Handbook of Implicit Social Cognition: Measurement, Theory, and Applications

Handbook of Implicit Social Cognition

Handbook of

Implicit Social

Cognition Measurement, Theory, and Applications

Edited by

Bertram Gawronski B. Keith Payne

THE GUILFORD PRESS New Yorkâ•…â•…London

©â•›2010 The Guilford Press A Division of Guilford Publications, Inc. 72 Spring Street, New York, NY 10012 www.guilford.com All rights reserved No part of this book may be reproduced, translated, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, microfilming, recording, or otherwise, without written permission from the Publisher. Printed in the United States of America This book is printed on acid-free paper. Last digit is print number:â•… 9â•… 8â•… 7â•… 6â•… 5â•… 4â•… 3â•… 2â•… 1 Library of Congress Cataloging-in-Publication Data Handbook of implicit cognition : measurement, theory, and applications / edited by Bertram Gawronski, B. Keith Payne. â•…â•… p. cm. â•… Includes bibliographical references and index. â•… ISBN 978-1-60623-673-4 (hbk.) â•… 1.╇ Subliminal perception.â•… 2.╇ Social perception.â•… I.╇ Gawronski, Bertram.â•… II.╇ Payne, B. Keith. â•… BF323.S8H36 2010 â•… 302′.12—dc22 2009041100

About the Editors

Bertram Gawronski, PhD, is Professor and Canada Research Chair in Social Psychology in the Department of Psychology at the University of Western Ontario, Canada. His research focuses on associative and propositional processes underlying social judgments and social behavior. Dr. Gawronski’s work has been recognized with the Theoretical Innovation Prize from the Society for Personality and Social Psychology, the Early Career Award from the International Social Cognition Network, the Early Researcher Award from the Ministry of Research and Innovation of Ontario, the Charlotte and Karl Bühler Award from the German Psychological Society, and the Faculty Scholar Award from the University of Western Ontario. B. Keith Payne, PhD, is Associate Professor in the Department of Psychology at the University of North Carolina at Chapel Hill. His research is concerned with the development of innovative approaches for studying automatic and controlled processes underpinning social behavior. Dr. Payne is a recipient of the Early Career Award from the International Social Cognition Network and the SAGE Young Scholars Award from the Foundation for Social and Personality Psychology and Sage Publications. He was profiled as a “Rising Star” in psychology in the Association for Psychological Science Observer and was cited by the Observer as one of the Top Ten most highly cited Social/Personality Psychologists at the Assistant Professor rank in the United States.



v

Contributors

Thomas J. Allen, MA, Department of Psychology, University of California, Davis, Davis, California David M. Amodio, PhD, Center for Neural Science and Department of Psychology, New York University, New York, New York Jens B. Asendorpf, PhD, Institute for Psychology, Humboldt University of Berlin, Berlin, Germany Mark W. Baldwin, PhD, Department of Psychology, McGill University, Montreal, Quebec, Canada Galen V. Bodenhausen, PhD, Department of Psychology, Northwestern University, Evanston, Illinois Pablo Briñol, PhD, Department of Social Psychology, Autonomous University of Madrid, Madrid,€Spain C. Daryl Cameron, MA, Department of Psychology, University of North Carolina at Chapel Hill, Chapel€Hill,€North€Carolina Don Carlston, PhD, Department of Psychological Sciences, Purdue University, West Lafayette, Indiana Elise M. Clerkin, MA, Department of Psychology, University of Virginia, Charlottesville, Virginia Meghan W. Cody, MA, Department of Psychology, University of Virginia, Charlottesville, Virginia Jan De Houwer, PhD, Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium Peter de Jong, PhD, Department of Clinical and Developmental Psychology, University of Groningen, Groningen, The Netherlands Juliane Degner, PhD, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands Roland Deutsch, PhD, Department of Psychology, University of Würzburg, Würzburg, Germany Yarrow Dunham, EdD, Department of Psychological Sciences, University of California, Merced, Merced,€California Sara Etchison, BA, Department of Psychology, McGill University, Montreal, Quebec, Canada Melissa J. Ferguson, PhD, Department of Psychology, Cornell University, Ithaca, New York Mark Forehand, PhD, Michael G. Foster School of Business, University of Washington, Seattle,€Washington Bertram Gawronski, PhD, Department of Psychology, University of Western Ontario, London,€Ontario,€Canada

vi

Contributors

vii

Jesse Graham, MA, Department of Psychology, University of Virginia, Charlottesville, Virginia Nicola S. Gray, PhD, School of Psychology, Cardiff University, Cardiff, Wales, United Kingdom; Department€of Psychology, Swansea University, Swansea, Wales, United Kingdom Carlee Beth Hawkins, BS, Department of Psychology, University of Virginia, Charlottesville, Virginia Wilhelm Hofmann, PhD, Department of Psychology, University of Würzburg, Würzburg, Germany; Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands Katrijn Houben, PhD, Department of Clinical Psychological Science, Faculty of Psychology, Maastricht University, Maastricht, The Netherlands Tiffany A. Ito, PhD, Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, Colorado Christian H. Jordan, PhD, Department of Psychology, Wilfrid Laurier University, Waterloo,€Ontario,€Canada Karl Christoph Klauer, PhD, Institute of Psychology, University of Freiburg, Freiburg, Germany John E. Lydon, PhD, Department of Psychology, McGill University, Montreal, Quebec, Canada M. Joy McClure, PhD, Department of Psychology, McGill University, Montreal, Quebec, Canada Allen R. McConnell, PhD, Department of Psychology, Miami University, Oxford, Ohio Saaid A. Mendoza, PhD, Department of Psychology, New York University, New York, New York Agnes Moors, PhD, Department of Experimental Clinical and Health Psychology, Ghent University, Ghent,€Belgium Brian A. Nosek, PhD, Department of Psychology, University of Virginia, Charlottesville, Virginia Kristina R. Olson, PhD, Department of Psychology, Yale University, New Haven, Connecticut B. Keith Payne, PhD, Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina Andrew Perkins, PhD, Jesse H. Jones Graduate School of Business, Rice University, Houston, Texas Marco Perugini, PhD, Faculty of Psychology, University of Milan–Bicocca, Milan, Italy Richard E. Petty, PhD, Department of Psychology, Ohio State University, Columbus, Ohio Shanette C. Porter, MA, Department of Psychology, Cornell University, Ithaca, New York Juliette Richetin, PhD, Faculty of Psychology, University of Milan–Bicocca, Milan, Italy Anne Roefs, PhD, Department of Clinical Psychological Science, Maastricht University, Maastricht,€The€Netherlands Robert J. Rydell, PhD, Department of Psychological and Brain Sciences, Indiana University, Bloomington,€Indiana Konrad Schnabel, PhD, Institute for Psychology, Humboldt University of Berlin, Berlin, Germany Denise Sekaquaptewa, PhD, Department of Psychology, University of Michigan, Ann Arbor, Michigan Jenessa R. Shapiro, PhD, Department of Psychology, University of California, Los Angeles, Los€Angeles,€California Jeffrey W. Sherman, PhD, Department of Psychology, University of California, Davis, Davis, California Robert J. Snowden, PhD, School of Psychology, Cardiff University, Cardiff, Wales, United Kingdom Adriaan Spruyt, PhD, Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium Rajees Sritharan, BA, Department of Psychology, University of Western Ontario, London, Ontario, Canada

viii Contributors Alan W. Stacy, PhD, School of Community and Global Health, Claremont Graduate University, San€Dimas,€California Fritz Strack, PhD, Department of Psychology, University of Würzburg, Würzburg, Germany Bethany A. Teachman, PhD, Department of Psychology, University of Virginia, Charlottesville, Virginia Sarah Teige-Mocigemba, PhD, Institute of Psychology, University of Freiburg, Freiburg, Germany Andrew R. Todd, PhD, Faculty of Human Sciences, Institute of Psychology, University of Cologne, Cologne,€Germany Sophie Trawalter, PhD, Department of Psychology, University of North Carolina at Chapel Hill, Chapel€Hill,€North€Carolina Patrick Vargas, PhD, Department of Advertising, University of Illinois at Urbana–Champaign, Urbana, Illinois William von Hippel, PhD, School of Psychology, University of Queensland, St Lucia, Queensland, Australia Dirk Wentura, PhD, Department of Psychology, Saarland University, Saarbruecken, Germany Reinout W. Wiers, PhD, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands Timothy D. Wilson, PhD, Department of Psychology, University of Virginia, Charlottesville, Virginia Virgil Zeigler-Hill, PhD, Department of Psychology, University of Southern Mississippi, Hattiesburg,€Mississippi Cristina Zogmaister, PhD, Faculty of Psychology, University of Milan–Bicocca, Milan, Italy

Preface

O

ur aim for this handbook was to provide the first comprehensive overview of the key findings, theories, and practical applications of implicit social cognition research. That’s easier said than done. Implicit social cognition is one of the largest, and fastest growing, subfields of psychology. A PsychINFO search using the key words social cognition and either implicit or automatic turned up 14,811 references as of this writing. More than half were published since 2000. To tackle this mammoth job, we enlisted the help of some of the world’s leading researchers, who have distilled this massive literature into 29 tightly focused chapters. The challenge we gave to each author was to write a review of an area of work that goes beyond individual research programs, to provide a broad overview of the field’s key findings, unanswered questions, and future directions from a particular point of view. We asked them to give special attention to conceptual clarity when discussing the meaning of terms like implicit and explicit. And we have included an emphasis on the empirical methods used to study these ideas. We think the contributors have delivered beautifully. The result is a unique volume that provides precision of thought with an expansive vision of the field as a whole. We have divided the book into five sections. The first section lays the foundation for what follows by providing conceptual analyses of automatic and controlled processes, reviewing dual-process models from the perspectives of mental representations and behavior, and surveying the increasingly bidirectional links between implicit social cognition and the neurosciences. The second section provides an in-depth look at the methods and procedures on which the field depends. The chapters in this section compare and contrast priming methods, the Implicit Association Test and related methods, low-tech but often hi-fidelity paper-and-pencil approaches, and mathematical modeling. Some of these methods were driving forces at the origin of implicit social cognition, and others are new cutting-edge advances. All of them form the lifeblood of the field, because our scientific conclusions are only as strong as the methods on which they are based. The third section reviews the many ties that have been forged between implicit social cognition and other topics of interest beyond the borders of social psychology, as researchers have come to increasingly recognize the role of implicit processes across virtually all aspects of mental life. This section highlights links to big-picture questions and broad topics, including consciousness, cognitive plasticity,



ix

x Preface

child development, prediction of behavior, judgment and decision making, cognitive consistency, and goal pursuit. After reviewing the divergent thinking that has fostered these many cross-cutting links, the fourth section digs deeper into classic topics of perennial interest in social psychology. This section looks at the roles of implicit social cognition in attitudes, prejudice and intergroup relations, self-concept and self-esteem, interpersonal relationships, and the complicated meanings of implicit processes for issues of social justice. Finally, the fifth section tackles the practical implications for real-world concerns, including mental and physical health, forensics, consumer behavior, and political behavior. We believe the Handbook to be an indispensable resource for any scholar who is interested in obtaining an up-to-date summary of the exponentially growing field of implicit social cognition. It also seems ideally suited as a text for graduate-level and advanced undergraduate courses and seminars. It will be an essential source for researchers who are using, or are interested in adopting, the theories and methods of implicit social cognition in their own research. Given the widespread adoption of theories and methods from implicit social cognition in virtually all areas of psychology, the Handbook will be of use not only to social psychologists but to any researcher who is interested in the unknown, unwanted, or unintended influences on human behavior. This volume was made possible with the help of many friends, mentors, and colleagues. We thank Carolyn Graham and Seymour Weingarten at The Guilford Press for their invaluable support throughout the editorial process; Thorsten Erle for his help in rigorously proofreading the final chapters; and Rainer Banse, Galen Bodenhausen, Larry Jacoby, Alan Lambert, and Fritz Strack for their invaluable mentorship. Their many influences, known and unknown but certainly wanted, have set the foundation for the editorial work on this handbook.

Contents

â•⁄ 1.

A History of Implicit Social Cognition: Where Is It Coming From? Where Is It Now? Where Is It Going?

1

B. Keith Payne and Bertram Gawronski

I. BASICS â•⁄ 2.

In Search of a Measure That Qualifies as Implicit: Recommendations Based on a Decompositional View of Automaticity

19

Models of Implicit and Explicit Mental€Representation

38

Building Blocks of Social Behavior: Reflective and Impulsive Processes

62

Implicit Social Cognition: Insights from Social Neuroscience

80

Agnes Moors, Adriaan Spruyt, and Jan De Houwer

â•⁄ 3.

Don Carlston â•⁄ 4.

Roland Deutsch and Fritz Strack â•⁄ 5.

Tiffany A. Ito

II. METHODS AND PROCEDURES â•⁄ 6.

A Practical Guide to Sequential Priming and€Related Tasks Dirk Wentura and Juliane Degner

â•⁄ 7.

A Practical Guide to Implicit Association Tests and Related Tasks Sarah Teige-�Mocigemba, Karl Christoph Klauer, and€Jeffrey€W.€Sherman



xi

95 117

xii Contents â•⁄ 8.

A Practical Guide to Paper-and-�Pencil Implicit Measures of Attitudes

140

Mathematical Modeling of Implicit Social Cognition: The Machine in the Ghost

156

Implicit Measures: Similarities and Differences

176

Denise Sekaquaptewa, Patrick Vargas, and William von Hippel â•⁄ 9.

Jeffrey W. Sherman, Karl Christoph Klauer, and€Thomas€J.€Allen 10.

Jan De Houwer and Agnes Moors

III. CROSS-CUTTING PERSPECTIVES 11.

Consciousness, Introspection, and€the€Adaptive Unconscious

197

Formation, Change, and Contextualization of Mental Associations: Determinants and Principles of Variations in€Implicit€Measures

216

The Development of Implicit Social Cognition

241

Prediction of Behavior

255

Automatic Aspects of Judgment and€Decision Making

278

Consistency and Inconsistency in Implicit Social Cognition: The Case of Implicit and Explicit Measures of Attitudes

295

What Is Implicit about Goal Pursuit?

311

Wilhelm Hofmann and Timothy D. Wilson 12.

Bertram Gawronski and Rajees Sritharan 13.

Kristina R. Olson and Yarrow Dunham 14.

Marco Perugini, Juliette Richetin, and Cristina Zogmaister 15.

Galen V. Bodenhausen and Andrew R. Todd 16.

Robert J. Rydell and Allen R. McConnell

17.

Melissa J. Ferguson and Shanette C. Porter

IV. DOMAIN-SPECIFIC PERSPECTIVES 18.

Attitude Structure and Change: Implications for Implicit Measures

335

Implicit Intergroup Bias: Cognitive, Affective, and Motivational Underpinnings

353

Racial Bias and Stereotyping: Interpersonal Processes

375

Two Faces of Self-�Esteem: Implicit and Explicit Forms of Self-�Esteem

392

The Self-�Concept: New Insights from Implicit Measurement€Procedures

408

Richard E. Petty and Pablo Briñol 19.

David M. Amodio and Saaid A. Mendoza 20.

Sophie Trawalter and Jenessa R. Shapiro 21.

Virgil Zeigler-Hill and Christian H. Jordan 22.

Konrad Schnabel and Jens B. Asendorpf

Contents 23.

Measuring Implicit Processes in€Close€Relationships

426

Divided Minds, Divided Morals: How Implicit Social Cognition Underpins and€Undermines Our Sense of Social Justice

445

Mark W. Baldwin, John E. Lydon, M. Joy McClure, and€Sara€Etchison 24.

xiii

B. Keith Payne and C. Daryl Cameron

V. APPLIED PERSPECTIVES 25.

Implicit Cognition in Health Psychology: Why Common Sense Goes Out the Window

463

Clinical Applications of Implicit Social€Cognition Theories and€Methods

489

Implicit Social Cognition in€Forensic€Settings

522

Implicit Social Cognition and Indirect Measures in Consumer Behavior

535

Implicit Political Cognition

548

Author Index

565

Subject Index

584

Reinout W. Wiers, Katrijn Houben, Anne Roefs, Peter de Jong, Wilhelm Hofmann, and Alan W. Stacy 26.

Bethany A. Teachman, Meghan W. Cody, and Elise M. Clerkin 27.

Robert J. Snowden and Nicola S. Gray 28.

Andrew Perkins and Mark Forehand 29.

Brian A. Nosek, Jesse Graham, and Carlee Beth Hawkins

Chapter 1

A History of Implicit Social Cognition Where Is It Coming From? Where Is It Now? Where Is It Going? B. Keith Payne and Bertram Gawronski

W

In every topic of study, implicit social cognition is concerned with automatic/implicit/unconscious processes underlying judgments and social behavior. An indispensable part of this endeavor is the use of a new class of indirect measurement procedures such as the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) and different kinds of sequential priming tasks (e.g., Fazio, Jackson, Dunton, & Williams, 1995; Payne, Cheng, Govorun, & Stewart, 2005; Wittenbrink, Judd, & Park, 1997), which play a crucial role in the chapters of this book. Thumbing through its pages makes it clear how implicit social cognition has brought new insights, and also new controversies, wherever it has led. In fact, implicit social cognition has grown at such an incredible rate over the past years that it seems almost impossible to keep track of the rapidly progressing developments in this area. The purpose of this handbook is to provide a comprehensive overview of the current state of the field, including key findings, current directions, and emerging themes in the area of implicit social cognition. In this introduction, we aim to provide a context for the chapters that follow by highlighting some of the themes that keep surfacing in the field, tracing them to their historical roots, and identifying

ithin the space of two decades, virtually every intellectual question in social psychology, and many outside of it, has been shaped by the theories and methods of implicit social cognition. Many of those questions are pondered in this volume, involving the role of automatic/implicit/ unconscious processes in attitudes (Petty & Briñol, Chapter 18), social judgment and decision making (Bodenhausen & Todd, Chapter 15), goal pursuit (Ferguson & Porter, Chapter 17), prejudice and stereotyping (Amodio & Mendoza, Chapter 19; Trawalter & Shapiro, Chapter 20), self-Â�concepts and self-Â�esteem (Schnabel & Asendorpf, Chapter 22; Zeigler-Hill & Jordan, Chapter 21), social cognitive development (Olson & Dunham, Chapter 13), romantic relationships (Baldwin, Lydon, McClure, & Etchison, Chapter 23), and social justice (Payne & Cameron, Chapter 24). Expanding beyond the traditional boundaries of social psychology, the basic ideas of implicit social cognition have also had an impact in many applied areas, including health psychology (Wiers et al., Chapter 25), clinical psychology (Teachman, Cody, & Clerkin, Chapter 26), forensic psychology (Snowden & Gray, Chapter 27), consumer psychology (Perkins & Forehand, Chapter 28), and political psychology (Nosek, Graham, & Hawkins, Chapter 29).



1

2

1. History of Implicit Social Cognition

emerging themes that may guide future research. We hope that our historical synopsis will not only provide an orientation for the in-depth discussions of particular topics in the following chapters but also circumscribe the characteristics that define implicit social cognition as a particular way of studying, understanding, and explaining human behavior. In addition, we hope that our introduction helps illuminate the historical roots of previous and ongoing debates, which seems valuable for critical appraisals of theoretical interpretations in implicit social cognition.

Where Is It Coming From? A history of implicit social cognition could easily enough start with Freud and the psychoanalytic unconscious; or with Augustine and Aquinas, who had a lot to say about the limits of introspection in knowing thyself; or even with Plato and Aristotle, who commented extensively on consciousness and intentional behavior. Our aim in this introduction is more modest. It is to take a selective look at roughly the last two decades in which implicit social cognition has comprised a recognizable enterprise in its own right, establishing itself as one of the most influential approaches in social psychology. This process began as researchers adapted ideas and methods from cognitive psychology to answer social psychological questions. As we shall see, many of the lively debates in implicit social cognition can be traced to which particular tradition of cognitive psychology was appropriated.

Two Roots of Implicit Social Cognition Reports on implicit social cognition include a lot of hyphens and slashes in their key terms. Phrases like automatic/implicit/unconscious processing and controlled/explicit/conscious processing abound. Aside from being less than precise, such phrases reveal a tension at the heart of the discipline. One theme that recurs in the field, and in this book, is an uneasy relationship between notions of automaticity and unconsciousness. When these terms are thrown together, they sometimes leave readers with only a vague idea of what is being studied. However, a sharper picture emerges when we take a step back and distinguish two intellectual traditions that gave rise to separate terminologies. One tradition is found in research on selective attention. The other is found in research on implicit memory.

Roots of Automatic and Controlled Processing in Attention Research Some of the seminal articles that can be subsumed under the umbrella term implicit social cognition emphasize the differences between automatic and controlled cognition, with little mention of the unconscious. These reports took as their point of departure the cognitive psychology work of Shiffrin and Schneider (1977) and Posner and Snyder (1975), among others. This work developed from a tradition of research on selective attention and short-term memory (e.g., Broadbent, 1971; Treisman, 1969). The key idea in these articles was that information processing could be divided into controlled and automatic modes. Controlled processing was defined as demanding attention, limited in capacity, and voluntarily initiated and altered. Automatic processing was defined as needing little attention, unlimited in capacity, and difficult to suppress voluntarily. These criteria will, of course, sound very familiar to social cognition researchers because they are the forerunners, but not identical to, Bargh’s (1994) “four horsemen” of automaticity (awareness, efficiency, intention, control). Although in Bargh’s scheme conscious awareness is the first feature, the earlier cognitive theories hardly mentioned consciousness. Fazio and colleagues’ seminal work showing that attitudes can be automatically activated built on these cognitive theories of automatic and controlled processing (Fazio, Sanbonmatsu, Powell, & Kardes, 1986; see also Dovidio, Evans, & Tyler, 1986; Gaertner & McLaughlin, 1983). Here too there was little concern with consciousness. The term conscious appears only once in Fazio and colleagues’ (1986) article and the term aware appears twice. For Fazio and colleagues, “The key feature of such automatic activation, then, is inescapability” (p.€229). In the work of Shiffrin and Schneider (1977) and Posner and Snyder (1975), the degree of learning was critically important for automaticity. Well-Â�learned items were detected or retrieved from memory automatically, whereas poorly learned items required cognitive effort to search for them. Based on these assumptions, Fazio and colleagues distinguished between well-Â�learned (i.e., strong) attitudes that should be activated automatically and poorly learned (i.e., weak) attitudes that should not. The connection between the strength of attitudes and their ability to be automatically activated set the stage for the use of sequential priming techniques to measure attitudes without asking subjects to report them (Fazio et al., 1995; see also



1. History of Implicit Social Cognition

Wentura & Degner, Chapter 6, this volume). This novel approach to indirect measurement would soon become important across many areas of psychology and ultimately a central characteristic of implicit social cognition. Initially, the usefulness of this approach was most obvious for studying racial attitudes (see Amodio & Mendoza, Chapter 19, and Trawalter & Shapiro, Chapter 20, this volume). Racial attitudes had always presented challenges for researchers because self-Â�presentation motives meant many subjects would not honestly report their attitudes. Techniques such as the bogus pipeline showed that subjects typically hold more prejudiced attitudes than they are willing to report under ordinary circumstances (Jones & Sigall, 1971). However, such deceptive procedures are ethically controversial and practically cumbersome. Priming methods seemed to offer the potential for a bona fide pipeline to reach respondents’ true attitudes (Fazio et al., 1995). The challenge of racial attitudes was taken up by both Devine (1989) and Fazio and colleagues (1995). Both studies were informed by cognitive theories of automatic and controlled processing (Posner & Snyder, 1975; Shiffrin & Schneider, 1977) and priming techniques (Neely, 1977); both had little to say about conscious awareness of attitudes; and both emphasized the idea that well-Â�learned associations should be activated automatically, but weakly learned associations require cognitive effort to be retrieved. For Fazio, the implication was that individuals with weak or neutral racial attitudes simply would not show any priming effects, whereas those with strongly negative attitudes or strongly positive attitudes would show corresponding priming effects. However, for Devine the important distinction was between the knowledge of a social stereotype versus the personal endorsement of the stereotype. Because everyone in a culture learns stereotypes in the same way they learn about other categories, such as birds or foods, knowledge of stereotypes should be well learned for virtually everyone. Stereotypes should, therefore, be automatically activated for everyone. Personally endorsed beliefs, on the other hand, should vary with one’s values and motivations. Thus, in Devine’s approach, inescapable stereotype activation was universal; it was endorsed beliefs that distinguished prejudiced from unprejudiced individuals. Here we see a second theme that reappears in debates throughout implicit social cognition research: Are the results of cognitive measurements, such as the ones revealed by sequential priming tasks, best considered a reflection of the person’s

3

inner attitudes and beliefs, or are they instead the products of the cultural environment? This debate is complex and multifaceted, and it recurred in different variants. Examples include the early controversy between Devine (1989) and Fazio and colleagues (1995), debates about the generality versus variability of automatic attitude activation (Bargh, Chaiken, Govender, & Pratto, 1992; Chaiken & Bargh, 1993; Fazio, 1993), and recent controversies about the role of personal versus extrapersonal associations in the IAT (Gawronski, Peters, & LeBel, 2008; Nosek & Hansen, 2008a, 2008b; Olson & Fazio, 2004; Olson, Fazio, & Han, 2009). We consider three different aspects of the debate in turn. First, the debate has sometimes been understood as questioning whether automatic attitudes are “real.” Both Fazio and Devine showed that automatically activated stereotypes or attitudes were associated with biased impressions and behaviors. Insofar as the reality of an attitude is a question about whether it has consequences for thought and behavior, all sides of the debate agree that the automatic responses are real. A second aspect of this debate is whether there are meaningful individual differences in automatic responses. If automatic responses were like language and virtually every healthy member of a culture possessed them, variability in sequential priming scores would simply reflect measurement error. A great deal of subsequent research showed that individual differences in priming and other cognitive tasks are indeed meaningful and predictive of behavior (Perugini, Richetin, & Zogmaister, Chapter 14, this volume). A thornier aspect of this debate is whether people who show negative automatic associations with ethnic groups should be considered prejudiced (Arkes & Tetlock, 2004; Banaji, Nosek, & Greenwald, 2004). This is a philosophical and moral issue more than an empirical one. Not surprisingly, it remains a topic of hot debate (see Payne & Cameron, Chapter 24, this volume). The roots of the debate can be traced as far as concepts of virtue in ancient Greek philosophy. Plato argued that virtue is about developing the right inclinations. The virtuous person has good inclinations, whereas a wicked person has evil inclinations. However, Aristotle argued that both good and bad people sometimes have wicked impulses, and that virtue consists in regulating and channeling them well. Contemporary philosophers have begun integrating insights from implicit social cognition with theories of ethics, creating an exciting exchange of ideas across disciplines (e.g., Appiah, 2008; Doris, 2002; Kennett & Fine, 2009).

4

1. History of Implicit Social Cognition

Roots of Unconscious and Conscious Processing in Implicit Memory Research The studies we have discussed so far focused on the distinction between automatic and controlled processing, emphasizing notions of inescapability rather than unconsciousness. However, in the following years, the automatic/controlled distinction often gave way to the dichotomy between explicit and implicit processes, interpreted as synonyms for the terms conscious and unconscious. This shift was ignited in part by Greenwald and Banaji’s (1995) influential review, which built on a different research tradition of cognitive psychology. Rather than building on theories of attention and shortterm memory, they drew on research on implicit memory (Banaji, 2001). Although measured in a variety of ways, implicit memory has been defined fairly consistently as influences of past experience on later performance, in the absence of conscious memory for the earlier experience (Jacoby & Dallas, 1981; Schacter, 1987). Amnesic patients, for example, tend to perform at chance when asked to recognize which words they have previously studied. When asked, however, to guess how to complete word fragments, they perform more accurately for studied than for unstudied words (Warrington & Weiskrantz, 1968). The effect of prior study on later performance, despite the inability to consciously remember it, is taken to indicate implicit memory. This conceptualization strongly shaped Greenwald and Banaji’s (1995) definition of implicit attitudes as “introspectively unidentified (or inaccurately identified) traces of past experience that mediate favorable or unfavorable feeling, thought, or action toward social objects” (p.€8). Just as implicit memory was defined as consequences of past experience in the absence of conscious awareness for the experience, implicit attitudes were defined as traces of past experience in the absence of conscious awareness for the experience. Implicitness was identified with unconsciousness. In contrast to earlier reports emphasizing the notion of automaticity in the sense of inescapability (Fazio et al., 1986), variants of the words conscious and awareness are mentioned nearly a hundred times in Greenwald and Banaji’s paper. Although the parallel between concepts of implicit memory and implicit attitudes is close, it is not complete. There was a subtle shift between the two concepts that has important consequences for implicit social cognition. In the case of implicit memory, it is the experience of some past event that is not consciously available. In memory stud-

ies, the “event” is often the presentation of a particular word or a picture in a learning task. The experimenter has perfect control over whether a particular stimulus was presented and, therefore, knows with certainty whether the subject experienced a particular learning episode. Moreover, the definition of implicit memory focuses on performance, which is an operational definition. From this perspective, implicit memory is based on the effects of past experiences, controlled by the experimenter, on later performance, which is directly observable. For implicit attitudes, in contrast, the definition refers to traces of past experience, which mediate later responses. This definition is decidedly more mentalistic than for implicit memory. In fact, implicit social cognition researchers usually do not control conscious awareness of past experiences giving rise to attitudes, stereotypes, and other such traces. Unlike implicit memory tests, the measures used in implicit social cognition research do not test for awareness of the formative experiences. Moreover, although Greenwald and Banaji (1995) clearly referred to unawareness of the experiences giving rise to attitudes, subsequent writers have sometimes conflated awareness of the source with awareness of the attitude itself. This conflation has led to the fallacy that if one assesses a construct with a measure that does not presuppose conscious introspection, then the Â�assessed construct must be introspectively inaccessible. The available evidence clearly speaks against this assumption (Gawronski, Hofmann, & Wilbur, 2006), but claims of unconsciousness remain common. The degree to which the constructs measured with cognitive tests may be unconscious and how to empirically confirm such claims remains a topic of debate. This debate, in turn, has inspired novel ways of thinking about consciousness and introspection (Hofmann & Wilson, Chapter 11).

What Is “Implicit” about Implicit Social Cognition? Since Greenwald and Banaji (1995) coined the term implicit social cognition, the two roots of implicit social cognition have contributed to lively debates about the proper use of terminology, in particular with regard to the term implicit. Some researchers have used the term to describe a particular characteristic of measurement procedures, namely measures that provide indicators of psychological attributes (e.g., attitudes) without having to ask participants to verbally report the desired information (e.g., Fazio & Olson, 2003). Yet



1. History of Implicit Social Cognition

other researchers have used implicit to describe the constructs assessed by a particular class of measurement procedures, namely constructs assessed by tasks that do not require conscious introspection and, therefore, might reflect psychological attributes that are introspectively inaccessible (e.g., Banaji, 2001). Because such terminological issues are a matter of semantics and linguistic conventions, they cannot be resolved empirically. At the same time, different use of the same terms often produces conceptual confusion, which can have disruptive effects on cumulative research progress (Machado & Silva, 2007). To overcome these problems, researchers have developed detailed taxonomies of the various properties of measurement procedures (De Houwer & Moors, Chapter 10, this volume) and mental representations (Carlston, Chapter 3, this volume). The most comprehensive conceptual analysis has been provided by De Houwer and colleagues, who suggested using the terms direct and indirect to describe features of measurement procedures and the terms implicit and explicit to describe features of the psychological attributes that are assessed by different measurement procedures (e.g., De Houwer, Teige-Â�Mocigemba, Spruyt, & Moors, 2009). With regard to the term implicit, De Houwer further suggested using this description in the meaning of the term automatic, such that it may describe the unconscious, efficient, unintentional, or uncontrollable nature of the assessed constructs (Moors, Spruyt, & De Houwer, Chapter 2, this volume). Even though terminological systems like the one proposed by De Houwer are a matter of convention, the normative request implied by these taxonomies is the call for researchers to be precise about what they mean when they use the term implicit. These norms encourage researchers to specify in which particular sense a process is automatic (De Houwer et al., 2009) and what exactly remains outside of conscious awareness (e.g., past experiences that give rise to attitudes vs. attitudes per se; see Gawronski et al., 2006). Detailed taxonomies and clarity in “defining our terms” will not themselves settle empirical questions, but they can strongly facilitate research progress by preventing conceptual misunderstandings.1

A NEW INDUSTRY OF RESEARCH Greenwald and Banaji (1995) ended their review with a call for the refinement of individual difference measures of implicit social cognition. They

5

predicted that “when such measures do become available, there should follow the rapid development of a new industry of research on implicit cognitive aspects of personality and social behavior” (p.€20). Their prediction has not been disappointed. With the development of the IAT (Greenwald et al., 1998), implicit social cognition research seemed to hit a tipping point. Although sequential priming tasks had been available for several years (e.g., Dovidio et al., 1986; Fazio et al., 1986; Gaertner & McLaughlin, 1983), researchers now had a task that made implicit social cognition research much easier. Facilitation scores from priming procedures often suffered from low reliability and relatively small effect sizes. However, the IAT had reasonably good reliability and very large effects. In fact, even though the term implicit social cognition was originally interpreted in a much broader sense (Greenwald & Banaji, 1995), it has become almost synonymous with research using indirect measurement procedures, such as the IAT and its derivates (Teige-Â�Mocigemba, Klauer, & Sherman, Chapter 7, this volume), sequential priming tasks (Wentura & Degner, Chapter 6, this volume), and sophisticated indirect paper-and-Â�pencil methods (Sekaquaptewa, Vargas, & von Hippel, Chapter 8, this volume). When the number of studies using indirect measurement procedures started to skyrocket around the turn of the millennium, the different roots of implicit social cognition left their own traces by inspiring two parallel, largely independent streams of research.

Two Parallel Streams of Research The first stream had its origin in the idea of inescapability, derived from the notion of automaticity in attention research. This stream was mainly theory driven, in that empirical predictions were derived from established theories of attitude–Â� behavior relations, most notably Fazio’s motivation and opportunity as determinants (MODE) model (Fazio, 1990; Fazio & Towles-Â�Schwen, 1999). The central assumption in the MODE model is that direct and indirect measures assess the behavioral effects of the same underlying mental representation, the only difference being the degree of control that participants have over their responses. Whereas responses on self-Â�report measures can be easily altered, indirect measures such as the IAT and sequential priming tasks constrain participants’ opportunity to control their responses. This distinction captures the theoretical core of the MODE model, which argues that automatically activated attitudes will guide judgments and

6

1. History of Implicit Social Cognition

behavior if either the motivation or the opportunity to engage in deliberate processing is low. If, however, both motivation and opportunity are high, the impact of automatic attitudes on self�reports may be diluted or inhibited by deliberate processes. These assumptions imply that the predictive validity of a given measure should depend on the overlap between the processing conditions during the completion of the measure and the processing conditions of the relevant behavior. For instance, indirect measures should be better predictors of spontaneous behavior, whereas direct self-�report measures should be better predictors of deliberate behavior (Perugini et al., Chapter 14, this volume). A second, parallel stream of research had its origin in the notion of unconsciousness, as derived from cognitive research on implicit memory. In contrast to the single-�representation assumption implied by the MODE model, this stream was characterized by an operational equation of measurement outcomes with distinct psychological constructs, which favored a dualism of two independent representations in memory: a conscious, explicit representation and an unconscious, implicit representation (Greenwald & Banaji, 1995). Because there was no theoretical framework available that could specify how the two kinds of representations may differentially influence judgments and behavior, empirical research in this stream was largely effect focused and method driven. The main topics of interest were whether indirect measures predict behaviors that direct measures do not predict or whether indirect measures explain variance in behavior over and above variance that can be explained by direct measures. As reviewed by Perugini and colleagues (Chapter 14, this volume), research has documented several distinct patterns by which indirect and direct measures may differentially predict behavior. For example, in some cases one measure or the other alone predicted behavior; in other cases they predicted behavior additively; and in other cases they synergistically combined to predict behavior. There is replicable evidence for each of these patterns, but this seems to be a case in which theory has not yet caught up to the data. Many models such as the MODE model and others reviewed in this chapter can accommodate these findings, but it is not clear that any current models make unambiguous a priori predictions for when each pattern should be expected. Developing such theories is an important next step we hope researchers will pursue.

Unexpected Malleability The explosion of research using indirect measures also led to discoveries that forced a reexamination of some of the field’s core assumptions. One such discovery was that the scores obtained with indirect measures often increased, decreased, or even reversed as a function of the context (Gawronski & Sritharan, Chapter 12). Spending 5 minutes imagining a “strong woman” led to weaker implicit gender stereotyping (Blair, Ma, & Lenton, 2001), and viewing photos of admired African Americans such as Martin Luther King, Jr., along with disliked white Americans such as serial killer Jeffrey Dahmer reduced implicit race bias (Dasgupta & Greenwald, 2001). Racial bias on indirect measures was even reversed by the background context. In one study, pictures of African Americans in the role of prisoners evoked negative implicit evaluations, but presenting the same individuals in the role of lawyers elicited positive implicit evaluations (Barden, Maddux, Petty, & Brewer, 2004; see also Judd, Simpson, & Carver, 2001; Wittenbrink, Judd, & Park, 2001). The ease of shifting measurement scores challenged a dominant assumption at the time that the associations assessed by indirect measures had become automatized by a long history of learning and must, therefore, be difficult to change (e.g., Wilson, Lindsey, & Schooler, 2000; see also Olson & Dunham, Chapter 13, this volume). This assumption was based on earlier research using perceptual learning tasks, and Shiffrin and Schneider (1977) had indeed shown that information was retrieved automatically only after hundreds or thousands of trials of learning. Thus, findings that the scores of indirect measures could be swayed one way or the other by immediate changes in context presented a puzzle. The two answers that have been offered to resolve this puzzle introduced a third theme to implicit social cognition, namely the debate about whether responses on indirect measures reflect stable representations in long-term memory or fluid constructions that are generated on the spot. In the eyes of construction theorists, the high malleability of indirect measurement scores confirmed their assumption that contexts influence what information is used to construct an attitude from one moment to the next, and that these principles apply equally to direct and indirect measurement procedures (e.g., Schwarz, 2007). In fact, the very idea that indirect measurement procedures would assess rigid “things” in memory independent of the context was seen as ill founded.



1. History of Implicit Social Cognition

In contrast to this view, representation theorists argued that malleability in measurement scores does not reflect context-�dependent changes in mental representations but, rather, responses to fundamentally different objects (e.g., Fazio, 2007). According to this view, contextual information simply influences how a given object is categorized. To the degree that different categories are associated with different evaluations in long-term memory, contextual cues can lead to different responses to the same object, even though the respective category associations that underlie these responses are highly stable. As Schwarz and Bohner (2001) pointed out, this debate cannot be resolved on empirical grounds because any result predicted by one theoretical framework can be reinterpreted by the other one. Yet, inspired by this debate, researchers have started to study the learning mechanisms that lead to context-�dependent or context-�independent responses on indirect measures (e.g., Rydell & Gawronski, 2009). By shifting the focus from mere demonstrations of context effects to experimentally controlled learning experiences and their directly observable effects on performance, this line of research could possibly lay the groundwork for a new way of studying context effects on indirect measures that goes beyond the debate between representational and constructionist models.

Unexpected Correspondence to Self-Â�Report Measures If the context dependence of indirect measurement scores made researchers rapidly reexamine their assumptions, another cause for reexamination accumulated more slowly over several years. Early studies often found little or no correspondence between direct and indirect measurement scores (Rydell & McConnell, Chapter 16, this volume). This divergence reinforced the idea that indirect measures reflected unconscious representations that are inaccessible to verbal self-Â�report. If implicit representations were unconscious, then self-Â�reports would certainly not be expected to track them. However, as the field’s data base grew, more and more studies showed surprisingly high correspondence under certain conditions (Hofmann, Gschwendner, Nosek, & Schmitt, 2005). One of the critical factors, identified in early research under the framework of the MODE model (Fazio et al., 1995), was the motivation to control the expression of one’s attitudes. For instance, in the context of racial attitudes the correlation

7

between directly and indirectly assessed evaluations was much higher for those who were willing to openly express prejudice compared with those who were motivated to control prejudiced reactions (e.g., Dunton & Fazio, 1997; Fazio et al., 1995; Gawronski, Geschke, & Banse, 2003; Payne, 2001). Expanding on these findings, research has shown that the spontaneity of self-�reports plays a significant role in this regard, such that direct and indirect measurement scores show higher correspondence when self-�reports are based on quick, intuitive gut reactions rather than deliberate analyses (e.g., Gawronski & LeBel, 2008; Hofmann, Gawronski, Gschwendner, Le, & Schmitt, 2005; Jordan, Whitfield, & Zeigler-Hill, 2007). A second critical factor was measurement error. Cunningham, Preacher, and Banaji (2001) demonstrated that when latent-�variable analyses were used to correct for measurement error, the correlation between directly and indirectly assessed racial attitudes was quite substantial (see also Hofmann, Gawronski, et al., 2005). A third factor concerns whether the attitude objects evaluated in direct and indirect measures are similar or not. Conceptual correspondence (Hofmann, Gawronski, et al., 2005) refers to whether different measures assess conceptually comparable constructs; structural fit (Payne, Burkley, & Stokes, 2008) refers to whether direct and indirect measures involve similar stimuli and responses. Overall, measurement scores of direct and indirect tests show much higher correspondence when they are matched in their concepts and test structures than when they do not align. Finally, the correspondence of measurement scores depends on several aspects of the underlying representations. For instance, direct and indirect measurement scores are more strongly related when the assessed attitudes are strong and perceived as distinctive (Nosek, 2005). The discovery of these moderators shifted the question from whether responses on direct and indirect measures are related to identifying the conditions under which they are related (Hofmann, Gschwendner, et al., 2005). This research suggested that direct and indirect measures, in fact, reveal highly similar results when the tasks are reliable, the stimulus materials are presented in similar ways, and participants base their self�reports on quick, intuitive gut reactions rather than deliberate analyses. Taken together, these findings posed a challenge to earlier claims that indirect measures assess unconscious representations (Gawronski, LeBel, & Peters, 2007). In fact, unconsciousness of a mental representation, like

8

1. History of Implicit Social Cognition

the nonexistence of black swans or the absence of light in a closed refrigerator, is notoriously difficult to prove. Even when there is no evidence for their presence, it is always possible you have not looked in the right place or at the right time (Popper, 1934). But together, these findings suggested that, as long as researchers look in the right way, people seem to have much greater introspective access to their mental representations than was commonly assumed.

SECOND-GENERATION MEASURES Most of the early research on implicit social cognition used either the IAT (Greenwald et al., 1998) or available variants of sequential priming (e.g., Fazio et al., 1995; Wittenbrink et al., 1997). However, both types of indirect measures had their problems. Whereas sequential priming tasks often suffered from low measurement reliability, the IAT involved a number of structural problems that produced method-�related confounds. These issues inspired researchers to develop new measurement procedures that tried to overcome the problems of existing tasks. The result was a second generation of measurement procedures that expanded on early work on the IAT and priming tasks.

IAT-Inspired Methods An early recognized limitation of the IAT was the fact that it provides relative assessments involving two target objects (Teige-Â�Mocigemba et al., Chapter 7, this volume). For instance, if a person shows a preference for candy bars over apples, we cannot tell whether he or she has a particular yen for Snickers, an aversion to Granny Smiths, or some combination of both (Blanton, Jaccard, Gonzales, & Christie, 2006; Fiedler, Messner, & Bluemke, 2006). Nosek and Banaji (2001) were the first to address this issue by developing the Go/No-Go Association Task (GNAT). The GNAT includes one target object, such as apples, and attributes, such as good and bad words. In one block of trials, subjects press a key when they see apples or good words, and in the other block they press a key when they see apples or bad words. If subjects are faster and more accurate when apples are paired with good rather than bad words, it is inferred that they have a positive attitude toward apples. Related approaches to measuring attitudes toward single-Â�target objects have been developed by simplifying the IAT’s dual-Â�target structure to a single

target, as in the Single-Category IAT (Karpinski & Steinman, 2006) and the Single-Target IAT (Wigboldus, Holland, & van Knippenberg, 2004). Because there is only one target object, these measures avoid the problem of relative comparisons. However, these tasks still involve comparisons across separate blocks of compatible versus incompatible trials, exposing them to another set of methodological critiques (Teige-Â�Mocigemba et al., Chapter 7, this volume). In a nutshell, these tasks assume that participants’ attitudes, stereotypes, or other knowledge structures provide the source of compatibility effects. That is, candy bars and pleasant words are compatible because both are positively evaluated. However, the valence of the items may be only one source of compatibility effects. As noted by Rothermund and Wentura (2001), participants might pair the items along any dimension that is salient at the time. Hence, participants might simplify the sorting task by subjectively recoding the task using any salient heuristic. Such “salience asymmetries” could create compatibility effects on the tasks for reasons that are unrelated to the associations of interest to the researcher. This issue has been addressed by several new methods that present congruent and incongruent trials randomly in a single block rather than blocked. The first IAT-derived task that avoided its original block structures is De Houwer’s (2003) Extrinsic Affective Simon Task. More recent examples include the Single-Block IAT (Teige-Â�Mocigemba, Klauer, & Rothermund, 2008) and the Recoding-Free IAT (Rothermund, Teige-Â�Mocigemba, Gast, & Wentura, 2009).

Priming-Â�Inspired Methods These methods all aimed at remedying some specific limitations of the IAT, perhaps because the IAT’s popularity made it an exciting target for refining measurement techniques. However, during the same period, research was progressing to refine priming methods as well (Wentura & Degner, Chapter 6, this volume). Whereas Fazio and colleagues (1986) had used sequential priming to measure attitudes, Banaji and Hardin (1996) applied sequential priming to measure semantic relations (e.g., stereotypes), and Wittenbrink and colleagues (1997) extended priming paradigms to measure semantic and affective dimensions of meaning independently. Priming paradigms have the considerable benefit of simplicity. Subjects typically respond to the target items with a simple judgment, making these paradigms simpler for subjects to complete and for



1. History of Implicit Social Cognition

researchers to interpret compared with IAT-related tasks. However, the response latency scores yielded by priming tasks often involve large proportions of measurement error. One way around this problem is to require participants to respond quickly, thus shifting influences of the primes from response latencies to accuracy rates (Draine & Greenwald, 1998). Payne (2001) used this strategy to measure the relationship between racial stereotypes and perceptions of weapons. Target items were more likely to be misidentified as guns when they were preceded by black face primes than white face primes. Priming effects in accuracy were found to be more reliable than priming effects in response latencies (Payne, 2001, 2005). Another approach to this problem is to dispense with latency and accuracy altogether and instead use judgments of ambiguous stimuli. Murphy and Zajonc (1993) showed that participants rated Chinese ideographs more favorably when they were primed with emotionally pleasant pictures and less favorably when they were primed with unpleasant pictures. Payne, Cheng, and colleagues (2005) adapted this paradigm to measure individual differences in attitudes toward the prime stimuli. Their affect misattribution procedure proved to have large effect sizes and high reliability, helping to address some of the key difficulties with sequential priming methods.

THEORETICAL DEVELOPMENTS Parallel to the development of second-�generation measures, the field of implicit social cognition has been reshaped by two major theoretical advances. One involves the emergence of generalized, domain-�independent dual-�process models; the other involves the development of formal process models that describe and quantify the contributions of distinct processes to performance on cognitive tasks. Together, these theoretical developments have had a significant impact on how researchers interpret the scores obtained with indirect measures.

Generalized Dual-�Process Models At the dawn of the new millennium, social psychology witnessed a remarkable shift in the generality of theorizing. Research in the 1980s and 1990s was characterized by a strong influence of dual-�process theories, which divided the realm of social cognitive processes into effortless, automatic processes versus effortful, controlled processes. To

9

a large extent, these theories were domain specific in that they were concerned with particular phenomena in social psychology, including persuasion (e.g., Chaiken, 1980; Petty & Cacioppo, 1986), impression formation (e.g., Brewer, 1988; Fiske & Neuberg, 1990), and dispositional attribution (Gilbert, 1989; Trope, 1986). In 2000, Smith and DeCoster published an influential review article in which they reconceptualized the domain-Â�specific processes proposed by earlier theories in terms of a general set of processes underlying a variety of phenomena. Their distinction between associative and rule-based processes set the foundation for a theoretical reinterpretation of direct and indirect measures as reflecting the outcomes of two qualitatively distinct processes (e.g., Gawronski & Bodenhausen, 2006; Rydell & McConnell, 2006; Strack & Deutsch, 2004; see also Deutsch & Strack, Chapter 4, this volume). The important difference from earlier theorizing is that these models emphasize the principles by which processes operate (e.g., associative vs. rule based) rather than the conditions under which they operate (e.g., unconscious, efficient, unintentional, uncontrollable). These operating conditions then become empirical questions to be tested. The most influential model in this regard is Strack and Deutsch’s (2004) reflective–Â�impulsive model, which distinguishes between associative and propositional processes. Associative processes involve the activation of associations in memory, which is guided by the principles of similarity and spatiotemporal contiguity. Propositional processes, in contrast, are conceptualized as the validation of the information implied by activated associations, which depends on syllogistic principles of logical consistency (Gawronski & Strack, 2004). The central difference between the two kinds of processes lies in the role of subjective truth or accuracy. Whereas the activation of associations is assumed to occur independently of whether these associations are regarded as accurate or inaccurate, propositional processes are inherently concerned with the validation of activated information. This distinction has been related to the outcomes of direct and indirect measures. Direct self-Â�report measures assess the subjective validity of propositional statements about some state of affairs (e.g., “How much do you agree with the statement .â•›.â•›. ”), whereas indirect measures assess the activation— that is, the momentary accessibility—of associations independent of whether they are regarded as accurate or inaccurate. Despite their impact on the field, generalized dual-Â�process models have also inspired some

10

1. History of Implicit Social Cognition

controversy, with some authors arguing that the distinctions they draw are too narrow and others arguing that they are too broad. For example, Kruglanski, Erb, Pierro, Mannetti, and Chun (2006) argued that it is unnecessary to distinguish between processes on the basis of operating principles such as associative versus propositional processing because virtually all judgment can be interpreted as the result of a single inference process so long as researchers specify parameters, such as relevance of information, processing difficulty, and cognitive resources needed to reach a judgment. In contrast, Sherman (2006) argued that more than two processes are often needed to describe the interplay between automatic and controlled processes. Still, generalized dual-�process models have advanced the field, at a minimum, by providing heuristically useful frameworks for interpreting distinctions between indirect and direct measures (Hofmann, Gawronski, et al., 2005). In addition, the models have provided theoretical links to recent developments in social neuroscience (Ito, Chapter 5, this volume), and they inspired novel ways of thinking about reflective versus impulsive determinants of social behavior (Strack & Deutsch, 2004) and the malleability of associative representations (Gawronski & Bodenhausen, 2006).

Formal Process Models of Indirect Measures Whereas generalized dual-�process models seek to explain a wide variety of findings on the basis of a broad central distinction, another new direction shaping implicit social cognition aims to get very specific about the processes underlying individual effects, tasks, or behaviors. Formal process models describe and quantify the contributions of multiple processes to performance on particular tasks and have recently been applied to indirect measures (Sherman, Klauer, & Allen, Chapter 9, this volume). Indirect measures operate on the premise that their scores are driven by underlying attitudes, stereotypes, or whatever representation is of interest to the researcher. However, a great deal of research suggests that the reality is more complicated than that. Many variables have been shown to affect task performance besides the mental contents they were meant to measure. For example, Payne (2001) observed greater stereotype bias in weapon misidentifications when participants responded under time pressure than when they responded at their own pace. If one assumes that the only process at work was automatic stereotyping, then such findings would lead to the strange con-

clusion that people had stronger stereotypical associations when they responded quickly. Instead, the most likely interpretation is that stereotypical representations were the same for participants responding fast and those responding slow. Other processes critical to responding accurately must have been influenced by speeded responding. Findings like these suggest that indirect measures reflect not only automatic processes but also controlled processes. The distinction between automatic and controlled processes allows researchers to predict, for example, that when people are tired, distracted, or rushed, they are more likely to respond based on automatic impulses than when they are energetic, focused, and unhurried (Fazio & Towles-Â�Schwen, 1999; Strack & Deutsch, 2004). The fact that many dual-Â�process theories predict these results is both a strength and a weakness for the theories. On the one hand, predictive power is vital for a theory’s value. On the other hand, dual-Â�process models do not usually specify exactly how automatic and controlled processes relate to each other. However, in understanding conflicts between automatic and controlled influences, it is often essential to know how they are related. Formal process models have been developed to answer these more specific questions. One such model is the process dissociation (PD) model, originally developed by Jacoby (1991) to separate implicit and explicit memory processes. Payne (2001) applied the model to separating automatic and controlled influences in a priming task, illustrating its usefulness for studying implicit social cognition. One variant of this model assumes that automatic processes drive behavior only when control over behavior fails (Jacoby, 1991). A second variant of the model assumes that automatic processes are instead dominant, and that controlled processes only drive behavior in the absence of an automatic influence (Lindsay & Jacoby, 1994). Testing how well different models describe experimental data can be used both to answer theoretical questions about how unobservable processes are related to each other and also to create quantitative estimates of those underlying processes (Payne, Jacoby, & Lambert, 2005). A second related model is the Quad model (Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005; Sherman et al., 2008). Whereas the PD variants assume that either controlled or automatic processing is dominant whenever they conflict, the Quad model assumes that either kind of process can be dominant. The Quad model adds a third parameter that reflects whether automatic or controlled processes “win” when they are in



1. History of Implicit Social Cognition

conflict and a fourth parameter for guessing biases. Bishara and Payne (2009) have recently described a unified framework for understanding PD and the Quad model, as well as other related models such as Stahl and Degner’s (2007) ABC model, within a single family of models (Payne & Bishara, 2009). These models are all multinomial models, which describe a variety of unobserved processes as probabilities. Multinomial models are agnostic about temporal order, and so they cannot differentiate between processes that occur relatively early or late. However, Klauer, Voss, Schmitz, and TeigeÂ�Mocigemba (2007) proposed a diffusion model that incorporates both accuracy (as with multinomial models) and also reaction times. Multinomial and diffusion models are exciting new directions because they offer means to test theories about the processes underlying indirect measures and simultaneously quantifying those processes. These models also offer potential for integrating insights across many different kinds of indirect measures because the same models can be applied across many different tasks to identify their common and distinct components. One of the earliest insights provided by the application of formal process models is that experimentally induced variations in indirect measurement scores can be due to different underlying mechanisms (Gawronski & Sritharan, Chapter 12, this volume). Whereas some effects have been found to reflect genuine changes in underlying associations (e.g., Sherman et al., 2008; Stewart & Payne, 2008), others stemmed from experimentally induced impairments in executive control (e.g., Govorun & Payne, 2006; Sherman et al., 2008). Given that changes in indirect measurement scores are typically interpreted as indicating changes in the underlying mental representations, formal process models provide an important means to avoid misinterpretations of empirical data and distortions in general theorizing.

WHERE IS IT GOING? Implicit social cognition is arguably one of the liveliest and most active research areas in social psychology. The steady increase in the number of publications is just one indicator in this regard. Given the exponential growth of implicit social cognition over the past decade, we want to consider some of the emerging themes that may shape the future of implicit social cognition. As the quantum physicist Niels Bohr put it, prediction is very difficult, especially about the future. Nevertheless, in

11

reviewing the chapters of this Handbook, we identified three themes that may guide future research and theorizing in implicit social cognition. A first theme—one that has been recurring in different variants—is the notorious tension between person-based and situation-based approaches. This tension is reflected in early debates about whether automatically activated associations reflect mere knowledge of cultural stereotypes (Devine, 1989) or personal attitudes (Fazio et al., 1995). It is reflected in the current controversy about the role of personal versus extrapersonal associations in the IAT (Gawronski et al., 2008; Nosek & Hansen, 2008a, 2008b; Olson & Fazio, 2004; Olson et al., 2009). And it is reflected in the debate about whether responses on indirect measures reveal stable representations in memory (Fazio, 2007) or context-Â�dependent evaluations that are constructed on the spot (Schwarz, 2007). Even though the details of these debates differ in various ways, they are all concerned with the question of whether responses on indirect measures tell us something about the individual or something about the individual’s environment. Framed in this way, the conflicting positions might bring up painful memories of the person–Â�situation debate in social and personality psychology. However, we believe that looking back at this debate can be helpful in identifying novel, integrative ways of thinking about implicit social cognition. A particularly useful direction in this regard may be social cognitive approaches to study person × situation interactions (e.g., Cervone, Shadel, & Jencius, 2001; Mischel & Shoda, 1995). One way to do this is by investigating the role of individual experiences in shaping an individual’s responses in various situational contexts (e.g., Rydell & Gawronski, 2009). A complementary approach is to study the ways that dispositions lead individuals to select different situations. Even though such an approach cannot answer every question of the justÂ�mentioned debates, it has the potential to provide a more comprehensive picture by including (1) a person’s individual experiences, (2) the context in which these experiences occurred, and (3) the role of contextual cues in activating individual experiences associated with a given context. Given that research on implicit social cognition heavily relies on both individual difference (e.g., prediction of behavior; Perugini et al., Chapter 14, this volume) and experimental designs (e.g., attitude formation and change; Gawronski & Sritharan, Chapter 12, this volume), future research adopting a person × situation interaction perspective could be helpful to shed new light on some of the recurring disputes

12

1. History of Implicit Social Cognition

about whether indirect measures tell us something about the person or about the person’s environment. A second trend that seems to be emerging from implicit social cognition is enhanced cross-talk and continuously shrinking boundaries between subdisciplines. The adoption of indirect measures and theoretical models from social psychology in other fields is only one example in this regard. In fact, it seems as though many social psychologists interested in basic questions have discovered applied research as an exciting context to demonstrate the power of their methods and theories. Great examples in this regard are provided by the chapters in the last section of this book, which review insights gained from implicit social cognition in the areas of health psychology (Wiers et al., Chapter 25), clinical psychology (Teachman, Cody, & Clerkin, Chapter 26), forensic psychology (Snowden & Gray, Chapter 27), consumer psychology (Perkins & Forehand, Chapter 28), and political psychology (Nosek, Graham, & Hawkins, Chapter 29). After decades of increasing compartmentalization in psychology, we see this development as an exciting trend that may ultimately lead to a more comprehensive picture of the human mind. A final theme that we identified in several chapters in this volume is a reanalysis of the role of mental concepts (as opposed to behavioral responses) in psychological theorizing. This trend is most explicit in requests not to interpret the responses on psychological measures as direct reflections of mental concepts. After all, responses on any kind of direct or indirect measures are behavioral responses (Amodio & Mendoza, Chapter 19; De Houwer & Moors, Chapter 10; Gawronski & Sritharan, Chapter 12; Perugini et al., Chapter 14; Sherman et al., Chapter 9). To be sure, it makes sense to assume that these behavioral responses have their roots in people’s minds. However, the undisputable fact that behavioral responses are mediated by mental processes does not allow researchers to equate these responses with the mental processes that presumably underlie them. A useful example to illustrate this idea is the basic notion of formal process models of indirect measures, which treat performance on indirect measures as behavioral outcomes that are jointly determined by multiple distinct processes. From the perspective of these models, the challenge for researchers is to develop theories of mental functioning that integrate the available set of behavioral observations in a parsimonious manner. Ideally, these models also imply novel predictions about behavior, so that they do

not simply provide a mentalistic redescription of the available behavioral observations. An important insight that can help to prevent circularity is the treatment of behavioral observations (e.g., performance on indirect measures) as behaviors that need to be explained rather than as mental constructs that explain behavior. From this perspective, mental constructs can still serve an important function in explaining behavior, but only to the degree that the theoretical assumptions about these constructs imply novel predictions about behavior that can be empirically scrutinized. Recent advances in implicit social cognition have already made significant steps in this direction, and the emerging concern with these issues indicates that they might become even more important in the future. Thus, even though implicit social cognition started as an inherently mentalistic approach, it clearly has the potential to make a unique contribution to the closing “decade of behavior,” and we look forward to the insights it will convey in the years to come. Acknowledgment We are grateful for the thoughtful comments of Galen Bodenhausen and Russ Fazio on an earlier draft of this chapter.

Note 1. For the sake of conceptual clarity, we follow De Houwer and colleagues’ (2009) recommendation to use the terms direct and indirect to describe procedural characteristics of measurement procedures and the terms implicit and explicit to describe psychological features of the constructs assessed by different kinds of measurement procedures (see also De Houwer & Moors, Chapter 10, this volume).

References Appiah, K. A. (2008). Experiments in ethics. Cambridge, MA: Harvard University Press. Arkes, H. R., & Tetlock, P. E. (2004). Attributions of implicit prejudice, or “would Jesse Jackson ‘fail’ the Implicit Association Test?” Psychological Inquiry, 15, 257–278. Banaji, M. R. (2001). Implicit attitudes can be measured. In H. L. Roediger, J. S. Nairne, I. Neath, & A. Surprenant (Eds.), The nature of remembering: Essays in remembering Robert G. Crowder (pp.€117–150). Washington, DC: American Psychological Association.



1. History of Implicit Social Cognition

Banaji, M. R., & Hardin, C. D. (1996). Automatic stereotyping. Psychological Science, 7, 136–141. Banaji, M. R., Nosek, B. A., & Greenwald, A. G. (2004). No place for nostalgia in science: A response to Arkes & Tetlock. Psychological Inquiry, 15, 279–289. Barden, J., Maddux, W. W., Petty, R. E., & Brewer, M. B. (2004). Contextual moderation of racial bias: The impact of social roles on controlled and automatically activated attitudes. Journal of Personality and Social Psychology, 87, 5–22. Bargh, J. A. (1994). The four horsemen of automaticity: Awareness, intention, efficiency, and control in social cognition. In R. S. Wyer, Jr. & T. K. Srull (Eds.), Handbook of social cognition (pp.€1–40). Hillsdale, NJ: Erlbaum. Bargh, J. A., Chaiken, S., Govender, R., & Pratto, F. (1992). The generality of the automatic attitude activation effect. Journal of Personality and Social Psychology, 62, 893–912. Bishara, A. J., & Payne, B. K. (2009). Multinomial process tree models of control and automaticity in weapon misidentification. Journal of Experimental Social Psychology, 45, 524–534. Blair, I. V., Ma, J., & Lenton, A. (2001). Imagining stereotypes away: The moderation of implicit stereotypes through mental imagery. Journal of Personality and Social Psychology, 81, 828–841. Blanton, H., Jaccard, J., Gonzales, P. M., & Christie, C. (2006). Decoding the Implicit Association Test: Implications for criterion prediction. Journal of Experimental Social Psychology, 42, 192–212. Brewer, M. B. (1988). A dual process model of impression formation. In T. K. Srull & R. S. Wyer, Jr. (Eds.), Advances in social cognition (Vol. 1, pp.€1–36). Hillsdale, NJ: Erlbaum. Broadbent, D. (1971). Decision and stress. Oxford, UK: Academic Press. Cervone, D., Shadel, W. D., & Jencius, S. (2001). SocialÂ�cognitive theory of personality assessment. Personality and Social Psychology Review, 5, 33–51. Chaiken, S. (1980). Heuristic versus systematic processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39, 752–766. Chaiken, S., & Bargh, J. A. (1993). Occurrence versus moderation of the automatic attitude activation effect: Reply to Fazio. Journal of Personality and Social Psychology, 64, 759–765. Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K., & Groom, C. (2005). Separating multiple processes in implicit social cognition: The quadmodel of implicit task performance. Journal of Personality and Social Psychology, 89, 469–487. Cunningham, W. A., Preacher, K. J., & Banaji, M. R. (2001). Implicit attitude measurement: Consistency, stability, and convergent validity. Psychological Science, 12, 163–170. Dasgupta, N., & Greenwald, A. G. (2001). On the malleability of automatic attitudes: Combating automatic prejudice with images of admired and disliked individuals. Journal of Personality and Social Psychology, 81, 800–814.

13

De Houwer, J. (2003). The Extrinsic Affective Simon Task. Experimental Psychology, 50, 77–85. De Houwer, J., Teige-Â�Mocigemba, S., Spruyt, A., & Moors, A. (2009). Implicit measures: A normative analysis and review. Psychological Bulletin, 135, 347–368. Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of Personality and Social Psychology, 56, 5–18. Doris, J. M. (2002). Lack of character: Personality and moral behavior. Cambridge, UK: Cambridge University Press. Dovidio, J. F., Evans, N., & Tyler, R. B. (1986). Racial stereotypes: The contents of their cognitive representations. Journal of Experimental Social Psychology, 22, 22–37. Draine, S. C., & Greenwald, A. G. (1998). Replicable unconscious semantic priming. Journal of Experimental Psychology: General, 127, 286–303. Dunton, B. C., & Fazio, R. H. (1997). An individual difference measure of motivation to control prejudiced reactions. Personality and Social Psychology Bulletin, 23, 316–326. Fazio, R. H. (1990). Multiple processes by which attitudes guide behavior: The MODE model as an integrative framework. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 23, pp.€ 75–109). San Diego, CA: Academic Press. Fazio, R. H. (1993). Variability in the likelihood of automatic attitude activation: Data reanalysis and commentary on Bargh, Chaiken, Govender, and Pratto (1992). Journal of Personality and Social Psychology, 64, 753–758. Fazio, R. H. (2007). Attitudes as object-Â�evaluation associations of varying strength. Social Cognition, 25, 603–637. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Fazio, R. H., & Olson, M. A. (2003). Implicit measures in social cognition research: Their meaning and use. Annual Review of Psychology, 54, 297–327. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. Fazio, R. H., & Towles-Â�Schwen, T. (1999). The MODE model of attitude-Â�behavior processes. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€97–116). New York: Guilford Press. Fiedler, K., Messner, C., & Bluemke, M. (2006). Unresolved problems with the “I,” the “A” and the “T”: Logical and psychometric critique of the Implicit Association Test (IAT). European Review of Social Psychology, 17, 74–147. Fiske, S. T., & Neuberg, S. L. (1990). A continuum of impression formation, from category-based to individuating processes: Influences of information and motivation on attention and interpretation. In M. P. Zanna (Ed.), Advances in experimental social psychol-

14

1. History of Implicit Social Cognition

ogy (Vol. 23, pp.€ 1–74). San Diego, CA: Academic Press. Gaertner, S. L., & McLaughlin, J. P. (1983). Racial stereotypes: Associations and ascriptions of positive and negative characteristics. Social Psychology Quarterly, 46, 23–30. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. Gawronski, B., Geschke, D., & Banse, R. (2003). Implicit bias in impression formation: Associations influence the construal of individuating information. European Journal of Social Psychology, 33, 573–589. Gawronski, B., Hofmann, W., & Wilbur, C. (2006). Are “implicit” attitudes unconscious? Consciousness and Cognition, 15, 485–499. Gawronski, B., & LeBel, E. P. (2008). Understanding patterns of attitude change: When implicit measures show change, but explicit measures do not. Journal of Experimental Social Psychology, 44, 1355–1361. Gawronski, B., LeBel, E. P., & Peters, K. R. (2007). What do implicit measures tell us?: Scrutinizing the validity of three common assumptions. Perspectives on Psychological Science, 2, 181–193. Gawronski, B., Peters, K. R., & LeBel, E. P. (2008). What makes mental associations personal or extraÂ�personal?: Conceptual issues in the methodological debate about implicit attitude measures. Social and Personality Psychology Compass, 2, 1002–1023. Gawronski, B., & Strack, F. (2004). On the propositional nature of cognitive consistency: Dissonance changes explicit, but not implicit attitudes. Journal of Experimental Social Psychology, 40, 535–542. Gilbert, D. T. (1989). Thinking lightly about others: Automatic components of the social inference process. In J. S. Uleman & J. A. Bargh (Eds.), Unintended thought (pp.€189–211). New York: Guilford Press. Govorun, O., & Payne, B. K. (2006). Ego-Â�depletion and prejudice: Separating automatic and controlled components. Social Cognition, 24, 111–136. Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-Â�esteem, and stereotypes. Psychological Review, 102, 4–27. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., & Schmitt, M. (2005). A meta-Â�analysis on the correlation between the Implicit Association Test and explicit self-Â�report measure. Personality and Social Psychology Bulletin, 31, 1369–1385. Hofmann, W., Gschwendner, T., Nosek, B. A., & Schmitt, M. (2005). What moderates implicit-Â�explicit consistency? European Review of Social Psychology, 16, 335–390. Jacoby, L. L. (1991). A process dissociation framework: Separating automatic from intentional uses of memory. Journal of Memory and Language, 30, 513–541. Jacoby, L. L., & Dallas, M. (1981). On the relationship between autobiographical memory and perceptual learning. Journal of Experimental Psychology: General, 110, 306–340.

Jones, E. E., & Sigall, H. (1971). The bogus pipeline: A new paradigm for measuring affect and attitude. Psychological Bulletin, 76, 349–364. Jordan, C. H., Whitfield, M., & Zeigler-Hill, V. (2007). Intuition and the correspondence between implicit and explicit self-Â�esteem. Journal of Personality and Social Psychology, 93, 1067–1079. Judd, C. M., Simpson, J. A., & Carver, C. S. (Eds.). (2001). Attitudes and social cognition [Special issue]. Journal of Personality and Social Psychology, 81(5). Karpinski, A., & Steinman, R. B. (2006). The single category Implicit Association Test as a measure of implicit social cognition. Journal of Personality and Social Psychology, 91, 16–32. Kennett, J., & Fine, C. (2009). Will the real moral judgment please stand up? Ethical Theory and Moral Practice, 12, 77–96. Klauer, K. C., Voss, A., Schmitz, F., & Teige-Â�Mocigemba, S. (2007). Process-Â�components of the Implicit Association Test: A diffusion model analysis. Journal of Personality and Social Psychology, 93, 353–368. Kruglanski, A., Erb, H., Pierro, A., Mannetti, L., & Chun, W. (2006). On parametric continuities in the world of binary either ors. Psychological Inquiry, 17, 153–165. Lindsay, D. S., & Jacoby, L. L. (1994). Stroop processÂ�dissociations: The relationship between facilitation€ and interference. Journal of Experimental Psychology: Human Perception and Performance, 20, 219–234. Machado, A., & Silva, F. J. (2007). Toward a richer view of the scientific method: The role of conceptual analysis. American Psychologist, 62, 671–681. Mischel, W., & Shoda, Y. (1995). A cognitive-Â�affective system theory of personality: Reconceptualizing situations, dispositions, dynamics, and invariance in personality structure. Psychological Review, 102, 246–268. Murphy, S. T., & Zajonc, R. B. (1993). Affect, cognition, and awareness: Affective priming with optimal and suboptimal stimulus exposures. Journal of Personality and Social Psychology, 64, 723–739. Neely, J. H. (1977). Semantic priming and retrieval from lexical memory: Roles of inhibitionless spreading activation and limited-Â�capacity attention. Journal of Experimental Psychology: General, 106, 226–254. Nosek, B. A. (2005). Moderators of the relationship between implicit and explicit evaluation. Journal of Experimental Psychology: General, 134, 565–584. Nosek, B. A., & Banaji, M. R. (2001). The Go/No-Go Association Task. Social Cognition, 19, 625–666. Nosek, B. A., & Hansen, J. (2008a). The associations in our head belong to us: Searching for attitudes and knowledge in implicit evaluation. Cognition and Emotion, 22, 553–594. Nosek, B. A., & Hansen, J. (2008b). Personalizing the Implicit Association Test increases explicit evaluation of target concepts. European Journal of Psychological Assessment, 24, 226–236. Olson, M. A., & Fazio, R. H. (2004). Reducing the influence of extra-Â�personal associations on the Implicit Association Test: Personalizing the IAT. Journal of Personality and Social Psychology, 86, 653–667. Olson, M. A., Fazio, R. H., & Han, H. A. (2009). Con-



1. History of Implicit Social Cognition

ceptualizing personal and extrapersonal associations. Social and Personality Psychology Compass, 3, 152–170. Payne, B. K. (2001). Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon. Journal of Personality and Social Psychology, 81, 181–192. Payne, B. K. (2005). Conceptualizing control in social cognition: How executive control modulates the expression of automatic stereotyping. Journal of Personality and Social Psychology, 89, 488–503. Payne, B. K., & Bishara, A. J. (2009). An integrative review of process dissociation and related models in social cognition. European Review of Social Psychology, 20, 272–314. Payne, B. K., Burkley, M., & Stokes, M. B. (2008). Why do implicit and explicit attitude tests diverge?: The role of structural fit. Journal of Personality and Social Psychology, 94, 16–31. Payne, B. K., Cheng, C. M., Govorun, O., & Stewart, B. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. Journal of Personality and Social Psychology, 89, 277–293. Payne, B. K., Jacoby, L. L., & Lambert, A. J. (2005). Attitudes as accessibility bias: Dissociating automatic and controlled components. In R. R. Hassin, J. S. Uleman, & J. A. Bargh (Eds.), The new unconscious (pp.€393–420). Oxford, UK: Oxford University Press. Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 19, pp.€123–205). New York: Academic Press. Popper, K. R. (1934). Logic of scientific discovery. New York: Basic Books. Posner, M. I., & Snyder, C. R. R. (1975). Attention and cognitive control. In R. L. Solso (Ed.), Information processing and cognition: The Loyola symposium. Hillsdale, NJ: Erlbaum. Rothermund, K., Teige-Â�Mocigemba, S., Gast, A., & Wentura, D. (2009). Minimizing the influence of recoding in the IAT: The Recoding-Free Implicit Association Test (IAT-RF). Quarterly Journal of Experimental Psychology, 62, 84–98. Rothermund, K., & Wentura, D. (2001). Figure-Â�ground asymmetries in the Implicit Association Test (IAT). Zeitschrift für Experimentelle Psychologie, 48, 94–106. Rydell, R. J., & Gawronski, B. (2009). I like you, I like you not: Understanding the formation of contextÂ�dependent automatic attitudes. Cognition and Emotion, 23, 1118–1152. Rydell, R. J., & McConnell, A. R. (2006). Understanding implicit and explicit attitude change: A systems of reasoning analysis. Journal of Personality and Social Psychology, 91, 995–1008. Schacter, D. (1987). Implicit memory: History and current status. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 501–518.

15

Schwarz, N. (2007). Attitude construction: Evaluation in context. Social Cognition, 25, 638–656. Schwarz, N., & Bohner, G. (2001). The construction of attitudes. In A. Tesser & N. Schwarz (Eds.), Blackwell handbook of social psychology: Intraindividual processes (pp.€436–457). Malden, MA: Blackwell. Sherman, J. (2006). On building a better process model: It’s not only how many, but which ones and by which means? Psychological Inquiry, 17, 173–184. Sherman, J. W., Gawronski, B., Gonsalkorale, K., Hugenberg, K., Allen, T. J., & Groom, C. J. (2008). The selfÂ�regulation of automatic associations and behavioral impulses. Psychological Review, 115, 314–335. Shiffrin, R., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. Psychological Review, 84, 127–190. Smith, E. R., & DeCoster, J. (2000). Dual-Â�process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4, 108–131. Stahl, C., & Degner, J. (2007). Assessing automatic activation of valence: A multinomial model of EAST performance. Experimental Psychology, 54, 99–112. Stewart, B. D., & Payne, B. K. (2008). Bringing automatic stereotyping under control: Implementation intentions as efficient means of thought control. Personality and Social Psychology Bulletin, 34, 1332– 1345. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Teige-Â�Mocigemba, S., Klauer, K. C., & Rothermund, K. (2008). Minimizing method-Â�specific variance in the IAT: The single block IAT. European Journal of Psychological Assessment, 24, 237–245. Treisman, A. (1969). Strategies and models of selective attention. Psychological Review, 76, 282–299. Trope, Y. (1986). Identification and inferential processes in dispositional attribution. Psychological Review, 93, 239–257. Warrington, E. K., & Weiskrantz, L. (1968). A new method of testing long-term retention with special reference to amnesic patients. Nature, 217, 972–974. Wigboldus, D. H. J., Holland, R. W., & van Knippenberg, A. (2004). Single target implicit associations. Unpublished manuscript. Wilson, T. D., Lindsey, S., & Schooler, T. Y. (2000). A model of dual attitudes. Psychological Review, 107, 101–126. Wittenbrink, B., Judd, C. M., & Park, B. (1997). Evidence for racial prejudice at the implicit level and its relationship with questionnaire measures. Journal of Personality and Social Psychology, 72, 262–274. Wittenbrink, B., Judd, C. M., & Park, B. (2001). Spontaneous prejudice in context: Variability in automatically activated attitudes. Journal of Personality and Social Psychology, 81, 815–827.

PA R T I

BASICS

Chapter 2

In Search of a Measure That Qualifies as Implicit Recommendations Based on a Decompositional View of Automaticity Agnes Moors, Adriaan Spruyt, and Jan De Houwer

I

n recent years, investigators interested in measuring attributes of individuals, such as their attitudes toward stimuli (e.g., products, minorities), have increasingly turned to implicit measures such as the affective priming task (e.g., Fazio, Jackson, Dunton, & Williams, 1995) and the Implicit Association Task (Greenwald, McGhee, & Schwartz, 1998). We analyze the term implicit measure with the aim of formulating recommendations for investigators in search of a measure that qualifies as implicit. To analyze the term implicit measure requires an answer to three questions: (1) What is implicit? (2) What is a measure? (3) To which aspect of the measure does the term implicit refer? With regard to the first question, De Houwer (2006; De Houwer & Moors, 2007, Chapter 10, this volume; De Houwer, Teige-�Mocigemba, Spruyt, & Moors, 2009) proposed to replace the concept of implicit with the concept of automatic. Regardless of whether one accepts that both concepts have exactly the same meaning (e.g., one might argue that the concept automatic is broader than the concept implicit), it is useful to replace the question of implicit measures with the question of automatic measures (and thus to broaden the question). With regard to the second question, De Houwer argued that the term measure can be used in two senses. In the first sense, it refers to a procedure (e.g., a priming task) designed to measure an



attribute of the person (e.g., his or her attitude toward the stimuli presented as primes). In the second sense, it refers to the outcome or effect of the measurement procedure (e.g., the size of a priming effect to a particular stimulus) and is supposed to reflect the attribute of the person. With regard to the third question, De Houwer argued that in the phrase “implicit measure” the term implicit is not used as a predicate of the measurement procedure but as a predicate of the measurement outcome or the effect (it tells us how the effect came about) or of the processes underlying the measurement outcome (it tells us what the processes that produced the effect are like).1 This definition of the term implicit measure has important implications for investigators in search of a measure that qualifies as implicit. To classify a particular measurement outcome as implicit, one has to generate (or review) empirical evidence supporting that the processes underlying the measure are automatic. Before we can illustrate how to assess the automaticity of processes underlying certain measurement outcomes, we need to take a few preparatory steps. In the first part, we clarify our view of automaticity. In the second part, we consider one popular group of measurement outcomes, irrelevant feature effects, and address the question of what processes underlie these effects. In the third part, we consider the affective priming effect as one example of an irrelevant feature effect that 19

20

I. BASICS

has been used as an implicit measure. We review a number of concrete arguments to diagnose the automaticity of the processes underlying affective priming effects.

AUTOMATICITY We advocate a feature-based view of automaticity, defining automaticity as an umbrella term for a number of features such as uncontrolled, unconscious, efficient, and fast. In this section, we first list definitions for a variety of automaticity features (see Moors & De Houwer, 2006, 2007, for a review). Next, we argue that automaticity features inform us about the conditions under which processes operate. In addition, we propose to extend the set of operating conditions beyond those that derive from automaticity features. Finally, we defend a decompositional view with regard to the relations among automaticity features or, more broadly, operating conditions (see also Bargh, 1992).

Definitions of Automaticity Features The features to be defined in this section are (un) controlled, (un)intentional, (non)autonomous, goal (in)dependent, (not) purely stimulus driven, (un)conscious, (non)efficient, and (slow) fast (Table 2.1). The first five features in this list are somehow related to goals. We call them the family of goal-�related features. Perhaps the most central of these is the feature controlled. To say that a process is controlled implies that one has a goal regarding the process and that activation of this goal causes the end state put forward in the goal. Processing goals can be of the promoting type (i.e., the goal to engage in the process) or of the counteracting type (e.g., the goals to alter, stop, or avoid the process). In the case of a promoting goal, the end state is the actual occurrence of a process. In the case of a counteracting goal, the end state is a change, the interruption, or the prevention of a process. To say that a process is uncontrolled can refer to different cases. It can refer to the case in which the goal about the process is present but the effect is absent. It can also refer to the case in which the effect is present (i.e., that the process occurs, changes, is interrupted, is prevented) but the goal about the process is absent. For instance, a process can be called uncontrolled when it is interrupted but the person did not have the goal to interrupt it. Uncontrolled can also describe the case in which both the goal and the desired effect are present but the effect was not caused by the goal. Imagine that

you have the goal to kneel down to tie your shoes. Suppose that you do kneel down not because of your goal but because you are pushed by a person walking by. In this case, both the goal and the desired effect are present, but it would be wrong to regard this act as controlled because the goal did not cause the desired effect. Concluding that a process or act is controlled thus requires demonstration of a causal relation between goal and effect, something that might not always be easy to achieve. Several other goal-Â�related features are closely related to the feature (un)controlled. To say that a process is intentional means that one has the goal to engage in a process and that this goal results in the occurrence of the process. Intentional is identical to controlled in the promoting sense. Intentional processes are thus a subset of controlled processes. Likewise, unintentional processes are a subset of uncontrolled processes. The feature autonomous is also related to the feature controlled. Moors and De Houwer (2006) defined autonomous as uncontrolled in terms of every possible processing goal. A process is autonomous when it is not caused by a promoting goal (i.e., uncontrolled in the promoting sense or unintentional) and when a counteracting goal does not result in the envisioned effect (i.e., uncontrolled in the counteracting sense). Another feature that is closely related to (un) intentional is the feature goal (in)dependent. Intentional processes are a subclass of goal-Â�dependent processes (because intentions are a subclass of goals; intentions are goals that have an act or a process as their content). A process that is caused by a goal is goal dependent. This goal may be either the proximal goal to engage in the process or a remote goal. In the first case, the process is intentional; in the second case, the process is unintentional but goal dependent. For instance, the act of moving one’s arm toward an apple may be caused by the proximal goal to move one’s arm (in this case the act is intentional) or directly by the remote goal to eat the apple (in this case the process is unintentional but goal dependent). Another scenario is that the remote goal causes the proximal goal and that the latter, in turn, causes the process. For example, the goal to eat an apple may cause the goal to move one’s arm toward the apple, and this may, in turn, cause the arm to move toward the apple. A process that is not caused by a (proximal or remote) goal is goal independent. A subset of goal-Â�independent processes are purely stimulus-Â�driven processes. These are processes that, in addition to being goal independent (i.e.,



2. A Measure That Qualifies as Implicit

21

TABLE 2.1.╇ (Non)automaticity Features: Types and Ingredients Ingredients Conditions

Effect

Causal connection

Controlled in the promoting sense = intentional

Promoting goal present

Effect present

Present

Controlled in the counteracting sense

Counteracting goal present

Effect present

Present

Goal pertaining to process absent Goal pertaining to process absent Goal pertaining to process present Goal pertaining to process present

Effect absent

Not applicable

Effect present

Not applicable

Effect absent

Not applicable

Effect present

Absent

Effect absent

Not applicable

Effect present

Not applicable

Effect absent

Not applicable

Effect present

Absent

Goal present

Process present

Present

Goal absent Goal present

Process present Process present

Not applicable Absent

Purely stimulus driven

Stimulus (+ usual background conditions) present

Process present

Present

Autonomous = uncontrolled in promoting and counteracting sense

Promoting goal absent Promoting goal present Counteracting goal present

Process present Process present Effect absent

Not applicable Absent Not applicable

Features

Uncontrolled in the promoting sense (note: only in case a2 and b2 uncontrolled in the promoting sense = unintentional)

Types

(a1) (a2) (b1) (b2)

Uncontrolled in the counteracting sense

(a1) (a2) (b1) (b2)

Goal dependent Goal independent

Conscious

(a) (b)

A-conscious P-conscious

Goal pertaining to process absent Goal pertaining to process absent Goal pertaining to process present Goal pertaining to process present

Intentional aspect present Phenomenal aspect present

Unconscious

Consciousness (A and/or P) absent

Efficient

Minimal amount of attentional capacity present

Nonefficient

Large amount of attentional capacity present

Fast

Minimal amount of time present

Slow

Large amount of time present

22

I. BASICS

do not causally depend on any type of goal), also do not depend on other factors such as awareness or attention. The occurrence of these processes depends only on the presence of a stimulus and certain basic conditions that ensure that the stimulus can be physically registered (e.g., in case of visual stimuli, that the eyes are not closed). It should be clear from the prior discussion that it is difficult, if not impossible, to demonstrate that a process is entirely goal independent or purely stimulus driven. The best one can do is demonstrate that the process does not depend on certain goals or other conditions and make these goals and conditions explicit. Now that we discussed all goal-Â�related features, we turn to the features conscious and unconscious. It is notoriously difficult to define the concepts of conscious and unconscious. Many theorists have distinguished two aspects of consciousness: an Intentional aspect and a phenomenal aspect (see also Hofmann & Wilson, Chapter 11, this volume). The Intentional aspect refers to the fact that consciousness is about something, that it has content; the phenomenal aspect is subjective feeling. The first depends on attention, whereas the second escapes attention. Some theorists have even posited that both aspects can be separated, thus arguing for two types of consciousness (Block, 1995). Note that the word “Intentional” is written here with a capital to distinguish it from the feature intentional discussed above. It is important to keep in mind that the concepts conscious and unconscious can be used as a predicate of several things. They can be applied to (1) the stimulus input of a process, (2) the output of a process, or (3) the process itself (e.g., Bargh, 1994). Processes that operate on conscious input can be unconscious themselves. Therefore, it is important to always specify what the concepts conscious and unconscious are a predicate of (see later discussion). Another correlate of (non)automaticity is the feature (non)efficient. A process is efficient when it consumes little or no attentional capacity. Attention can be said to have two aspects: quantity and direction. Efficiency is related to the quantity aspect. Efficient processes operate without or with very little of this quantity. It should be noted that processes that depend on the direction of attention do not necessarily use a large quantity of capacity. Consider, for example, the observation that Stroop interference effects sometimes diminish when relevant and irrelevant features were spatially separated (e.g., Kahneman & Chajczyk, 1983). This indicates that in a standard Stroop task process-

ing of the irrelevant feature requires it to be in the focus of attention. Nevertheless, it is possible that it sufficed with a very small amount of attention and, therefore, still deserves to be called efficient. Attention may be directed at the process or at the stimulus input on which the process operates. One should thus clearly indicate whether one uses the term efficient as a predicate of the critical process or of the initial perceptual process that operates on the raw stimulus input. The final feature that we consider is the feature fast (slow). A fast process is one with a short duration. The duration of a process should not be confounded with the duration of the stimulus input on which the process operates. Slow processes may be triggered by briefly presented stimuli. The feature fast is clearly gradual. There is no objective threshold for calling something fast or slow, so investigators need to rely on conventions.

Features Provide Information about€Operating Conditions Most theorists accept that the features discussed are not fixed features of processes. A process can be unintentional on some occasions but intentional on others. This has led to the proposal to view and redefine features of (non)automaticity in terms of operating conditions (Bargh, 1992). For example, a process is uncontrolled in the promoting sense (unintentional) when it operates under the condition of the absence of a (causally efficacious) promoting goal. A process is uncontrolled in the counteracting sense when it operates under the condition of the presence of a counteracting goal. A process (or input or output) is unconscious when it operates under the condition of a lack of awareness of the process (or input or output). A process is efficient when it operates under the condition of minimal attentional capacity. A process is fast when it can be completed under the condition of minimal time. It is important to note that the range of possible conditions that influence the occurrence of processes can be extended beyond conditions that derive from automaticity features. One example of a condition that is not included in the automaticity concept is salience, the quality of a stimulus (or stimulus feature) to stand out relative to other stimuli (or stimulus features) (Itti, 2007). Salience is a property of a stimulus, but a stimulus is always salient for a person. Thus, salience corresponds to the subjective intensity of a stimulus relative to other stimuli. This factor must be delineated from the objective intensity2 of a stimulus



2. A Measure That Qualifies as Implicit

relative to other stimuli. Salience of a stimulus can be influenced by external factors such as the objective intensity, clarity, frequency, and size of a stimulus but also by internal factors such as goals, expectations, attention, and recency (whether or not a representation of the stimulus was recently activated). The salience of a stimulus is likely to influence the occurrence of processes that use the stimulus as their input: The more salient a stimulus, the more likely it is to serve as the input of processes. To broaden our definition of implicit measures even further: An implicit measure is a measurement outcome that is based on processes that operate under suboptimal conditions, such as when there is no promoting goal, when there is a counteracting goal, when there is minimal time, when there is minimal attentional capacity, when the stimulus input is unconscious, and when the stimulus input is not salient.

Relations among Operating€Conditions Various feature-based views of automaticity differ with regard to the features they emphasize most and the amount of coherence they assume among features. The best known feature-based view is the all-or-none view. This view assumes a perfect coherence among the features of each mode: Automatic processes hold all automatic features; nonautomatic processes hold all nonautomatic features. Thus, evidence for the presence of one feature of one mode can be generalized to the remaining features of that mode. It has become clear, however, that the all-or-none view is incorrect. Studies have demonstrated that most processes possess a combination of automatic and nonautomatic features. An alternative view that we advocate is the decompositional view (Bargh, 1992; Moors & De Houwer, 2006). This view assumes no (a priori) coherence among groups of features. Thus, the presence of features should be investigated separately and cannot be inferred from the presence of other features. The decompositional view rests on two central assumptions with regard to the interrelations among features. A first assumption is that automaticity features can be conceptually separated, that is, they can be defined in such a way that they do not share ingredients. In the definitions presented previously, we did point out that there is partial conceptual overlap among several pairs of goal-�related features. Intentional processes are a subset of controlled processes as well as a subset of goal�dependent processes. Unintentional processes and autonomous processes are both subsets of uncon-

23

trolled processes. Purely stimulus-�driven processes are a subset of unintentional processes. On the other hand, we do not assume conceptual overlap among different types of controlled processes that are based on different types of goals. (Un)controlled in the promoting sense does not overlap with (un)controlled in the counteracting sense. In our definitions, we also avoided conceptual overlap among goal-�related features and the feature (un)conscious. It is often assumed that consciousness is an ingredient of control (in the promoting and counteracting sense). To be in control seems to ask for a conscious subject. However, recent research has convincingly demonstrated that goals to engage in or to counteract a process can also be activated and implemented in an unconscious way (Moskowitz, Li, & Kirk, 2004). Hence, one can have control without being aware of it. Our definitions also avoid conceptual overlap among all other feature sets (goal-�related features and efficient; unconscious and efficient; fast and efficient; fast and goal-�related features). A second assumption on which the decompositional approach rests is that automaticity features do not influence each other in a one-to-one fashion. That is, one feature is not a necessary and/ or sufficient condition for another feature. We do acknowledge, however, that there are relations among features or operating conditions. This is because the central ingredients of the features or operating conditions (e.g., goals, consciousness, time, attention, and salience) can influence each other (see Figure 2.1; see also Moors & De Houwer, 2007). Examples are that (1) goals influence the direction of attention; (2) the direction of attention influences the content of consciousness; (3) the amount of attention spent, the objective stimulus intensity, salience, and time influence the possibility of consciousness; (4) the presence of a salient and/or conscious stimulus (or process) influences the possibility of implementing a conscious goal of the promoting (or of the counteracting) kind; and (5) goals, expectations, the direction and amount of attention, and objective stimulus intensity influence salience. Importantly, the relations among these ingredients are not one-to-one relations. With regard to example 1, attention may be directed by other factors than goals, such as expectations and salience. With regard to example 2, there may be aspects or types of consciousness (e.g., phenomenal consciousness) that are not dependent of the direction of attention. The relations mentioned in example 3 show that consciousness of a stimulus (or process) is dependent of the interplay among several

24

I. BASICS

ingredients. The role of these ingredients may be additive. This means, for instance, that a decrease in presentation time can be compensated by an increase in stimulus intensity and/or an increase in attention. Likewise, a decrease of stimulus intensity (e.g., when the stimulus is degraded) can be compensated by an increase in presentation time and/or an increase in attention. Similar claims can be made with regard to the relations described in example 5. The assumptions of a lack of conceptual overlap and a lack of one-to-one relations justify the decompositional approach to investigate features separately. Indeed, it only makes sense to invest effort in the separate study of features when they do not have conceptual overlap and when they are not related in a one-to-one fashion. If consciousness would be included in control, evidence for the presence of control would be evidence for the presence of consciousness. If conscious input would be necessary for control, evidence for the absence of conscious input would be evidence for the absence of control. If attention would be sufficient for consciousness, evidence for the presence of attention would be evidence for the presence of consciousness. The additional point that conditions are interdependent has the implication that it is ultimately not very informative to have knowledge about a single operating condition. It is best to progressively investigate and map out the larger set of interdependent conditions at stake. The present section delivers the following recommendations for the issue of implicit measures. When calling a measure implicit, one should specify the features of automaticity (or operating conditions) one has in mind or, even better, accompany them with empirical arguments (De Houwer, 2006). In addition, one should attempt to uncover the interrelations among the features (or conditions).

UNDERLYING PROCESSES The question of which processes underlie measurement outcomes cannot be answered in general. Different types of measurement outcomes have different underlying processes (see De Houwer & Moors, Chapter 10, this volume). We choose to discuss the underlying processes for one popular group of measurement outcomes: irrelevant feature effects, that is, effects observed with irrelevant feature tasks. In this section, we first explain what irrelevant feature tasks are (i.e., the procedure) and how they can be used to measure attributes. We then discuss the processes that underlie irrel-

evant feature effects. We argue that two processes are necessary for obtaining an irrelevant feature effect. We also note that both processes can be described at a higher or lower level of analysis, and that several automaticity features can be applied to entire processes or to parts of them. Irrelevant feature tasks are experimental tasks consisting of a series of trials. On each trial, one or more stimuli are presented. Two features of the stimulus or stimuli are manipulated: a relevant feature and an irrelevant feature. The relevant feature is the feature that participants are asked to respond to; the irrelevant feature is a feature that participants are not asked to respond to but that is of interest to the investigator. For example, an affective priming task consists of a series of trials. On each trial, two stimuli are presented sequentially: a prime stimulus and a target stimulus. The relevant feature is some feature of the targets (e.g., valence, lexical status, nonaffective semantic feature); the irrelevant feature is the valence of the primes. Participants are asked, for example, to respond to the valence of the targets but not to the valence of the primes. Trials are created with different combinations of prime valence and target valence. Congruent trials are those on which prime and target have the same valence (positive–Â� positive or negative–Â�negative). Incongruent trials are those on which prime and target have an opposite valence (positive–Â�negative or negative–Â� positive). The typical result is that performance (reaction times, accuracy) is better on congruent than on incongruent trials. Irrelevant feature tasks have been used for several purposes, including as a tool for measuring attributes of persons (e.g., their attitudes toward stimuli, i.e., the valence that they assign to the stimuli). Suppose an investigator wants to measure an individual’s attitude toward a category of stimuli (e.g., alcoholic drinks). He or she may set up an affective priming task in which primes are exemplars of the category under study and exemplars of a contrasting category (e.g., nonalcoholic drinks). Targets are stimuli with a well-Â�established positive or negative valence. Relying on the knowledge that the typical result in affective priming tasks is that performance is better on congruent than on incongruent trials, the reaction times on the targets can be used to infer the valence of the primes for the individual (and hence his or her attitudes toward the primes). The occurrence of an effect in an irrelevant feature task is typically dependent on two steps. In the first step, the irrelevant feature is processed. In the second step, the output of the first step influ-



2. A Measure That Qualifies as Implicit

ences responding to the relevant feature. In other words, there are always at least two processes that contribute to irrelevant feature effects. The first process is concerned with the processing of the irrelevant feature. The second process is concerned with the influence that the output of the first process has on the response to the relevant feature. The second process is the one that translates the output of the first process into an effect or measurement outcome. For example, in the affective priming task, the first process is the evaluation of the prime (also called the processing of the prime valence). This process takes the prime as its input and produces a positive or negative valence as its output. The second process is involved in the influence of the prime valence on the target response. In irrelevant feature tasks, the second process is typically dependent on the first process. The second process uses the output of the first process as its input. As a consequence, when the first process has not been completed and an output is not produced, the second process has no input and cannot operate.3 The failure to find an irrelevant feature effect can thus have two causes. First, it is possible that the first process fails to occur (e.g., the prime valence was not processed). As a consequence, the second process also fails to occur (e.g., because there is no prime valence to influence the target responses). Second, it is possible that the first process occurred but the second process did not (e.g., the prime valence was processed but it did not influence the target responses). Both the processing of the irrelevant feature and the translation process can be described at different levels of analysis. At a high level, a process can be described as the relation between input and output. For example, in an affective priming task, the process of evaluating the primes relates the prime stimulus to a positive or negative valence (represented in memory). The translation process relates the prime valence to a target response of a certain quality (fast vs. slow and correct vs. incorrect). At a lower level, a process can be described in terms of the mechanisms and the format of the representations that intervene in the transition of input into output. There is disagreement about the nature of the low-level process underlying evaluation of the primes (see De Houwer, 2009). Some authors have argued that evaluation is always a constructive process, based on the weighing of information coming from a variety of sources (context, past experiences; e.g., Schwarz, 2007). Others have argued that once an evaluation is constructed, an association is formed in memory between a representation of

25

the stimulus and a representation of valence. A subsequent encounter with the stimulus leads to an activation of the stimulus representation and spreads to the associated valence representation (Fazio, 2007). The association in memory between the stimulus representation and the valence representation is what Fazio calls an attitude. Evaluation, in Fazio’s view, is nothing but the activation of this attitude. There are also rivaling accounts of the nature of the low-level process underlying translation. In the case of affective priming, some authors (e.g., De Houwer, Hermans, & Spruyt, 2001; Fazio, 2001; Spruyt, Hermans, De Houwer, & Eelen, 2002; Spruyt, Hermans, De Houwer, Vandekerckhove, & Eelen, 2007; Spruyt, Hermans, De Houwer, Vandromme, & Eelen, 2007) have argued that encoding of the primes preactivates encoding of targets with the same valence. Because of the preactivation of same-Â�valence targets (in congruent trials), processing of and responding to these targets are facilitated compared with oppositeÂ�valence targets (in incongruent trials). Other authors (De Houwer, Hermans, Rothermund, & Wentura, 2002; Klauer & Musch, 2003; Klauer, Rossnagel, & Musch, 1997; Klinger, Burton, & Pitts, 2000; Wentura, 1999) have argued that the prime valence activates the tendency to give a positive or negative response. This response tendency either corresponds (on congruent trials) or conflicts (on incongruent trials) with the correct target response and thus facilitates or hinders responding. Whether a process is described at one level of analysis or another, it is still the same process. Therefore, operating conditions apply equally to processes described at lower or higher levels of analyses. This being said, the description of internal operating conditions (i.e., the presence of a goal to engage in or to counteract a process, the presence of awareness of a process, and the presence of attention directed to a process) all include reference to a process (or part of a process). It is reasonable to assume that the processes figuring in the descriptions of these conditions are high level but not low level. For example, a person can become aware of the fact that she evaluates a stimulus (i.e., high-level process) but probably not of the fact that her evaluation is based on memory retrieval (i.e., low-level process). As another example, a person can have the (conscious or unconscious) goal to counteract her stereotypical evaluations (i.e., high-level process) but probably not the goal to stop the flow of activation in her memory network (i.e., low-level process).

26

I. BASICS

Automaticity features can be used not only as predicates of effects and processes underlying those effects but also as predicates of parts of processes, that is, inputs and outputs. For example, considering the evaluation process in affective priming, participants can be conscious or unconscious of (1) the process (that they are evaluating the primes), (2) the input of the process (that a stimulus input is present and/or which one), and (3) the output of the process (that prime valence is produced and/or which one). It may be noted that the output of some processes is a representation, whereas the output of other processes is an observable response. For example, in affective priming, the output of the evaluation process is a valence representation, whereas the output of the translation process is a target response. The recommendation of the present section for the issue of implicit measures is that, in addition to listing the automaticity features that apply (i.e., the recommendation of a previous section), it is necessary to specify what the features are a predicate of: an effect, a process (process 1 or process 2), or a part of a process (an input or an output).

ARGUMENTS FOR THE€AUTOMATICITY OF THE€PROCESSES UNDERLYING AFFECTIVE PRIMING EFFECTS The present section shows how investigators may proceed when the aim is to evaluate the extent to which outcomes obtained with certain tasks are implicit. We illustrate this with one popular irrelevant feature task: the affective priming task. Our aim is to exemplify how the recommendations formulated in the previous sections can be followed. That is, we consider two processes that we identified as central in the production of an affective priming effect: the evaluation of the primes (process 1) and the process that translates the prime valence into the target responses (process 2). For each process (and/or their parts), we review empirical arguments for a set of conditions that are related to automaticity features: time (related to fast), a promoting goal (related to unintentional), a counteracting goal (related to uncontrolled in the counteracting sense), awareness (related to unconscious), and attentional capacity (related to efficient). We also discuss evidence for one operating condition that is not included in the automaticity concept: the salience of valence. In addition to evidence for individual conditions, we discuss evidence for the interrelations among pairs of conditions.

Time On a typical affective priming trial, the prime is presented very briefly and immediately followed by the target. Consequently, the time between the onset of the prime and the onset of the target (i.e., stimulus-onset asynchrony [SOA]) is very brief. Affective priming effects have been obtained with SOAs less than 300 msec (Fazio, Sanbonmatsu, Powell, & Kardes, 1986; Hermans, De Houwer, & Eelen, 1994, 2001; Klauer & Musch, 2003; Klauer et al., 1997; Klauer, Teige-Â�Mocigemba, & Spruyt, 2009; Spruyt, Hermans, De Houwer, Vandromme, & Eelen, 2007) and even with a negative SOA of –100 msec (i.e., when targets precede primes; e.g., Fockenberg, Koole, & Semin, 2006). In addition, participants are typically instructed to give speeded responses to the targets, which results in short response times. The occurrence of a priming effect under the condition of a short SOA combined with short response times supports the idea that both the evaluation process and the translation process can be completed fast. In addition, affective priming effects tend to disappear when the SOA exceeds 300 msec (but see Klauer et al., 1997, 2009). This may indicate that the output of the evaluation process (process 1), the prime valence, remains active only for a restricted period of time, beyond which it is unable to exert an influence on the target responses (process 2). It may also indicate that, with longer SOAs, participants do not evaluate the primes. The occurrence of a priming effect indicates that the processes of evaluation (process 1) and translation (process 2) take place in the time interval between the onset of the prime and the onset of the target response. The duration of the evaluation process equals the time between the onset of the prime and the onset of the target response minus the time required for the translation process and the preparation of the target response. The duration of the translation process equals the time between the onset of the prime and the onset of the target response minus the time required for the evaluation process and the preparation of the target response. It is important to note that the evaluation process may extend beyond the SOA and that the translation process may start before the onset of the target. This implies that it is not possible to draw precise conclusions about the individual duration of the processes of evaluation and translation. It is only possible to mention the maximum duration of the sum of both processes or to give approximations of their individual durations. Another implication is that the use of a short SOA



2. A Measure That Qualifies as Implicit

is not a sufficient argument to conclude that the processes of evaluation and translation are fast. It must be complemented with the argument that response times were short as well. To make sure that response times are short, investigators may ask participants to respond as fast as possible or to respond before an early predetermined response deadline (Draine & Greenwald, 1998; Klauer & Teige-�Mocigemba, 2007; Moors, De Houwer, Hermans, & Eelen, 2005). Whether or not a particular time interval between prime onset and response onset is sufficient for the completion of the processes of evaluation and translation is likely to be influenced by other characteristics of the procedure. For example, the complexity of the target task (e.g., categorization of words vs. pronunciation of words) is likely to determine the time required for processing of the target and hence the time that is left for evaluation and translation. Another factor is the nature of the prime stimuli. Extracting the valence of the primes may take longer in the case of written words or compounds of words (e.g., Deutsch, Gawronski, & Strack, 2006) than in the case of simple pictures. On a final note, the feature fast is a gradual notion. Whether some process is called fast or slow depends on a standard for comparison that one has in mind and for which there may be consensus. Arguing that a process is fast, without specifying a maximal or approximate duration, is not very informative unless there is a strong consensus about the standard for comparison used.

Promoting Goal Priming researchers have argued that the processes underlying affective priming do not require a promoting goal or intention (i.e., the goal to engage in the process; e.g., Bargh, Chaiken, Raymond, & Hymes, 1996). It may be noted that in most cases the claim is that these processes do not require a conscious goal to engage in them. Recently, however, several researchers have questioned this view, at least with regard to the evaluation process. They have argued that the process of evaluation does require a promoting goal (e.g., Spruyt, De Houwer, & Hermans, 2009). This goal may be conscious or unconscious. Empirical arguments for both views are discussed separately. Before turning to these arguments, we wish to note that, depending on the content of the goal, the same process can be labeled (1) intentional or (2) goal dependent but unintentional. The process of evaluating the primes can be identified as intentional when it caused by the goal to evaluate the

27

primes. It can be identified as goal dependent but unintentional when it is caused by another goal (e.g., the goal to evaluate the targets). We wish to add that the process of evaluating the primes can also be identified as intentional when it is caused by the goal to evaluate stimuli in general (see following discussion). Given the fact that primes belong to the class of stimuli, the goal to evaluate stimuli can simply be applied to the primes. Put differently, the process of evaluating stimuli, including the primes, is intentional when it is caused by the goal to evaluate stimuli, which include primes. It must be recognized, however, that it is difficult to verify the content of goals.

Evaluation and Translation Do Not Require a Promoting Goal To demonstrate that the processes of evaluation and translation do not require a promoting goal or the goal to engage in them, researchers should find evidence that (1) the processes are present and (2) the goals to engage in them are absent. Support for point 1 is delivered by the occurrence of a priming effect. Support for point 2 is delivered by specific characteristics of the procedure. We discuss these procedural characteristics for each of the two processes separately. Evaluation Process

The affective priming task is an irrelevant feature task; that is, participants are asked to respond to and, therefore, to process some feature of the targets (relevant feature) and not the valence of the primes (irrelevant feature). As a consequence, they are not encouraged to adopt the goal to process the valence of the primes. In addition, processing of the prime valence is not useful for increasing performance on the target task. Even in priming tasks in which participants have to respond to the valence of the targets, the prime valence differs from the target valence on half of the trials. Thus, participants cannot rely on the valence of the primes to predict the valence of the targets. It is possible, however, that the instruction to evaluate the targets induces in participants the goal to evaluate stimuli in general, which they apply not only to the targets but also to the primes. In that case, evaluation of the primes can be labeled intentional (see prior discussion). Another scenario is that the goal to evaluate the targets directly causes evaluation of the primes without the intermediate goal of evaluating stimuli in general or the primes

28

I. BASICS

in particular. In that case, evaluation of the primes can be labeled goal dependent but unintentional. Stronger support for the unintentional nature of the process of prime evaluation comes from priming studies in which participants performed a nonaffective target task. With a nonaffective target task, participants are less encouraged to focus on the valence of stimuli in general, including the valence of the primes. Affective priming effects have been reported when participants were asked to name the targets (e.g., Bargh, 1994; Spruyt et al., 2002; Wentura & Frings, 2008). It must be noted, however, that many efforts to replicate these effects have failed (e.g., Klauer & Musch, 2001; Spruyt et al., 2002; Spruyt, Hermans, Pandelaere, De Houwer, & Eelen, 2004). Affective priming effects have also been obtained with other nonaffective target tasks. For example, Wentura (2000) obtained affective priming effects when using a lexical decision task. Positive and negative targets were intermixed with pronounceable nonwords, and participants had to categorize targets as words or nonwords. For another example, Klauer and Musch (2002) conducted affective priming experiments in which participants were asked to determine whether primes and targets matched (say “yes”) or mismatched (say “no”) with regard to a nonaffective feature (color, grammatical category, letter case, or location). They observed that performance on these nonaffective target tasks was influenced by the match or mismatch in valence between primes and targets. In the studies discussed previously, activation of the goal to evaluate the primes (or stimuli in general) may have been discouraged, but it was not actually prevented. Some investigators have argued that the use of short SOAs (including short prime durations) combined with short response times prevents implementation of the goal to evaluate the primes (or stimuli in general) should the participant have such a goal (e.g., Hermans et al., 2001). This seems a reasonable claim, but the question is, how short is too short? In the case of short SOAs and subliminal prime presentations, it is likely that implementation of the conscious goal to evaluate the primes is hindered (but not implementation of the unconscious goal to evaluate the primes). Affective priming effects obtained with subliminal prime presentations (e.g., Draine & Greenwald, 1998) thus support the idea that participants can evaluate the primes without the conscious goal to do so. In the case of short SOAs but supraliminal prime presentations, it is less clear whether implementation of the conscious goal to evaluate the primes (process 1) is

hindered. It is possible that participants formed a so-�called implementation intention (Gollwitzer, 1993). This is an association between a goal and the particular circumstance in which the goal is to be implemented. In this case, the association is between the goal to evaluate the prime (or stimuli in general) and the moment of prime presentation. Gollwitzer showed that when an implementation intention is in place, the actual implementation of the goal may happen instantly upon detection of the predetermined circumstances. Thus, short SOAs with supraliminal prime durations may be sufficient for implementation of the conscious goal to evaluate the primes (or stimuli in general). To conclude this section, we wish to call into question the search for fixed time intervals that do or do not allow the implementation of conscious goals. Whether or not a particular time interval is sufficient for the implementation of these goals is dependent on the presence of other conditions, such as the nature of the target task and the nature of the prime stimuli (cf. prior discussion). Translation Process

Several arguments that support the possibility of the unintentional nature of the evaluation process (process 1) extend to the translation process (process 2). Procedural characteristics that discourage participants from pursuing the goal to evaluate the primes (or stimuli in general) most likely also discourage them from pursuing the goal to use the prime valence to influence the target responses. Likewise, procedural characteristics (such as short time intervals) that prevent implementation of the conscious goal to evaluate the primes (or stimuli in general) can be expected to also prevent implementation of the conscious goal to use the prime valence to influence the target responses. Turning it around, it is possible that participants pursue the goal to evaluate the primes (or stimuli in general) but not the goal to use the prime valence to influence the target responses. In addition, given that the translation process can only set in when the evaluation process is completed, it can be assumed that there are time intervals that allow implementation of the goal to engage in the evaluation process but not the goal to engage in the translation process. Short time intervals (created by short SOAs and short response times) with supraliminal prime presentations may be long enough to permit implementation of the conscious goal to evaluate the primes but not long enough to also implement the (conscious or unconscious) goal to use the prime valence to influence the



2. A Measure That Qualifies as Implicit

target responses. It may be noted, however, that investigators have often generalized conclusions about process 2 to process 1 (e.g., Fazio et al., 1986). They have taken Neely’s (1991) conclusions from semantic priming research that short SOAs of 300 msec and less do not allow strategic influencing of target responses to also mean that these short SOAs do not permit intentional evaluation of the primes. Another problem already mentioned is that short SOAs must be combined with short response times in order to conclude that the available time interval was short.

Evaluation Does Require a Promoting Goal Several researchers have questioned that the evaluation process can be unintentional. In the previous section, we listed procedural characteristics that may discourage pursuing the goal to evaluate the primes (or stimuli in general). In many of these experiments, however, one can still point at procedural characteristics that may encourage pursuing such a goal. It can be argued that in all these experiments valence was somehow salient (i.e., more subjectively intense than other features), and that this induced the (conscious or unconscious) goal to process valence (Lavender & Hommel, 2007; Moors, 2007; Spruyt et al., 2008).4 It is important to note that the assumption that salience of valence induces the goal to engage in evaluation is at present not verified. It is possible that the salience of valence is a condition that influences processing directly, without the mediation of goals. Arguments for the idea that the salience of valence is a necessary condition for obtaining an affective priming effect are discussed in the next section.

Salience Many affective priming studies have procedural characteristics that may increase the salience of valence (i.e., the subjective intensity of valence relative to other stimulus features). The mere fact, for example, that all stimuli in a prototypical affective priming study have a polarized valence might be sufficient to increase the salience of valence (see also Moors, 2007; Moors & De Houwer, 2006; Spruyt, Teige-�Mocigemba, Everaert, & Klauer, 2008). Differences in the salience of valence can be held responsible for the mixed results that are obtained with the naming task (see prior discussion). When targets were words and participants were asked to name (i.e., pronounce) them, affective priming effects have sporadically been found

29

(e.g., Bargh et al., 1996; Hermans et al., 1994), but most studies produced null findings (e.g., Klauer & Musch, 2001; Spruyt et al., 2004). In contrast, when primes and targets were pictures and participants were asked to name the target pictures, affective priming effects were readily obtained (e.g., Spruyt et al., 2002; Spruyt, Hermans, De Houwer, Vandromme, & Eelen, 2007; Spruyt & Hermans, 2008; Spruyt, Teige-Â�Mocigemba, et al., 2008; Wentura & Frings, 2008). Spruyt and colleagues (2002) have explained this data pattern in terms of the memory model of Glaser and Glaser (1989; see also Glaser, 1992). According to this model, pictures have privileged access to a semantic system (i.e., a memory system that contains semantic information), whereas words first need to access a nonsemantic lexical system before they can activate semantic information. Crucially, the naming of pictures requires processing of semantic information, whereas the naming or pronunciation of words does not. Pronunciation can be based on lexical information alone. Given the assumption that valence is stored within the semantic system (e.g., Bower, 1981; De Houwer & Hermans, 1994; De Houwer & Randell, 2002; Fiske & Pavelchak, 1986), one can thus expect that picture naming is more likely to entail the processing of valence than word naming. This might explain the finding that affective priming effects are more reliable in the picture–Â�picture naming task than in the word–word pronunciation task. This data pattern can, however, also be explained by assuming that pictures and words differ in the extent to which they render valence salient. Pictures that are used in affective priming research are typically either extremely positive (e.g., a picture of an attractive woman cuddling a baby) or extremely negative (e.g., a picture of a corpse). It seems implausible that this salient manipulation would go unnoticed by the participants. Thus, the divergent findings that are obtained with the picture–Â�picture naming task and the word–word pronunciation task might be due to salience effects. To the best of our knowledge, however, this hypothesis has not yet been verified empirically. In addition to the polarized valence of the stimuli, there may be other characteristics of the procedure that increase the salience of valence. One such characteristic is the nature of the response set. In affective priming studies in which an affective target task is administered, valence is salient because participants are asked to process valence. In the studies of Klauer and Musch (2002) discussed previously, participants were asked to judge whether the primes and the targets matched or

30

I. BASICS

mismatched with regard to a nonaffective feature (color, grammatical category, letter case, or location). It could be argued, however, that the response labels “yes” and “no” still had a positive and negative valence, which led to an increase in the salience of valence (Moors, 2007; Spruyt, De Houwer, et al., 2008; for related arguments, see Eder & Klauer, 2007; Eder & Rothermund, 2008). Tentative studies conducted at our lab suggest that this is at least a viable hypothesis (Spruyt, De Houwer, et al., 2008). In an experiment modeled after Klauer and Musch (2002), in which participants had to determine whether valenced primes and targets matched or mismatched with regard to a nonaffective feature, we manipulated the degree to which the response labels had valence. In one condition, participants were asked to respond with valenced response labels (e.g., “flower” vs. “cancer”). In another condition, participants were asked to respond with neutral response labels (e.g., “square” vs. “circle”). Results showed a significant impact of the (task-Â�irrelevant) affective match between the primes and the targets only in the condition with valenced response labels. Storbeck and Robinson (2004) manipulated the salience of valence relative to the salience of other features of the stimulus material. They obtained nonaffective semantic priming effects but not affective priming effects when the stimulus material varied with regard to valence as well as a more salient nonaffective semantic feature (religion vs. texture). They only obtained an affective priming effect when all stimuli were chosen from the same semantic category (e.g., religion) and when an affective target task was administered. Simmons and Prentice (2006) manipulated the salience of valence by manipulating the attention directed to the primes.5 They obtained stronger priming effects when participants were instructed to attend to the primes than when participants were instructed to ignore the primes. Gawronski, Cunningham, LeBel, and Deutsch (in press) conducted a priming task in which primes varied on two variables that are related to valence: race and age. In one condition, participants had to count the number of black and white faces; in the other condition, they had to count the number of old and young faces. Affective priming effects were found only for the variable that participants had to focus on. Spruyt and colleagues (2008) manipulated the salience of valence by varying the degree to which the context had valence. In their series of priming studies, they manipulated both affective and nonaffective semantic congruency (see also Spruyt,

De Houwer, Hermans, & Eelen, 2007). They were able to show that priming effects occurred only for features that were made salient. In their Experiment 3, for example, participants were presented with prime and target words that were all positive or negative exemplars of the categories of objects or humans (e.g., gun, mother). They were asked to pronounce the targets as quickly as possible on experimental trials but to categorize them on filler trials. In one condition targets in the filler trials were categorized as positive or negative and in a second condition as object or human. Priming effects on the critical pronunciation trials emerged only for the stimulus feature (valence vs. animacy) that was rendered salient by the task in the filler trials (i.e., the context). Taken together, the results discussed in this section suggest that affective priming effects are found only when valence is made salient by some characteristic of the procedure (target task, stimulus set, prime task, context). It is important to note that evidence for the idea that valence must be salient in order to be processed can never be conclusive. This idea is valid until the opposite is proven. The same can be said about the idea that evaluation requires a promoting goal. Nevertheless, these ideas become increasingly plausible with increasing amounts of corroborating evidence.

Counteracting Goal Priming researchers have assumed that the processes underlying affective priming occur despite the presence of a counteracting goal (Wittenbrink, 2007). Recently, however, several researchers have questioned this assumption. They have argued that the processes of evaluation and translation can be counteracted when participants have the (conscious) goal to do so. Empirical arguments for both views are discussed separately.

Evaluation and Translation Occur Despite the Presence of Counteracting Goals To demonstrate that the processes of evaluation and translation are not controlled in the counteracting sense requires showing that (1) the goals to counteract (stop, alter, or avoid) the processes are present and (2) the processes are not counteracted (not interrupted, not altered, or still present). We argued previously that the mere fact that the valence of the primes is task irrelevant in an affective priming task supports the idea that the processes of evaluation and translation do not re-



2. A Measure That Qualifies as Implicit

quire a promoting goal. The task irrelevance of the prime valence has also been invoked as an argument for the idea that these processes occur despite the presence of counteracting goals. During the course of an affective priming task, participants may notice that primes and targets have an opposite valence on a substantial part of the trials. They may even notice that the valence of the primes interferes with giving speedy responses to the targets. If so, they may adopt the goal to avoid evaluating the primes (process 1) or the goal to suppress the influence of prime valence on target responses (process 2). To the extent that participants indeed had the goal to avoid evaluating the primes (process 1) or the goal to suppress the influence of prime valence on target responses (process 2), the occurrence of an affective priming effect demonstrates that participants were not successful in their attempts. Another argument for the idea that evaluation of the primes occurs despite the presence of a counteracting goal comes from affective priming studies in which participants received the explicit instruction to ignore (i.e., avoid processing) the primes (e.g., Hermans et al., 1994, Experiment 1; Klauer & Musch, 2003). The occurrence of a priming effect in these studies indicates that participants were not successful at ignoring the primes. The arguments discussed so far are rather indirect. In none of these studies was it verified whether participants actually pursued the goal to avoid processing the primes or, better, the prime valence. Only a handful of studies investigated in a more direct manner whether, and under which other conditions, participants were successful or unsuccessful at counteracting the processes of evaluation and translation. In the present section, we only discuss results showing that counteraction was unsuccessful. Results showing that counteraction was successful are discussed in the next section. Degner (2009) found that affective priming effects were unaffected when participants pursued the conscious goal to counteract the influence of prime valence on target responses (process 2) and when a short response deadline (< 600 msec) was installed. This finding suggests that participants were unsuccessful at counteracting the translation process under the additional condition of a lack of time. It must be noted that the prediction that counteracting goals have no influence (under certain conditions) amounts to demonstrating a null finding. This is not the case with studies that demonstrate that counteracting goals lead to an ironic increase in the effect (Wegner, 1994). For example, Payne, Lambert, and Jacoby (2002) in-

31

vestigated the influence of racial primes (black/ white faces) on the perceptual discrimination of objects (guns/tools). They obtained an enhanced stereotype-�congruent influence when participants were instructed either to use or to avoid using the primes as a cue compared with when participants did not receive instructions regarding the primes. Moreover, the effects increased when the response deadline decreased. Similarly, Glaser and Banaji (1999; see also Glaser, 2003) reported reverse priming effects (i.e., faster responses on incongruent than on congruent trials) when using primes with a strong valence and when participants had a high motivation to respond accurately.

Evaluation and Translation Are Counteracted in the Presence of€Counteracting Goals The goal to counteract a process is successful when the process is actually counteracted. Whether an attempt to counteract a process is successful depends, first, on the goal at stake. The goal to avoid a process is successful when the process is absent. The goal to alter (e.g., to diminish) a process is successful when the process is altered (e.g., diminished). The goal to stop a process is successful when the process is stopped at some point. A process that is stopped halfway is not entirely absent. As such, it may still exert an influence on subsequent processing. To determine the absence or diminution of a process is difficult. All that investigators can observe is the size of priming effects. The absence of a priming effect may mean that (1) process 1 is absent and, as a consequence, process 2 is absent as well; (2) process 1 is present but diminished, causing the absence of process 2; or (3) process 1 is present (not diminished), but process 2 is absent. A diminished priming effect may indicate that (1) process 1 is diminished, causing the diminution of process 2; or (2) process 1 is present (not diminished), but process 2 is diminished. Despite this complex set of possibilities, most studies show the absence of a priming effect and conclude that the underlying processes (evaluation and/or translation) are successfully counteracted (without further specification). Teige-�Mocigemba and Klauer (2008) showed that priming effects disappeared when participants pursued the conscious goal to alter (fake) their evaluation of the primes (process 1). Moreover, they showed that priming effects disappeared for specific primes when participants were motivated (with monetary reward) to minimize the influence of these primes on target responses (pro-

32

I. BASICS

cess 2). Likewise, Degner (2009) found that priming effects disappeared when participants pursued the conscious goal to counteract the influence of prime valence on target valence (process 2) and when no response deadline was installed. These findings suggest that both processes 1 and 2 can be controlled in the counteracting sense. Taken together, the results presented here and in the previous section suggest that the processes underlying affective priming sometimes are and sometimes are not controlled in the counteracting sense. Both positions can perhaps be reconciled if additional conditions are taken into account. One additional condition is time. Degner (2009; cf. prior discussion) showed that participants’ ability to successfully counteract process 2 depended on the presence or absence of a response deadline. Another additional condition is sufficient attentional capacity. This is suggested by the studies of Klauer and Teige-Â�Mocigemba (2007) discussed later (in the Attention section).

Consciousness Participants can be conscious or unconscious of the affective priming effect (that performance is better on congruent than incongruent trials). They can also be conscious or unconscious of the processes that we identified as underlying the affective priming effect: evaluation and translation. For each of these processes, it is useful to distinguish among the input, the output, and the process itself (i.e., the relation between input and output). With regard to the evaluation process (process 1), participants can be aware or unaware of (1) the input, that is, the prime; (2) the output, that is, the valence of the prime; and (3) the process itself, that is, the fact that the primes generate valence. With regard to the translation process (process 2), participants can be aware or unaware of (1) the input, that is, the prime valence; (2) the output, that is, the quality of the target responses (speed, accuracy); and (3) the process itself, that is, the fact that the valence of the primes influences the quality of the target responses. Given our (highlevel) definition of a process as the relation between input and output, unawareness of input or output is sufficient to conclude that the process is unaware as well. We reiterate that only processes described at a high level of analysis can serve as the object of awareness. Processes described at a low level of analysis (the particular mechanisms and format of representations) are, in all likelihood, not available for introspection.

Several studies have demonstrated affective priming effects when primes were presented subliminally so that participants were unaware of the input of the evaluation process (e.g., Abrams & Greenwald, 2000; Abrams, Klinger, & Greenwald, 2002; Banse, 2001; Croizet, 1998; Greenwald, Klinger, & Liu, 1989; Greenwald, Klinger, & Schuh, 1995; Hermans, Spuyt, De Houwer, & Eelen, 2003; Klauer, Eder, Greenwald, & Abrams, 2007; Klauer, Mierke, & Musch, 2003; Klinger et al., 2000; Otten & Wentura, 1999). This indicates that the evaluation process can operate on unconscious input and, by inference, that the evaluation process itself can operate unconsciously. What about the output of the evaluation process? It is not impossible that when the prime is unconscious the positive or negative valence generated by the prime does become conscious. In case participants are unaware of the valence generated by the primes (output of process 1, input of process 2), they must logically also be unaware that this valence influenced the quality of their target responses (process 2). It is still possible, however, that they are aware of (some aspects of) the output of process 2. For instance, they may be aware that a particular target response is fast and that it is accurate.

Attention We distinguished between two aspects of attention—Â�amount and direction—and argued that only the first is related to the feature efficient. Research about the role of each aspect of attention for the processes underlying affective priming is discussed next.

Amount of Attention Priming effects obtained with a standard affective priming task do not provide arguments about the feature efficient. In a standard affective priming task, the irrelevant feature (prime valence) and the relevant feature (target valence) are not presented simultaneously. It is thus possible that participants spend all their attentional capacity on evaluating the primes before spending all their attentional capacity on preparation of the target responses. The degree of capacity consumption is best operationalized as dual-task interference. In dual-task studies, participants perform a primary task simultaneously with a secondary task that is assumed to consume attentional capacity. If manipulation of the amount of capacity used by the



2. A Measure That Qualifies as Implicit

secondary task does not influence performance on the primary task, it is concluded that the processes underlying the primary task are efficient. For example, Hermans, Crombez, and Eelen (2000) had participants perform an affective priming task (the primary task) while simultaneously reciting a series of digits (the secondary task). They found that manipulation of the mental load imposed by the digit series did not influence the magnitude of the priming effect. This suggests that both the evaluation process (process 1) and the translation process (process 2) are efficient. Klauer and Teige�Mocigemba (2007) investigated efficiency by comparing groups of participants with working memory capacity that was larger versus smaller than average. Increases in mental load led to an increase of the priming effect in the smaller capacity group but not in the larger capacity group. This finding is consistent with the idea that participants use attentional capacity to counteract the influence of prime valence on target responses. A lack of attentional capacity (in the case of high mental load for participants with smaller capacity) produces a failure to counteract the translation process, leading to an increase in the affective priming effect. If this interpretation of the findings of Klauer and Teige-�Mocigemba is correct, they provide support for a relation between the features efficient and controlled in the counteracting sense. In addition, the fact that an affective priming effect occurred in both capacity groups is consistent with the idea that the processes underlying this effect (evaluation and translation) are efficient. Taken together, the results indicate that the processes of evaluation and translation are efficient but that the processes that counteract them are nonefficient. It must be kept in mind that efficiency is a gradual notion. It is impossible to determine whether a secondary task exhausts all processing resources. Therefore, it can never be demonstrated that the processes underlying the primary task (the affective priming task) are totally independent of attentional capacity.

Direction of Attention Musch and Klauer (2001) presented primes and targets at different locations on a computer screen. Affective priming effects occurred when target location was uncertain and participants had to distribute attention over the two locations where prime and target could be presented. When target location was certain and attention was focused on the location of the target only, affective priming

33

effects vanished completely. These results suggest that evaluation of the prime depends on attention being directed to the location of the primes. Several of the studies discussed in the Salience section suggest that evaluation of the primes requires not only that attention is directed at the location of the primes but also that attention is directed at the valence of the primes. This is akin to the distinction between the spatial versus conceptual direction of attention (Logan, 1985).

CONCLUSION We analyzed the term implicit measure with the aim of formulating recommendations for investigators in search of a measure that qualifies as implicit. On the basis of earlier suggestions by De Houwer (e.g., 2006), we started from a broad definition of implicit measure as a measurement outcome that is automatic or based on automatic processes. In the first section, we argued for a feature-based and decompositional view of automaticity, which urges us to investigate automaticity features separately instead of inferring the presence of one feature on the basis of another. We further argued that automaticity features refer to the conditions under which a process can operate. This proposal opened the door for the study of operating conditions that do not derive from automaticity features (e.g., salience). This led to an even broader definition of implicit measure as a measurement outcome that is based on processes that operate under (to-be�specified) suboptimal conditions, such as when there is no promoting goal, when there is a counteracting goal, when time is scarce, when there is little attentional capacity, when the stimulus input is unconscious, and when the stimulus input is not salient. A further point that we have emphasized is that operating conditions are not independent of each other. Thus, in addition to investigating operating conditions separately, it is ultimately important to investigate the interrelated set of operating conditions that pertain to the processes underlying the measurement outcome. In the second section, we considered irrelevant feature effects as one popular type of implicit measures. We identified two processes that underlie the production of irrelevant feature effects: one that is concerned with processing of the irrelevant feature and one that is concerned with the influence of the irrelevant feature on responding to the relevant feature. This analysis led us to conclude that, in addition to specifying the operating con-

34

I. BASICS

ditions at stake, investigators should specify the process (or the part of the process) that the operating conditions pertain to. In the third section, we provided an example of how our recommendations can be (started to be) put into practice. We considered one irrelevant feature task, the affective priming task, which has prime valence as the irrelevant feature and target valence as the relevant feature. Affective priming tasks can be used to measure people’s attitudes toward the primes (i.e., the valence they assign to the primes). The valence of the primes is inferred from the quality of the target responses (speed, accuracy) that follow the primes: A prime is positive/negative when it leads to facilitation of positive/negative target responses relative to negative/positive target responses. We identified two processes that underlie the affective priming effect: the process of evaluating the primes (short, the evaluation process or process 1) and the process by which the prime valence influences the quality of the target responses (short, the translation process or process 2). We listed empirical arguments related to operating conditions with the following ingredients: time, a promoting goal, salience, a counteracting goal, consciousness, and the capacity and direction of attention. We also documented the idea that several of these ingredients are interdependent. For example, some studies showed that conscious control in the counteracting sense is possible when there is sufficient time and/ or sufficient attentional capacity. In summary, we have the following recommendations for researchers who want to investigate or evaluate the implicitness (in a broad sense) of irrelevant feature effects: It is necessary to be specific about the operating conditions at stake and the process (or the parts of the process) that these features pertain to. It is ultimately important to investigate the interplay among several operating conditions. Finally, we discuss the implications of our view for the issue of the predictive validity of implicit versus explicit measures (see Perugini, Richetin, & Zogmaister, Chapter 14, this volume). It has been argued that the conditions under which an attribute is measured inform us about the conditions under which the attribute influences processing and behavior in real life (cf. De Houwer, 2006). This is because measurement of the attribute (e.g., the attitude toward the primes) is itself based on the influence that the attribute has on behavior (e.g., the target responses). Attributes revealed by implicit measures are expected to influence processing and behavior that occur under suboptimal conditions; attributes revealed by explicit

measures are expected to influence processing and behavior that occur under optimal conditions. For example, implicit attitudes are expected to influence behavior when opportunity (time, attentional capacity) and motivation (intention) are low. Explicit attitudes are expected to influence behavior when opportunity and motivation are high (cf. Fazio’s, 1990, motivation and opportunity as determinants model). We believe that specifying the (extended set) of optimal and/or suboptimal conditions at stake and detailing the interrelations among them can help optimize the predictive power of the measure under consideration. In addition, we believe that process 1 and process 2 should be distinguished here also. It is not enough to call a measure implicit or explicit and to detail the operating conditions. One must also indicate the process that the conditions pertain to. Thus, the claim that attributes revealed by implicit/explicit measures are expected to influence processing and behavior when conditions are suboptimal/ optimal is not specific enough. It is better to argue that attributes that are generated under such and such condition in the laboratory are likely to be generated under similar conditions in real life; and that attributes that influence processing and behavior in the lab under such and such conditions are likely to influence processing and behavior under similar conditions in real life. Acknowledgments Agnes Moors and Adriaan Spruyt are Postdoctoral Fellows of the Research Foundation, Flanders, Belgium. Preparation of this chapter was supported by Grant No. BOF/GOA2006/001 of Ghent University and by Methusalem Grant No. BOF09/01M00209 of Ghent University to Jan De Houwer.

Notes 1. It must be noted, however, that measurement outcomes and measurement procedures are not unrelated. An implicit measure is an outcome based on automatic processes, and the automaticity of these processes is inferred from characteristics of the measurement procedure that produce the measurement outcome. 2. Objective stimulus intensity corresponds to luminance in the visual domain, to amount of decibels in the auditory domain, and to amount of pressure in the tactile domain. 3. This claim can be nuanced to make it more consistent with accumulative process models of priming. According to these models, the output of the



2. A Measure That Qualifies as Implicit

evaluation process, the prime valence, builds up gradually. Partial completion of the evaluation process delivers a minimal representation of valence, which already influences target responses. With each gradual step, the representation of the prime valence is strengthened and so is its influence on the target responses. One could thus argue that the evaluation process need not be fully completed before the translation process can set in. In any case, it remains true that the evaluation process must deliver a minimal output before the translation process can set in. In other words, the partial evaluation process delivering a minimal output must be completed before the (partial) translation process can set in. 4. Goals can enter this picture as a determinant of salience or as a mediating variable between salience and the process. In other words, salience can both be caused by a goal and cause activation of a goal. This leads to several possible scenarios for the causation of a process. One scenario is that the goal to process some feature causes processing of the feature. A second scenario is that the salience of a feature causes processing of the feature. A third scenario is that the goal to process a feature increases salience of the feature, which, in turn, causes processing of the feature. A fourth scenario is that the salience of the feature causes activation of the goal to process the feature, which, in turn, causes processing of the feature. A final scenario is that the goal to process a feature increases salience of the feature, which, in turn, increases activation of the goal to process the feature, which, in turn, causes processing of the feature. The last scenario can be extended to resemble a feedback loop between goal activation and salience, in which the probability that the process occurs increases with each cycle. 5. The relation between attention and salience is probably as complex as that between salience and goals (see Note 4). Attention to a stimulus feature may increase the salience of the feature. Salience of a stimulus feature may increase attention to the feature. The possible scenarios for the causation of a process are analogous to what we presented in Note 4. If we would consider the possible causal relations among goals, attention, and salience, the range of possible scenarios for the causation of processes would even increase.

References Abrams, R. L., & Greenwald, A. G. (2000). Parts outweigh the whole (word) in unconscious analysis of meaning. Psychological Science, 11, 118–124. Abrams, R. L., Klinger, M. R., & Greenwald, A. G. (2002). Subliminal words activate semantic categories (not automated motor responses). Psychonomic Bulletin and Review, 9, 100–106.

35

Banse, R. (2001). Affective priming with liked and disliked persons: Prime visibility determines congruency and incongruency effects. Cognition and Emotion, 15, 501–520. Bargh, J. A. (1992). The ecology of automaticity. Toward establishing the conditions needed to produce automatic processing effects. American Journal of Psychology, 105, 181–199. Bargh, J. A. (1994). The four horsemen of automaticity: Awareness, intention, efficiency, and control in social cognition. In R. S. Wyer, Jr. & T. K. Srull (Eds.), Handbook of social cognition (Vol. 1, pp.€1–40). Hillsdale, NJ: Erlbaum. Bargh, J. A., Chaiken, S., Raymond, P., & Hymes, C. (1996). The automatic evaluation effect: Unconditional automatic activation with a pronunciation task. Journal of Experimental Social Psychology, 32, 104–128. Block, N. (1995). On a confusion about a function of consciousness. Behavioral and Brain Sciences, 18, 227–287. Bower, G. H. (1981). Mood and memory. American Psychologist, 36, 129–148. Croizet, J. C. (1998). Unconscious perception of affective information and its impact on personality trait judgment. Cahiers de Psychologie Cognitive—Â�Current Psychology of Cognition, 17, 53–70. De Houwer, J. (2006). What are implicit measures and why are we using them. In R. W. Wiers & A. W. Stacy (Eds.), The handbook of implicit cognition and addiction (pp.€11–28). Thousand Oaks, CA: Sage. De Houwer, J. (2009). How do people evaluate objects? A brief review. Social and Personality Psychology Compass, 3, 36–48. De Houwer, J., & Hermans, D. (1994). Differences in the affective processing of words and pictures. Cognition and Emotion, 8, 1–20. De Houwer, J., Hermans, D., Rothermund, K., & Wentura, D. (2002). Affective priming of semantic categorization responses. Cognition and Emotion, 16, 643–666. De Houwer, J., Hermans, D., & Spruyt, A. (2001). Affective priming of pronunciation responses: Effects of target degradation. Journal of Experimental Social Psychology, 37, 85–91. De Houwer, J., & Moors, A. (2007). How to define and examine the implicitness of implicit measures. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes: Procedures and controversies (pp.€ 179– 194). New York: Guilford Press. De Houwer, J., & Randell, T. (2002). Attention to primes modulates affective priming of pronunciation responses. Cognition and Emotion, 49, 163–170. De Houwer, J., Teige-Â�Mocigemba, S., Spruyt, A., & Moors, A. (2009). Implicit measures: A normative analysis and review. Psychological Bulletin, 135, 347–368. Degner, J. (2009). On the (un-)controllability of affective priming: Strategic manipulation is feasible but can possibly be prevented. Cognition and Emotion, 23, 327–354. Deutsch, R., Gawronski, B., & Strack, F. (2006). At the boundaries of automaticity: Negation as reflective

36

I. BASICS

operation. Journal of Personality and Social Psychology, 91, 385–405. Draine, S. C., & Greenwald, A. G. (1998). Replicable unconscious semantic priming. Journal of Experimental Psychology: General, 127, 286–303. Eder, A. B., & Klauer, K. C. (2007). Common valence coding in action and evaluation: Affective blindness towards response-Â�compatible stimuli. Cognition and Emotion, 21, 1297–1322. Eder, A. B., & Rothermund, K. (2008). When do motor behaviors (mis)match affective stimuli?: An evaluative encoding view of approach and avoidance reactions. Journal of Experimental Psychology: General, 137, 262–281. Fazio, R. H. (1990). Multiple processes by which attitudes guide behavior: The MODE model as an integrative framework. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 23, pp.€ 75–109). San Francisco: Academic Press. Fazio, R. H. (2001). On the automatic activation of associated evaluations: An overview. Cognition and Emotion, 15, 115–141. Fazio, R. H. (2007). Attitudes as object-Â�evaluation associations of varying strength. Social Cognition, 25, 603–637. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an obtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. Fiske, S. T., & Pavelchak, M. A. (1986). Category-based versus piecemeal-based affective responses: Developments in schema-Â�triggered affect. In R. M. Sorrentino & E. T. Higgins (Eds.), Handbook of motivation and cognition: Vol. 1. Foundations of social behavior (pp.€167–203). New York: Guilford Press. Fockenberg, D. A., Koole, S. L., & Semin, G. R. (2006). Backward affective priming: Even when the prime is late, people still evaluate. Journal of Experimental Social Psychology, 42, 799–806. Gawronski, B., Cunningham, W. A., LeBel, E. P., & Deutsch, R. (in press). Attentional influences on affective priming: Does categorization influence spontaneous evaluations of multiply categorizable objects? Cognition and Emotion. Glaser, J. (2003). Reverse priming: Implications for the (un)conditionality of automatic evaluation. In J. Musch & K. C. Klauer (Eds.), The psychology of evaluation: Affective processes in cognition and emotion (pp.€87–108). Mahwah, NJ: Erlbaum. Glaser, J., & Banaji, M. R. (1999). When fair is foul and foul is fair: Reverse priming in automatic evaluation. Journal of Personality and Social Psychology, 77, 669–687. Glaser, W. R. (1992). Picture naming. Cognition, 42, 61–105. Glaser, W. R., & Glaser, M. O. (1989). Context effects in Stroop-like word and picture processing. Journal of Experimental Psychology: General, 118, 13–42. Gollwitzer, P. M. (1993). Goal achievement: The role of

intentions. European Review of Social Psychology, 4, 141–185. Greenwald, A. G., Klinger, M. R., & Liu, T. J. (1989). Unconscious processing of dichoptically masked words. Memory and Cognition, 17, 35–47. Greenwald, A. G., Klinger, M. R., & Schuh, E. S. (1995). Activation by marginally perceptible (“subliminal”) stimuli: Dissociation of unconscious from conscious cognition. Journal of Experimental Psychology: General, 124, 22–42. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Hermans, D., Crombez, G., & Eelen, P. (2000). Automatic attitude activation and efficiency: The fourth horseman of automaticity. Psychologica Belgica, 40, 3–22. Hermans, D., De Houwer, J., & Eelen, P. (1994). The affective priming effect: Automatic activation of evaluative information in memory. Cognition and Emotion, 8, 515–533. Hermans, D., De Houwer, J., & Eelen, P. (2001). A time course analysis of the affective priming effect. Cognition and Emotion, 15, 143–165. Hermans, D., Spruyt, A., De Houwer, J., & Eelen, P. (2003). Affective priming with subliminally presented pictures. Canadian Journal of Experimental Psychology, 57, 97–114. Itti, L. (2007). Visual salience. Scholarpedia, 2, 3327. Kahneman, D., & Chajczyk, D. (1983). Test of the automaticity of reading: Dilution of Stroop effects by colorÂ�irrelevant stimuli. Journal of Experimental Psychology: Human Perception and Performance, 9, 497–509. Klauer, K. C., Eder, A. B., Greenwald, A. G., & Abrams, R. L. (2007). Priming of semantic classifications by novel subliminal prime words. Consciousness and Cognition, 16, 63–83. Klauer, K. C., Mierke, J., & Musch, J. (2003). The positivity proportion effect: A list context effect in masked affective priming. Memory and Cognition, 31, 953–967. Klauer, K. C., & Musch, J. (2001). Does sunshine prime loyal? Affective priming in the naming task. Quarterly Journal of Experimental Psychology, 54, 727–751. Klauer, K. C., & Musch, J. (2002). Goal-Â�dependent and goal-Â�independent effects of irrelevant evaluations. Personality and Social Psychology Bulletin, 28, 802–814. Klauer, K. C., & Musch, J. (2003). Affective priming: Findings and theories. In J. Musch & K. C. Klauer (Eds.), The psychology of evaluation: Affective processes in cognition and emotion (pp.€7–49). Mahwah, NJ: Erlbaum. Klauer, K. C., Rossnagel, C., & Musch, J. (1997). ListÂ�context effects in affective priming. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 246–255. Klauer, K. C., & Teige-Â�Mocigemba, S. (2007). Controllability and resource dependence in automatic evaluation. Journal of Experimental Social Psychology, 43, 648–655. Klauer, K. C., Teige-Â�Mocigemba, S., & Spruyt, A. (2009). Contrast effects in spontaneous evaluations:



2. A Measure That Qualifies as Implicit

A psychophysical account. Journal of Personality and Social Psychology, 96, 265–287. Klinger, M. R., Burton, P. C., & Pitts, G. S. (2000). Mechanisms of unconscious priming: I. Response competition, not spreading activation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 441–455. Lavender, T., & Hommel, B. (2007). Affect and action: Towards an event-Â�coding account. Cognition and Emotion, 21, 1270–1296. Logan, G. D. (1985). Skill and automaticity: Relations, implications, and future directions. Canadian Journal of Psychology, 39, 367–386. Moors, A. (2007). Can cognitive methods be used to study the unique aspect of emotion: An appraisal theorist’s answer. Cognition and Emotion, 21, 1238– 1269. Moors, A., & De Houwer, J. (2006). Automaticity: A theoretical and conceptual analysis. Psychological Bulletin, 132, 297–326. Moors, A., & De Houwer, J. (2007). What is automaticity: An analysis of its component features and their interrelations. In J. A. Bargh (Ed.), Social psychology and the unconscious: The automaticity of higher mental processes (pp.€11–50). New York: Psychology Press. Moors, A., De Houwer, J., Hermans, D., & Eelen, P. (2005). Unintentional processing of motivational valence. Quarterly Journal of Experimental Psychology, 58A, 1043–1063. Moskowitz, G. B., Li, P., & Kirk, E. R. (2004). The implicit volition model: On the preconscious regulation of temporarily adopted goals. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 34, pp.€317–414). San Diego, CA: Academic Press. Musch, J., & Klauer, K. C. (2001). Locational uncertainty moderates affective congruency effects in the evaluation decision task. Cognition and Emotion, 15, 167–188. Neely, J. H. (1991). Semantic priming effects in visual word recognition: A selective review of current findings and theories. In D. Besner & G. W. Humphreys (Eds.), Basic processes in reading: Visual word recognition (pp.€264–336). Hillsdale, NJ: Erlbaum. Otten, S., & Wentura, D. (1999). About the impact of automaticity in the minimal group paradigm: Evidence from affective priming tasks. European Journal of Social Psychology, 29, 1049–1071. Payne, B. K., Lambert, A. J., & Jacoby, L. L. (2002). Best laid plans: Effects of goals on accessibility bias and cognitive control in race-based misperceptions of weapons. Journal of Experimental Social Psychology, 38, 384–396. Schwarz, N. (2007). Attitude construction: Evaluation in context. Social Cognition, 25, 638–656. Simmons, J. P., & Prentice, D. A. (2006). Pay attention! Attention to the prime increases attitude assessment accuracy. Journal of Experimental Social Psychology, 42, 784–791. Spruyt, A., De Houwer, J., & Hermans, D. (2009). Modulation of semantic priming by feature-Â�specific attention allocation. Journal of Memory and Language, 61, 37–54.

37

Spruyt, A., De Houwer, J., Hermans, D., & Eelen, P. (2007). Affective priming of non-Â�affective semantic categorization responses. Experimental Psychology, 54, 44–53. Spruyt, A., De Houwer, J., Moors, A., & Hermans, D. (2008). Salience effects in automatic stimulus processing. Manuscript in preparation. Spruyt, A., & Hermans, D. (2008). Affective priming of naming responses does not depend on stimulus repetition. Canadian Journal of Experimental Psychology, 62, 237–241. Spruyt, A., Hermans, D., De Houwer, J., & Eelen, P. (2002). On the nature of the affective priming effect: Affective priming of naming responses. Social Cognition, 20, 227–256. Spruyt, A., Hermans, D., De Houwer, J., Vandekerckhove, J., & Eelen, P. (2007). On the predictive validity of indirect attitude measures: Prediction of consumer choice behavior on the basis of affective priming in the picture–Â�picture naming task. Journal of Experimental Social Psychology, 43, 599–610. Spruyt, A., Hermans, D., De Houwer, J., Vandromme, H., & Eelen, P. (2007). On the nature of the affective priming effect: Effects of stimulus onset asynchrony and relatedness proportion in naming and evaluative categorization. Memory and Cognition, 35, 95–106. Spruyt, A., Hermans, D., Pandelaere, M., De Houwer, J., & Eelen, P. (2004). On the replicability of the affective priming effect in the pronunciation task. Experimental Psychology, 51, 109–115. Spruyt, A., Teige-Â�Mocigemba, S., Everaert, T., & Klauer, K. C. (2008). Affective priming by multiple primes in a naming task: Further evidence for the psychophysical account of contrast effects in spontaneous evaluation. Unpublished manuscript, Ghent University. Storbeck, J., & Robinson, M. D. (2004). Preferences and inferences in encoding visual objects: A systematic comparison of semantic and affective priming. Personality and Social Psychology Bulletin, 30, 81–93. Teige-Â�Mocigemba, S., & Klauer, K. C. (2008). “Automatic” evaluation?: Strategic effects on affective priming. Journal of Experimental Social Psychology, 44, 1414–1417. Wegner, D. M. (1994). Ironic processes of mental control. Psychological Review, 101, 34–52. Wentura, D. (1999). Activation and inhibition of affective information: Evidence for negative priming in the evaluation task. Cognition and Emotion, 13, 65–91. Wentura, D. (2000). Dissociative affective and associative priming effects in the lexical decision task: Yes versus no responses to word targets reveal evaluative judgment tendencies. Journal of Experimental PsyÂ�chology: Learning, Memory, and Cognition, 26, 456–469. Wentura, D., & Frings, C. (2008). Response-bound primes diminish affective priming in the naming task. Cognition and Emotion, 22, 374–384. Wittenbrink, B. (2007). Measuring attitudes through priming. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp.€17–58). New York: Guilford Press.

Chapter 3

Models of Implicit and Explicit Mental€Representation Don Carlston

Theories of mental representation are inherently metaphorical. —Robert S. Wyer, Jr. (2007, p.€285)

M

ental representations are the residue of a lifetime of observation, thought, and experience. Sometimes this material is generated by conscious processes, as when we explicitly reflect on a recent acquaintance and decide that that individual is someone we would like to know better. But sometimes it is the product of subconscious observation, categorization, or reaction, as when someone we meet evokes feelings that we are not fully aware of and may be at a loss to explain. The nature of this representational residue—for example, whether it reflects retrievable information or a momentary construction (see, e.g., Smith, 1998)—is complicated and controversial. And it becomes even more so when one includes among the plethora of representational varieties some that are alleged to be implicit or subconscious. This chapter attempts to shed some light on the nature of mental representation, primarily by placing such representations in the context of past theories of memory. In doing so, it considers how implicit and explicit representations might be characterized by these theories. And it ultimately concludes by examining some common varieties of mental representation and outlining evidence for implicit and explicit versions of each. This re

view differs from past social psychological reviews of mental representation (e.g., Carlston & Smith, 1996; Smith, 1998; Wyer, 2007; Wyer & Carlston, 1994) primarily in being integrative rather than discriminative and in its focus on what makes a representation implicit or explicit. The integrative approach is in service of the focus because the goal here is not so much to differentiate alternative models of representation as it is to provide a theoretical context for thinking about the meaning of implicitness and explicitness from the viewpoint of social memory. Several limitations of this approach (and thus this chapter) should be acknowledged. First, undoubtedly details and distinctions that many memory theorists care deeply about are skimmed over. Each of the models described herein has variants, and the proponents of these different variants care a great deal about the particulars that distinguish one model from another. Given space limitations and the focus of this chapter, many of these nuanced distinctions are not dealt with explicitly. Second, the concept of mental representation is central to many other areas outside of social cognition, including philosophy, linguistics, computer science, and the studies of consciousness 38



3. Models of Implicit and Explicit Mental Representation

and visual imagery, among others. The interested reader is referred to Pitt (2008) for a recent, comprehensive, online review. Again, however, given the focus, these alternative perspectives are underrepresented in this chapter. Finally, although psychologists know a great deal about memory and mental representation, much of what they know about the explicit–Â�implicit distinction is covered only implicitly in the published literature. Consequently, the review that follows should be viewed as an initial, tentative, speculative effort to characterize the nature of differences between explicit and implicit representations, based on publications that were sometimes trying to do something else.

Defining Mental Representations Past social psychological reviews of mental representation suggest (at least implicitly) that representations are cognitive structures that reflect acquired knowledge and experience, and that provide the material on which cognitive processes operate. They are sometimes equated with such cognitive constructions as memories, concepts, scripts, and schemas and with such social psychological concepts as impressions, attitudes, and stereotypes. Cognitive psychologists occasionally constrain the definition of mental representations, arguing either that they are higher level aggregations of lower order concepts (cf. Fodor, 1990) or momentary constructions of experience that comprise situational characteristics, motivational goals, and other activated thoughts and stimuli (cf. Zwaan, Magliano, & Graesser, 1995). Additional constraints on the definition of mental representations typically depend on the theoretical metaphor adopted to characterize thought processes. Smith (1996) suggests that, as construed within most traditional (“symbolic”) models, mental representations are constructed by a perceiver from more atomic representations through certain “rule-Â�governed processes” and then stored in memory as “discrete and separate units” that are “distinct from the processes that operate on them” (p.€ 894). This is essentially a conception of representations as informational “things” (cf. Abelson & Prentice, 1989) that can be created, stored, accessed, and lost. An alternative view, according to Smith (1996), is that representations reflect a meaningful, learned pattern of activation weights across a set of features, such that the nature of every representation is inextricably intertwined with the weighting processes in-

39

volved in its creation and reactivation. This connectionist perspective differs from the symbolic view in providing a more dynamic integration of representational content and process and a more situationally variable conception of what representations represent. These and other approaches to mental representation differ in a number of details that are described later in this chapter. However, at a conceptual level, there are also many similarities in the conceptualizations each provides of representational processes. Taking a different tack, Carlston and Smith (1996) characterize mental representations in terms of five important principles that govern them. First, they come in many different forms, ranging from episodic and semantic representations to visual and affective ones. Second, the nature of representations generated by a stimulus is critically determined by the kind of processing that stimulus receives. Third, cognitive representations must be interconnected or interrelated in some way. Fourth, representations differ in their accessibility, and this has important consequences for thought and behavior. Finally, mental representations of different kinds tend to be coactivated during most cognitive processes, but tend to be experienced as a unified “mental mush” rather than as discrete sources of information. (Exactly how this occurs has been referred to as “the binding problem,” and despite historical controversies, combinatorial mechanisms that produce the experience of mush are readily envisioned within most contemporary models of cognition.)

Implicit versus Explicit Forms of€Representation Given these definitions and principles, how might implicit mental representations be differentiated from explicit ones? One possibility is to argue that implicit and explicit representations are separate and independent, and that some kinds of representation (e.g., affect and procedural memory) tend to be more implicit than others (e.g., verbal inferences and episodic memory) (see Tulving, 1985; Zajonc, 1980). Such possibilities are considered briefly toward the end of this chapter. An alternative view is that implicit and explicit representations overlap considerably in form, function, and characteristics (e.g., Turk-Â�Browne, Yi, & Chun, 2006) and are usefully viewed as different “states” of the same concepts. My prejudice, which is apparent throughout this chapter, is that both these views are correct, except that separable representational forms are still a long way from independent because these

40

I. BASICS

interact promiscuously during normal memory functioning (Dienes & Perner, 1999) rather than operating in a kind of cognitive apartheid. If one views implicitness and explicitness as different states of representations rather than as different representations altogether, what might distinguish the implicit state of a representation from its explicit state? One set of possible answers is provided by the models of representation to be examined shortly. Preliminarily, however, this question might be addressed in terms of Bargh’s (1994) argument that automatic processes reflect some or all of four qualities: lack of awareness, lack of intentionality, lack of controllability, and high efficiency (nonreliance on cognitive resources). Similarly, implicit mental representations might be defined as those of which a perceiver is unaware, which were not retrieved intentionally, whose effects are relatively uncontrollable, and/or which are processed using few cognitive resources (for a similar conception, see De Houwer, TeigeÂ�Mocigemba, Spruyt, & Moors, 2009). This construal is consistent with the distinction in cognitive psychology between explicit memory and implicit memory (Schacter, 1987, 1994). Explicit memory involves the conscious or intentional recall of material from memory, whereas implicit memory refers to the effects of memories in the absence of conscious or intentional recall. As an example, prior exposure to a word (e.g., plethora) can facilitate completion of a word fragment (pl_ _h_ra) even when one has no explicit memory of the prior exposure (Tulving, Schacter, & Stark, 1982). Consistent with the “overlapping concept” view, cognitive theorists have emphasized that the explicit–Â�implicit distinction refers to the way in which memory is accessed or used, not to qualitatively different kinds of memory (Jacoby & Kelley, 1987; Smith, 1996). However, as is briefly considered later in this chapter, qualitatively different kinds of memory may also be differentially amendable to explicit representation. Bargh (1989) made the important point that many (perhaps most) processes have both characteristics of automatic and controlled processes (see also Moors & De Houwer, 2006). Along similar lines, I suggest here that mental representations may not always be neatly categorizable as implicit or explicit, even in a particular context or at a particular point in time. Awareness is presumably a graduated, rather than an all-or-Â�nothing, state, with the intermediate condition sometimes characterized as “vague awareness.” Intentionality and control may similarly be matters of degree rather than absolutes. It is also possible that some sub-

components of representations may be represented explicitly while others are not. As a consequence, a mental representation’s state may fall along a continuum of implicit–Â�explicitness rather into one category or the other.

MODELS OF MEMORY AND MENTAL REPRESENTATION Mental representations are the basic building blocks of the human memory system. Theories of memory consequently provide the clearest picture of what representations are and how they are formed, organized, and retrieved. Unfortunately, there are a number of different models of memory, so the picture that emerges is not totally clear. However, I suggest that, at least for our purposes in social cognition, the alternative theories do not so much contradict each other as provide different metaphors regarding the operations of memory. So, unlike other reviews that attempt to clearly differentiate these models (e.g., Smith, 1998; Wyer, 2007; Wyer & Carlston, 1994), the present review attempts some reconciliation.

Schema Theory Schema theory provides one of the oldest models of mental representation, with roots in early German philosophy (e.g., Kant, 1781/1969), gestalt theory (e.g., Koffka, 1935), Bartlett’s (1932) work on human memory, and ultimately the New Look in perception (e.g., Bruner, 1957). The central tenet of the approach is that past experience is summarized and stored in organized representational structures that shape subsequent perceptions and memories. Because schemas are defined as “summary” representations, they do not typically refer to specific episodes in a given time and place (Smith, 1998) but are most often characterized as higher level abstractions. Nonetheless, schemas may represent concrete objects, persons, and behaviors, summarizing generic qualities of these across different occasions and contexts. In social cognition, schemas are often equated with scenarios (Heider & Simmel, 1944) and stereotypes (Stangor & Schaller, 1996). The schema approach emphasizes the effects of top-down processing in which existing knowledge shapes the perception of new stimuli, in contrast to many more contemporary memory theories, which seemingly emphasize bottom-up processes of stimulus perception and processing (Carlston & Smith, 1996). Schema theory also places a



3. Models of Implicit and Explicit Mental Representation

great deal of emphasis on the “organized” nature of mental representations, although many instantiations of the theory are vague about the exact nature of that organization or how it comes about (Smith, 1998), focusing instead on the impact of schemas on the interpretation of, and memory for, new information (Markus & Zajonc, 1985). In fact, although schemas possess internal organization, they lack external organization. For example, because schemas are not interlinked, retrieving one schema does not affect the retrievability of others (Smith, 1998). Similarly, changing one will not necessarily change others, or at least the mechanism by which such changes occur is largely unspecified.

Status of the Schema Model Social psychologists have nonetheless found the concept of schema to be useful in conceptualizing how generalized knowledge influences thoughts about events (e.g., Gilovich, 1981), groups of people (e.g., Cantor & Mischel, 1977), the self (Markus, 1977; Rogers, Kuiper, & Kirker, 1977), and other social concepts. Gilovich’s (1981) Experiment 3 provides a nice example, showing that political science students interpret an international conflict quite differently (and make different recommendations regarding U.S. intervention) when irrelevant cues bring a World War II schema versus a Vietnam schema to mind. However, critics of schema theory have noted that it is so vague that it is sometimes difficult to tell what constitutes a schema and what does not (Alba & Hasher, 1983, Fiske & Linville, 1980). The approach is also often vague about external structure, processes underlying schema formation and change, and other details that one would want a complete theory of memory representation to have (Smith, 1998). On the other hand, many writers (e.g., Fiske & Taylor, 1991; Markus & Zajonc, 1985; Smith, 1998; Wyer & Carlston, 1994) have described mechanics of schema accessibility and retrieval that closely parallel those in associative network models, and it is plausible that such models also provide guidance regarding other, underdeveloped aspects of schema theory. Indeed, Fiske and Linville (1980) suggest that the schema concept is best utilized within some broader theory of memory, and all of the models described in the following sections provide good candidates. At the very least, schema theory provides a reminder that models of memory need to provide mechanisms for top-down conceptual influences as well as bottomup, stimulus-Â�driven ones.

41

Implicit and Explicit Representations in€Schema Theory In his review of mental representation, Smith (1998, p.€403) notes that “the effects of a schema are generally considered to occur at a preconscious level. That is, the perceiver generally believes that the result of schematic processing is ‘out there’ and remains unaware of the contribution of his or her own knowledge structures to what is consciously perceived.” This view would clearly classify schematic mental representations as implicit. Yet Smith goes on to note that many researchers assess people’s schemas simply by asking for verbal reports, assuming some level of conscious accessibility. In the Gilovich (1981) study described previously, participants may well have been able to articulate the general nature of their World War II and Vietnam schemas, although it is likely that they were unaware of how these were affecting their judgments. This was probably also true in Heider and Simmel’s (1944) classic schema experiment, in which participants interpreted moving geometric shapes in terms of common cultural scenarios. Similarly, people may sometimes be aware of their own stereotypes, although sometimes they are not, and frequently they are oblivious to the ways in which these shape their perceptions and memories (Bargh, 1999). We earlier defined implicit representations as those of which people are unaware, do not intend, cannot control, or can use relatively effortlessly. By these criteria, schemas would appear to sometimes have all of the qualities of implicit representations but sometimes only a subset (perhaps uncontrollability and effortlessness) combined with some of the qualities of explicit ones (perhaps awareness and intentionality). Thus, we might admit to holding a stereotype of lawyers and even apply that stereotype willingly to someone we just met, without being able to control the manner in which it implicitly biases our interpretation of everything the lawyer says. In this latter case, we might characterize the schematic representation as largely explicit, although some of its aspects and effects might remain implicit, as is undoubtedly true for many, if not most, explicit representations. As a rule, the fact that a representation is explicit does not inevitably bestow its holder with control over the manner in which that representation functions within deeper reaches of his or her cognitive system. If not already obvious, this point should become more so as we consider the nature of implicit and explicit representations within other cognitive frameworks.

42

I. BASICS

The Standard or Modal Model Most readers will be familiar with such basic components of the memory system as long-term memory, short-term memory, and executive control (Atkinson & Shiffrin, 1968; see Hastie & Carlston, 1980, for an introduction). The basic components of this model are an unlimited-�capacity, longterm memory store that contains representations of everything a person knows, a short-term store that contains currently active representations that have been drawn either from sensory buffers or long-term memory, and an executive that governs the activation, maintenance, and manipulation of information in short-term memory. These basic components have been elaborated, reconstrued, and even renamed over the years, and it is virtually impossible in this space to adequately represent the many variations that have been proposed. What follows, therefore, is a generic (and somewhat idiosyncratic) version of the model that I believe is useful for our purposes here.

Long-Term Memory Long-term memory constitutes the reservoir of all knowledge and experience, including an episodic record of experienced events and a conceptual store of abstracted knowledge and beliefs. Tulving (1972) proposed a distinction between these episodic and semantic storage systems that has proven somewhat controversial (e.g., Horner, 1990; McKoon, Ratcliff, & Dell, 1986) but is now partially supported by neuropsychological evidence that there is considerable independence between episodic and semantic systems (e.g., Menon, Boyett�Anderson, Schatzberg, & Reiss, 2002). However, whether or not they are physically distinct is not critical to social cognitionists, who have found the distinction to be useful in any case (e.g., Almeida, 2007; Carlston, 1980; Kihlstrom & Cantor, 1984).

Short-Term and Working Memories Environmental stimuli are fed into long-term memory from short-term memory after passing through a sensory buffer. Within short-term memory, stimulus features combine with knowledge stored in long-term memory to produce a meaningful symbolic representation (Atkinson & Shiffrin, 1968). Hastie and Carlston (1980) described this “symbolic representation as being held in a limited capacity ‘location,’ or alternately as being in the ‘active’ portion of the larger memory system,

or as being in the focus of current conscious attention” (p.€10). Terminology

The terms short-term memory and working memory have been used interchangeably at times, although the latter was originally introduced to emphasize a more active, constructive role for a short-term resource that had previously been described in more passive, associationistic terms (Repovs & Baddeley, 2006). To confuse matters further, Hastie and Carlston (1980) provide an account of working memory drawn from Klatzky (1975) and Bower (1975), in which this aspect of memory is described as an intermediate-term, “fringe-of-Â�consciousness” store that is arguably more passive than short-term memory rather than less so. This store was said to hold working representations of the environment, and perhaps of goals and inferences (see Zwaan et al., 1995), and to transform rough environmental input into serviceable representations. Two novel terms are used here that more directly specify the assumed nature of the underlying short-term memory stores and thus are less confusable than the shifting, and disputed, meanings of working memory in cognitive psychology. In place of short-term memory, we suggest the term focal activated memory (or focal memory) to emphasize that this is a limited-Â�capacity resource representing those activated concepts on which a perceiver is currently focused. Representations within focal memory can be manipulated by executive routines of one kind or another, causing quick and dramatic changes in the structure of memory when these representations pass into long-term storage. We contrast this with another memory resource, semiactivated associative memory (or semiactive memory), which is the “fringe-of-Â�awareness” store that passively holds representations from sensory buffers, processing goals, and previously activated memories in reserve, partially activated, so that they are available to focal memory. Importantly, semiactive memory provides a mechanism by which stimulus material outside of focal awareness can influence processing and ultimately find its way into long-term memory. Moreover, given the severe capacity limitations of focal memory, semiactive memory provides a convenient way of construing the larger sets of motivational, environmental, and conceptual representations that must be held in readiness by the cognitive system as it devotes its limited resources to other concepts of more central interest.



3. Models of Implicit and Explicit Mental Representation

The notion that short-term memory stores may have multiple components (e.g., focal and semiactive memory) is consistent with neurological evidence that working memory is not a unitary system (D’Esposito & Postle, 2000; Postle, 2006), although most theories partition it in different ways than proposed here. The best neurological evidence probably differentiates the rehearsal functions of focal memory from the storage functions of long-term memory (Smith & Jonides, 1999) rather than from the associative processes of semiactive memory. Thus, semiactive memory might actually be construed as just a subset of momentarily accessible representations within long-term memory (Cowan, 1995; Hastie & Carlston, 1980). Effects

Representations from both focal and semiactive memory are ultimately passed to long-term memory where they are stored until needed further. When this occurs, these representations retain qualities acquired during their stay in the two short-term memory stores. These include linkages to other stimuli, concepts, and goals that resided there at the same time as well as levels of strength reflecting the length of their stay. As we shall see, these qualities are represented differently in different models of mental representation. The important point here is that spending time in short-term memory inevitably has consequences for mental representations; these consequences can be fairly dramatic for those held in focal memory but likely are more gradual and incremental for those held in semiactive memory.

The Executive Perhaps the most quixotic component of the basic memory system is the executive. The executive embodies a perceiver’s goals, including especially those in semiactive memory, directing the focus of attention and the stream of thought and determining what gets held in focal memory and for how long (Baddeley & Hitch, 1974). Rehearsal of material in focal memory was once viewed as the primary mechanism by which the executive alters the representational structure of memory, but today more elaborate attentional (Norman & Shallice, 1986) and reasoning (Gawronski & Bodenhausen, 2007; Repovs & Baddeley, 2006) processes are also theorized to play a role. What makes the executive potentially problematic is that it appears to provide a kind of “homunculus” into which theorists

43

might dump all the goal directedness and rationality of the memory system that is otherwise hard to understand. Why does some material get held in short-term memory, leading the perceiver progressively toward some goal, while other material slips away out of consciousness? If the answer is “because that’s what the executive demands,” one must next answer why the executive demands it. Fortunately, psychologists have made tremendous strides in recent years in understanding the nature and organization of executive control (see, e.g., Deutsch & Strack, Chapter 4, and Ferguson & Porter, Chapter 17, this volume). Interestingly, much recent work in this area treats goals and reasoning rules as mental representations that exist within an associative memory structure that functions according to conventional principles of memory organization and retrieval (cf. Kruglanski, 1996). Consequently, in the same way that shortterm memory has been defined as the activated portion of long-turn memory, it may be possible to characterize the executive as simply a portion of semiactive memory. However, the representations that make up the executive are distinct and special in that they embody (1) rules that govern attention to other representations, (2) procedures for executing relevant cognitive and behavioral sequences, and (3) links to representations and systems responsible for producing behavior. Thus, rather than representing the executive as some external homunculus-like force, this perspective views it as an integral part of a unified memory system. Many theorists view one complex form of mental manipulation, propositional reasoning, as a collaborative effort between focal memory and the executive. In other words, the ability to reason about and with propositional assertions (“People shouldn’t run with scissors”) involves activation of concepts in focal memory combined with directive routines from an executive system. Propositional reasoning seemingly includes the ability to evaluate associative linkages for their truth value, an ability generally described as absent from purely associative processing (Gawronski & Bodenhausen, 2007). Gawronski and Bodenhausen’s (2007) associative–Â�propositional evaluation model suggests further that both kinds of processing can influence the other, as propositional reasoning produces associations, and associations can provide the grist for propositional reasoning. Similar assumptions underlie Carlston and Skowronski’s (2005) distinction between “linking” and “thinking” in impression judgments.

44

I. BASICS

Implicit and Explicit Representations in€the Standard Model Clearly, mental representations that are activated within focal memory qualify as explicit. It is tempting, then, to categorize all of those that reside in less accessible memory stores as implicit. However, the term implicit has typically been used for representations and processes that affect people’s thoughts and actions, although they do so outside awareness, without intention, and so on, in a manner that was earlier characterized as largely automatic. Until activated, representations within long-term memory are essentially dormant and therefore incapable of affecting either thoughts or actions. Consequently, it may be more useful to restrict the term implicit to representations in semiactive memory, which I have characterized as a broad reservoir of partially activated motivational, environmental, and stimulus concepts that are potentially accessible and that may influence both focal and long-term memory, even without achieving full activation.

Associative Network Models The most prevalent models of human memory are associative network models, which focus on the patterns and strength of associative linkages among concepts in memory (Anderson & Bower, 1973; Collins & Loftus, 1975; Wyer & Carlston, 1979). The term concept is broadly used in such models and encompasses all of the different kinds of mental representation described in this chapter. For all practical purposes, then, there is no difference between what we have thus far labeled “representations” and what we here label “concepts,” using language more common among network theorists. In associative network models, concepts are represented by nodes that are interconnected by pathways that vary in strength (characterized as either width or length), reflecting the degree of association between each pair of concepts. The nature of the resultant associative network dictates the course of memory search efforts. However, the exact manner in which this happens varies somewhat between parallel and sequential search models.

Parallel Search Models Parallel search models, sometimes also called spreading activation models, are prevalent within both cognitive and social psychology (Collins &

Loftus, 1975; Higgins, Bargh, & Lombardi, 1985; Wyer & Carlston, 1979). The models provide a general account of the organization of memory and the dynamics of associative learning and retrieval. Moreover, they have proven to have considerable utility in understanding constrained phenomena such as priming effects in person perception (Higgins et al., 1985). In spreading activation models, there is an equivalence between a concept acquiring threshold levels of activation and its being “thought about.” Although it is tempting at times to relate these characterizations causally, they are, in fact, simply different construals of the same event. It is, therefore, merely convenient shorthand to say that concepts spring into consciousness when they are activated or that their activation increases when they are thought about. Concepts that are activated can be construed to be in focal memory, and in general, the longer those concepts persist in focal memory, the higher their levels of activation become (although some synapse models assume a ceiling at or near the threshold for activation; Higgins et al., 1985). While held in focal memory, activated concepts spread excitation through connecting pathways to other concepts with which they are associated. When sufficient excitation accumulates at these associated concepts, they, in turn, become activated, come to mind, and enter focal memory. Activation spreads in accordance with some basic hydraulic principles.1 For example, it spreads more quickly through fat or short connecting pathways, meaning that it spreads first to other concepts that are strongly associated with the activated concept. In these models, activation also spreads in parallel through all connecting pathways, so that the spread down any particular pathway, to any particular concept, is reduced when there are a large number of pathways leading away from the activated concept. This is called the fan effect, and it explains why concepts that are associated with many things are less effective retrieval cues than those that are only associated with a few (Anderson, 1974). Sometimes overlooked is the logical flip side of the fan effect, which might be called a hub effect: The more activated material that a concept is linked to, the more rapidly will excitation reach it through the array of pathways to which it is connected. Focal memory, by itself, can comprise five to nine concepts, more or less (Miller, 1956), and all of these presumably serve as sources of spreading activation. Consequently, the particular concepts to achieve secondary activation are likely to



3. Models of Implicit and Explicit Mental Representation

be those that are most closely connected to the whole collection of representations momentarily occupying focal memory. Hub concepts, which are widely associated with many other things, should have an advantage in this competition to accumulate excitation and achieve activation. Rehearsal in Focal Memory

After a concept has been activated and drawn into short-term memory, it may be maintained there for a time as the perceiver mulls over other concepts that also come to mind. As noted earlier, this serves to strengthen the pathway between coactivated concepts, creating small changes in the network underlying long-term memory. Over time, such changes may slowly aggregate, so that repeated coactivation of concepts produces structural changes in memory that reflect associative learning and may account for the increased accessibility of frequently activated constructs. However, some theorists (cf. Higgins, 1996) suggest that repeated rehearsal also serves to strengthen the representation of the concept itself, causing it to become chronically activated and enhancing its subsequent retrievability. Essentially, the argument is that frequent activation of concepts serves as a source of future accessibility comparable to that described next for recent activation (Higgins & King, 1981; Sedikides & Skowronski, 1991). However, the two sources of activation have been shown to behave differently (Bargh, Lombardi, & Higgins, 1988; Smith & Branscombe, 1987), and some theorists (e.g., Carlston, 1991) argue that different mechanisms are involved. After an activated concept retreats from focal memory, its residual activation generally dissipates over a period of time. An exception is that goalÂ�related concepts tend to be quickly inhibited once a goal is attained (Förster, Liberman, & Friedman, 2007; Förster, Liberman, & Higgins, 2005). Other concepts tend to remain semiactivated for a period of time, in a state above baseline but below the threshold necessary for activation. While in this state, a concept is primed for reactivation because it requires less excitation from other sources to surpass threshold and be brought back into focal memory. This mechanism is presumed to underlie the superior accessibility and impact of recently primed material compared with unprimed material (although there are alternative accounts; see Ratcliff & McKoon, 1988). The rapid dissipation may also explain why recently primed material has more impact after short time intervals, whereas the structural changes noted previously explain

45

the more durable effects of frequently primed material (Bargh, Bond, Lombardi, & Tota, 1986). Activation in Working Memory

In parallel processing models, excitation presumably spreads through the memory system much as water spreads through a series of interconnected pipes, except that the volume of spreading excitation is seemingly supplemented whenever a nodal concept is promoted into focal memory, so that the system is never (or at least rarely) left with a series of poorly activated puddles incapable of activating any useful thoughts. Given such a network of nodes and pipes, with a replenishing supply of excitation coursing through it and concepts (especially “hub” concepts) here and there becoming activated because of their relatively numerous and relatively strong connections with other activated concepts, many less connected concepts throughout the system will achieve only subthreshhold levels of excitation, insufficient to cause them to be activated. Research on subliminal priming effects (e.g., Bargh & Pietromonaco, 1982) indicates that such semiactivated material also spreads excitation to neighboring concepts, just as do those that have been fully activated. In theory, this semiactivated material may thereby contribute to the activation of other concepts without itself becoming activated. What we have just labeled “semiactivated material” would seem to qualify for that fringe-ofÂ�consciousness memory store described earlier: semiactive memory. Thus, semiactive memory may comprise a broad set of vague associations and memories that have collected some activation but not enough to bring them into focal memory. Concepts in semiactive memory that have never actually risen to conscious awareness might nonetheless be characterized as possessing residual activation, so that they are potentially more accessible in the near future. If these vague associations and memories pass excitation through to other related concepts, they may play a role in the gradual aggregation of changes in long-term memory and in determining what other concepts ultimately enter awareness. This role appears to be conceptually related to the slow-Â�learning system proposed by Smith and DeCoster (2000) in that it gradually learns associative regularities without much effort or reliance on the limited capacity of focal memory. Characterizations of working memory (see, e.g., Hastie & Carlston, 1980) suggest that it frequently serves to “represent information about the actor’s immediate environment” (p.€ 11; see also Zwaan

46

I. BASICS

et al., 1995). This implies that many features of people’s surroundings might achieve sufficient activation to join working memory (or what we call semiactive memory), although perhaps not enough to achieve explicit awareness. Such contextual material should nonetheless provide sources of concept activation. If environmental stimuli do play such a role, this would go along way toward explaining context effects, where cues that prime a memory (or a behavior) in one situation fail to do so in another (Greenspoon & Ranyard, 1957; Smith, Glenberg, & Bjork, 1978). Godden and Baddeley (1975), for example, found that when divers learned words underwater, they recalled a higher percentage of these when tested underwater than when tested on land. The opposite was true for words that the divers learned on land. Smith (1979) conducted a similar study where students learned and were tested for words in two very different rooms and found that context effects replicated when students simply thought about the room in which they had learned the words. Such results support the notion that features of the learning environment, which were probably only peripherally attended to, nonetheless served as sources of activation that aided in the recovery of associated memories. Similar context-Â�specific effects have been demonstrated for evaluations of attitude objects, under conditions in which such evaluations were arguably automatic. Thus, for example, Wittenbrink, Judd, and Park (2001) found that whites’ implicit evaluations of blacks were shaped by momentary exposure to pictures of different environmental settings (e.g., a church vs. a street corner). Rydell and Gawronski (2009) showed that such situational specificity can be a consequence of heterogeneous experiences that associate an object with different evaluations in different contexts, in contrast to homogenous experiences that cause the same evaluation to generalize across contexts. In the former case, then, different combinations of attitude object and situational setting are associated in memory with different evaluations, whereas in the latter such combinations are associated with the same evaluation (see Bassili & Brown, 2005; Conrey & Smith, 2007). The situated cognition perspective (Niedenthal, Barsalou, Winkielman, Krauth-Â�Gruber, & Ric, 2005; Smith & Semin, 2004) essentially argues that all cognitions are inextricably tied to the contexts in which they occur. In other words, according to this perspective, cognitions are not “formed” or “retrieved” as invariant concepts in a vacuum; rather, they are on-the-fly creations that

embody situational context along with an organisms’ goals, behavior, and so on. Although this perspective is very different from that adopted here, it does serve to emphasize the importance of the total pattern of activation, including situational context, in cognitive activity. Spreading Inhibition

One potential shortcoming in the associative network metaphor is that there may be too many puddles of excitation lying around to permit a disciplined and orderly progression of concepts through focal memory. Suppose that a friend of yours were to mention an acquaintance named Bill in the course of a conversation. The context of the conversation might ensure that enough other cues are present so that, in conjunction, they direct the desired excitation to the right memory node, namely one representing the Bill your friend is actually referring to. However, given the scheme described so far, some excitation would likely flow as well to nodes representing the cash in your wallet, the features of ducks, and a former president, not to mention innumerable other characters who possess the same first name. Over time, the accumulation of excitation at these inappropriate associates could build up, trickling into irrelevant corners of your associative structure and provoking thoughts that substantially interfere with the conversation you are trying to have, particularly if other situational primes relate to money, waterfowl, or politics. In essence, too many possible interpretations of too many environmental stimuli could leave people drowning in a soup of excitation. Some mechanism is required to keep people’s thoughts swimming in the right direction as excitation spreads among concepts that are both relevant and irrelevant to the goals of the perceiver. Theorists have, therefore, posited that once a concept node is activated, alternatives to that concept are actually inhibited. In other words, once you think of your acquaintance, Bill, other meanings of that name become temporarily less accessible. This implies more than just a quick moppingup operation that removes excitation from where it is no longer needed. Rather, access to inhibited concepts seems to actually be blocked, reducing accessibility to levels even below their initial baseline (Neumann, Cherau, Hood, & Steinnagel, 1993). Continuing with the hydraulic metaphor, spreading inhibition might be thought of as a kind of sludge that reduces the flow of excitation through pathways for a time until it gets washed away. It is important to note, however, that inhibitory sludge



3. Models of Implicit and Explicit Mental Representation

does not spread down pathways that are simply irrelevant; it appears to spread only toward concepts that are in actual competition with the meanings on which a perceiver focuses. The idea of spreading inhibition has excited a good deal of interest in recent psychology. It helps to explain why perceivers tend to derive only one conclusion from behaviors that have multiple implications (Carlston, 1980), tend to categorize individuals as Chinese or female but not both (Macrae, Bodenhausen, & Milne, 1995), and tend to evaluate targets by race or occupation, disregarding the less salient category (Mitchell, Nosek, & Banaji, 2003). It has also been suggested that inhibitory processes are key to shaping the flow of thought (Constantinidis, Williams, & GoldmanRakic, 2002; Hasher & Zacks, 1988), which might otherwise spread out in all directions. In these ways, spreading inhibition serves to focus spreading activation, exerting some degree of linearity even in the absence of (or perhaps at the direction of) executive control. In general, spreading activation models actually have little to say explicitly about the role of executive control. The entire system is mechanistic, with the spread of activation being stimulated principally by external stimuli, coupled with internal representations to which they are linked, with little explicit role for decisions or processing goals. Consequently, such models incorporate a kind of unbridled faith that the ebb and flow of activation and inhibition will somehow produce a meaningful succession of thoughts. Other theorists have placed greater emphasis on mechanisms that might underlie this succession, suggesting sequential search models such as those discussed in the next section.

Sequential Search Models In sequential search models (e.g., Anderson & Bower, 1973; Carlston & Skowronski, 1986; Hastie & Kumar, 1979), memory search begins with an activated cue concept and proceeds sequentially down connecting pathways until a goal concept is reached. As described by Wyer (2007): A search that begins at one node proceeds to a second node along the pathway that connects them, from this node to a third that is connected to the second, and so on. When two or more paths are connected to a given node, the path that reflects the strongest association is selected. If a “dead end” is reached (i.e., an element is identified to which no new paths are connected), the search is reinitiated. (p.€286)

47

Serial search is also affected by residual patterns of activation, connections via multiple pathways, and so on and thus necessarily incorporates aspects of parallel search. Consequently, distinctions between the two kinds of models can sometimes become blurred. Within some constrained contexts, serial search models have proven very useful. One example from social cognition is the Srull (1981) model of incongruity effects in person memory. Srull sought to explain why perceivers who form an impression of an individual are subsequently more likely to remember behaviors that are incongruent with that impression than behaviors that are congruent with it (Hastie & Kumar, 1979). Srull reasoned that when perceivers encounter an incongruent behavior, they hold it in short-term memory while sequentially retrieving other congruent and incongruent behaviors in an attempt to reconcile their seemingly contradictory implications. The result is that incongruent behaviors become linked with many other behaviors, whereas congruent behaviors (which do not require reconciliation) do not, giving the former a recall advantage. Srull’s model predicts not only the recall advantage enjoyed by incongruent behaviors but also the sequence in which congruent and incongruent behaviors are likely to be recalled, although whether his data support these latter predictions is a matter of some dispute (Skowronski & Welbourne, 1997).

Status of Associative Network Models Wyer (2007) suggests that both parallel and sequential search models have their uses: The parallel-�processing version [of associative network models] is particularly useful in conceptualizing the spontaneous activation of knowledge that occurs in the absence of any particular goal that requires it. The sequential processing version more easily characterizes a deliberate, goal-�directed search of memory in pursuit of a specified objective. (p.€286)

Although this characterization is fair, many (perhaps most) of the processes that operate on people’s stored mental representations have both undirected, spontaneous components and deliberate, goal-Â�directed components. Consequently, associative network models need mechanisms for explaining both the passive spread of activation, particularly within semiactive memory, and the motivated retrieval and maintenance of concepts within focal memory. Though neither type of model has taken full advantage of the leverage

48

I. BASICS

provided by the other, some combination of parallel and sequential processing is ultimately likely to provide the best metaphor for human memory processes. Labeled and Unlabeled Links

Several other issues also need to be addressed. First, social psychological models have generally treated associative pathways as generic and interchangeable (see Wyer & Srull, 1989, chap. 7), so that concepts may differ in how strongly they are associated but not in how they are associated. In contrast, most cognitive network models assume that associative links come in different varieties, so that, for example, two concepts may be associated hierarchically (because one “ISA” subcategory of the other) or, alternatively, one may be linked to the other as a feature (Collins & Quillian, 1969; see also Anderson, 1983). Smith (1998) suggests that without labeled links, associative structures have difficulty distinguishing the proposition “Dog bites man” from “Man bites dog.” The failure to differentiate different kinds of associations may contribute to the recent controversy over whether prejudiced associations detected by the Implicit Association Test (Greenwald, McGhee, & Schwartz, 1998) are equivalent to actual beliefs or attitudes (Karpinski & Hilton, 2001). Most people are familiar with some propositions (e.g., “The world is flat”) that they regard differently than others (e.g., “The world is round”), and in one way or another associative networks need to represent such differences. Olson and Fazio (2004) make essentially this same point by distinguishing between personal and extrapersonal associations. A variety of different mechanisms could allow associative network models to incorporate such distinctions. One involves links marked with different kinds of associative labels (e.g., “true property” or “false belief”). However, a computationally equivalent alternative simply assumes the simultaneous activation of additional concepts (e.g., “I believe this” or “This is what some people think”). It is important not to confuse processes involving labeled links with propositional reasoning processes. Identifying a particular belief (e.g., “Government is bad”) as reflecting the view of others (e.g., by tagging or associating it with views characteristic of “anarchists”) rather than as reflecting one’s own personal beliefs is not the same as elaborating on, or counterarguing with, the statement and concluding that it represents others’ be-

liefs rather than one’s own. Such counterarguing could sometimes underlie the initial formation of the tag or association, but associative processes by themselves can implicate different kinds of associations, producing discriminable effects, even in the absence of more elaborative thought. Nonetheless, it is worth reiterating that the distinctions between automatic and controlled processes, or associative versus deliberative ones, obscure the fact that many processes may sometimes lie between these “ideal” characterizations. Level of Representation

Associative network models are frequently vague about the abstractness of the concepts represented by network nodes. As Smith (1998) suggests: Theorists disagree concerning the conceptual level at which the nodes are interpreted (see Wyer & Carlston, 1994, p.€7). That is, a node could be a feature, a concept, or a whole body of knowledge (schema). (p.€394)

In fact, in many social applications (see, e.g., Carlston & Skowronski, 1986; Srull, 1981), associative networks simultaneously comprise features (especially traits), concepts (especially people), and schemas (especially events). The problem here is not conceptual diversity, of which we are proponents (e.g., Carlston, 1994). The problem is that it may be desirable to segregate the operations of associative networks at different levels. At a very basic level, associations among simple featural properties (e.g., edges, shapes, colors) may serve to activate simple objects. At a higher level, associations among such objects may help to define more complex object schemas (e.g., a bookcase or a messy desk), and associations among such schemas may activate settings (e.g., an office). Associations among such settings and other higher order constructions (e.g., persons or behaviors) may bring to mind still higher level schemas (e.g., events), and when these are coupled with activated motivations, superordinate goals may be activated. Although this is not simply a bottom-up construction process, neither is it likely to reflect the simultaneous activation and interplay of all component features and representations with each other at the same time. Therefore, associative network models need to posit some level of modularity, where activation spreads within constrained domains, which, once properly resolved, serve as concepts in other domains. An apt metaphor may be the mosaic pic-



3. Models of Implicit and Explicit Mental Representation

ture (representing a higher order concept), which is actually composed of numerous photographs (representing lower order concepts). Notably, it is the highest order representation of which we are generally most aware, although we can sometimes recognize at least a few of the component photographs. In the same way, explicit representations, of which we are aware, likely are built upon numerous more implicit ones, of which we are not. Individual versus Separate Concepts

Finally, associative network models often sidestep the difficult question of when multiple instances of a feature or category are to be represented within the network as a single node, when they should be represented as two separate but linked nodes, and when they might be stored completely independently. If two people are both named Bill, is this a property of each individual separately, or is it a single property node to which both people are linked? What if both people have mustaches? What if both people are members of the same organization? The equivalence of such representations may depend on perceivers’ focus: If the common property is salient, it may serve to link two people; if it is not, such linkage is less likely. But this creates the awkward paradox in which the nature of the associative structure depends on what people are focusing on, at the same time that the structure is supposed to be a determinant of that focus. All of these complications can be dealt with in more elaborate associative network models. But they may be even more effectively addressed through a completely different kind of model, derived from an approach termed connectionism, as discussed shortly.

Implicit and Explicit Representations in€Associative Network Theory From an associative network viewpoint, explicit mental representations are those that have been fully activated through spreading excitation and are, therefore, accessible to consciousness and to controlled processing routines, such as propositional reasoning (cf. Gawronski & Bodenhausen, 2007). That is, explicit representations are those that are in focal memory. In contrast, implicit mental representations are those that are only partially activated and are thus capable of influencing the spread of activation within the associative network, even though they remain outside of awareness, and are principally subject to simple

49

associative processes. This is essentially the same as saying that they are in semiactive memory, as we have construed it here.

Connectionist Models Many theorists, particularly in cognitive psychology, artificial intelligence, and computer science, have largely abandoned associative network models of memory for an approach that treats mental representations as weighted combinations of a fixed set of features (for reviews, see Rumelhart, Smolensky, McClelland, & Hinton, 1986; Smolensky, 1988). This connectionist approach relies on a fundamentally different metaphor than models of memory we have already discussed, although it uses some similar terminology (and some related ideas). In essence, connectionist models assume that memory consists of a large network of feature units (like nodes) that share activation via weighted interconnections (like pathways). Mental representations are thus distributed across the entire network of units, essentially embedded in the weights that govern the parallel spread of activation through the network (which is why this approach is sometimes referred to as parallel distributed processing). For example, a set of input units may be activated when a given object is observed, and these units then spread activation to each other as well as to other output units in amounts dictated by the interunit weights. Weights can be negative or zero as well as positive, and given the massive interconnectedness of the network, some units will receive both positive and negative activation from other units whose activation levels they may themselves be influencing. Consequently, the overall pattern of activation will oscillate and eventually converge on a set of values that most closely accommodates the constraints set by the input units and by the existing network weights (through what is termed parallel constraint satisfaction). This convergence process effects slow, incremental changes in the interitem weights that amounts to learning, and the ultimate pattern of weights determines the pattern of activation levels across these common units (in other words, what mental representation is constructed when there are inputs). Consequently, representations are not construed as nodes located somewhere in the memory network; rather, they are simply activation patterns that are generated by the weights in conjunction with the external input to the network. Moreover, long-term memory simply consists of a pattern of weights that has

50

I. BASICS

accumulated across a lifetime of convergence processes. Importantly, this pattern can accomplish pattern completion, essentially “recognizing” representations even when some components of the representational pattern are missing. One attraction of the connectionist models is that, at least within certain domains of applicability, they tend to be more computationally tractable than the other described models of memory. It is possible to create mathematical algorithms that embody the central assumptions of constructionist models and to test the fit between these mathematically generated predictions and empirical results collected from psychological studies in that domain. Other models of memory generally have not been specified at a level that encourages mathematical modeling, leaving “fit” with psychological data more of a qualitative than a quantitative judgment.

Comparisons and Contributions of€Connectionism It is not difficult to meld the connectionist model with the associative network model described earlier. Imagine an associative network model in which all the nodes represent features rather than higher level constructs, where pathway strength is characterized as a weight that may be positive (excitatory) or negative (inhibitory), and where past learning shapes the strength of connecting pathways so that excitation tends to converge toward distinct patterns that serve to activate higher level constructs. The equivalence of the two models is far from perfect, but the overlap provides a useful launching pad for considering some of the unique contributions of the connectionist viewpoint. Spreading Activation

One important difference between associative network models and connectionist models lies in the principles underlying the spread of activation. In the associative network model, excitation (or inhibition) spreads through a network much as a liquid would, pooling in areas with the strongest connections to activated concepts and then passing through to other related concepts. This spread of excitation has a slow effect on the connecting pathways, strengthening them to reflect some degree of association. (It is likely that this effect is more rapid in focal memory and more gradual in semiactive memory.) The spread is affected to some extent by executive routines that maintain some concepts in focal memory (and even ma-

nipulate them) while allowing others to fall by the wayside. These routines thus affect the spread of excitation simply by affecting the sources of excitation in focal memory. However, there is no inherent learning or adjustment mechanism that reacts to the success or failure of the entire pattern of spread. A perceiver may perseverate over certain concepts indefinitely in the hopes that appropriate target representations will be triggered and come to mind. But it is not clear how the system learns from the entire exercise, adjusting pathway strengths so as to promote faster and more functional retrieval patterns in the future. In contrast, such self-Â�corrective learning processes lie at the heart of connectionist models. At one level, these are simply designed to match activated patterns of features with previously encountered patterns, essentially “learning” concepts. But at another level, these same processes fine-tune the associative system to maximize its functionality. Levels of Representation

We suggested earlier that associative network models tend to be vague about the level at which concepts are represented, and that it might be useful to segregate the operations of networks at different levels. Connectionist models address this issue directly, assuming both that units function in different layers and that there are different kinds of connectionist networks that interact to accomplish different purposes. Feed-Â�forward networks comprise multiple layers of units, each of which serves to activate another layer in a manner ideal for representing the transformation of input representations (a series of letters or a pattern of observed behaviors) into output representations (words or trait inferences). In contrast, recurrentÂ�network modules contain units that are complexly interconnected with each other, providing the ideal architecture for pattern completion mechanisms composing long-term memory. Chappel and Humphries (1994) have explored connectionist models that combine recurrent and feed-Â�forward modules to fit known memory data. In their model, a central recurrent network appropriately reflected patterns of explicit recognition memory, whereas weight changes in a feed-Â�forward, input–Â�output associator module reflected results of repetition priming, a form of implicit memory. Nonconnectionist models could presumably also posit separate levels and modules to better represent the complexity of human memory processes, but perhaps because they lack the same degree of



3. Models of Implicit and Explicit Mental Representation

computational tractability, few efforts have been made to do so. Representational Similarity

We noted earlier that associative network models are generally vague about when a concept like Bill is represented as a single concept, two linked concepts, or two unrelated concepts. This issue never arises in connectionist modeling, where each Bill is defined by a pattern of features, some of which necessarily overlap and some of which differ. This feature-�overlap characteristic is a very useful way to conceptualize some forms of priming, which can be construed as a natural consequence of the prior activation of a subset of the units necessary to activate a construct. Thus, for example, the subliminal presentation of evaluative primes (Fazio, Sanbonmatsu, Powell, & Kardes, 1986) facilitates the recognition of an evaluatively congruent stimulus because the priming activates one feature that composes the representation of the congruent stimulus (namely, the evaluation), short-�circuiting the need for the input units to do so. When the stimulus is evaluatively incongruent, prior activation of the wrong evaluation inhibits stimulus recognition by working against the pattern emerging from the input units. Remote versus Local Effects

Associative networks models are often characterized as localist to reflect their premise that representations are located in particular nodes and thus in constrained areas of the associative network. In contrast, connectionist models assume distributed representations, which have no specific location and exist only as a pattern distributed across the network of feature nodes (Smith, 1998). Associative network theories also tend to imply that concepts are primarily activated by other local concepts, that is, by those to which they are closely related and which are, therefore, proximal in associative space. In contrast, connectionist networks imply a much broader range of influences, in that concept activation can be affected by the plethora of concept features within the network, whether these are normally associated with the concept or not. At least on the surface, these differences would appear to make connectionist models better suited for representing the kinds of context effects described earlier, where, for example, material learned underwater is better retrieved underwater (e.g., Godden & Baddeley, 1975). Water (along

51

with diving equipment and various other contextÂ�specific cues) would serve to activate a number of features that contribute to the pattern of activation embodying the learned material, facilitating recall despite the absence of any a priori associations between water and the material to be learned. However, there is nothing inherently parochial about activation effects in associative networks. Concepts that are usually only distantly related can be simultaneously activated within focal or semiactive memory, and the spread of excitation in parallel from these typically unrelated concepts might then have the potential to activate material that would not be primed by any of these concepts individually. Nonetheless, it is evident that associative network theorists have largely failed to make the case for “nonlocal” priming effects, which would reflect rare but unique convergences of activation into concept nodes that benefit from numerous, though weak, connections with other activated concepts. Reconstructive Memory

Smith (1998 p.€ 420) argues that distributed representations are essentially “recreated or evoked or reconstructed” rather than searched for or retrieved, and that, in contrast, most associative models of representation assume “that representations are static, able to be stored and retrieved again in invariant form.” Certainly this argument is consistent with the characterization of concepts as singular nodes that become activated and then deactivated in the course of thought. On the other hand, as suggested in the preceding section, the set of concepts activated during thinking may well include material primed by extraneous contextual material, motivational goals, or anything else momentarily active in short-term or long-term memory. Some nonconnectionist models (e.g., Loftus & Palmer, 1974) explicitly recognize that memory is a reconstruction based on all such activated elements rather than an invariant reproduction of retrieved representations. However, the variable nature of mental representation is arguably more integral to the connectionist account. Content and Process

Smith (1996, 1998) also suggests that connectionist models do a better job of integrating content and process than do associative models. This claim follows from the premise that interitem weights in these models underlie learning and retrieval processes as well as the actual content of connection-

52

I. BASICS

ist representations. This is partially a difficult-toÂ�evaluate argument that connectionist models are more parsimonious, which is also reflected in his assertion that they are more compatible with the known architecture of the brain. At a conceptual level, however, these claims serve to emphasize the interconnectedness of content and process, which merits additional mention here. Mental representations are not simply packets of information— they are intimately tied to what a person has done (Carlston & Smith, 1996; Morris, Bransford, & Franks, 1977) as well as what he or she is thinking of doing (Fiske, 1992). Consequently, whether one adopts a strict connectionist metaphor or not, the point that mental representations are intimately tied to ongoing cognitive (as well as to affective and behavioral) processes is an important one, which is discussed again in the final section.

Implicit and Explicit Representations in€Connectionism Some connectionist theorists (e.g., Sloman, 1996; Smolensky, 1988) have directly addressed differences between implicit and explicit processing (which is equivalent to addressing differences between implicit and explicit representation because processes and representations are isomorphic in connectionist models). They essentially suggest the operation of two different processing modules, one of which is a “top-level conscious processor” and the other an “intuitive processor.” The top-level processor is involved in conscious effortful reasoning and operates by manipulating linguistically encoded information by executing symbolic rules in a more or less sequential manner. The operations of this processing module are thus similar to the propositional reasoning described by Gawronski and Bodenhausen (2007). The embedded mental representations, which are conscious, intentional, effortful, and perhaps controllable, can be considered to be explicit. The intuitive processor conforms more closely to the usual distributed processing mechanisms posited by connectionist theories and operates through associative activation processes described previously. Smith (1998) suggests that this processor “mediates most human behavior (and all animal behavior), including perception, skilled motor behavior, and intuitive problem solving and pattern matching” (p.€426). This mode of processing is consistent with Gawronski and Bodenhausen’s (2007) “associative” processing mechanism as well as with Smith and DeCoster’s (2000) “slowÂ�processing” system. The embedded mental repre-

sentations would clearly be characterized as implicit.

Summary Comments on Models of€Representation Focal versus Semiactive Memory The distinction we have made here between an active focal memory store and a broader, associative, semiactive store is not unique (Bower, 1975), although neither is it universal (Nairne, 2002). The distinction is important not only because it embodies the notion of discrete “fast” (and often propositional) and “slow” (and usually associative) processing resources (Gawronski & Bodenhausen, 2007; Smith & DeCoster, 2000), but because it emphasizes the important role of focal attention in information processing. Available concepts on which we focus are characterized as being within focal memory, which has the capacity not only to reason with them but also to create schemas, associations, or interitem weights (depending on the preferred metaphor) that create instant and often dramatic changes in memory networks. Consequently, if the executive (or the processing routines that it embodies) were to do nothing but shine the spotlight of focal attention on some concepts while allowing others to remain in the dark, this, by itself, would have considerable impact. Consider these two examples. First, imagine that you park in a novel spot in a large parking lot that you commonly frequent. As you leave your car, if you spend 2 seconds noting its location, either by visually scanning where you are or by noting some space or section identifier, your chances of quickly finding your car later will be substantially enhanced. Two seconds is not a long time to hold something in focal memory, particularly compared with the several minutes that it may have taken you to find the parking space, gather up whatever you needed, exit, lock the vehicle, and walk away. But all the perceptual inputs from these other activities, if not specifically noted, simply pass through semiactive memory to long-term storage. And then, when you return to the lot at a later time, the weak associative traces they have left may be overwhelmed by other associations, particularly those reinforced by all those times you parked in some other spot. As a second example, suppose that many mornings, when you arrive at your office, there is a seemingly personal e-mail from a colleague at another institution welcoming you to work. Over the years, strong associations are likely to be formed



3. Models of Implicit and Explicit Mental Representation

between coming to work, receiving e-mails, your colleague, and feeling welcomed. If you give these events little thought, you are likely to view the e-Â� mailer as friendly and reliable, and if you reflect more deeply, you may conclude as well that this colleague has too much spare time or is stalking you. Suppose that, after years of these e-mails, you learn that they are actually generated by a spamming website that has no connection with your colleague. This is not much information, relative to the hundreds of e-mails that you have received and the associations these have created. But it changes everything. By focusing even briefly on this new information in focal memory, the implications of your past associations may be completely overcome, and your conclusions about your colleague radically changed. On the other hand, although focal attention can have dramatic effects on memory and judgment, those effects do not necessarily imply the eradication or reorganization of prior associations. Some models of implicit memory (Petty, Tormala, Briñol, & Jarvis, 2006; Wilson, Lindsey, & Schooler, 2000) suggest that earlier associations can continue to have an influence under some circumstances. Thus, when preoccupied, you may still walk to the spot where your car is usually parked or think of your colleague as a friendly stalker. The implication is that the effect of focal memory is to create powerful new associations or strengthen some that existed previously without actually overwriting the prior associative network.

Levels of Analysis It could be argued that the different models of mental representation described earlier represent conceptually different levels of analysis, with schema theories reflecting the most general (and vaguest) level, followed by the standard structural model of long-term and short-term memory, associative network models, and then finally connectionist models at the most concrete (and best specified) level. The connectionist model can easily capture essential aspects of the schema approach (Rumelhart et al., 1986), but probably, given its flexible nature, schema theory might also be embellished to capture essential aspects of the connectionist approach. What we have attempted to highlight in this review are some of the areas of overlap and interplay among the different levels of analysis because each would seem to have something to offer to our understanding of mental representation. It is likely true that the more general models, like schema theory, will never lend themselves to the

53

kind of computational precision characterizing connectionist approaches. But they make important points, nonetheless, and allow these points to be understood through metaphors appropriate to their levels of analysis.

Specific Forms of Mental Representation Each of the memory models discussed earlier is commonly described as capable of representing a variety of disparate kinds of information. For example, Smith (1996) suggests that connectionist models can accommodate many, if not most, of the sorts of mental representations with which social cognitionists are concerned. Wyer and Carlston’s (1979) associative network model included representations of people, behaviors, traits, events, schemas, scripts, and categorizations. And schema theory has been applied to all of these and more (Fiske & Linville, 1980). There are literally dozens of different kinds of representations, according to past taxonomic schemes for classifying just the varieties found in open-ended descriptions of people (e.g., Beach & Wertheimer, 1961; Fiske & Cox, 1979; see Carlston, 1994, for a summary). However, this probably says more about the power of language to make subtle distinctions than it does about the power of the brain to represent information in different forms.

Basic Systems of Representation It is nonetheless useful to distinguish several major kinds of representations from each other. Specifically, we suggest that mental representations derive from four major processing systems: verbal, visual, behavioral, and affective. These four systems correspond closely to four identified by Martindale (1991) in his neural network approach to cognitive psychology and to four representational codes discussed by Fiske and Taylor (1991). They have different neural bases and different functions and arguably require different kinds of mental representations to carry out those functions (see Carlston, 1994, for a review). However, the four systems may also have several things in common. First, each is theorized to be hierarchically organized, with physical representations linked to neurological mechanisms at the lowest levels and increasingly more complex and potentially accessible representations at higher levels (Konorski, 1967; Martindale, 1991). Second, these representations are qualitatively different

54

I. BASICS

from system to system, with each tailored to the particular kinds of informational inputs and outputs with which each must ordinarily deal. Third, at the highest, most accessible levels, these representations may be readily characterized by verbal descriptors commonly identified within taxonomies such as those mentioned previously (Carlston, 1994). Fourth, these verbal descriptions, which are accessible to consciousness and to manipulation in focal memory, can be characterized as explicit representations when activated and as implicit when only partially activated. Finally, these higher level representations are not likely to be isomorphic to lower level representations, which may be less amenable to explicit representation but which may nonetheless be implicitly activated. The basic logic, and much of the supporting evidence, for this systematic approach to mental representation is described in Carlston’s (1994) associated systems theory (AST). This theory suggests that, at the highest levels, forms of representation from different systems interact within a common associative structure. Yet the activation of any representation has the capacity to reactivate lower level structures and representations within the system that originally produced it (Damasio, 1989; Farah, 1989). To propose a new analogy, higher level representations from each system are like immigrants to a common land, all now living together under common rules and sharing characteristics and goals, yet all also having ties to their individual homelands that affect what they are like and with whom they can most easily communicate. When the higher levels of representation are activated and perceivers are aware of them, we characterize these as explicit mental representations. When they are in a state of semiactivation and perceivers are largely unaware of them, as occurs in a great deal of subliminal priming research, we characterize these as implicit mental representations. The same logic applies to lower level representations within each system, except that it is often more difficult for perceivers to become fully aware of some of these, to activate them intentionally, or to control them, so they are more likely to remain in an implicit state.

Verbal Forms of Representation Higher level mental representations associated with the verbal system include words and propositions, both of which are intimately involved with reasoning processes in focal memory. Attitudes and evaluations also have a strong verbal component (along with an affective one), as do categori-

zations and stereotypes (often with a visual component). Consequently, it is reasonable to expect most of these verbal forms of representation, or at least their verbal subcomponents, to be accessible to focal memory, making them common forms of explicit representation. Zajonc (1980) argued that verbal representations tend to be more explicit than affective representations; consequently, some kinds or components of attitudes (which are affective as well as verbal) may tend toward implicitness. And, of course, when only partially activated, even verbal forms of representation could be characterized as implicit. There is ample evidence for the effects of such implicit verbal representations in the semantic priming literature (Greenwald, Draine, & Abrams, 1996; Kemp-�Wheeler & Hill, 1988), where activating one verbal representation contributes to the activation of related verbal representations.

Visual Forms of Representation Higher level visual representations are essentially visual images. Activation of such images has been shown to reactivate the same neural structures as concurrent perception of those images (Farah, 1989) and to be subject to the same kinds of interference (McConnell & Quinn, 2000, 2003–2004). At the same time, such activation clearly provides a degraded form of representation compared with perception, and much vividness and detail are lost in recalled images. AST theory (Carlston, 1994) proposed that visual representations are the primary way of mentally representing unfamiliar people, with more familiar ones increasingly represented verbally (as well as behaviorally and affectively), a prediction recently confirmed for inanimate objects (Lacey & Campbell, 2006). Clearly, images can be explicit representations in focal memory. Repovs and Baddeley (2006) have even posited a “visuospatial sketchpad” to be a basic component of this memory store. There is also evidence that visual images can be represented implicitly (Schacter, Cooper, & Delaney, 1990). Additional evidence is provided by research on social categorizations, which AST suggests have both visual and verbal components. Categorizations and stereotypes of social groups have been successfully primed with subliminally presented pictures (Degner, Wentura, Bniewosz, & Noack, 2007; Fazio, Jackson, Dunton, & Williams, 1995) as well as with subliminally presented words (Wittenbrink, Judd, & Park, 1997), with these effects presumably reflecting the operations of implicit visual and verbal representations, respectively.



3. Models of Implicit and Explicit Mental Representation

Behavioral Forms of Representation There have been numerous theories regarding the representational underpinnings of behavior since William James (1890) wrote on the topic more than 100 years ago (for more recent examples, see Bargh & Morsella, 2009; Shallice, 1978; Vallacher & Wegner, 1985). Many such theories posit the existence of higher order representations that reflect goals and that are linked to lower level representations reflecting procedural knowledge (Tulving, 1985) and ultimately to even lower level representations governing motor movement. The highest levels of such hierarchical systems are available to short-term memory and may thus be explicit when activated or implicit when only semiactivated. However, as one moves down the hierarchy to more procedural and motoric representations, explicit representation becomes more difficult, and to the extent that people are aware of such representations at all, they may be focused on cognitive analogues that lack essential features of the underlying implicit representations. The activation of implicit behavioral representations should be evident through experimental priming manipulations, as with verbal and visual representations (see Bargh & Morsella, 2009, for a review of other evidence). However, because priming internal states is more complex than priming external stimuli, such experiments take a rather different form than subliminal presentations of words or images. An example would be Chartrand and Bargh’s (1999) work on mimicry, which can be characterized as priming implicit behavioral representations (e.g., tapping one’s foot) through confederate behavior, with both stimulus and response occurring outside of participant awareness. Such priming may take advantage of mirror neurons, which have been discovered to fire in the same manner when specific behaviors are either observed or performed (Rizzolatti & Craighero, 2004). The existence of such neurons is consistent with AST’s assumption of overlap in representations of behavioral observations and behavioral responses. Along the same lines, AST suggests that episodic memories (Tulving, 1972) reflect a combination of visual and behavioral processing. Consistent with this premise, research has confirmed that the mental representation of some kinds of spatial actions implicates a “cooperative process” involving both visual and motor imagery (Stevens, 2005). There is also some evidence for motor or action system involvement in episodic-like memories, but it is unclear whether this enactment effect reflects the impact of implicit procedural representations

55

or more explicit processes (Daprati, Nico, Saimpont, Franck, & Sirigu, 2004). Finally, it should be noted that the recent situated cognition approach essentially argues that all of a person’s cognitions are inseparable from that person’s actions and behavior and the context in which these occur (Niedenthal et al., 2005; Smith & Semin, 2004).

Affective Forms of Representation Mood and emotion are basic affective representations, which can be expressed at a high level, as when one verbalizes love, hate, anger, and so on (see Ortony & Clore, 1990). As with visual images, recalled affects probably reactivate portions of the affective system involved in the original production. However, as with visual images, recalled affect may often be a degraded form of the original experience, lacking some of the intensity and components that were felt when the mood or emotion first welled up inside. Moreover, verbal representations of emotional experiences may be misrepresentations, which are biased by social stereotypes of such experiences (Scherer, 1992). The higher level representations, which are expressible in verbal form, should be accessible to focal memory and thus capable of explicit representation. The lower level representations, implicated in the true experience of emotion, are likely to remain implicit much of the time (Zajonc, 1980). As mentioned in the previous section, it is difficult to prime internal states in the way that one can internal representations of external objects. However, Schachter and Singer’s classic (1962) research using drugs to surreptitiously manipulate participants’ arousal levels and Strack, Martin, and Stepper’s (1988) more recent work manipulating facial expressions might be viewed as priming underlying components of implicit emotional states. According to AST, both attitudes/evaluations and relationships/orientations are representational forms that are partially affiliated with the affective system (the former jointly with the verbal system and the latter jointly with the action system). Both have been shown to be affected by subliminal primes that could be characterized as activating low-level forms of these representations outside of awareness (e.g., Baldwin, Carrell, & Lopez, 1990; Draine, 1997).

Summary of Specific Forms of€Mental€Representation Carlston’s (1994) AST suggests that the different forms of representation described previously

56

I. BASICS

are systematically related to each other by virtue of their connections with underlying processing systems. The result is an organizational structure that has implications regarding the formation, interrelation, transformation, and effects of different forms of representation. Space precludes reviewing these here, but one additional implication, not discussed in the original theory, merits discussion. Based on the material reviewed earlier, it seems plausible that the target/self-Â�reference dimension, which contrasts visual and verbal representations with behavioral and affective ones, relates also to the ease with which implicit representations can become explicit. Visual and verbal representations are characterized in the theory as target focused, which means that these are internal representations of external stimuli. (The verbal representations in this person perception model are essentially semantic encodings of target features.) Given that such internal representations are mentally constructed models of an external reality, it may be relatively easy for perceivers to become aware of these, to intentionally retrieve them, and essentially to “activate” them into explicit representations. In contrast, behavioral and affective representations are self-Â�focused, which means that these are internal representations of internal states. Because such states have their roots in lower level physiological structures that are not readily accessible through introspection, it may be difficult to construct higher level representations that adequately reflect their experiential features. Consequently, these sorts of representations may be more difficult to make explicit, or at least the explicit versions of these representations may lose something in the process. Whether or not the specific organizational structure posited by AST is correct, the theoretical principle here is worth emphasizing. If representations are immigrants from basic brain systems, then brain architecture and organization, whatever it turns out to be, will have implications for the relationships among different representational forms (Postle, 2006). Moreover, this should be true whichever of the representational models discussed earlier one chooses to adopt.

CONCLUSIONS In this review, we described how mental representations are construed in the historically preeminent models of memory and in some specific subdomains of social psychology and social cognition.

The picture that emerges of implicit representations is that these can be of two types: (1) potentially explicit representations that are not sufficiently activated to enter focal attention or (2) partially activated representatives of processing systems (especially the action and affective systems) whose representational underpinnings are generally more difficult to translate into explicit form. Both kinds of implicit representations may become sufficiently activated to be characterized as momentary components of the “fringe-of-Â�awareness” domain of working memory that we termed semiactivated associationistic memory; thus, both kinds may have effects on cognition and other processing systems, and both may have effects (albeit small, incremental ones) on the structure of long-term memory. The nature of those effects is elaborated in many other chapters in this volume. In contrast to implicit representations, explicit ones are a part of a very limited-Â�capacity memory store that embodies the handful of concepts on which a perceiver is momentarily focused. Such explicit representations are likely to be predominantly verbal and visual because these symbolic codes are relatively amenable to manipulation by executive processing systems. Possibly, however, biased or impoverished representations of behavioral and affective systems can also become explicit at times. In any case, entry into focal memory permits mental manipulation of explicit representations, with the consequence that these may produce more dramatic effects on processing systems and may cause more dramatic effects on long-term memory than do implicit ones. Importantly, however, it is the continuous blending of implicit and explicit representations that ultimately creates both the stream of consciousness and the flow of human experience. Acknowledgments I am is grateful for detailed feedback and suggestions offered by the editors of this volume and by Eliot Smith, Indiana University.

Note 1. It is probably more common today to couch these “hydraulic” mechanisms in terms of the spread of excitation through neural synapses (e.g., Higgins et al., 1985), which seemingly emphasizes the plausibility of associative network theory as a working model of human memory. However, because readers are more likely familiar with the



3. Models of Implicit and Explicit Mental Representation principles of plumbing than the firing of neurons, we here retain the simpler metaphor of water flowing through connecting pipes.

References Abelson, R. P., & Prentice, D. A. (1989). Beliefs as possessions: A functional perspective. In A. R. Pratkanis, S. J. Breckler, & A. G. Greenwald (Eds.), Attitude structure and function (pp.€361–381). Hillsdale, NJ: Erlbaum. Alba, J. W., & Hasher, L. (1983). Is memory schematic? Psychological Bulletin, 93, 203–231. Almeida, J. (2007). The semantic/episodic distinction: The case for social information processing. Journal of Experimental Social Psychology, 43, 842–849. Anderson, J. R. (1974). Retrieval of prepositional information from long-term memory. Cognitive Psychology, 6, 451–474. Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press. Anderson, J. R., & Bower, G. H. (1973). Human associative memory. Washington, DC: Winston. Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. Spence & J. T. Spence (Eds.), The psychology of learning and motivation (Vol. 2, pp.€90–197). New York: Academic Press. Baddeley, A. D., & Hitch, G. J. L. (1974). Working memory. In G. A. Bower (Ed.), Advances in research and theory: Vol. 8. The psychology of learning and motivation (pp.€47–89). New York: Academic Press. Baldwin, M. W., Carrell, S. E., & Lopez, D. F. (1990). Priming relationship schemas: My advisor and the Pope are watching me from the back of my mind. Journal of Experimental Social Psychology, 26, 435– 454. Bargh, J. A. (1989). Conditional automaticity: Varieties of automatic influence in social perception and cognition. In J. S. Uleman & J. A. Bargh (Eds.), Unintended thought (pp.€3–51). New York: Guilford Press. Bargh, J. A. (1994). The four horsemen of automaticity: Awareness, intention efficiency and control in social cognition. In R. S. Wyer, Jr. & T. K. Srull (Eds.), Handbook of social cognition (Vol. 1, 2nd ed., pp.€1–40). Hillsdale, NJ: Erlbaum. Bargh, J. A. (1999). The cognitive monster: The case against the controllability of automatic stereotype effects. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€361–382). New York: Guilford Press. Bargh, J. A., Bond, R. N., Lombardi, W. J., & Tota, M. E. (1986). The additive nature of chronic and temporary sources of construct accessibility. Journal of Personality and Social Psychology, 50, 869–879. Bargh, J. A., Lombardi, W. J., & Higgins, E. T. (1988). Automaticity of chronically accessible constructs in person × situation effects on person perception: It’s just a matter of time. Journal of Personality and Social Psychology, 55, 599–605. Bargh, J. A., & Morsella, E. (2009). Unconscious behavioral guidance systems. In C. Agnew, D. Carlston, J.

57

Kelly, & W. Graziano (Eds.), Then a miracle occurs: From social cognition to social behavior (pp.€ 89–118). New York: Oxford University Press. Bargh, J. A., & Pietromonaco, P. (1982). Automatic information processing and social perception: The influence of trait information presented outside of conscious awareness on impression formation. Journal of Personality and Social Psychology, 43, 437–449. Bartlett, F. C. (1932). Remembering. Cambridge, UK: Cambridge University Press. Bassili, J. N., & Brown, R. D. (2005). Implicit and explicit attitudes: Research, challenges, and theory. In D. Albarracin, B. T. Johnson, & M. P. Zanna (Eds.), Handbook of attitudes and attitude change (pp.€ 543– 574). Mahwah, NJ: Erlbaum. Beach, L., & Wertheimer, M. (1961). A free-Â�response approach to the study of person cognition. Journal of Abnormal and Social Psychology, 62, 367–374. Bower, G. H. (1975). Cognitive psychology: An introduction. In W. K. Estes (Ed.), Handbook of learning and cognitive processes (Vol. 1, pp.€25–80). Hillsdale, NJ: Erlbaum. Bruner, J. S. (1957). Going beyond the information given. In H. Gruber, G. Terrell, & M. Wertheimer (Eds.), Contemporary approaches to cognition (Vol. 1, pp.€119–160). Hillsdale, NJ: Erlbaum. Cantor, N., & Mischel, W. (1977). Traits as prototypes: Effects on recognition memory. Journal of Personality and Social Psychology, 35, 38–49. Carlston, D. E. (1980). Events, inferences and impression formation. In R. Hastie, T. M. Ostrom, E. E. Ebbesen, R. S. Wyer, Jr., D. L. Hamilton, & D. E. Carlston (Eds.), Person memory: The cognitive basis of social perception (pp.€ 89–119). Hillsdale, NJ: Erlbaum. Carlston, D. E. (1991). How universal is the universal law of cognitive structure activation? Psychological Inquiry, 2, 185–187. Carlston, D. E. (1994). Associated systems theory: A systematic approach to the cognitive representation of persons and events. In R. S. Wyer (Ed.), Advances in social cognition: Vol. 7. Associated systems theory (pp.€1–78). Hillsdale, NJ: Erlbaum. Carlston, D. E., & Skowronski, J. J. (1986). Trait memory and behavior memory: The effects of alternative pathways on impression judgment response times. Journal of Personality and Social Psychology, 50, 1–9. Carlston, D. E., & Skowronski, J. J. (2005). Linking versus thinking: Evidence for the different associative and attributional bases of spontaneous trait transference and spontaneous trait inference. Journal of Personality and Social Psychology, 89, 884–898. Carlston, D. E., & Smith, E. R. (1996). Principles of mental representation. In E. T. Higgins & A. W. Kruglanski (Eds.), Social psychology: Handbook of basic principles (pp.€ 184–210). New York: Guilford Press. Chappel, M., & Humphries, M. S. (1994). An autoÂ�associative neural network for sparse representations: Analysis and application to models of recognition and cued recall. Psychological Review, 101, 103–128. Chartrand, T. L., & Bargh, J. A. (1999). The chameleon effect: The perception-Â�behavior link and social in-

58

I. BASICS

teraction. Journal of Personality and Social Psychology, 76, 893–910. Collins, A. M., & Loftus, E. F. (1975). A spreading activation theory of semantic processing. Psychological Review, 82, 407–428. Collins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior, 8, 240–247. Conrey, F. R., & Smith, E. R. (2007). Attitude representation: Attitudes as patterns in a distributed, connectionist representational system. Social Cognition, 25, 718–735. Constantinidis, C., Williams, G. V., & Goldman-Rakic, P. S. (2002). A role for inhibition in shaping the temporal flow of information in prefrontal cortex. Nature Neuroscience, 5, 175–180. Cowan, N. (1995). Attention and memory: An integrated framework. New York: Oxford University Press. Damasio, A. (1989). Multiregional retroactivation: A systems level model for some neural substrates of cognition. Cognition, 33, 25–62. Daprati, E., Nico, D., Saimpont, A., Franck, N., & Sirigu, A. (2004). Memory and action: An experimental study on normal subjects and schizophrenic patients. Neuropsychologia, 43, 281–293. De Houwer, J., Teige-Â�Mocigemba, S., Spruyt, A., & Moors, A. (2009). Implicit measures: A normative analysis and review. Psychological Bulletin, 135, 347–368. Degner, J., Wentura, D., Bniewosz, B., & Noack, P. (2007). Hostility-Â�related prejudice against Turks in adolescents: Masked affective priming allows for a differentiation of automatic prejudice. Basic and Applied Social Psychology, 29, 245–256. D’Esposito, M., & Postle, B. R. (2000). Neural correlates of processes contributing to working-Â�memory function: Evidence from neuropsychological and pharmacological studies. In S. Monsell & J. Driver (Eds.), Control of cognitive processes: Attention and performance XVIII (pp.€ 579–602). Cambridge, MA: MIT Press. Dienes, Z., & Perner, J. (1999). A theory of implicit and explicit knowledge. Behavioural and Brain Sciences, 22, 735–755. Draine, S. C. (1997). Analytic limitations of unconscious language processing. Unpublished doctoral dissertation, University of Washington. Farah, M. J. (1989). The neural basis of mental imagery. Trends in Neurosciences, 12, 395–399. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes. A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. Fiske, S. T. (1992). Thinking is for doing: Portraits of social cognition from daguerreotype to laserphoto. Journal of Personality and Social Psychology, 63, 877–889. Fiske, S. T., & Cox, M. G. (1979). Person concepts: The effects of target familiarity and descriptive purpose

on the process of describing others. Journal of Personality, 47, 136–161. Fiske, S. T., & Linville, P. W. (1980). What does the concept of schema buy us? Personality and Social Psychology Bulletin, 6, 543–557. Fiske, S., & Taylor, S. E. (1991). Social cognition (2nd ed.). New York: McGraw-Hill. Fodor, J. A. (1990). A theory of content and other essays. Cambridge, MA: MIT Press. Förster, J., Liberman, N., & Friedman, R. (2007). Seven principles of self regulation: A systematic approach to distinguishing goal priming from priming of non goal concepts. Personality and Social Psychology Review, 11, 211–235. Förster, J., Liberman, N., & Higgins, E. T. (2005). Accessibility from active and fulfilled goals. Journal of Experimental Social Psychology, 41, 220–239. Gawronski, B., & Bodenhausen, G. V. (2007). Unraveling the processes underlying evaluation: Attitudes from the perspective of the APE model. Social Cognition, 25, 687–717. Gilovich, T. (1981). Seeing the past in the present: The effect of associations to familiar events on judgments and decisions. Journal of Personality and Social Psychology, 40, 797–808. Godden, D. R., & Baddeley, A. D. (1975). ContextÂ�dependent memory in two natural environments: On land and under water. British Journal of Psychology, 66, 325–331. Greenspoon, J., & Ranyard, R. (1957). Stimulus conditions and retroactive inhibition. Journal of Experimental Psychology, 53, 55–59. Greenwald, A. G., Draine, S. C., & Abrams, R. L. (1996). Three cognitive markers of unconscious semantic activation. Science, 273, 1699–1702. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Hasher, L., & Zacks, R. T. (1988). Working memory, comprehension, and aging: A review and a new view. In G. H. Bower (Ed.), The psychology of learning and motivation: Vol. 22 (pp.€ 192–225). New York: Academic Press. Hastie, R., & Carlston, D. E. (1980). Theoretical issues in person memory. In R. Hastie, T. M. Ostrom, E. E. Ebbesen, R. S. Wyer, Jr., D. L. Hamilton, & D. E. Carlston (Eds.), Person memory: The cognitive basis of social perception (pp.€1–53). Hillsdale, NJ: Erlbaum. Hastie, R., & Kumar, P. A. (1979). Person memory: Personality traits as organizing principles in memory for behaviors. Journal of Personality and Social Psychology, 37, 25–38. Heider, F., & Simmel, M. (1944). An experimental study of apparent behavior. American Journal of Psychology, 57, 243–249. Higgins, E. T. (1996). Knowledge activation: Accessibility, applicability, and salience. In E. T. Higgins & A. W. Kruglanski (Eds.), Social psychology: Handbook of basic principles (pp.€184–210). New York: Guilford Press. Higgins, E. T., Bargh, J. A., & Lombardi, W. (1985). Nature of priming effects on categorization. Journal



3. Models of Implicit and Explicit Mental Representation

of Experimental Psychology: Learning, Memory, and Cognition, 11, 59–69. Higgins, E. T., & King, G. (1981). Accessibility of social constructs: Information processing consequences of individual and contextual variability. In N. Cantor & J. Kihlstrom (Eds.), Personality, cognition, and social interaction (pp.€69–122). Hillsdale, NJ: Erlbaum. Horner, M. D. (1990). Psychobiological evidence for the distinction between episodic and semantic memory. Neuropsychology Review, 1, 281–321. Jacoby, L., & Kelley, C. (1987). Unconscious influences of memory for a prior event. Personality and Social Psychology Bulletin, 13, 314–336. James, W. (1890). The principles of psychology. New York: Henry Holt. Kant, I. (1969). Critique of pure reason. New York: St. Martin’s Press. (Original work published 1781) Karpinski, A., & Hilton, J. L. (2001). Attitudes and the Implicit Association Test. Journal of Personality and Social Psychology, 81, 774–778. Kemp-Â�Wheeler, S. M., & Hill, A. B. (1988). Semantic priming without awareness: Some methodological considerations and replications. Quarterly Journal of Experimental Psychology, 40, 671–692. Kihlstrom, J. F., & Cantor, N. (1984). Mental representations of the self. In L. Berkowitz (Ed.), Advances in experimental social psychology: Vol. 17 (pp.€2–48). New York: Academic Press. Klatzky, R. L. (1975). Human memory: Structures and processes. San Francisco: Freeman. Koffka, K. (1935). Principles of gestalt psychology. New York: Harcourt, Brace & World. Konorski, J. (1967). Integrative activity of the brain. Chicago: University of Chicago Press. Kruglanski, A. W. (1996). Goals as knowledge structures. In P. M. Gollwitzer & J. A. Bargh (Eds.), The psychology of action: Linking cognition and motivation to behavior (pp.€599–618). New York: Guilford Press. Lacey, S., & Campbell, C. (2006). Mental representation in visual/haptic cross-modal memory: Evidence from interference effects. Quarterly Journal of Experimental Psychology, 59, 361–376. Loftus, E. F., & Palmer, J. C. (1974). Reconstruction of automobile destruction: An example of the interaction between language and memory. Journal of Learning and Verbal Behavior, 13, 585–589. Macrae, C. N., Bodenhausen, G. V., & Milne, A. B. (1995). The dissection of selection in person perception: Inhibitory processes in social stereotyping. Journal of Personality and Social Psychology, 69, 397–407. Markus, H. (1977). Self-Â�schemata and processing information about the self. Journal of Personality and Social Psychology, 35, 63–78. Markus, H., & Zajonc, R. B. (1985). The cognitive perspective in social psychology. In G. Lindzey & H. Aronson (Eds.), Handbook of social psychology (3rd ed., Vol. 1, pp.€137–230). New York: Random House. Martindale, C. (1991). Cognitive psychology: A neuralÂ�network approach. Pacific Ocean, CA: Brooks/Cole. McConnell, J., & Quinn, J. G. (2000). Interference in visual working memory. Quarterly Journal of Experimental Psychology, 53A, 53–67. McConnell, J., & Quinn, J. G. (2003–2004). Cognitive

59

mechanisms of visual memories and visual images. Imagination, Cognition and Personality, 23, 201–207. McKoon, G., Ratcliff, R., & Dell, G. (1986). A critical evaluation of the semantic/episodic distinction. Journal of Experimental Psychology: Learning, Memory, and Cognition, 12, 295–306. Menon, V., Boyett-Â�Anderson, J. M., Schatzberg, A. F., & Reiss, A. L. (2002). Relating semantic and episodic memory systems. Brain Research. Cognitive Brain Research, 13, 261–265. Miller, G. A. (1956). The magical number seven, plus or minus two. Psychological Review, 63, 81–97. Mitchell, J. P., Nosek, B. A., & Banaji, M. R. (2003). Contextual variations in implicit evaluation. Journal of Experimental Psychology: General, 132, 455–469. Moors, A., & De Houwer, J. (2006). Automaticity: A conceptual and theoretical analysis. Psychological Bulletin, 132, 297–326. Morris, C. D., Bransford, J. D., & Franks, J. J. (1977). Levels of processing versus transfer appropriate processing. Journal of Verbal Learning and Verbal Behavior, 16, 519–533. Nairne, J. S. (2002). Remembering over the short-term: The case against the standard model. Annual Review of Psychology, 53, 53–81. Neumann, E., Cherau, J. F., Hood, K. L., & Steinnagel, S. L. (1993). Does inhibition spread in a manner analogous to spreading activation? Memory, 1, 81–105. Niedenthal, P. M., Barsalou, L. W., Winkielman, P., Krauth-Â�Gruber, S., & Ric, F. (2005). Embodiment in attitudes, social perception, and emotion. Personality and Social Psychology Review, 9, 184–211. Norman, D. A., & Shallice, T. (1986). Attention to action: Willed and automatic control of behaviour. In€ R. J. Davidson, G. E. Schwarts, & D. Shapiro (Eds.), Consciousness and self-Â�regulation: Advances in research and theory (Vol. 4, pp.€ 1–18). New York: Plenum. Olson, M. A., & Fazio, R. H. (2004). Reducing the influence of extra-Â�personal associations on the Implicit Association Test: Personalizing the IAT. Journal of Personality and Social Psychology, 86, 653–667. Ortony, A., & Clore, G. L. (1990). The cognitive structure of emotions. New York: Cambridge University Press. Petty, R. E., Tormala, Z. L., Briñol, P., & Jarvis, W. B. G. (2006). Implicit ambivalence from attitude change: An exploration of the PAST model. Journal of Personality and Social Psychology, 90, 21–41. Pitt, D. (2008). Mental representation. In Stanford encyclopedia of philosophy. Retrieved October 19, 2009, from plato.stanford.edu/entries/mental-Â�representation. Postle, B. R. (2006). Working memory as an emergent property of the mind and brain. Neuroscience, 139, 23–38. Ratcliff, R., & McKoon, G. (1988). A retrieval theory of priming in memory. Psychological Review, 95, 385–408. Repovs, G., & Baddeley, A. D. (2006). Multi-Â�component model of working memory: Explorations in experimental cognitive psychology. Neuroscience, 139, 5–21. Rizzolatti, G., & Craighero, L. (2004). The mirror-

60

I. BASICS

Â� neuron system. Annual Review in Neuroscience, 27, 169–192. Rogers, T. B., Kuiper, N. A., & Kirker, W. S. (1977). Self-Â�reference and the encoding of personal information. Journal of Personality and Social Psychology, 35, 677–688. Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. E. (1986). Schemata and sequential thought processes in PDP models. In J. L. McClelland & D. E. Rumelhart (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 2, pp.€7–57). Cambridge, MA: MIT Press. Rydell, R. J., & Gawronski, B. (2009). I like you, I like you not: Understanding the formation of contextÂ�dependent automatic attitudes. Cognition and Emotion, 23, 1118–1152. Schachter, S., & Singer, J. E. (1962). Cognitive, social, and physiological determinants of emotional states. Psychological Review, 69, 379–399. Schacter, D. L. (1987). Implicit memory: History and current status. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 501–518. Schacter, D. L. (1994). Priming and multiple memory systems: Perceptual mechanisms of implicit memory. In D. L. Schacter & E. Tulving (Eds.), Memory systems (pp.€233–268). Cambridge, MA: MIT Press. Schacter, D. L., Cooper, L. A., & Delaney, S. M. (1990). Implicit memory for visual objects and the structural description system. Bulletin of the Psychonomic Society, 28, 367–372. Scherer, K. R. (1992). On social representations of emotional experience: Stereotypes, prototypes, or archetypes? In M. von Cranach, W. Doise, & G. Mugney (Eds.), Social representations and the social bases of knowledge (pp.€ 30–36). Ashland, OH: Hogrefe & Huber. Sedikides, C., & Skowronski, J. J. (1991). The law of cognitive structure activation. Psychological Inquiry, 2, 169–184. Shallice, T. (1978). The dominant action system: An information-Â�processing approach to consciousness. In K. S. Pope & J. L. Singer (Eds.), The stream of consciousness: Scientific investigations into the flow of human experience (pp.€ 117–157). New York: Plenum Press. Skowronski, J. J., & Welbourne, J. (1997). Conditional probability may be a flawed measure of associative strength. Social Cognition, 15, 1–12. Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119, 3–22. Smith, E. E., & Jonides, J. (1999). Storage and executive processes in the frontal lobes. Science, 283, 1657– 1661. Smith, E. R. (1996). What do connectionism and social psychology offer each other? Journal of Personality and Social Psychology, 70, 893–912. Smith, E. R. (1998). Mental representation and memory. In D. T. Gilbert, S. T. Fiske, & G. Lindsey (Eds.), Handbook of social psychology (4th ed., Vol. 2, pp.€391–445). New York: McGraw Hill. Smith, E. R., & Branscombe, N. R. (1987). Procedurally mediated social inferences: The case of category accessibility effects. Journal of Experimental Social Psychology, 23, 361–382.

Smith, E. R., & DeCoster, J. (2000). Dual-Â�process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems.€ Personality and Social Psychology Review, 4, 108–131. Smith, E. R., & Semin, G. R. (2004). Socially situated cognition: Cognition in its social context. Advances in Experimental Social Psychology, 36, 53–117. Smith, S. M. (1979). Remembering in and out of context. Journal of Experimental Psychology: Human Learning and Memory, 5, 460–471. Smith, S. M., Glenberg, A. M., & Bjork, B. A. (1978). Environmental context and human memory. Memory and Cognition, 6, 342–353. Smolensky, P. (1988). On the proper treatment of connectionism. Behavioral and Brain Sciences, 11, 1–74. Srull, T. K. (1981). Person memory: Some tests of associative storage and retrieval models. Journal of Experimental Psychology: Human Learning and Memory, 7, 440–463. Stangor, C., & Schaller, S. (1996). Stereotypes as individual and collective representations. In N. C. Macrae, C. Stangor, & M. Hewstone (Eds.), Stereotypes and stereotyping (pp.€3–40). New York: Guilford Press. Stevens, J. A. (2005). Interference effects demonstrate distinct roles for visual and motor imagery during the mental representation of human action. Cognition, 95, 329–350. Strack, F., Martin, L. L., & Stepper, S. (1988). Inhibiting and facilitating conditions of the human smile: A nonobtrusive test of the facial feedback hypothesis. Journal of Personality and Social Psychology, 54, 768–777. Tulving, E. (1972). Episodic and semantic memory. In E. Tulving & W. Donaldson (Eds.), Organization of memory (pp.€381–403). New York: Academic Press. Tulving, E. (1985). How many memory systems are there? American Psychologist, 40, 385–398. Tulving, E., Schacter, D. L., & Stark, H. (1982). Priming effects in word-Â�fragment completion are independent of recognition memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 8, 336–342. Turk-Â�Browne, N. B., Yi, D., & Chun, M. M. (2006). Linking implicit and explicit memory: Common encoding factors and shared representations. Neuron, 49, 917–927. Vallacher, R. R., & Wegner, D. M. (1985). A theory of action identification. Hillsdale, NJ: Erlbaum. Wilson, T. D., Lindsey, S., & Schooler, T. Y. (2000). A model of dual attitudes. Psychological Review, 107, 101–126. Wittenbrink, B., Judd, C. M., & Park, B. (1997). Evidence for racial prejudice at the implicit level and its relationship with questionnaire measures. Journal Personality and Social Psychology, 72, 264–274. Wittenbrink, B., Judd, C. M., & Park, B. (2001). Evaluative versus conceptual judgments in Â�automatic stereotyping and prejudice. Journal of€Experimental Social Psychology, 37, 244–252. Wyer, R. S., Jr. (2007). Principles of mental representations. In A. W. Kruglanski & E. T. Higgins (Eds.), Social psychology: Handbook of basic principles (2nd ed., pp.€285–307). New York: Guilford Press.



3. Models of Implicit and Explicit Mental Representation

Wyer, R. S., Jr., & Carlston, D. E. (1979). Social cognition, inference and attribution. Hillsdale, NJ: Erlbaum. Wyer, R. S., Jr., & Carlston, D. E. (1994). The cognitive representation of persons and events. In R. S. Wyer, Jr. & T. K. Srull (Eds.), Handbook of social cognition (2nd ed., Vol. 1, pp.€41–98). Hillsdale, NJ: Erlbaum. Wyer, R. S., Jr., & Srull, T. K. (1989). Memory and cognition in its social context. Hillsdale, NJ: Erlbaum.

61

Zajonc, R. B. (1980). Feelings and thinking: Preferences need no inferences. American Psychologist, 35, 151–175 Zwaan, R. A., Magliano, J. P., & Graesser, A. C. (1995). Dimensions of situation model construction in Â�narrative comprehension. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21, 386–397.

Chapter 4

Building Blocks of Social Behavior Reflective and Impulsive Processes Roland Deutsch and Fritz Strack

F

rom the actor’s perspective, her or his own behaviors appear to be simple, coherent, and a direct consequence of intentions. Factually, however, a multitude of subprocesses ranging from fineÂ�grained sensorimotor coordination (Rosenbaum, Vaughan, Meulenbroek, Jax, & Cohen, 2009) to action-Â�contingent affect generation (Carver, 2001) are part of most behaviors, without the actor being necessarily aware or in control of their operation (for a review, see Strack, Deutsch, & Krieglmeyer, 2009). However, the multiple facets of behavior become obvious when some of them are in conflict (Strack & Deutsch, 2007). Doing something unintended out of habit (Verplanken & Aarts, 1999), experiencing hunger while voluntarily dieting (Seibt, Häfner, & Deutsch, 2007), or discriminating against minority group members without actually endorsing biased attitudes (Payne, 2001) are examples for a misalignment of processes that jointly determine action. Such conflicts are of particular scientific and practical interest. Just as cognitive biases or perceptual illusions are informative about the processes underlying nonÂ�biased cognition and perception (Strack & Deutsch, 2002), behavioral conflicts can advance knowledge about behavior in general. From an applied perspective, dissociations between intent and actual behavior are at the core of many problematic behaviors, such as social discrimination (Payne, 2001), addiction (Deutsch & Strack, 2005; Robinson & Berridge, 2003), and



overspending (Hoch & Loewenstein, 1991; Strack, Werth, & Deutsch, 2006). The topic of this book—Â�implicit and explicit social cognition1—is tightly bound to the notion of multiple and potentially dissociating determinants of behavior. Particularly, the paradigm of implicit social cognition rests on the notion that attitudes, prejudice, stereotypes, and the self may have an impact on behavior that sometimes opposes beliefs and intentions (Deutsch & Strack, 2005; Fazio, 1990; Gawronski & Bodenhausen, 2006; Greenwald et al., 2002; Smith & DeCoster, 2000; Wilson, Lindsey, & Schooler, 2000). Consequently, a plethora of studies investigated the relation between implicit and explicit social cognition on one hand and behaviors on the other. This fascinating and extremely fruitful program of research not only led to numerous demonstrations of behavioral effects of implicit social cognition beyond explicit beliefs and intentions, but it was also successful in unveiling the situational and inner conditions under which such influences are likely to occur (for reviews, see Friese, Hofmann, & Schmitt, 2009; Perugini, Richetin, & Zogmaister, Chapter 14, this volume). Given that implicit social cognition has a specific impact on behavior in well-Â�defined situations; by which mechanisms exactly does this influence occur? This vital mediational question has received relatively little attention in the recent past, and in the present 62



4. Reflective and Impulsive Processes

chapter, we aim at shifting the focus to this question. Toward this end, we review those psychological constructs (such as goals or habits) that we deem as most indispensable for the explanation of social behavior. We briefly review existing research on how implicit and explicit social cognition may affect each of these constructs. We then turn to describing different forms of conflict that may result from the operation of these behavioral determinants. We end our chapter with a brief overview of current attempts to capture the interplay of behavioral determinants in broad theories of social cognition and behavior. Before doing so, however, we discuss the distinction between implicit and explicit social cognition in a more general manner and briefly review different theoretical perspectives on these concepts.

Dividing the Realm of€Social Cognition The paradigm of implicit social cognition is intimately tied to the idea that cognitive processes can be subdivided into at least two groups, typically labeled implicit and explicit social cognition. But what exactly do we mean when we locate a phenomenon within the realm of implicit or explicit social cognition? There is still no generally agreed-on answer to this broad question (for detailed discussions, see De Houwer, Teige�Mocigemba, Spruyt, & Moors, 2009; Greenwald & Nosek, 2009; Olson & Fazio, 2009), but most current theories are in agreement about two basic assumptions, which center on indirect measures. Research within the paradigm of implicit social cognition rests on a class of indirect measures of attitudes, stereotypes, or the self, such as evaluative priming (Fazio, Sanbonmatsu, Powell, & Kardes, 1986), the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998), the extrinsic affective Simon task (De Houwer, 2003), semantic priming (Wittenbrink, Judd, & Park, 1997), and the affect misattribution procedure (Payne, Cheng, Govorun, & Stewart, 2005). Different from traditional self-�report measures, these indirect measures aim at assessing attitudes, stereotypes, or self aspects by means of simple categorization tasks, which do not require the participant to make judgments about the issue under consideration. Instead, mental representations are typically inferred from response latencies, error rates, or interpretations of ambiguous stimuli. The first shared assumption of most theories is that indirect measures not only are valid measures

63

of attitudes, stereotypes, and the self, but also that these measures somehow differ from direct measures in their functional characteristics (De Houwer & Moors, 2007). For example, it is generally assumed that participants can influence indirect measures to a lesser degree than direct measures. Consequently, one can expect indirect measures to be less susceptible to self-Â�presentational tendencies, demand effects, or correction for unwanted biases than direct measures. More generally, indirect measures are considered to operate in a more automatic fashion than direct measures (De Houwer et al., 2009). The second shared assumption is that indirect and direct measures differ not only in how they capture a mental process but also in which process they capture in the first place. This assumption comes in many variants. For example, some theories suggest that indirect measures rather assess the representations of attitudes, stereotypes, and the self in memory, whereas direct measures are more influenced by deliberate reasoning processes about these representations if participants have the motivation and opportunity to do so (e.g., Fazio & Olson, 2003; Olson & Fazio, 2009). Thus, from this perspective, a different mixture of processes is captured by direct and indirect measures. However, the processes that specifically influence direct measures are rather considered to transform the to-be-Â�measured construct instead of generating completely new and independent explicit versions of attitudes, stereotypes, or the self. Diverging from this view, other theories even suggest that direct and indirect measures provide access to separate and perhaps independent representations in long-term memory (e.g., Wilson et al., 2000). It is not easy to decide which particular perspective on the different constructs assessed by indirect measures is correct (e.g., Greenwald & Nosek, 2009; Moors & De Houwer, 2006b). At the same time, the shared assumption that the constructs that are assessed with direct and indirect measures differ at least to some degree in a psychologically meaningful manner is well in line with evidence from two decades of intensive research (for overviews, see Petty, Fazio, & Briñol, 2009; Wittenbrink & Schwarz, 2007). For example, indirect and direct measures respond differently to the same experimental manipulations (e.g., Deutsch, Gawronski, & Strack, 2006; Gawronski & Strack, 2004; Gregg, Seibt, & Banaji, 2006; Rydell & McConnell, 2006), predict different behaviors (e.g., Asendorpf, Banse, & Mücke, 2002; Dovidio, Kawakami, Johnson, & Johnson, 1997; Dunton & Fazio, 1997; Friese, Hofmann, & Wänke, 2008), and are only weakly related under many conditions (e.g.,

64

I. BASICS

Greenwald & Nosek, 2009). At the same time, it is plausible that there is some overlap in the constructs that are assessed because both types of measures may reflect mixtures of processes (Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005), which may influence the measure to varying degrees depending on situational conditions (Hofmann, Gschwendner, Nosek, & Schmitt, 2005). Although the available evidence is no final proof that indirect and direct measures at least partially assess different psychological constructs, we argue that this interpretation is the most viable one. So, given that the constructs assessed by direct and indirect measures differ in a psychologically meaningful way, are there commonalities in how they differ? We address this question next.

Operating Principles From the outset of the implicit social cognition paradigm, researchers have developed theories about the nature of the processes that are captured by indirect measures. Probably the two most important features are the associative nature of underlying representations and the automaticity of their activation and influence on further processing. This perspective was, for instance, taken in Fazio and colleagues’ (1986) seminal article on automatic attitude activation. Particularly, attitudes were conceptualized as being based on object–Â�evaluation associations in memory, which may become activated automatically on the perception of the attitude object. These two features have inspired many later models within the implicit social cognition paradigm (e.g., Gawronski & Bodenhausen, 2006; Greenwald et al., 2002; Lieberman, Gaunt, Gilbert, & Trope, 2002; Smith & DeCoster, 2000; Strack & Deutsch, 2004). In the following sections, we briefly review research on these two features in the realm of social cognitive processes.

Automaticity From the beginning, indirect measures were thought to tap into processes that are more automatic than those underlying direct measures. Automatic processes are mostly defined as being fast, unintentional, uncontrollable, efficient, and perhaps unconscious (Bargh, 1994; Moors & De Houwer, 2006a; Schneider & Chein, 2003; Schneider & Schiffrin, 1977). Indeed, a plethora of studies suggests that those processes feeding into indirect measures can be considered as being relatively automatic. At the same time, recent stud-

ies provide a more differentiated picture (e.g., De Houwer et al., 2009). First, it has become clear that automaticity features do not correlate perfectly. For example, a cognitive process may be extremely fast but still depend on intentions (Moors & De Houwer, 2006a). This is probably also the case for those processes that are captured with indirect measures of attitudes or stereotypes. For example, evaluative processes as assessed in the evaluative priming paradigm (Fazio et al., 1986) were shown to depend on participants’ intention to evaluate (De Houwer, Hermans, Rothermund, & Wentura, 2002; Klauer & Musch, 2002) and to be enhanced by participants being attentive to the primes (Simmons & Prentice, 2006). Research based on the IAT (Greenwald et al., 1998) suggests that participants can, in part, control their immediate evaluative or stereotypical responses to stimuli (e.g., De Houwer, Beckers, & Moors, 2007; Kim, 2003), and participants may often be aware of their evaluative and stereotypic responses (Gawronski, Hofmann, & Wilbur, 2006). At the same time, it is important to note that different measures yield diverging results when it comes to evaluating the automaticity of evaluations and stereotypes. For example, evaluative priming in the pronunciation task does not seem to depend on evaluative intentions (Spruyt, Hermans, Pandelaere, De Houwer, & Eelen, 2004), whereas it does in the evaluative decision task (Klauer & Musch, 2002). It is, therefore, likely that some variability in automaticity features must be attributed to factors that belong to the measure but not to the construct under consideration (Gawronski, Deutsch, LeBel, & Peters, 2008). Further research based on a better understanding of the measurement procedures is needed to disentangle method- and construct-Â�related effects.

Association versus Proposition Many theories of implicit social cognition assume the existence of evaluative and stereotypic associations in memory, which can be activated or primed upon the perception of fitting stimuli (Bassili & Brown, 2005; Fazio & Olson, 2003; Fazio et al., 1986; Gawronski & Bodenhausen, 2006; Greenwald et al., 2002; Smith & DeCoster, 2000; Strack & Deutsch, 2004). Although the concept association has numerous implications, we have argued that one of their most distinctive features is that they do not possess a truth value (Strack & Deutsch, 2004). Associated elements prime each other, but this priming does not itself suggest that the association describes a true state of affairs. For example, a person may associate Arab with terror



4. Reflective and Impulsive Processes

just because extended media exposure has created such a link. At the same time, the person may not believe in any factual link between being an Arab person and being a terrorist (Devine, 1989). In the reflective–Â�impulsive model (RIM; Strack & Deutsch, 2004), we suggested that associations are formed in an impulsive system (IS) on the bases of temporal contiguity and frequency of pairing without containing any additional information on their veracity. In the RIM, propositional representations in general and assessments of truth in particular are reserved to a reflective system (RS), which is assumed to generate propositions based on what is retrieved from associative memory, thereby drawing on syllogistic reasoning and adhering to the principle of cognitive consistency. At the same time, we suggested that indirect measures reflect processes of the IS to a great degree, whereas direct measures reflect processes of the RS to a greater degree. Two lines of evidence support the usefulness of this distinction. First, evidence suggests that evaluations that are assessed with indirect measures are acquired on grounds of slow learning processes based on repeated pairings, whereas direct measures respond more quickly to propositional information (e.g., Rydell & McConnell, 2006). Second, studies suggest that the evaluative processes underlying indirect measures are insensitive to the propositional meaning of the stimuli. For example, affirmed and negated stimuli had comparable effects in evaluative priming and stereotype training studies (e.g., Deutsch et al., 2006; Draine, 1997; Gawronski, Deutsch, Mbirkou, Seibt, & Strack, 2008). Also directly measured evaluations were shown to adhere to the principles of cognitive consistency, which can be considered propositional, whereas indirect measures were not (Gawronski & Strack, 2004). These findings notwithstanding, some evidence suggests that a pure associative theory of the constructs assessed by indirect measures may not be feasible. First, diverging from the principle of frequent contiguous coactivation, some studies show that a single pairing between a person and a feature can be sufficient to result in effects on indirect measures of evaluation (e.g., Castelli, Zogmaister, Smith, & Arcuri, 2004; Gregg et al., 2006). Second, experiments suggest that processes that we clearly consider propositional can have an impact on indirect measures under specific conditions. For example, research by Moors, De Houwer, and Eelen (2004) demonstrated that indirect measures were influenced by stimulus–goal comparisons. Recent experiments conducted in our laboratory suggest that the propositional operation of nega-

65

tion can affect indirect measures as long as participants’ working memory is not taxed (Deutsch, Kordts-Â�Freudinger, Gawronski, & Strack, 2009). In essence, although a large number of studies seem compatible with the notion that indirect measures mainly assess associative processes, some measures seem to be sensitive to propositional processes under specific conditions. This pattern of results can be interpreted in two ways. First, it may suggest that propositional and associative processes interact under some conditions instead of operating isolated from each other. Indeed, this perspective was taken by some current theories such as Strack and Deutsch’s (2004) RIM or Gawronski and Bodenhausen’s (2006) associative–Â�propositional evaluation model. In both theories, associative representations are assumed to serve as an input for propositional processes. Propositional reasoning, in turn, is assumed to be capable of altering associative representations under specific conditions. From this perspective, propositional influences on indirect measures reflect true changes in associative processing. Second, the pattern of results may suggest that common implicit measures do not purely reflect associations, but also reflect other processes, including propositional processing (cf. Conrey et al., 2005). From this perspective, propositional influences on indirect measures reflect contaminations of the measure. Deciding which of the two interpretations is more valid is not an easy task (Gawronski, Deutsch, LeBel, et al., 2008), and it is theoretically plausible that both contribute to the pattern of results observed so far.

BUILDING BLOCKS OF SOCIAL BEHAVIOR For a long time, psychological theories of social behavior have taken the perspective of the actor, thereby highlighting the role of deliberating about beliefs, evaluations, coming to behavioral decisions, and forming intentions (e.g., Ajzen & Fishbein, 1980; Bandura, 1986). Thus, from the actors’ perspective, behavior occurs based on their reflection on the current situation, their preferences and expectancies, and a cumulative assessment of what is best under the given circumstances. Following earlier conceptions (e.g., Hofmann, Friese, & Strack, 2009; Strack & Deutsch, 2004), we refer to such behavior as reflective actions. The importance of behavioral decisions and intentions notwithstanding, a number of other constructs are necessary to arrive at a complete explanation of

66

I. BASICS

human behavior. Tightly bound to an increasing interest in automaticity (Bargh, 1997) and implicit measures (Fazio et al., 1986), nonreflective precursors of behavior, such as habits or motivational orientations, have gained immense interest. Because these processes are often conceptualized as being strongly influenced by situational inputs and having fast access to those systems that finally execute behaviors, the label impulsive has been used (e.g., Hofmann et al., 2009; Strack & Deutsch, 2004). From its start, predicting behavior was an important aim of the implicit social cognition paradigm, and by now numerous studies have generated a wealth of knowledge on how and when implicit and explicit social cognition is related to actual behavior (see Perugini et al., Chapter 14, this volume). In line with Fazio and colleagues’ motivation and opportunity as determinants (MODE) model (Fazio, 1990; Fazio & Towles-Â�Schwen, 1999), motivation and opportunity to engage in more complex reasoning processes have proven to moderate the relation between implicit and explicit social cognition on one hand and behavior on the other. With little motivation or opportunity, the processes underlying indirect measures of attitudes or stereotypes seem to have a privileged access to behavior. When both motivation and opportunity are present, those processes that determine explicit measures seem to gain control over behavior (for reviews, see Fazio & Towles-Â�Schwen, 1999; Friese et al., 2009; Olson & Fazio, 2009). Whereas the

moderation of the link between implicit and explicit social cognition on the one hand and behavior on the other is very well studied, there has been less attention to the mediation of such influences (e.g., Amodio & Devine, 2006).2 In what follows, we provide a brief overview of what we consider the most important mechanisms that potentially mediate the influence of implicit social cognition on behavior (Figure 4.1). On one hand, implicit social cognition may have an indirect effect on behavioral mediators by biasing the construal of the situation (Olson & Fazio, 2009). Thus, the construal of the situation can be considered a perceptual or cognitive mediator. On the other hand, implicit social cognition may have a direct effect on behavioral mediators independent of how the situation is construed.

Construal of the Situation Social behavior typically does not occur in a vacuum but rather in response to social situations. What matters for behavior, however, are not the objective properties of the physical and social surrounding but their inner representation instead. Inner representations are always abstractions of the outer world, which were shown to be strongly influenced by attitudes and stereotypes at various stages of processing. This starts at the earliest stages of cognitive processing, as is evidenced by well�documented effects of attitudes and stereotypes

FIGURE 4.1.╇ Mechanisms that may mediate the influence of implicit social cognition (attitudes, stereotypes, self) on behavior.



4. Reflective and Impulsive Processes

on attentional processes. For example, research by Roskos-Â�Ewoldsen and Fazio (1992) suggests that objects are the more attended the stronger participants hold attitudes toward them. Another example is a study by Eberhardt, Goff, Purdie, and Davies (2004), which suggests that activating stereotype-Â�relevant concepts in memory induces attentional biases toward stereotype-Â�consistent exemplars. But later stages of information processing are affected too. For example, attitudes were shown to influence categorizations of attitude objects (Smith, Fazio, & Cejka, 1996) or to bias the visual perception of facial expressions (Hugenberg & Bodenhausen, 2003), facial identity (Fazio, Ledbetter, & Towles-Â�Schwen, 2000), and guns and tools (Payne, 2001). Stereotype-Â�consistent contents not only can be activated automatically upon the processing of stereotype-Â�related cues but also can bias later person perception (Devine, 1989; Lepore & Brown, 1997; Wittenbrink, Judd, & Park, 1997). The automatic activation of stereotypes and attitudes and their biasing influence on attention, perception, and judgment, however, are not as ubiquitous as previously thought. Instead, activation and application were demonstrated to be moderated by numerous situational and personal factors (e.g., Devine, Plant, Amodio, HarmonJones, & Vance, 2002; Kunda & Spencer, 2003; Spencer, Fein, Wolfe, Fong, & Dunn, 1998). Yet it is clear that, under many circumstances, such a biasing influence can occur. The more the influence occurs in an automatic fashion, the less likely it is that an actor will recognize the influence and that he or she will be able to control for it should it be unwanted (Gawronski, Geschke, & Banse, 2003). Consequently, even when there is motivation and opportunity for responding in a way that is unbiased by stereotypes, attitudes, and self-Â�related associations, these constructs may have a strong influence on an actors’ decisions and other behavioral mediators such as habits or reflex behaviors.

Behavioral Mediators Once a representation of the situation is formed, which processes then determine behaviors? And how does such an influence occur? Relatively little attention has been devoted to this mediational question in recent years. At the same time, this question is a vital part of understanding the behavioral relevance of implicit social cognition. Our brief review of research on these questions is centered on a number of behavioral processes that have been studied in social psychology and

67

other fields of psychology. Generally, all of these mediators may be influenced by implicit social cognition via the route of the construal of the situation (Olson & Fazio, 2009). At the same time, at least some of the mediators may be influenced by implicit social cognition in a more direct manner. Moreover, at least for some mediators, there is evidence suggesting a bidirectional link with implicit social cognition.

Behavioral Decisions Behavioral decisions are one of the most successful constructs in explaining human behavior. Behavioral decisions are those processes that underlie the formation of intentions based on, broadly speaking, the potential costs and benefits of performing a particular behavior (Feather, 1982). Theories emphasizing behavioral decisions and intentions exist in many variants and have been widely used not only in social psychology (Bandura, 1986; Fishbein & Ajzen, 1975) but also in health psychology (Schwarzer, 1999), organizational behavior (Vroom, 1964), and economic theory (Becker, 1993). Although these theories differ in detail, they share the assumptions that (1) intentions are important determinants of behavior and (2) intentions follow from thoughts about the likelihood and desirability of behavioral outcomes. Such behavior is presumed to be primarily a function of its anticipated outcomes and, therefore, constitutes a prime example of what we defined as reflective behavior. An extensive body of research supports the usefulness of both assumptions (Ajzen, 2000; Armitage & Conner, 2001; Fishbein & Ajzen, 1975). Most important to the present volume, research suggests that direct measures of attitudes and intentions specifically predict behaviors that are well controllable and not habitualized, particularly when the actor’s motivation and self-Â�regulatory capacity are high (Fazio & Towles-Â�Schwen, 1999; Friese et al., 2009; Greenwald, Poehlman, Uhlmann, & Banaji, 2009; Perugini, 2005). But there is also reason to expect automatic evaluations and activated stereotypes to influence both the expectancy and the value component of decisions (Gawronski & Bodenhausen, 2006; Perugini & Bagozzi, 2004). Automatic evaluations and resulting feelings can serve as input to propositional judgments (Olson & Fazio, 2001), such as utility assessments, which may then drive decision making (Bechara, Damasio, Tranel, & Damasio, 1997; Loewenstein, Weber, Hsee, & Welch, 2001). Stereotypes can be conceived as implicit representa-

68

I. BASICS

tions of the probability that certain stereotypical features will apply to the target person given that he or she belongs to the stereotyped category. Decision makers may sometimes use such representations to form judgments of likelihoods (Tversky & Kahneman, 1983). Overall, such influences are particularly likely when decision makers have a preference for intuitive or affect-based thinking (Hofmann & Baumert, in press; Scarabis, Florack, & Gosejohann, 2006).

Goal Pursuit Theories of goal pursuit aim at explaining how goals, once they are set, translate into overt behavior. In essence, goal pursuit can be best understood as a regulative process, which monitors the distance between the actual and the desired state and selects appropriate means to minimize the distance to the goal (Carver & Scheier, 1998). Extensive research has helped to unveil basic mechanisms that support these functions (Elliot & Fryer, 2008; Fishbach & Ferguson, 2007; Förster, Liberman, & Friedman, 2007; Kruglanski et al., 2002). One important feature of goal pursuit is that it is entwined with affect. Positive and negative emotions serve a feedback function during and after goal pursuit, informing the actor about the current progress toward the goal (Carver & Scheier, 1990, 2009). At the same time, pursuing goals tunes the evaluation of stimuli in a way that supports goal attainment (Markman, Brendl, & Kim, 2009). Items that signal goal attainment become more positive (Ferguson, 2008; Ferguson & Bargh, 2004; Moors & De Houwer, 2001), and items that are in conflict with actual goals may be devaluated (Brendl, Markman, & Messner, 2003; Fishbach, Friedman, & Kruglanski, 2003). Processes in support of goal attainment are not limited to affect. Goal pursuit increases the accessibility of goal-Â�promoting concepts (Förster, Liberman, & Higgins, 2005) and may decrease the accessibility of conflicting representations (Fishbach et al., 2003). Differentiating goal pursuit from other sources of accessibility, goal-Â�dependent accessibility was demonstrated to drop sharply after goal pursuit while accessibility from other sources fades gradually (Förster et al., 2005; Liberman, Förster, & Higgins, 2007). As reviewed by Förster and colleagues (2007), other important features of goal pursuit are that its intensity increases with greater proximity to the goal, that it follows from behavioral decisions, that it involves self-Â�control, and that typically multiple means exist to reach

a given goal (Kruglanski et al., 2002). As part of its regulative nature, goal pursuit includes the selection of new means if obstacles block a planned path to the goal. Further in support of goal attainment, implementation intentions increase the accessibility of intention-�relevant representations in memory (Webb & Sheeran, 2007). This way, the appropriate behavior is activated more easily by situational cues (Gollwitzer & Sheeran, 2006). Processes of goal pursuit are intertwined with implicit and explicit social cognition phenomena in various ways (see Ferguson & Porter, Chapter 17, this volume). For example, valuation and devaluation resulting from goal pursuit were shown to influence not only explicit valence judgments but also automatic evaluations (Ferguson, 2008; Ferguson & Bargh, 2004; Moors & De Houwer, 2001). Thus, goal pursuit can be considered as a source of automatic valence. Another interaction results from the semiautomatic nature of implementation intentions (Gollwitzer & Sheeran, 2006). As such, they were demonstrated not only to efficiently promote goal attainment but also to be a means to suppress automatic responses that result from evaluative and stereotypic associations (Achtziger & Gollwitzer, 2008; Hofmann, Deutsch, Lancaster, & Banaji, 2010; Stewart & Payne, 2008).

Habits, Schemas, and Scripts Traditionally, habits were defined as learned associations between stimuli and behaviors, which determine behavior through situational stimulation without a mediation through goals or decisions (e.g., Hull, 1943; James, 1890/1950; Watson, 1913). Thus, habits represent a core feature of our definition of impulsive processes. In modern social psychology, some successful explanatory concepts resemble the original idea of stimulus–Â�response links, among them modern variants of the habit concept (Aarts & Dijksterhuis, 2000; Verplanken & Aarts, 1999; Wood & Neal, 2007), behavioral schemas (Strack & Deutsch, 2004), and scripts (Schank & Abelson, 1977). What these concepts have in common is the idea that frequent practice creates dispositions to act similarly in comparable situations in a relatively automatic manner. Research and theory differ, however, with respect to at least two features (Wood & Neal, 2007). The first feature is the rigidity of responding. In line with research on the automatization of simple skills (Logan, 1988; Schneider & Chein, 2003), consistent execution of the same behavior in a given context may result in rigid stimulus–Â�



4. Reflective and Impulsive Processes

response links that lack the properties of flexible goal regulation (Strack & Deutsch, 2004; Wood & Neal, 2007). On the other hand, evidence suggests that extended practice of goal pursuit results in associations between goals and particular actions (Aarts & Dijksterhuis, 2000). Such structures, albeit supporting the efficient execution of behaviors, are more flexible because they are not bound to specific situations and may follow the principles of regulated behavior (Förster et al., 2007). Closely related to the previous feature, habit-like concepts in social psychology differ in the assumed degree of propositional processing. In the case of scripts (Schank & Abelson, 1977), habitualization results in well-Â�established knowledge about what to do in a given situation. Whereas this knowledge may efficiently guide the interpretation of other people’s behaviors and one’s own decision making, it is not self-Â�sufficient to trigger behavior. On the other end of the spectrum, theorists have argued in favor of direct situational cuing of behavior with minimal cognitive mediation in the case of habits (Neal & Wood, 2009; Wood & Neal, 2007). Given the empirical support for mediation and flexibility on the one hand and nonmediation and rigidity on the other, it seems appropriate to differentiate between different forms of habitualized behaviors, ideally using distinct labels. Surprisingly, only a few studies have addressed the interplay of implicit social cognition and habits. One study suggests that evaluative associations determine behavior more the stronger the behavior is habitualized (Conner, Perugini, O’Gorman, Ayres, & Prestwich, 2007). A few other studies suggest that evaluative associations may also have a facilitating effect on habitualized goal–Â�action associations. Particularly, such behavioral representations had a facilitating (inhibitory) influence on behavior when they were paired with positive (negative) affect (Aarts, Custers, & Holland, 2007; Custers & Aarts, 2005a, 2005b). In sum, these studies suggest that habits increase the impact of evaluative associations on behavior, and that evaluative associations fuel the execution of habits. Other studies have addressed the interaction of habits and propositional, belief-based evaluations. Although it is a dominant finding that habits contribute uniquely to behavior beyond intentions, it was also found that habits may contribute to the formation of explicit evaluations and intentions (Ouellette & Wood, 1998; Wood & Neal, 2007). That is, people may infer what they like and what their want from their recollections of past behaviors. To further study how implicit social cognition

69

may interact with habits in determining behavior, methods that were developed to study the relation among explicit attitudes, habits, and behaviors (e.g., Ouellette & Wood, 1998) could be adopted.

Motivational Orientations Motivational processes and their influence on social cognition and behavior have gained enormous interest during the past two decades. Besides specific goals, more global motivational forces were assumed to exert a directional influence on affect, cognition, and behavior. Whereas goals typically result from behavioral decisions, a subset of motivational processes are presumably set in motion as a consequence of organismic factors outside of reflection, such as physiological need states or affect. One area of research has focused on basic motivational orientations. Motivational orientations are inner states that prepare the organism toward approaching or avoiding stimuli (Neumann, Förster, & Strack, 2003). Numerous studies suggest that participants are faster to decrease the distance between positive stimuli and the self and to increase the distance to negative stimuli (Castelli et al., 2004; De Houwer, Crombez, Baeyens, & Hermans, 2001; Duckworth, Bargh, Garcia, & Chaiken, 2002; Markman & Brendl, 2005; Mogg, Bradley, Field, & De Houwer, 2003; Neumann, Hülsenbeck, & Seibt, 2004; Seibt, Neumann, Nussinson, & Strack, 2008). Additionally, distance regulation has a feedback effect on information processing: While approaching or avoiding, evaluatively compatible information is processed more easily (Förster & Strack, 1997), whereas evaluatively incompatible information is more attention grabbing (Gawronski, Deutsch, & Strack, 2005). Approach and avoidance orientations are also an integral part of many emotions (Carver, Avivi, & Laurenceau, 2008; Lang, 1995) and may be, in part, responsible for rushed, spontaneous action that often follows from intense emotional experiences (Cyders & Smith, 2008; Loewenstein, 1996). Either explicitly or implicitly, many theories suggest that approach and avoidance reactions are intimately linked to the activation of evaluative and stereotypic associations (e.g., Fazio, 1990; Lang, Bradley, & Cuthbert, 1990; Metcalfe & Mischel, 1999; Strack & Deutsch, 2004). In direct support of this idea, results by Neumann and colleagues (2004) indicate that implicit, but not explicit, measures of prejudice against persons with AIDS predict participants’ spontaneous–Â�approach avoidance tendencies. Other evidence for this no-

70

I. BASICS

tion is, however, indirect. It mainly rests on the observation that compatibility effects of stimulus valence and approach–Â�avoidance behaviors occur even when participants are not intentionally evaluating the stimuli (Chen & Bargh, 1999; Krieglmeyer & Deutsch, in press; Moors & De Houwer, 2001), such that automatic evaluations are their likely basis. Recently, the notion of automaticity has been questioned based on failures to observe compatibility effects in the absence of intentions to evaluate (Rotteveel & Phaf, 2004). Future research should investigate potential moderators of automatic approach–Â�avoidance effects. Although it remains subject to investigation whether approach–Â�avoidance tendencies based on automatic evaluations are possible, it has become clear that other motivational factors such as goals and needs also have an impact on approach–Â� avoidance reactions, presumably by changing evaluations of stimuli in a need- or goal-Â�congruent manner (Moors & De Houwer, 2001; Seibt et al., 2007).

Innate Behaviors and Reflexes Many personality traits and preferences that affect social behavior were theorized to be partially inherited. For example, evidence suggests a partial genetic determination for socially relevant traits such as aggressiveness (Miles & Carey, 1997), agreeableness, and extraversion (Jang, McCrae, Angleitner, Riemann, & Livesley, 1998). Moreover, preferences for reciprocal altruism (Trivers, 1971), social inclusion (Kerr & Levine, 2008), and particular features of mating partners (Thornhill & Gangestad, 1999) were theorized to have biological bases as an outcome of evolutionary processes. Traits and preferences dispose to act in a specific way, but they are not identical to the behaviors they facilitate. For example, although aggressiveness may have a strong genetic component, this does not imply that behaviors like hitting or shooting are inherited. Indeed, only very few innate behaviors in humans are of social relevance. The most important category of such behaviors is probably that of facial expressions. Although cultural conventions have a strong impact on the expression of emotions (Elfenbein & Ambady, 2002), many empirical findings are supportive of Darwin’s (1872) notion that emotional expressions have a genetic basis (e.g., Ekman, 1972). At least in part, emotional expressions do not occur as the consequence of decisions or intentions but rather as an immediate consequence of experiencing an emotion (e.g., Buck, 1994). Therefore, they can

be considered as impulsive behaviors under many conditions. Facial expressions are important not only because they fulfill a communicative function in social contexts (Fridlund, 1994) but also because they have a regulative function in that they contribute to the experience of emotions (Izard, 1971; Strack, Martin, & Stepper, 1988). Although humans are endowed with a number of reflexes, almost none of them have been considered important for human social behavior and cognition. Even the extensively studied startle reflex (Lang et al., 1990) has mainly served as an indicator of avoidance motivation. A number of studies suggest that facial expressions and other nonverbal behaviors are particularly well predicted by indirect measures of prejudice and stereotypes (Dasgupta & Rivera, 2006; Dovidio et al., 1997; Fazio, Jackson, Dunton, & Williams, 1995; McConnell & Leibold, 2001). Also stimulus valence immediately facilitates congruent facial expressions (Dimberg, Thunberg, & Grunedal, 2002; Neumann, Hess, Schulz, & Alpers, 2005). Facial expressions were also studied as an independent variable in numerous studies. This evidence suggests that facial expressions influence explicit judgments in various domains (Strack et al., 1988; Strack & Neumann, 2000), presumably by inducing affective and cognitive feelings or by biasing information processing (Deutsch & Strack, 2008).

CONFLICTS AND IRRATIONAL€BEHAVIOR So far, we have reviewed various determinants of social behavior and how they might relate to implicit and explicit social cognition. In what follows, we discuss how these processes may contribute to conflicts and irrational behavior. By conflict, we refer to the case that two or more psychological processes have incommensurable implications for cognition or behavior. By irrational, we refer to the case in which behavior occurs against the actor’s explicit beliefs. The latter possibility is particularly important because concepts of implicit social cognition are often invoked to explain behavioral phenomena that cannot be accommodated by theories of rational choice, which construe behavior as being based on decisions and judgments of value and probability. Examples are thoughtless behaviors (Langer, Blank, & Chanowitz, 1978), social discrimination despite the actor’s egalitarian beliefs (Payne, 2001), and behaviors that people pursue although they expect their overall



4. Reflective and Impulsive Processes

outcomes to be negative, as is often the case in addictive behaviors (Robinson & Berridge, 2003). Wilson and colleagues (2000) suggested that implicit and explicit attitudes may be in conflict for various reasons. Fazio’s MODE model (e.g., Fazio, 1990) rests on the assumption that people may have evaluative associations in memory that they do not endorse and may thus may be motivated to prevent their influence. Recurring to concepts such as habits or motivational orientations, seems to be better suited to explain these phenomena than rational choice theory.

Types of Conflicts and Irrationality Such conflicts may come in at least two broad variants, which may be quite different with respect to their underlying mechanisms and may be considered irrational in different ways (Berridge, 2003). The first form is an intertemporal conflict, in which thoughts or actions that occur at one point in time are at odds with what one thinks or decides at another (Loewenstein, Read, & Baumeister, 2003). Examples are shopping sprees that later lead to regret or recognizing in hindsight that one has treated someone in an unfair manner because of his or her gender. Labeling such behaviors as irrational may be a misnomer because when they were made, the choices may have appeared perfectly aligned with one’s values. Because a lack of knowledge or awareness has led to this behavior, some theorists have called it miswanting (Gilbert & Wilson, 2000; Winkielman & Berridge, 2003). In a pure form, this type of conflict does not involve anticipated regret on the actor’s side while acting. Quite often, however, such feelings may be present and trigger tendencies to refrain from the action. In this case, the intertemporal conflict has become a simultaneous conflict, in which two or more incommensurable thoughts or action tendencies are active at the same time (Sloman, 1996). If such a conflict is resolved by following one’s explicit beliefs about what is true or most useful, the situation is often described as one in which self-Â�control was successfully operating (Baumeister & Heatherton, 1996; Carver, 2005; Hofmann et al., 2009). If this conflict is solved by yielding to influences that contradict one’s explicit beliefs, the behavior is often described as impulsive or truly irrational (Berridge, 2003; Carver, 2005). While the former case suggests that self-Â�regulative resources were strong enough to prevent actors from doing something against their better knowledge, the latter case suggests they were not (Heatherton & Baumeister, 1996). Beyond these prototypic

71

forms of conflict, decision makers may experience a prolonged phase of oscillation between the options before either self-�control or impulse sets in. For example, when in conflict about healthy but not tasty food and unhealthy but tasty food, an actor may first focus his or her attention on the taste of the food, thereby increasing the strength of the impulse to consume the unhealthy food. As this impulse increases, self-�regulatory efforts may set in, thereby shifting the prepotent action tendencies from the unhealthy to the healthy option. Attention may then turn back to taste, shifting the prepotent action tendencies to the tasty option and so forth. Such oscillations of preferences before action have received some attention in decision research (Busemeyer & Townsend, 1993; Fink, Kaplowitz, & Hubbard, 2002) and may also play an important role in self-�regulation.

Relation to Reflective and Impulsive Determinants Reflective and impulsive precursors of behavior may contribute to temporal and simultaneous conflicts in a complex way. Much of the current research and theorizing on self-�regulatory conflicts draws on a metaphor of strength. Accordingly, irrational behavior is seen as a relative weakness of reflective determinants (e.g., Baumeister, Heatherton, & Tice, 1994; Carver, 2005). This perspective has been exceptionally successful in explaining the occurrence of self-�controlled versus irrational behavior (for extensive collections of evidence, see Baumeister & Vohs, 2004; Hofmann et al., 2009). Numerous studies suggest that straining the capacities of reflective processing decreases self-�control, whereas buffering reflective processing capacities increases self-�control (Baumeister, Bratslavsky, Muraven, & Tice, 1998; Gailliot et al., 2007). Recent research provides more detailed insights into the cognitive underpinnings of reflective self-�control. For example, executive functions such as attentional control contribute to self-�control (Hofmann, Friese, & Roefs, 2009). As another example, implementation intentions were shown to help prevent unwanted responses (Hofmann et al., 2010; Stewart & Payne, 2008). Often impulsive determinants such as habits, motivational orientations, and expressive behavior work in harmony with decisions and goal pursuit and do not generate self-�regulatory conflict (Strack & Deutsch, 2004). To the degree that impulsive processes are more difficult to change and adapt more slowly to changing environments or goals, they are a prime source for conflict (Betsch, Hab-

72

I. BASICS

erstroh, Molter, & Glockner, 2004; Wood & Neal, 2007). Obviously, the strength model was developed to explain simultaneous conflicts. However, it cannot explain intertemporal conflicts that do not involve a simultaneous “battle” between two opposing forces. A number of theories were developed to explain intertemporal conflicts (e.g., Ainslie, 2005; Fujita, Trope, Liberman, & Levin-Sagi, 2006). Given that a feature of temporal conflicts is that the decision maker feels in full harmony with his or her goals and beliefs, these theories must explain why beliefs differ as a function of time. One theoretical and empirical thrust has been to focus on how people estimate the probability and value of future events. Many studies suggest that the cognitive construals of temporally close versus distant events differ significantly and predictably (Ainslie, 2005; Fujita et al., 2006; Gilbert & Wilson, 2000). As a consequence, what seemed desirable at time 1 will appear foolish at time 2. Another source of temporal conflicts is the lack of awareness of unwanted influences. Numerous studies on stereotype activation suggest that activated constructs can shape social perception (Hugenberg & Bodenhausen, 2003) and eventually decision making without the perceiver being aware of their impact (Gawronski et al., 2003). Other impulsive factors, such as habits (Wood & Neal, 2007), facial expressions (Strack et al., 1988), and evaluative associations (Galdi, Arcuri, & Gawronski, 2008) similarly may bias reflective judgment and decision making outside of awareness. Awareness and knowledge about the direction of an unwanted influence, however, is a precondition for engaging in correctional activities (Strack & Hannover, 1996). To the degree that the actor achieves awareness at a later point in time, such biasing influences can be considered as contributing to temporal conflict.

THEORETICAL INTEGRATION AND OPEN QUESTIONS The development of a science typically goes hand in hand with specialization. The same is true for single research programs, which often start with a few interesting questions or phenomena but often quickly turn into a differentiated field of research with highly specialized subgroups of researchers, theories, methods, and questions. The paradigm of implicit social cognition seems to have already reached such a state. On one hand, this is clearly a sign of scientific progress toward a high degree of precision. Moreover, many other fields of psycholo-

gy (e.g., clinical, consumer behavior) adopted core ideas and methods of the implicit social cognition paradigm, which may provide a basis for improved cross-talk between these branches. On the other hand, specialization can result in a fragmentation of a research field, which may have numerous negative consequences, the worst being a lack of cumulative progress (Kruglanski, 2001). It is, therefore, an important goal to search for commonalities between the various processes described so far and to integrate them within a broader theoretical framework. Fortunately, the field of implicit social cognition has been based on integrative theories for quite a while now (e.g., Fazio, 1990; Greenwald et al., 2002; Smith & DeCoster, 2000; Wilson, et al., 2000). These theories stand in the tradition of dual-�process models (Chaiken & Trope, 1999) and are based on the conviction that two qualitatively different classes of psychological processes are largely responsible for the phenomena observed in the realm of implicit social cognition. A number of theories have invoked the notion of psychological systems in order to group and systemize the various processes (Lieberman et al., 2002; Smith & DeCoster, 2000; Strack & Deutsch, 2004). In essence, these models suggest that subgroups of processes correlate and thus form mental systems (Deutsch & Strack, 2006a). The RIM (Strack & Deutsch, 2004) particularly aims at integrating cognitive and behavioral processes that underlie overt social behavior. The core tenet of the RIM is that social cognition and behavior is a joint function of an RS and an IS, each operating according to different representations and computations. In this model, the two systems operate interactively, serve different functions, and have different conditions for optimal functioning. The IS combines the concept of an associative memory that contains evaluative and semantic associations with the notion of a procedural memory that contains habits. It further can be oriented toward approach or avoidance, and its operation is modulated by the deprivation of needs. It is also responsible for generating affect. In part, the processes of the IS resemble those constructs that have been theorized to drive implicit social cognition, and it contains two important building blocks of behavior that were reviewed previously (i.e., habits, motivational orientations). The RS has features complementary to the IS, serving different regulatory goals. It is specialized in generating propositional representations based on what is activated in the IS and, if necessary, in fulfilling executive functions such as overcoming habitual



4. Reflective and Impulsive Processes

responses or putting together action plans in new situations or when habits fail (Lieberman, 2003). It generates judgments, decisions, and intentions, which can result in verbal or nonverbal behavior. The processing capacity of the RS is limited compared with the IS, and processing in the RS depends on intentions. The RS is the system that generates behavioral decisions and translates them into overt behavior via intending and goal regulation. The RS thus contains two other building blocks of social behavior (i.e., decisions and goal regulation), and its processes rather resemble typical conceptualizations of explicit social cognition. The two systems work in an interactive fashion while navigating through the social environment. On one hand, the RS influences the IS in several ways. For instance, it activates associative clusters while construing propositional representations and directs activation to behavioral schemata after decisions. Both processes alter the reactivity of the IS. On the other hand, the IS has several influences on reflection. The accessibility of associative clusters varies depending on perception, previous use, motivational orientation, and deprivation and shapes how the RS generates judgments and decisions. In addition, feelings of different qualities enter into reflection as heuristic cues, and high emotional arousal disrupts reflection. Principles of dual-�system models have been applied to many topics within social cognition research and beyond, including evaluative processes (De Houwer, 2009; Gawronski & Bodenhausen, 2006), addiction (Deutsch & Strack, 2005), self�control (Hofmann, Friese, & Strack, 2009), and consumer behavior (Strack et al., 2006). Dual�system models serve to highlight commonalities and differences between the multitude of processes and phenomena of implicit social cognition. We believe that they can serve as a means to ensure cumulative process within the field. At present, the majority of broad theories within the field do indeed follow a dual-�process or dual-�system approach. They do, however, partially differ in their scope, language, and the specific types of processes they address. Moreover, single-�process alternatives have been developed (e.g., Kruglanski & Thompson, 1999) and proved useful in some domains. At the same time, the idea of dividing the realm of psychological processes has been criticized for empirical and theoretical reasons (e.g., Gigerenzer & Regier, 1996; Keren, van Rooij, & Schul, 2007; Moors & De Houwer, 2006b). For a more elaborated discussion of the function and potential problems of dual-�process and dual-�system models, see Deutsch and Strack (2006a, 2006b).

73

An important future task will be to improve our understanding of exactly how implicit social cognition relates to overt behavior. As we have argued in the present chapter, there is a wealth of knowledge that suggests a relatively complicated relationship between implicit and explicit cognition on the one hand and overt behavior on the other. Not only do different behaviors seem to be affected by implicit and explicit social cognition, but this relation also seems to be moderated by factors that can be broadly identified as motivation and opportunity to control the overt behavior (Fazio, 1990; Friese et al., 2009). The available knowledge on the processes that mediate the relation between implicit social cognition and behavior, however, seems to be less rich. At the same time, we argue that such knowledge is important for not only developing a more comprehensive understanding of implicit social cognition but also facilitating exchange with those other branches of psychology that are immediately interested in explaining behavior. Acknowledgments We thank Bertram Gawronski, Wilhelm Hofmann, and Keith Payne for valuable comments on a previous version of this chapter.

Notes 1. For the purpose of the present analysis, we use the term implicit social cognition in a rather narrow sense. Particularly, by this term we refer to attitudes, stereotypes, and cognitions relating to the self-�concept (Greenwald et al., 2002), which exert their influence in a relatively automatic manner (De Houwer et al., 2009). 2. Of course, moderator models often allow at least indirect hints on mediating mechanisms, and quite often moderator models are inspired by mediator models.

References Aarts, H., Custers, R., & Holland, R. W. (2007). The nonconscious cessation of goal pursuit: When goals and negative affect are coactivated. Journal of Personality and Social Psychology, 92, 165–178. Aarts, H., & Dijksterhuis, A. (2000). Habits as knowledge structures: Automaticity in goal-Â�directed behavior. Journal of Personality and Social Psychology, 78, 53–63. Achtziger, A., & Gollwitzer, P. M. (2008). Implementation intentions and shielding goal striving from unwanted thoughts and feelings. Personality and Social Psychology Bulletin, 34, 381–393.

74

I. BASICS

Ainslie, G. (2005). Precis of breakdown of will. Behavioral and Brain Sciences, 28, 635–673. Ajzen, I. (2000). Attitudes and the attitude-Â�behavior relation: Reasoned and automatic processes. In W. Stroebe & M. Hewstone (Eds.), European review of social psychology (pp.€1–33). New York: Wiley. Ajzen, I., & Fishbein, M. (Eds.). (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall. Amodio, D. M., & Devine, P. G. (2006). Stereotyping and evaluation in implicit race bias: Evidence for independent constructs and unique effects on behavior. Journal of Personality and Social Psychology, 91, 652–661. Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behavior: A meta-Â�analytic review. British Journal of Social Psychology, 40, 471–499. Asendorpf, J. B., Banse, R., & Mücke, D. (2002). Double dissociation between implicit and explicit personality self-Â�concept: The case of shy behavior. Journal of Personality and Social Psychology, 83, 380–393. Bandura, A. (1986). Social foundations of thought and action: A social cfcognitive theory. Englewood Cliffs, NJ: Prentice Hall. Bargh, J. A. (1994). The four horsemen of automaticity: Awareness, intention, efficiency, and control in social cognition. In R. S. Wyer, Jr. & T. K. Srull (Eds.), Handbook of social cognition (Vol. 1, pp.€1–40). Hillsdale, NJ: Erlbaum. Bargh, J. A. (1997). The automaticity of everyday life. In R. S. Wyer, Jr. (Ed.), Advances in social cognition (Vol. 10, pp.€1–61). Mahwah, NJ: Erlbaum. Bassili, J. N., & Brown, R. D. (2005). Implicit and explicit attitudes: Research, challenges, and theory. In D. Albarracin, B. T. Johnson, & M. P. Zanna (Eds.), The handbook of attitudes (pp.€ 543–574). Mahwah, NJ: Erlbaum. Baumeister, R. F., Bratslavsky, M., Muraven, M., & Tice, D. M. (1998). Ego depletion: Is the active self a limited resource? Journal of Personality and Social Psychology, 74, 1252–1265. Baumeister, R. F., & Heatherton, T. F. (1996). SelfÂ�regulation failure: An overview. Psychological Inquiry, 7, 1–15. Baumeister, R. F., Heatherton, T. F., & Tice, D. M. (1994). Losing control: How and why people fail at selfregulation. San Diego, CA: Academic Press. Baumeister, R. F., Heatherton, T. F., Tice, D. M., Boekaerts, M., Pintrich, P. R., & Zeidner, M. (2002). Losing control: How and why people fail at self-Â�regulation and handbook of self-Â�regulation. Journal of Psychiatry and Law, 30, 283–288. Baumeister, R. F., & Vohs, K. D. (Eds.). (2004). Handbook of self-Â�regulation: Research, theory, and applications. New York: Guilford Press. Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 275, 1293–1295. Becker, G. S. (1993). The economic way of looking at behavior. Journal of Political Economy, 101, 385–409. Berridge, K. C. (2003). Irrational pursuits: HyperÂ�incentives from a visceral brain. In L. Brocas & J. Carrillo (Eds.), The psychology of economic decisions (Vol. 1, pp.€17–40). Oxford, UK: Oxford University Press.

Betsch, T., Haberstroh, S., Molter, B., & Glockner, A. (2004). Oops, I did it again—Â�Relapse errors in routinized decision making. Organizational Behavior and Human Decision Processes, 93, 62–74. Brendl, C. M., Markman, A. B., & Messner, C. (2003). The devaluation effect: Activating a need devalues unrelated objects. Journal of Consumer Research, 29, 463–473. Buck, R. (1994). Social and emotional functions in facial expression and communication: The readout hypothesis. Biological Psychology, 38, 95–115. Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: A dynamic-Â�cognitive approach to decision making in an uncertain environment. Psychological Review, 100, 432–459. Carver, C. S. (2001). Affect and the functional bases of behavior: On the dimensional structure of affective experience. Personality and Social Psychology Review, 5, 345–356. Carver, C. S. (2005). Impulse and constraint: Perspectives from personality psychology, convergence with theory in other areas, and potential for integration. Personality and Social Psychology Review, 9, 312–333. Carver, C. S., Avivi, Y. E., & Laurenceau, J.-P. (2008). Approach, avoidance, and emotional experiences. In A. J. Elliot (Ed.), Handbook of approach and avoidance motivation (pp.€385–397). New York: Psychology Press. Carver, C. S., & Scheier, M. F. (1990). Origins and functions of positive and negative affect: A control process view. Psychological Review, 97, 19–35. Carver, C. S., & Scheier, M. F. (1998). On the selfÂ�regulation of behavior. New York: Cambridge University Press. Carver, C. S., & Scheier, M. F. (2009). Action, affect, and two-mode models of functioning. In E. Morsella, J. A. Bargh, & P. M. Gollwitzer (Eds.), Oxford handbook of human action (pp.€298–327). New York: Oxford University Press. Castelli, L., Zogmaister, C., Smith, E. R., & Arcuri, L. (2004). On the automatic evaluation of social exemplars. Journal of Personality and Social Psychology, 86, 373–387. Chaiken, S., & Trope, Y. (Eds.). (1999). Dual-Â�process theories in social psychology. New York: Guilford Press. Chen, M., & Bargh, J. A. (1999). Consequences of automatic evaluation: Immediate behavioral predispositions to approach or avoid the stimulus. Personality and Social Psychology Bulletin, 25, 215–224. Conner, M., Perugini, M., O’Gorman, R., Ayres, K., & Prestwich, A. (2007). Relations between implicit and explicit measures of attitudes and measures of behavior: Evidence of moderation by individual difference variables. Personality and Social Psychology Bulletin, 33, 1727–1740. Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K., & Groom, C. J. (2005). Separating multiple processes in implicit social cognition: The quad model of implicit task performance. Journal of Personality and Social Psychology, 89, 469–487. Custers, R., & Aarts, H. (2005a). Beyond priming effects: The role of positive affect and discrepancies in implicit processes of motivation and goal pursuit. European Review of Social Psychology, 16, 257–300.



4. Reflective and Impulsive Processes

Custers, R., & Aarts, H. (2005b). Positive affect as implicit motivator: On the nonconscious operation of behavioral goals. Journal of Personality and Social Psychology, 89, 129–142. Cyders, M. A., & Smith, G. T. (2008). Emotion-based dispositions to rash action: Positive and negative urgency. Psychological Bulletin, 134, 807–828. Darwin, C. (Ed.). (1872). The expression of the emotions in man and animal. London: Murray. Dasgupta, N., & Rivera, L. M. (2006). From automatic antigay prejudice to behavior: The moderating role of conscious beliefs about gender and behavioral control. Journal of Personality and Social Psychology, 91, 268–280. De Houwer, J. (2003). The extrinsic affective Simon task. Experimental Psychology, 50, 77–85. De Houwer, J. (2009). How do people evaluate objects? Social and Personality Psychology Compass, 3, 36–48. De Houwer, J., Beckers, T., & Moors, A. (2007). Novel attitudes can be faked on the Implicit Association Test. Journal of Experimental Social Psychology, 43, 972–978. De Houwer, J., Crombez, G., Baeyens, F., & Hermans, D. (2001). On the generality of the affective Simon effect. Cognition and Emotion, 15, 189–206. De Houwer, J., Hermans, D., Rothermund, K., & Wentura, D. (2002). Affective priming of semantic categorisation responses. Cognition and Emotion, 16, 643–666. De Houwer, J., & Moors, A. (2007). How to define and examine the implicitness of implicit measures. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp.€179–194). New York: Guilford Press. De Houwer, J., Teige-Â�Mocigemba, S., Spruyt, A., & Moors, A. (2009). Implicit measures: A normative analysis and review. Psychological Bulletin, 135, 347–368. Deutsch, R., Gawronski, B., & Strack, F. (2006). At the boundaries of automaticity: Negation as reflective operation. Journal of Personality and Social Psychology, 91, 385–405. Deutsch, R., Kordts-Â�Freudinger, R., Gawronski, B., & Strack, F. (2009). Fast and fragile: A new look at the automaticity of negation processing. Experimental Psychology, 56, 434–446. Deutsch, R., & Strack, F. (2005). Reflective and impulsive determinants of addictive behaviors. In R. W. Wiers & A. W. Stacy (Eds.), Handbook of implicit cognition and addiction (pp.€45–57). Thousand Oaks, CA: Sage. Deutsch, R., & Strack, F. (2006a). Duality models in social psychology: From dual processes to interacting systems. Psychological Inquiry, 17, 166–172. Deutsch, R., & Strack, F. (2006b). Duality models in social psychology: Response to commentaries. Psychological Inquiry, 17, 265–268. Deutsch, R., & Strack, F. (2008). Variants of judgment and decision making: The perspective of the reflective-Â�impulsive model. In H. Plessner, C. Betsch, & T. Betsch (Eds.), Intuition in judgment and decision making (pp.€39–53). Mahwah, NJ: Erlbaum. Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of Personality and Social Psychology, 56, 5–18.

75

Devine, P. G., Plant, E. A., Amodio, D. M., HarmonJones, E., & Vance, S. L. (2002). The regulation of explicit and implicit race bias: The role of motivation to respond without prejudice. Journal of Personality and Social Psychology, 82, 835–848. Dimberg, U., Thunberg, M., & Grunedal, S. (2002). Facial reactions to emotional stimuli: Automatically controlled emotional responses. Cognition and Emotion, 16, 449–471. Dovidio, J. F., Kawakami, K., Johnson, C., & Johnson, B. (1997). On the nature of prejudice: Automatic and controlled processes. Journal of Experimental Social Psychology, 33, 510–540. Draine, S. C. (1997). Analytic limitations of unconscious language processing. Dissertation Abstracts International: Section B: The Sciences and Engineering, 58, 3331. Duckworth, K., Bargh, J. A., Garcia, M., & Chaiken, S. (2002). The automatic evaluation of novel stimuli. Psychological Science, 13, 513–519. Dunton, B. C., & Fazio, R. H. (1997). An individual difference measure of motivation to control prejudiced reactions. Personality and Social Psychology Bulletin, 23, 316–326. Eberhardt, J. L., Goff, P. A., Purdie, V. J., & Davies, P. G. (2004). Seeing black: Race, crime, and visual processing. Journal of Personality and Social Psychology, 87, 876–893. Ekman, P. (1972). Universals and cultural differences in facial expressions of emotion. Nebraska Symposium on Motivation, 19, 207–283. Elfenbein, H. A., & Ambady, N. (2002). Is there an ingroup advantage in emotion recognition? Psychological Bulletin, 128, 243–249. Elliot, A. J., & Fryer, J. W. (2008). The goal construct in psychology. In J. Y. Shah & W. L. Gardner (Eds.), Handbook of motivation science (pp.€ 235–250). New York: Guilford Press. Fazio, R. H. (1990). Multiple processes by which attitudes guide behavior: The MODE model as an integrative framework. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 23, pp.€ 75–109). New York: Academic Press. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Fazio, R. H., Ledbetter, J. E., & Towles-Â�Schwen, T. (2000). On the costs of accessible attitudes: Detecting that the attitude object has changed. Journal of Personality and Social Psychology, 78, 197–210. Fazio, R. H., & Olson, M. A. (2003). Implicit measures in social cognition research: Their meaning and uses. Annual Review of Psychology, 54, 297–327. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. Fazio, R. H., & Towles-Â�Schwen, T. (1999). The MODE model of attitude-Â�behavior processes. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€97–116). New York: Guilford Press. Feather, N. T. (Ed.). (1982). Expectations and actions:

76

I. BASICS

Expectancy-value models in psychology. Hillsdale, NJ: Erlbaum. Ferguson, M. J. (2008). On becoming ready to pursue a goal you don’t know you have: Effects of nonconscious goals on evaluative readiness. Journal of Personality and Social Psychology, 95, 1268–1294. Ferguson, M. J., & Bargh, J. A. (2004). Liking is for doing: The effects of goal pursuit on automatic evaluation. Journal of Personality and Social Psychology, 87, 557–572. Fink, E. L., Kaplowitz, S. A., & Hubbard, S. M. (2002). Oscillation in beliefs and decisions. In J. P. Dillard & M. Pfau (Eds.), The persuasion handbook: Developments in theory and practice (pp.€17–38). Thousand Oaks, CA: Sage. Fishbach, A., & Ferguson, M. J. (2007). The goal construct in social psychology. In A. W. Kruglanski & E. T. Higgins (Eds.), Social psychology: Handbook of basic principles (2nd ed., pp.€ 490–515). New York: Guilford Press. Fishbach, A., Friedman, R. S., & Kruglanski, A. W. (2003). Leading us not unto temptation: Momentary allurements elicit overriding goal activation. Journal of Personality and Social Psychology, 84, 296–309. Fishbein, M., & Ajzen, I. (Eds.). (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Â�Wesley. Förster, J., Liberman, N., & Friedman, R. (2007). Seven principles of goal activation: A systematic approach to distinguishing goal priming from priming of nongoal constructs. Personality and Social Psychology Review, 11, 211–233. Förster, J., Liberman, N., & Higgins, E. T. (2005). Accessibility from active and fulfilled goals. Journal of Experimental Social Psychology, 41, 220–239. Förster, J., & Strack, F. (1997). Motor actions in retrieval of valenced information: A motor congruence effect. Perceptual and Motor Skills, 85, 1419–1427. Fridlund, A. J. (1994). Human facial expression: An evolutionary view. San Diego, CA: Academic Press. Friese, M., Hofmann, W., & Schmitt, M. (2009). When€ and why do implicit reaction time measures predict behavior?: Empirical evidence for the moderating role of motivation, opportunity, and process reliance. European Review of Social Psychology, 19, 285–338. Friese, M., Hofmann, W., & Wänke, M. (2008). When impulses take over: Moderated predictive validity of explicit and implicit attitude measures in predicting food choice and consumption behaviour. British Journal of Social Psychology, 47, 397–419. Fujita, K., Trope, Y., Liberman, N., & Levin-Sagi, M. (2006). Construal levels and self-Â�control. Journal of Personality and Social Psychology, 90, 351–367. Gailliot, M. T., Baumeister, R. F., DeWall, C. N., Maner, J. K., Plant, E. A., & Tice, D. M. (2007). Self-Â�control relies on glucose as a limited energy source: Willpower is more than a metaphor. Journal of Personality and Social Psychology, 92, 325–336. Galdi, S., Arcuri, L., & Gawronski, B. (2008). Automatic mental associations predict future choices of undecided decision-Â�makers. Science, 321, 1100–1102. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An

integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. Gawronski, B., Deutsch, R., LeBel, E. P., & Peters, K. R. (2008). Response interference as a mechanism underlying implicit measures: Some traps and gaps in the assessment of mental associations with experimental paradigms. European Journal of Psychological Assessment, 24, 218–225. Gawronski, B., Deutsch, R., Mbirkou, S., Seibt, B., & Strack, F. (2008). When “just say no” is not enough: Affirmation versus negation training and the reduction of automatic stereotype activation. Journal of Experimental Social Psychology, 44, 370–377. Gawronski, B., Deutsch, R., & Strack, F. (2005). Approach/avoidance-Â�related motor actions and the processing of affective stimuli: Incongruency effects in automatic attention allocation. Social Cognition, 23, 182–203. Gawronski, B., Geschke, D., & Banse, R. (2003). Implicit bias in impression formation: Associations influence the construal of individuating information. European Journal of Social Psychology, 33, 573–589. Gawronski, B., Hofmann, W., & Wilbur, C. (2006). Are implicit attitudes unconscious? Consciousness and Cognition, 15, 485–499. Gawronski, B., & Strack, F. (2004). On the propositional nature of cognitive consistency. Dissonance changes explicit, but not implicit attitudes. Journal of Experimental Social Psychology, 40, 535–542. Gigerenzer, G., & Regier, T. (1996). How do we tell an association from a rule?: Comment on Sloman (1996). Psychological Bulletin, 119, 23–26. Gilbert, D. T., & Wilson, T. D. (2000). Miswanting: Some problems in the forecasting of future affective states. In J. P. Forgas (Ed.), Feeling and thinking: The role of affect in social cognition (pp.€ 178–197). New York: Cambridge University Press. Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta-Â�analysis of effects and processes. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 38, pp.€69–119). San Diego, CA: Elsevier. Greenwald, A. G., Banaji, M. R., Rudman, L. A., Farnham, S. D., Nosek, B. A., & Mellott, D. S. (2002). A unified theory of implicit attitudes, stereotypes, selfÂ�esteem, and self-Â�concept. Psychological Review, 109, 3–25. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Greenwald, A. G., & Nosek, B. A. (2009). Attitudinal dissociation. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€65–82). New York: Psychology Press. Greenwald, A. G., Poehlman, T. A., Uhlmann, E., & Banaji, M. R. (2009). Understanding and using the Implicit Association Test: III. Meta-Â�analysis of predictive validity. Journal of Personality and Social Psychology, 97, 17–41. Gregg, A. P., Seibt, B., & Banaji, M. R. (2006). Easier done than undone: Asymmetry in the malleability of implicit preferences. Journal of Personality and Social Psychology, 90, 1–20.



4. Reflective and Impulsive Processes

Heatherton, T. F., & Baumeister, R. F. (1996). SelfÂ�regulation failure: Past, present, and future. Psychological Inquiry, 7, 90–98. Hoch, S. J., & Loewenstein, G. F. (1991). TimeÂ�inconsistent preferences and consumer self-Â�control. Journal of Consumer Research, 17, 492–507. Hofmann, W., & Baumert, A. (in press). Immediate affect as a basis for moral judgement: An adaptation of the affect misattribution procedure. Cognition and Emotion. Hofmann, W., Deutsch, R., Lancaster, K., & Banaji, M. R. (2010). Cooling the heat of temptation: Mental self-Â�control and the automatic evaluation of tempting stimuli. European Journal of Social Psychology, 40, 17–25. Hofmann, W., Friese, M., & Roefs, A. (2009). Three ways to resist temptation: The independent contributions of executive attention, inhibitory control, and affect regulation to the impulse control of eating behavior. Journal of Experimental Social Psychology, 45, 431–435. Hofmann, W., Friese, M., & Strack, F. (2009). Impulse and self-Â�control from a dual-Â�systems perspective. Perspectives on Psychological Science, 4, 162–176. Hofmann, W., Gschwendner, T., Nosek, B. A., & Schmitt, M. (2005). What moderates implicit-Â�explicit consistency? European Review of Social Psychology, 16, 335–390. Hugenberg, K., & Bodenhausen, G. V. (2003). Facing prejudice: Implicit prejudice and the perception of facial threat. Psychological Science, 14, 640–643. Hull, C. L. (1943). Principles of behavior. New York: Appleton-Â�Century-Â�Crofts. Izard, C. E. (1971). The face of emotion. New York: Appleton-Â�Century-Â�Crofts. James, W. (1950). The principles of psychology (Vol. 1). New York: Dover. (Original work published 1890) Jang, K. L., McCrae, R. R., Angleitner, A., Riemann, R., & Livesley, W. J. (1998). Heritability of facet-level traits in a cross-Â�cultural twin sample: Support for a hierarchical model of personality. Journal of Personality and Social Psychology, 74, 1556–1565. Keren, G., van Rooij, I., & Schul, Y. (2007). One wrong does not justify another: Accepting dual processes by fallacy of false alternatives. Behavioral and Brain Sciences, 30, 269–270. Kerr, N. L., & Levine, J. M. (2008). The detection of social exclusion: Evolution and beyond. Group Dynamics: Theory, Research, and Practice, 12, 39–52. Kim, D. Y. (2003). Voluntary controllability of the Implicit Association Test (IAT). Social Psychology Quarterly, 66, 83–96. Klauer, K. C., & Musch, J. (2002). Goal-Â�dependent and goal-Â�independent effects of irrelevant evaluations. Personality and Social Psychology Bulletin, 28, 802–814. Krieglmeyer, R., & Deutsch, R. (in press). Comparing measures of approach-Â�avoidance behaviour: The manikin task vs. two versions of the joystick task. Cognition and Emotion. Kruglanski, A. W. (2001). That “vision thing”: The state of theory in social and personality psychology at the edge of the new millennium. Journal of Personality and Social Psychology, 80, 871–875. Kruglanski, A. W., Shah, J. Y., Fishbach, A., Friedman,

77

R., Chun, W. Y., & Sleeth-Â�Keppler, D. (2002). A theory of goal systems. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 34, pp.€331–378). San Diego, CA: Academic Press. Kruglanski, A. W., & Thompson, E. P. (1999). Persuasion by a single route: A view from the unimodel. Psychological Inquiry, 10, 83–109. Kunda, Z., & Spencer, S. J. (2003). When do stereotypes come to mind and when do they color judgment?: A goal-based theoretical framework for stereotype activation and application. Psychological Bulletin, 129, 522–544. Lang, P. J. (1995). The emotion probe: Studies of motivation and attention. American Psychologist, 50, 372–385. Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1990). Emotion, attention, and the startle reflex. Psychological Review, 97, 377–395. Langer, E., Blank, A., & Chanowitz, B. (1978). The mindlessness of ostensibly thoughtful action: The role of “placebic” information in interpersonal interaction. Journal of Personality and Social Psychology, 36, 635–642. Lepore, L., & Brown, R. (1997). Category and stereotype activation: Is prejudice inevitable? Journal of Personality and Social Psychology, 72, 275–287. Liberman, N., Förster, J., & Higgins, E. T. (2007). Completed vs. interrupted priming: Reduced accessibility from post-Â�fulfillment inhibition. Journal of Experimental Social Psychology, 43, 258–264. Lieberman, M. D. (2003). Reflexive and reflective judgment processes: A social cognitive neuroscience approach. In J. P. Forgas, K. D. Williams, & W. von Hippel (Eds.), Social judgments: Implicit and explicit processes (pp.€44–67). New York: Cambridge University Press. Lieberman, M. D., Gaunt, R., Gilbert, D. T., & Trope, Y. (2002). Reflexion and reflection: A social cognitive neuroscience approach to attributional inference. Advances in Experimental Social Psychology, 34, 199–249. Loewenstein, G. F. (1996). Out of control: Visceral influences on behavior. Organizational Behavior and Human Decision Processes, 65, 272–292. Loewenstein, G. F., Read, D., & Baumeister, R. (2003). Time and decision: Economic and psychological perspectives on intertemporal choice. New York: Russell Sage Foundation. Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127, 267–286. Logan, G. D. (1988). Toward an instance theory of automatization. Psychological Review, 95, 492–527. Markman, A. B., & Brendl, C. M. (2005). Constraining theories of embodied cognition. Psychological Science, 16, 6–10. Markman, A. B., Brendl, C. M., & Kim, K. (2009). From goal activation to action: How does preference and use of knowledge intervene? In E. Morsella, J. A. Bargh, & P. M. Gollwitzer (Eds.), Oxford handbook of human action (pp.€ 328–349). New York: Oxford University Press. McConnell, A. R., & Leibold, J. M. (2001). Relations among the Implicit Association Test, discrimina-

78

I. BASICS

tory behavior, and explicit measures of racial attitudes. Journal of Experimental Social Psychology, 37, 435–442. Metcalfe, J., & Mischel, W. (1999). A hot/cool system analysis of delay of gratification: Dynamics of willpower. Psychological Review, 106, 3–19. Miles, D. R., & Carey, G. (1997). Genetic and environmental architecture on human aggression. Journal of Personality and Social Psychology, 72, 207–217. Mogg, K., Bradley, M. M., Field, M., & De Houwer, J. (2003). Eye movements to smoking-Â�related pictures in smokers: Relationship between attentional biases and implicit and explicit measures of stimulus valence. Addiction, 98, 825–836. Moors, A., & De Houwer, J. (2001). Automatic appraisal of motivational valence: Motivational affective priming and Simon effects. Cognition and Emotion, 15, 749–766. Moors, A., & De Houwer, J. (2006a). Automaticity: A theoretical and conceptual analysis. Psychological Bulletin, 132, 297–326. Moors, A., & De Houwer, J. (2006b). Problems with dividing the realm of processes. Psychological Inquiry, 17, 199–204. Moors, A., De Houwer, J., & Eelen, P. (2004). Automatic stimulus-goal comparisons: Support from motivational affective priming studies. Cognition and Emotion, 18, 29–54. Neal, D. T., & Wood, W. (2009). Automaticity in situ and in the lab: The nature of habit in daily life. In E. Morsella, J. A. Bargh, & P. M. Gollwitzer (Eds.), Oxford handbook of human action (pp.€442–457). New York: Oxford University Press. Neumann, R., Förster, J., & Strack, F. (2003). Motor compatibility: The bidirectional link between behavior and evaluation. In J. Musch & K. C. Klauer (Eds.), The psychology of evaluation (pp.€ 371–391). London: Erlbaum. Neumann, R., Hess, M., Schulz, S. M., & Alpers, G. W. (2005). Automatic behavioural responses to valence: Evidence that facial action is facilitated by evaluative processing. Cognition and Emotion, 19, 499–513. Neumann, R., Hülsenbeck, K., & Seibt, B. (2004). Attitudes towards people with AIDS and avoidance behavior: Automatic and reflective bases of behavior. Journal of Experimental Social Psychology, 40, 543–550. Olson, M. A., & Fazio, R. H. (2001). Implicit attitude formation through classical conditioning. Psychological Science, 12, 413–417. Olson, M. A., & Fazio, R. H. (2009). Implicit and explicit measures of attitudes: The perspective of the MODE model. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes. Insights from the new implicit measures (pp.€19–63). New York: Psychology Press. Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124, 54–74. Payne, B. K. (2001). Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon. Journal of Personality and Social Psychology, 81, 181–192. Payne, B. K., Cheng, C. M., Govorun, O., & Stewart, B.

D. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. Journal of Personality and Social Psychology, 89, 277–293. Perugini, M. (2005). Predictive models of implicit and explicit attitudes. British Journal of Social Psychology, 44, 29–45. Perugini, M., & Bagozzi, R. P. (2004). An alternative view of pre-Â�volitional processes in decision making: Conceptual issues and empirical evidence. In G. Haddock & G. R. Maio (Eds.), Contemporary perspectives on the psychology of attitudes (pp.€169–201). Hove, UK: Psychology Press. Petty, R. E., Fazio, R. H., & Briñol, P. (Eds.). (2009). Attitudes: Insights from the new implicit measures. New York: Psychology Press. Robinson, T. E., & Berridge, K. C. (2003). Addiction. Annual Review of Psychology, 54, 25–53. Rosenbaum, D. A., Vaughan, J., Meulenbroek, R. G. J., Jax, S., & Cohen, R. G. (2009). Smart moves: The psychology of everyday perceptual-motor acts. In E. Morsella, J. A. Bargh, & P. M. Gollwitzer (Eds.), Oxford handbook of human action (pp.€121–135). New York: Oxford University Press. Roskos-Â�Ewoldsen, D. R., & Fazio, R. H. (1992). On the orienting value of attitudes: Attitude accessibility as a determinant of an object’s attraction of visual attention. Journal of Personality and Social Psychology, 63, 198–211. Rotteveel, M., & Phaf, R. H. (2004). Automatic affective evaluation does not automatically predispose for arm flexion and extension. Emotion, 4, 156–172. Rydell, R. J., & McConnell, A. R. (2006). Understanding implicit and explicit attitude change: A systems of reasoning analysis. Journal of Personality and Social Psychology, 91, 995–1008. Scarabis, M., Florack, A., & Gosejohann, S. (2006). When consumers follow their feelings: The impact of affective or cognitive focus on the basis of consumers’ choice. Psychology and Marketing, 23, 1015– 1034. Schank, R. C., & Abelson, R. P. (1977). Scripts, plans, goals and understanding: An inquiry into human knowledge structures. Oxford, UK: Erlbaum. Schneider, W., & Chein, J. M. (2003). Controlled and automatic processing: Behavior, theory, and biological mechanisms. Cognitive Science, 27, 525–559. Schneider, W., & Schiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review, 84, 1–66. Schwarzer, R. (1999). Self-Â�regulatory processes in the adoption and maintenance of health behaviors: The role of optimism, goals, and threats. Journal of Health Psychology, 4, 115–127. Seibt, B., Häfner, M., & Deutsch, R. (2007). Prepared to eat: How immediate affective and motivational responses to food cues are influenced by food deprivation. European Journal of Social Psychology, 37, 359–379. Seibt, B., Neumann, R., Nussinson, R., & Strack, F. (2008). Movement direction or change in distance?: Self- and object-Â�related approach-Â�avoidance motions. Journal of Experimental Social Psychology, 44, 713–720.



4. Reflective and Impulsive Processes

Simmons, J. P., & Prentice, D. A. (2006). Pay attention! Attention to the primes increases attitude assessment accuracy. Journal of Experimental Social Psychology, 42, 784–791. Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119, 3–22. Smith, E. R., & DeCoster, J. (2000). Dual-Â�process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4, 108–131. Smith, E. R., Fazio, R. H., & Cejka, M. A. (1996). Accessible attitudes influence categorization of multiply categorizable objects. Journal of Personality and Social Psychology, 71, 888–898. Spencer, S. J., Fein, S., Wolfe, C. T., Fong, C., & Dunn, M. A. (1998). Automatic activation of stereotypes: The role of self-image threat. Personality and Social Psychology Bulletin, 24, 1139–1152. Spruyt, A., Hermans, D., Pandelaere, M., De Houwer, J., & Eelen, P. (2004). On the replicability of the affective priming effect in the pronunciation task. Experimental Psychology, 51, 109–115. Stewart, B. D., & Payne, B. K. (2008). Bringing automatic stereotyping under control: Implementation intentions as efficient means of thought control. Personality and Social Psychology Bulletin, 34, 1332– 1345. Strack, F., & Deutsch, R. (2002). Urteilsheuristiken [Judgmental heuristics]. In D. Frey & M. Irle (Eds.), Theorien der sozialpsychologie (Vol. III, pp.€352–384). Bern, Switzerland: Huber. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Strack, F., & Deutsch, R. (2007). The role of impulse in social behavior. In A. W. Kruglanski & E. T. Higgins (Eds.), Social psychology: Handbook of basic principles (pp.€408–431). New York: Guilford Press. Strack, F., Deutsch, R., & Krieglmeyer, R. (2009). The two horses of behavior. In E. Morsella, J. A. Bargh, & P. M. Gollwitzer (Eds.), The psychology of action (pp.€104–117). New York: Oxford University Press. Strack, F., & Hannover, B. (1996). Awareness of influence as a precondition for implementing correctional goals. In P. M. Gollwitzer & J. A. Bargh (Eds.), The psychology of action: Linking cognition and motivation to behavior (pp.€579–596). New York: Guilford Press. Strack, F., Martin, L. L., & Stepper, S. (1988). Inhibit-

79

ing and facilitating conditions of the human smile: A nonobtrusive test of the facial feedback hypothesis. Journal of Personality and Social Psychology, 54, 768–777. Strack, F., & Neumann, R. (2000). Furrowing the brow may undermine perceived fame: The role of facial feedback in judgments of celebrity. Personality and Social Psychology Bulletin, 26, 762–768. Strack, F., Werth, L., & Deutsch, R. (2006). Reflective and impulsive determinants of consumer behavior. Journal of Consumer Psychology, 16, 205–216. Thornhill, R., & Gangestad, S. W. (1999). Facial attractiveness. Trends in Cognitive Sciences, 3, 452–460. Trivers, R. (1971). The evolution of reciprocal altruism. Quarterly Review of Biology, 46, 35–57. Tversky, A., & Kahneman, D. (1983). Extensional versus intensional reasoning: The conjunction fallacy in probability judgment. Psychological Review, 90, 293–315. Verplanken, B., & Aarts, H. (1999). Habit, attitude and planned behaviour: Is habit an empty construct or an interesting case of automaticity. European Review of Social Psychology, 10, 101–134. Vroom, V. H. (1964). Work and motivation. New York: Wiley. Watson, J. B. (1913). Psychology as the behaviorist views it. Psychological Review, 20, 158–177. Webb, T. L., & Sheeran, P. (2007). How do implementation intentions promote goal attainment?: A test of component processes. Journal of Experimental Social Psychology, 43, 295–302. Wilson, T. D., Lindsey, S., & Schooler, T. Y. (2000). A model of dual attitudes. Psychological Review, 107, 101–126. Winkielman, P., & Berridge, K. (2003). Irrational wanting and subrational liking: How rudimentary motivational and affective processes shape preferences and choices. Political Psychology, 24, 657–680. Wittenbrink, B., Judd, C. M., & Park, B. (1997). Evidence for racial prejudice at the implicit level and its relationship with questionnaire measures. Journal of Personality and Social Psychology, 72, 262–274. Wittenbrink, B., & Schwarz, N. (Eds.). (2007). Implicit measures of attitudes: Procedures and controversies. New York: Guilford Press. Wood, W., & Neal, D. T. (2007). A new look at habits and the habit–goal interface. Psychological Review, 114, 843–863.

Chapter 5

Implicit Social Cognition Insights from Social Neuroscience Tiffany A. Ito

T

he marriage of social cognition and social neuroscience may have struck some as unlikely, but it is actually quite a compatible match. For one, they have common ground in their underlying approaches. Social cognition has sought to understand social behavior by examining its constituent processes. Neuroscience has adopted an analogous focus of understanding the neural bases of constituent information-Â�processing operations. Social cognition’s interest in implicit processes also makes the union compelling. Physiological and neural measures can be obtained without participants’ subjective awareness of the process of interest. Moreover, because these measures can be made without participants’ awareness of what exactly is being measured, they can be particularly informative in situations where explicit and implicit responses diverge or when participants might be motivated to modify their initial responses (Ito & Cacioppo, 2007). As a reflection of the success of social neuroscience, we have progressed well past the point where any one chapter can give a comprehensive overview of all relevant research. Accordingly, the goal of this review is to highlight select areas in which social neuroscience has been particularly productive addressing issues relevant to implicit social cognition. Two broad approaches to the use of neuroscience in the study of social processes

are discussed. One perspective is aimed primarily at identifying the brain functions that underlie a particular behavior, such as explicating the function of the amygdala in social cognition. Another approach takes a behavioral phenomenon such as the control of implicit biases as its primary interest and uses the neural evidence to refine theoretical conceptualizations of psychological processes. Although we have only begun to understand the complex way in which brain areas interact in the service of social cognition, a great deal of convergence exists. Table 5.1 summarizes some of the main findings that are highlighted in this review, demonstrating several brain areas consistently associated with particular processes of relevance to implicit social cognition.

NEURAL SUBSTRATES UNDERLYING SOCIAL COGNITION: ATTITUDES AND€EVALUATIVE PROCESSES Research investigating the neural substrates of socially relevant processes is progressing rapidly on many fronts. There is now a good deal of research examining the neural bases of face perception (e.g., Amodio & Frith, 2006; Eimer, 2000; Haxby, Hoffman, & Gobbini, 2000; Kanwisher, McDermott, & 80



5. Insights from Social Neuroscience

81

TABLE 5.1.╇ Select Brain Areas Involved in Implicit Social Cognition Brain area

Relevance to social cognition

Amygdala

Sensitivity to explicit and implicit evaluative processes; greater activity to negative and positive than neutral stimuli and to high- versus lowintensity stimuli

Anterior cingulate cortex

Greater activity when response conflict is high

Prefrontal cortex (e.g., ventrolateral and dorsolateral prefrontal cortex)

Greater activity in situations where behavioral control is exerted

Temporoparietal attention networks

Sensitivity to implicit and explicit categorization processes; greater attention to negative and social stimuli

Chun, 1997), mental state inferences (e.g., Harris & Fiske, 2007; Mitchell, Macrae, & Banaji, 2006), and emotion regulation (e.g., Ochsner et al., 2004) to name a few, but perhaps most active of all has been research on the neural underpinnings of evaluative processes. This dovetails nicely with social cognition research, where attitudes and evaluative processes have been of enduring interest. They are also one of the most studied aspects of implicit social cognition, with ample evidence demonstrating the extent to which attitudes and evaluations can be activated with little conscious awareness or effort (e.g., Bargh, Chaiken, Govender, & Pratto, 1992; Fazio, Sanbonmatsu, Powell, & Kardes, 1986). Early research on the neural substrates of evaluative processes focused on the amygdala, originally stimulated by demonstrations of its role in emotional learning, such as the acquisition of conditioned fear responses (e.g., LaBar, Gatenby, Gore, LeDoux, & Phelps, 1998; for a review, see LeDoux, 2000). More specific examinations of amygdala activation in response to valenced stimuli confirm its role in emotional processes more generally. Greater amygdala activation has been repeatedly observed following exposure to negative compared with neutral and positive stimuli (e.g., Lane et al., 1997; Morris et al., 1996; for reviews, see Phan, Wager, Taylor, & Liberzon, 2002; Zald, 2003). Of particular relevance to the issue of implicit social cognition, these effects occur not only when participants adopt an explicit evaluative set and stimuli are presented above conscious threshold but also when evaluation is not the explicit task and/ or when stimuli are presented very briefly. As an example, Whalen and colleagues (1998) exposed participants to 33-msec presentations of happy and fearful facial expressions that were immediately backward-�masked by faces showing neutral expressions. Although participants reported seeing only the neutral expressions, amygdala activation

was significantly greater to fearful than to happy faces. The involvement of the amygdala with socially relevant implicit evaluations is not limited to emotional expressions. Faces idiosyncratically judged as untrustworthy are associated with greater amygdala activation. This occurs both when perceivers are explicitly attending to trustworthiness and when they are attending to another dimension (age) (Winston, Strange, O’Doherty, & Dolan, 2002). Implicit effects of target race have also been observed. Among white participants, briefly presented (30 msec) black faces are associated with greater amygdala activation than white faces (Cunningham, Johnson, et al., 2004). Although much of the research on implicit evaluative effects on amygdala activation has used facial stimuli, the amygdala responds more broadly, as shown in research examining attitudinal judgments of lexical stimuli. Greater amygdala activation is elicited by names associated with negative (e.g., Adolph Hitler) as opposed to positive (e.g., Mahatma Gandhi) attitudes, both when the names are explicitly being evaluated as good or bad and when they are being judged along a nonevaluative dimension (whether the names refer to current or historical persons) (Cunningham, Johnson, Gatenby, Gore, & Banaji, 2003). What are the implications of this research for implicit social cognition? Can we conclude that amygdala activation represents the neural substrate for negative evaluations and attitudes? Several factors argue against this simplistic equation. First, there is growing evidence that the amygdala is sensitive to more than just valence. As the preceding review illustrates, increased amygdala activation has been consistently associated with negative stimuli, but greater amygdala activation has also been observed to positive compared with neutral stimuli (Canli, Sivers, Whitfield, Gotlib,

82

I. BASICS

& Gabrieli, 2002; Hamann & Mao, 2002; Zald, 2003). Whalen (1998) suggested that amygdala activation to negative stimuli occurs because the amygdala is important for modulating vigilance. From this perspective, amygdala activation may be seen most consistently to negative, especially threatening, stimuli but should also be seen to any stimulus signaling biological relevance (cf. Norris, Chen, Zhu, Small, & Cacioppo, 2004). Cunningham, Raye, and Johnson (2004) have also noted that when both the valence and intensity dimensions of stimuli are considered, amygdala activation increases for both high-�intensity negative and positive stimuli. Even expanding amygdala function to include sensitivity to both positive and negative stimuli, it is unlikely that the amygdala represents the sole neural substrate of these social cognitive processes. Instead, a distributed compared with highly localized neural mechanism is more likely. An investigation of implicit racial bias in a patient with bilateral amygdala damage illustrates this point (Phelps, Cannistraci, & Cunningham, 2003). If the amygdala represents a necessary neural substrate for implicit evaluations, the ability to make evaluative differentiations at the implicit level should be compromised by amygdala damage. By contrast, this patient showed an implicit preference for ingroup whites compared with outgroup blacks, as measured with the Implicit Association Test (IAT). Moreover, the level of bias the patient demonstrated was indistinguishable from control participants at both implicit and explicit levels (the latter measured with the Modern Racism Scale). The involvement of other brain areas in implicit evaluations, still intact in this particular patient, could account for the dissociation between amygdala damage and implicit bias. In fact, studies of brain imaging often identify several other areas associated with evaluative process, including the basal ganglia, medial prefrontal cortex (mPFC), ventral lateral prefrontal cortex (VLPFC), orbitofrontal cortex, and insula (e.g., Beer et al., 2008; Britton, Taylor, Sudheimer, & Liberzon, 2006; Cunningham et al., 2003; Cunningham, Raye, & Johnson, 2004; Norris et al., 2004; Phan et al., 2002; Satpute & Lieberman, 2006). In sum, extant research supports a more nuanced view of the role of the amygdala in implicit social cognition, which encompasses both positive and negative evaluative responses as well as sensitivity to other dimensions such as intensity and personal relevance. There are also numerous other brain areas likely to be involved in implicit and explicit evaluative processes.

In addition to shaping our understanding of the specific brain areas that support aspects of social cognition, social neuroscience research on the role of the amygdala in evaluative processes converges with several key theoretical assumptions in social cognition. One deals with the malleability of implicit social cognition. Although the amygdala responds implicitly along dimensions such as valence, intensity, and personal relevance, numerous studies show that amygdala activity can also be modulated by processing goals and attentional sets (e.g., Chiao et al., 2008; Cunningham, Van Bavel, & Johnsen, 2008; Lieberman, Hariri, Jarcho, Eisenberger, & Bookheimer, 2005; Richeson, Todd, Trawalter, & Baird, 2008; Touryan et al., 2007; Van Bavel, Packer, & Cunningham, 2008; Wheeler & Fiske, 2005). In trying to integrate these many effects, Cunningham and colleagues have suggested that amygdala activation is sensitive to the fit between the current processing goal and evaluative features of the stimulus (Cunningham et al., 2008). The sensitivity of the amygdala to processing goals converges nicely with our current understanding about the malleability of implicit evaluative responses (e.g., Blair, 2002). The involvement of the amygdala in implicit evaluative processes is also broadly consistent with the working assumption in social cognition research that implicit evaluative processes are important in part because of their potential for wide-�ranging behavioral and judgmental effects (e.g., Bargh, 1999; Devine, 1989; Fiske, 1989). The amygdala is richly connected to numerous other brain regions involved in a range of cognitive, motoric, and endocrine responses. Such connections could support the kinds of effects theoretically linked to implicit evaluations. For example, projections from the amygdala to association cortex, prefrontal cortex, and hippocampus may provide the mechanisms through which initial evaluative reactions activate a cascade of subsequent beliefs and judgments. Similarly, projections to the hypothalamus could support behavioral changes consequent to evaluations through its effects on the endocrine and autonomic nervous system.

INFERENCES ABOUT UNDERLYING MENTAL PROCESSES As a complement to research aimed at elucidating the role of specific brain areas in particular psychological processes—Â�illustrated here with work on the amygdala—other social neuroscience research



5. Insights from Social Neuroscience

has more explicitly focused on understanding the psychological processes that underlie a particular behavior. Social cognition models can be quite sophisticated and specific in their mechanistic details, yet it is also recognized that individuals frequently lack conscious awareness of the underlying reasons for their behavior (Nisbett & Wilson, 1977). This creates an inferential conundrum, whereby explicit self-�report measures may provide useful information on the products of higher order thought processes (e.g., the attitude judgment) or lay theories of cause and effect and yet not reflect the full complement of factors mediating the behavior. Neuroscience measures may provide more direct evidence of their operation and, in so doing, represent a powerful way to test very specific mechanistic hypotheses. There are numerous examples of this type of work. Two particularly fruitful applications have focused on understanding implicit and explicit mechanisms mediating behavior regulation and the automaticity and malleability of automatic evaluative and categorization processes. Representative research from each area is reviewed next.

Detection of Conflict Cognitive neuroscience models have postulated the operation of two interrelated neural systems for the effective regulation of behavior. First, a conflict-�monitoring system is thought to continuously and preconsciously monitor for incongruities between activated representations, such as between a prepotent response tendency and countervailing stimulus characteristics. When conflict is detected, it alerts a second, regulatory system that implements top-down attentional control (e.g., Botvinick, Carter, Braver, Barch, & Cohen, 2001). Such a two-part system calls to mind dual�process models in social cognition, which have similarly specified a more automatic comparator and more controlled regulative modules (e.g., Wegner, 1994). These conceptual similarities suggest that, although the cognitive neuroscience and social cognition models were originally applied to distinct phenomena, common neural mechanisms may underlie their operation. Several studies examining operation of the preconscious conflict detection system in particular have made use of event-�related brain potentials (ERPs). Conflict detection often occurs transiently, making the high temporal resolution of ERPs informative for examining both the presence and timing of such detection. One study recorded ERPs as participants performed the Shooter task,

83

which involves making decisions to shoot or not shoot black and white men based on the presence of a weapon. Behavioral results from this paradigm show a consistent pattern of bias, with faster and more accurate responses to shoot armed black men compared with white men but faster and more accurate responses to not shoot unarmed white men compared with black men (Correll, Park, Judd, & Wittenbrink, 2002). To quantify the operation of conflict-�monitoring processes online as decisions are made, the N200 ERP component was examined as participants completed the task (Correll, Urland, & Ito, 2006). The N200 has been linked to cognitive control processes (Kopp, Rist, & Mattler, 1996; Ritter, Simson, Vaughan, & Friedman, 1979). Source modeling implicates involvement of the anterior cingulate cortex (ACC) in the generation of the N200 (Nieuwenhuis, Yeung, Van Den Wildenberg, & Ridderinkhof, 2003; van Veen & Carter, 2002), which plays a critical role in the detection of conflict, including the inhibition of a prepotent response (Carter et al., 1998; Swainson et al., 2003). In this paradigm, participants are given the task of deciding who to shoot. Moreover, points are added or deducted to a running score after every trial. The point contingencies further promote a tendency to shoot, with greater rewards for correct shooting than not shooting and greater penalties for failing to shoot than not. (These contingencies were originally implemented to model possible influences on the decision making of police officers, where failing to shoot an armed suspect and erroneously shooting an unarmed one are particularly costly outcomes.) Together, these features make shoot prepotent in the task. Against this general response set, correctly overriding shoot when encountering an unarmed target should require detection of the inconsistency between shoot and stimulus cues indicating the absence of a gun. In accord with this analysis, N200s are larger to unarmed than to armed individuals (Correll et al., 2006). Consistent with the pattern of racial bias in the behavioral results, N200s are also larger to white than black targets, suggesting that even at preconscious levels regulatory processes are not being applied equally to black and white targets. Finally, the racial differences in the N200 predict individual variability in behavioral responses. Those individuals who show relatively larger N200s to whites than blacks show greater racial bias in their behavioral responses. Neural measures of response conflict and conflict detection have also been used to probe the locus of implicit stereotyping effects (Bartholow

84

I. BASICS

& Dickter, 2008). This was assessed using a modified flanker procedure in which black and white faces were presented centrally, simultaneous with a word above, below, to the right, and to the left of it. These flanking words were either stereotypical of blacks or whites (with the same word shown in each position on a given trial). If participants implicitly process the flankers, racial categorization of the central faces should be faster when the flankers are stereotypically congruent (e.g., a black face surrounded by a black stereotypical word) but slower when they are incongruent (e.g., a black face surrounded by a white stereotypical word). If this difference in response latency is due to conflict between the racial category of the central face and stereotypicality of the surrounding words, then N200s should be elevated in the stereotypeÂ�incongruent condition. Both predictions were confirmed: Stereotype-Â�incongruent arrays were associated with longer response latencies and larger N200s. This study also provides evidence that conflict at the response level—Â�derived from the activation of competing response tendencies by the central and flanking stimuli—Â�contributes to differences in response latency. This was examined by quantifying neural responses measured over motor cortex. The resultant ERP response, called the lateralized readiness potential (LRP), provides an indication of the degree to which a manual response is activated and is sensitive enough to detect preparation that occurs even in the absence of an overt movement (Gratton, Coles, Sirevaag, Eriksen, & Donchin, 1988, 1992). The LRP showed initial activation of the motor response associated with the incorrect response on stereotype-Â�incongruent arrays (i.e., the response implied by the flankers). These analyses were restricted to correct trials. Thus, the initial preparation of the motor response occurred in the context of ultimately overriding that response and making the appropriate one based on the central stimulus. Moreover, the magnitude of response conflict was correlated with response latencies. A greater tendency to initially activate the incorrect response on stereotypeÂ�incongruent compared with congruent arrays was correlated with slower response latencies on stereotype-Â�incongruent versus congruent arrays. The authors suggest that response competition is an important mechanism explaining patterns of implicit bias, as reflected in longer response latencies on stereotype-Â�incongruent trials (see also De Houwer, Hermans, Rothermund, & Wentura, 2002; Klinger, Burton, & Pitts, 2000).

The N200 reported in Correll and colleagues (2006) and Bartholow and Dickter (2008) is thought to reflect conflict detection processes occurring before, and mediating execution of, correct responses. However, conflict detection is also thought to operate on trials on which errors are made. In these instances, the conflict is typically maximal shortly after the commission of the error, as continued processing of stimulus characteristics activates the correct response, leading to conflict with stimulus processing that initiated the incorrect one (Carter et al., 1998). Posterror conflict detection has been linked to the error-Â�related negativity (ERN), an ERP component occurring roughly 50 to 150 msec after errors and also thought to be generated by the ACC (van Veen & Carter, 2002). Like the N200, the ERN has also been used to assess conflict detection during implicit social cognition tasks. Specifically, Amodio and colleagues have recorded the ERN in several studies using the Weapon Identification task (Payne, 2001), a sequential priming procedure in which participants classify pictures of guns and tools shown after brief presentations of black and white primes. Participants are typically faster and more accurate in responding to guns after presentation of black compared with white faces. In this paradigm, incorrectly classifying a tool as a gun following a black prime can be viewed as particularly relevant to race bias. Thus, although elevated conflict should be expected following any error in this task, conflict may be even greater in this condition because of its additional race-bias implications (assuming one generally wishes to avoid the display of prejudice). In the studies reviewed here, the racial implications were made particularly salient by telling participants at the onset of the task that errors on black–tool trials would represent an inappropriate application of the stereotype. As expected, ERNs were observed following all errors but were significantly larger on black–tool trials (Amodio et al., 2004; Amodio, Kubota, Harmon-Jones, & Devine, 2006). Moreover, as predicted from models of behavior regulation, conflict detection predicted behavior. Higher levels of conflict following black–tool errors were associated with greater accuracy following errors and with slower responses following errors. Both can be seen as the implementation of greater control following the detection of conflict. These effects have been found in a sample of participants who were unselected but drawn from a population with generally nonprejudiced attitudes (Amodio et al., 2004). In another study, par-



5. Insights from Social Neuroscience

ticipants were specifically selected to vary on level of motivation to control bias to allow examination of differences in the automatic activation of egalitarian intentions (Amodio, Devine, & HarmonJones, 2008). If the larger ERNs observed following errors on black–tool trials reflect enhanced conflict between the commission of a race-Â�biased response and a desire to be nonprejudiced, ERNs should be elevated only for participants for whom egalitarian intentions are automatically activated. This was expected to occur for participants who are primarily internally motivated to control their prejudice (i.e., report high levels of internal but low levels of external motivations to control prejudice) but not for participants reporting both high internal and external sources of motivation or for participants with low internal motivation. As predicted, ERNs following errors on black–tool trials were increased only for participants with high internal and low external motivations to control prejudice. As can be seen, social neuroscience has been used in different ways to test mechanisms predicted to underlie behavior. In each case, predictions were derived from a rich theoretical and empirical social cognition foundation, and results provide evidence for important mediators of behavior that would be otherwise difficult to obtain. Of importance, in all of the examples reviewed, the neural signals of conflict were closely related to behavior, predicting patterns of racial bias in the case of the Shooter task, implicit stereotyping in a flanker task, and more accurate and slower responses following errors in the Weapon Identification task.

Cognitive Control The conflict detection processes assessed in the studies just reviewed are thought to operate in concert with regulatory processes that recruit more effortful cognitive processes to override conflicting responses tendencies and execute the desired response (Botvinick et al., 2001). Neuroscience studies have also been useful in observing operation of this second aspect of behavioral regulation, typically associated with areas in the prefrontal cortex (PFC) (e.g., Badre, Poldrack, Paré-Blagoev, Insler, & Wagner, 2005; Badre & Wagner, 2002; Banich et al., 2000; Braver, Cohen, Jonides, Smith, & Noll, 1997; Bunge, Ochsner, Desmond, Glover, & Gabrieli, 2001; Bunge, Wendelken, Badre, & Wagner, 2005). This has been seen in several studies examining brain activation associated with coactivation of conflicting evaluations.

85

A study by Cunningham and colleagues (2003) was previously reviewed in which participants viewed names of current and historical figures (e.g., Bill Clinton, Yasser Arafat). In addition to examining amygdala activity during viewing of the names (and finding greater amygdala activation to negative than to positive names), separate ratings of positivity and negativity toward each name were also obtained from the participants after functional magnetic resonance imaging scanning. These were used to compute idiosyncratic ambivalence scores for each participant to each target (Priester & Petty, 1996). Ambivalence was then examined with respect to brain activation obtained during initial judgment of the names. More ambivalent names were associated with greater activation in VLPFC. This was true when participants were making explicit good–bad judgments of the stimuli during scanning but not when they judged whether the figure was a current or a historical figure. Another study related brain activity to explicit ratings of the degree to which a stimulus typically elicits attempts to reflectively control or suppress initial evaluative responses (Cunningham, Raye, & Johnson, 2004). Stimuli were verbal concepts (e.g., freedom, poetry, murder, terrorism), and during scanning participants made either binary good–bad or abstract–Â�concrete judgments about them. Ratings of control were obtained after scanning and regressed onto brain activation, revealing an association between need for greater control and greater activation in the VLPFC as well as other areas in the PFC, such as the right anterior PFC and mPFC. This study also found increased activation in the ACC as a function of control ratings, consistent with the expectation that stimuli perceived as eliciting greater control are ones that elicit conflicting responses. As with Cunningham and colleagues (2003), activation in these areas was stronger when participants were making explicit evaluative judgments compared with the abstract–Â� concrete judgment. The effects of task on the recruitment of areas associated with controlled processes may suggest that controlled processes aimed at resolving conflicting evaluations are more likely when evaluation is the explicit goal. The implementation of controlled processes has also been assessed in the context of racial bias, revealing greater activation in prefrontal regions by white participants when viewing pictures of blacks. This was seen in the previously discussed Cunningham, Johnson, and colleagues (2004) study, which manipulated exposure duration. It was assumed that primarily implicit reactions would occur to

86

I. BASICS

faces presented below conscious awareness (for 30 msec), whereas controlled processes would occur when faces were presented consciously (for 525 msec). Consistent with this assumption, there was greater VLPFC and dorsolateral prefrontal cortex (DLPFC) activation to black faces compared with white faces when presented for 525 msec, but no differences in prefrontal areas were found when faces were presented for 30 msec. The ACC also showed more activation to black than white faces in the 525-msec condition. Recall that in this study black faces were associated with greater amygdala activation but only in the 30-msec presentation condition. The pattern of activation in the PFC, ACC, and amygdala suggests that conflict was detected in the 525-msec condition in response to black faces, possibly between the activation of implicit bias and egalitarian goals. This, in turn, engaged cognitive control processes, which eliminated the racial differences in amygdala activation observed in the 30-msec condition. In fact, the degree to which the ACC and DLPFC were more strongly activated in the 525-msec condition compared with the 30-msec condition for blacks versus whites was correlated with the black–white difference in greater amygdala activation at 30 msec compared with 525 msec. Converging evidence for the role of the DLPFC in the control of racial bias is also reported by Richeson and colleagues (2003). In the initial session of this study, white participants completed an IAT to measure implicit racial bias favoring whites compared with blacks. They next engaged in an interracial interaction with a black confederate. To increase the likelihood that participants would expend regulatory effort to monitor their behavior during the conversation, one of the topics involved racial issues. After the discussion, participants completed a Stroop task, selected to detect decrements in executive function that would result from having depleted regulatory resources in the interracial interaction. Stroop performance was affected by the interracial interaction. Participants with higher levels of implicit bias (as reflected in the initial IAT) showed greater impairment on postinteraction Stroop performance (see also Richeson & Shelton, 2003). Of particular relevance to understanding the mechanisms involved in the control of bias, participants returned several weeks later for an ostensibly unrelated second session, in which brain activity was recorded as they viewed pictures of black and white faces while making spatial location judgments. Activation in the DLPFC, as well in the ACC, was associated with implicit bias scores from session 1. Partici-

pants who exhibited greater implicit bias showed more activation in these areas associated with cognitive control and conflict detection when viewing black faces. Moreover, the degree to which these areas associated with behavior regulation were spontaneously activated when viewing black faces predicted Stroop performance after the interracial interaction; greater activation in the DLPFC and ACC predicted poorer postinteraction Stroop performance. Mediational analyses indicated that the degree to which viewing black faces spontaneously increased activation in DLPFC mediated the relation between implicit bias (measured with the IAT) and postinteraction decrements in Stroop performance. Finally, prefrontal areas are implicated during completion of incongruent IAT blocks. Responding using stereotype-�incongruent response mappings, as when the same hand is used to categorize a face as black and a word as positive, should be associated with both higher conflict detection and the need for greater regulatory control. In line with this expectation, several studies have found greater activation in the VLPFC, DLPFC, and ACC during incongruent compared with congruent blocks in IAT tasks (Beer et al., 2008; Chee, Sriram, Soon, & Lee, 2000; Knutson, Mah, Manly, & Grafman, 2007). The studies reviewed in this section show good convergence in the brain areas activated across different types of stimuli. One difference, though, is in the degree to which activation depends on an explicit evaluation goal. The two studies dealing with racial bias suggest spontaneous application of more controlled processes when viewing racial outgroup members (Cunningham, Johnson, et al., 2004; Richeson et al., 2003). In both studies, the brain imaging data were obtained while participants judged whether the stimulus was to the left or to the right of fixation, with no explicit instructions to make evaluative judgments. By contrast, the first two studies reviewed, dealing with names and concepts, found greater activation in prefrontal areas when evaluations were explicit (Cunningham et al., 2003; Cunningham, Raye, & Johnson, 2004). Such results imply that controlled processes aimed at resolving conflicting evaluations are more likely when evaluation is the explicit goal. Individuals are known to desire consistency (Heider, 1958); it is not surprising that focusing attention on the dimension along which inconsistency is aroused would increase the likelihood of activating this motivation. That such explicit attention was not required in the studies dealing with racial attitudes is interesting. This may reflect a domain



5. Insights from Social Neuroscience

effect, such that motivations to control evaluations associated with racial groups are more potent and can be aroused even when evaluation is not the explicit goal (cf. Moskowitz, Gollwitzer, Wasel, & Schaal, 1999; Moskowitz, Salomon, & Taylor, 2000). It is also possible that the particular participants in these studies were especially motivated to control their expressions of bias. In fact, one of the studies collected data on motivation to control prejudice (Cunningham, Johnson, et al., 2004) and reported relatively high levels of internal motivation to control prejudiced reactions, suggesting that motivational effects may be relevant. Regardless of the explanation, the results across these different studies are informative in demonstrating instances in which explicit focus on the dimension of conflict may be required for the implementation of control and activation of such control in the absence of an explicit task focus.

Implicit Categorization In addition to the broad question of whether conflict detection and cognitive control processes underlie social behavior, neural processes have also been useful in investigating basic questions regarding implicit categorization processes. Ito and Cacioppo (2000) recorded ERPs as participants viewed pictures that varied in both valence (positive or negative) and social content (including people or not). Some participants made explicit categorizations along the valence dimension, whereas others categorized along the sociality dimension. The P300 ERP component, which is sensitive to categorization processes (Coles, 1989; Rugg & Coles, 1995), varied as a function of both the explicit categorization dimension and the implicitly varying one. For example, among participants explicitly counting the number of positive or negative pictures, P300s varied as a function of both stimulus valence and social content. P300s also showed evidence of processing biases in favor of both negative stimuli and stimuli containing people; more attention was directed toward pictures that were negative, showed people, or possessed both features (see also Ito, Larsen, Smith, & Cacioppo, 1998; Smith, Cacioppo, Larsen, & Chartrand, 2003). Of importance, the bias in favor of negative and social content was observed both when participants were explicitly attending to those dimensions and when they were directing attention to other features. For social creatures trying to minimize harm and maximize benefit, both evaluative and social cues convey information of tremendous relevance, making implicit attention

87

and orienting to these cues sensible (cf. Norris et al., 2004). Similar paradigms have been used to examine the degree to which perceivers implicitly attend to distinctions within social stimuli, specifically focusing on attention to social category information. When exposed to faces that vary as a function of group membership, neural responses indicate that participants implicitly categorize in terms of both gender and race (Ito & Urland, 2003). Examining the timing of the effects has been particularly beneficial in understanding how quickly these social cues become available during processing. Effects are seen in the same P300 component that showed sensitivity to valence and social content in Ito and Cacioppo (2000), peaking in this latter study at about 500 msec after stimulus onset. However, target gender and race also affected earlier ERP components that peaked within the first 100 to 200 msec after stimulus onset. Specifically, race effects have been seen in anterior brain regions as early as 120 msec and gender effects as early as 180 msec. Such quick effects support assumptions about the automatic nature of social categorization (Brewer, 1988; Bruner, 1957; Fiske & Neuberg, 1990; Stangor, Lynch, Duan, & Glass, 1992). Other studies have manipulated processing goals to examine the degree to which this implicit attention to category information can be attenuated. Manipulations have been examined that direct attention deeper than the social category, in the form of focusing on more personalized judgments, as well as those that lead to more shallow processing, by directing attention away from the social stimuli altogether (Ito & Urland, 2005). Race and gender information still modulates ERP responses in all task conditions, and effects are seen just as quickly as when participants are explicitly focused on social categorization. The fast and implicit nature of social categorization demonstrated in these studies has implications for considering how to change the expression of prejudice. One theoretically possible option is to decrease attention to category-�specifying information, but these results suggest this may be difficult. Instead, it may be more effective to intervene at subsequent processes, such as when category information activates feelings and beliefs or in the application of this information. Neural measures can be useful in these applications by testing for dissociations in the effects of manipulations on categorization and subsequent behavior. In one such examination, Tomelleri and Ito (2009) recorded ERPs as participants completed a sequential priming task in which brief presentations of either black

88

I. BASICS

or white face primes preceded pictures of guns and insects (Judd, Blair, & Chapleau, 2004). Participants made a manual response to categorize the gun and insect pictures while mentally performing one of two processing tasks with respect to the faces: either categorizing them in terms of race or determining whether the picture had a small dot on it. ERP results replicated past research, showing encoding of racial information both when participants were explicitly focused on it and when their attention was directed away from the social nature of the stimuli through the dot task. Behaviorally, though, priming effects were seen only in the race categorization task. There, participants were faster and more accurate in responding to guns following black than white primes. Participants responded equally fast and accurate to guns following black and white primes when they made dot-�present or absent judgments about the faces (see also Macrae, Bodenhausen, Milne, Thorn, & Castelli, 1997). In sum, the ERPs reveal a manipulation that leaves the ability to encode social category information intact while disrupting the activation of stereotypical associations just a few hundred milliseconds later.

CONCLUSIONS AND FUTURE€tDIRECTIONS The research reviewed in this chapter examines theoretical questions of central interest in social cognition (e.g., attitudes, racial bias, behavior regulation) using a variety of paradigms familiar to social cognition researchers (e.g., sequential priming, attitude judgments, subliminal presentations). The integration of these content and methodological domains with neuroscience measures and models contributes to our understanding of implicit social cognition in a number of ways. For one, social neuroscience provides a window into aspects of mental life not easily reflected in other measures. In some cases, where results closely align with theoretical expectations, this work provides converging evidence for existing theoretical models. Such confirmatory results also open up new avenues for exploration. Research by Amodio and colleagues (2004, 2006) illustrates this point. Their initial research showing that ERNs are larger after errors in identifying tools primed by black faces compared with white faces provides support for existing models of behavior regulation. Their subsequent work extends this basic model to examine how individuals who are seemingly nonprejudiced can display different levels of bias (Amodio et al., 2008). The

degree to which egalitarian motivations are spontaneously activated is critical to their explanation, and ERNs were used to test a specific hypothesis about the impact of sources of motivation to control prejudice and conflict between the commission of a race-�biased response. Similarly, research by Tomelleri and Ito (2009) built on extensive ERP research examining the automaticity of encoding of social category information. By examining both ERPs and a behavioral outcome, they were able to examine separable effects of processing manipulations on encoding of social category information and implicit stereotyping, respectively. In addition to examining specific theoretical issues, social neuroscience will also likely influence our understanding of broader issues, such as how implicit and explicit processes differ. They at times appear qualitatively distinct; they can be characterized as differing along such dimensions as speed, awareness, and intention (Wegner & Bargh, 1998), and varying the contributions of implicit and explicit processing can lead to notably different responses (e.g., Dovidio, Kawakami, & Gaertner, 2002; Fazio, Jackson, Dunton, & Williams, 1995; Wittenbrink, Judd, & Park, 2001). It might follow that the neural mechanisms will also show a corresponding qualitative distinction. In fact, Lieberman and colleagues argue for the neural specialization of automatic versus controlled social cognition (Lieberman, 2007; Satpute & Lieberman, 2006). In this model, brain structures are classified as supporting either reflexive (automatic) or reflective (controlled) processes. The basis for these distinctions is well grounded in empirical research, yet this distinction must take into account studies showing activation of the same area in both implicit and explicit tasks. Studies of amygdala activation to implicit and explicit evaluative tasks, reviewed in the first section of this chapter, are a perfect example. Resolution may depend on our evolving understanding of specialization within brain areas. The amygdala, for instance, is composed of several distinct subdivisions (Amunts et al., 2005). It is possible that what appears to be activation in the same regions for implicit and explicit processes at current levels of resolution is actually activation in spatially close yet distinct subregions. However, it is also likely that part of the overlap in brain activity between seemingly qualitatively distinct tasks reflects the true joint operation of implicit and explicit processes. That is, performance of an explicit task may also invoke implicit processes (e.g., making an explicit evaluation is likely to also engage implicit evaluative processes), and implicit



5. Insights from Social Neuroscience

tasks are increasingly recognized as capturing the operation of explicit processes as well (e.g., Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005; Payne, 2008). Studies have already begun that seek to separately quantify implicit and explicit contributions to task performance and relate them to activity in different brain areas (cf. Amodio et al., 2004, 2008; Beer et al., 2008). Studies using this approach will likely be informative in shaping our understanding of the interplay between implicit and explicit processes. Social cognition research has a rich tradition of crossing disciplinary boundaries at both theoretical and methodological levels. The studies reviewed in this chapter illustrate the ways in which social neuroscience continues the interdisciplinary tradition. However, the exchange should not be restricted to a single direction. This review has focused on the implications of neuroscience research for social cognition theory, but a firm understanding of social behavior is also important to interpreting brain function. Consider our rapidly evolving understanding of the functional complexities of the amygdala. The numerous dimensions to which the amygdala responds—Â�negativity, positivity, high intensity, personal relevance— could seem disordered but make sense based on social cognitive research supporting the inherent importance of social stimuli and the fundamental nature of valence and arousal (Eagly & Chaiken, 1998; Osgood, Suci, & Tannenbaum, 1957). The tremendous volume of sophisticated social cognition research will be an invaluable partner as we further unravel the workings of our social brain. References Amodio, D. M., Devine, P. G., & Harmon-Jones, E. (2008). Individual differences in the regulation of intergroup bias: The role of conflict monitoring and neural signals for control. Journal of Personality and Social Psychology, 94, 60–74. Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: The medial frontal cortex and social cognition. Nature Reviews Neuroscience, 7, 268–277. Amodio, D. M., Harmon-Jones, E., Devine, P. G., Curtin, J. J., Hartley, S. L., & Covert, A. E. (2004). Neural signals for the detection of unintentional race bias. Psychological Science, 15, 88–93. Amodio, D. M., Kubota, J. T., Harmon-Jones, E., & Devine, P. G. (2006). Alternative mechanisms for regulating racial responses according to internal vs. external cues. Social Cognitive and Affective Neuroscience, 1, 26–36. Amunts, K., Kedo, O., Kindler, M., Pieperhoff, P., Mohlberg, H., Shah, N., et al. (2005). Cytoarchitectonic mapping of the human amygdala, hippocampal re-

89

gion and entorhinal cortex: Intersubject variability and probability maps. Anatomy and Embryology, 210, 343–352. Badre, D., Poldrack, R. A., Paré-Blagoev, E. J., Insler, R. Z., & Wagner, A. D. (2005). Dissociable controlled retrieval and generalized selection mechanisms in ventrolateral prefrontal cortex. Neuron, 47, 907–918. Badre, D., & Wagner, A. D. (2002). Semantic retrieval, mnemonic control, and prefrontal cortex. Behavioral and Cognitive Neuroscience Reviews, 1, 206–218. Banich, M. T., Milham, M. P., Atchley, R. A, Cohen, N. J., Webb, A., Wszalek, T., et al. (2000). Prefrontal regions play a predominant role in imposing an attentional “set”: Evidence from fMRI. Cognitive Brain Research, 10, 1–9. Bargh, J. A. (1999). The cognitive monster: The case against the controllability of automatic stereotype effects. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€361–382). New York: Guilford Press. Bargh, J. A., Chaiken, S., Govender, R., & Pratto, F. (1992). The generality of the automatic attitude activation effect. Journal of Personality and Social Psychology, 62, 893–912. Bartholow, B. D., & Dickter, C. L. (2008). A response conflict account of the effects of stereotypes on racial categorization. Social Cognition, 26, 273–291. Beer, J. S., Stallen, M., Lombardo, M. V., Gonsalkorale, K., Cunningham, W. A., & Sherman, J. W. (2008). The quadruple process model approach to examining the neural underpinnings of prejudice. NeuroImage, 43, 775–783. Blair, I. V. (2002). The malleability of automatic stereotypes and prejudice. Personality and Social Psychology Review, 6, 242–261. Botvinick, M. M., Carter, C. S., Braver, T. S., Barch, D. M., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108, 624–652. Braver, T. S., Cohen, J. D., Jonides, J., Smith, E. E., & Noll, D. C. (1997). A parametric study of prefrontal cortex involvement in human working memory. NeuroImage, 5, 49–62. Brewer, M. C. (1988). A dual process model of impression formation. In T. K. Srull & R. S. Wyer, Jr. (Eds.), Advances in social cognition (Vol. 1, pp.€1–36). Hillsdale, NJ: Erlbaum. Britton, J. C., Taylor, S. F., Sudheimer, K. D., & Liberzon, I. (2006). Facial expressions and complex IAPS pictures: Common and differential networks. NeuroImage, 31, 906–919. Bruner, J. S. (1957). On perceptual readiness. Psychological Review, 64, 123–151. Bunge, S. A., Ochsner, K. N., Desmond, J. E., Glover, G. H., & Gabrieli, J. D. E. (2001). Prefrontal regions involved in keeping information in and out of mind. Brain, 124, 2074–2086. Bunge, S. A., Wendelken, C., Badre, D., & Wagner, A. D. (2005). Analogical reasoning and prefrontal cortex: Evidence for separable retrieval and integration mechanisms. Cerebral Cortex, 15, 239–249. Canli, T., Sivers, H., Whitfield, S. L., Gotlib, I. H., & Gabrieli, J. D. E. (2002). Amygdala response to

90

I. BASICS

happy faces as a function of extraversion. Science, 296, 2191. Carter, C. S., Braver, T. S., Barch, D. M., Botvinick, M. M., Noll, D., & Cohen, J. D. (1998). Anterior cingulate cortex, error detection, and the online monitoring of performance. Science, 280, 747–749. Chee, M. W., Sriram, N., Soon, C. S., & Lee, K. M. (2000). Dorsolateral prefrontal cortex and the implicit association of concepts and attributes. NeuroReport, 11, 135–140. Chiao, J. Y., Iidaka, T., Gordon, H. L., Nogawa, J., Bar, M., Aminoff, E., et al. (2008). Cultural specificity in amygdala response to fear faces. Journal of Cognitive Neuroscience, 20, 2167–2174. Coles, M. G. H. (1989). Modern mind–brain reading: Psychophysiology, physiology, and cognition. Psychophysiology, 26, 251–269. Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K., & Groom, C. J. (2006). Separating multiple processes in implicit social cognition: The quad model of implicit task performance. Journal of Personality and Social Psychology, 89, 469–487. Correll, J., Park, B., Judd, C. M., & Wittenbrink, B. (2002). The police officer’s dilemma: Using ethnicity to disambiguate potentially threatening individuals. Journal of Personality and Social Psychology, 83, 1314–1329. Correll, J., Urland, G. R., & Ito, T. A. (2006). EventÂ�related potentials and the decision to shoot: The role of threat perception and cognitive control. Journal of Experimental Social Psychology, 42, 120–128. Cunningham, W. A., Johnson, M. K., Gatenby, J. C., Gore, J. C., & Banaji, M. R. (2003). Neural components of social evaluation. Journal of Personality and Social Psychology, 85, 639–649. Cunningham, W. A., Johnson, M. K., Raye, C. L., Gatenby, J. C., Gore, J. C., & Banaji, M. R. (2004). Separable neural components in the processing of black and white faces. Psychological Science, 15, 806–812. Cunningham, W. A., Raye, C. L., & Johnson, M. K. (2004). Implicit and explicit evaluation: fMRI correlates of valence, emotional intensity, and control in the processing of attitudes. Journal of Cognitive Neuroscience, 16, 1717–1729. Cunningham, W. A., Van Bavel, J. J., & Johnsen, I. R. (2008). Affective flexibility: Evaluative processing goals shape amygdala activity. Psychological Science, 19, 152–160. De Houwer, J., Hermans, D., Rothermund, K., & Wentura, D. (2002). Affective priming of semantic categorization responses. Cognition and Emotion, 16, 643–666. Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of Personality and Social Psychology, 56, 5–18. Dovidio, J. F., Kawakami, K., & Gaertner, S. L. (2002). Implicit and explicit prejudice and interracial interaction. Journal of Personality and Social Psychology, 82, 62–68. Eagly, A. H., & Chaiken, S. (1998). Attitude structure and function. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (4th ed., Vol. 1, pp.€269–322). New York: McGraw-Hill.

Eimer, M. (2000). Event-Â�related brain potentials distinguish processing stages involved in face perception and recognition. Clinical Neurophysiology, 111, 694–705. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic€Â�activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Â�Personality and Social Psychology, 69, 1013–1027. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. C. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. Fiske, S. T. (1989). Examining the role of intent: Toward understanding its role in stereotyping and prejudice. In J. S. Uleman & J. A. Bargh (Eds.), Unintended thought (pp.€253–283). New York: Guilford Press. Fiske, S. T., & Neuberg, S. L. (1990). A continuum of impression formation, from category-based to individuating processes: Influences of information and motivation on attention and interpretation. Advances in Experimental Social Psychology, 23, 1–74. Gratton, G., Coles, M. G. H., Sirevaag, E. J., Eriksen, C. W., & Donchin, E. (1988). Pre- and poststimulus activation of response channels: A psychophysiological analysis. Journal of Experimental Psychology, 14, 331–344. Gratton, G., Coles, M. G. H., Sirevaag, E. J., Eriksen, C. J., & Donchin, E. (1992). Optimizing the use of information: Strategic control of activation of responses. Journal of Experimental Psychology, 121, 480–506. Hamann, S., & Mao, H. (2002). Positive and negative emotional verbal stimuli elicit activity in the left amygdala. NeuroReport, 13, 15–19. Harris, L. T., & Fiske, S. T. (2007). Social groups that elicit disgust are differentially processed in mPFC. Social Cognitive and Affective Neuroscience, 2, 45–51. Haxby, J. V., Hoffman, E. Z., & Gobbini, M. I. (2000). The distributed human neural system for face perception. Trends in Cognitive Sciences, 4, 223–233. Heider, F. (1958). The psychology of interpersonal relations. New York: Wiley. Ito, T. A., & Cacioppo, J. T. (2000). Electrophysiological evidence of implicit and explicit categorization processes. Journal of Experimental Social Psychology, 36, 660–676. Ito, T. A., & Cacioppo, J. T. (2007). Attitudes as mental and neural states of readiness: Using physiological measures to study implicit attitudes. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp.€125–158). New York: Guilford Press. Ito, T. A., Larsen, J. T., Smith, N. K., & Cacioppo, J. T. (1998). Negative information weighs more heavily on the brain: The negativity bias in evaluative categorizations. Journal of Personality and Social Psychology, 75, 887–900. Ito, T. A., & Urland, G. R. (2003). Race and gender on the brain: Electrocortical measures of attention to race and gender of multiply categorizable individuals. Journal of Personality and Social Psychology, 85, 616–626. Ito, T. A., & Urland, G. R. (2005). The influence of processing objectives on the perception of faces: An



5. Insights from Social Neuroscience

ERP study of race and gender perception. Cognitive, Affective, and Behavioral Neuroscience, 5, 21–36. Judd, C. M., Blair, I. V., & Chapleau, K. M. (2004). Automatic stereotypes versus automatic prejudice: Sorting out the possibilities in the Payne (2001) weapon paradigm. Journal of Experimental Social Psychology, 40, 75–81. Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17, 4302–4311. Klinger, M. R., Burton, P. C., & Pitts, G. S. (2000). Mechanisms of unconscious priming: I. Response competition, not spreading activation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 441–455. Knutson, K. M., Mah, L., Manly, C. F., & Grafman, J. (2007). Neural correlates of automatic beliefs about gender and race. Human Brain Mapping, 28, 915– 930. Kopp, B., Rist, F., & Mattler, U. (1996). N200 in the flanker task as a neurobehavioral tool for investigating executive control. Psychophysiology, 33, 282–294. LaBar, K. S., Gatenby, J. C., Gore, J. C., LeDoux, J. E., & Phelps, E. A. (1998). Human amygdala activation during conditioned fear acquisition and extinction: A mixed-trial fMRI study. Neuron, 20, 937–945. Lane, R. D., Reiman, E. M., Bradley, M. M., Lang, P. J., Ahern, G. L., Davidson, R. J., et al. (1997). Neuroanatomical correlates of pleasant and unpleasant emotion. Neuropsychologia, 35, 1437–1444. LeDoux, J. E. (2000). Emotion circuits in the brain. Annual Review of Neuroscience, 23, 155–184. Lieberman, M. D. (2007). Social cognitive neuroscience: A review of core processes. Annual Review of Psychology, 58, 259–289. Lieberman, M. D., Hariri, A., Jarcho, J. M., Eisenberger, N. I., & Bookheimer, S. Y. (2005). An fMRI investigation of race-Â�related amygdala activity in AfricanÂ�American and Caucasian-Â�American individuals. Nature Neuroscience, 8, 720–722. Macrae, C. N., Bodenhausen, G. V., Milne, A. B., Thorn, T. M. J., & Castelli, L. (1997). On the activation of social stereotypes: The moderating role of processing objectives. Journal of Experimental Social Psychology, 33, 471–489. Mitchell, J. P., Macrae, C. N., & Banaji, M. R. (2006). Dissociable medial prefrontal contributions to judgments of similar and dissimilar others. Neuron, 50, 655–663. Morris, J. S., Frith, C. D., Perrett, D. I., Rowland, D., Young, A. W., Calder, A. J., et al. (1996). A differential neural response in the human amygdala to fearful and happy facial expressions. Nature, 383, 812–815. Moskowitz, G. B., Gollwitzer, P. M., Wasel, W., & Schaal, B. (1999). Preconscious control of stereotype activation through chronic egalitarian goals. Journal of Personality and Social Psychology, 77, 167–184. Moskowitz, G. B., Salomon, A. R., & Taylor, C. M. (2000). Preconsciously controlling stereotyping: Implicitly activated egalitarian goals prevent the activation of stereotypes. Social Cognition, 18, 151–177. Nieuwenhuis, S., Yeung, N., Van Den Wildenberg, W., & Ridderinkhof, K. R. (2003). Electrophysiological

91

correlates of anterior cingulate function in a go/ no-go task: Effects of response conflict and trial type frequency. Cognitive, Affective and Behavioral Neuroscience, 3, 17–26. Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84, 231–259. Norris, C. J., Chen, E. E., Zhu, D. C., Small, S. L., & Cacioppo, J. T. (2004). The interaction of social and emotional processes in the brain. Journal of Cognitive Neuroscience, 16, 1818–1829. Ochsner, K. N., Knierim, K., Ludlow, D. H., Hanelin, J., Ramachandran, T., Glover, G., et al. (2004). Reflecting upon feelings: An fMRI study of neural systems supporting the attribution of emotion to self and other. Journal of Cognitive Neuroscience, 16, 1746–1772. Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The measurement of meaning. Urbana: University of Illinois Press. Payne, B. K. (2001). Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon. Journal of Personality and Social Psychology, 81, 181–192. Payne, B. K. (2008). What mistakes disclose: A process dissociation approach to automatic and controlled processes in social psychology. Social and Personality Psychology Compass, 2, 1073–1092. Phan, K. L., Wager, T. D., Taylor, S. F., & Liberzon, I. (2002). Functional neuroanatomy of emotion: A meta-Â�analysis of emotion activation studies in PET and fMRI. NeuroImage, 16, 331–348. Phelps, E. A., Cannistraci, C. J., & Cunningham, W. A. (2003). Intact performance on an indirect measure of race bias following amygdala damage. Neuropsychologia, 41, 203–208. Priester, J. R., & Petty, R. E. (1996). The gradual threshold model of ambivalence: Relating the positive and negative bases of attitudes to subjective ambivalence. Journal of Personality and Social Psychology, 71, 431– 449. Richeson, J. A., Baird, A. A., Gordon, H. L., Heatherton, T. F., Wyland, C. L., Trawalter, S., et al. (2003). An fMRI investigation of the impact of interracial contact on executive function. Nature Neuroscience, 6, 1323–1328. Richeson, J. A., & Shelton, J. N. (2003). When prejudice€ does not pay: Effects of interracial contact on executive function. Psychological Science, 14, 287– 290. Richeson, J. A., Todd, A. R., Trawalter, S., & Baird, A. A. (2008). Eye-gaze direction modulates race-Â�related amygdala activity. Group Processes and Intergroup Relations, 11, 233–246. Ritter, W., Simson, R., Vaughan, H. G., & Friedman, D. (1979). A brain event related to the making of a sensory discrimination. Science, 203, 1358–1361. Rugg, M. D., & Coles, M. G. H. (Eds.). (1995). Electrophysiology of mind: Event-Â�related brain potentials and cognition. New York: Oxford University Press. Satpute, A. B., & Lieberman, M. D. (2006). Integrating automatic and controlled processing into neurocognitive models of social cognition. Brain Research, 1079, 86–97.

92

I. BASICS

Smith, N. K., Cacioppo, J. T., Larsen, J. T., & Chartrand, T. L. (2003). May I have your attention, please: Electrocortical responses to positive and negative stimuli. Neuropsychologia, 41, 171–183. Stangor, C., Lynch, L., Duan, C., & Glass, B. (1992). Categorization of individuals on the basis of multiple social features. Journal of Personality and Social Psychology, 62, 207–218. Swainson, R., Cunnington, R., Jackson, G. M., Rorden, C., Peters, A. M., Morris, P., et al. (2003). Cognitive control mechanisms revealed by ERP and fMRI: Evidence from repeated task-Â�switching. Journal of Cognitive Neuroscience, 15, 785–799. Tomelleri, S., & Ito, T. A. (2009). On the flexibility of social perception: Categorization and stereotype activation as functionally independent processes. Unpublished raw data. Touryan, S. R., Johnson, M. K., Mitchell, K. J., Farb, N., Cunningham, W. A., & Raye, C. L. (2007). The influence of self-Â�regulatory focus on encoding of, and memory for, emotional words. Social Neuroscience, 2, 14–27. Van Bavel, J. J., Packer, D. J., & Cunningham, W. A. (2008). The neural substrates of in-group bias: A functional magnetic resonance imaging investigation. Psychological Science, 11, 1131–1139. van Veen, V., & Carter, C. S. (2002). The timing of action-Â�monitoring processes in the anterior cingulate cortex. Journal of Cognitive Neuroscience, 14, 593–602.

Wegner, D. M. (1994). Ironic processes of mental control. Psychological Review, 101, 34–52. Wegner, D. M., & Bargh, J. A. (1998). Control and automaticity in social life. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (pp.€446–496). New York: McGraw-Hill. Whalen, P. J. (1998). Fear, vigilance, and ambiguity: Initial neuroimaging studies of the human amygdala. Current Directions in Psychological Science, 7, 177– 188. Whalen, P. J., Rauch, S. L., Etcoff, N. L., McInerney, S. C., Lee, M. B., & Jenike, M. A. (1998). Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge. Journal of Neuroscience, 18, 411–418. Wheeler, M. E., & Fiske, S. T. (2005). Controlling racial prejudice: Social cognitive goals affect amygdala and stereotype activation. Psychological Science, 16, 56–63. Winston, J. S., Strange, B. A., O’Doherty, J., & Dolan, R. J. (2002). Automatic and intentional brain responses during evaluation of trustworthiness of faces. Nature Neuroscience, 5, 277–283. Wittenbrink, B., Judd, C. M., & Park, B. (2001). Evaluative versus conceptual judgments in automatic stereotyping and prejudice. Journal of Experimental Social Psychology, 37, 244–252. Zald, D. H. (2003). The human amygdala and the emotional evaluation of sensory stimuli. Brain Research Reviews, 41, 88–123.

PA R T II

METHODS AND PROCEDURES

Chapter 6

A Practical Guide to Sequential Priming and€Related Tasks Dirk Wentura and Juliane Degner

T

his chapter is about a family of simple tasks—Â� sequential priming tasks—that have become increasingly popular in social psychology throughout the last decade (see Wittenbrink, 2007). The defining characteristic of these tasks is a common experimental procedure: Participants work through a sequence of trials in which two stimuli are subsequently or simultaneously presented but only one—the target—has to be processed with regard to a basic feature while the other—the prime—is task irrelevant. Starting from this procedural commonality, the family splits into two dominant branches based on the kind of relation between prime and target and the kind of target processing task implemented. When it is the participants’ task to classify letter strings as words or nonwords (lexical decision task) and primes and targets vary according their semantic relatedness, we describe a variant of a semantic priming paradigm. When it is the participants’ task to categorize targets as positive versus negative (evaluation task) and primes and targets vary according their evaluative congruence, we describe a variant of a response priming paradigm (as we prefer to name it for reasons that will become clearer in the course of this chapter). The basic result and dependent variable of interest is a difference in speed and/or accuracy between the conditions defined by the prime–Â�target relationship within each trial. Typically, mean

reaction latencies (error rates) in related or congruent trials are lower compared with those in unrelated or incongruent trials. Because primes are task irrelevant and (prima facie) of no help for participants, such results reveal what is typically referred to as automatic processing of the prime (ignoring the complexities of the concept of automatic for a moment; see, e.g., Moors & De Houwer, 2006). Congruency effects in the aforementioned lexical decision task are assumed to shed light on the mental representation of these stimuli. This is per se an interesting phenomenon, but it becomes even more interesting if prime–Â�target pairs of a priori known relatedness are replaced by pairs of assumed relatedness (e.g., primes denoting a social category and targets denoting presumed stereotypic traits (e.g., Wittenbrink, Judd, & Park, 1997). Congruency effects in the aforementioned evaluative categorization task are assumed to reflect automatic processing of the prime’s valence. This is per se an interesting phenomenon, but it becomes even more interesting if primes of a priori valence are replaced by primes of assumed valence (e.g., pictures of black vs. white persons) (e.g., Fazio, Jackson, Dunton, & Williams, 1995). Although these two branches of tasks seem to be highly similar on first sight, we emphasize that they have to be accurately distinguished, as we elaborate on in this chapter. The purpose of this chapter is to describe different variations of 95

96

II. METHODS AND PROCEDURES

sequential priming tasks and explain their different underlying mechanisms. We furthermore give an overview of typical designs, tasks, procedural features, and peculiarities of data analysis and potential application in social cognition research. We see this chapter as an introductory guide into sequential priming research that cannot answer all questions but gives valuable hints in how to do good priming research.

MIND THE GAP!: SEMANTIC PRIMING VERSUS€RESPONSE PRIMING The first branch of the priming family is known as the semantic priming paradigm, which was developed in the early 1970s (Meyer & Schvaneveldt, 1971). There are two typical variants, depending on the participants’ task. The first one was already mentioned: Targets are words and (pronounceable) nonwords and the time to press a word (or nonword) key is recorded as the dependent variable (lexical decision task). In the second variant, targets (now words only) have to be read as quickly as possible (naming or pronunciation task), and the latency of naming is the dependent variable. The essential experimental manipulation in both tasks is whether the prime is semantically related or unrelated to the target. The typical difference between mean reaction latencies for related and unrelated pairs is explained by the activation of prime meaning, which facilitates encoding of related targets compared with unrelated targets (for exceptions to this rule, see later discussion for reviews, see Lucas, 2000; McNamara, 2005; McNamara & Holbrook, 2003; Neely, 1991). The metaphor of spreading activation in a semantic network is typically used to illustrate the underlying mechanisms of such response facilitation effects (see, e.g., Collins & Loftus, 1975). Note, however, that this is not the only way to explain these effects (see, e.g., Masson, 1995; Ratcliff & McKoon, 1988). The second branch of the priming family comprises different variants of response priming. The most prominent member of this branch in social cognition research is the evaluative (or affective) priming paradigm using the evaluation task, as developed by Fazio, Sanbonmatsu, Powell, and Kardes (1986). We have already outlined this paradigm: Participants categorize target stimuli with regard to their valence as positive or negative; the primes are positive or negative as well. Usually, primes are described as being evaluatively congruent or incongruent to the target, which is correct

but somewhat misleading because it obscures the crucial difference to semantic priming. More appropriately, primes should be described as being congruent or incongruent to the response that has to be given to the target. The most straightforward explanation for a congruency effect in a response priming task is to assume that the irrelevant prime’s response-Â�relevant feature (e.g., the valence) is nonintentionally processed and prepares a response (see De Houwer, Teige-Â�Mocigemba, Spruyt, & Moors, 2009; Klauer & Musch, 2003, for reviews): If the prime triggers the same response as the target, responding is facilitated (thus, responses are faster and errors less likely); if the prime triggers a different response, it conflicts with the response triggered by the target and this conflict has to be solved; thus slowing responses (or an error will more likely occur). At a more abstract level, response priming is structurally equivalent to the classic Stroop color interference paradigm (Stroop, 1935) or the Eriksen flanker task (Eriksen & Eriksen, 1974). For example, in the Stroop task, participants name the print color of words in congruent trials (e.g., the word green printed in green), incongruent trials (the word green printed in red), and neutral trials (“XXXX” printed in red). In incongruent trials, a response conflict occurs because the task-Â�relevant word color suggests one response and the task-Â�irrelevant word content suggests another. Before the response can be given, the response conflict has to be resolved (i.e., the task-Â�relevant information has to be separated from the task-Â�irrelevant information), resulting in slower and less accurate responses compared with congruent and neutral trials. Thus, in such response interference paradigms, a task-Â�irrelevant feature (i.e., the word content in the Stroop task) or a task-Â�irrelevant stimulus (i.e., the prime in the priming task) is automatically processed, triggering specific responses and thus interfering with responding in incongruent cases. Beside the evaluative priming paradigm, there are other examples of response priming in social cognition research. The seminal article on automatic stereotyping by Banaji and Hardin (1996) is a good example for (1) a different version of response priming and (2) for the comparison of response priming and semantic priming, using the same materials and focusing on different facets of a research hypothesis (see also Wittenbrink, Judd, & Park, 2001). In their first experiment, the authors instructed participants to quickly categorize pronouns (e.g., he, she) with regard to gender. The pronouns were preceded by different categories of primes: nouns that were related to gender either



6. Sequential Priming and Related Tasks

by definition (e.g., father vs. mother) or by normative base rates (e.g., doctor vs. nurse). Results indicated faster responding after generic genderÂ�congruent prime–Â�target pairs compared with gender-Â�incongruent pairs. Given our prior definition, we have a clear example of response priming: The primes related to gender by definition can be categorized as male versus female on an a priori basis and can thus trigger the response “male” or “female.” Thus, the observed priming effect can be interpreted analogous to evaluative priming: In the context of a gender classification task, a briefly presented gender-Â�related word is automatically categorized as either male or female; this categorization helps or hinders the target categorization. This is per se an interesting phenomenon, but it becomes even more interesting looking at primes that were only indirectly gender connoted. Did nurse (doctor) automatically evoke a female (male) classification? Yes, it did: The congruency effect with normative base rate primes was significant as well. In their second experiment, Banaji and Hardin (1996) changed the task from gender categorization to pronoun categorization (i.e., Is the target a pronoun or not?). Of course, trials with nonÂ�pronoun targets (e.g., do, as) were added. By this change in task instructions, we are faced by a version of semantic priming: The variation of interest (i.e., whether prime and target are matching with regard to gender or not) is completely orthogonal to the binary decision made with regard to the target. In this task, Banaji and Hardin found a relatedness effect for primes that were gender related by definition but not for primes that were only indirectly gender connoted. In two aspects, these priming effects differ from the effects found in the response priming version. First, automatic gender categorization of the generic gender-Â�related primes occurs even when no explicit gender classification is instructed, thus indicating a different degree of goal independence (or nonintentionality) of the process (see, e.g., Bargh, 1996). Second, the specific relation between prime and target counts: A concrete female-Â�related word (i.e., she) is processed somewhat easier if it is preceded by a “female” noun. To add more emphasis to this point, imagine a further experiment in which the target set consists of adjectives (and adjective-like nonwords), and participants are instructed to categorize targets according to their lexical status (see, e.g., Wittenbrink et al., 2001). If adjectives are related to gender stereotypes, a priming effect would mean that encoding of a stereotypical “female” adjective (e.g., caring) is facilitated by a

97

gender-�related prime. Now the explanation of this facilitation has to draw on assumptions about the mental representation of stereotypes. A transition between response priming and semantic priming as in the experiments by Banaji and Hardin (1996) can be made for evaluative priming as well. You can, of course, study evaluative congruency effects in a semantic priming design if you vary prime and target valence, for example, in a naming task (e.g., Bargh, Chaiken, Raymond, & Hymes, 1996; De Houwer & Hermans, 1994; Spruyt, Hermans, De Houwer, & Eelen, 2002) or a lexical decision task (e.g., Bessenoff & Sherman, 2000; Wentura, 2000). Note, however, that, in comparison to the response priming variant of evaluative priming, you add two essential assumptions. First, you assume that the valence of the prime is automatically processed even in a task context that does not ask for evaluation. Second and more importantly, you assume that the mental representation of valent stimuli is structured in a way that allows for encoding facilitation. This is an exciting research hypothesis on its own (see, e.g., Spruyt, Hermans, De Houwer, Vandromme, & Eelen, 2007; Wentura & Frings, 2008, for recent research).1

THE DEVIL INSIDE: STRATEGIC€PRODUCTION OF€PRIMING EFFECTS Undoubtedly, the great interest in the priming paradigm is due to the belief that the priming effect could be a window to the inner structure of our cognitive (-affective) apparatus and the basic processes that operate within this structure. Why do we believe that? We treat participants in our studies as our confederates, as persons with the capability to act intentionally (a miraculous capability from a cognitive perspective), who are willed to tenaciously press keys in response to target stimuli in order to show a good performance in terms of accuracy and speed. As a mere side effect caused by the automatisms of their cognitive apparatus, and thus believed to be out of their control, their performance data reflect the priming effects we are interested in. However, we should carefully rethink whether our data can alternatively be explained by strategic efforts of the participants. For the semantic priming branch, this question is closely associated with the seminal article by Neely (1977). What will participants do if they realize the basic structure of a semantic priming experiment, that is, that a prime

98

II. METHODS AND PROCEDURES

is often followed by a semantically related target? They might generate expectations about specific targets to appear after certain primes and, if an expected target appears, react faster compared with the unrelated condition. Neely pitted presumed automatic processes against expectancy-based priming by presenting in the majority of trials a priori unrelated pairs with a structure known to participants (i.e., they knew, for example, that whenever body was the prime, a part of a building [e.g., door] could be expected as the target; whenever building was the prime, a part of the body [e.g., heart] could be expected). Additionally, an unexpected minority of trials was composed of a priori related prime–Â� target pairs (i.e., body–heart). In comparison to a neutral baseline, there was large facilitation for expected targets and large inhibition for unexpected targets. However, this pattern was moderated by the delay in onset of prime and target (stimulusonset asynchrony [SOA]). It was strongest at an SOA of 700 msec and rather weak at an SOA of 400 msec. At an SOA of 250 msec, however, there was facilitation for a priori related targets (and no inhibition for unrelated but expected ones). What do we learn from this pattern? In semantic priming experiments, effects of a priori relatedness succeed even if participants are instructed to work against them, but only for short SOAs. Semantic priming effects found with long SOAs are more fragile and open for alternative explanations. Another challenge of the interpretation of semantic priming effects in terms of encoding automatisms is posed by possible backward-Â�checking strategies (e.g., Neely, Keefe, & Ross, 1989). Typically, the set of word trials (with half of them preceded by a related prime) is supplemented by the same number of nonword trials. Of course, nonword trials are preceded by word primes as well, however, typically without relationship to the nonword (e.g., botter as a nonword is usually not preceded by bread). Word as well as nonword targets initially activate entries in the mental lexicon (i.e., the word target butter as well as the nonword target botter activate butter). If a backward check yields the result that the activated entry has a relationship to the prime, it must be a word! We discuss how to prevent such strategies in the Procedural Details section. The previous discussion applies exclusively to semantic priming designs. For response priming designs, we cannot claim to measure automatic effects just because we rely on a short SOA (e.g., 200 msec). Klauer, Rossnagel, and Musch (1997) conducted a study that can be regarded as an equivalent to the experiment by Neely (1977) for

evaluative response priming. In this study, SOA as well as the proportion of congruent pairs was varied, which should lead to similar expectancies: If, for example, only 25% of all trials are congruent prime–Â�target pairs, participants can expect a negative prime to be followed by a positive target (and vice versa) in the majority of trials. If evaluative priming follows the same laws as semantic priming, a manipulation of the relatedness proportion should determine the priming effects only at longer SOAs. That, however, was not what Klauer and colleagues found. Relatedness proportion moderated priming effects already at SOAs of 0 msec (i.e., prime and target were presented simultaneously one above the other) and 200 msec, with larger priming effects for larger relatedness proportions. However, even in the case of a minority of 25% congruent trials, the priming effect did not reverse, which might have been expected if participants intentionally prepare a response dependent on the prime category. Reversed effects (i.e., shorter reaction times [RTs] for incongruent trials) were only found at SOA = 1,200 msec.2 What do we learn from these studies? First, given the analogy of evaluative priming to the Stroop paradigm (see earlier discussion), the result of relatedness proportion effects at SOA = 0 msec is not surprising; the pattern was known from the Stroop literature (e.g., Logan, 1980). Second, this moderation shows indeed that the process underlying response priming effects is somewhat more complicated than a simple automatism. Logan (1980) explains the moderation by relatedness proportion in the Stroop task by an attentionÂ�controlled adaptation of the weights of ink color and color word in generating a response. If the relatedness proportion is very low, the weight for the color word is reduced to a very low level and no longer interferes with ink color naming. If the relatedness proportion is high, a higher weight is given to the word content as an additional information source helping to achieve fast and correct responses. Third, this result is, however, not attributable to the same expectancy mechanism as the one tested by Neely because (1) an SOA = 0 msec does not allow for response preparation and (2) if participants would base their reactions fully on the contingencies, a reversed effects should have occurred for low relatedness proportions. The picture gets even more complicated because recent results suggest that evaluative priming can be influenced by participants’ goals (Degner, 2009; Klauer & Teige-Â�Mocigemba, 2007). Interestingly, relatedness proportion effects were not observed for masked versions of response prim-



6. Sequential Priming and Related Tasks

ing (Klauer & Musch, 2003; Musch, 2000), that is, for versions with primes for whom participants are at least subjectively unaware. This result indicates that supraliminal (unmasked) and masked versions differ somewhat in their processing characteristic.

MASKED AND UNMASKED€PRIMING Masking procedures are used to prevent participants from becoming fully aware of the priming event and/or the prime content. For word stimuli, for example, a so-Â�called sandwich masking procedure is often used: The prime is displayed very briefly (e.g., 10–40 msec) and embedded into letter strings serving as forward and backward masks. Typically, to the participant, the mask–prime– mask sequence appears as a brief flickering of letter strings, implying subjective unawareness of the prime. Objective unawareness is demonstrated by a so-Â�called direct test: Participants work again through a sequence of the same kind of prime–Â� target pairs (or a most comparable version of it) but are now instructed to categorize the prime instead of the target (see, e.g., Draine & Greenwald, 1998; see also, e.g., Dehaene et al., 1998, for a different version). The detection sensitivity parameter of signal detection theory (d′) is used as an index of (un)awareness. The most conservative evidence of subliminal priming consists of significant priming effects associated with a mean d′ of 0 (see, e.g., Schmidt & Vorberg, 2006). Others (e.g., Draine & Greenwald, 1998) suggest using regression analysis to provide evidence for priming in the absence of awareness: Regress the priming difference (incongruent RT minus congruent RT) on the d′ of the direct test. If the intercept deviates significantly from zero, you can claim that there is priming at the zero point of the awareness measure. Of course, this only makes sense if the distribution of d′ includes 0 as a nonrare case (i.e., the scatter plot encloses the y-axis; see Klauer, Greenwald, & Draine, 1998, for a further problem). For this kind of research in general and for applications in social cognition especially, a further differentiation is very important, although it seems to be a more technical detail at first sight. You can distinguish between experiments in which primes and targets are drawn from the same (small) set of stimuli (without, of course, presenting the same stimulus as prime and target in the same trial), such that the same stimulus is used as a prime in some trials and as a target in others, and experi-

99

ments with primes that never appear as targets. For the former case, it is known that masked priming effects are robust (Draine & Greenwald, 1998). However, it seems as if they are caused by primeÂ�induced retrieval of stimulus–Â�response episodes that are created in trials where the prime was used as a target (see, e.g., Abrams & Greenwald, 2000). The latter case, however, is the more interesting one for social cognition researchers who want to use the masked affective priming paradigm as a tool in the attitude domain. Isn’t it highly attractive to use primes that were never visibly presented (not to say evaluated) during the experiment? In fact, you can find effects in such experiments (see, e.g., Klauer, Eder, Greenwald, & Abrams, 2007; Wentura & Degner, in press, for basic research on this topic; see, e.g., Degner & Wentura, 2009; Degner, Wentura, Gniewosz, & Noack, 2007; Frings & Wentura, 2003; Otten & Wentura, 1999; Wentura, Kulfanek, & Greve, 2005, for applications); however, they tend to be rather small. The evidence for masked semantic priming effects is even more complex. There is evidence for semantic priming at very short SOAs (e.g., the prime is overwritten by the target after 50-msec exposure; see Perea & Gotor, 1997; Perea & Rosa, 2002). For subliminal semantic priming, however, evidence is not as clear-cut as for response priming. Bodner and Masson (2003) found evidence for masked semantic priming (although direct prime identification was not perfectly at chance level), whereas Klinger, Burton, and Pitts (2000) contrasted response and semantic priming effects and found only evidence for the former but not the latter (while using the regression method of Draine & Greenwald, 1998). In the social cognition domain, for example, Wittenbrink and colleagues (2001) found stereotype-Â�related priming by using masked presentation of category labels (i.e., black vs. white). The most important difference between the Klinger and colleagues and the Wittenbrink and colleagues experiments seems to be the massive repetition of the prime labels in the latter studies compared with the former. However, in our own experiments using repetition of category primes (albeit nonsocial ones) but a somewhat different masking procedure, we found reversed semantic priming (Wentura & Frings, 2005). To summarize our report on masked versions of priming, we can state that response priming does produce masked effects. Even in the case with unpracticed primes (i.e., primes that are never openly presented throughout the course of the study), there is now enough evidence to rely on the existence of this (albeit rather small) effect. For se-

100

II. METHODS AND PROCEDURES

mantic priming, one cannot take masked effects for granted. There is some evidence for priming effects, especially if one relies on the “weaker” criterion of subjective unawareness.3 Nevertheless, we strongly recommend pilot testing the procedure using standard materials.

DESIGN AND TASKS For both branches of the priming family, the design of a standard experiment is straightforward. We briefly discuss some details and variations with special consideration of social cognition research.

Basic Design of Semantic Priming For semantic priming, semantically related and unrelated conditions have to be compared in a within-Â�participants design (for the possible inclusion of a neutral priming condition, see later discussion). The gold standard of semantic priming experiments, however, is to present a given target only once to a given participant (to eliminate potential effects of target repetition). Thus, the design is basically a counterbalanced one (i.e., a Latin square): Two parallel lists of prime–Â�target pairs are generated. Sample A receives pairs of list 1 as related pairings (related condition) and list 2 with a scrambled assignment of primes to targets (unrelated condition). For sample B the lists are presented the other way round. Thus, across the full sample, each target word appears equally often in the related and unrelated conditions. In social cognition research, you might only be interested in testing whether a certain set of stimuli A (e.g., first names associated with African Americans) prime a certain set of stimuli B (e.g., stereotypical trait words). It seems most straightforward to simply compare mean RTs to B targets preceded by A primes to mean RTs to B targets preceded by control primes (e.g., first names associated with Caucasian Americans) and to interpret a difference as a priming effect. This, however, is not legitimate because a difference might be due to an unspecific main effect of prime set; that is, RTs to any target might be faster if preceded by words of set A in comparison to the control set (e.g., because words of set A are attention grabbing; to illustrate, imagine an experiment using taboo words as primes). Thus, you are obliged to add a further set of target words as a control condition. Your priming effect corresponds to the interaction in a 2 (prime variation) × 2 (target variation)! Similarly, it is not recommended to use one list of related pairs and

another list of unrelated pairs (with target words matched for some stimulus characteristics) for the whole sample. Any priming effect found might be due to subtle differences in word access or to subtle main effects of the two different prime lists. Thus, use the same list of targets preceded by related and unrelated primes. In social cognition research, the material of interest (e.g., stereotypes) is often limited. Also, Latin square designs are not well suited for those social cognition studies that want to use priming indices as individual difference measures because these make it preferable that all participants complete the same version of the task. Thus, targets have to be repeated in related and unrelated priming trials. We do not believe that repetition of targets (e.g., one time preceded by a related prime and one time by an unrelated one) is a problem per se. One should keep in mind, however, that results might become noisier. For example, repeated targets might reach a ceiling level of accessibility throughout the task or automatic target–Â�response bindings might emerge such that primes cannot have any effect on target responses any more (see also McNamara, 2005, with regard to this topic). What kind of tasks can be used for semantic priming experiments (see also Table 6.1)?

Lexical Decision Task As indicated early in this chapter, lexical decision is the classic and most frequently used task for semantic priming research. Word and nonword trials are presented, and participants decide as quickly as possible whether the target is a legal word or not. Without challenging the status of this task, we want to mention three problems. First, nonword trials are in some sense “waste” trials because analyses are (almost always) restricted to word trials. Second, because typically nonword trials are semantically unrelated trials, participants are invited to use strategies that are not intended by the experimenter (see prior discussion on backward checking). Third, lexical decision processes are sometimes believed to be not elaborate enough for semantic analysis (see, e.g., De Houwer, Hermans, Rothermund, & Wentura, 2002).

Naming Task The naming task has already been introduced in this chapter. The main advantage is that response�related explanations become rather implausible because a multitude of different responses have to be emitted by participants throughout the task.



101 (Valenced) target evaluation (Neutral) target evaluation Target categorization (e.g., gender) “Shoot”/ “Don’t shoot” decision

Prime valence × target valence

Prime valence

Prime category × target category

Black versus white × weapon versus no weapon

Evaluative priming (masked version)

AMP

Semantic category

Weapon task

Payne (2001); Klauer & Voss (2008); Payne, Shimizu, et al. (2005)

Draine & Greenwald (1998); Klinger et al. (2000); Dehaene et al. (1998)

Payne, Cheng, et al. (2005); Murphy & Zajonc (1993)

Draine & Greenwald (1998); Klauer et al. (2007)

Fazio et al. (1986); Klauer & Musch (2003)

Note. AMP, affect misattribution procedure. aIncludes articles that either established a task, review evidence on this task, or report on recent studies using the task in basic research questions. bIncludes publications that apply the paradigm to social psychological research questions concerning, for example, stereotypes and prejudice.

(Valenced) target evaluation

Bodner & Mason (2003); Klinger et al. (2000); Wentura & Frings (2005)

De Houwer et al. (2002); Klinger et al. (2000) Warren (1972, 1974)

Semantic decision (e.g., object vs. person) Color naming See above.

Neely (1991); McNamara (2005) Neely (1991); McNamara (2005)

Basic referencesa

Lexical decision Pronunciation

Task

Prime valence × target valence

See above.

Semantic relationship, for example: •• Association •• Category label (prime), exemplar (target) •• category coordinates

Basic prime–target variation

Evaluative priming

Response priming

Semantic priming (masked versions)

Semantic priming

Semantic priming

Paradigm

TABLE 6.1.╇Overview of Tasks That Dominate Priming Research

Correll et al. (2002)

Banaji & Hardin (1996, Study 1)

Payne, Cheng, et al. (2005)

Degner & Wentura (2009)

Fazio et al. (1995)

Wittenbrink et al. (2001)

Kawakami et al. (1999)

Wittenbrink et al. (1997) Spruyt, Hermans, De Houwer, Vandekerckhove, & Eelen (2007) Banaji & Hardin (1996; Study 2)

Sample application in social psychologyb

102

II. METHODS AND PROCEDURES

However, the task has some disadvantages in practical implementation: (1) A voice key apparatus is required and (2) the responses must be monitored because one must constantly check whether the voice key was triggered by the correct utterance (and not, e.g., by a wrong word or a cough). Thus, either an attentively listening experimenter has to categorize each response online as valid versus invalid, or naming responses are recorded and arduously coded for accuracy afterward.4 A final point is to always be aware of possible confounds with regard to an unbalanced mixture of initial phonemes. If you were interested, for example, in simple differences to name positive versus negative words, you should carefully check whether one set of words contains more plosives (e.g., the “p” [p] in “positive”) while the other contains more fricatives (e.g., the “sh” [] in “shark”). Of course, this problem is not very severe in priming designs because throughout the experiment each target is presented equally often in related/congruent and unrelated/incongruent conditions.

Semantic Decision Task De Houwer and colleagues (2002) tested for evaluative congruency effects in a semantic priming design by using targets that had to be categorized as either persons (e.g., friend, enemy) or objects (e.g., gift, poison). Advantages in comparison to a lexical decision are (1) presumably deeper processing of targets and (2) no waste trials (like the nonword trials in lexical decision). Banaji and Hardin’s (1996) Experiment 2 mentioned previously provides a second example of semantic decision task. Of course, it has to be carefully considered whether a specific research hypothesis allows for such task variations.

Stroop Priming Task An interesting but rarely used task is the Stroop priming task (see Warren, 1972, 1974). It has its name as a result of a superficial similarity to the original Stroop color-�naming paradigm: The target stimuli of a semantic priming task are presented in one of four or five colors and the color has to be named. It is assumed that mere variations in accessibility of concepts partly determine interference in color naming. Note that this cannot be inferred from the original Stroop studies because the original paradigm is dominantly a response interference paradigm (like response priming or the flanker task; see prior discussion). Nevertheless, Warren found increased color-�naming latencies for

targets that were preceded by semantically related primes. Rothermund and Wentura (1998) pointed out a potential advantage of this task, which can be made clear by a thought experiment. Imagine that you disambiguate the word cancer by a prime to refer either to the disease or to the astrological sign. Both priming conditions might result in increased accessibility of cancer in comparison to a neutral prime condition, thus principally enabling faster lexical decisions compared with the neutral prime condition. In the disease-prime condition, however, an additional effect might come into play: The possibly increased accessibility of a threat-Â�related meaning of the prime might grab participants’ attention and distract them from the task. The hypothesis for the response speed in a lexical decision task becomes uncertain: Increased accessibility of target meaning predicts fast RTs; attention grabbing of prime meaning predicts slow RTs. However, in a color-Â�naming task, both hypothetical effects work in the same direction: They increase latencies. However, we hasten to remark that, in a control condition, Rothermund and Wentura tried to replicate Warren’s results by using associated prime–Â�target pairs. This was successful when elaborate processing of the primes was assured (i.e., the prime word had to be memorized), which is rather unusual in semantic priming research. Without doing so, a more complex pattern of results was observed that made clear there are some intricacies associated with this task. In another application, Kawakami and colleagues (Kawakami, Dion, & Dovidio, 1999; Kawakami, Dovidio, Moll, Hermsen, & Russin, 2000) successfully used the task for stereotype assessment, but with a rather unusual long SOA of 1,000 msec (including a prime presentation of 950 msec). Thus, we can give no “easy-and-Â�straightforward-to-use” recommendation for this task.

Further Suggestions In semantic priming research, the single-Â�target procedure has some reputation to reflect automatisms of memory retrieval: It consists of a sequence of binary categorizations (e.g., lexical decision) to single stimuli (i.e., there is no prime in the sense of a preceding task-Â�irrelevant stimulus). The prime is simply the target of the foregoing trial (e.g., Shelton & Martin, 1992). Wentura and Brandtstädter (2003) used complete sentences as primes (see also Wentura & Greve, 2004, 2005). Finally, in the domain of text-Â�processing psychology, recognition priming was successfully introduced: After reading some scenarios, participants went through a series



6. Sequential Priming and Related Tasks

of recognition trials with phrases out of the scenarios as targets. It was tested whether presenting one part of a scenario as a prime facilitates retrieval of another (see, e.g., Ratcliff & McKoon, 1978).

Basic Design of Response Priming Basic response priming design comprises (at least; but see the Procedural Details section) the 2 × 2 variation of prime category and target category: The targets fall into two clearly defined categories, A and B (e.g., positive and negative). In basic research on response priming, the primes clearly differentiate into the same two categories A and B, and prime category and target category are orthogonally varied. In applying the design to research questions of social cognition (e.g., “implicit” prejudice), the primes differentiate into two sets as well, one whose exemplars are supposed to be automatically categorized as being a member of A and a control category that should not fall into the category A. In contrast to semantic priming, it is common to use rather small sets of stimuli. (That habit might have its origin in the structural equality to Stroop and flanker designs, which are always based on small sets of stimuli.) For semantic priming designs, it was easy to give an overview of different tasks that, despite their differences, test the same research hypothesis (“Do these primes facilitate encoding of those targets?”). For response priming designs, the task is intimately tied to the specific research hypothesis. For example, if you are interested in automatically evoked gender categorization of primes, use a male versus female categorization task (see, e.g., Banaji & Hardin, 1996; see prior discussion). If you are interested in automatically evoked intergroup evaluations, use an evaluation task (see, e.g., Fazio et al., 1995). It is rather difficult to give an exhaustive list of tasks for the response priming paradigm. There are, nevertheless, three popular tasks in social cognition research that are variants of response priming designs. They are discussed in the following section (see also Table 6.1).

Evaluative (or Affective) Priming Task We have already mentioned the evaluative priming task several times, and there is no need to elaborate further on this task itself. Participants are simply instructed to categorize target stimuli as positive or negative as quickly as possible while ignoring the prime stimulus. A reasonable advancement is the use of response deadline or response window instructions.5 If we consider response fa-

103

cilitation and/or interference as the driving force behind response priming effects, the accuracy of responding is in some sense the more “natural” dependent variable: If the prime supports the correct response, the likelihood of an error decreases; if the prime triggers the wrong response, the likelihood of an error increases. In somewhat more methodical terms, the probability of emitting a positive (vs. negative) response is a function of weighted target valence and weighted prime valence. Of course, if participants have enough time to process the target, they will finally press the correct response key in almost all trials, and facilitation or interference will dominantly show up in RTs. Thus, one trick to shift priming effects into accuracy data is to instruct participants to emit responses earlier than they would do in the nonrestricted case. In this regard, response window techniques can be very helpful (Draine & Greenwald, 1998). Response windows force participants to give extremely fast reactions within a predefined response window that is typically too short for high accuracy (e.g., within 300–450 msec after target onset). Thus, as participants are forced to perform with a more or less constant response speed, prime processing typically influences response accuracy, which becomes the main dependent variable. Simple response deadlines can have the same effect. Such techniques come with three further advantages: (1) The response window can be defined so close to the target onset that the target might not be fully processed when the response has to be given and thus prime influence on response accuracy is comparatively strong (i.e., the weight of the prime valence relatively increases) (Draine & Greenwald, 1998; Wentura & Rothermund, 2003); (2) there is preliminary evidence that time pressure evoked by response deadlines impedes participants from implementing manipulating or faking strategies (at least in evaluative priming; Degner, 2009); and (3) besides any theoretical advantage of accuracy as the dependent variable, response windows shift potential priming effects into one dependent variable for all participants (i.e., an effect is not partitioned between accuracy, for fast participants, and response times, for accurate participants).

Affect Misattribution Procedure Murphy and Zajonc (1993) developed a different version of affective priming. On each trial, a prime (i.e., an emotional face) was very briefly presented and directly followed (and thereby masked) by a neutral Chinese ideograph whose pleasantness had to be rated. They found that ratings were partly

104

II. METHODS AND PROCEDURES

determined by the valence of the picture. Payne, Cheng, Govorun, and Stewart (2005) adapted this design to the assessment of automatic evaluation. In their affect misattribution procedure (AMP), the prime is briefly (i.e., 75 msec) but visibly presented, followed by a blank screen (i.e., 125 msec) and an again briefly (i.e., 100 msec) but visibly presented Chinese ideograph as a target, which is finally overwritten by a mask. In a reduced binary categorization task, participants have to quickly decide whether the ideograph is less or more “visually pleasing than average” (p.€280). The priming effect in this paradigm consists of the actual evaluative responses to the neutral targets, that is the proportions of “more pleasant” versus “less pleasant” responses. Although participants are warned about the potentially biasing influence of the primes, the target categorizations typically reflect the valence of the primes: That is, “pleasant” responses are more/less frequent after positive/negative primes. This paradigm is in some sense the logical culmination of response priming: If we consider the probability of choosing one response category instead of the other as a function of weighted target valence and weighted prime valence (see prior discussion), the weight of target valence is almost reduced to zero if a neutral target has to be evaluated.6 Further evidence for the role of response priming in the AMP is the finding that the procedure also produces priming effects for nonevaluative variants of the task (e.g., Deutsch & Gawronski, 2009), which are difficult to explain with an affect misattribution mechanism. Results are impressive, and the authors have done much to refute the criticism that participants follow a demand strategy (see Deutsch & Gawronski, 2009, for a comparison of AMP and standard affective priming).

Weapon Identification Task With some reservations, we describe the weapon identification task (Correll, Park, Judd, & Wittenbrink, 2002; Payne, 2001) within the section on response priming. In a computer game of sorts, participants have to decide quickly whether (pictorially presented) target persons hold a weapon or a nonweapon object in their hands. In the former case, participants have “to shoot” (i.e., to press one key), and in the latter case they have “not to shoot” (i.e., to press another key). Target persons are of either black or white ethnicity. It is typically found that RTs and/or errors are decreased for black/weapon and white/nonweapon trials. On an abstract level, we are faced by a response priming design: A task-Â�irrelevant feature (i.e., target ethnicity) varies orthogonal to the manipula-

tion of a task-Â�relevant feature (i.e., whether there is a weapon or not). What is different, of course, is that the mapping of the task-Â�irrelevant feature to the categorization task is not as straightforward as, for example, in a basic affective priming experiment. (Only an extreme racist would cynically reconstruct the ethnicity variation in the experiment as a variation of “someone to shoot at” vs. “someone not to shoot at.”) Thus, the paradigm invites one to think about ethnicity biasing early perceptual processes of the target object, such that a nonweapon object held by a black person is sometimes misperceived as a weapon. In recent studies, however, this was shown to be rather unlikely (Klauer & Voss, 2008; Payne, Shimizu, & Jacoby, 2005). According to sophisticated analysis of data, response-bias explanations (and, therefore, the analogy to response priming) are more in line with the evidence.

PROCEDURAL DETAILS For the novice, programming a priming experiment asks for many procedural decisions for which no clear-cut rules can be found in the literature. For example, how many stimuli are needed? How often should each prime and target category be repeated during the task? How should timing routines be defined? Which control conditions are needed to interpret effects? How should instructions be formulated? We now provide some rules of thumb. We emphasize, however, that their application to specific research questions might depend on a variety of factors that we cannot discuss in detail in this chapter.

Target Stimuli in Semantic Priming As mentioned, words are typically not presented repeatedly in basic semantic priming research. For applications in social cognition research, often this goal cannot be fulfilled because the set of materials for a given research hypothesis is typically quite small. However, any RT paradigm needs many trials (see later discussion). Thus, you have to find a balance between stimulus number (which ideally should be large), number of repetitions (which should be low), and overall number of trials (which should be large). Finally, finding the hypothesized, confound-free, and replicable semantic priming effect will justify your choices. For a lexical decision task, nonwords should closely resemble words (e.g., generate nonwords from words by replacing one or two letters): if nonwords are too easily recognizable as nonwords



6. Sequential Priming and Related Tasks

(e.g., letter strings like dfxsvg), participants might use a quick “Is it a random string or not?” strategy and will thus not process the target stimuli beyond a perceptual level. In this case, semantic priming effects become highly unlikely. Finally, you might consider degrading targets (e.g., bUtTer or %b%u%t%t%e%r% instead of butter) because it is has been shown that semantic priming effects are enlarged if target processing is impeded (e.g., Becker & Killion, 1977; De Houwer, Hermans, & Spruyt, 2001; Williams, 1996). To prevent backward-Â�checking strategies (see prior discussion of strategic processes), you can think of using nonword trials that mimic the structure of the word trials (i.e., half of the nonword trials are “related” trials insofar as the prime and the word that was the basis of the nonword are related; see, e.g., Wentura & Frings, 2005).

Target Stimuli in Response Priming In response priming experiments, it is important, of course, to choose target stimuli that are easily categorized with regard to the task at hand. For the evaluation task, use positive and negative words or pictures that can be unambiguously categorized as positive or negative according to some norm or pilot data. Note, however, that Chan, Ybarra, and Schwarz (2006) found reversed affective priming for high-Â�frequency targets (while observing the usual positive priming effect for low-Â�frequency targets). Beyond its value for understanding affective priming effects, these results should be taken as a hint that target characteristics might play a larger role than usually assumed. On which dimensions should positive and negative target stimuli be matched? There is more than one answer to this question. The most conservative strategy is to prevent any confound. We would cling to that strict recommendation if we were writing about experiments exploring differences between positive and negative stimuli (e.g., whether one or the other are associated with longer RTs in the color-Â�naming task) because any confound might be critical for interpreting differences in terms of valence (see, e.g., Delplanque, N’Diaye, Scherer, & Grandjean, 2007; Larsen, Mercer, & Balota, 2006, for possible confounds). However, in affective priming tasks, we test for the difference of RTs (and/or errors) to congruent and incongruent prime–Â�target pairs, with the positive and negative stimuli entering both into the congruent and incongruent aggregates. Thus, confounds cannot distort the effect in a straightforward way. At this point, we integrate an important issue that is often misunderstood: It is not legitimate

105

to split the 2 (prime valence) × 2 (target valence) table into single effects, for example, to test whether affective priming occurs for positive but not for negative prime valence (or vice versa). Such single-Â�difference analyses can be distorted by main effects of prime and/or target valence. To illustrate this, imagine a perfectly symmetrical crossover interaction pattern (that is the result of priming processes) in the 2 (target valence) × 2 (prime valence) table of means; for example, the congruent cells are associated with 580 msec and the incongruent ones with 600 msec. Now add main effects of prime and target valence to the table; that is, add 20 msec for all responses to targets following negative primes and 20 msec for all responses to negative targets. It appears as though priming works for one valence but not the other. Affective priming is the difference of mean RT to incongruent minus congruent priming. If you stick to this principle, you need not worry too much about possible stimulus confounds. Nevertheless, if confounds are very blatant, they might be used by participants to facilitate responding (e.g., if positive targets are clearly shorter than negative ones), lowering the chances of finding reliable priming effects. It would be even worse if prime valence and target valence are both confounded in the same blatant way. In this case, the interpretation of congruency effects in terms of valence might no longer be justified because congruency versus incongruency in the confounded feature might be responsible for the effect. Thus, try your best to prevent blatant confounds: For example, positive and negative words should be matched according to length and other perceptible characteristics. If you use the International Affective Picture System (IAPS) pictures (Lang, Bradley, & Cuthbert, 2008), positive and negative pictures should not be distinguishable on the basis of perceptible characteristics, and the positive–Â�negative distinction should not be replaceable by a more narrow pair of categories (e.g., weapon vs. erotic). Because arousal is the second dominant variable in the IAPS, it might be wise to match positive and negative picture according to arousal. Finally, if there are confounds that cannot be avoided, you might want to use regression analytical techniques (see later discussion) to demonstrate their (un-) importance.

Prime Stimuli For social cognition research, you will typically select several stimuli as primes that represent the concept or category in question. Asked to give a number, we would advise using four to 10 stimuli

106

II. METHODS AND PROCEDURES

for each prime category. However, there might be research questions that suggest a single stimulus as the cardinal one for the concept. For example, if you want to explore ingroup favoritism in the minimal group paradigm, the names of the ingroup and the outgoup might simply be the best (and only) available stimuli (Otten & Wentura, 1999). In such cases (see Frings & Wentura, 2003; Wentura et al., 2005, for other examples), you might want to mask primes in order to hide their massive repetition (see the research by Wittenbrink and colleagues, 1997, 2001, for a comparable example in the semantic priming domain).

Number of Trials Priming experiments are typically composed of a triple-digit number of trials.7 The best planning strategy is to orient toward the smallest unit in terms of either (1) set size or (2) number of conditions of the design. An example of the former is an affective priming experiment using set sizes of 10 items (i.e., 10 positive and 10 negative targets as well as 10 positive and 10 negative primes). Accordingly, a block of trials would contain 40 trials with each prime and each target presented twice: once in a congruent and once in an incongruent pairing. In the second step, decide how many blocks are manageable in the context of your experiment (typically resulting in an aggregate of 100–300 trials). An example of the latter case is a study by Frings, Wentura, and Holtz (2007), whose design consisted of no fewer than 36 withinÂ�participants conditions. In such a case, align the trial number with the minimum number of trials needed for analyses. In our study, we did not want to fall below 10 trials per condition. Thus, the experiment eventually comprised 360 trials. Would it have been acceptable to accept fewer than 10 trials per condition? Yes and no. Note that in the affective priming experiment outlined previously the number of congruent and incongruent trials would be between 60 and 140 (depending on whether the total number of trials would have been 120 or 280), which are typical values for RT-based experiments in cognitive psychology. Compared with this standard, 10 trials per condition is rather low. However, we have done semantic priming experiments (to confess, with some headaches) in which we have gone below this number (Wentura & Brandtstädter, 2003; Wentura & Greve, 2004). If you do so, be aware of the risk of finding data too noisy for significant priming effects and also the risk of triggering skepticism in reviewers. For masked priming experiments, the number of trials per cell of your design should be even

higher compared with supralimal studies. Masked priming might depend on a high number of prime repetitions before primes actually display the wanted activations. For example, in the aforementioned minimal group study (Otten & Wentura, 1999), reliable priming effects occurred only in the second of two blocks of 100 trials each. Moreover, these blocks were preceded by an additional practice block of 100 trials (as a result of using the response window technique; see prior and later discussions)! Each priming experiment should include some practice trials (e.g., 10) in the instruction phase— their number depending on the complexity of the task—and some warm-up trials (e.g., five to 10) that precede the main trials unbeknownst to participants. Finally, filler trials can be interspersed if you need to hide the structure of the trials of interest.

Neutral Priming Condition In social cognition research, you might occasionally be interested in priming effects related to a single prime category. For example, in evaluative priming experiments on intergroup bias, it might be of theoretical importance to separate spontaneous ingroup evaluations from outgroup evaluations. You might be tempted to compute separate ingroup and outgroup priming effects by subtracting responses to positive targets from those to negative targets and interpret positive scores as relative positive attitudes and negative scores as negative attitudes. As mentioned, such difference scores can be highly biased by various target effects (e.g., participants typically reacting slower to negative targets; participants reacting faster with the dominant hand). To compute priming effects, you always need some sort of baseline that helps you to separate main target effects from the priming effects of interest. For this reason, it might be wise to include a neutral prime condition into your experiment. Another reason for the inclusion of neutral primes is a cost–Â�benefit analysis, that is, an analysis to separate facilitation (as a result of congruent/related priming) from inhibition (as a result of incongruent/unrelated priming). Although the use of neutral primes seems to be self-Â�suggesting for this aim, things are a bit more complicated (see also Jonides & Mack, 1984). A simple consideration shows that the comparison of mean RTs for congruent and incongruent trials with the neutral baseline is ambiguous: Assume that the mean RT for congruent trials is 500 msec and the mean RT for incongruent trials amounts to 530 msec. If the mean RT for the neutral baseline is 530 msec, it



6. Sequential Priming and Related Tasks

seems as if priming was entirely due to facilitation mechanisms. Now assume that prime valence is associated with a main effect: For example, besides facilitating negative responses for most of the trials negative primes capture attention in some trials, thereby increasing RTs for positive as well as negative targets. This main effect will increase mean RTs for the congruent as well as for the incongruent conditions. To extend our example, if this increase amounts to 30 msec, we would be faced with a result suggesting that priming is entirely due to inhibition (because the mean RTs for congruent trials and incongruent trials are now 530 msec and 560 msec, respectively). What would be the most appropriate baseline condition? You might decide to use words or pictures that have been rated as neutral regarding the dimension of interest. In this case, be aware that neutral as well as ambivalent stimuli reach neutral average ratings in a pretest. Thus, if you don’t want to run the risk that your neutral primes are actually positive for some participants and negative for others (thereby highly biasing your baseline), take the variance of ratings or rating differences between groups of participants into account when choosing your stimuli. Alternatively, you may decide to use some nonsense primes, for example, nonwords, random (consonant) letter strings, scrambled pictures, or nonsense patterns for a neutral baseline. Of course, the problem of nonneutrality is not completely ruled out (e.g., nonsense might act like negative stimuli because of contrast effects).

Adding Standard Priming Materials Priming effects are well-Â�documented phenomena. However, it would be a misconception to think that priming effects are as easy to replicate as, for example, the Stroop interference effect. Although response priming effects with the evaluation task are rather robust, we occasionally find zero effects. Also there are reports in the literature on reversed effects (Chan et al., 2006; Glaser & Banaji, 1999; Wentura & Rothermund, 2003). The world of semantic priming effects is even more complex: Using clearly associated materials (e.g., bread–Â� butter), you will certainly find a reliable semantic priming effect. However, if you enter the literature on nonassociative semantic priming (e.g., whether category coordinates prime each other), finding robust effects cannot be taken for granted (for reviews, see Hutchison, 2003; Lucas, 2000). Thus, if you plan to use priming experiments to test specific hypotheses (e.g., “Is there evidence for automatic ageism?”), especially if you do so for the first time, it is important to verify that the spe-

107

cific procedure and instructions you use actually do allow for the occurrence of the priming effects in question. For example, in using the evaluation task for assessing automatically activated valence, we typically use additional control primes of clear positive and negative valence (according to norm data). In semantic priming experiments, prime–Â� target pairs whose semantic relatedness has been demonstrated in previous experiments might be included.

Timing Routines Ideally we would provide standards on presentation durations and timing intervals. Unfortunately, there is little research systematically exploring these procedural details, except for SOA. Therefore, any value given here is not carved in stone but simply reflects what we see as typical. We do not rule out that a systematic exploration of some of these values might lead to discoveries of theoretical interest. A trial typically starts with a fixation stimulus (e.g., a “+” presented for 500 msec). Its only function is to orient the participant’s attention toward the location of prime and target presentation and inform about the beginning of a new trial. Subsequently, the prime is presented typically with a duration varying between 100 and 300 msec (of course, we focus on supraliminal priming at the moment). Primes are replaced by either a blank screen or the target directly. The most important, and best explored, timing parameter in priming research is the SOA between prime and target. We have already reported findings related to SOA and strategy effects. At this point, we want to give some SOA recommendations for finding robust priming effects. For response priming, an often-used SOA is 300 msec (with a prime presentation of 200 msec), which is the value introduced in the original experiments by Fazio and colleagues (1986). Using this SOA, you will typically obtain effects. A recommendation, however, is to use SOAs less than 300 msec (e.g., 150–200 msec; see also Klauer & Musch, 2003): Klauer and colleagues (1997) as well as Hermans, De Houwer, and Eelen (2001) tested for several SOAs and found reliable affective priming at SOA = 150 msec but not at SOA = 300 msec (or above). In semantic priming studies, it is common to use SOAs of 250 msec or less because of the automaticity issue (see prior discussion). A typical, and unfortunately misled, assumption is that short SOAs (less than 300 msec) guarantee automatic processing (e.g., that participants do not strategically manipulate priming effects). However, if

108

II. METHODS AND PROCEDURES

participants take their time to see and profoundly process the target and give a response (e.g., after 2,000 msec), you cannot assume that the response would be free of strategic influences. A further means to reduce strategic influences might be the implementation of a response deadline or window (see, e.g., McKoon & Ratcliff, 1986; see also Degner, 2009). Thus, whereas targets are typically presented until the participant presses a response key, response deadline and response window techniques shorten target presentation (e.g., to 500 msec) and oblige participants to respond within a restricted time frame. A final word on timing routines: Response window timing is typically adaptive. That is, the center of the window adapts to the current performance of a participant to ensure a rather large error rate. Procedural details are reported in Draine and Greenwald (1998). Studies vary in whether direct accuracy feedback is given or not. In response window experiments or any experiment with speed instructions, you typically do not give instantaneous error feedback. For an accuracy-�stressing experiment, that is, when your main dependent variables are reactions latencies, you might consider direct error feedback and forced corrections (i.e., the next trial starts only after the correct response has been given). This not only reminds participants of accuracy instructions but has the additional advantage that participants do not start pressing the response keys randomly hoping the experiment would be over soon. As an intertrial interval, values between 1,000 and 2,000 msec are typically implemented.

Masking To prevent participants from becoming (subjectively or objectively) aware of the prime event, use brief prime presentations and masks. Duration of prime presentation is usually between one and three refresh cycles of the screen (see later discussion), that is, for example, between 14 and 43 msec (12–35 msec) for a 70-Hz (85-Hz) screen. Typically, sandwich masking procedures are implemented, in which the briefly presented prime is embedded into a forward and backward mask. Word stimuli are typically easy to mask; a random sequence of letters (consonants) will do. The advantage is that participants often get the impression that the computer would shuffle through the letters of the alphabet before presenting the target stimulus and are thus less suspicious about the priming event. It is more difficult to choose good masking stimuli for pictures, especially for faces. Be aware that there is a large amount of basic research on subliminal perception that you should rely on for finding the best

masking procedure (e.g., Breitmeyer & Ögmen, 2006). For example, to mask facial expressions, you might consider using spatially quantized face pictures (e.g., Bachmann, Luiga, & Pöder, 2005). Note that masking success is a function of the specific prime and mask stimuli as well as presentation duration. You thus should carefully pilot test whether the masking procedure you choose fulfills your expectations regarding subjective or objective unawareness of the priming event (see prior discussion).

Screen Synchronizing The shorter the durations, the more a technical aspect comes to the fore, especially if you use masked priming. To present a stimulus on a screen means (in technical terms) that the stimulus is stored into a screen buffer. With a certain frequency (typically between 70–100 Hz), the cathode ray of the traditional type of computer screen starts in the upper left corner of the screen to display the content of the buffer on the screen. If you do not synchronize the cathode ray with your “present” command, the following scenario will unfold. Assume that you have a “present-the-prime” command in your experimental software, followed by a “delay” command of 20 msec, and finally a “present-the-mask” command. Additionally assume that your screen is updated 70 times per second, that is, one cycle of the cathode ray lasts approximately 14 msec. Imagine the following: First, your “present-theprime” command alters the screen buffer just before the cathode ray arrives at the location of the prime. Thus, the prime will be almost immediately presented on the screen. The “present-the-mask” command will alter the buffer after 20 msec, that is, when the cathode ray has written the prime a second time. Thus, the prime will be presented for 28 msec. Second, your “present-the-prime” command alters the screen buffer just after the cathode ray had passed the location of the prime. Thus, the prime will appear on the screen after approximately 14 msec has elapsed. After a further 6 msec, the buffer will be changed according to the “presentthe-mask” command, such that in the next cycle of the screen the mask is presented. That means that your prime will be on the screen for a total of only 14 msec. To prevent these unpredictable variations, you have to synchronize updating of the buffer with the cathode ray refresh signal (i.e., that synchronous to the “present-the-prime” command, a refresh cycle will start). Typically, software packages for creating experimental programs include this feature. Furthermore, choose presentation times that represent a multiple of the cycle



6. Sequential Priming and Related Tasks

implied by the refresh rate (e.g., presentation times 14 msec, 28 msec, etc., for a cycle of 14 msec implied by a refresh rate of 70 Hz). It is recommended to use a “delay” after the “present-the-prime” command that is some milliseconds less that value (e.g., 10 msec instead of 14 msec, 24 msec instead of 28 msec) to be sure that in the follow-up cycle the mask will be presented. Some programming software packages for creating experimental programs already include corresponding features (e.g., by defining stimulus presentation times in terms of frames instead of milliseconds).

Randomization According to standards of experimental cognitive psychology, priming experiments should consist of fully randomized trial sequences as well as full counterbalancing of all experimental conditions. Randomization is needed if the main result of your study is a general one, that is, one that should hold for the average participant because it precludes alternative explanations of results (e.g., based on specific trial sequences). However, it might be necessary to constrain randomization. For example, direct repetitions of stimuli should be avoided (it would lead to artificially large facilitation effects) and each prime category, target category, or prime–Â� target condition should be presented equally often, without too many direct repetitions. If the most important result of your study is concerned with individual differences, it might be wise to use the same randomized sequence of trials for all participants to reduce error variance that is due to different randomized sequences. (If you conduct an intelligence test, you typically do not shuffle the items for each participant either.) You might even renounce counterbalancing. Of course, without randomization and counterbalancing, main effects cannot be straightforwardly interpreted anymore. It is as Banse (2001, p.€517) has phrased it: “One has to decide whether to optimise the design .â•›.â•›. for the analysis of treatment effects or for the analysis of individual differences, one cannot have it both ways.”

Instructions In typical priming experiments, participants are simply informed about the presentation of two stimuli, and they are asked to perform a given task on the second one. Thus, the instruction states (either implicitly or explicitly) to ignore the prime. Apart from this standard, there are some studies that explicitly instruct participants to process the prime. We have mentioned the Stroop priming

109

task in which Rothermund and Wentura (1998) found straightforward results only if the prime had to be memorized. It is interesting to note that Fazio and colleagues (1986) followed the same strategy introducing the evaluative priming paradigm. In their later studies using evaluative priming for assessing social attitudes (Fazio et al., 1995), they even used a prime-Â�related task to disguise their intention: Prime faces had to be processed for a later memory task. In fact, there is evidence from different subdomains of priming research that processing of the prime matters. In “implicit” attitudes research, Olson and Fazio (2003) found different results depending on whether primes had to be categorized or not. In basic research on semantic priming, there are results that show that semantic priming disappears if the prime is processed very superficially (see, e.g., Stolz & Besner, 1999; but see Heil, Rolke, & Pecchinenda, 2004). Thus, there is a need to explore these issues in more detail. In the instructions you should emphasize speed of responding while simultaneously conserving a rather high rate of accuracy (unless you use the response window technique; see prior discussion). Typically, you will achieve the appropriate speed–Â� accuracy trade-off by mentioning to participants that they will inevitably make some errors if they are as fast as they should be.

DETAILS OF ANALYZING PRIMING DATA Preparation of Data RT data are characterized by some peculiarities that ask for careful data preparation. This includes trimming of individual data and sample screening for outlying participants. The first is important because not all priming trials result in valid RTs. The latter is important because any priming paradigm requires that participants intentionally fulfill the task at hand (and delivers the priming result as a by-Â�product). If there are indications that participants did not fulfill instructions, it might be wise to discard their data from analyses. It is a must to screen distribution of RT data for outliers. On the left of the RT distribution (the “fast side”), you often have no or few outlying values according to distribution-based criteria. However, an RT of, for example, 90 msec cannot be valid in a lexical decision task. Thus, you have to discard extremely short RTs. Typically, a value between 150 and 300 msec is chosen as a minimum inclusion criterion. On the right of the distribution, there are typically some outlying values that reflect lapses of attention or a second thought

110

II. METHODS AND PROCEDURES

about the decision. In different articles about RT studies, slightly different solutions to this problem are offered. Often, an experimentwise criterion is defined, marking implausible long RTs for the task at hand. For example, RTs greater than 1,500 msec are certainly invalid in a task that is associated with mean RTs in the range of 500 to 700 msec. A good solution to find this criterion for a given task is to accumulate all valid RTs (i.e., RTs associated with correct responses) of all participants and to discard either all values that are “far out” or “outside” (i.e., values that are 3 or 1.5 interquartile ranges, respectively, above the third quartile of the distribution; Tukey, 1977).8 The advantage of this procedure compared with one that defines outliers with regard to standard deviation units above the mean is obvious: The extremeness of outlying values themselves do not distort the definition procedure. In case of experiments with different conditions that are a priori associated with huge differences in mean RT (e.g., words and nonwords in a lexical decision task), it might be wise to define outlier criterions on the basis of the conditionwise RT distributions. Of course, each participant performs at his or her own speed. Thus, it is consequent to define outliers for the individual distribution as well, for example, based on the criteria proposed by Tukey (see prior discussion). Finally, you might combine sample-based and individually defined criteria. What is a typical rate of outliers? Instead of giving a rule of thumb, we have simply checked our last 10 publications on priming experiments (including 23 experiments). Outlier rates were between 0.29 and 5.80%. Note, however, that Ratcliff (1993) concluded from simulation studies that it might be wise to discard up to 10% of the upper tail of the RT distribution. (Because we do not see such high rates published often, it might be wise to cite Ratcliff to convince skeptical reviewers.) Calculation of mean RTs is based on all nonoutlying RTs that are associated with a correct answer. To adjust for the skewed distribution of RTs, you can transform raw data (for suggestions, see, e.g., Fazio, 1990; Ratcliff, 1993) with the log-Â�transformation being most frequently used. Note that instead of using means of transformed, trimmed RTs, you can alternatively use the individual medians as the repeated measures (see Ratcliff, 1993, for recommendations; see Miller, 1988, for a warning given some specified conditions). Data of participants with extreme error rates and/or extreme mean RTs should be discarded (see prior discussion). Exclusion criteria are, of course, dependent on the concrete materials and task used

in a specific experiment. You are well advised to box-plot participants’ mean reaction latencies and mean error rates and exclude participants with outlying or extreme values.

Analysis of Data We briefly consider some aspects of data analysis that might be useful in analyzing priming data (and other RT data).

Difference Scores Personally, we rarely use mean RTs or error rates, preferring instead to use priming difference scores as main dependent variables of a priming experiment (i.e., mean RTs or errors for unrelated/incongruent trials minus mean RTs or errors for related/ congruent trials). Especially if the priming factor is embedded into a more complex design, this facilitates understanding of results. Although it should be clear to anyone who handles such data, we state it explicitly: Analyzing such difference scores is formally equivalent to analyzing the nonreduced plan (with, e.g., the F test for the effect of prime/ target relatedness in the nonreduced analysis being equivalent to the one-�sample t test of whether the mean difference score is significantly different from zero). What is the advantage? First, you eliminate the reporting of main effects and interactions that do not involve your priming factor. Second, thinking about differences and their significant deviance from zero reintroduces the idea of directional tests. Third, it naturally links experimental and correlational designs because the difference scores can be correlated with individual differences variables.

Individual Regressions In the world of between-�participants designs, it is well known that nominal-�scaled predictors (e.g., coding variables for experimental designs) and interval-�scaled variables as well as their interaction terms can be introduced into a single regression model. Less well known is that this applies for repeated measures designs as well (like priming designs; see Lorch & Myers, 1990). The most simple and straightforward application is to (1) calculate regression models for each individual data set (with trials as the unit of analysis) and (2) test whether the means (across participants) of the regression weights are significantly above or below zero. For example, if you want to test whether RT in an evaluation task is better predicted by valence congruency or by a (continuous) index of semantic



6. Sequential Priming and Related Tasks

relatedness of prime–Â�target pairs, you can easily do so by testing a model with valence congruency and semantic relatedness as predictors (see Wentura & Degner, in press). If the mean of the regression weights for affective congruency is significantly above zero but the mean for semantic relatedness is not, you can infer that affective congruency is the better predictor of reaction latencies. Lorch and Myers (1990) describe how to calculate these tests in a single step by using the complete N (number of participants) × M (number of trials) data set. More advanced and up to date would be the use of hierarchical linear models (HLMs; see later discussion). The basic rationale, however, remains the same.

Reliability As mentioned repeatedly, priming indices are often used as individual differences measures. This raises the issue of reliability of priming indices. If you follow classic test theory, you calculate two parallel priming scores, correlate them, and adjust according to the Spearman–Brown formula. Two parallel scores might be calculated by splitting the entire trial sequence into two halves. However, be aware that the first half and the second half of a priming experiment might be different with regard to the processes involved (see earlier discussion of number of trials). Just as well, you should be cautious to use an odd–even splitting. If there are autocorrelations (i.e., the response in trial n + 1 is not independent from trial n), the split-half correlation is biased. An alternative might be to use several random splittings of the trial sequence and report the average reliability score that results. As an alternative to the Spearman–Brown adjusted split-half correlation, you can calculate more than two independent scores, which you then consider as item scores for calculation of Cronbach’s alpha. For example, in an affective priming study with 10 positive and 10 negative targets that are repeatedly presented, you assign each negative target to one positive target and calculate priming scores for each positive–Â�negative pair (see, e.g., Payne et al., 2005, for a comparable solution). Reliability indices are typically rather low for indirect measures (but see Payne, Cheng, et al., 2005, for the AMP). This seems to be especially true for priming measures (see, e.g., Banse, 2001; Cunningham, Preacher, & Banaji, 2001; Olson & Fazio, 2003; Wentura et al., 2005, who report reliability indices). A simple consideration might help to identify some aspects of the problem. By using attitude object-Â�related primes

111

(e.g., pictures of blacks vs. whites) in an affective priming task, the variance in the priming effect is interpreted as individual difference variance in the automatically activated evaluation of that attitude object. That is, for participants scoring about zero, it is inferred that there is no difference in automatically activated valence for blacks and whites. For participants having a positive priming score, it is inferred that pictures of blacks evoke a more negative evaluation in contrast to pictures of whites. However, what is taken for granted in this logic is that the procedure itself works for everyone. In other words, primes of a priori given universal valence (e.g., some items of the IAPS set) or individually selected positive and negative primes (see Fazio et al., 1986) would yield a robust affective priming effect for everyone. This, however, is not the case: Even if you successfully replicate the basic affective priming effect (i.e., if you use primes with universal positive and negative evaluation), you will get huge variance in the priming difference scores, with some participants even having a negative value. The same is true for semantic priming effects (see Stolz, Besner, & Carr, 2005). This error variance, of course, dilutes the measure of attitude-Â�related evaluation (i.e., if you use, e.g., pictures of blacks and whites as primes) as well. If reliability of attitude-Â�related priming scores is a severe issue, two routes need to be taken in future research: First, we should improve the basic procedure in such a way that it delivers a positive priming score for each participant if one uses clear positive and negative primes (in the affective priming paradigm) or if one uses clearly semantically related prime–Â�target pairs (in the semantic priming paradigm). Variance around the mean should be low in this case. Note that in the ideal case (i.e., very low variance by using a set of nonambiguous positive and negative pictures) reliability of this variance is expected to be zero. If you have larger variance and this variance turns out to be reliable, there is the possibility of unwanted method variance (i.e., the method does not operate in the same way for each participant). The second route should be to check carefully (e.g., by simulation studies) whether RT-based measures have some intricacies that lower the applicability of classic test theory (see also, e.g., De Houwer, 2009).

Other Recommendations Because of space restrictions, we do not present detailed discussion of further points. We will, however, briefly mention them.

112

II. METHODS AND PROCEDURES

1.╇ If you have a low number of trials per cell, a counterbalanced design (in which a given participant receives a given target only in one condition of the priming factor), and you expect only small effects (not unusual in priming research), you should consider increasing the power of your experiment by subtracting the main effect of balancing lists from error variance (see Pollatsek & Well, 1995). 2.╇ Well known to psycholinguists is the longÂ�standing issue of “the-Â�language-as-a-fixed-Â�effect fallacy,” as introduced by Clark (1973). This often leads to the recommendation to add “by-item analyses” to the standard analyses. With regard to priming experiments, this is usually not necessary (see Raaijmakers, 2003; see also McNamara, 2005). 3.╇ Modern statistical packages like SPSS are very convenient and offer many attractive opportunities. For example, the menu for the repeated measures analysis allows for inclusion of betweenÂ�participants factors as well as inclusion of covariates. This might be taken as an invitation to reduce error variance to increase test power. If you are tempted to do so, please first consider Delaney and Maxwell (1981; especially p.€ 108) to prevent publication of nonsense.

Advanced Data Analytical Methods The present chapter does not allow for lengthy discussions of elaborate data analysis techniques. However, there are some interesting options that will certainly attract even more interest in the near future. Although we cannot present these methods of analysis in detail, we want to briefly mention them and draw attention to alternative ways of analyzing priming data

Diffusion Model As long as you use a binary decision task (e.g., lexical decision or evaluative decision) in your priming experiments, the application of Ratcliff’s (e.g., 1978) diffusion model is a fascinating option. The model allows for separation of different components of the response process (i.e., decision threshold, bias, information uptake, and motor response; see Voss, Rothermund, & Voss, 2004; Voss & Voss, 2007, 2008).

Multinomial Modeling Another option might be the application of multinomial modeling (Riefer & Batchelder, 1988)

to error data (e.g., Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005; Payne, 2001; Stahl & Degner, 2007). Depending on which processes you assume to determine a participants’ performance in a priming task, you can design a model. For example, in the model by Payne (2001), the controlled processing of the target is separated from the automatic processing of the prime. Conrey and colleagues (2005) refined this model by separating different controlled and automatic processes; Stahl and Degner (2007) developed a model that included a further parameter to take guessing and response tendencies into account.

Hierarchical Linear Models Basically, the structure of priming data is of hierarchical character: Trials are nested within participants. HLMs take the multilevel structure of repeated measures data into account (see, e.g., Locker, Hoffman, & Bovaird, 2007; Richter, 2006). Using them, you can model the influence of mixed predictor variables at the level of trials and participants as well as within-level and crosslevel interactions. The advantage of these tools lies (1) in the straightforward application of regression analytical thinking to repeated measures experimental data and (2) the test of moderation of a within-�participants effect by individual differences. HLM is the more advanced statistical technique compared with the older suggestions by Lorch and Myers (1990; see prior discussion).

CONCLUSIONS Priming paradigms are a fascinating tool to study hypotheses of implicit social cognition. However, priming processes are a research domain all their own with a growing body of evidence that makes clear how complex the field is. We have tried to find a balance between giving an overview that informs about this complexity and giving practical advice for conducting priming experiments. Always remember that a priming result is, first of all, the outcome of an experimental procedure that might be open to different interpretations (see also Gawronski, LeBel, & Peters, 2007). Acknowledgment The research reported in this chapter was supported by a grant from the Deutsche Forschungsgemeinschaft to Dirk Wentura (No. WE 2284/3).



6. Sequential Priming and Related Tasks

Notes 1. Note that the variation of prime and target valence in a binary categorization task (e.g., the lexical decision task) allows for a further valenceÂ�specific process (see Wentura, 2000; see also Klauer & Stern, 1992). 2. We should note that at SOA = 1,200 msec, the effect no longer linearly depended on the relatedness proportion. Without going into detail here, this does not undermine our argument in the main text (see Klauer et al., 1997, for explanations). 3. After finishing our chapter, a meta-Â�analysis of masked priming was published (Van den Bussche, Van den Noortgate, & Reynvoet, 2009) that gives a more comprehensive review on that topic. Note that the terms “unconscious” and “subliminal” are used a bit less constrained by the authors than in the present paragraph. 4. You might think of instructing participants to categorize each trial on their own with regard to whether the voice key was triggered correctly. Of course, it should be clear that this is a less well-Â�conducted experiment compared with the experimenter-Â�controlled version. Whether a manuscript based on such experiments would find acceptance is your own risk. 5. Of course, the response window procedure is not exclusively bound to the evaluation task. Its use might be considered for any response priming task. 6. To prevent a misunderstanding; in line with Gawronski, Deutsch, LeBel, and Peters (2008), we do not assume the AMP to be driven by response interferences simply because a neutral target cannot trigger a clear positive or negative response. However, the affective priming effect in the evaluation task might have in common with the AMP that usually participants do not differentiate between the two sources of affect (i.e., prime and target). If they would try to do so, effects might be different from what is usually observed (see Wentura & Rothermund, 2003, for related arguments). 7. The AMP is an exception to the rule because most studies have used 24–72 trials. 8. “Far out” (“outside”) are the values marked by a * (a °) in a SPSS box plot.

References Abrams, R. L., & Greenwald, A. G. (2000). Parts outweigh the whole (word) in unconscious analysis of meaning. Psychological Science, 11, 118–124. Bachmann, T., Luiga, L., & Pöder, E. (2005). Variations in backward masking with different masking stimuli: II. The effects of spatially quantised masks in the light of local contour interaction, interchannel inhi-

113

bition, perceptual retouch, and substitution theories. Perception, 34, 139–154. Banaji, M. R., & Hardin, C. D. (1996). Automatic stereotyping. Psychological Science, 7, 136–141. Banse, R. (2001). Affective priming with liked and disliked persons: Prime visibility determines congruency and incongruency effects. Cognition and Emotion, 15, 501–520. Bargh, J. A. (1996). Automaticity in social psychology. In E. T. Higgins & A. W. Kruglanski (Eds.), Social psychology: Handbook of basic principles (pp.€ 169– 183). New York: Guilford Press. Bargh, J. A., Chaiken, S., Raymond, P., & Hymes, C. (1996). The automatic evaluation effect: Unconditional automatic attitude activation with a pronunciation task. Journal of Experimental Social Psychology, 32, 104–128. Becker, C. A., & Killion, T. H. (1977). Interaction of visual and cognitive effects in word recognition. Journal of Experimental Psychology: Human Perception and Performance, 3, 389–401. Bessenoff, G. R., & Sherman, J. W. (2000). Automatic and controlled components of prejudice toward fat people: Evaluation versus stereotype activation. Social Cognition, 18, 329–353. Bodner, G. E., & Masson, M. E. J. (2003). Beyond spreading activation: An influence of relatedness proportion on masked semantic priming. Psychonomic Bulletin and Review, 10, 645–652. Breitmeyer, B. G., & Ögmen, H. (2006). Visual masking: Time slices through conscious and unconscious vision (2nd ed.). New York: Oxford University Press. Chan, E., Ybarra, O., & Schwarz, N. (2006). Reversing the affective congruency effect: The role of target word frequency of occurrence. Journal of Experimental Social Psychology, 42, 365–372. Clark, H. H. (1973). The language-as-fixed-Â�effect fallacy: A critique of language statistics in psychological research. Journal of Verbal Learning and Verbal Behavior, 12, 335–359. Collins, A. M., & Loftus, E. F. (1975). A spreadingÂ�activation theory of semantic processing. Psychological Review, 82, 407–428. Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K., & Groom, C. J. (2005). Separating multiple processes in implicit social cognition: The quad model of implicit task performance. Journal of Personality and Social Psychology, 89, 469–487. Correll, J., Park, B., Judd, C. M., & Wittenbrink, B. (2002). The police officer’s dilemma: Using ethnicity to disambiguate potentially threatening individuals. Journal of Personality and Social Psychology, 83, 1314–1329. Cunningham, W. A., Preacher, K. J., & Banaji, M. R. (2001). Implicit attitude measures: Consistency, stability, and convergent validity. Psychological Science, 121, 163–170. De Houwer, J. (2009). Comparing measures of attitudes at the functional and procedural level: Analysis and implications. In R. E. Petty, R. H. Fazio, & P. Briñol (Ed.), Attitudes: Insights from the new implicit measures (pp.€361–390). New York: Psychology Press. De Houwer, J., & Hermans, D. (1994). Differences in

114

II. METHODS AND PROCEDURES

the affective processing of words and pictures. Cogntion and Emotion, 8, 1–20. De Houwer, J., Hermans, D., Rothermund, K., & Wentura, D. (2002). Affective priming of semantic categorization responses. Cognition and Emotion, 16, 643–666. De Houwer, J., Hermans, D., & Spruyt, A. (2001). Affective priming of pronunciation responses: Effects of target degradation. Journal of Experimental Social Psychology, 37, 85–91. De Houwer, J., Teige-Â�Mocigemba, S., Spruyt, A., & Moors, A. (2009). Implicit measures: A normative analysis and review. Psychological Bulletin, 135, 347–368. Degner, J. (2009). On the (un)controllability of affective priming: Strategic manipulation is feasible but can possibly be prevented. Cognition and Emotion, 23, 327–354. Degner, J., & Wentura, D. (2009). Not everybody likes the thin and despises the fat: One’s weight matters in the automatic activation of weight-Â�related social evaluation. Social Cognition, 27, 202–220. Degner, J., Wentura, D., Gniewosz, B., & Noack, P. (2007). Hostility-Â�related prejudice against Turks in adolescents: Masked affective priming allows for a differentiation of automatic prejudice. Basic and Applied Social Psychology, 29, 245–256. Dehaene, S., Naccache, L., Le Clec, H. G., Koechlin, E., Mueller, M., Dehaene-Â�Lambertz, G., et al. (1998). Imaging unconscious semantic priming. Nature, 395, 597–600. Delaney, H. D., & Maxwell, S. E. (1981). On using analysis of covariance in repeated measures design. Multivariate Behavioral Research, 16, 105–123. Delplanque, S., N’Diaye, K., Scherer, K., & Grandjean, D. (2007). Spatial frequencies or emotional effects?: A systematic measure of spatial frequencies for IAPS pictures by a discrete wavelet analysis. Journal of Neuroscience Methods, 165, 144–150. Deutsch, R., & Gawronski, B. (2009). When the method makes a difference: Antagonistic effects on “automatic evaluations” as a function of task characteristics of the measure. Journal of Experimental Social Psychology, 45, 101–114. Draine, S. C., & Greenwald, A. G. (1998). Replicable unconscious semantic priming. Journal of Experimental Psychology: General, 127, 286–303. Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception and Psychophysics, 16, 143–149. Fazio, R. H. (1990). A practical guide to the use of response latency in social psychological research. In C. Hendrick & M. S. Clark (Eds.), Review of personality and social psychology: Vol. 11. Research methods in personality and social psychology (pp.€74–97). Newbury Park, CA: Sage. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of

attitudes. Journal of Personality and Social Psychology, 50, 229–238. Frings, C., & Wentura, D. (2003). Who is watching “Big Brother”?: TV consumption predicted by masked affective priming. European Journal of Social Psychology, 33, 779–791. Frings, C., Wentura, D., & Holtz, M. (2007). Dysphorics cannot ignore unpleasant information. Cognition and Emotion, 21, 1525–1534. Gawronski, B., Deutsch, R., LeBel, E. P., & Peters, K. R. (2008). Response interference as a mechanism underlying implicit measures: Some traps and gaps in the assessment of mental associations with experimental paradigms. European Journal of Psychological Assessment, 24, 218–225. Gawronski, B., LeBel, E. P., & Peters, K. R. (2007). What do implicit measures tell us?: Scrutinizing the validity of three common assumptions. Perspectives on Psychological Science, 2, 181–193. Glaser, J., & Banaji, M. R. (1999). When fair is foul and foul is fair: Reverse priming in automatic evaluation. Journal of Personality and Social Psychology, 77, 669–687. Heil, M., Rolke, B., & Pecchinenda, A. (2004). Automatic semantic activation is no myth: Semantic context effects on the N400 in the letter-Â�search task in the absence of response time effects. Psychological Science, 15, 852–857. Hermans, D., De Houwer, J., & Eelen, P. (2001). A time course analysis of the affective priming effect. Cognition and Emotion, 15, 143–165. Hutchison, K. A. (2003). Is semantic priming due to association strength or feature overlap?: A microanalytic review. Psychonomic Bulletin and Review, 10, 785–813. Jonides, J., & Mack, R. (1984). On the cost and benefit of cost and benefit. Psychological Bulletin, 96, 29–44. Kawakami, K., Dion, K. L., & Dovidio, J. F. (1999). Implicit stereotyping and prejudice and the primed Stroop task. Swiss Journal of Psychology, 58, 241–250. Kawakami, K., Dovidio, J. F., Moll, J., Hermsen, S., & Russin, A. (2000). Just say no (to stereotyping): Effects of training in the negation of stereotypic associations on stereotype activation. Journal of Personality and Social Psychology, 78, 871–888. Klauer, K. C., Eder, A. B., Greenwald, A. G., & Abrams, R. L. (2007). Priming of semantic classifications by novel subliminal prime words. Consciousness and Cognition, 16, 63–83. Klauer, K. C., Greenwald, A. G., & Draine, S. C. (1998). Correcting for measurement error in detecting unconscious cognition: Comment on Draine and Greenwald (1998). Journal of Experimental Psychology: General, 127, 318–319. Klauer, K. C., & Musch, J. (2003). Affective priming: Findings and theories. In J. Musch & K. C. Klauer (Eds.), The psychology of evaluation: Affective processes in cognition and emotion (pp.€7–49). Mahwah, NJ: Erlbaum. Klauer, K. C., Rossnagel, C., & Musch, J. (1997). List context effects in evaluative priming. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 246–255. Klauer, K. C., & Stern, E. (1992). How attitudes guide



6. Sequential Priming and Related Tasks

memory-based judgments: A two-Â�process model. Journal of Experimental Social Psychology, 28, 186–206. Klauer, K. C., & Teige-Â�Mocigemba, S. (2007). Controllability and resource dependence in automatic evaluation. Journal of Experimental Social Psychology, 43, 648–655. Klauer, K. C., & Voss, A. (2008). Effects of race on responses and response latencies in the weapon identification task: A test of six models. Personality and Social Psychology Bulletin, 34, 1124–1140. Klinger, M. R., Burton, P. C., & Pitts, G. S. (2000). Mechanisms of unconscious priming: I. Response competition, not spreading activation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 441–455. Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (2008). International Affective Picture System (IAPS): Affective ratings of pictures and instruction manual (Tech. Rep. A-8). Gainesville: University of Florida. Larsen, R. J., Mercer, K. A., & Balota, D. A. (2006). Lexical characteristics of words used in emotional Stroop experiments. Emotion, 6, 62–72. Locker, L., Jr., Hoffman, L., & Bovaird, J. A. (2007). On the use of multilevel modeling as an alternative to items analysis in psycholinguistic research. Behavior Research Methods, 39, 723–730. Logan, G. D. (1980). Attention and automaticity in Stroop and priming tasks: Theory and data. Cognitive Psychology, 12, 523–553. Lorch, R. F., & Myers, J. L. (1990). Regression analyses of repeated measures data in cognitive research. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 149–157. Lucas, M. (2000). Semantic priming without association: A meta-Â�analytic review. Psychonomic Bulletin and Review, 7, 618–630. Masson, M. E. J. (1995). A distributed memory model of semantic priming. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21, 3–23. McKoon, G., & Ratcliff, R. (1986). Inferences about predictable events. Journal of Experimental Psychology: Learning, Memory, and Cognition, 12, 82–91. McNamara, T. P. (2005). Semantic priming: Perspectives from memory and word recognition. New York: Psychology Press. McNamara, T. P., & Holbrook, J. B. (2003). Semantic memory and priming. In A. F. Healy & R. W. Proctor (Eds.), Experimental psychology (pp.€ 447–474). New York: Wiley. Meyer, D. E., & Schvaneveldt, R. W. (1971). Facilitation in recognizing pairs of words: Evidence of a dependence between retrieval operations. Journal of Experimental Psychology, 90, 227–234. Miller, J. (1988). A warning about median reaction time. Journal of Experimental Psychology: Human Perception and Performance, 14, 539–543. Moors, A., & De Houwer, J. (2006). Automaticity: A theoretical and conceptual analysis. Psychological Bulletin, 132, 297–326. Murphy, S. T., & Zajonc, R. B. (1993). Affect, cognition, and awareness: Affective priming with optimal and suboptimal stimulus exposures. Journal of Personality and Social Psychology, 64, 723–739. Musch, J. (2000). Affektives Priming: Kongruenzeffekte

115

bei der evaluativen Bewertung [Affective priming: Congruency effects in evaluative judgments]. Unpublished doctoral dissertation, University of Bonn, Germany. Neely, J. H. (1977). Semantic priming and retrieval from lexical memory: Roles of inhibitionless spreading activation and limited-Â�capacity attention. Journal of Experimental Psychology: General, 106, 226–254. Neely, J. H. (1991). Semantic priming effects in visual word recognition: A selective review of current findings and theories. In D. Besner & G. W. Humphreys (Eds.), Basic processes in reading: Visual word recognition (pp.€264–336). Hillsdale, NJ: Erlbaum. Neely, J. H., Keefe, D. E., & Ross, K. L. (1989). Semantic priming in the lexical decision task: Roles of prospective prime-Â�generated expectancies and retrospective semantic matching. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 1003–1019. Olson, M. A., & Fazio, R. H. (2003). Relations between implicit measures of prejudice: What are we measuring? Psychological Science, 14, 636–639. Otten, S., & Wentura, D. (1999). About the impact of automaticity in the minimal group paradigm: Evidence from affective priming tasks. European Journal of Social Psychology, 29, 1049–1071. Payne, B. K. (2001). Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon. Journal of Personality and Social Psychology, 81, 181–192. Payne, B. K., Cheng, C. M., Govorun, O., & Stewart, B. D. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. Journal of Personality and Social Psychology, 89, 277–293. Payne, B. K., Shimizu, Y., & Jacoby, L. L. (2005). Mental control and visual illusions: Toward explaining raceÂ�biased weapon misidentifications. Journal of Experimental Social Psychology, 41, 36–47. Perea, M., & Gotor, A. (1997). Associative and semantic priming effects occur at very short stimulus-onset asynchronies in lexical decision and naming. Cognition, 62, 223–240. Perea, M., & Rosa, E. (2002). The effects of associative and semantic priming in the lexical decision task. Psychological Research, 66, 180–194. Pollatsek, A., & Well, A. D. (1995). On the use of counterbalanced designs in cognitive research: A suggestion for a better and more powerful analysis. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21, 785–794. Raaijmakers, J. G. W. (2003). A further look at the “language-as-fixed-Â�effect fallacy.” Canadian Journal of Experimental Psychology, 57, 141–151. Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59–108. Ratcliff, R. (1993). Methods for dealing with reaction time outliers. Psychological Bulletin, 114, 510–532. Ratcliff, R., & McKoon, G. (1978). Priming in item recognition: Evidence for the propositional structure of sentences. Journal of Verbal Learning and Verbal Behavior, 17, 403–417. Ratcliff, R., & McKoon, G. (1988). A retrieval theory of priming in memory. Psychological Review, 95, 385–408. Richter, T. (2006). What is wrong with ANOVA and

116

II. METHODS AND PROCEDURES

multiple regression?: Analyzing sentence reading times with hierarchical linear models. Discourse Processes, 41, 221–250. Riefer, D. M., & Batchelder, W. H. (1988). Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318–339. Rothermund, K., & Wentura, D. (1998). Ein fairer Test für die Aktivationsausbreitungshypothese: Affektives Priming in der Stroop-Â�Aufgabe [An unbiased test of a spreading activation account of affective priming: Analysis of affective congruency effects in the Stroop task]. Zeitschrift für Experimentelle Psychologie, 45, 120–135. Schmidt, T., & Vorberg, D. (2006). Criteria for unconscious cognition: Three types of dissociation. Perception and Psychophysics, 68, 489–504. Shelton, J. R., & Martin, R. C. (1992). How semantic is automatic semantic priming? Journal of Experimental Psychology: Learning, Memory, and Cognition, 18, 1191–1210. Spruyt, A., Hermans, D., De Houwer, J., & Eelen, P. (2002). On the nature of the affective priming effect: Affective priming of naming responses. Social Cognition, 20, 227–256. Spruyt, A., Hermans, D., De Houwer, J., Vandekerckhove, J., & Eelen, P. (2007). On the predictive validity of indirect attitude measures: Prediction of consumer choice behavior on the basis of affective priming in the picture-Â�picture naming task. Journal of Experimental Social Psychology, 43, 599–610. Spruyt, A., Hermans, D., De Houwer, J., Vandromme, H., & Eelen, P. (2007). On the nature of the affective priming effect: Effects of stimulus onset asynchrony and congruency proportion in naming and evaluative categorization. Memory and Cognition, 35, 95–106. Stahl, C., & Degner, J. (2007). Assessing automatic activation of valence: A multinomial model of EAST performance. Experimental Psychology, 54, 99–112. Stolz, J. A., & Besner, D. (1999). On the myth of automatic semantic activation in reading. Current Directions in Psychological Science, 8, 61–65. Stolz, J. A., Besner, D., & Carr, T. H. (2005). Implications of measures of reliability for theories of priming: Activity in semantic memory is inherently noisy and uncoordinated. Visual Cognition, 12, 284–336. Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662. Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Â�Wesley. Van den Bussche, E., Van den Noortgate, W., & Reynvoet, B. (2009). Mechanisms of masked priming: A meta-Â�analysis. Psychological Bulletin, 135, 452–477. Voss, A., Rothermund, K., & Voss, J. (2004). Interpreting the parameters of the diffusion model: An empirical validation. Memory and Cognition, 32, 1206–1220. Voss, A., & Voss, J. (2007). Fast-dm: A free program for efficient diffusion model analysis. Behavior Research Methods, 39, 767–775.

Voss, A., & Voss, J. (2008). A fast numerical algorithm for the estimation of diffusion model parameters. Journal of Mathematical Psychology, 52, 1–9. Warren, R. E. (1972). Stimulus encoding and memory. Journal of Experimental Psychology, 94, 90–100. Warren, R. E. (1974). Association, directionality, and stimulus encoding. Journal of Experimental Psychology, 102, 151–158. Wentura, D. (2000). Dissociative affective and associative priming effects in the lexical decision task: Yes vs. no responses to word targets reveal evaluative judgment tendencies. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 456–469. Wentura, D., & Brandtstädter, J. (2003). Age stereotypes in younger and older women: Analyses of accommodative shifts with a sentence-Â�priming task. Experimental Psychology, 50, 16–26. Wentura, D., & Degner, J. (in press). Automatic evaluation isn’t that crude! Moderation of masked affective priming by type of valence. Cognition and Emotion. Wentura, D., & Frings, C. (2005). Repeated masked category primes interfere with related exemplars: New evidence for negative semantic priming. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 108–120. Wentura, D., & Frings, C. (2008). Response-bound primes diminish affective priming in the naming task. Cognition and Emotion, 22, 374–384. Wentura, D., & Greve, W. (2004). Who wants to be .â•›.â•›. erudite? Everyone!: Evidence for automatic adaptation of trait definitions. Social Cognition, 22, 30–53. Wentura, D., & Greve, W. (2005). Assessing the structure of self-Â�concept: Evidence for self-Â�defensive processes by using a sentence priming task. Self and Identity, 4, 193–211. Wentura, D., Kulfanek, M., & Greve, W. (2005). Masked affective priming by name letters: Evidence for a correspondence of explicit and implicit self-Â�esteem. Journal of Experimental Social Psychology, 41, 654–663. Wentura, D., & Rothermund, K. (2003). The “meddling-in” of affective information: A general model of automatic evaluation effects. In J. Musch & K. C. Klauer (Eds.), The psychology of evaluation: Affective processes in cognition and emotion (pp.€51–86). Mahwah, NJ: Erlbaum. Williams, J. N. (1996). Is automatic priming semantic? European Journal of Cognitive Psychology, 8, 113–161. Wittenbrink, B. (2007). Measuring attitudes through priming. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp.€17–58). New York: Guilford Press. Wittenbrink, B., Judd, C. M., & Park, B. (1997). Evidence for racial prejudice at the implicit level and its relationship with questionnaire measures. Journal of Personality and Social Psychology, 72, 262–274. Wittenbrink, B., Judd, C. M., & Park, B. (2001). Evaluative versus conceptual judgments in automatic stereotyping and prejudice. Journal of Experimental Social Psychology, 37, 244–252.

Chapter 7

A Practical Guide to Implicit Association Tests and Related Tasks Sarah Teige-�Mocigemba, Karl Christoph Klauer, and€Jeffrey€W.€Sherman

T

ON A NEW APPROACH TO€AN€OLD PROBLEM

he story of the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) is one of an incredible boom. Eleven years after its first publication, more than 450 articles have been published that either applied the method to the assessment of various “implicit” constructs (for a definition of the term implicit and its different uses, see De Houwer, 2006; De Houwer & Moors, 2007; De Houwer, Teige-Â�Mocigemba, Spruyt, & Moors, 2009) or investigated the underlying processes and possible confounds of IAT effects. In the present chapter, we trace this story of the IAT by reviewing research on the IAT and related tasks. In particular, we initially identify factors that contributed to the explosion of IAT research. We then introduce the IAT methodology, review findings of its psychometric properties, and present process models that have been proposed to account for IAT effects. In the course of discussing research on contaminations of IAT effects, we finally introduce IAT-related tasks, most of which have been developed as possible solutions to IAT confounds. Other possible remedies for the respective confounds are also presented.

Since researchers aim at assessing core psychological processes, they are faced with two key problems of direct measures (e.g., self-Â�reports), namely introspective limits (e.g., Nisbett & Wilson, 1977) and susceptibility to self-Â�presentation or socially desirable responding (e.g., Paulhus, 1984). The idea that there might be more about ourselves than we can tell or want to tell promoted the development of several indirect measures. Such measures were developed in the hope of obtaining diagnostic inferences about a person’s dispositions without having to ask the person directly. This hope, however, was soon dampened as findings of unacceptable reliability and validity questioned the usefulness of early indirect measures such as projective tests (e.g., Lilienfeld, Wood, & Garb, 2000). With technological progress making it possible to present stimuli and record response times with highly accurate computer-based methods, a new class of indirect measures emerged, namely response time measures (also termed “implicit” 117

118

II. METHODS AND PROCEDURES

measures).1 Assuming that response time patterns may reflect the associative processes of interest appropriately (see Strack & Deutsch, 2004), such measures are expected to offer straightforward access to cognitive structures or processes. Researchers applied experimental paradigms of cognitive psychology such as sequential priming (Neely, 1977) or response interference tasks (Kornblum, Hasbroucq, & Osman, 1990) to the assessment of attitudes, stereotypes, self-Â�esteem, and personality traits (for reviews, see Fazio & Olson, 2003; Schnabel, Asendorpf, & Greenwald, 2008; Wittenbrink & Schwarz, 2007; for similarities of and differences between response time measures, see De Houwer, 2001, 2003b, 2008). For the new computer-based measures, the known problems of early indirect measures initially seemed to recur: The new measures proved to be useful tools to examine differences at the group level, but scarcely any of them met the test-Â�theoretical criteria required for the assessment of differences at the individual level. Thus, like for the early indirect measures, most implicit response time measures suffered from low to, at best, moderate reliability. In 1995, Greenwald and Banaji argued that because such unreliable measures fail to detect interindividual differences, their application to the assessment of implicit constructs at the individual level is highly problematic. Three years later, the IAT was introduced as the first implicit response time measure that proved to be reliable, at least in terms of internal consistency (see later discussion). Thus, the great demand for reliable indirect measures helped to make the IAT widely accepted. The IAT’s easy applicability and effective promotion might also have contributed

to its popularity and widespread use in diverse subdisciplines of psychological research. In the following section, we give more detailed information about the general procedure, implementation details, and different scoring procedures of the IAT. We then turn to the IAT’s psychometric properties.

The IAT Procedure The IAT is thought to assess the strength of associations between target categories (e.g., black persons vs. white persons) and attribute categories (e.g., negative vs. positive), both arranged on bipolar dimensions, by comparing the response latencies for two differently combined categorization tasks. Participants are instructed to categorize stimuli that represent the four categories (e.g., names typical for blacks vs. whites and negative vs. positive words) with the help of two response keys, each assigned to two of the four categories. The IAT’s basic assumption is that if two concepts are highly associated, categorization will be easier when the two associated categories share the same response (in the so-Â�called compatible block; De Houwer, 2003b) than when they require different responses (in the so-Â�called incompatible block; De Houwer, 2003b). Table 7.1 presents a typical task sequence of the IAT (here, a racial attitude IAT) consisting of seven blocks, some of which are practice blocks to acquaint participants with the stimulus material and categorization rules. In a racial attitude IAT,

TABLE 7.1.╇ Example of a Racial Attitude Implicit Association Test (IAT): Task€Sequence Response key assignment Block

N trials

Task

Left key

Right key

1 2 3 4 5 6 7

20 20 20 40 20 or 40 20 40

Target discrimination Attribute discrimination Initial combined task Initial combined task Reversed target discrimination Reversed combined task Reversed combined task

Black Negative Black, negative Black, negative White White, negative White, negative

White Positive White, positive White, positive Black Black, positive Black, positive

Note. Nosek, Greenwald, and Banaji (2005) recommend increasing the number of trials in the fifth block as an effective means to reduce compatibility-order effects (see Confounding Factors of the IAT Effect section). Therefore, some IAT procedures present 40 instead of 20 trials in this block, in which participants practice the reverse target discrimination task.



7. IAT and Related Tasks

for instance, participants are trained to press a left key for “black” stimuli and a right key for “white” stimuli in the first block of 20 trials (target discrimination). In the second block of 20 trials, they are trained to press the same left key for “negative” stimuli and the same right key for “positive” stimuli (attribute discrimination). The diagnostically relevant third block (20 trials) and fourth block (40 trials) combine the attribute and target discrimination. Participants now are to respond left to negative and black stimuli and right to positive and white stimuli. In the fifth block (20 or 40 trials; see later discussion), target discrimination is reversed: Participants are trained to press the left key for white stimuli and the right key for black stimuli. The diagnostically relevant sixth block (20 trials) and seventh block (40 trials) again combine the attribute and the previously reversed target discrimination. Participants now are to respond left to negative and white stimuli, and right to positive and black stimuli. The difference in performance between the initial combined blocks (third and fourth blocks) and the reversed combined blocks (sixth and seventh blocks) is called the IAT effect. Subdivision of the initial combined and reversed combined categorization tasks into two blocks of different lengths (20 vs. 40 trials), respectively, has mainly historical reasons: In contrast to current scoring procedures (see later discussion) that include data from both the shorter combined blocks (third and sixth blocks) and the longer combined blocks (fourth and seventh blocks) in the algorithms (Greenwald, Nosek, & Banaji, 2003), early scoring procedures denoted the third and the sixth blocks as practice blocks and excluded them from the analyses (Greenwald et al., 1998). The IAT effect is interpreted in its size and direction as revealing the relative association strength between the target and attribute categories. Accordingly, individuals with implicit prejudices against blacks are expected to respond faster and more accurately when black stimuli and negative attributes are assigned to the same response key (and white stimuli and positive attributes to the other key) compared with the reverse configuration (black and positive are assigned to one key, white and negative to the other key). It is important to note that IAT effects always have to be interpreted in a relative manner (for more details, see Relative Measure section): A racial attitude IAT effect, for instance, does not permit any conclusions about an individual’s evaluation of blacks but provides only information about an individual’s preference for blacks over whites (or whites over blacks).

119

Implementation Details IATs as well as other response time–based measures can be easily implemented using software packages such as Inquisit, E-Prime, Direct-RT, and SuperLab to name just a few (see Stahl, 2006, for a comparison of these four software packages). Sample programs can be downloaded from several websites (e.g., faculty.washington.edu/agg). Different procedural details have been used in IAT implementations. In most instances, procedural variations did not considerably affect IAT effects or their correlations (see Nosek, Greenwald, & Banaji, 2007). Those procedural variations that have been shown to have an impact on IAT effects are discussed later (see Confounding Factors of IAT Effects section). Implementation details of current standard IAT procedures comprise (1) the instruction to respond as quickly and accurately as possible, (2) correction of erroneous responses as indicated by an error cue (e.g., presentation of a red X or the word error below the stimulus), (3) display of category labels assigned to the left or right response key in the corresponding upper screen corners throughout all blocks, (4) intertrial interval of 150 msec–750 msec (250 msec may be most often used), (5) five to six stimuli per category (at least two), (6) alternation between target and attribute stimuli in the combined blocks, (7) otherwise randomized trial order if group differences are the main focus of the experiment, but (8) a fixed random trial order for all participants in correlational studies in order to reduce confounds of procedural and interindividual variance (e.g., Banse, Seise, & Zerbes, 2001).

Scoring Procedures Different scoring procedures have been proposed for calculating IAT effects (see Greenwald et al., 2003). All scoring procedures compare the performance between the initial combined blocks (see Table 7.1, third and fourth blocks) and the reverse combined blocks (see Table 7.1, sixth and seventh blocks). Because of the variety of available IAT score calculations and different recommendations about which score should be used, most researchers report analyses based on both the so-�called conventional IAT score and the so-�called D measures. According to the conventional algorithm (see Greenwald et al., 2003), the performance difference between the two combined tasks is based on log-�transformed response latencies, with latencies smaller than 300 msec or greater than 3,000 msec being recoded to 300 msec and 3,000 msec,

120

II. METHODS AND PROCEDURES

respectively. For descriptive statistics, however, IAT scores based on raw response latencies are often reported. Greenwald and colleagues (2003) suggested improved scoring procedures for the IAT, the D measures, which were optimized with regard to the IAT’s psychometric criteria (e.g., increased internal consistency, higher correlations with explicit measures, resistance to some extraneous procedural influences). D measures differ from the conventional algorithm in several aspects, including modified upper and lower tail treatment of latencies, inclusion of both correct and incorrect responses, with incorrect response latencies being increased by an error penalty, and an individual standardization similar to that in Cohen’s effect size measure d (see Greenwald et al., 2003; Nosek et al., 2007). SPSS syntaxes for different D measures can be downloaded from Greenwald’s website (faculty.washington.edu/agg/iat_materials.htm). It should be noted that although D measures are widely used by now, there is ongoing debate regarding the adequacy of the criteria according to which the algorithms of D measures have been developed (e.g., Wentura & Rothermund, 2007). In particular, researchers have criticized that D measures have been selected by maximizing the IAT’s correlations with explicit measures. This might make the measure more direct on an indirect–Â�direct dimension, which contravenes the basic idea of developing implicit measures. Clearly, more research is needed to evaluate strengths and weaknesses of the D measures compared with the conventional scores. Regardless of the particular chosen algorithm, the practice of reducing eight distinct response latencies or error rates (for two targets and two attributes in both compatible and incompatible blocks) to a single number representing an IAT effect may have significant shortcomings. Difference scores may conceal important information about which particular trials are responsible for differences across conditions. Likewise, when reduced to a single index, it is impossible to examine whether different IAT scores in different experimental conditions, for example, reflect differences in responses to target trials, attribute trials, or both. Similarly, it is impossible to tell whether the effects are driven by responses on compatible trials, incompatible trials, or both. Important empirical and theoretical questions may be addressed by examining response latencies and error rates for each of the conditions separately (e.g., Brendl, Markman, & Messner, 2001). Finally, recent research has suggested the use of more complex mathematical procedures such as

diffusion model analysis (Klauer, Voss, Schmitz, & Teige-�Mocigemba, 2007) or multinomial modeling (Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005) to analyze IAT data. Such mathematical models acknowledge that hardly any measure used in psychology is process pure in the sense that its outcome covaries only with differences in the construct that is to be measured. Thus, the modeling approach attempts to quantify the relative contributions of qualitatively different processes within a given measure (for a review, see Sherman, Klauer, & Allen, Chapter 9, this volume). To what extent the modeling approach will become accepted as a standard tool for analyzing the outcome of response time measures such as the IAT will probably depend on (1) accessibility of manageable software applications (e.g., Stahl & Klauer, 2007; Voss & Voss, 2007), (2) superiority to the conventional and/or D measures (e.g., regarding confounds), and (3) psychometric properties of the process components.

PSYCHOMETRIC PROPERTIES OF€THE IAT Reliability Much of the IAT’s popularity may be attributable to its comparatively satisfactory reliability estimates. In particular, internal consistencies (split-half correlations or Cronbach’s alpha) have been shown to be satisfactory, with scores ranging from .70 to .90 (Nosek et al., 2007), differing slightly depending on the method of calculation (see Schnabel et al., 2008). By contrast, test–Â�retest reliability has been found to be less satisfactory, ranging from .25 to .69 with mean and median estimates of about .50 (Lane, Banaji, Nosek, & Greenwald, 2007), varying little with retest interval (Egloff, Schwerdtfeger, & Schmukle, 2005). Explaining the discrepancy between the IAT’s satisfactory internal consistency on the one hand and its lower test–Â�retest reliability on the other is still an unresolved puzzle regarding the IAT’s ability to capture temporally stable implicit constructs (e.g., personality traits or stable attitudes). Researchers have put forward different explanations for this discrepancy. First, it has been argued that the IAT might measure states rather than traits, as suggested by studies showing the IAT’s sensitivity to context effects and experimental manipulations (for reviews, see Blair, 2002; Gawronski & Bodenhausen, 2006). Drawing on such findings, some researchers have even questioned the existence of invariant, trait-like cognitive structures (e.g., Schwarz, 2007).



7. IAT and Related Tasks

Second, it has been hypothesized that additional sources of construct-Â�unrelated variance might lower test–Â�retest reliability. For instance, the two measurement occasions may exert different influences on the processes underlying the IAT (not on the to-be-Â�measured construct itself!) and thus lead to changes in the extent to which the construct in question causes variation in the IAT outcome (e.g., De Houwer, 2008; Gawronski, Deutsch, LeBel, & Peters, 2008). Such processing differences may comprise differences in (1) test-Â�taking strategies (Egloff et al., 2005), (2) attentional foci when completing IATs (Gawronski et al., 2008), (3) learning effects (Schmukle & Egloff, 2004), or (4) other component response processes that do not reflect associations per se (e.g., Sherman et al., 2008). Context effects may thus rely on (1) the IAT’s sensitivity to changes in the construct of interest (i.e., IAT as a state measure), (2) its susceptibility to changes in additional sources of constructÂ�unrelated variance (i.e., IAT as an insufficiently reliable trait measure), or (3) both (i.e., IAT as capturing both state- and trait-Â�specific variation; Schmukle & Egloff, 2005; Sherman et al., 2008). In this regard, Gschwendner, Hofmann, and Schmitt (2008) recently emphasized the impact of construct accessibility on the IAT’s temporal stability. They showed that (1) the IAT’s test–Â�retest reliability was enhanced in situations in which contextual background features activated specific construct-Â�relevant concepts and that (2) this effect was particularly pronounced for individuals with chronically high accessibility for the relevant concept. These findings suggest that the IAT’s ability to assess traits is enhanced by activating more specific representations via context information. Such representations may be assumed to reflect interindividually different, temporally stable patterns of associative activation (see also Conrey & Smith, 2007).

Validity Group Level Universal Attitudes

At the group level, a priori assumptions have been used to examine the IAT’s validity. For example, normative studies and a priori arguments suggest that there are objects toward which most people have relatively uniform attitudes (e.g., most people prefer flowers over insects). Accordingly, such universal attitudes should be reflected in the IAT effect. Indeed, flower–Â�insect IATs have repeatedly

121

been found to show more positive attitudes toward flowers than toward insects (for the first demonstration of this effect, see Greenwald et al., 1998) indicating validity of the IAT. Known-Group Approach

The so-�called known-group approach contrasts groups that are assumed a priori to differ regarding the construct of interest. For some domains, the IAT proved to be valid as it revealed such differences (see Greenwald & Nosek, 2001). For instance, white and black individuals differed in their racial attitude IAT effects (Nosek, Banaji, & Greenwald, 2002), and a homosexuality attitude IAT distinguished between homosexuals and heterosexuals (Banse et al., 2001). In other domains, particularly those related to addictive behavior, the IAT did not consistently differentiate between groups (such as smokers vs. nonsmokers; Swanson, Rudman, & Greenwald, 2001; but see Perugini, 2005). The strength of the known-group approach, of course, hinges on the certainty with which the groups (e.g., smokers and nonsmokers) can be assumed to differ on a priori grounds. Experimentally Manipulated Attitudes

Assuming that the IAT effect reflects the construct in question, experimental manipulation of this construct should influence the IAT effect in the expected manner. Olson and Fazio (2001), for instance, drew on this assumption and found evidence for the IAT’s validity. Novel attitudes were formed by pairing previously unknown stimuli with other, clearly positive or negative stimuli. Results showed that IAT effects reflected these new attitudes, even when participants were unaware of its origins. Criticism of Validation Approaches at€the Group Level

Importantly, the experimental validation approach has its limits when constructs are to be assessed that are expected to be stable over time. Per definition, such stable constructs (e.g., the personality trait of anxiousness) should not be affected by short-term manipulations. For instance, Schmukle and Egloff (2004) experimentally induced state anxiety by a public speaking task and found no effects on anxiety IATs. They interpreted their findings not in terms of the IAT’s invalidity, but rather in terms of the IAT’s validity as a measure of trait

122

II. METHODS AND PROCEDURES

anxiety (see state–trait issue discussed previously). Similarly, the conclusiveness of the validation approaches by universal attitudes and known groups is limited: It cannot be ruled out that there are other, uncontrolled variables confounded with universal attitudes (e.g., stimulus selection) or group membership (e.g., cognitive abilities) that also account for the findings.

Individual Level Most research on the IAT’s validity adopted the correlational approach. Specifically, the IAT’s validity has been investigated in terms of its correlations with (1) explicit measures and (2) other implicit measures and (3) by its predictive validity for behavioral measures. Correlations with Explicit Measures

Most studies have concentrated on implicit–Â� explicit consistency. A meta-Â�analysis over various content domains (including attitudes, stereotypes, and self-Â�concept) revealed a somewhat low correlation of .24 between IATs and explicit measures (Hofmann, Gawronski, Gschwendner, Le, & Schmitt, 2005), whereas a large-scale analysis of Internet data from the IAT website yielded a higher implicit–Â�explicit correlation of .37 (Nosek, 2005). Nosek’s analysis might reveal somewhat higher correlations because, first, his data refer to attitude domains for which higher implicit–Â�explicit consistency is expected. Second, Nosek exclusively used relative thermometer scales as explicit measures, which may better correspond to the IAT in that they are relative measures and more directly tap into an affective component. Third, greater variability in Nosek’s Internet data might also have contributed to the higher implicit–Â�explicit correlations. There is still considerable controversy, however, as to whether such low to moderate correlations between the IAT and explicit measures should be interpreted as indices of discriminant validity or convergent validity (Payne, Burkley, & Stokes, 2008; see also Nosek & Smyth, 2007). The core of this debate traces back to the question of differences and similarities of the cognitive structures that underlie implicit and explicit measures. Some researchers postulate independent representations of implicit versus explicit constructs and thus interpret implicit–Â�explicit correlations as indices of discriminant validity (e.g., Wilson, Lindsey, & Schooler, 2000). Other researchers postulate only one representation that can be tapped differently

(i.e., using implicit or explicit measures) and, consequently, interpret implicit–Â�explicit correlations as indices of convergent validity (e.g., Fazio, 1990; Nier, 2005). It is to be noted, however, that recent research advised caution in interpreting implicit–Â�explicit correlations as evidence for underlying cognitive structures. For instance, Payne and colleagues (2008) argued that measures differ with regard to several (structural) features and showed that structural fit has a strong impact on implicit–Â�explicit correlations: The more similar the task demands of implicit and explicit measures, the higher the correlation between them, even when controlling for common method-Â�specific variance. Implicit–Â� explicit correlations might, therefore, reflect (structural) fit, or misfit, of the underlying measures rather than of the underlying cognitive structures (see also Hofmann, Gschwendner, Nosek, & Schmitt, 2005). Correlations with Other Implicit€Measures

Considering the interpretation problems of implicit–Â�explicit correlations, it has been suggested to focus on implicit–Â�implicit correlations. Assuming that implicit measures capture the same (i.e., implicit) construct, the IAT’s correlations with other implicit measures should reflect the IAT’s convergent validity (e.g., Banaji, 2001). Interestingly, correlations between IATs and other implicit measures have typically been found to be weak (e.g., Olson & Fazio, 2003; Rudolph, Schröder-Abé, Schütz, Gregg, & Sedikides, 2008; Sherman, Presson, Chassin, Rose, & Koch, 2003; Teige, Schnabel, Banse, & Asendorpf, 2004). Low implicit–Â�implicit consistency, however, is often not attributed to the IAT’s invalidity but rather to two other factors. First, implicit measures other than the IAT often show unacceptable reliability estimates (Nosek et al., 2007). Because reliability sets upper limits on the to-be-Â�expected correlation, implicit–Â�implicit relations might necessarily be underestimated (Teige et al., 2004; see Cunningham, Preacher, & Banaji, 2001, for an approach to correct for such measurement error through latent variable analysis). Second, not only implicit–Â�explicit consistency but also implicit–Â�implicit consistency might be influenced by the structural fit of the measures (Payne et al., 2008; see also De Houwer, 2008). Empirical evidence for this assumption is provided by studies that approximated formerly dissimilar features of implicit measures and indeed found



7. IAT and Related Tasks

higher implicit–Â�implicit correlations. For example, Olson and Fazio (2003) argued that, as a result of different task demands of the IAT and affective priming, the IAT reveals evaluations of superordinate categories, whereas affective priming reveals evaluations of specific category exemplars used as stimuli. When affective priming was made more similar to the IAT by encouraging the primes’ categorization in terms of the superordinate category, the correlation between affective priming and IAT was increased. In a similar vein, Steffens, Kirschbaum, and Glados (2008) equated the IAT and a response-Â�window priming task with regard to stimulus selection (i.e., both tasks used only the concept categories as stimuli). Again, IAT effects and priming effects correlated significantly. Taken together, the IAT’s low correlations with other implicit measures do not necessarily indicate the IAT’s invalidity but can be accounted for by (1) low reliability estimates of implicit measures other than the IAT and (2) structural differences among the measures (Rudolph et al., 2008). Predictive Validity for Behavioral Measures

Most convincing in light of these discussions are correlational studies that have demonstrated the IAT’s ability to predict behavior over and above explicit measures. Perugini (2005) distinguished between three different models of predictive validity of implicit and explicit measures: the additive, the multiplicative, and the double-Â�dissociation model. Research on the IAT found evidence for all three models. As proposed by the additive model, the IAT and explicit measures explained separate portions of relevant criterion variance (e.g., Schnabel, Banse, & Asendorpf, 2006a). As suggested by the multiplicative model, the IAT and explicit measures interacted in predicting relevant behavior (e.g., Schröder-Abé, Rudolph, & Schütz, 2007). Finally, as proposed by the doubleÂ�dissociation model, only the IAT predicted spontaneous behavior, whereas only explicit measures predicted controlled behavior (e.g., Asendorpf, Banse, & Mücke, 2002). Evidence for the predictive validity of IATs across various behavioral domains is also provided by a recent meta-Â�analysis by Greenwald, Poehlman, Uhlmann, and Banaji (2009). In socially sensitive domains such as stereotyping and prejudice, the IAT showed better predictive validity than explicit measures. This might have been expected, given that, particularly in these domains, socially desirable responding may bias explicit measures. In

123

contrast, the meta-Â�analysis revealed lower predictive validity for IATs than for explicit measures in studies that explored brand preferences or political attitudes. Importantly, in domains related to health behavior, the IAT has been shown to have weaker predictive validity: An IAT designed to assess preferences for apples versus candy bars did not predict the subsequent choice between an apple and a candy bar (e.g., Karpinski & Hilton, 2001; Spruyt, Hermans, De Houwer, Vandekerckhove, & Eelen, 2007). The IAT’s insufficiency in such domains has been argued to be due to its sensitivity to so-Â�called “extrapersonal” knowledge (Olson & Fazio, 2004), that is, societal views that do not necessarily correspond to the personal view (see later discussion). Criticism of Validation approaches at€the Individual Level

As with the validation approach at the group level, the conclusiveness of the validation approach at the individual level is also limited (see Borsboom, Mellenbergh, & van Heerden, 2004). Correlations between IAT effects (e.g., an aggressiveness IAT effect) and criterion variables (e.g., aggressive behavior) might emerge because of a third variable (e.g., impaired cognitive skills) that influences both the IAT effect and the criterion variable. For example, the finding that the IAT predicts aggressive behavior does not necessarily attest to the IAT’s validity as a measure of implicit aggressiveness. This correlation might also result from individual differences in cognitive skills such as the ability to inhibit impulsive responses that may impact on both aggressive behavior and the IAT effect (see the IAT’s confound by cognitive abilities as discussed later). All in all, however, the evidence provided by correlational studies is, to a large extent, in line with the assumption that IAT effects can capture meaningful construct-Â�related variance.

CRITICISM OF THE IAT In summary, the IAT has been shown to capture valid construct-Â�related variance with regard to both the group and individual levels. Although these findings are encouraging and indicate the IAT’s validity, several studies have seriously challenged the assumption that IAT effects are driven primarily by the to-be-Â�measured associations (for general criticism of the IAT, see Fiedler, Messner, & Bluemke, 2006). First and foremost, research-

124

II. METHODS AND PROCEDURES

ers criticized that, in contrast to the explosion of IAT applications in diverse psychological areas, the processes underlying the IAT are still unclear (e.g., Fazio & Olson, 2003). For most if not all implicit measures (including the IAT), it is not yet sufficiently understood how the to-be-�measured construct translates into observed responses (De Houwer et al., 2009). Identifying the underlying processes of the IAT is particularly important because several factors have been found to contribute to the IAT effect independent of the to-be�measured construct and thus cause additional, but construct-�unrelated, variance in the IAT effect (e.g., Sherman et al., 2008; Wentura & Rothermund, 2007). Hence, although the construct in question may be sufficient to cause an IAT effect, other factors might also lead to IAT effects independently of this construct, thereby affecting not only the absolute size but possibly also the rank order of IAT effects. Unfortunately, there is no comprehensive, testable process model that takes all confounding factors into account and allows their relative influences on the IAT effect to be disentangled. Not even modeling approaches (see Scoring Procedures section) permit the integration of all such factors. However, process models have been proposed that account for at least some, albeit not all, factors that can cause systematic variance in IAT effects. A brief overview of these models is given next.

(i.e., associated with the same valence) when the “negative” key has to be pressed for black names and the “positive” key for white names (black/ negative–white/positive block). When the same individuals are asked to press the “negative” key for white names and the “positive” key for black names (white/negative–black/positive block), stimuli and responses are incompatible. Because stimulus–Â�response compatibility varies between the compatible and incompatible blocks of an IAT, De Houwer (2001, 2003b, 2008) hypothesized that IAT effects are due to the activation of responses by the presented stimuli. It is important to note that not only relevant stimulus features (such as category membership) but also irrelevant stimulus features (such as perceptual form) might activate responses in an IAT (De Houwer, 2008; De Houwer, Geldof, & De Bruycker, 2005). Thus, the feature according to which stimulus and response in an IAT are compatible might not necessarily (and exclusively) be construct related and thus relevant (such as valence in the prior example), but could also be construct unrelated and thus irrelevant (i.e., features other than valence in attitude IATs such as, e.g., perceptual form; De Houwer et al., 2005). The stimulus–Â�response compatibility account thereby predicts that both construct-Â�related and constructÂ�unrelated variance can contribute to the IAT effect.

Process Models of the IAT

Random-Walk Model

Stimulus–Â�Response Compatibilities

Brendl and colleagues (2001) proposed that the IAT effect reflects the result of a random-walk process in which evidence is accumulated on a joint response-�related decision dimension. The time required before a response criterion is reached depends on whether all incoming information pushes an internal counter in the same direction. It is hypothesized that both information of the target categories (i.e., category membership such as black vs. white) and information of the attribute categories (e.g., valence) drive the counter. Therefore, stimuli of the target categories (e.g., black vs. white names) should have a lower net accumulation rate in the incompatible than in the compatible IAT condition because information of the category membership (i.e., black vs. white) and valence of a stimulus (i.e., negative vs. positive) disagree in the former, but not in the latter, condition. Again, let us take the example of the racial attitude IAT. For individuals with implicit prejudices against blacks, black stimuli do not only belong to the category black but are also negatively evalu-

According to De Houwer (2001, 2003b), the IAT effect is based on stimulus–Â�response compatibility. The basic assumption in this model is that response keys acquire the meaning of the stimulus category to which they are assigned. Compatibility between the meaning of a response key and stimulus features then facilitates responses. This mechanism can explain the IAT effect because compatibility between stimulus features and responses is consistently given in the compatible, but not the incompatible, block. To illustrate this process, consider the racial attitude IAT introduced previously. By asking participants to press one key for negative words and another key for positive words, the a priori neutral keys are assumed to become associated with negative and positive valence, respectively (see also Eder & Rothermund, 2008). Hence, for prejudiced individuals who like white persons but dislike black persons, stimuli and responses are compatible



7. IAT and Related Tasks

ated. If a black stimulus has to be categorized in the compatible block (here, black/negative vs. white/positive), both sources of information (i.e., the membership of the category black as well as the negative valence) push the accumulation process toward the same response (i.e., the common response for black names and negative words). In contrast, in the incompatible block (here, white/ negative vs. black/positive), the two sources of information move the accumulation process in opposite directions because now black names and negative words are to be mapped onto different responses. Usually category membership will have the stronger impact resulting in correct responses in most trials. All in all, however, the net evidence accumulation rate for black stimuli should be lower in the incompatible block than in the compatible block, thus leading to slower responses in the former than in the latter task. Brendl and colleagues (2001) predicted that differences in net accumulation rate are accompanied by a shift in response criteria in the incompatible block of an IAT. The authors assume that because the incompatible block is perceived as more difficult, participants adopt a more conservative response criterion, leading to slower responses in the incompatible block compared with the compatible block. Accordingly, Brendl and colleagues suggest two mechanisms by which IAT effects are produced, namely, different rates of information accumulation and different response criteria. Whereas Brendl and colleagues did not mathematically formalize their conceptualization of a random-walk model for the IAT, Klauer and colleagues (2007) applied diffusion model analyses to IAT data and found evidence for the two proposed mechanisms. They also confirmed Brendl and colleagues’ assumption that the first mechanism, different rates of information accumulation, produces constructÂ�related variance in the IAT effect, whereas the second mechanism, different response criteria, produces construct-Â�unrelated variance of strategic, situational, and/or trait-Â�related influences on the IAT effect.

Task Switching According to Mierke and Klauer (2001, 2003; Klauer & Mierke, 2005), task-�switching costs contribute to the IAT effect because they affect the two crucial blocks of the IAT asymmetrically. Thus, the central assumption of the task-�switching account is that the IAT involves executive control processes (i.e., identifying and switching to the appropriate task set). It is argued that in the com-

125

patible block of an IAT the structure of the task provides participants with an overlapping feature. Again, think of prejudiced individuals who like white persons but dislike black persons. For these individuals, negative words and black names share the feature negativity, whereas positivity is shared by positive words and white names. In the black/ negative–white/positive block (here the compatible block) of the racial attitude IAT, categories that share a feature, namely valence, are thus mapped onto one response key. Categorizing a black or white stimulus according to valence (negative or positive) or category membership (black or white) should thus lead to the same response (Mierke & Klauer, 2001, 2003). Consequently, the task-Â�switching account assumes that participants derive their responses from an arbitrary feature (not necessarily valence) shared by the attribute and target category in the compatible block (see also De Houwer et al., 2005). Because the process of deriving responses is thereby simplified, responses should be fast in this condition. In contrast, responses cannot be derived from an overlapping feature in the incompatible IAT block. For instance, if the same prejudiced individuals complete the white/negative–black/positive block of an IAT, responding to a black name on the basis of its valence (here negative) would lead to an incorrect response. In the incompatible block, attribute-Â�related information thus needs to be ignored for stimuli of the target categories but has to be processed for stimuli of the attribute categories. Hence, in the incompatible block, participants are required to perform every switch between attribute and target discrimination tasks, whereas the compatible block can be completed without performing all such task switches. Because task switching is associated with performance costs (Rogers & Monsell, 1995) and affects both blocks asymmetrically, task-Â�switching ability should contribute to the IAT effect (see Klauer, Schmitz, Teige-Â�Mocigemba, & Voss, in press, for evidence). The task-Â�switching account thereby predicts that both construct-Â�related and constructÂ�unrelated variance contribute to the IAT effect: An attitude IAT effect, for instance, should comprise construct-Â�related variance inasmuch as participants derive their responses from valence as the arbitrary feature shared by the attribute and target category in the compatible block. Construct-Â�unrelated variance should constitute the IAT effect inasmuch as participants derive their responses from an arbitrary feature other than valence. Also, construct-Â�unrelated influences of task-Â�switching ability should be larger the more

126

II. METHODS AND PROCEDURES

participants tend to save costly task switches in the compatible block by simplifying the task via deriving responses from some overlapping feature.

Figure–Â�Ground Asymmetry According to Rothermund and Wentura (2001, 2004), the IAT measures differences in the salience of stimulus categories. Figure–Â�ground asymmetries within the target (e.g., black vs. white) and attribute (e.g., negative vs. positive) dimensions are the central explanatory concept of this account. The authors assume that participants simplify, either spontaneously or strategically (Rothermund, Wentura, & De Houwer, 2005), the compatible block, in which the salient categories are mapped onto one response key, by recoding both categorization tasks as figure–Â�ground discriminations. This way, all salient (i.e., figure) stimuli are assigned to one key and all nonsalient (i.e., ground) stimuli to the other, so that the salient stimuli constitute the figure against the background of the less salient stimuli. Importantly, such a recoding is impossible in the incompatible block in which the salient categories are mapped onto different response keys. Hence, performance differences between the two blocks are argued to be the result of salience asymmetries. Applying the figure–Â�ground asymmetry account to the racial attitude IAT, it may be assumed that black names are more salient than white names because they are unfamiliar. Simultaneously, negative words are more salient than positive words because of the attention-Â�grabbing power of negative information (Pratto & John, 1991). Hence, participants should respond faster and more accurately if the salient categories black and negative share one response (black/negative–white/positive block) than if the salient categories black and negative are mapped onto different responses (white/negative–black/positive block). This is because in the former, but not in the latter, case participants can reduce the complex four-to-two categorization task to a single binary decision of whether the stimulus belongs to the salient (i.e., figure) or the nonsalient (i.e., ground) category. As in Mierke and Klauer’s (2001, 2003) task-Â�switching account, the figure–Â� ground asymmetry account thus assumes that the compatible, but not the incompatible, block of an IAT provides participants with an overlapping feature shared by the target and attribute category that directly contributes to the IAT effect (see also De Houwer et al., 2005). In contrast to the task-Â�switching model, however, it is assumed that the feature used for recoding the IAT task must be

salience. Hence, associations between categories are argued to play a subordinate role in the IAT effect compared with salience asymmetries (but see Kinoshita & Peek-O’Leary, 2006). Note, however, that the figure–Â�ground asymmetry account still allows for an influence of valence associations inasmuch as they may serve as determinants of salience asymmetries. For instance, for a prejudiced individual, stimuli of the category black such as names typical for blacks might be salient not only because they are unfamiliar but also because they are negatively valenced (remember that negative stimuli are assumed to be salient). This should lead to better performance when black and negative (i.e., figure) stimuli share one response. Thus, inasmuch as valence is one determinant of salience, salience may serve as a mediator between valence and IAT scores. Consequently, according to the figure–Â�ground asymmetry account, IAT effects might comprise both construct-Â�related variance (if valence of the target categories is a determinant of salience) and construct-Â�unrelated variance (if factors other than valence determine salience).

Summary Despite the absence of a comprehensive process model, fruitful proposals have been made about the processes by which variables may cause variations in IAT effects. As discussed later, research has confirmed some, albeit not all, predictions of the respective process models. We do not evaluate strengths and weaknesses of every account here. Instead, we focus on a shared assumption underlying all process models, namely that IAT effects are influenced not only by the to-be-�measured associations between categories but also by other construct-�unrelated factors.

Confounding Factors of the IAT Effect Unfortunately, in the absence of a comprehensive process model, the relative contribution of construct-�related and construct-�unrelated influences on the IAT effect cannot be determined and thus cannot be controlled for statistically. Confounding factors, therefore, exert an uncontrollable influence on the absolute size and possibly also on the rank order of IAT effects. Accordingly, the interpretation of IAT effects in an absolute manner is compromised: An IAT effect of zero cannot be interpreted as reflecting a neutral attitude, nor does a positive (negative) IAT effect necessarily reflect a positive (negative) attitude. Thus,



7. IAT and Related Tasks

descriptive statements such as “72% of test takers show anti-black bias” cannot be derived from IAT effects. Given that some confounding factors (e.g., cognitive abilities) differ between individuals, they do not exert the same influence on IAT effects for all individuals but rather exert interindividually different influences. Accordingly, such interindividually differing contaminants should distort not only the size but also the rank order of IAT effects, thereby restricting the IAT’s predictive power and, hence, affect conclusions about validity. In the absence of empirical evidence, it is difficult to predict whether or not a specific contaminant will affect the rank order of IAT effects. In principle, all of the confounding factors discussed next might distort the rank order, at least to the extent to which participants systematically differ in being subject to the respective contaminant. Confounding factors thus pose a problem for both the interpretation of absolute IAT effects and the interpretation of the IAT effect as a measure of interindividual differences. In the following, we discuss the different factors that have been shown to contaminate the IAT effect. If available, we also present possible remedies for the respective confounds, some of which involve procedural changes to the IAT. Remarkably, research on IAT contaminants has promoted the development of several implicit measures (for an overview, see Table 7.2) that are conceptually similar to the IAT but aim for overcoming one or more of its confounds. We start the section with a more general (structural) problem of the IAT, before we concentrate on specific confounding factors.

Recoding Many of the confounding influences on the IAT effect may be argued to have their roots in soÂ�called recoding processes (see Rothermund, TeigeÂ�Mocigemba, Gast, & Wentura, 2009). As just elaborated, recoding in the IAT means that participants simplify—Â�spontaneously or strategically— one of the double-Â�discrimination tasks of an IAT. Instead of following the instructions to categorize all stimuli according to their category membership, participants may recode the four categories of an IAT into two. Such a simplification may rely on any feature that helps to distinguish between the two groups of stimuli that are assigned to different response keys (Mierke & Klauer, 2003). Participants might even draw on societal views (i.e., extrapersonal knowledge; Olson & Fazio, 2004) to simplify the task (Rothermund & Wentura, 2004).

127

Hence, the IAT effect might, in part, reflect those overlapping features that participants (decided to) use for categorization (e.g., valence, meaning, salience, perceptual form; De Houwer et al., 2005). Recently, it has been argued that recoding processes rely heavily on the IAT’s block structure (Rothermund et al., 2009; Teige-Â�Mocigemba, Klauer, & Rothermund, 2008; see also De Houwer, 2003a). It is assumed that the different mappings of categories onto response keys that are implemented in the compatible versus incompatible block of the IAT promote different processes in the two blocks. Because the IAT effect is based on a comparison of performance in the two separate IAT blocks, such processing differences have a direct impact on the IAT effect. A straightforward remedy thus seems to be the elimination of the IAT’s block structure, as has been realized in two paradigms called Single Block IAT (SB-IAT; Teige-Â�Mocigemba et al., 2008) and Recoding Free IAT (IAT-RF; Rothermund et al., 2009). The basic principle of both the SB-IAT and the IAT-RF is that the mapping of categories onto response keys may randomly change from trial to trial instead of blockwise. The SB-IAT

The SB-IAT provides participants with a structural feature, namely word position, which signals the mapping of categories onto response keys (i.e., compatible vs. incompatible) for each trial. All stimuli are randomly presented above or below a dashed line that divides the screen into an upper and a lower half. If, for instance, a stimulus appears in the upper half, the compatible mapping is valid (i.e., compatible categories share one response). If a stimulus appears in the lower half, the incompatible mapping is valid (i.e., incompatible categories share one response). To illustrate the procedure, let us consider a racial attitude SB-IAT: For attribute stimuli (e.g., positive and negative words), word position is irrelevant because attribute stimuli always have to be assigned to the same response keys irrespective of word position (e.g., positive words to the right key, negative words to the left key). For target stimuli (e.g., names typical for whites vs. blacks), however, word position is highly relevant. For instance, if target stimuli appear above the dashed line, names of whites have to be assigned to the right (positive) key, whereas names of blacks have to be assigned to the left (negative) key. Conversely, if target stimuli appear below the dashed line, names of whites have to be assigned to the



128

Uses go/no-go task, GNAT scores typically based on signal detection analyses of error rates

Eliminates IAT’s block structure, requires categorizing two stimuli (one target, one attribute) at once to one of four possible target–attribute category pairs

Uses substantially fewer trials, requires focusing on just two categories (one target, one attribute category) in each four-category test block

GNAT

SPF

BIAT

Time required for task

Relative measure, compatibility order, recoding (?)

Relative measure

Relative measure

See EAST

Recoding, relative measure, compatibility order, limitation to assessment of single constructs

Extrapersonal associations

Relative measure

Strategic control (?)

Recoding, task-switching costs, stimulus influences, compatibility order

Recoding, method-specific variance, compatibility order

Problem addressed

Sparse empirical evidence at this point

Sparse empirical evidence at this point

Lower reliability

Recoding (?)

Sparse empirical evidence at this point

Insufficient psychometric properties in most domains

Vague conceptualization of extrapersonal associations; more direct measure

See IAP; does not allow for betweenindividual comparisons

Sparse empirical evidence at this point, implementation effort higher

Sparse empirical evidence at this point

Sparse empirical evidence at this point

Caveat

Note. IAT, Implicit Association Test; SB-IAT, Single-Block IAT; IAT-RF, Recoding-Free IAT; IAP, Implicit Association Procedure; EMA, Evaluative Movement Assessment; EAST, Extrinsic Affective Simon Task; ID-EAST, Identification-EAST; SC-IAT, Single Category IAT; GNAT, Go/No-Go Association Test; SPF,= Sorting Paired Features Task; BIAT, brief IAT.

Uses three categories instead of four (one target, two attribute categories)

Removes error feedback, uses more personalized attribute category labels, requires participants to categorize attribute stimuli according to (explicit) personal preference (instead of normative valence)

Personalized IAT

SC-IAT

Requires approach–avoidance behavior by using horizontal joystick movement instead of key press

EMA

Similar to EAST, main difference: requires processing of target categories

Requires approach–avoidance behavior by changing the response modality (joystick movement instead of key press)

IAP

ID-EAST

Eliminates IAT’s block structure

IAT-RF

Differs in many aspects, main difference: Uses “valenced” responses for color discrimination task of target words, EAST effect relies on stimulus– response compatibility of task-irrelevant valence of target words and valence of responses

Eliminates IAT’s block structure

SB-IAT

EAST

Procedural differences from the IAT

Measure

TABLE 7.2.╇Overview of Measures Reviewed in the Present Chapter, Including Their Procedural Differences to the IAT, the IAT Confound They Address, and Their Possible Caveat



7. IAT and Related Tasks

left (negative) key and names of blacks have to be assigned to the right (positive) key. As in the IAT, the performance difference between the two kinds of mappings is interpreted as reflecting the relative association strength between the target and attribute categories. For individuals with implicit prejudices against blacks, responses to stimuli that appear in the upper half of the screen (white/positive and black/negative share one response) should thus be faster and more accurate than responses to stimuli appearing in the lower half (black/positive and white/negative share one response). The IAT-RF

In the IAT-RF, a structural feature such as word position that signals the mapping of categories onto response keys for each trial is absent. Instead, response assignments are indicated at the beginning of each trial by presenting the category labels in the respective left and right corners of the screen (e.g., black/negative left, white/positive right) that correspond to the response keys to which the categories are assigned in the upcoming trial. For each trial, participants are thus required to read the category labels that indicate the mapping for the respective trial. The main difference between the SB-IAT and the IAT-RF on the one hand and the standard IAT on the other is that both the SB-IAT and the IAT-RF compare performance on compatible versus incompatible trials within the same (i.e., a single) block, whereas the standard IAT compares performance on compatible versus incompatible trials between two different (i.e., compatible vs. incompatible) blocks. Thus, in the SB-IAT and the IAT-RF, the response mapping (compatible vs. incompatible) may randomly change from trial to trial and is not consistently blocked anymore. This should impede any kind of recoding strategies because recoding processes are assumed to rely on a consistent mapping of categories onto response keys (Strayer & Kramer, 1994). The SB-IAT and the IAT-RF indeed showed reduced susceptibility to markers of recoding processes (see later discussion): Confounding influences on the IAT such as those of method-Â�specific variance, task-Â�switching costs, and biased selection of stimuli were clearly diminished (Rothermund et al., 2009; Teige-Â�Mocigemba et al., 2008), suggesting usefulness and effectiveness of eliminating the IAT’s block structure. At the same time, first findings indicated satisfactory reliability and validity estimates at both the group (e.g., known group approach) and the individual (implicit–Â�explicit

129

consistency) level. It should be noted, however, that effect sizes of both the SB-IAT and the IATRF were clearly reduced compared with the IAT, thereby leaving less room for markers of recoding processes to appear. Definitely, more research is needed to evaluate the potential of both the SBIAT and the IAT-RF.

Cognitive Abilities Maybe best documented and acknowledged is the confounding influence of cognitive abilities on the IAT effect. For instance, overall response speed and the size of IAT effects have been found to be correlated (McFarland & Crouch, 2002). Because overall response speed is associated with cognitive abilities, these results suggest that IAT effects are at least partially determined by the participants’ cognitive skills. Indirect evidence comes from studies showing larger IAT effects for older individuals compared with younger individuals (e.g., Hummert, Garstka, O’Brien, Greenwald, & Mellott, 2002). Given that cognitive abilities tend to decline with age, such findings also suggest that IAT effects are influenced by cognitive abilities (see also Gonsalkorale, Sherman, & Klauer, 2009; Sherman et al., 2008). Further indirect evidence for a cognitive skill confound on the IAT is provided by studies showing correlations between different IATs that were supposed to capture different, unrelated constructs and, therefore, should not be intercorrelated (e.g., Back, Schmukle, & Egloff, 2005; McFarland & Crouch, 2002; Mierke & Klauer, 2003). Such findings indicate that some of the systematic variance in IAT effects is due to factors that affect different IATs similarly, irrespective of contents, reflecting so-Â�called method variance. Method-Â�specific variance in the IAT has been accounted for by speed–Â�accuracy trade-offs (Klauer et al., 2007) and by cognitive abilities, in particular task-Â�switching ability (Klauer et al., in press), as is to be expected following Mierke and Klauer’s (2003) task-Â�switching model. Different techniques have been proposed to decrease the cognitive skill confound on the IAT effect. First, D measures (Greenwald et al., 2003) have proven to be less susceptible to cognitive skills than the conventional score. Second, modeling approaches (e.g., the Quad model; Conrey et al., 2005) may help to dissociate construct-Â�related and construct-Â�unrelated components of the IAT effect (see also Klauer et al., 2007), including, for instance, a component for cognitive abilities (Sherman et al., 2008). Finally, the SB-IAT effect and the IAT-RF effect have been found to be less

130

II. METHODS AND PROCEDURES

contaminated by method variance (see prior discussion).

Salience In support of their figure–Â�ground asymmetry account, Rothermund and Wentura (2001, 2004) reported experimental data (i.e., manipulations of salience influence IAT effects) and correlational data (i.e., IAT effects are related to measures of salience). These findings corroborate the assumption that salience asymmetries have the potential to contribute to IAT effects as acknowledged by the developers of the IAT (see Greenwald, Nosek, Banaji, & Klauer, 2005). It is still controversial, however, how pervasive the impact of salience asymmetries is (see Rothermund et al., 2005). Recent studies indicate that only part of the IAT effect can be accounted for by construct-Â�unrelated salience asymmetries because construct-Â�related compatibilities between the nominal categories have been shown to simultaneously contribute to IAT effects (e.g., Kinoshita & Peek-O’Leary, 2006). Moreover, there is still uncertainty at the conceptual level about how salience should be measured (e.g., Greenwald et al., 2005) and how it is related to other constructs such as familiarity and polarity (e.g., Kinoshita & Peek-O’Leary, 2006; Proctor & Cho, 2006). Given that recoding processes have been argued to form the basis of contaminations by salience asymmetries (e.g., Wentura & Rothermund, 2007), prevention of recoding processes should also prevent confounding influences of salience on the IAT effect (see Rothermund et al., 2009). Also, measuring salience asymmetries using a visual search task might enable one to estimate its impact on the IAT effect (Rothermund & Wentura, 2004).

Stimuli The IAT effect has been found to be determined both by the superordinate nominal categories according to which the stimuli have to be categorized (i.e., the category labels such as black vs. white) (De Houwer, 2001, 2008; Olson & Fazio, 2003) and by the stimuli used to represent the categories (e.g., a particular black or white face) (Bluemke & Friese, 2006; Govan & Williams, 2004; Mitchell, Nosek, & Banaji, 2003; Steffens & Plewe, 2001). Influences at the level of the category labels are desired. They ensure the experimenter’s control over the nominal categories according to which

participants categorize and process the stimuli (see the relevant feature account by De Houwer, 2008). This allows for determining the construct that the IAT effect should reflect, and also adds to the IAT’s easy applicability to various domains. Influences at the level of the stimuli, however, are often unintended. For example, several studies indicated that stimulus selection may force participants to categorize stimuli according to other than the specified category labels (see the irrelevant feature account by De Houwer, 2008). As Govan and Williams (2004) proposed, participants may redefine the category labels in order to reconcile meaning and/or valence of category labels with meaning and/or valence of stimuli. Biased selections of stimuli can thus dramatically influence magnitude and even direction of IAT effects (Bluemke & Friese, 2006; Govan & Williams, 2004; Rothermund et al., 2009), which poses a threat to the IAT’s validity. Careful stimulus selection is thus required to exert as much control as possible over the nominal categories according to which participants categorize stimuli. First and foremost, stimuli should be representative for the respective category, and any confounds of stimulus features of the attribute versus target categories should be avoided (Steffens & Plewe, 2001). In an attitude IAT, for instance, both positively and negatively valenced stimuli should be selected for the target categories (De Houwer, 2001). Furthermore, the distinctiveness of the attribute versus target stimuli may be enhanced by distinct colors, fonts, or other stimulus modalities (Nosek et al., 2007). Finally, recent findings indicated that elimination of the IAT’s block structure may be an effective means to reduce stimulus influences (Rothermund et al., 2009), and an IAT variant that uses the category labels (or synonyms of them) as stimuli also provided promising results (Steffens et al., 2008).

Strategic Effects Evidence for strategic effects on the IAT comes from studies that investigated the fakeability of the IAT. These studies revealed that the IAT outcome can be strategically controlled (1) if participants are told how to fake (Fiedler & Bluemke, 2005), (2) if participants are high on self-�monitoring and highly motivated to fake (Czellar, 2006), or (3) if participants had experience with at least one prior IAT (Fiedler & Bluemke, 2005; Steffens, 2004). If, however, participants were exposed to an IAT for the very first time (Banse et al., 2001; but see



7. IAT and Related Tasks

De Houwer, Beckers, & Moors, 2007) or were not advised on how to fake (Asendorpf et al., 2002; Egloff & Schmukle, 2002; but see Experiment 3 of Lowery, Hardin, & Sinclair, 2001), there was little evidence for strategic control over the IAT outcome. Accordingly, under certain circumstances, participants might strategically influence the IAT effect. It is, however, probably much easier to exert strategic control over self-�reports than over an IAT (Steffens, 2004). To the best of our knowledge, no straightforward solution to control for faking attempts in IATs has been proposed so far. Promising approaches may comprise the development of algorithms that allow for distinguishing fakers from nonfakers or the use of modeling approaches as discussed previously. Furthermore, procedural changes might reduce the risk of strategically altered IAT effects. For instance, modifying the Evaluative Movement Assessment (EMA; Brendl, Markman, & Messner, 2005),2 Schnabel, Banse, and Asendorpf (2006b) used what they called the Implicit Association Procedure (IAP). The IAP

The IAP is methodologically very similar to the IAT, the main difference being the use of a different response modality (i.e., joystick movement instead of key press) that is thought to trigger approach (pulling the joystick toward oneself) and avoidance (pushing the joystick away from oneself) behavior. In an initial combined block of a racial attitude IAP, for instance, participants have to pull the joystick to themselves when white or positive stimuli are presented and to push it away from themselves when black or negative stimuli are presented. In the reverse combined block, participants have to pull the joystick to themselves when black or positive stimuli are presented and to push it away from themselves when white or negative stimuli are presented. As in the IAT, the difference in performance between the two kinds of pairings is interpreted as revealing the relative association strength between the target and attribute categories. Accordingly, individuals with implicit prejudices against blacks are expected to respond faster and more accurately when black and negative stimuli require avoidance behavior (pushing the joystick away from oneself) and white and positive stimuli require approach behavior (pulling the joystick toward oneself) compared with the reverse configuration. The IAP showed satisfactory psychometric properties, and

131

unlike the IAT, it was not susceptible to faking (Schnabel et al., 2006b). This might indicate that it is more difficult to exert strategic control over the outcome of a procedure that uses approach–Â� avoidance responses.

Extrapersonal Associations Olson and Fazio (2004) identified another confounding influence on the IAT effect: so-Â�called “extrapersonal” associations (see also Karpinski & Hilton, 2001). The term extrapersonal knowledge refers to culturally shared assumptions (e.g., apples are healthy and thus are positive) that do not necessarily correspond to personal evaluations (e.g., I don’t like apples). To the extent that the measurement purpose is to reveal personal rather than societal views, the IAT’s sensitivity to extrapersonal associations poses a threat to its validity (Olson & Fazio, 2004; but see Nosek & Hansen, 2008a). Evidence for the IAT’s contamination by extrapersonal associations is provided by experiments in which the manipulation of extrapersonal views changed IAT effects (Han, Olson, & Fazio, 2006). Furthermore, when groups with diverging personal and societal views completed IATs, IAT effects at least sometimes seemed to be in line with societal views (e.g., Olson & Fazio, 2004; Spruyt et al., 2007). Finally, the assumption that extrapersonal associations contaminate IAT effects is consistent with studies showing the IAT’s weakness to predict behavior in health-Â�related domains (e.g., Spruyt et al., 2007), where societal views are prevalent. To prevent a confounding influence of extrapersonal associations, Olson and Fazio (2003) suggested procedural changes to the IAT. In their personalized IAT variant, error feedback is removed, more personalized attribute category labels are used (i.e., “I like” vs. “I don’t like” instead of “positive” vs. “negative”), and participants are asked to categorize attribute stimuli according to their (explicit) personal preference (instead of normative valence). Although the personalized IAT was less affected by societal views (Han et al., 2006; Olson & Fazio, 2004), recent research seriously questioned the usefulness of this IAT variant as an implicit measure: Nosek and Hansen (2008b) showed that personalized IATs foster participants to evaluate not only attribute stimuli but also target stimuli explicitly, thereby basically making the measurement more direct on an indirect–Â�direct dimension. Moreover, doubts have been raised about the validity and theoretical significance of the extrapersonal account of IAT effects. At the empirical

132

II. METHODS AND PROCEDURES

level, recent correlational studies provided little evidence for a link between IAT effects and measures of societal views (Nosek & Hansen, 2008a). At the conceptual level, it has been argued that the distinction between personal and extrapersonal views actually makes little sense, especially when considering the automatic effects of personal and extrapersonal associations (Gawronski & Bodenhausen, 2006; Nosek & Hansen, 2008a). Furthermore, there is still uncertainty about how extrapersonal associations can be conceptualized (see Gawronski, Peters, & LeBel, 2008). Finally, the processes via which extrapersonal associations may contaminate IAT effects are not yet identified (but see Rothermund & Wentura, 2004, for a strategic recoding account).

Compatibility Order The IAT effect is also known to be confounded by compatibility order: IAT effects tend to be larger if the compatible block precedes the incompatible block than vice versa (see Nosek et al., 2007). A theoretical account for compatibility-order effects was provided by Klauer and Mierke (2005). Drawing on their task-Â�switching model, the authors suggested that differences in the accessibility of attribute information in the compatible versus incompatible block of the IAT may account for compatibilityorder effects. Such effects are difficult to control for, given that compatibility is a function of interindividual differences in the construct of interest and cannot a priori be determined in many applied contexts. First attempts to reduce the confounding impact of compatibility order have focused on slight changes to the IAT procedure (Nosek, Greenwald, & Banaji, 2005), namely by increasing numbers of trials in the reversed target discrimination task (fifth block of Table 7.1). Also, implicit measures that abandon the IAT’s block structure such as the SB-IAT, the IAT-RF, or the Extrinsic Affective Simon Task (EAST; De Houwer, 2003a) should not be subject to compatibility-order effects (Teige-Â�Mocigemba et al., 2008). The EAST

In the EAST, participants are asked to respond to attribute words that are colored white and to target words colored blue or green by pressing one of two keys. For the white attribute words (e.g., positive, negative), participants have to respond based on valence. As a result, one key is assumed to become associated with positive valence (posi-

tive key) and the other key with negative valence (negative key). For the colored target words (e.g., blacks), participants need to select one of those same valenced responses but this time based on word color while ignoring valence of the word. Results typically show that participants perform better when the irrelevant valence of a colored target word corresponds to the valence of the response than when stimulus and response have a different valence (De Houwer, 2003a). To illustrate the procedure, let us consider a racial attitude EAST: In such an EAST application, attribute words might comprise positive and negative words (e.g., happy, sad) that are presented in white color, and target words might comprise names typical for blacks versus whites that are presented in blue color on some trials and in green color on others, respectively. Participants are to press a left key when they see a white word of negative valence (e.g., sad) or a word printed in blue (i.e., black or white name) and to press a right key when they see a white word of positive valence (e.g., happy) or a word printed in green (i.e., black or white name). To the degree that participants show faster or more accurate responses to a colored (target) word (e.g., black name) when the required response is combined with a negative compared with a positive response, it is inferred that participants have negative associations with the object depicted by the colored word. Accordingly, individuals with implicit prejudices against blacks are expected to respond faster and more accurately when the word color of black stimuli requires them to press the negative key compared with the positive key. Although in some domains the EAST provided promising results (e.g., De Houwer & De Bruycker, 2007c), several studies questioned its usefulness as a measure of interindividual differences and showed clearly inferior psychometric properties compared with the IAT (e.g., De Houwer & De Bruycker, 2007b; Teige et al., 2004). In part, the EAST’s unsatisfactory reliability could be improved by some procedural variations: The soÂ�called identification-EAST (ID-EAST; De Houwer & De Bruycker, 2007a), for instance, differs from the EAST in that it requires participants to process the target categories in order to perform the task. The few studies that used the ID-EAST so far (De Houwer & De Bruycker, 2007a; Rudolph et al., 2008) indeed found that the psychometric properties for this variant were superior to the EAST, although still inferior to the IAT. Given the sparse empirical data on the ID-EAST, however, it ap-



7. IAT and Related Tasks

pears to be premature to evaluate the usefulness of this EAST variant at present.

Relative Measure Last but not least, it has been criticized that the IAT is restricted to the assessment of relative association strengths between nominal categories. For instance, a positive score in a racial attitude IAT does not indicate that one evaluates blacks negatively and whites positively. It rather reflects that blacks are evaluated more negatively than whites. Thus, an IAT effect does not permit conclusions about a person’s evaluation of the single categories, nor does the same IAT effect of different persons necessarily reflect the same attitude (e.g., Blanton, Jaccard, Gonzales, & Christie, 2006). As a second problem, the IAT’s applicability is limited to constructs that have a natural counterpart. Several constructs of interest, however, do not meet this requirement. For example, if researchers are interested in a person’s fear of spiders (e.g., Teachman, 2007), it is difficult to think of a suitable counterpart that could serve as a contrast category. Research has, therefore, suggested different solutions such as contrasting the target category with a neutral category (e.g., Sherman et al., 2003) or using other implicit measures that allow for the assessment of associations between a single-Â�target category and attribute categories. Among these measures are the Single Category IAT (SC-IAT; Karpinski & Steinman, 2006), the Go/No-Go Association Test (GNAT; Nosek & Banaji, 2001), the Sorting Paired Features Task (SPF; Bar-Anan, Nosek, & Vianello, 2009), and the EAST (De Houwer, 2003a). The SC-IAT

The SC-IAT is very similar to the IAT, except that it uses three categories instead of four (one target, two attribute categories): The combined blocks involve assigning two categories to one response and one category to the other response (e.g., black/ negative left, positive right or negative left, black/ positive right). Although reliability of the SC-IAT has been shown to be somewhat lower than that of the IAT (see Schnabel et al., 2008), validity estimates are encouraging (Bluemke & Friese, 2008; Friese, Hofmann, & Schmitt, 2008). However, because of its high methodological similarity to the IAT, many of the confounding influences on the IAT may also affect SC-IATs (Karpinski & Steinman, 2006).

133

The GNAT

The GNAT uses a go/no-go task in which participants are first asked to show a “go” response (e.g., key press) to stimuli of a target and an attribute category (e.g., blacks and negative in a racial attitude GNAT) and a “no-go” response (i.e., no key press) to distracter stimuli, some of which belong to the opposed attribute category (here positive). In a second block, the responses for the attribute categories are reversed such that the former go category (here negative) becomes the no-go category and the former no-go category (here positive) becomes the go category. Accordingly, in the second block of a racial attitude GNAT, participants are to show a go response to black and positive stimuli and a no-go response to distracter stimuli, including negative stimuli. Go responses typically have to be made within a given response deadline (e.g., 600 msec). The performance difference between the two pairings of go trials (here black/negative vs. black/ positive) is then interpreted as reflecting the association between the target category and the attribute categories. Note that, in contrast to the other implicit measures discussed in the present chapter, GNAT scores are based on signal detection analyses of error rates rather than response latencies (for details, see Nosek & Banaji, 2001). Thus, individuals with implicit prejudices against blacks are expected to perform better in terms of sensitivity scores (d′) when black and negative stimuli require a go response compared with when black and positive stimuli require a go response. As with the SC-IAT, the GNAT’s reliability has been found to be lower than the IAT’s (e.g., Nosek & Banaji, 2001). However, some studies have attested to the GNAT’s validity (e.g., Boldero, Rawlings, & Haslam, 2007; Gonsalkorale, von Hippel, Sherman, & Klauer, 2009; Teachman, 2007). The SPF

Similar to the SB-IAT and the IAT-RF, the SPF eliminates the IAT’s block structure. Unlike the former two tasks, the SPF uses four response options instead of two: Each trial requires participants to categorize two stimuli (one target, one attribute) at once to one of four possible target–Â� attribute category pairs, thereby forcing the simultaneous processing of both (i.e., target and attribute) association components. In a racial attitude SPF, for instance, the category pairs whites–good, whites–bad, blacks–good, and blacks–bad are each

134

II. METHODS AND PROCEDURES

mapped onto one response key. If, for example, a name typical for whites (target stimulus) and the word wonderful (attribute stimulus) are simultaneously presented, participants are to press the “whites–good” key. If a stimulus pair consists of a name typical for whites and the word awful, participants are to press the “whites–bad” key and so on. According to Bar-Anan and colleagues (2009), the SPF has the unique feature of allowing for separable assessments of the four association strengths involved (e.g., whites–good, whites–bad, blacks– good, blacks–bad). Although the authors present some evidence for differential predictive validity of the four specific scores, they acknowledge the interdependence of the four scores: Each association strength can only be interpreted relative to the other three but not in isolation. Initial studies showed that reliability estimates of the SPF are considerably lower than for the IAT but indicated its validity, both at the group level (known-group approach) and at the individual level (implicit–Â� explicit and implicit–Â�implicit consistency; BarAnan et al., 2009). Note that the SPF has been shown to be sensitive to both focal, attended concepts (e.g., race in a racial attitude SPF) and nonfocal, unattended stimulus features (e.g., gender of race stimuli in a racial attitude SPF). Future research will have to clarify the extent to which the sensitivity to nonfocal stimulus features is advantageous or disadvantageous (cf. confounding influence of stimulus effects in the IAT). The Brief IAT

Finally, Sriram and Greenwald (2009) recently introduced the Brief Implicit Association Test (BIAT). The BIAT differs from the IAT in that it uses substantially fewer trials (about one-third the number of IAT trials) and requires participants to focus on just two, so-Â�called focal, of the four categories in each four-Â�category test block. In the initial combined test block of a racial attitude BIAT, for instance, participants have to press one response key when a stimulus matches either the “whites” or the “good” category and have to press the other response key for “anything else” (here blacks and negative stimuli). In the reverse combined test block, the other target category becomes one of the focal categories: Participants thus have to press one response key when a stimulus matches either the “blacks” or the “good” category and have to press the other response key for “anything else” (here whites and negative stimuli). As in the

IAT, the performance difference between the initial and the reverse combined blocks is interpreted as reflecting the association between the target categories and the attribute category. First findings indicate the crucial role of determining which category is focal: Reliability and validity estimates were satisfactory only when “good” (but not “bad”) was a focal category in attitude BIATs and when “self” (but not “others”) was a focal category in identity BIATs (Sriram & Greenwald, 2009). Note that, in principle, the BIAT may be adapted to allow for the assessment of associations between a single target category and two attribute categories. In the initial combined block of a racial attitude BIAT, for instance, “blacks” and “positive” could serve as the focal categories, whereas “blacks” and “negative” could be focal in the reverse combined block. Clearly, more research is needed to evaluate potentials and limits of the newly developed BIAT, particularly regarding the impact of determining the focal categories on the BIAT’s psychometric properties.

CONCLUSION Since its publication, the IAT has stimulated an enormous amount of fruitful research, revealing its strengths as well as its shortcomings. In the present chapter, we have reviewed this research on the IAT and related tasks. Although many findings regarding the IAT’s psychometric properties appear to be promising, the IAT effect has been shown to be contaminated by several variables operating via different processes. We considered different remedies for the respective contaminants, some of which involve procedural changes to the IAT. The review reveals that, after 11 years of IAT research, many questions are still open, obliging one to show scientific responsibility in using the IAT (and any of its derivates) for individual diagnosis (e.g., for employee selection). Nevertheless, IAT research has clearly contributed to a deeper understanding of human experience and behavior and will certainly continue to do so in the future. Acknowledgments The development of this chapter was supported by Grant No. Kl 614/13-1 from the Deutsche Forschungsgemeinschaft to Karl Christoph Klauer and by Grant No. BCS 0802855 from the U.S. National Science Foundation to Jeffrey W. Sherman.



7. IAT and Related Tasks

Notes 1. Note that there is some ambiguity in the use of the terms implicit and explicit. In the literature, the term implicit measure is commonly used to refer to indirect measures such as response-time measures. The term explicit measure is commonly used to refer to direct measures such as questionnaires or other kinds of self-Â�reports. It has been argued that this terminology makes it difficult to disentangle the empirical measurement level and the construct level. As a result, the outcome of direct and indirect measures might be inadmissibly equated with the underlying implicit and explicit constructs (De Houwer & Moors, 2007; Fazio & Olson, 2003). Furthermore, it is rarely defined what the term implicit measure actually means, which leads to a somewhat arbitrary use of the term (for an attempt to contribute to definitional clarity, see De Houwer et al., 2009). For these and other reasons, De Houwer (2006) regarded the terms direct measure and indirect measure as more appropriate. Although we acknowledge the problems posed by the wording, we decided to stick to the more commonly used terminology of implicit and explicit measures in the present chapter (see Fazio & Olson, 2003). We use these terms, however, without making specific assumptions about their exact relations to automaticity and/ or unconsciousness. In line with the literature, we reserve the term implicit measure for indirect computer-based measures (such as the IAT), whereas other indirect measures not necessarily relying on highly accurate computer-based methods (such as projective tests) will be denoted as indirect measures. 2. In the EMA, participants are presented with distracter words (i.e., positive and negative words) and target words (i.e., words describing the attitude objects of interest, e.g., “flower”) that are displayed to the right or to the left of the participants’ name. Using a joystick, participants have to horizontally move the stimuli either toward their own name, which is assumed to trigger approach behavior, or away from it, which is assumed to trigger avoidance behavior. There are two critical blocks in the EMA: In one block target words have to be moved toward the participant’s name, whereas in the other block, target words have to be moved away from the participant’s name. For distracter words, participants always have to move positive words toward their own name and negative words away from it independent of the block condition. Valence of each target word can thus be estimated as the difference between the response times for moving the respective target word away from the participant’s name (avoidance) versus toward the participant’s name (ap-

135

proach). Thus, it is assumed that for positively evaluated objects (such as “flower”), it is easier to show approach behavior compared with avoidance behavior. The EMA has been shown to measure rank orders of preferences for different objects within the same individual: It allows for assigning multiple attitude objects to a single scale, retaining their rank order on a good–bad dimension, and meaningfully centering this scale around a neutral point (Brendl et al., 2005). Importantly, the EMA thereby differs from the other implicit measures discussed in the present chapter that aim at assessing differences between individuals in their evaluation of the same object.

References Asendorpf, J. B., Banse, R., & Mücke, D. (2002). Double dissociation between implicit and explicit personality self-Â�concept: The case of shy behavior. Journal of Personality and Social Psychology, 83, 380–393. Back, M. D., Schmukle, S. C., & Egloff, B. (2005). Measuring task-Â�switching ability in the Implicit Association Test. Experimental Psychology, 52, 167–179. Banaji, M. R. (2001). Implicit attitudes can be measured. In H. L. Roediger & J. S. Nairne (Eds.), The nature of remembering: Essays in honor of Robert G. Crowder (pp.€117–150). Washington, DC: American Psychological Association. Banse, R., Seise, J., & Zerbes, N. (2001). Implicit attitudes toward homosexuality: Reliability, validity, and controllability of the IAT. Zeitschrift für Experimentelle Psychologie, 48, 145–160. Bar-Anan Y., Nosek, B. A., & Vianello, M. (2009). The Sorting Paired Features Task: A measure of association strengths. Experimental Psychology, 56, 329–343. Blair, I. V. (2002). The malleability of automatic stereotypes and prejudice. Personality and Social Psychology Review, 6, 242–261. Blanton, H., Jaccard, J., Gonzales, P. M., & Christie, C. (2006). Decoding the Implicit Association Test: Implications for criterion prediction. Journal of Experimental Social Psychology, 42, 192–212. Bluemke, M., & Friese, M. (2006). Do features of stimuli influence IAT effects? Journal of Experimental Social Psychology, 42, 163–176. Bluemke, M., & Friese, M. (2008). Reliability and validity of the Single-Target IAT (ST-IAT): Assessing automatic affect toward multiple political groups. European Journal of Social Psychology, 38, 977–997. Boldero, J. M., Rawlings, D., & Haslam, N. (2007). Convergence between GNAT-assessed implicit and explicit personality. European Journal of Personality, 21, 341–358. Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2004). The concept of validity. Psychological Review, 111, 1061–1071. Brendl, C. M., Markman, A., & Messner, C. (2001). How do indirect measures of evaluation work?: Evaluating the inference of prejudice in the Implicit Association

136

II. METHODS AND PROCEDURES

Test. Journal of Personality and Social Psychology, 81, 760–773. Brendl, C. M., Markman, A. B., & Messner, C. (2005). Indirectly measuring evaluations of several attitude objects in relation to a neutral reference point. Journal of Experimental Social Psychology, 41, 346–368. Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K., & Groom, C. (2005). Separating multiple processes in implicit social cognition: The quadmodel of implicit task performance. Journal of Personality and Social Psychology, 89, 469–487. Conrey, F. R., & Smith, E. R. (2007). Attitude representation: Attitudes as patterns in a distributed, connectionist representational system. Social Cognition, 25, 718–735. Cunningham, W. A., Preacher, K. J., & Banaji, M. R. (2001). Implicit attitude measures: Consistency, stability, and convergent validity. Psychological Science, 12, 163–170. Czellar, S. (2006). Self-Â�presentational effects in the Implicit Association Test. Journal of Consumer Psychology, 16, 92–100. De Houwer, J. (2001). A structural and process analysis of the Implicit Association Test. Journal of Experimental Social Psychology, 37, 443–451. De Houwer, J. (2003a). The extrinsic affective Simon task. Experimental Psychology, 50, 77–85. De Houwer, J. (2003b). A structural analysis of indirect measures of attitudes. In J. Musch & K. C. Klauer (Eds.), The psychology of evaluation: Affective processes in cognition and emotion (pp.€ 219–244). Mahwah, NJ: Erlbaum. De Houwer, J. (2006). What are implicit measures and why are we using them? In R. W. Wiers & A. W. Stacy (Eds.), Handbook of implicit cognition and addiction (pp.€11–28). Thousand Oaks, CA: Sage. De Houwer, J. (2008). Comparing measures of attitudes at the functional and procedural level: Analysis and implications. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€361–390). New York: Psychology Press. De Houwer, J., Beckers, T., & Moors, A. (2007). Novel attitudes can be faked on the Implicit Association Test. Journal of Experimental Social Psychology, 43, 972–978. De Houwer, J., & De Bruycker, E. (2007a). The identification-EAST as a valid measure of implicit attitudes toward alcohol-Â�related stimuli. Journal of Behavior Therapy and Experimental Psychiatry, 38, 133–143. De Houwer, J., & De Bruycker, E. (2007b). The Implicit Association Test outperforms the Extrinsic Affective Simon Task as a measure of interindividual differences in attitudes. British Journal of Social Psychology, 46, 401–421. De Houwer, J., & De Bruycker, E. (2007c). Implicit attitudes toward meat and vegetables in vegetarians and non-Â�vegetarians. International Journal of Psychology, 42, 158–165. De Houwer, J., Geldof, T., & De Bruycker, E. (2005). The Implicit Association Test as a general measure of similarity. Canadian Journal of Experimental Psychology, 59, 228–239. De Houwer, J., & Moors, A. (2007). How to define and examine the implicitness of implicit measures. In B.

Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp.€179–194). New York: Guilford Press. De Houwer, J., Teige-Â�Mocigemba, S., Spruyt, A., & Moors, A. (2009). Implicit measures: A normative analysis and review. Psychological Bulletin, 135, 347–368. Eder, A., & Rothermund, K. (2008). When do motor behaviors (mis)match affective stimuli?: An evaluative coding view of approach and avoidance reactions. Journal of Experimental Psychology: General, 137, 262–281. Egloff, B., & Schmukle, S. C. (2002). Predictive validity of an Implicit Association Test for assessing anxiety. Journal of Personality and Social Psychology, 83, 1441–1455. Egloff, B., Schwerdtfeger, A. & Schmukle, S. C. (2005). Temporal stability of the Implicit Association TestÂ�anxiety. Journal of Personality Assessment, 84, 82–88. Fazio, R. H. (1990). Multiple processes by which attitudes guide behavior: The MODE model as an integrative framework. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 23, pp.€ 75–109). San Diego, CA: Academic Press. Fazio, R. H., & Olson, M. A. (2003). Implicit measures in social cognition research: Their meaning and use. Annual Review of Psychology, 54, 297–327. Fiedler, K., & Bluemke, M. (2005). Faking the IAT: Aided and unaided response control on the Implicit Association Test. Basis and Applied Social Psychology, 27, 307–316. Fiedler, K., Messner, C., & Bluemke, M. (2006). Unresolved problems with the “I”, the “A” and the “T”: Logical and psychometric critique of the Implicit Association Test (IAT). European Review of Social Psychology, 17, 74–147. Friese, M., Hofmann, W., & Schmitt, M. (2008). When and why do implicit measures predict behavior? Empirical evidence for the moderating role of opportunity, motivation, and process reliance. European Review of Social Psychology, 19, 285–338. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. Gawronski, B., Deutsch, R., LeBel, E. P., & Peters, K. R. (2008). Response interference as a mechanism underlying implicit measures: Some traps and gaps in the assessment of mental associations with experimental paradigms. European Journal of Psychological Assessment, 24, 218–225. Gawronski, B., Peters, K. R., & LeBel, E. P. (2008). What makes mental associations personal or extraÂ�personal? Conceptual issues in the methodological debate about implicit attitude measures. Social and Personality Psychology Compass, 2, 1002–1023. Gonsalkorale, K., Sherman, J. W., & Klauer, K. C. (2009). Aging and prejudice: Diminished regulation of automatic race bias among older adults. Journal of Experimental Social Psychology, 45, 410–414. Gonsalkorale, K., von Hippel, W., Sherman, J. W., & Klauer, K. C. (2009). Bias and regulation of bias in intergroup interactions: Implicit attitudes toward Muslims and interaction quality. Journal of Experimental Social Psychology, 45, 161–166.



7. IAT and Related Tasks

Govan, C. L., & Williams, K. D. (2004). Changing the affective valence of the stimulus items influences the IAT by re-Â�defining the category labels. Journal of Experimental Social Psychology, 40, 357–365. Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-Â�esteem, and stereotypes. Psychological Review, 102, 4–27. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Greenwald, A. G., & Nosek, B. A. (2001). Health of the Implicit Association Test at age 3. Zeitschrift für Experimentelle Psychologie, 48, 85–93. Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the Implicit Association Test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85, 197–216. Greenwald, A. G., Nosek, B. A., Banaji, M. R., & Klauer, K. C. (2005). Validity of the salience asymmetry interpretation of the IAT: Comment on Rothermund and Wentura. Journal of Experimental Psychology: General, 134, 420–425. Greenwald, A. G., Poehlman, T. A., Uhlmann, E., & Banaji, M. R. (2009). Understanding and using the Implicit Association Test: III. Meta-Â�analysis of predictive validity. Journal of Personality and Social Psychology, 97, 17–41. Gschwendner, T., Hofmann, W., & Schmitt, M. (2008). Differential stability: The effects of acute and chronic construct accessibility on the temporal stability of the Implicit Association Test. Journal of Individual Differences, 29, 70–79. Han, H. A., Olson, M. A., & Fazio, R. H. (2006). The influence of experimentally created extrapersonal associations on the Implicit Association Test. Journal of Experimental Social Psychology, 42, 259–272. Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., & Schmitt, M. (2005). A meta-Â�analysis on the correlation between the Implicit Association Test and explicit self-Â�report measures. Personality and Social Psychology Bulletin, 31, 1369–1385. Hofmann, W., Gschwendner, T., Nosek, B. A., & Schmitt, M. (2005). What moderates implicit-Â�explicit consistency? European Review of Social Psychology, 16, 335–390. Hummert, M. L., Garstka, T. A., O’Brien, L. T., Greenwald, A. G., & Mellott, D. S. (2002). Using the Implicit Association Test to measure age differences in implicit social cognitions. Psychology and Aging, 17, 482–495. Karpinski, A., & Hilton, J. L. (2001). Attitudes and the Implicit Association Test. Journal of Personality and Social Psychology, 81, 774–778. Karpinski, A., & Steinman, R. B. (2006). The single category Implicit Association Test as a measure of implicit social cognition. Journal of Personality and Social Psychology, 91, 16–32. Kinoshita, S., & Peek-O’Leary, M. (2006). Two bases of the compatibility effect in the Implicit Association Test (IAT). Quarterly Journal of Experimental Psychology, 59, 2102–2120. Klauer, K. C., & Mierke, J. (2005). Task-set inertia, attitude accessibility, and compatibility-order effects:

137

New evidence for a task-set switching account of the IAT effect. Personality and Social Psychology Bulletin, 31, 208–217. Klauer, K. C., Schmitz, F., Teige-Â�Mocigemba, S., & Voss, A. (in press). Understanding the role of executive control in the Implicit Association Test: Why flexible people have small IAT effects. Quarterly Journal of Experimental Psychology. Klauer, K. C., Voss, A., Schmitz, F., & Teige-Â�Mocigemba, S. (2007). Process components of the Implicit Association Test: A diffusion-model analysis. Journal of Personality and Social Psychology, 93, 353–368. Kornblum, S., Hasbroucq, T., & Osman, A. (1990). Dimensional overlap: Cognitive basis for stimulusÂ�response compatibility—A model and taxonomy. Psychological Review, 97, 253–270. Lane, K. A., Banaji, M. R., Nosek, B. A., & Greenwald, A. G. (2007). Understanding and using the Implicit Association Test: IV: What we know (so far) about the method. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp.€59–102). New York: Guilford Press. Lilienfeld, S. O., Wood, J. M., & Garb, H. N. (2000). The scientific status of projective techniques. Psychological Science in the Public Interest, 1, 27–66. Lowery, B. S., Hardin, C. D., & Sinclair, S. (2001). Social influence effects on automatic racial prejudice. Journal of Personality and Social Psychology, 81, 842– 855. McFarland, S. G., & Crouch, Z. (2002). A cognitive skill confound on the Implicit Association Test. Social Cognition, 20, 483–510. Mierke, J., & Klauer, K. C. (2001). Implicit association measurement with the IAT: Evidence for effects of executive control processes. Zeitschrift für Experimentelle Psychologie, 48, 107–122. Mierke, J., & Klauer, K. C. (2003). Method-Â�specific variance in the Implicit Association Test. Journal of Personality and Social Psychology, 85, 1180–1192. Mitchell, J. P., Nosek, B. A., & Banaji, M. R. (2003). Contextual variations in implicit evaluation. Journal of Experimental Psychology: General, 132, 455–469. Neely, J. H. (1977). Semantic priming and retrieval from lexical memory: Roles of inhibitionless spreading activation and limited capacity attention. Journal of Experimental Psychology: General, 106, 226–254. Nier, J. A. (2005). How dissociated are implicit and explicit racial attitudes?: A bogus pipeline approach. Group Processes and Intergroup Relations, 8, 39–52. Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84, 231–259. Nosek, B. A. (2005). Moderators of the relationship between implicit and explicit evaluation. Journal of Experimental Psychology: General, 134, 565–584. Nosek, B. A., & Banaji, M. R. (2001). The Go/No-Go Association Task. Social Cognition, 19, 625–666. Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). Harvesting implicit group attitudes and beliefs from a demonstration website. Group Dynamics, 6, 101–115. Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2005). Understanding and using the Implicit Association Test: II. Method variables and construct validity. Personality and Social Psychology Bulletin, 31, 166–180.

138

II. METHODS AND PROCEDURES

Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2007). The Implicit Association Test at age 7: A methodological and conceptual review. In J. A. Bargh (Ed.), Social psychology and the unconscious: The automaticity of higher mental processes (pp.€265–292). New York: Psychology Press. Nosek, B. A., & Hansen, J. J. (2008a). The associations in our heads belong to us: Searching for attitudes and knowledge in implicit evaluation. Cognition and Emotion, 22, 553–594. Nosek, B. A., & Hansen, J. J. (2008b). Personalizing the Implicit Association Test increases explicit evaluation of target concepts. European Journal of Psychological Assessment, 24, 226–236. Nosek, B. A., & Smyth, F. L. (2007). A multitraitÂ�multimethod validation of the Implicit Association Test: Implicit and explicit attitudes are related but distinct constructs. Experimental Psychology, 54, 14–29. Olson, M. A., & Fazio, R. H. (2001). Implicit attitude formation through classical conditioning. Psychological Science, 12, 413–417. Olson, M. A., & Fazio, R. H. (2003). Relations between implicit measures of prejudice: What are we measuring? Psychological Science, 14, 636–639. Olson, M. A., & Fazio, R. H. (2004). Reducing the influence of extrapersonal associations on the Implicit Association Test: Personalizing the IAT. Journal of Personality and Social Psychology, 86, 653–667. Paulhus, D. L. (1984). Two-Â�component models of socially desirable responding. Journal of Personality and Social Psychology, 46, 598–609. Payne, B. K., Burkley, M. A., & Stokes, M. B. (2008). Why do implicit and explicit attitude tests diverge?: The role of structural fit. Journal of Personality and Social Psychology, 94, 16–31. Perugini, M. (2005). Predictive models of implicit and explicit attitudes. British Journal of Social Psychology, 44, 29–45. Pratto, F., & John, O. P. (1991). Automatic vigilance: The attention-Â�grabbing power of negative social information. Journal of Personality and Social Psychology, 61, 380–391. Proctor, R. W., & Cho, Y. S. (2006). Polarity correspondence: A general principle for performance of speeded binary classification tasks. Psychological Bulletin, 132, 416–442. Rogers, R., & Monsell, S. (1995). Costs of a predictable switch between simple cognitive tasks. Journal of Experimental Psychology: General, 124, 207–231. Rothermund, K., Teige-Â�Mocigemba, S., Gast, A., & Wentura, D. (2009). Minimizing the influence of recoding processes in the Implicit Association Test: The Recoding-Free Implicit Association Test (IATRF). Quarterly Journal of Experimental Psychology, 62, 84–98. Rothermund, K., & Wentura, D. (2001). Figure-Â�ground asymmetries in the Implicit Association Test (IAT). Zeitschrift für Experimentelle Psychologie, 48, 94–106. Rothermund, K., & Wentura, D. (2004). Underlying processes in the Implicit Association Test (IAT): Dissociating salience from associations. Journal of Experimental Psychology: General, 133, 139–165. Rothermund, K., Wentura, D., & De Houwer, J. (2005).

Validity of the salience asymmetry account of the IAT: Reply to Greenwald, Nosek, Banaji, and Klauer (2005). Journal of Experimental Psychology: General, 134, 426–430. Rudolph, A., Schröder-Abé, M., Schütz, A., Gregg, A. P., & Sedikides, C. (2008). Through a glass, less darkly?: Reassessing convergent and discriminant validity in measures of implicit self-Â�esteem. European Journal of Psychological Assessment, 24, 273–281. Schmukle, S. C., & Egloff, B. (2004). Does the Implicit Association Test for assessing anxiety measure trait and state variance? European Journal of Personality, 18, 483–494. Schmukle, S. C., & Egloff, B. (2005). A latent state-trait analysis of implicit and explicit personality measures. European Journal of Psychological Assessment, 21, 100–107. Schnabel, K., Asendorpf, J. B., & Greenwald, A. G. (2008). Using Implicit Association Tests for the assessment of implicit personality self-Â�concept. In G. J. Boyle, G. Matthews, & D. H. Saklofske (Eds.), The Sage handbook of personality theory and assessment: Vol. 2. Personality measurement and testing (pp.€508– 528). Thousand Oaks, CA: Sage. Schnabel, K., Banse, R., & Asendorpf, J. B. (2006a). Assessment of implicit personality self-Â�concept using the Implicit Association Test (IAT): Concurrent assessment of anxiousness and angriness. British Journal of Social Psychology, 45, 373–396. Schnabel, K., Banse, R., & Asendorpf, J. B. (2006b). Employing automatic approach and avoidance tendencies for the assessment of implicit personality selfÂ�concept: The Implicit Association Procedure (IAP). Experimental Psychology, 53, 69–76. Schröder-Abé, M., Rudolph, A., & Schütz, A. (2007). High implicit self-Â�esteem is not necessarily advantageous: Discrepancies between explicit and implicit self-Â�esteem and their relationship with anger expression and psychological health. European Journal of Personality, 21, 319–339. Schwarz, N. (2007). Attitude construction: Evaluation in context. Social Cognition, 25, 638–656. Sherman, J. W., Gawronski, B., Gonsalkorale, K., Hugenberg, K., Allen, T. J., & Groom, C. J. (2008). The selfÂ�regulation of automatic associations and behavioral impulses. Psychological Review, 115, 314–335. Sherman, S. J., Presson, C. C., Chassin, L., Rose, J. S., & Koch, K. (2003). Implicit and explicit attitudes toward cigarette smoking: The effects of context and motivation. Journal of Social and Clinical Psychology, 22, 13–39. Spruyt, A., Hermans, D., De Houwer, J., Vandekerckhove, J., & Eelen, P. (2007). On the predictive validity of indirect attitude measures: Prediction of consumer choice behavior on the basis of affective priming in the picture–Â�picture naming task. Journal of Experimental Social Psychology, 43, 599–610. Sriram, N., & Greenwald, A. G. (2009). The brief Implicit Association Test. Experimental Psychology, 56, 283–294. Stahl, C. (2006). Software for generating psychological experiments. Experimental Psychology, 53, 218–232. Stahl, C., & Klauer, K. C. (2007). HMMTree: A computer program for latent-class hierarchical multino-



7. IAT and Related Tasks

mial processing tree models. Behavior Research Methods, 39, 267–273. Steffens, M. C. (2004). Is the Implicit Association Test immune to faking? Experimental Psychology, 51, 165– 179. Steffens, M. C., Kirschbaum, M., & Glados, P. (2008). Avoiding stimulus confounds in Implicit Association Tests by using the concepts as stimuli. British Journal of Social Psychology, 47, 217–243. Steffens, M. C., & Plewe, I. (2001). Items’ cross-Â�category associations as a confounding factor in the Implicit Association Test. Zeitschrift für Experimentelle Psychologie, 48, 123–134. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Strayer, D. L., & Kramer, A. F. (1994). Strategies and automaticity: I. Basic findings and conceptual framework. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 318–341. Swanson, J. E., Rudman, L. A., & Greenwald, A. G. (2001). Using the Implicit Association Test to investigate attitude-Â�behaviour consistency for stigmatised behaviour. Cognition and Emotion, 15, 207–230. Teachman, B. A. (2007). Evaluating implicit spider fear

139

associations using the Go/No-Go Association Task. Journal of Behavior Therapy and Experimental Psychiatry, 38, 156–167. Teige, S., Schnabel, K., Banse, R., & Asendorpf, J. B. (2004). Assessment of multiple implicit self-Â�concept dimensions using the Extrinsic Affective Simon Task (EAST). European Journal of Personality, 18, 495–520. Teige-Â�Mocigemba, S., Klauer, K. C., & Rothermund, K. (2008). Minimizing method-Â�specific variance in the IAT: A single block IAT. European Journal of Psychological Assessment, 24, 237–245. Voss, A., & Voss, J. (2007). Fast-dm: A free program for efficient diffusion model analysis. Behavioral Research Methods, 39, 767–775. Wentura, D., & Rothermund, K. (2007). Paradigms we live by: A plea for more basic research on the Implicit Association Test. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp.€ 195–215). New York: Guilford Press. Wilson, T. D., Lindsey, S., & Schooler, T. Y. (2000). A model of dual attitudes. Psychological Review, 107, 101–126. Wittenbrink, B., & Schwarz, N. (Eds.). (2007). Implicit measures of attitudes. New York: Guilford Press.

Chapter 8

A Practical Guide to Paper-and-�Pencil Implicit Measures of Attitudes Denise Sekaquaptewa, Patrick Vargas, and William von Hippel

S

and negative valence. When Thurstone (1928) declared that “attitudes can be measured,” he was facing a much broader conceptualization of the attitude construct. He wrote, “It will be conceded at the outset that an attitude is a complex affair which cannot be measured by any single numerical index” (p.€530). Allport, for example, defined attitude as “a mental and neural state of readiness, organized through experience, exerting a directive or dynamic influence upon the individual’s response to all objects and situations with which it is related” (1935, p.€810; see also definitions by Krech & Crutchfield, 1948; Smith, Bruner, & White, 1956). When Thurstone focused on measuring the evaluative dimension of attitudes, he (perhaps inadvertently) guided attitudes researchers toward a more restrictive conceptualization of attitudes. More recently, we have seen a similar influence of method on theory, with the development and increasing popularity of priming techniques (Fazio, Jackson, Dunton, & Williams, 1995) and the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) to implicitly measure attitudes. We wonder, though, whether limiting attitude measures to implicit and explicit categories and focusing so much of the implicit attitude research on priming techniques and the IAT might divert attention from other types of measures that are grounded in classic theorizing on attitudes but do not fall easily into the implicit–Â�explicit dichotomy. Indeed, the

ocial psychologists have long been interested in indirect or implicit1 measures of attitudes for what they can tell us about the nonconscious components of attitudes and for their utility in controlling socially desirable responding and demand effects on measurement (Fazio & Olson, 2003). A variety of implicit measures have emerged and proven useful, many of which rely on computerized, reaction time (RT)-based assessments. In this chapter, we review measures that are indirect but are decidedly more low-tech than RT-based assessments and describe their design, implementation, and scoring. Many of these low-tech measures are based on early theories and definitions of attitude, an important consideration given the strong influence of measurement on theorizing about the attitude construct (Ostrom, 1989).

How Measurement Technologies Influence Our Concept of “Attitude” Modern definitions of attitudes are quite different from classic definitions. Consider the widely cited definition of attitude as “a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor” (Eagly & Chaiken, 1993, p.€ 1). The definition is relatively simple, limiting the attitude construct to positive

140



8. Paper-and-�Pencil Implicit Measures of Attitudes

implicit–Â�explicit attitude distinction is preceded by a five-Â�category taxonomy of attitude measures developed by Cook and Selltiz (1964). We believe this more complex classification of attitude measures might remain useful today. Just as implicit and explicit measures are thought to reflect the operation of associative and propositional processes, respectively, other classes of measures might reflect the operation of other types of cognitive processes. Cook and Selltiz’s five classes were self-Â�report, physiological, behavioral, partially structured, and performance on objective tasks. The first three categories are fairly consistent with contemporary conceptualizations. Self-Â�report measures are presently known as explicit measures; examples include Thurstone, semantic differential, and Likert scales. Recent physiological measures include facial electromyographic activity (e.g., Cacioppo & Petty, 1979) and event-Â�related brain potentials

141

(Cacioppo, Crites, Berntson, & Coles, 1993). Behavioral measures range from the lost-Â�letter technique (people are more likely to mail “lost” letters addressed to organizations toward which they are favorably disposed; Milgram, Marm, & Harter, 1965) to the tendency for people to affiliate with similar others (Byrne, Ervin, & Lamberth, 1970). In a modern update of the lost-Â�letter technique, emails were “accidentally” sent to research participants who had to decide whether to reply informing the sender that the e-mail had not reached its intended recipient (Bushman & Bonacci, 2004). In contemporary attitude research, behavior is generally considered an outcome rather than a measure of attitudes (but see Albarracin & Wyer, 2000; Ouellette & Wood, 1998; Patterson, 2001). Cook and Selltiz’s last two categories (and an additional category that we introduce here) are of particular relevance for the present chapter (Table 8.1).

TABLE 8.1.╇Three General Classes of Paper-and-Pencil Measures of Attitudes Classification

Example

Description

Representative research application

Partially structured measures

Thematic Apperception Test (TAT)

Open-ended responses to ambiguous stimuli coded as to the themes reflected

Need for affiliation (Winter et al., 1998)

Name-letter effect

The tendency to prefer letters that are in one’s own initials or name

Self-esteem (Kitayama & Karasawa, 1997; Pelham et al., 2002)

Vignette measures

Ambiguous behaviors presented in a vignette are contrasted away from one’s own attitude

Religious and ethical attitudes (Vargas, von Hippel, & Petty, 2004)

Linguistic intergroup bias (LIB)

The tendency to describe attitudeconsistent events than attitudeinconsistent events with more abstract language

Intergroup attitudes (Maass et al., 1989; von Hippel et al., 1997)

Breadth-Based Adjective Rating Task (BART)

The tendency to describe attitudeconsistent behaviors than attitudeinconsistent behavior with broader adjectives

Group and self attitudes (Hamilton et al., 1992; Karpinski et al., 2007)

Stereotypic explanatory bias (SEB)

The tendency to spontaneously explain stereotype-incongruent than stereotypecongruent behavior

Race and gender stereotypes (Sekaquaptewa et al., 2003; Sekaquaptewa & Espinoza, 2004)

Information error test

Choice of response options that are false, but in attitudinally opposite directions and equidistant to the true answer

Political attitudes (Hammond, 1948)

Paper-and-pencil IAT

Correct classification of target and evaluative category words within a fixed amount of time

Racial attitudes (Lemm et al., 2003)

Word fragment completion

Word fragments or word stems completed with target attitude words

Ethnic attitudes (Son Hing et al., 2002, 2008)

Measures of linguistic bias

Performance on objective tasks

142

II. METHODS AND PROCEDURES

Partially Structured Measures Partially structured measures are those in which respondents are presented with ambiguous stimuli and are asked to interpret the stimuli in some manner. A classic example is the Thematic Apperception Test (TAT; Morgan & Murray, 1935; Murray, 1965). The TAT consists of a set of set of ambiguous images that are shown to a respondent, whose task is to make up stories about the images. Trained coders review the stories and assign scores on dimensions of interest (e.g., need for power [Winter, John, Stewart, Klohnen, & Duncan, 1998]; attitudes toward union labor [Proshansky, 1943]). A variety of different partially structured measures have been developed, including some that do not rely on open-ended responses (e.g., Sokolowski, Schmalt, Langens, & Puca, 2000; Vargas, von Hippel, & Petty, 2004). Partially structured measures are implicit in that they do not require respondents to intentionally retrieve stored evaluative information about attitude objects or evaluate propositional information. Rather, partially structured measures simply allow respondents to describe, or react to, some ambiguous stimulus involving a particular attitude object. According to Cook and Selltiz (1964), these measures are based on the idea that “perception of stimuli that are not clearly structured is influenced by the perceiver’s own needs and dispositions .â•›.â•›. the expressed response corresponds directly to the individual’s attitude” (pp.€47–48).

Performance on Objective€Tasks Attitude measures examining respondents’ performance on objective tasks are presented as “specific tasks to be performed .â•›.â•›. tests of information or ability, or simply as jobs that need to be done” (Cook & Selltiz, 1964, p.€50). Contemporary implicit measures relying on response times, such as the IAT and the evaluative priming task, fit into this category. Early performance measures include Hammond’s information error test, in which participants were presented with a quiz featuring questions in which the response options were both false but in opposite directions and equidistant to the true answer. For example, to assess attitudes toward U.S. President George W. Bush, this item might be used: “The percentage of U.S. citizens living in poverty by the end of G. W. Bush’s first term was (a) 8.5% (b) 15.3%.” Other performance mea-

sures examined respondents’ proclivity to accept or reject logically flawed but emotionally charged arguments or to accept attitude-Â�congruent arguments as most plausible (Saucier & Miller, 2003; Thistlethwaite, 1950; Waly & Cook, 1965). Like partially structured measures, performance measures are also implicit in that they do not require respondents to intentionally indicate whether an attitude object is good or bad. Rather, as noted by Cook and Selltiz, these measures operate on the assumption that “performance may be influenced by attitude, and that a systematic bias in performance reflects the influence of the attitude” (p.€50).

Measures of Linguistic€Biases Although Cook and Selltiz’s (1964) taxonomy of attitude measures rather neatly encompasses most contemporary attitude measures, an additional category that focuses on linguistic processing is also useful, given the growing body of work on the subtle and nuanced ways language choices reflect interpersonal processes and perceptions (Semin, 2008; von Hippel, Sekaquaptewa, & Vargas, 2009). Based on the assumption that different aspects of socially relevant information are encoded in natural language, researchers have turned to analyzing language to reveal how people interact with the world. Personality researchers have analyzed natural language to identify different dimensions of personality (John, Angleitner, & Ostendorf, 1988; Saucier & Goldberg, 1996) and, even more broadly, the measurement of meaning itself (Osgood, Suci, & Tannenbaum, 1957; Pinker, 2007). Attitudes researchers have similarly analyzed natural language to identify markers of various types of beliefs and feelings (Pennebaker, Mehl, & Niederhoffer, 2003; Semin, 2008; von Hippel et al., 2009). Our organization of paper-and-Â�pencil measures into three general classes (partially structured, performance, and linguistic biases) is but one way to represent the current state of research using lowtech implicit measures, and other equally valid distinctions between the various measures could be made. A more fundamental question may be to ask why it is important to consider multiple classes of attitude measures. We believe that the answer to this question lies in the fact that attitudes are complex constructs, capable of influencing thoughts and behaviors in many ways. Most researchers are wary of problems with single operationalizations of complex constructs. Repeated use of a single operation may emphasize some elements irrelevant to



8. Paper-and-�Pencil Implicit Measures of Attitudes

the central concept and under- and overemphasize other elements of importance. As Rosenthal and Rosnow (2008) explained, single operations tend to “underdetermine” concepts (p.€ 46) because a single measure may not fully capture a complex latent construct. The current focus on implicit versus explicit, with the accompanying emphasis on priming techniques and the IAT, has clearly generated a great deal of useful information and theorizing. Nevertheless, that emphasis may now be limiting attitude research as much as advancing it. The use of multiple attitude measures that tap different underlying cognitive processes should allow a more complete understanding of the attitude construct, improve behavioral prediction, and help generate novel hypotheses about how attitudes, cognition, and behavior influence one another. Additionally, as we have argued elsewhere (Vargas, Sekaquaptewa, & von Hippel, 2007), attitude measures tapping deliberative versus spontaneous cognitive processes might be differentially related to behaviors driven by deliberative versus spontaneous processes (cf. Ajzen & Fishbein, 1977; Wilson, Lindsey, & Schooler, 2000). In the remainder of this chapter, we review various ways that paper-and-Â�pencil measures of attitudes have been used in research as implicit indicators of attitudes and describe how such measures are designed, implemented, and scored.

PAPER-AND-PENCIL MEASURES: WHAT THEY DO Research Using Partially Structured Measures Projective Measures The classic projective measure is the TAT (Morgan & Murray, 1935). Although the TAT has suffered reliability problems, its predictive validity can be impressive. For example, Winter and colleagues (1998) obtained explicit measures of extraversion and projective measures of the need for affiliation. These measures were weakly correlated with each other and interacted to predict, among other things, relationship dissatisfaction and low levels of intimacy 30 years after the projective measures had been administered. Women who were extroverted but low in implicit affiliation motive and women who were introverted but high in implicit affiliation motive (i.e., those who showed an implicit–Â�explicit conflict) suffered more dissatisfaction and experienced more low points in intimacy.

143

Projective measures have been used in a variety of other ways. In an early example, Proshansky (1943) presented students known to have pro- and antilabor union attitudes with a series of images previously judged to be neither pro- nor antilabor and asked them to write about each picture. Judges coded the responses as either favorable or unfavorable toward labor. Proshansky’s projective technique was highly correlated with a traditional measure of attitudes toward labor unions. Such implicit–Â�explicit consistency is no longer a hallmark of projective measures or other implicit measures for that matter (see Hofmann, Gschwendner, Nosek, & Schmitt, 2005), but projective techniques have also been used to explore the nature of implicit–Â�explicit discrepancies. For example, using a measure similar to that of Proshansky (1943), Thrash, Elliot, and Schultheiss (2007) demonstrated that low selfÂ�monitors show greater implicit–Â�explicit consistency on the need for achievement than high self-Â�monitors. More recently, measures tapping biased information processing have revealed that people with opposing but strong preexisting attitudes can become more convinced of their original position when presented with mixed evidence because of their selective rejection of attitude-Â�incongruent information (Ditto, Munro, Apanovich, Scepansky, & Lockhart, 2003; Ditto, Scepansky, Munro, Apanovich, & Lockhart, 1998; Lord, Ross, & Lepper, 1979; Saucier & Miller, 2003). This sort of biased rejection of attitude-Â�inconsistent information has not been used as a predictor variable in many studies, but Vargas, von Hippel, and Petty (2004) have shown that contrasting a relatively neutral attitude or behavior away from one’s own can be used to predict unique variance in behavior (beyond that predicted by the IAT or explicit measures).

Name-Â�Letter Preferences Most attitudes regarding the self should be accessible via implicit measurement, but the brunt of the literature has focused on assessment of implicit self-Â�esteem. The most widely used measure of implicit self-Â�esteem is assessment of the name-Â�letter effect, or the tendency to prefer letters that are in one’s own initials (Nuttin, 1985). Interest in this measure stems not only from its ease of use but also from its reliability, predictive validity, and robustness, even in cultures where people do not report high levels of explicit self-Â�esteem (Kitayama & Karasawa, 1997; Koole, Dijksterhuis, & van Knip-

144

II. METHODS AND PROCEDURES

penberg, 2001). Name-�letter preferences are associated with having nurturing parents (DeHart, Pelham, & Tennen, 2006); manifest themselves in occupational, geographic, and romantic partner preferences (Jones, Pelham, Carvallo, & Mirenberg, 2004; Pelham, Mirenberg, & Jones, 2002); and are sensitive to threats to the self-�concept (Jones, Pelham, Mirenberg, & Hetts, 2002), negative self-�thinking (Verplanken, Friborg, Wang, Trafimow, & Woolf, 2007), and daily life events (DeHart & Pelham, 2007). Perhaps most impressively, name-�letter preferences are associated with self-�sabotaging behavior among people whose name letters are associated with negative academic and sporting outcomes (Nelson & Simmons, 2007). Name-�letter effects also appear to be useful in discriminating defensive individuals from others (Bosson, Brown, Zeigler-Hill, & Swann, 2003), and different varieties of name-�letter effects have even been documented, as narcissists find their name letters attractive but not particularly likable (Sakellaropoulo & Baldwin, 2007). Name-�letter effects have the important advantage that they can be assessed via archival records (as in Pelham et al., 2002), and thus they open up a wide variety of sources that would otherwise be unavailable for the study of implicit attitudes. However, name-�letter effects are only one example of how people can show differential levels of preference for items associated with the self. For example, people also show a preference for their birthday numbers (Pelham et al., 2002), and they are more likely to complete word stems with positive words after answering questions about the self (Hetts, Sakuma, & Pelham, 1999). Another implicit measure of self-�esteem is based on signature size (e.g., Stapel & Blanton, 2004). Measures such as signature size are also amenable to historical analysis, and one can easily imagine studies that would be possible with such measures (e.g., correlating signature size of famous historical figures with other aspects of their behavior, such as the audacity of their exploits). A final partially structured attitude measure that has shown some success in predicting behavior is a procedure developed by von Hippel, Lakin, and Shakarchi (2005), in which participants are given success and failure feedback on two novel tasks and asked which task is more important. Participants who were self-�serving by rating the task at which they succeeded as more important than the task at which they failed also tended to be self-�serving by cheating on a math task, but only when they could justify their cheating as unintentional.

Research using Measures of Linguistic Bias Linguistic Intergroup Bias Linguistic intergroup bias (LIB) is the tendency to describe attitude-Â�consistent events with more abstract language than is used to describe attitudeÂ�inconsistent events. LIB has been documented among many social groups, including Italian horseÂ�racing clubs (Maass, Salvi, Arcuri, & Semin, 1989), Japanese baseball fans (Tanabe & Oka, 2001), and nations (Maass, Montalcini, & Biciotti, 1998) and in many types of interpersonal behaviors, including doctor–Â�patient interactions (Watson & Gallois, 2002) and personnel selection processes (Rubini & Menegatti, 2008). LIB has even been shown to emerge in minimal groups (Moscatelli, Albarello, & Rubini, 2008) and among previously unacquainted groups who cooperate versus compete with each other (de Montes, Semin, & Valencia, 2003). To the extent that LIB supports favorable ingroup perception and maintains outgroup stereotypes (Maass, 1999), LIB may be considered an implicit marker of intergroup attitudes and stereotyping. Consistent with this idea, LIB has been shown to predict evaluations of outgroup members (von Hippel, Sekaquaptewa, & Vargas, 1997). Although some research has shown LIB to be correlated with explicit prejudice (Schanke & Ruscher, 1998), other research has failed to find a relationship between the two (von Hippel et al., 1997). The relationship between explicit prejudice and LIB may be moderated by social desirability concerns because explicit prejudice and LIB have been found to be more highly correlated for groups that are not protected by social norms of nondiscrimination, such as Islamic fundamentalists, than for groups that are normatively protected, such as Jews (Franco & Maass, 1999; for an alternative interpretation, see Gawronski, Peters, Brochu, & Strack, 2008). One interpretation of this result is that social desirability norms influence selfÂ�reported attitudes but not the LIB, but it should be noted that individuals can inhibit the LIB if the situation draws their attention to it (Douglas, Sutton, & Wilkin, 2008). Language abstraction has been used most extensively to study intergroup and interpersonal relations, but it has also been used to study other attitudinally relevant phenomena. For example, abstract language is associated with a promotion focus, whereas concrete language is more typical of a prevention focus (Semin, Higgins, de Montes, Estourget, & Valencia, 2005). This pattern is thought to occur because promotion-Â�focused indi-



8. Paper-and-�Pencil Implicit Measures of Attitudes

viduals are inclusive and broad in their emphasis on a successful outcome, whereas prevention�focused individuals are more concerned with concrete details that may go awry. Abstract language use also leads to recall of earlier information than concrete language because older memories are stored in more abstract form than more recent memories (Semin & Smith, 1999). Finally, powerful people are more likely to use abstract language than powerless people (Smith & Trope, 2006) because of the increased psychological distance felt by powerful people (Trope & Liberman, 2003). As a consequence of these various influences on the use of abstract language, care must be taken in designing manipulations and measures of language abstraction to ensure that it is tapping the construct of interest.

Breadth-Based Adjectives Adjective breadth refers to the number of behaviors that could be subsumed by a particular adjective; for example, friendly is a broader descriptor than punctual (Hampson, John, & Goldberg, 1987). As with the LIB, people describe attitude-�consistent behaviors with broader adjectives than they use for attitude-�inconsistent behavior. This effect has been shown with ingroups and outgroups (Hamilton, Gibbons, Stroessner, & Sherman, 1992), with liked and disliked politicians (McGraw, Fischle, Stenner, & Lodge, 1996), and with the self (Karpinski, Steinberg, Versek, & Alloy, 2007). For example, in Hamilton and colleagues (1992), participants rated liked and disliked nationalities on a series of broad and narrow desirable and undesirable traits. Hamilton et al. found that participants believed that liked nationalities exhibited more of the broad desirable traits than the narrow desirable traits, whereas disliked nationalities exhibited more of the narrow than broad desirable traits. Although there is not yet any research demonstrating that this measure is particularly well suited for the implicit assessment of attitudes (with the possible exception of self-�attitudes; Karpinski et al., 2007), it has the advantage that differential evaluations can be measured even though people are only providing positive evaluations of groups. Thus, it seems possible that the measure may be nonreactive and suitable for implicit measurement when social desirability is an important concern.

Stereotypic Explanatory Bias Stereotypic explanatory bias (SEB) is the tendency to spontaneously explain stereotype-�incongruent

145

compared with stereotype-�congruent behavior. To date, research has primarily focused on SEB as an implicit linguistic marker of gender and racial stereotyping. SEB has been shown to correlate with measures of implicit stereotyping (lexical decision making: Espinoza & Sekaquaptewa, 1999; LIB: von Hippel et al., 1997) but not a measure of implicit prejudice (the race IAT: Sekaquaptewa et al., 2003), suggesting that SEB may have convergent validity as a measure of implicit stereotyping. SEB is also increased by factors known to augment stereotyping, such as mortality salience (Schimel et al., 1999), positive mood (Chartrand, van Baaren, & Bargh, 2007), threats to collective self-�esteem (Gonsalkorale, Carlisle, & von Hippel, 2007), and suggestions that stereotyping is normative (Gonsalkorale et al., 2007). SEB is also greater in response to low- than high-�social-status targets (Sekaquaptewa & Espinoza, 2004). Perhaps most important in establishing SEB as an indicator of implicit stereotyping are findings supporting its utility in predicting the nature of intergroup interactions. SEB has been shown to predict nonverbal behaviors in intergroup interactions with African Americans (Sekaquaptewa, Espinoza, Thompson, Vargas, & von Hippel, 2003) and male versus female job applicants (Vargas, Sekaquaptewa, & von Hippel, 2004). In both studies, explicit measures of bias (the Modern Racism Scale and Attitudes toward Women Scale) did not emerge as significant predictors of these outcomes. The predictive validity of SEB also appears to differ depending on the type of explanation provided for stereotype inconsistency on the SEB measure. Explanations for stereotype-�relevant behaviors can be categorized as internal (attributed to the person) or external (attributed to the situation). By categorizing SEB into its internal and external forms, Sekaquaptewa and colleagues (2003) found that engaging in external SEB (attributing stereotype inconsistency to external or situational forces) predicted having a more negative social interaction with an African American confederate. Conversely, engaging in internal SEB (attributing stereotype inconsistency to internal dispositions) predicted having a more positive social interaction with the African American confederate. This pattern may have emerged because external SEB is associated with stereotype maintenance, as stereotype inconsistency is attributed to less controllable and perhaps fleeting situational forces. Internal SEB, on the other hand, is associated with stereotype change because stereotype inconsistency is attributed to stable and enduring characteristics of

146

II. METHODS AND PROCEDURES

the actor. Therefore, an important feature of SEB is that it appears to predict positive as well as negative social interaction outcomes, depending on the locus of explanation. This use of SEB is in need of further research.

Research Using Performance on€Objective Tasks In an early study using a performance-based measure, Hammond (1948) presented the information error test to respondents as a simple multipleÂ�choice test of their knowledge. Hammond devised two different types of questions. As noted, the first type presented respondents with two response options that were equidistant and in opposite directions from the truth. The second type involved presenting response options that are indeterminate; a current example may be “President George W. Bush is known around the world for his (a) affable (b) bumbling demeanor.” Hammond found that both types of questions enabled him to differentiate between known groups of responders. A conceptually related measure examines respondents’ proclivity to accept or reject logically flawed but emotionally charged arguments toward a socially sensitive topic. In one such study, respondents from northern and southern U.S. colleges were shown a series of premises and conclusions about integration and were asked to evaluate the validity of the arguments (Thistlethwaite, 1950). Prejudiced students tended to accept flawed arguments that were emotionally charged and attitude congruent but to reject more neutral, attitudeÂ�congruent arguments.

Paper-and-�Pencil IAT The IAT is designed for computer-based administration, but paper-and-�pencil versions have been developed for situations when computer administration is not feasible. One such paper-and-�pencil IAT features on each page a column of stimuli from the target and attribute categories (e.g., a flower/insect IAT might include daffodil, rainbow, cockroach, and vomit). Respondents categorize the stimuli by marking circles on either side of the column. The critical dependent measure is the difference in the number of stimuli correctly categorized in a fixed amount of time (30 seconds in Mast, 2004; 20 seconds in Lemm, Lane, Sattler, Khan, & Nosek, 2008) between evaluatively congruent and incongruent blocks. The Lemm and colleagues (2008) research has the advantage that it compared different versions

of the IAT. These authors administered two different versions of the IAT, one on computer and the other via paper and pencil. One version featured black and white names as stimuli, and another featured cropped pictures of black and white faces. Lemm and colleagues found that the paper-andÂ�pencil name version of the IAT produced a larger effect size, better test–Â�retest reliability, and higher correlations with a computerized IAT than did the paper-and-Â�pencil picture version of the IAT.

Word Fragment Completion Perhaps the first paper-and-�pencil implicit measure to be used in research on stereotyping and prejudice was word fragment completion, in a study that demonstrated that stereotype activation was not inevitable upon presentation of a stereotyped group member (Gilbert & Hixon, 1991). Since then, word fragment completion tasks have been widely used as implicit evidence that a stereotype has been activated (e.g., Sinclair & Kunda, 1999; Spencer, Fein, Wolfe, Fong, & Dunn, 1998; Steele & Aronson, 1995). However, word fragment completion can also be used as a predictor variable. In one such study (Son Hing, Li, & Zanna, 2002), word fragment completion was used in conjunction with measures of explicit racism to identify aversive racists (individuals who endorse egalitarianism but nevertheless retain unconscious negative feelings toward racial outgroups; Gaertner & Dovidio, 1986). Participants who scored low on an explicit measure of prejudice (the Asian Modern Racism Scale) but high on the implicit word fragment completion measure were identified as aversive racists, and their responses were compared with those of individuals who scored low on both measures. Aversive racists felt more guilty and showed less discrimination toward Asians after being reminded of previous racist transgressions against Asians (consistent with aversive racism and prejudice reduction theories; Monteith, 1996), whereas these outcomes did not emerge among individuals who scored low on both measures. Recently, Son Hing and colleagues have extended this finding using the IAT as the implicit prejudice measure, suggesting that word fragment completion measures might be interchangeable with the IAT under some circumstances (Son Hing, ChungYan, Hamilton, & Zanna, 2008).

Memory Associations Research in health psychology has revived a measure of memory associations that is similar to



8. Paper-and-�Pencil Implicit Measures of Attitudes

Freud’s use of free associations to reveal people’s unconscious attitudes and motivations (Ames, Franken, & Coronges, 2006; Stacy & Wiers, 2006). In an early example of this work, people were asked to provide the first word that comes to mind when they read words ambiguously related to alcohol- or marijuana (Stacy, 1997). Greater frequency of responding with alcohol- and marijuanaÂ�related words in this context was associated with greater subsequent alcohol and marijuana use (Kelly, Masterman, & Marlatt, 2005; Stacy, 1997). Although this measure has not been used in social psychological research, and indeed may possibly be reactive under some circumstances, its predictive ability in health psychology suggests that it would be a useful measure for attitudes researchers to explore, particularly if the valence of the associated word response is considered.

HOW TO: DEVELOPING AND USING PAPER-AND-PENCIL MEASURES Designing, implementing, and scoring paper-andÂ�pencil measures of implicit attitudes involves many of the same issues that are important in using any type of attitude assessment; the measure should reliably assess the intended construct. Yet there are also important differences between the three classes of paper-and-Â�pencil measures and other measures. Similar to all self-Â�report psychological measures, responses on various types of paper-and-Â�pencil measures are subject to biases regarding question wording, context, and response alternatives (Schwarz, 1999). For example, research on survey methodology has revealed that higher means are observed in data collected using rating scales ranging from –5 to 5 than when the rating scale used ranges from 1 to 10 because of people’s avoidance of endorsing negative-Â�numbered response options (Schwarz, Knäuper, Hippler, Noelle-Â�Neumann, & Clark, 1991). To the extent that a paper-andÂ�pencil measure involves properties of questionnaires known to be subject to context and wording effects (such as the use of rating scales in LIB measures), the same care should be taken to reduce the influence of such effects when developing any selfÂ�report scale (see Sudman, Bradburn, & Schwarz, 1996). Unlike most RT measures of attitudes, paperand-Â�pencil measures that involve simple multipleÂ�choice responses or endorsements have the advantage of being easy to administer because no

147

special equipment or computer is needed, and easy to score given their closed-�choice response format. However, scoring of paper-and-�pencil measures can involve significantly more effort when openended responses are involved. Although this may seem at first a disadvantage of using open-ended measures, there are positives. For example, coding schemes can uncover responses or patterns in the data that may not have been evident when respondents are given distinct answer choices (Schuman & Presser, 1981). Overall, open-ended responses may provide opportunity for rich and detailed interpretations of ambiguous information for some paper-and-�pencil measures (e.g., partially structured measures and the TAT), but are not critical for use in all paper-and-�pencil measures (e.g., information error test, LIB). Finally, it is worth noting that some implicit measures assess the accessibility of a single concept in memory (e.g., TAT, word fragment completion tasks), whereas others assess the strength of association between two concepts (e.g., paper-and�pencil IAT, name-�letter preference assessments). Thus, when deciding on a paper-and-�pencil assessment method, consideration of the conceptual representation of the attitude at hand (as a single concept or associated pair) is important.

Developing and Using Partially Structured Measures The TAT Many partially structured measures have the advantage of being relatively easy to administer. The main goal is to present respondents with a stimulus that is ambiguous in regard to the attitude object of interest and let their underlying attitudes guide their interpretation of it. Several guides and coding manuals have been published on using and scoring the TAT (Jenkins, 2008; Winter, 1991). Among the issues to consider when using this technique are cultural differences: Certain symbols and images have clear meanings in some cultures but do not transfer across cultures (Hofer & Chasiotis, 2004). When readministering the TAT, test–Â�retest reliability can be improved by instructing respondents that they should not be concerned about whether their story is the same or different from what they generated initially (Lundy, 1985).

Name-�Letter Preference Assessments Many demonstrations of the name-�letter effect use archival analysis of existing public records (e.g.,

148

II. METHODS AND PROCEDURES

Pelham et al., 2002). Laboratory studies have examined name-Â�letter preferences by having respondents rate all the letters in the alphabet or rate attitude objects, of which some begin with one of the respondent’s initials (e.g., Hodson & Olson, 2005). Name-Â�letter preference is indicated by comparing ratings of letters in one’s name to nonname letters or by assessing preferences for attitude objects that begin with the letters in one’s initials relative to ones that do not. Name-Â�letter preferences can be examined between individuals (“Does Sara like Saran Wrap more than Bob does?”) or within an individual (“Does Sara like Saran Wrap more than foil?”). The within-Â�individual comparison provides an index of the extent to which an individual shows a name-Â�letter preference, which can serve as an indirect indicator of self-Â�esteem. Various scoring algorithms have been developed (see LeBel & Gawronski, 2009, for a comparison of algorithms) in which ratings of objects that begin and do not begin with one of a respondent’s initials, or of letters that are contained or not contained in the respondent’s name, are compared. A difference score is computed between ratings of the letters that appear in the attitude object or in one’s name and a baseline evaluation of non-self-Â�related letters or objects to yield a relative name preference score. Positive scores reflect the name-Â�letter effect: greater preference for objects containing one’s initials than for those that do not or more positive ratings of the letters in one’s own name versus letters not appearing in one’s name.

Other Partially Structured Measures The vignette measures used in Vargas, von Hippel, and Petty (2004) described different target characters engaged in ambiguously conflicting behaviors. For example, an item used to measure religiosity was as follows: “June didn’t go to church once the whole time she was in college, but she claimed that she was still a very religious person. She said that she prayed occasionally, and that she believed in Christian ideals. Sometimes she watched religious programs on TV like the 700 Club or the Billy Graham Crusade.” Following each vignette, participants were asked to rate, using 11-point scales anchored by not at all religious and extremely religious, how religious they thought the main character’s behaviors were and how religious the main character was in general. Responses to the measures are averaged to create a partially structured attitude measure score. Other sets of vignettes were designed to measure attitudes toward dis-

honesty and political orientations. The vignette measures were created using a typical scale development procedure (e.g., Churchill, 1979), so other sets of vignettes assessing attitudes toward a variety of different attitude objects may be developed relatively easily.

Developing and Using Measures of€Linguistic Bias Linguistic Intergroup Bias Initial demonstrations of LIB used cartoon illustrations to show ingroup and outgroup members performing positive and negative behaviors. Cartoon illustrations were used because they are free of language (thus avoiding the potential of abstractions in the language of the stimulus material influencing encoding; Maass et al., 1989), although later demonstrations of LIB successfully used written passages (e.g., von Hippel et al., 1997) and videotaped footage (Gorham, 2006) to convey ingroup and outgroup behaviors. In Maass and colleagues (1989), participants were shown the illustrations and asked to select from among four descriptions the one they thought best described the scene. LIB response choices are based on Semin and Fiedler’s (1988) linguistic category model. This model presents four linguistic categories with which individuals can describe other people. At the most concrete level are descriptive action verbs that refer to single, observable events bound to a particular physical action (such as “to hit”). Next are interpretive action verbs, which are single events but not tied to specific actions (such as “to hurt someone”). The third category, state verbs, includes psychological states without reference to a particular action but which are directed at a specific object (such as “to detest”). Finally, at the most abstract level of description is the fourth category, adjectives, which describes dispositions without reference to particular situations, targets, or behaviors (such as “violent”). LIB response alternatives were structured according to this model, and the respondent’s choice of more abstract descriptions for positive ingroup and negative outgroup behaviors was evidence of LIB. LIB studies using cartoon drawings to show ingroup–Â�outgroup behaviors have the advantage of strong control over extraneous background factors that may influence respondents. Additionally, it is an easy matter to convey group membership by changing simple visual cues (such as shirt colors associated with particular horse-Â�racing contrada;



8. Paper-and-�Pencil Implicit Measures of Attitudes

Maass et al., 1989). LIB measures using written passages may also easily convey group membership using simple cues, such as conveying the gender of the protagonist by using gender-Â�suggestive names or by attaching a photograph said to be of the protagonist (e.g., von Hippel et al., 1997). When photographs are used to convey group membership (such as a black man vs. a white man), it is wise to use more than one picture of a target category (such as black male) because idiosyncratic facial characteristics can influence social judgments (Berry & Wero, 1993). Photographs can also be pretested to ensure similarity in extraneous facial characteristics using Rhodes’s (1988) facial rating scales (as in Gorham, 2006). In creating LIB scores, different procedures have been used successfully. Maass and her colleagues (1989) simply asked respondents to choose the best description from the four alternatives and compared the level of abstraction chosen for ingroup-Â�positive/outgroup-Â�negative behaviors with ingroup-Â�negative/outgroup-Â�positive behaviors. Von Hippel and colleagues (1997) used a subtraction method, in which participants were asked to rate each of the four descriptions on how well it described the passage; the endorsement of the most abstract description of a stereotypeÂ�inconsistent behavior was subtracted from the endorsement of the most abstract description of stereotype-Â�consistent behavior. Positive difference scores indicated LIB.

The Breadth-Based Adjective Rating Task The Breadth-Based Adjective Rating Task (BART; Karpinski et al., 2007) was introduced as an indirect measure of self-�esteem. A list of 144 adjectives is presented, shown in previous research to vary in terms of valence and breadth (Hampson et al., 1987) and selected such that broad and narrow traits are equated on valence (see Karpinski et al., 2007, for the complete list of BART adjectives). Respondents rate how well each adjective describes the attitude object on a scale from 1 (does not describe at all) to 9 (describes very well). To compute a BART score, ratings within each of the four BART categories (broad positive, narrow positive, broad negative, narrow negative) are averaged. The averages from the broad negative and narrow positive are summed, and this sum is subtracted from the sum of the broad positive and narrow negative categories. Higher final scores indicate more positive attitudes toward the self, person, or group. One advantage of the BART is that potentially useful alternative scores can easily be computed,

149

such as a total trait valence score (positive trait average minus negative trait average) and a total breadth score (broad trait ratings minus narrow trait ratings). One disadvantage of the BART is that respondents may find it tedious to rate 144 adjectives; however, meaningful results have been found using only half of the entire list of traits (retaining the traits that are at the extremes of valence and breadth; Karpinski et al., 2007, Experiment 2).

Stereotypic Explanatory Bias A measure of SEB typically consists of a series of sentence beginnings representing group members performing behaviors consistent or inconsistent with their group stereotype. SEB measures involve the comparison of responses to targets from two social groups. To illustrate, we use the example of male and female to assess gender SEB. Four types of SEB items are required, reflecting the crossing of target gender and stereotype consistency: Male targets performing male-Â�stereotypic behaviors; male targets performing female-Â�stereotypic behaviors; female targets performing femaleÂ�stereotypic behaviors; female targets performing male-Â�stereotypic behaviors (see Sekaquaptewa & Espinoza, 2004, for a useful example of how to pretest SEB items). Previous SEB studies have used three items of each type plus filler items designed to be stereotype neutral. Respondents are asked to complete the sentence with the first response that comes to mind, using any ending they wish as long as it forms a grammatical sentence. In computing an SEB score, it is important to use predetermined criteria to classify SEB responses. Responses should be coded by independent raters as to whether they explain the behavior presented in the sentence beginning (e.g., “Susan went to the auto show .â•›.â•›. because her dad wanted her to”) or simply continue the sentence without providing an explanation (e.g., “Susan went to the auto show .â•›.â•›. last weekend”). A general SEB score can be computed by subtracting the number of explanations provided for stereotype-Â�consistent SEB items from the number provided for stereotypeÂ�inconsistent items. Positive difference scores indicate SEB: greater explanation of stereotype inconsistency over stereotype consistency. SEB subscores can also be computed according to whether the explanations reflect internal or external attributions. For example, the response “Susan went to the auto show .â•›.â•›. because her dad wanted her to” would be scored as an external explanation because her behavior was explained by

150

II. METHODS AND PROCEDURES

a force external to her, whereas the response “because she loved cars” would be scored as an internal explanation because it places the motivation for Susan’s behavior within her own disposition or preferences. Because of the implications of locus of explanation for stereotype maintenance, using these internal and external SEB subscales may provide an indicator of SEB that is more sensitive in predicting different behavioral outcomes (Sekaquaptewa et al., 2003).

Developing and Using Measures of€Performance on Objective Tasks Paper-and-Â�Pencil IAT Recent reviews and methodological discussions of the IAT tend to raise issues that are probably relevant to both the computerized and paperand-Â�pencil versions of the IAT (e.g., see TeigeÂ�Mocigemba, Klauer, & Sherman, Chapter 7, this volume). Such issues include the categories and category labels chosen (e.g., whether to contrast science with humanities, arts, or some other nonscience discipline; use of the label “homosexual” or “gay”), specific stimuli/category exemplars (for the category “USA,” a picture of Vice President Dick Cheney vs. a picture of an American flag), number of trials (60 being recommended for the critical trials), and order of presentation of trials within an IAT (Greenwald et al., 1998) as well as order of IAT and other (explicit) measures (Nosek, Greenwald, & Banaji, 2005). Scoring procedures for paper-and-Â�pencil IATs have been defined based on correct categorization of listed stimuli within a predetermined time frame; similar to computerized IATs, the number of correct categorizations is compared between critical stages (i.e., evaluatively congruent and incongruent blocks; Lemm et al., 2003; Mast, 2004).

Information Error Test The information error test requires effort in item development but is easy to score. Items should present informational questions about an attitude object with response choices that are not the true answer to the question but that are equally wrong (i.e., equidistant from the truth). As stated by Hammond, “In constructing an item the principle requirement was to eliminate reality, the truth of the matter, as a factor and thus force the respondent into a choice of errors” (1948, p.€ 40, original emphasis). The direction of the error reveals the respondent’s underlying attitude. The original

questionnaire developed by Hammond used only two response alternatives and assigned a point for choosing the pro-Â�attitude object response alternative and no point for choosing the anti-Â�attitude object alternative to produce an index in which positive scores reflect positive attitudes. However, Hammond did note that future tests could be “constructed with four error-Â�choices to provide for ‘intensity’ of error” (p.€43). Thus, conceivably an information error test could be devised with response options that vary in their distance from the truth (from being slightly off to being substantially distant from the true answer), providing a greater range of scores.

Word Fragment Completion Generally, one creates a list of target words and fashions word fragments by leaving out at least two specific letters or word stems by leaving out word endings. For example, to assess death accessibility, Schimel, Hayes, Williams, and Jahrig (2007) developed target words such as coffin and created the word fragment “coff _ _ “ (in this case, a word stem “coff__” could also have been created). Typically, word fragments or stems are designed so they can be completed with alternative completions or endings (such as “coffee” in this example). Pretesting can confirm that word fragments can be completed with nontarget as well as target words (e.g., Steele & Aronson, 1995). In the implicit memory literature, however, word fragments have been used in which the fragments were designed to have only one solution, and memory was demonstrated by an increased likelihood of arriving at a solution (e.g., Tulving, Schacter, & Stark, 1982). Such a procedure could theoretically be used with word fragment or stem completion that is intended to measure attitudes or construct accessibility. Researchers may use word frequency tables (e.g., Dahl, 1979) and databases (e.g., www.psych. rl.ac.uk; Wilson, 1988) to compare frequency and familiarity of target words to alternatives. Coffee may be more often used in the English language than coffin, for example. In addition, published work on word usage may be used to develop filler items that can be completed with equally frequent nontarget concept words. Norms even exist for different stem completions (Graf & Williams, 1987) and for free associations to specific words (w3.usf.edu/FreeAssociation/; Nelson, McEvoy, & Schreibner, 1998). In coding word fragments, one may use a stringent criterion of assigning one point for each item completed with the target word. Alternatively, one



8. Paper-and-�Pencil Implicit Measures of Attitudes

could use more lax criteria by assigning points for word completions that are close in meaning to the target concept, even for a filler item (e.g., completing the intended filler item _ r _ _ e as grave.

CONCLUSION Paper-and-Â�pencil implicit measures of attitudes, in conjunction with recently developed computerized assessments, provide an interesting example of the evolution of our concept of attitude as influenced by available technologies. Early definitions noted that defining attitudes was a “complex affair” (Allport, 1935), and early assessment techniques typically fell short of capturing this complexity. Advancements in technology led to adjustments in theory as the use of specialized equipment and computer technologies allowed microassessment of responses to stimuli. Thus, today’s conceptualizations of attitude represent the confluence of our available methodologies, distinguishing, for example, between associative (as measured by RT assessments) and propositional processes (as measured using self-Â�reports; Gawronski & Bodenhausen, 2007). The taxonomy proposed by Cook and Selltiz (1964), and revised in the present chapter, might help us to consider how attitudes involve cognitive processes other than propositional and associative. For example, partially structured measures require respondents to report on their interpretation of ambiguous stimuli. The way in which ambiguous stimuli are interpreted may tap perceptual processes (a close play in a sporting event may be perceived very differently by fans of opposing teams; Hastorf & Cantril, 1954) or a variety of cognitive biases (e.g., the hostile media phenomenon; Vallone, Ross, & Lepper, 1985; for an excellent account of other biases that may influence the interpretation of [ambiguous] stimuli, see MacCoun, 1998). Partially structured measures requiring more elaborate thought might also tap patterns of spreading activation in semantic memory and other processes underlying more deliberative cognition. Linguistic measures, likewise, may tap different syntactical, morphological, and semantic processes. Although it is unlikely that any measure is process pure, reflecting the influence of only one type of cognitive process, different classes of measures may tap a variety of cognitive processes that may add to our understanding of how attitudes are formed, maintained, and expressed. Looking forward, one may predict that new technological advances (e.g., virtual reality tech-

151

nologies) will remain a continual influence on our evolving concept of attitude. However, it will be important to continue to investigate the utility of paper-and-�pencil measures of attitudes, which not only have emerged as useful predictors of important outcomes but also show promise in capturing components of the attitude construct that are untapped by measures of associative processing (Vargas et al., 2007). Although the popularity of various technologies may change over time, paperand-�pencil measures may continue to contribute to our understanding of attitudes by virtue of their potential to measure aspects of attitudes not reflected in explicit self-�report, physiological, or RTbased assessments. Note 1. The term implicit is generally used to refer to a lack of awareness, making an implicit measure one in which respondents are unaware that their attitudes are being measured. Similarly, an implicit attitude may be considered an evaluative tendency of which respondents are unaware (see Fazio & Olson, 2003; Petty, Wheeler, & Tormala, 2003). In comparison to an implicit measure, an indirect, or disguised, measure is one in which respondents are not directly asked to indicate their attitudes about an object. In the present chapter, we use the terms implicit and indirect interchangeably when referring to attitude measures.

References Ajzen, I., & Fishbein, M. (1977). Attitude–behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84, 888–918. Albarracin, D., & Wyer, R. S., Jr. (2000). The cognitive impact of past behavior: Influences of beliefs, attitudes, and future behavioral decisions. Journal of Personality and Social Psychology, 79, 5–22. Allport, G. W. (1935). Attitudes. In C. Murchison (Ed.), Handbook of social psychology (pp.€133–175). Worcester, MA: Clark University Press. Ames, S. L., Franken, I. H. A., & Coronges, K. (2006). Implicit cognition and drugs of abuse. In R. W. Wiers & A. W. Stacy (Eds.), Handbook of implicit cognition and addiction (pp.€ 363–378). Thousand Oaks, CA: Sage. Berry, D. S., & Wero, J. L. F. (1993). Accuracy in face perception: A view from ecological psychology. Journal of Personality, 61, 497–520. Bosson, J. K., Brown, R. P., Zeigler-Hill, V., & Swann, W. B., Jr. (2003). Self-Â�enhancement tendencies among people with high explicit self-Â�esteem: The moderating role of implicit self-Â�esteem. Self and Identity, 2, 169–187. Bushman, B. J., & Bonacci, A. M. (2004). You’ve got

152

II. METHODS AND PROCEDURES

mail: Using email to examine the effect of prejudiced attitudes on discrimination against Arabs. Journal of Experimental Social Psychology, 40, 753–759. Byrne, D., Ervin, C. R., & Lamberth, J. (1970). Continuity between the experimental study of attraction and real-life computer dating. Journal of Personality and Social Psychology, 16, 157–165. Cacioppo, J. T., Crites, S. L., Berntson, G. G., & Coles, M. G. (1993). If attitudes affect how stimuli are processed, should they not affect the event-Â�related brain potential? Psychological Science, 4, 108–112. Cacioppo, J. T., & Petty, R. E. (1979). Attitudes and cognitive response: An electrophysiological approach. Journal of Personality and Social Psychology, 37, 2181–2199. Chartrand, T. L., van Baaren, R. B., & Bargh, J. A. (2007). Linking automatic evaluation to mood and information processing style: Consequences for experienced affect, impression formation, and stereotyping. Journal of Experimental Psychology: General, 135, 70–77. Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16, 64–73. Cook, S. W., & Selltiz, C. (1964). A multiple-Â�indicator approach to attitude measurement. Psychological Bulletin, 62, 36–55. Dahl, H. (1979). Word frequency of spoken American English. Essex, CT: Verbatim. de Montes, L. G., Semin, G. R., & Valencia, J. F. (2003). Communication patterns in interdependent relationships. Journal of Language and Social Psychology, 22, 259–281. DeHart, T., & Pelham, B. W. (2007). Fluctuations in state implicit self-Â�esteem in response to daily negative events. Journal of Experimental Social Psychology, 43, 157–165. DeHart, T., Pelham, B. W., & Tennen, H. (2006). What lies beneath: Parenting style and implicit self-Â�esteem. Journal of Experimental Social Psychology, 42, 1–17. Ditto, P. H., Munro, G. D., Apanovich, A. M., Scepansky, J. A., & Lockhart, L. K. (2003). Spontaneous skepticism: The interplay of motivation and expectation in responses to favorable and unfavorable medical diagnoses. Personality and Social Psychology Bulletin, 29, 1120–1132. Ditto, P. H., Scepansky, J. A., Munro, G. D., Apanovich, A. M., & Lockhart, L. K. (1998). Motivated sensitivity to preference-Â�inconsistent information. Journal of Personality and Social Psychology, 75, 53–69. Douglas, K. M., Sutton, R. M., & Wilkin, K. (2008). Could you mind your language?: An investigation of communicators’ ability to inhibit linguistic bias. Journal of Language and Social Psychology, 27, 123– 139. Eagly, A. E., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth, TX: Harcourt Brace Jovanovich. Espinoza, P., & Sekaquaptewa, D. (1999, June). The role of biased processing in assessing prejudice. Paper presented at the 11th Annual Convention of the Association for Psychological Science, Denver, CO. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide

pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Fazio, R. H., & Olson, M. A. (2003). Implicit measures in social cognition research: Their meaning and use. Annual Review of Psychology, 54, 297–327. Franco, F. M., & Maass, A. (1999). Intentional control over prejudice: When the choice of the measure matters. European Journal of Social Psychology, 29, 469–477. Gaertner, S. L., & Dovidio, J. F. (1986). The aversive form of racism. In J. F. Dovidio & S. L. Gaertner (Eds.), Prejudice, discrimination, and racism (pp.€ 61– 89). San Diego, CA: Academic Press. Gawronski, B., & Bodenhausen, G. V. (2007). Unraveling the processes underlying evaluation: Attitudes from the perspective of the APE model. Social Cognition, 25, 687–717. Gawronski, B., Peters, K. R., Brochu, P. M., & Strack, F. (2008). Understanding the relations between different forms of racial prejudice: A cognitive consistency perspective. Personality and Social Psychology Bulletin, 34, 648–665. Gilbert, D. T., & Hixon, J. G. (1991). The trouble of thinking: Activation and application of stereotypic beliefs. Journal of Personality and Social Psychology, 60, 509–517. Gonsalkorale, K., Carlisle, C., & von Hippel, W. (2007). Intergroup threat increases implicit stereotyping. International Journal of Psychology and Psychological Therapy, 7, 189–200. Gorham, B. W. (2006). News media’s relationship with stereotyping: The linguistic intergroup bias in response to crime news. Journal of Communication, 56, 289–308. Graf, P., & Williams, D. (1987). Completion norms for 40 three-Â�letter word stems. Behavioral Research Methods, Instruments, and Computers, 19, 422–445. Greenwald, A. G., McGhee, D., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1364–1480. Hamilton, D. L., Gibbons, P. A., Stroessner, S. J., & Sherman, J. W. (1992). Stereotypes and language use. In G. R. Semin & K. Fiedler (Eds.), Language, interaction and social cognition (pp.€ 102–128). Thousand Oaks, CA: Sage. Hammond, K. R. (1948). Measuring attitudes by error choice: An indirect method. Journal of Abnormal and Social Psychology, 43, 38–48. Hampson, S. E., John, O. P., & Goldberg, L. R. (1987). Category-Â�breadth and social desirability values for 573 personality terms. European Journal of Personality, 1, 37–54. Hastorf, A. H., & Cantril, H. (1954). They saw a game: A case study. Journal of Abnormal Psychology, 49, 129–134. Hetts, J. J., Sakuma, M., & Pelham, B. W. (1999). Two roads to positive regard: Implicit and explicit selfÂ�evaluation and culture. Journal of Experimental Social Psychology, 35, 512–559. Hodson, G., & Olson, J. M. (2005). Testing the generality of the name letter effect: Name initials and everyday attitudes. Personality and Social Psychology Bulletin, 31, 1099–1111.



8. Paper-and-�Pencil Implicit Measures of Attitudes

Hofer, J., & Chasiotis, A. (2004). Methodological considerations of applying a TAT-type picture-story test in cross-Â�cultural research. Journal of Cross-Â�Cultural Psychology, 35, 224–241. Hofmann, W., Gschwendner, T., Nosek, B. A., & Schmitt, M. (2005). What moderates implicit–Â� explicit consistency? European Review of Social Psychology, 16, 335–390. Jenkins, S. R. (2008). A handbook of clinical scoring systems for thematic apperceptive techniques. Mahwah, NJ: Erlbaum. John, O. P., Angleitner, A., & Ostendorf, F. (1988). The lexical approach to personality: A historical review of trait taxonomic research. European Journal of Personality, 2, 171–203. Jones, J. T., Pelham, B. W., Carvallo, M., & Mirenberg, M. C. (2004). How do I love thee? Let me count the Js: Implicit egotism and interpersonal attraction. Journal of Personality and Social Psychology, 87, 665–683. Jones, J. T., Pelham, B. W., Mirenberg, M. C., & Hetts, J. J. (2002). Name letter preferences are not merely mere exposure: Implicit egotism as self-Â�regulation. Journal of Experimental Social Psychology, 38, 170–177. Karpinski, A., Steinberg, J. A., Versek, B., & Alloy, L. B. (2007). The Breadth-Based Adjective Rating Task (BART) as an indirect measure of self-Â�esteem. Social Cognition, 25, 778–818. Kelly, A. B., Masterman, P. W., & Marlatt, G. A. (2005). Alcohol related associative strength and drinking behaviours: Concurrent and prospective relationships. Drug and Alcohol Review, 24, 489–498. Kitayama, S., & Karasawa, M. (1997). Implicit selfÂ�esteem in Japan: Name letters and birthday numbers. Personality and Social Psychology Bulletin, 23, 736–742. Koole, S. L., Dijksterhuis, A., & van Knippenberg, A. (2001). What’s in a name: Implicit self-Â�esteem and the automatic self. Journal of Personality and Social Psychology, 80, 669–685. Krech, D., & Crutchfield, R. (1948). Theory and problems of social psychology. New York: McGraw-Hill. LeBel, E. P., & Gawronski, B. (2009). How to find what’s in a name: Scrutinizing the optimality of five scoring algorithms for the name-Â�letter task. European Journal of Personality, 23, 85–106. Lemm, K. M., Lane, K. A., Sattler, D. N., Khan, S. R., & Nosek, B. A. (2003). Assessing implicit Cognitions with a paper-Â�format Implicit Association Test. In M. A. Morrison & T. G. Morrison (Eds.), The psychology of modern prejudice (pp.€ 123–146). Hauppauge, NY: Nova Science. Lord, C. G., Ross, L., & Lepper, M. (1979). Biased assimilation and attitude polarization: The effects of prior theories on subsequently considered evidence. Journal of Personality and Social Psychology, 37, 2098–2109. Lundy, A. (1985). The reliability of the Thematic Apperception Test. Journal of Personality Assessment, 49, 141–145. Maass, A. (1999). Linguistic intergroup bias: Stereotype perpetuation through language. Advances in Experimental Social Psychology, 31, 79–121. Maass, A., Montalcini, F., & Biciotti, E. (1998). On the (dis)confirmability of stereotypic attributes. European Journal of Social Psychology, 28, 383–402.

153

Maass, A., Salvi, D., Arcuri, L., & Semin, G. (1989). Language use in intergroup contexts: The linguistic intergroup bias. Journal of Personality and Social Psychology, 57, 981–993. MacCoun, R. J. (1998). Biases in the interpretation and use of research results. Annual Review of Psychology, 49, 259–287. Mast, M. S. (2004). Men are hierarchical, women are egalitarian: An implicit gender stereotype. Swiss Journal of Psychology, 63, 107–111. McGraw, K. M., Fischle, M., Stenner, K., & Lodge, M. (1996). What’s in a word?: Bias in trait descriptions of political leaders. Political Behavior, 18, 263–287. Milgram, S., Marm, L., & Harter, S. (1965). The lostÂ�letter technique: A tool of social research. Public Opinion Quarterly, 29, 437–438. Monteith, M. J. (1996). Affective reactions to prejudiceÂ�related discrepant responses: The impact of standard salience. Personality and Social Psychology Bulletin, 22, 48–59. Morgan, C. D., & Murray, H. A. (1935). A method for investigating fantasies. Archives of Neurology and Psychiatry, 34, 289–304. Moscatelli, S., Albarello, F., & Rubini, M. (2008). Linguistic discrimination in minimal groups: The impact of status differentials. Journal of Language and Social Psychology, 27, 140–154. Murray, H. A. (1965). Uses of the Thematic Apperception Test. In B. I. Murstein (Ed.), Handbook of projective techniques. New York: Basic Books. Nelson, D. L., McEvoy, C. L., & Schreiber, T. A. (1998). The University of South Florida word association, rhyme, and word fragment norms. Retrieved December 16, 2009, 3.usf.edu/FreeAssociation/. Nelson, L. D., & Simmons, J. P. (2007). Moniker maladies: When names sabotage success. Psychological Science, 18, 1106–1112. Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2005). Understanding and using the Implicit Association Test: II. Method variables and construct validity. Personality and Social Psychology Bulletin, 31, 166–180. Nuttin, J. M. (1985). Narcissism beyond gestalt and awareness: The name letter effect. European Journal of Social Psychology, 15, 353–361. Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The measurement of meaning. Urbana: University of Illinois Press. Ostrom, T. M. (1989). The interdependence of attitude theory and measurement. In A. R. Pratkanis, S. J. Breckler, & A. G. Greenwald (Eds.), Attitude structure and function (pp.€11–36). Hillsdale, NJ: Erlbaum. Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The processes by which past behavior predicts future behavior. Psychological Bulletin, 124, 54–74. Patterson, M. L. (2001). Toward a comprehensive model of non-Â�verbal communication. In W. P. Robinson & H. Giles (Eds.), The new handbook of language and social psychology (pp.€159–176). Hoboken, NJ: Wiley. Pelham, B. W., Mirenberg, M. C., & Jones, J. T. (2002). Why Susie sells seashells by the seashore: Implicit egotism and major life decisions. Journal of Personality and Social Psychology, 82, 469–487. Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G.

154

II. METHODS AND PROCEDURES

(2003). Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology, 54, 547–577. Petty, R. E., Wheeler, S. C., & Tormala, Z. L. (2003). Persuasion and attitude change. In T. Mellon & M. J. Lerner (Eds.), Handbook of psychology: Vol. 5. Personality and social psychology (pp.€ 353–382). Hoboken, NJ: Wiley. Pinker, S. (2007). Language as an adaptation by natural selection. Acta Psychologica Sinica, 39, 431–438. Proshansky, H. M. (1943). A projective method for the study of attitudes. Journal of Abnormal and Social Psychology, 38, 393–395. Rhodes, G. (1988). Looking at faces: First-order and second-order features as determinants of facial appearance. Perception, 17, 43–63. Rosenthal, R., & Rosnow, R. L. (2008). Essentials of behavioral research: Methods and data analysis (3rd ed.). New York: McGraw-Hill. Rubini, M., & Menegatti, M. (2008). Linguistic bias in personnel selection. Journal of Language and Social Psychology, 27, 168–181. Sakellaropoulo, M., & Baldwin, M. W. (2007). The hidden sides of self-Â�esteem: Two dimensions of implicit self-Â�esteem and their relation to narcissistic reactions. Journal of Experimental Social Psychology, 43, 995–1001. Saucier, D. A., & Miller, C. T. (2003). The persuasiveness of racial arguments as a subtle measure of racism. Personality and Social Psychology Bulletin, 29, 1303–1315. Saucier, G., & Goldberg, L. R. (1996). Evidence for the Big Five in analyses of familiar English personality adjectives. European Journal of Personality, 10, 61–77. Schanke, S. B., & Ruscher, J. B. (1998). Modern racism as a predictor of the linguistic intergroup bias. Journal of Language and Social Psychology, 17, 484–491. Schimel, J., Hayes, J., Williams, T., & Jahrig, J. (2007). Is death really the worm at the core?: Converging evidence that worldview threat increases death–Â�thought accessibility. Journal of Personality and Social Psychology, 92, 789–803. Schimel, J., Simon, L., Greenberg, J., Pyszczynski, T., Solomon, S., Waxmonsky, J., et al. (1999). Stereotypes and terror management: Evidence that mortality salience enhances stereotypic thinking and preferences. Journal of Personality and Social Psychology, 77, 905–926. Schuman, H., & Presser, S. (1981). Questions and answers in attitude surveys. New York: Academic Press. Schwarz, N. (1999). Self-Â�reports: How the questions shape the answers. American Psychologist, 54, 93– 105. Schwarz, N., Knäuper, B., Hippler, H.-J., NoelleÂ�Neumann, E., & Clark, L. (1991). Rating scales: Numeric values may change the meaning of scale labels. Public Opinion Quarterly, 55, 570–582. Sekaquaptewa, D., & Espinoza, P. (2004). Biased processing of stereotype-Â�incongruency is greater for low than high status groups. Journal of Experimental Social Psychology, 40, 128–135. Sekaquaptewa, D., Espinoza, P., Thompson, M., Vargas, P., & von Hippel, W. (2003). Stereotypic explanatory bias: Implicit stereotyping as a predictor of discrimi-

nation. Journal of Experimental Social Psychology, 39, 75–82. Semin, G. R. (2008). Language puzzles: A prospective retrospective on the linguistic category model. Journal of Language and Social Psychology, 27, 197–209. Semin, G. R., & Fiedler, K. (1988). The cognitive functions of linguistic categories in describing persons: Social cognition and language. Journal of Personality and Social Psychology, 54, 558–568. Semin, G. R., Higgins, E. T., de Montes, L. G., Estourget, Y., & Valencia, J. F. (2005). Linguistic signatures of regulatory focus: How abstraction fits promotion more than prevention. Journal of Personality and Social Psychology, 89, 36–45. Semin, G. R., & Smith, E. R. (1999). Revisiting the past and back to the future: Memory systems and the linguistic representation of social events. Journal of Personality and Social Psychology, 76, 877–892. Sinclair, L., & Kunda, Z. (1999). Reactions to a black professional: Motivated inhibition and activation of conflicting stereotypes. Journal of Personality and Social Psychology, 77, 885–904. Smith, M. B., Bruner, J. S., & White, R. W. (1956). Opinions and personality. New York: Wiley. Smith, P. K., & Trope, Y. (2006). You focus on the forest when you’re in charge of the trees: Power priming and abstract information processing. Journal of Personality and Social Psychology, 90, 578–596. Sokolowski, K., Schmalt, H., Langens, T. A., & Puca, R. M. (2000). Assessing achievement, affiliation, and power motives all at once: The Multi-Â�Motive Grid (MMG). Journal of Personality Assessment, 74, 126–145. Son Hing, L. S., Chung-Yan, G. A., Hamilton, L. K., & Zanna, M. P. (2008). A two-Â�dimensional model that employs explicit and implicit attitudes to characterize prejudice. Journal of Personality and Social Psychology, 94, 971–987. Son Hing, L. S., Li, W., & Zanna, M. P. (2002). Inducing hypocrisy to reduce prejudicial responses among aversive racists. Journal of Experimental Social Psychology, 38, 71–78. Spencer, S. J., Fein, S., Wolfe, C., Fong, C., & Dunn, M. (1998). Stereotype activation under cognitive load: The moderating role of self-image threat. Personality and Social Psychology Bulletin, 24, 1139–1152. Stacy, A. W. (1997). Memory activation and expectancy as prospective predictors of alcohol and marijuana use. Journal of Abnormal Psychology, 106, 61–73. Stacy, A. W., & Wiers, R. W. (2006). An implicit cognition, associative memory framework for addiction. In M. R. Munafo & I. P. Albero (Eds.), Cognition and addiction (pp.€ 31–71). New York: Oxford University Press. Stapel, D. A., & Blanton, H. (2004). From seeing to being: Subliminal social comparisons affect implicit and explicit self-Â�evaluations. Journal of Personality and Social Psychology, 87, 468–481. Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of AfricanÂ�Americans. Journal of Personality and Social Psychology, 69, 797–811. Sudman, S., Bradburn, N. M., & Schwarz, N. (1996). Thinking about answers: The application of cognitive



8. Paper-and-�Pencil Implicit Measures of Attitudes

processes to survey methodology. San Francisco: JosseyBass. Tanabe, Y., & Oka, T. (2001). Linguistic intergroup bias in Japan. Japanese Psychological Research, 43, 104–111. Thistlethwaite, D. L. (1950). Attitude and structure as factors in the distortion in reasoning. Journal of Abnormal and Social Psychology, 45, 442–458. Thrash, T. M., Elliot, A. J., & Schultheiss, O. C. (2007). Methodological and dispositional predictors of congruence between implicit and explicit need for achievement. Personality and Social Psychology Bulletin, 33, 961–974. Thurstone, L. L. (1928). Attitudes can be measured. American Journal of Sociology, 33, 529–554. Trope, Y., & Liberman, N. (2003). Temporal construal. Psychological Review, 110, 403–421. Tulving, E., Schacter, D. L., & Stark, H. A. (1982). Priming effects in word-Â�fragment completion are independent of recognition memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 8, 336–342. Vallone, R. P., Ross, L., & Lepper, M. R. (1985). The hostile media phenomenon: Biased perception and perceptions of media bias in coverage of the “Beirut Massacre.” Journal of Personality and Social Psychology, 49, 577–585. Vargas, P. T., Sekaquaptewa, D., & von Hippel, W. (2004). It’s not just what you think, it’s also how you think: Prejudice as biased information processing. In W. N. Lee & J. Williams (Eds.), Diversity in advertising (pp.€93–119). Mahwah, NJ: Erlbaum. Vargas, P. T., Sekaquaptewa, D., & von Hippel, W. (2007). Armed only with paper and pencil: “Lowtech” measures of implicit attitudes. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp.€103–124). New York: Guilford Press. Vargas, P. T., von Hippel, W., & Petty, R. E. (2004). Using “partially structured” attitude measures to enhance the attitude-Â�behavior relationship. Personality and Social Psychology Bulletin, 30, 197–211.

155

Verplanken, B., Friborg, O., Wang, C. E., Trafimow, D., & Woolf, K. (2007). Mental habits: Metacognitive reflection on negative self-Â�thinking. Journal of Personality and Social Psychology, 92, 526–551. von Hippel, W., Lakin, J. L., & Shakarchi, R. J. (2005). Individual differences in motivated social cognition: The case of self-Â�serving information processing. Personality and Social Psychology Bulletin, 31, 1347–1357. von Hippel, W., Sekaquaptewa, D., & Vargas, P. (1997). The linguistic intergroup bias as an implicit indicator of prejudice. Journal of Experimental Social Psychology, 33, 490–509. von Hippel, W., Sekaquaptewa, D., & Vargas, P. (2009). Linguistic markers of implicit attitudes. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€ 429–458). New York: Psychology Press. Waly, P., & Cook, S. W. (1965). Effect of attitude on judgments of plausibility. Journal of Personality and Social Psychology, 2, 745–749. Watson, B., & Gallois, C. (2002). Patients’ interactions with health providers: A linguistic category model approach. Journal of Language and Social Psychology, 21, 32–52. Wilson, M. D. (1988). The MRC psycholinguistic database: Machine readable dictionary, version 2. Behavioural Research Methods, Instruments and Computers, 20, 6–11. Wilson, T. D., Lindsey, S., & Schooler, T. Y. (2000). A model of dual attitudes. Psychological Review, 107, 101–126. Winter, D. G. (1991). Measuring personality at a distance: Development of an integrated system for scoring motives in running text. In A. J. Stewart, J. M. Healy, Jr., & D. Ozer (Eds.), Perspectives in personality: Approaches to understanding lives (pp.€ 59–89). London: Jessica Kingsley. Winter, D. G., John, O. P., Stewart, A. J., Klohnen, E. C., & Duncan, L. E. (1998). Traits and motives: Toward an integration of two traditions in personality research. Psychological Review, 105, 230–250.

Chapter 9

Mathematical Modeling of Implicit Social Cognition The Machine in the Ghost Jeffrey W. Sherman, Karl Christoph Klauer, and€Thomas€J.€Allen

A

lthough implicit measures often are portrayed as pure indices of automatic associations, instead they reflect the joint contributions of a variety of processes that may be automatic or controlled component processes. Mathematical modeling of implicit task performance attempts to identify and measure these processes. This approach can shed light on a number of important theoretical and empirical issues surrounding implicit social cognition. Modeling also avoids some significant methodological problems inherent in other means of measuring automatic and controlled aspects of social cognition. The first part of this chapter describes some difficulties in the interpretation and use of implicit measures and the related advantages of modeling approaches. The second part of the chapter reviews specific models that have been used to account for performance on various implicit measures and the questions to which they have been applied. However, before turning to the central concerns of this chapter, some definitional issues require comment. In discussing implicit social cognition, it is important to distinguish among features of the measurement procedure, the behavioral responses obtained with those procedures, and the psychological constructs those responses are meant to reflect. In this chapter, we refer to measurement procedures that assess attitudes and knowledge indirectly (i.e., without explicitly asking people to

report their attitudes and knowledge) as implicit measures or implicit tasks. The term indirect measure may be technically more accurate for our intended meaning (see De Houwer & Moors, Chapter 10, this volume), but we nevertheless use the common terminology of implicit. Behavioral responses on these measures (e.g., button presses measured in reaction times or error rates) are referred to as behavioral responses or behavioral bias. In the implicit social cognition literature, these behavioral responses usually are labeled as implicit attitudes or implicit knowledge (e.g., stereotypes). We, too, use these terms when it would be awkward, abnormal, or confusing to do otherwise. However, their use may be a source of misunderstanding. In some cases, these terms are meant to imply a specific representational model, most typically that the behavioral responses reflect underlying associations in memory (the presumed underlying psychological construct). Yet in other cases, the labels are used simply as a way to describe the behavioral outcomes of implicit measures and do not carry strong implications about mental representation. One central aim of the modeling approach is to specify the extent to which behavioral responses reflect the activation of associations versus other response processes. As such, it would make little sense to promote the assumption that the behavioral responses reflect underlying associations. Thus, in this chapter, use of 156



9. Mathematical Modeling

the term implicit attitude or implicit stereotype carries no representational assumption. Because for purposes of this chapter implicit attitude simply refers to an attitude that is measured with an implicit measure, our use of the term also implies nothing about the automatic nature of the hypothetical constructs (e.g., evaluative associations) and processes (e.g., detection of correct responses) that generate behavioral response biases. Those underlying constructs and processes may or may not influence responses efficiently (e.g., in the absence of time or processing resources), without awareness or without intention, or be difficult to inhibit (Bargh, 1994). Which, if any, of these varying components of automaticity apply to a particular construct or process can be determined only through empirical research. The components do not necessarily covary with one another, and a given construct or process may possess features of both automaticity and control. Furthermore, these features may change over time (e.g., through extensive practice, an initially controlled process may acquire features of automaticity). Detailed analyses of the precise manners in which given constructs or processes are automatic is beyond the scope of this chapter (see Moors, Spruyt, & De Houwer, Chapter 2, this volume).

A Brief Overview of Mathematical Modeling This chapter is not meant to provide a comprehensive description of mathematical modeling approaches in psychology. Such accounts can be found elsewhere (e.g., Luce, 1995, 1999; Myung & Pitt, 2002). Nevertheless, before we turn to the core of this chapter, a very brief sketch of the nature of mathematical modeling may be useful for readers unfamiliar with the approach. Mathematical modeling of implicit measures seeks to identify and quantify the processes that account for performance on the measures. To do so, models attempt to describe outcomes on the measures (error rates, reaction times) via a set of variables (or parameters) and a set of equations that establish relationships among the variables. The variables in the equations represent the hypothesized component processes (e.g., activation of associations, detecting a correct response, overcoming bias, response bias). Solving for these variables yields estimates of the extent of the processes. In some cases, such as with Signal Detection Theory (SDT; Correll, Park, Judd, & Wittenbrink, 2002; Green & Swets, 1966) or Process Dissocia-

157

tion (PD; Jacoby, 1991; Payne, 2001), the equations can be solved algebraically. In other cases, such as with multinomial models (e.g., Batchelder & Riefer, 1999; Sherman et al., 2008; Stahl & Degner, 2007) or diffusion models (e.g., Klauer, Voss, Schmitz, & Teige-Â�Mocigemba, 2007; Ratcliff, 1978), parameter estimates are systematically varied through maximum-Â�likelihood estimation or related procedures to determine the values that most closely reproduce actual task performance. There are two main purposes of modeling. First, it is used to identify the processes that best account for performance on the task of interest (i.e., model fitting). Models can be compared on this dimension to determine their relative merits in describing a task. Second, modeling is used to extract measures of component processes that may then function as distinct variables. For example, the Quad model (e.g., Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005; Sherman et al., 2008) estimates four qualitatively distinct processes: Association Activation, Detection, Overcoming Bias, and Guessing. The component process estimates provided by a model may be used to predict individual differences in traits or motives (e.g., Amodio, Devine, & Harmon-Jones, 2008; Payne, 2005), group demographics (e.g., Gonsalkorale, Sherman, & Klauer, 2009), performance on explicit measures (e.g., Klauer et al., 2007; Payne, 2001), performance on different implicit measures (e.g., Payne, 2005), neuropsychological measures (e.g., Amodio et al., 2008; Beer et al., 2008), social judgments (e.g., Payne, 2005), and behavior (e.g., Gonsalkorale, von Hippel, Sherman, & Klauer, 2009). Model-Â�derived process estimates may predict these outcomes with greater specificity and acuity than raw implicit task performance. As an example, model components derived from two different implicit attitude measures (e.g., estimates of association activation derived from two different measures) may correlate more strongly with one another than the behavioral biases (e.g., based on reaction times or errors) demonstrated on the two measures (e.g., Payne, 2005). Likewise, model components derived from an implicit measure of an attitude may correlate more strongly or subtly than the reaction time–based behavioral bias with an explicit measure of the attitude or with an attitude-Â�relevant behavior (e.g., intergroup behavior; Gonsalkorale, Sherman, et al., 2009). In this way, modeling helps to clarify the meanings of the different measures and provides an enhanced method for determining the extent to which different measures (or a measure and a behavior) reflect the same or different underlying component

158

II. METHODS AND PROCEDURES

processes and representations. This process also provides a means of enhancing the demonstrated relationships among measures and behaviors that may appear to lack correspondence. These issues are addressed later in greater detail. The component process estimates provided by models also may be used as dependent variables. For example, one might examine the impact of exemplar exposure (e.g., Gonsalkorale, Allen, Sherman, & Klauer, 2009), social context (Conrey et al., 2005; Lambert et al., 2003); stimulus context (e.g., Allen, Sherman, & Klauer, 2009), processing goals (Payne, Lambert, & Jacoby, 2002), or training programs (e.g., Plant, Peruche, & Butz, 2005; Sherman et al., 2008) on specific process estimates derived from modeling an implicit measure. As described in more detail later, this permits a better understanding of the specific means by which these manipulations influence performance on implicit measures (see Gawronski & Sritharan, Chapter 12, this volume). It is important to note that applying or fitting a model provides estimates of the process parameters but, in itself, cannot validate the psychological meanings of those parameters. That is, whether the parameters reflect their intended processes must be established independently via construct validation studies. For example, if a parameter is meant to reflect an automatic association or process, then that automaticity must be demonstrated empirically. This might be achieved by showing that the influence of the underlying association or process on the parameter estimate in question is unaffected by a cognitive load or a short response deadline (i.e., the efficiency component of automaticity). In a similar way, the qualitative, psychological nature of a parameter must be established. For example, if a parameter is meant to represent the process of overcoming activated associations, this might be demonstrated by showing that estimates of the parameter are diminished among older adults (for whom self-�regulation is diminished) or are associated with reduced expressions of behavioral bias in a one-on-one intergroup interaction (e.g., Sherman et al., 2008). The equations that describe the relationships among parameters in a model may be consistent with many different psychological meanings of those parameters. For example, the equations in the Control Default model of PD (Payne, 2001) stipulate that automatic race bias influences responses only if controlled detection of the correct response fails (see later discussion). However, given this mathematical constraint, there are a number of potentially valid ways to describe the psycho-

logical meanings of the automatic and controlled components (Klauer & Voss, 2008). For instance, the automatic and controlled parameters may reflect processes that operate in parallel, with the controlled process resolving conflicts between simultaneously generated automatic biases and correct responses. Alternatively, the automatic process may generate an initial response that is either corrected or not by a subsequent controlled process. Another possibility is that the automatic process is engaged only after controlled processing fails to provide a response. Each of these possible accounts is consistent with the equations of the Control Default model and the requirement that the automatic component drives behavior only when control fails. The same multiplicity of processing accounts exists for all multinomial models, including the Quad model (Sherman et al., 2008) and the ABC model (Stahl & Degner, 2007). The only way to distinguish among different processing accounts and to determine the psychological meaning of the parameters is through careful validation studies. We now describe some of the methodological difficulties surrounding the use of implicit measures and the corresponding advantages of modeling approaches.

DIFFICULTIES WITH THE€USE AND INTERPRETATION OF€IMPLICIT MEASURES People may be unaware of their underlying knowledge and evaluations or unwilling to report them truthfully. These “willing and able” issues are two of the most difficult problems for research on attitudes and social cognition. Implicit measures were developed, in part, to avoid these obstacles by measuring attitudes and knowledge without directly requesting that respondents report them. In many cases, people are unaware that these constructs are being measured with such tasks. Many proponents of these measures further argue that, even if made aware of the nature of the task, people are unable to control their responses. Thus, these measures are frequently seen as reflecting the unintended, automatic activation of stored knowledge, whose expression largely cannot be altered or inhibited (e.g., Bargh, 1999; Devine, 1989; Fazio, Jackson, Dunton, & Williams, 1995; Greenwald, McGhee, & Schwartz, 1998; Kim, 2003). Taken in conjunction with explicit measures (e.g., questionnaires), implicit measures are often used to compare and contrast automatic and controlled (or implicit and explicit) social cognition.



9. Mathematical Modeling

Task Confounds Although this task dissociation approach has proven useful, it has significant drawbacks. First, assessing automatic and controlled components of knowledge with separate measures introduces a confound between process type (e.g., automatic vs. controlled) and measurement task (e.g., Implicit Association Test vs. questionnaire). Undoubtedly, implicit measures are less subject to awareness and intention than are explicit measures. However, there may be other important differences between any pair of implicit and explicit tasks beyond the extent to which they tap automatic versus controlled processing. Indeed, when these task-Â�specific differences are reduced, correlations between implicit and explicit measures are increased (Gawronski & LeBel, 2008; Payne, Burkley, & Stokes, 2008; Ranganath, Smith, & Nosek, 2008). An instructive example of the possible significance of confounding task and process can be found in the memory literature. As in the domain of implicit social cognition, for years different measures were used to assess what were thought to be independent implicit and explicit types or systems of memory. However, Roediger and his colleagues determined that, whereas implicit measures of memory had tapped perceptual encoding processes, explicit measures had tapped conceptual encoding processes (e.g., Roediger, 1990). Instantaneously, a whole generation of research depicting differences between implicit and explicit types of memory was open to reinterpretation as reflecting, instead, differences in measures that tapped perceptual and conceptual encoding processes. As of yet, no one has provided a similar overarching reinterpretation of dissociations between implicit and explicit measures of social cognition. However, Roediger’s example should serve as a cautionary tale for applying task dissociation logic to implicit social cognition.

Implicit Measures Are Not Process Pure The more general point is that no task is “process pure.” Any task that requires an observable response (e.g., a button press) cannot be entirely automatic, and no task is immune from the influence of automatic processes (e.g., Jacoby, Toth, & Yonelinas, 1993). Rather, all tasks involve an ongoing interplay among simultaneously occurring automatic and controlled processes. As such, a behavioral response, in and of itself, is incapable of specifying the nature of the underlying processes that produced the response.

159

Consider the Stroop task (Stroop, 1935). A fully literate adult and a young child who knows colors but does not know how to read may make an equally small number of errors on the task. However, very different processes are at work for the adult and the child. On incompatible trials (e.g., the word blue written in red ink), the adult must overcome a habit to read the word in order to name the color of the ink correctly. In contrast, the child has no habit to overcome; he or she simply responds to the color of the ink. The same principle applies to implicit measures in social cognition, many of which have a Stroop-like structure of compatible (e.g., pairing black faces with negative words and white faces with positive words) and incompatible (e.g., pairing black faces with positive words and white faces with negative words) trials (for a review, see Gawronski, Deutsch, LeBel, & Peters, 2008). Thus, the performance of two people who appear to have different implicit attitudes may reflect nonattitudinal processes. For example, the two people may have equally strong evaluative associations, but one person is better able to effectively overcome them in responding to the demands of the task. In the same way, the performance of two people who appear to have equally strong implicit attitudes on such measures may reflect very different underlying processes. Whereas one person may have strong automatic evaluative associations that are successfully overcome in responding, the other may have weaker associations that are not overcome so well. The measure itself cannot distinguish between the two cases. The distinction is well worth making because the causes, consequences, and cures of having strong automatic associations versus weak self-�regulatory abilities are very different. An important methodological implication is that, when taken as pure reflections of automatic associations, implicit measures underestimate the extent of cognitive control. The equally important corollary is that a strong ability to overcome automatic associations on implicit measures may mask the true extent of automatic bias (e.g., Sherman et al., 2008).

Simultaneous Processes A related drawback to the task dissociation approach is that it cannot reveal the simultaneous contributions of multiple-�component processes, both automatic and controlled. If we assume that responses on any implicit measure reflect the joint contributions of automatic and controlled process-

160

II. METHODS AND PROCEDURES

es (or even multiple automatic and controlled processes), then it would be advantageous to have a means to track those contributions independently. However, because implicit and explicit measures often are taken as self-�contained, process-pure estimates of a single, specific automatic or controlled component, there is no way to assess the ongoing interplay of these processes in producing a discrete response on a particular task. This necessarily produces an overly simplified depiction of the processes that underlie performance on implicit measures.

Implicit Social Cognition Is€Constructed, Not Revealed The preceding discussions all converge on the important point that responses on implicit measures are just that: responses on measures. As such, there are many factors and processes that may intervene in the translation of underlying representations into responses on implicit measures. Although the constructive nature of responses on explicit measures of attitudes has been well appreciated (e.g., Wilson & Hodges, 1992), generally, the same has not been true for implicit measures. Because responses on implicit measures typically are viewed as inevitable and uncontrollable (e.g., Bargh, 1999; Devine, 1989), they have been portrayed as reflecting a real, true, and singular underlying representation to a much greater extent than have responses on explicit tasks (e.g., Dovidio & Fazio, 1992; Fazio et al., 1995). However, although implicit measures are certainly less susceptible to intention and less reliant on awareness than are explicit measures, evidence makes clear that implicit attitudes are not the singular, stable entities they once were thought to be. For example, there is now considerable evidence that responses on implicit measures may be influenced by a variety of personal and contextual factors (e.g., Blair, 2002; Sherman et al., 2008). Moreover, implicit measures of attitudes show poorer test–Â�retest reliability than do explicit measures and smaller correlations across measures of the same attitude object than do explicit measures (e.g., Cunningham, Preacher, & Banaji, 2001; Kawakami & Dovidio, 2001). These findings are hard to reconcile with the view that implicit measures directly tap singular, true attitudes that are stable across contexts. Furthermore, these results indicate that implicit measures are no different than all other psychological measures; there is a translational gap between the construct and the way it is measured.

Advantages of Modeling Modeling approaches avoid these difficulties, assuming that no measure is process pure and that multiple processes (both automatic and controlled) exert independent and simultaneous influences on any task. The purpose of modeling is to identify and quantify these processes, describe how they interact to produce observed outcomes on the measures (e.g., biases in error rates or reaction times), and describe how they relate to other measures and behaviors. Because estimates of the component processes are derived from behavior on a single task, modeling techniques also avoid confounding task and process.

MODELS OF IMPLICIT MEASURES We now review formal models that have been proposed to account for performance on different implicit measures. This section is not meant to offer detailed descriptions of the procedures involved in applying the models. For that level of detail, interested readers should seek out the primary research articles that proposed and tested these models, and which are cited in this chapter. Signal Detection (Green & Swets, 1966), PD, and multinomial models (Batchelder & Riefer, 1999) have been used to model the accuracy data from implicit measures of social cognition. The multinomial models include variations of PD models (Bishara & Payne, 2009) and extensions of those models, including the Quad model (Sherman et al., 2008) and the ABC model (Stahl & Degner, 2007). Finally, Klauer and his colleagues (2007) have developed a diffusion model that takes into account both error rates and reaction times in accounting for performance on the IAT.

Signal Detection Theory SDT has been used primarily to account for two�option decisions in the domains of perception and memory (e.g., Green & Swets, 1966). More recently, it also has been applied to understanding responses on the Shooter task, an implicit measure of stereotyping (e.g., Correll et al., 2002; Greenwald, Oakes, & Hoffman, 2003). To our knowledge, it has not been applied to other implicit measures, although it is equipped to analyze data from the Weapons task (e.g., Payne, 2001) and other semantic and evaluative priming tasks. The purpose of SDT is to separate sensitivity (or discrimination accuracy) and response bias in



9. Mathematical Modeling

responding. For example, in the domain of recognition memory, SDT identifies one response component that reflects the extent to which perceivers are able to discriminate between items they have and have not encountered and a second component that reflects a bias to call an item old or new. Although many different equations have been offered to measure sensitivity (for a review, see Snodgrass & Corwin, 1988), all of them propose that it is reflected by a difference in the prevalence of hits (i.e., correctly calling an old item old) versus false alarms (i.e., incorrectly calling a new item old). By contrast, response bias has been defined by a number of alternative relationships between hits and false alarms (for reviews, see Macmillan & Creelman, 1990; Snodgrass & Corwin, 1988). An important point about SDT in the context of implicit social cognition is that the component processes are not assumed to map onto the distinction between automatic and controlled processes. In the Shooter task, SDT has been used to separate people’s ability to discriminate accurately whether a target is holding a gun or another object from a bias to provide a gun response (shoot) versus no-gun response (don’t shoot). Correll and colleagues (2002; see also Correll et al., 2007) reported that participants set a lower response criterion for reporting the presence of a gun for black targets than for white targets. However, the ability to discriminate accurately the presence of a gun did not differ between black and white targets. In contrast, Greenwald and colleagues (2003) found both the criterion effect reported by Correll and colleagues and also that participants were better able to discriminate between guns and non-guns for white targets than black targets. There are a variety of procedural differences in the studies by Correll and colleagues (2002, 2007) and Greenwald and colleagues, and the basis for the discrepant results is not clear.

Process Dissociation Concerns about task confounds and assumptions of process purity in the literature on implicit versus explicit memory led Jacoby (1991; Lindsay & Jacoby, 1994; see Jacoby, Kelley, & McElree, 1999, for a review) to develop PD techniques for separating the automatic and controlled components of memory from performance on a single task. The PD approach assumes that no measure is process pure, and that automatic and controlled processes exert independent and simultaneous influences on any task. Because estimates of the two components are derived from behavior on a single task,

161

PD techniques avoid confounding task and process. Payne (2001) recognized that the same issues that are problematic for separating implicit and explicit memory also are problematic for separating implicit and explicit social cognition, and adapted PD techniques for use in decomposing automatic and controlled components of implicit task performance. This technique has been applied primarily to the Weapons task (for a review, see Payne, 2008) but also to the Shooter task (e.g., Plant et al., 2005) and the IAT (e.g., Conrey et al., 2005; Huntsinger, Sinclair, & Clore, 2009; Sherman et al., 2008; Stewart, von Hippel, & Radvansky, 2009). In principle, PD is applicable to any task that compares compatible (e.g., black + bad/white + good) and incompatible (e.g., black + good/white + bad) trials, in which automatic and controlled processes are placed in concert with and in opposition to one another, including many varieties of semantic and evaluative priming tasks, the IAT, the Go/No-Go Association Task (GNAT; Nosek & Banaji, 2001), and the Extrinsic Affective Simon Task (EAST; De Houwer, 2003). Initially, Jacoby and his colleagues developed two different models of PD (Jacoby, 1991; Lindsay & Jacoby, 1994). The primary difference between the two models is whether automatic or controlled processes are assumed to be dominant.

The Control Default Model One model is designed to account for tasks in which automatic processes are thought to influence behavior only when control fails (Jacoby, 1991). For example, in recognition memory, Jacoby (1991) proposed that controlled, effortful recollective processes will determine judgments whenever possible. Only when controlled recollection fails to provide a response will automatically generated perceptions of an item’s familiarity drive recognition judgments. This is the PD model that Payne and others have applied to the Weapons task and the Shooter task (e.g., for a review, see Payne, 2008; Plant et al., 2005). Structure and Use of the Control Default Model

The logic of the model for these and other priming tasks is as follows (see Figure 9.1). On compatible trials (e.g., a black prime followed by a weapon target), participants may produce the correct response either by correctly identifying the target through controlled processing (C) or by relying

162

II. METHODS AND PROCEDURES

FIGURE 9.1.╇ The Control Default process dissociation model. Each path represents a likelihood. Parameters with lines leading to them are conditional upon all preceding parameters. The table on the right side depicts correct () and incorrect (×) responses as a function of process pattern and trial type.

on an automatic stereotypic bias (A) to produce a “weapon” response, when controlled detection of the object fails (A*(1 – C)). Thus, the equation for correct compatible responses is C + A (1 – C). In contrast, on incompatible trials (e.g., a black prime followed by a tool target), participants will produce an incorrect response only when controlled detection fails, and participants rely on an automatic bias (A*(1 – C)). Subtracting the equation for incompatible trials, in which automatic and controlled processes produce opposite responses, from the equation for compatible trials, in which automatic and controlled processes produce the same response, provides an estimate of controlled processing, C. Subsequently, an estimate of automatic processing (A) can be derived through simple algebra.1 As indicated by the equations, this version of PD proposes that the race of the prime influences judgments about the target object only if people fail to identify the target object through controlled discrimination processes. If the object can be correctly identified, then race will have no influence on judgments. Thus, the model permits no role for automatic processes (e.g., activation of stereotypic associations) that capture attention and influence behavior even though the correct response can be determined. Researchers applying this model must be careful to interpret the automatic component of this model accordingly. Validation of the Control Default€Model

A multinomial model implementation has shown that the Control Default model accurately predicts performance on the Weapons task, to which it has

been applied primarily (Bishara & Payne, 2009; Sherman et al., 2008). The parameters also have been shown to vary independently of one another. For example, implementing a response deadline reduced C but left A unaffected (e.g., Payne, 2001). This finding also provides evidence of the controlled and automatic natures of C and A, respectively. Other findings further support this conclusion. For example, depleting processing resources through a lengthy ego-�depletion manipulation reduced C but not A (Govorun & Payne, 2006). In yet another study, A correlated with responses on implicit measures of bias, whereas C correlated with other measures of cognitive control such as an antisaccade task (Payne, 2005). Finally, Stewart and colleagues (2009) showed that C but not A was associated with age-�related deficits in inhibitory ability. The specific qualitative natures of the A and C parameters are somewhat less clear. The status of the A parameter as representing a race-based bias is supported by its correlations with behavioral bias on implicit measures. However, the underlying basis for this A bias is unspecified. The C parameter appears to be associated with a variety of controlled processes. One study showed that the C parameter correlated with performance on an antisaccade task, which assesses the ability to inhibit attention from being directed to a distracting cue (Payne, 2005). The just-�mentioned data reported by Stewart and colleagues (2009) also shows that C is associated with inhibition ability. Other studies have shown that C correlates with motivations to control biased responding (Amodio et al., 2008; Payne, 2005). Still other data show that the C parameter correlates with event-�related brain potentials that have been linked to monitoring



9. Mathematical Modeling

conflicting response tendencies (Amodio et al., 2004, 2008). The detection of such conflict assists in the subsequent control of unwanted responses. Although these different indices of control are related, they also point to distinct processes. The C parameter may reflect each of these processes and more (e.g., Klauer & Voss, 2008). One challenge for future research will be to specify the qualitative natures of the PD component processes. Application of the Control Default Model

Predicting Other Measures and Behavior.╇ One application of the Control Default model has been to use the A and C parameters derived from Weapons task performance to predict responses on other measures of racial bias. In one study, the A parameter was shown to correlate with an explicit measure of bias only among those participants low in motivation to control prejudiced responses (Payne, 2001). This demonstrates the role of motivation in obscuring bias on explicit measures. Another study showed that the A parameter better predicted performance on both a Weapons task and an evaluative priming task (e.g., Fazio et al., 1995) among participants with lower levels of the C parameter (Payne, 2005). Importantly, the C estimates were derived from different tasks than the Weapons task and priming task. Thus, control, as measured on a separate task, predicted the extent to which automatic bias influenced implicit task performance. This demonstrates the role of control in implicit task performance. The same relationship between A and C was found in predicting stereotyping in an impression formation task. Payne (2005) also showed that A parameters derived from an evaluative priming task and a Weapons task correlated more strongly than did the behavioral biases (based on errors) demonstrated on the tasks. This shows that weak correlations among different implicit measures may conceal stronger correlations among the component processes of the different measures, and that the weak correlations may be due to aspects of the measures that are not of central interest (i.e., aspects that do not reflect automatic bias). Accounting for Changes in Implicit Bias.╇ This model also has been used to account for the effects of experimental manipulations on implicit bias. For instance, Lambert and colleagues (2003) found that, surprisingly, racial bias on the Weapons task increased when participants were told that their responses would be observed by others.

163

Application of the Control Default model demonstrated that the public context did not affect the A parameter but reduced C. This suggests that the public context increases bias by interfering with controlled processes that would otherwise prevent biased responses. In another study, Plant and colleagues (2005) showed that training reduced racial bias on the Shooter task. Application of the Control Default model showed that training increased C for both black and white trials but reduced A only for black trials. This suggests that training reduces biased responses by reducing the extent of automatic race bias. Accounting for Group Differences in Implicit Bias.╇ Finally, Stewart and colleagues (2009) used this model to account for group differences in implicit bias. Specifically, age-�related increases in implicit bias were associated with diminished C but were not related to A. At the same time, greater implicit bias among white than black participants was associated with greater A but was unrelated to C. Thus, it appears that aging is associated with greater IAT bias as a result of diminished control, whereas outgroup (vs. ingroup) status is associated with greater IAT bias because of enhanced automatic race bias.

The Automatic Default Model The other PD model (Lindsay & Jacoby, 1994) was designed to account for tasks in which automatic processes are thought to influence behavior, regardless of whether or not control succeeds. In this model, controlled processes drive responses only in the absence of automatic bias. For example, the model proposes that in the Stroop task, if present, an automatic habit to read the word will determine responses. Only in the absence of such a habit will the controlled process of determining the color drive responses. The logic of the model for the Weapons task and other priming tasks is as follows (see Figure 9.2). On compatible trials (e.g., a black prime followed by a weapon target), participants may produce the correct response either by relying on an automatic bias to produce a “weapon” response (A) or by correctly identifying the target through controlled processing (C) when no automatic bias is activated (1 – A). Thus, the equation for correct compatible responses is A + C (1 – A). In contrast, on incompatible trials (e.g., a black prime followed by a tool target), this model proposes that a correct response will be provided only if there is no automatic bias and controlled detection succeeds

164

II. METHODS AND PROCEDURES

FIGURE 9.2.╇ The Automatic Default process dissociation model. Each path represents a likelihood. Parameters with lines leading to them are conditional upon all preceding parameters. The table on the right side depicts correct () and incorrect (×) responses as a function of process pattern and trial type.

C*(1 – A). Subtracting the equation for incompatible trials, in which automatic and controlled processes produce opposite responses, from the equation for compatible trials, in which automatic and controlled processes produce the same response, provides an estimate of automatic processing, A. Subsequently, an estimate of controlled processing (C) can be derived through simple algebra. As indicated by the equations, this version of PD proposes that controlled detection of the target object influences judgments of the target only if the race prime fails to activate an automatic bias. If there is an automatic bias, then control will have no influence on judgments. Thus, the model permits no role for controlled processes that determine responses despite the presence of an automatic bias. As such, this model cannot account for perceivers’ ability to produce a correct response on an implicit measure despite the operation of an automatic bias. Researchers applying this model must be careful to interpret the automatic and controlled components of this model accordingly. One limitation of this model is that it does not distinguish between cases in which an automatic bias is not activated at all from cases in which the bias is activated but is overcome. On the Stroop task, people provide correct responses on most trials despite the fact that they have an automatic habit to read the word. In these cases, the habit is overcome. In contrast, as described previously, a child who cannot read will make few errors simply because he or she has no reading habit to overcome in the first place. This PD model cannot distinguish between these two cases. Likewise, on implicit measures of social cognition, the model cannot distinguish between a person who is able to overcome a strong automatic bias and a person

who has no bias in the first place (for more thorough discussions, see Conrey et al., 2005; Sherman et al., 2008). Although this model is applicable to all the same tasks as the Control Default model, it has been used very rarely. To our knowledge, it has been used only to model the Weapons task, and it has failed to provide adequate fit for task performance (e.g., Bishara & Payne, 2009), perhaps because of the limited role the model affords to control. Nevertheless, the limited use of this model is somewhat surprising, given that the automatic parameter in this model appears to be more akin to what researchers usually mean when they refer to automatic attitudes or stereotypes. For example, in most dual-�process models of social cognition (Chaiken & Trope, 1999), the role of the automatic process is to influence perception and behavior regardless of the status of controlled processes. Controlled processes may moderate the impact of the automatic process, but they do not preclude its influence (for a review, see Sherman et al., 2008). Yet the PD model used almost exclusively is the Control Default model, which assigns a secondary role to automatic processes, such that their influence is felt only when there is no controlled processing.

The Quad Model The Quad model was developed as an extension of PD (Conrey et al., 2005; Sherman et al., 2008). Whereas the PD models estimate a single automatic and a single controlled process, the Quad model estimates four processes, two of which are meant to reflect features of automatic processing and two of which are meant to reflect features of



9. Mathematical Modeling

more controlled processing. In addition to providing estimates of these four qualitatively distinct processes, the other primary purpose of the Quad model is to separate cases in which an automatic association is activated but overcome from cases in which the association simply is not activated. This is the problem described earlier in reference to adult versus child performance on the Stroop task, and it is not addressed by either PD model. Different dual-Â�process models of social cognition have proposed qualitatively distinct automatic and controlled processes (for a review, see Sherman et al., 2008). Most commonly, automaticity is represented as simple associations that are triggered by the environment without the perceiver’s awareness or intent and that influence subsequent processing (e.g., Fazio et al., 1995; Greenwald et al., 1998). This is the kind of automaticity to which researchers studying implicit social cognition typically refer, and it is assessed in the Automatic Default PD model. In other dual-Â�process models (e.g., Jacoby, 1991), however, automatic processes influence behavior only when control fails. This is the role of automaticity in the Control Default PD model. Dual-Â�process models also have generally been concerned with one of two different types of control. In some models (e.g., models of impression formation or persuasion), control is characterized by stimulus-Â�detection processes that attempt to provide an accurate depiction of the environment (e.g., Brewer & Feinstein, 1999; Chen & Chaiken, 1999; Fiske, Lin, & Neuberg, 1999; Petty & Wegener, 1999). This is how the C component has been described in both PD models. However, in

165

other dual-Â�process models, control is characterized by self-Â�regulatory processes that attempt to inhibit unwanted or inappropriate information. For example, in Devine’s (1989) model of stereotyping, control must be exerted to overcome the automatic influence of stereotypes (see also Wegner, 1994). The Quad model is a multinomial model (see Batchelder & Riefer, 1999) designed to measure the contributions of each of these processes to performance on implicit measures of social cognition. More formally, the four components of the model are (1) the activation of an association (Association aCtivation [AC]), (2) the ability to determine correct and incorrect responses (Detection [D]), (3) the success at overcoming activated associations when necessary (Overcoming Bias [OB]), and (4) the influence of a general response bias that might guide responses in the absence of other available guides to response (Guessing [G]). Whereas AC and G are meant to reflect features of automatic processing (although G need not; see later discussion), D and OB are meant to reflect features of controlled processing. The relatively automatic versus controlled nature of these processing components has been supported by empirical research (see later discussion).

Structure and Use of the Quad Model The structure of the Quad model is depicted as a processing tree in Figure 9.3. As an example of how the model works, consider performance on a standard black–white/positive–Â�negative IAT (Greenwald et al., 1998). The presentation of

FIGURE 9.3.╇ The Quadruple Process model. Each path represents a likelihood. Parameters with lines leading to them are conditional upon all preceding parameters. The table on the right side depicts correct () and incorrect (×) responses as a function of process pattern and trial type.

166

II. METHODS AND PROCEDURES

a black face may activate negative associations (AC), predisposing the participant to press the negative button. Depending on whether the trial is part of a compatible (black/bad and white/good) or incompatible (black/good and white/bad) block, this bias will be either congruent or incongruent with the correct answer “black” achieved through detection (D). On compatible trials, there is no conflict between activated associations and what is detected. As such, there is no need to overcome the bias (OB) in order to produce the correct response. However, on incompatible trials, AC and D generate conflicting responses. Which of these two processes ultimately directs the outcome is determined by whether or not the participant succeeds in overcoming his or her bias. Finally, if no association is activated and the correct response cannot be ascertained, then participants must guess (G). Guessing need not be random and may be quite strategic (rather than automatic), such as a bias to respond with the positive key when all else fails (Conrey et al., 2005). Other response biases reflect more automatic processes, such as a dominant-hand bias or a bias to press the button with the higher probability of providing a correct response (Conrey et al., 2005). Parameter estimates cannot be generated with simple algebra but must be established via maximum likelihood estimation. In Figure 9.3, each path represents a likelihood. Processing parameters with lines leading to them are conditional upon all preceding parameters. For instance, OB is conditional upon both AC and D. Similarly, G is conditional upon the lack of AC (1 – AC) and the lack of D (1 – D). Note that these conditional relationships do not imply a serial order in the onset and conclusion of the different processes. Rather, these relationships are mathematical descriptions of the manner in which the parameters interact to produce behavior. Thus, the activation of associations (AC), attempts to detect a correct response (D), and attempts to overcome associations (OB) may occur simultaneously. However, in determining a response on an incompatible trial, the status of OB determines whether AC or D drives responses. The conditional relationships described by the model form a system of equations that predict the number of correct and incorrect responses in different conditions (e.g., compatible and incompatible trials). For example, a black face on an incompatible trial will be responded to correctly with the probability: AC × D × OB + (1 – AC) × D + (1 – AC) × (1 – D) × (1 – G). This equation sums the three possible paths by which a correct

answer can be returned in this case. The first part of the equation, AC × D × OB, is the likelihood that the association between black and negative is activated, that the correct answer can be detected, and that the association is overcome in favor of the detected response. The second part of the equation, (1 – AC) × D, is the likelihood that the association is not activated and that the correct response can be detected. Finally, (1 – AC) × (1 – D) × (1 – G) is the likelihood that the association is not activated, that the correct answer cannot be detected, and that the participant guesses correctly. The respective equations for each item category (e.g., black faces, white faces, positive words, and negative words in both compatible and incompatible blocks) are then used to predict the observed proportions of errors in a given data set. The model’s predictions are then compared with the actual data to determine the model’s ability to account for the data. A chi-Â�square estimate is computed for the difference between the predicted and observed errors. To best approximate the model to the data, the four parameter values are changed through maximum likelihood estimation until they produce a minimum possible value of the chi-Â�square. The final parameter values that result from this process are interpreted as relative levels of the four processes. Although we have used the IAT as an example, the Quad model may be used to analyze data from any measure that compares compatible and incompatible trials, in which automatic and controlled processes are placed in concert with and in opposition to one another, including Stroop tasks, evaluative priming tasks (e.g., Allen et al., 2009; Conrey et al., 2005; Sherman et al., 2008), the Weapons task (e.g., Conrey et al., 2005), the Shooter task, and the GNAT (Gonsalkorale, von Hippel, et al., 2009).

Comparisons to PD Models The Quad model is an extension of PD models and includes features of both the Control Default and Automatic Default models. In particular, the AC and D parameters in the Quad model are analogous to the A and C parameters of the PD models in terms of their qualitative features (although these features are specified in greater detail in the Quad model). However, the hypothesized relationships among the components differ in important ways in PD and Quad models. PD models posit that automatic and controlled processes either dominate or are dominated by one another. In the Control Default model, control (C) dominates; in



9. Mathematical Modeling

the Automatic Default model, automaticity (A) dominates. In contrast, the Quad model suggests that either conflicting AC or D processes may act as the default, depending on the outcome of the OB process (another controlled process). When there is a conflict between AC and D (i.e., on incompatible trials), AC dominates D when OB fails, and D dominates AC when OB succeeds. Thus, whereas the final estimates of A and C in the PD models reflect only situations in which one or the other process has dominated, estimates of AC and D in the Quad model reflect cases in which the two parameters both were superordinate and subordinate to one another. That is, dominating or being dominated are defining features of automaticity and control in PD models, whereas the Quad model does not limit the influence of automatic and controlled processes in this way. Of course, the Quad model also differs from PD models in the specification of the OB and G parameters. OB represents a form of controlled process that is common in the dual-�process literature but is not represented as a distinct process in either of the PD models. It plays a critical role in the Quad model, acting as the arbiter of whether AC or D determines responses when they are in conflict. When successful, OB inhibits incorrect association-based responses in favor of detected correct responses. Similarly, the G parameter in the Quad model represents a form of response bias that is not represented as a distinct process in PD models. When there is no automatic activation of a biased response or controlled detection of a correct response, G determines the response that will be given. The addition of the G parameter in the Quad model implies very different outcomes than the PD models when both the automatic activation process and the controlled detection process fail. In the Control Default model, this circumstance leads to correct responses on compatible trials and incorrect responses on incompatible trials. That is, in the absence of all information, the default is to produce a response that is correct on compatible trials and incorrect on incompatible trials. In the Automatic Default model, this same circumstance always leads to incorrect responses. That is, if there is no automatic activation and no controlled detection, an incorrect response will always be provided on all trials. In contrast, the G parameter of the Quad model proposes that, in the absence of activation and detection processes, a guessing process occurs that will sometimes produce correct and sometimes produce incorrect responses.

167

Validation of the Quad Model The Quad model has shown its ability to predict performance accurately on a variety of priming tasks, IATs, and the GNAT, demonstrating good model fit for these tasks (Conrey et al., 2005; Gonsalkorale, von Hippel, et al., 2009; Sherman et al., 2008). The parameters also have been shown to vary independently of one another. For example, implementing a response deadline in an IAT reduced D and OB but left AC and G unaffected. Manipulating the base rate of left-hand versus right-hand responses in the same task affected G but none of the other three parameters (AC, D, OB). Aging was associated with an increased ability to detect stimuli accurately (D) but decreased success at overcoming bias (OB; Gonsalkorale, Sherman, et al., 2009). This agebased dissociation between OB and D is consistent with previous research (Rosano et al., 2005) showing that not all forms of controlled processing diminish with age. Rather, age-�related deficits in cognitive control appear to be related primarily to self-�regulatory processes. These results indicate that the four parameters of the Quad model can vary independently (for a review, see Sherman et al., 2008). The construct validity of the model parameters also has been established by a number of findings (Conrey et al., 2005; Sherman et al., 2008). For example, the fact that D and OB were reduced by a response deadline supports the claim that the two parameters reflect controlled processes that require cognitive capacity. In contrast, the finding that AC and G were unaffected by the response deadline is consistent with their depiction as relatively automatic processes that do not require significant cognitive capacity. Beyond the general automatic/controlled distinction, the specific qualitative nature of the different parameters also has been established. The status of AC as a measure of association activation is supported by the fact that AC was shown to be positively correlated with reaction time bias on the IAT (Conrey et al., 2005) and with activation of the amygdala and insula in a neuroimaging study (Beer et al., 2008). The amygdala and insula are known to be involved in emotional processing and arousal. The validity of OB as a measure of self�regulation is supported by demonstrations that it is impaired by alcohol consumption and decreases with age, two factors associated with impairments in self-�regulation (Gonsalkorale, Sherman, et al., 2009; Sherman et al., 2008). OB also has been shown to be negatively correlated with reaction

168

II. METHODS AND PROCEDURES

time bias on the IAT (Conrey et al., 2005) and positively correlated with favorable intergroup interactions (Gonsalkorale, von Hippel, et al., 2009), further evidence that OB measures the inhibition of bias. The fact that altering the base rate of left-hand and right-hand responses influenced G corroborates the portrayal of that parameter as a general response bias. The precise qualitative nature of the D parameter is less well established. Neuroimaging data showed that D was associated with activation in both the dorsal anterior cingulate cortex and the dorsolateral prefrontal cortex, areas of the brain associated with detecting the need for control and implementing control, respectively. This is consistent with the Quad model’s depiction of D as a controlled process that selects appropriate behavior and feeds into efforts to overcome inappropriate automatic influences. However, further research is needed to establish the precise psychological meaning of D.

Application of the Quad Model Malleability of Responses on Implicit Measures

The Quad model has been applied to a number of empirical and theoretical problems surrounding implicit social cognition. One set of questions pertains to understanding the contextual malleability of responses on implicit measures. Although they originally were assumed to be highly stable and resistant to change, considerable research now indicates that responses on these measures are highly context dependent (for reviews, see Blair, 2002; Gawronski & Bodenhausen, 2006; Sherman et al., 2008). When responses change as a result of situational manipulations, what accounts for it? Most typically, these effects are attributed to context-Â�dependent changes in the specific associations that are activated in the different contexts (e.g., Blair, 2002; Ferguson & Bargh, 2007; Gawronski & Bodenhausen, 2006) or to changes in the degree to which given associations are activated in the different contexts (e.g., Glaser & Knowles, 2007; Maddux, Barden, Brewer, & Petty, 2005; Moskowitz, Gollwitzer, Wasel, & Schaal, 1999; see also Klauer, 2009, for a different view). According to the Quad model, however, such effects could be due to changes in the nature of the activated associations, changes in respondents’ ability to determine appropriate behavior, changes in respondents’ ability to overcome associations when necessary, changes in response biases, or some combination of these processes.

In some cases, application of the Quad model suggests that changes in implicit task performance primarily reflect changes in the underlying associations that are activated (either the extent of activation or the nature of the associations activated). For example, when newly formed implicit attitudes about a target person are gradually altered (e.g., Rydell & McConnell, 2006), the effect is associated only with changes to the AC parameter (Sherman et al., 2008). Similarly, the well-Â�replicated finding that the presentation of prejudice-Â�inconsistent exemplars reduces implicit bias (e.g., Blair, Ma, & Lenton, 2001; Dasgupta & Greenwald, 2001; Govan & Williams, 2004; Mitchell, Nosek, & Banaji, 2003) is related solely to changes in the associations that are activated (AC; Gonsalkorale, Allen, et al., 2009; Sherman et al., 2008). Other interventions that change implicit bias appear to influence both automatic and controlled processes. For example, training to negate stereotypes has been shown to reduce subsequent implicit stereotyping (e.g., Kawakami, Dovidio, Moll, Hermsen, & Russin, 2000). A Quad model analysis of this effect showed that the negation training both reduced AC and increased the ability to detect correct responses (D). So training not only altered the associations that were activated, but improved participants’ ability to perform the task accurately (Sherman et al., 2008). A reanalysis of Lambert and colleagues’ data (2003) on the effects of public accountability on implicit stereotyping showed that, relative to the private condition, the public condition reduced D (this is similar to the C effect reported by Lambert et al.) and increased AC. Thus, unlike the PD analysis of these data, the Quad model analysis suggested that public accountability not only diminished control but also increased the activation of biased associations, supporting a social facilitation account of the finding, in which the dominant response (in this case, stereotype activation) is enhanced by the presence of others (Zajonc, 1965; see also Conrey et al., 2005). In still other cases, variations in implicit bias appear to have nothing to do with the underlying associations but rather reflect only variations in controlled processes. Bartholow, Dickter, and Sestir (2006) found that participants under the influence of alcohol demonstrated greater implicit stereotyping than their sober counterparts. A Quad model analysis of these data showed that alcohol reduced estimates of overcoming bias (OB) but had no effect on the activation of associations (Sherman et al., 2008). This finding is consistent



9. Mathematical Modeling

with research showing that alcohol impairs self�regulatory ability. Another example involves the effects of stimulus contexts on implicit prejudice. A number of studies have shown that implicit prejudice is reduced when pictures of black and white targets are presented in prejudice-�inconsistent contexts (e.g., black targets in front of a church; white targets in front of a jail). Allen and his colleagues replicated this finding in both an IAT and an evaluative priming task (Allen et al., 2009). In both cases, application of the Quad model showed that the inconsistent contexts increased OB but affected no other parameters. Thus, it appears that these contexts act as cues to initiate self-�regulatory processes that overcome biased associations. Variability in Responses on Implicit Measures

Another set of questions surrounds the meaning of individual and group differences in implicit task performance. To what extent do these differences reflect variation in underlying associations, the ability to determine appropriate behavior, the ability to overcome associations, response biases, or some combination of these processes? As is the case for malleability effects, variability effects sometimes are related to the automatic activation of associations, sometimes are related to a combination of automatic and controlled processes, and sometimes are related only to control. Not surprisingly, black people show less positivity toward whites and negativity toward blacks on implicit measures than do white people (e.g., Nosek, Banaji, & Greenwald, 2002). In one study, application of the Quad model showed that only the AC parameter differed between black and white participants (Gonsalkorale, Allen, et al., 2009). Thus, black people are not better at controlling their racial bias, they just have less biased associations in the first place, perhaps because of more frequent exposure to positive black exemplars. Internal and external motivations to respond in nonprejudiced ways have been shown to be important moderators of the extent of implicit bias (Amodio et al., 2008; Amodio, Harmon-Jones, & Devine, 2003; Devine, Plant, Amodio, HarmonJones, & Vance, 2002). Specifically, individuals who are internally but not externally motivated (high Internal Motivation to Respond Without Prejudice Scale [IMS]/low External Motivation to Respond Without Prejudice Scale [EMS]) to behave in nonprejudiced ways demonstrate less bias on measures of implicit bias than individuals who

169

are motivated by both internal and external reasons (high IMS/high EMS) or who lack internal motivation (low IMS). Quad model analyses of both Weapons task data collected by Amodio and colleagues (2008) and a new IAT study showed that high-IMS/low-EMS participants exhibited less activation of biased associations (AC) and a greater likelihood of detecting correct responses (D) than other participants (Sherman et al., 2008). Thus, like participants who are directly trained at overcoming bias, these individuals (who are believed to train themselves to act in nonbiased ways; Monteith, Ashburn-Nardo, Voils, & Czopp, 2002) have weaker associations and stronger detection of appropriate responses. Other individual differences in implicit bias appear to have nothing to do with activated associations and are, seemingly, based entirely on variations in controlled processes. Recent research has revealed a developmental trend, showing a positive correlation between age and implicit racial bias among white people (e.g., Nosek et al., 2002). This finding often is interpreted as evidence that older people’s racial associations are more biased than those of younger adults, reflecting generational changes in societal attitudes. However, an alternative explanation for age differences in prejudice is that deficits in self-Â�regulatory ability alter the attitudinal expression of older adults on implicit measures. Indeed, application of the Quad model to IAT data showed that the increase in IAT bias with age was associated only with a decreased ability to overcome bias (OB; Gonsalkorale, Sherman, et al., 2009). Thus, age differences in implicit racial bias appear to be due to age-Â�related losses in regulatory functions. Predicting Behavior

Finally, the Quad model also has been applied toward understanding the underlying automatic and controlled processes that predict social behavior. A recently published study used the Quad model to examine the processes that predict the quality of a social interaction between members of different social groups (Gonsalkorale, von Hippel, et al., 2009). White non-Â�Muslims interacted with a Muslim confederate and completed a GNAT measuring anti-Â�Muslim bias. The confederate’s ratings of how much he liked the interaction partners were predicted by an interaction between AC and OB. Specifically, when participants had low AC estimates of negative associations with Muslims, their level of OB was unrelated to how much they were liked by the confederate. In contrast, participants

170

II. METHODS AND PROCEDURES

with high AC estimates of negative associations with Muslims were liked to the extent that they had high OB parameter estimates. Thus, the ability to overcome negative associations on the GNAT predicted the quality of the social interaction when those associations were strong.

The ABC Model The ABC model was developed by Stahl and Degner (2007) to account for performance on the EAST task specifically (De Houwer, 2003). The ABC model is an extension of the Automatic Default PD model, in that it assumes that the automatic component (A) will drive responses if it is engaged. The controlled component (C) determines responses only if A is not engaged. The ABC model extends the Automatic Default model by adding a parameter that measures guessing that occurs when neither A nor C are engaged. Thus, the ABC model proposes that, in the absence of all other relevant information, a guess will be made that will yield either a correct or an incorrect response. This is in contrast to the assumption of the Automatic Default model that an incorrect response will always result when neither A nor C are engaged. In four studies, Stahl and Degner (2007) showed that the ABC model provided excellent fit for EAST data. They also showed that the A parameter corresponded to the automatic activation of evaluative associations, whereas the C parameter reflected the use of controlled processes to respond to task demands. As predicted, the A parameter was sensitive to variations in the evaluative nature of the stimuli that were irrelevant to task performance but not to variations in the difficulty of responding to the task-�relevant feature (reflecting the automatic nature of the evaluative process). In contrast, the C parameter was sensitive to the difficulty of task-�relevant demands but not to the task-�irrelevant evaluative features of the stimulus. Finally, they showed that only the guessing parameter (B) was influenced by a manipulation of the baseline proportions of responses requiring different keys. Specifically, participants were more likely to guess with the key most likely to produce a correct response.

The Diffusion Model of the IAT Recently, Klauer and his colleagues developed a diffusion model to account for IAT performance (Klauer et al., 2007; see also Brendl, Markman, & Messner, 2001, for a related random-walk model

that was proposed but not formally tested). Diffusion models differ from SDT, PD, and multinomial models in that the models’ parameters are estimated from both error rates and reaction times. Given that implicit bias is most typically presented in terms of reaction times, this is a distinct advantage of the Diffusion model. The Diffusion model assumes that the choice between the two responses on an IAT trial is based on an accumulation of information over time (see Figure 9.4). The diffusion process moves from a starting point between the two possible responses until one of the response thresholds is reached and the response associated with it is initiated. The model estimates seven parameters that contribute to the ultimate response. Parameter a describes the amount of information that must accumulate before a decision is made. Therefore, it represents speed–Â�accuracy trade-off settings. Parameter z is the starting point of information accumulation and measures response bias. For example, a starting point close to the upper threshold implies that comparatively little additional information must accumulate toward the upper threshold before it is crossed; conversely, comparatively more information must accumulate toward the lower threshold before it can be crossed. The result is a response bias toward the response associated with the upper threshold and against the response associated with the lower threshold. Parameter v is the mean drift rate. Drift rate quantifies the direction (toward lower vs. upper threshold) and speed with which relevant information accumulates. A high drift rate implies both fast and accurate decisions. Parameter to represents the contribution of nondecision processes relating to, for example, preparatory encoding of stimuli and motor responses. Finally, three parameters measure variability in the drift rate (h), variability in the starting point (sz), and variability in the nondecision processes (st). The parameters can be estimated via chi-Â�square and weighted least squares methods that rely on grouped data or on the maximum-Â�likelihood method that relies on ungrouped latencies. Overall model fit is assessed with a chi-Â�square distributed goodness-of-fit statistic (for a complete description, see Klauer et al., 2007). In an initial application of the model, Klauer and colleagues (2007) showed that the model provided adequate fit to IAT data. They also found that the speed–Â�accuracy trade-off was more conservative in incompatible (e.g., flowers–bad; insects–good) than compatible trials, a prediction that had been offered by Brendl and colleagues (2001) but not directly tested. Results also showed a slower drift



9. Mathematical Modeling

rate (i.e., accumulation of information) and slower nondecision components in incompatible than compatible trials. However, only the differential drift rate in compatible and incompatible trials predicted responses on an explicit measure of attitudes, suggesting that this parameter may be particularly indicative of attitudinal responses. In addition, results showed that the speed–Â�accuracy trade-off difference on compatible and incompatible trials reflected method variance rather than attitude-Â�specific responses. This suggests that unwanted sources of variance in the IAT related to factors that influence speed–Â�accuracy trade-offs, such as age, prevention versus promotion focus, instructions, strategies, processing styles, and so on may influence results.

Choosing among Models Although each of these models has been applied primarily (or only) to one specific implicit measure, each also may be applied to other measures (assuming that the model provides adequate fit for the data). How then should one choose which model to apply? In large part, the answer to this question depends on the purpose for which one is using the model. If the purpose of modeling is to find the set of processes (and relationships among them) that best describe task performance, then model fit would be the key criterion. However, if the purpose of the modeling is to extract estimates of processes of interest that may then function as distinct variables (e.g., for predicting other variables, measures, or behavior, or for use as dependent variables), then the criteria are more theoretical in nature (Sherman et al., 2008).

Model Fit All else being equal, the model that provides the best account of the data is preferred. In comparing the fits of different models, it is important to account for the complexity of the models because more complex models tend to fit given data better than simpler models. For example, because the Quad model estimates four parameters compared with the two parameters estimated by PD models, the Quad model will tend to provide superior model fit. As such, an important goal is to find the best compromise between fit and parsimony. To do so, one should use selection criteria that penalize models for complexity. Akaike’s information criterion and Bayes’ information criterion are two metrics of model fit that correct for model complexity (for a review, see Myung, 2000).

171

Theoretical Considerations If the purpose of modeling is to derive estimates of processes of interest, then the choice of a model should be based on theoretical considerations (assuming that the model provides adequate fit to the data and the meanings of the parameters have been adequately established via construct validation studies). In the same way one may choose to measure attention capture, attentional disengagement, perceptual encoding, conceptual encoding, or any number of other processes in standard behavioral research, when choosing a model, one must decide which processes are most relevant to the research questions at hand. The parameters estimated by each model are, in fact, separate variables representing distinct cognitive processes. Even when they are described in similar ways (e.g., the A parameter in the Control Default model and the AC parameter in the Quad model), there may be critical differences among the processes estimated by different models. Thus, if a researcher is interested in an automatic process that captures attention and influences behavior regardless of whether or not control succeeds, then the Control Default model would not be appropriate. In this model, the A parameter reflects a subordinate automatic process that influences behavior only when control has failed, and the model is not mathematically equipped to estimate a dominant automatic processes. Similarly, if a researcher is interested in speed–Â�accuracy trade-offs, only the Diffusion model will suffice. Hence, in selecting a model, a paramount concern should be which processes are theoretically relevant to the research. If the processes of interest are not represented in existing models, one may always develop and validate a new model.

Why Not Rely Solely on Fit to Choose a€Model? Some may feel that model fit should be the only criterion for choosing a model. Why would a researcher ever use a model other than the one that provides the best fit? First, a strictly atheoretical, bottom-up approach may require one to adopt a nonsensical model. Indeed, from this perspective, a researcher would be bound to test all potential models on each and every data set (even models that may not make theoretical sense). Logically, there is no basis to restrict model-�fitting efforts to even a few competing models. This is a recognized problem in all kinds of mathematical modeling of psychological data (e.g., multino-

172

II. METHODS AND PROCEDURES

Decision Axis

mial modeling, SEM, etc.), and is why theoretical plausibility is ascribed such a central role in model building. Second, in our experience, it is not the case that one model will always provide the absolute best fit to a given measure. For example, we have experimented with five slight variations of the Quad model (e.g., models that have one OB vs. two OB parameters). We have found that, although some variants of the model generally provide better fit than others, this is not the case 100% of the time. Were we to adopt a strict bottom-up approach, we would apply different versions of the model to different data sets based on (often slight) differences in model fit as the only justification. Our experience suggests that the search for a single model or single version of a model that will provide superior fit on all occasions would be a lengthy and fruitless endeavor. Third, although it is easy to test whether or not a model provides good fit to a data set, the standards for comparing levels of good fit are unclear. How great must the difference in fit be to command use of one model over another? For example, we (Sherman et al., 2008) compared the ability of the Control Default PD model and the Quad model to account for data from priming measures versus the IAT. We found that, overall, both models provided good fit to both measures. However, whereas the Control Default model provided better fit for the priming measures, the Quad model provided better fit for the IAT. Nevertheless, the effect sizes of these differences in model fit were tiny (all Cohen ds < 0.017). We conclude that either model may be applied to either task, provided that the model offers adequate fit for the given data

set. More generally, we argue that, given that the meaning of a model’s parameters have been validated via empirical research and that the model provides adequate fit to the current data set, the primary consideration for choosing the model (vs. another) should be theoretical.

CONCLUSION Implicit measures of attitudes and knowledge do not provide process-pure estimates of automatic biases. Rather, responses on implicit measures reflect the influence of a variety of automatic and controlled processes. Mathematical modeling of implicit task performance can help to disentangle these component processes and provide independent estimates of their prevalence, without relying on task dissociation techniques that confound processes with measures. In so doing, modeling can further our understanding of implicit social cognition in a number of ways. It can help to clarify the meanings of different implicit measures and distinguish among accounts of how people respond to them. It can clarify the meaning of relationships (or lack of relationships) among different implicit measures, explicit measures, neuropsychological measures, and measures of judgments and behavior and can enhance the strength of these relationships. Modeling also can help to explain variability in implicit measures and the effects of different manipulations on implicit task performance (i.e., task malleability). Finally, it can help researchers conceptualize the nature of automatic and controlled processes, how they are related, and how they interact to produce behavior.

a z 0

Decision Time

FIGURE 9.4.╇ The Diffusion model. The decision axis is the vertical axis, and the decision time axis is the horizontal axis. The lower threshold is positioned at zero and the upper threshold at a. Information accumulation begins at z with mean drift rate v.



9. Mathematical Modeling

Acknowledgments Preparation of this chapter was facilitated by a grant from the National Science Foundation (No. BCS 0820855) to Jeffrey W. Sherman.

Note 1. Following Payne, we refer to the two component processes of PD as the automatic (A) and the controlled (C) component. Nevertheless, we reiterate that the extent to which a construct or process (or model component) possesses one or more features of automaticity (or control) is an empirical question. That is, features of automaticity and control must be demonstrated empirically. In the case of the PD model, the characterizations of the A component as automatic and the C component as controlled are well supported by careful research, summarized later.

References Allen, T. J., Sherman, J. W., & Klauer, K. C. (2009). Social context and the self-Â�regulation of implicit bias. Unpublished manuscript. Amodio, D. M., Devine, P. G., & Harmon-Jones, E. (2008). Individual differences in the regulation of intergroup bias: The role of conflict monitoring and neural signals for control. Journal of Personality and Social Psychology, 94, 60–74. Amodio, D. M., Harmon-Jones, E., & Devine, P. G. (2003). Individual differences in the activation and control of affective race bias as assessed by startle eyeblink response and self-Â�report. Journal of Personality and Social Psychology, 84, 738–753. Amodio, D. M., Harmon-Jones, E., Devine, P. G., Curtin, J. J., Hartley, S. L., & Covert, A. E. (2004). Neural signals for the detection of unintentional race bias. Psychological Science, 15, 88–93. Bargh, J. A. (1994). The four horsemen of automaticity: Awareness, intention, efficiency, and control in social cognition. In R. S. Wyer, Jr. & T. K. Srull (Eds.), Handbook of social cognition: Vol. 1. Basic processes (2nd ed., pp.€1–40). Hillsdale, NJ: Erlbaum. Bargh, J. A. (1999). The cognitive monster: The case against the controllability of automatic stereotype effects. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€361–382). New York: Guilford Press. Bartholow, B. D., Dickter, C. L., & Sestir, M. A. (2006). Stereotype activation and control of race bias: Cognitive control of inhibition and its impairment by alcohol. Journal of Personality and Social Psychology, 90, 272–287. Batchelder, W. H., & Riefer, D. M. (1999). Theoretical and empirical review of multinomial process tree modeling. Psychonomic Bulletin and Review, 6, 57–86. Beer, J. S., Stallen, M., Lombardo, M. V., Gonsalkorale,

173

K., Cunningham, W. A., & Sherman, J. W. (2008). The Quadruple Process model approach to examining the neural underpinnings of prejudice. NeuroImage, 43, 775–783. Bishara, A. J., & Payne, B. K. (2009). Multinomial process tree models of control and automaticity in weapon misidentification. Journal of Experimental Social Psychology, 45, 524–534. Blair, I. V. (2002). The malleability of automatic stereotypes and prejudice. Personality and Social Psychology Review, 6, 242–261. Blair, I. V., Ma, J. E., & Lenton, A. P. (2001). Imagining stereotypes away: The moderation of implicit stereotypes through mental imagery. Journal of Personality and Social Psychology, 81, 828–841. Brendl, C. M., Markman, A. B., & Messner, C. (2001). How do indirect measures of evaluation work?: Evaluating the inference of prejudice in the Implicit Association Test. Journal of Personality and Social Psychology, 81, 760–773. Brewer, M. B., & Feinstein, A. S. H. (1999). Dual processes in the cognitive representations of persons and social categories. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€ 255– 270). New York: Guilford Press. Chaiken, S., & Trope, Y. (Eds.). (1999). Dual-Â�process theories in social psychology. New York: Guilford Press. Chen, S., & Chaiken, S. (1999). The Heuristic Systematic Model in its broader context. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€73–96). New York: Guilford Press. Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K., & Groom, C. (2005). Separating multiple processes in implicit social cognition: The quad model of implicit task performance. Journal of Personality and Social Psychology, 89, 469–487. Correll, J., Park, B., Judd, C. M., & Wittenbrink, B. (2002). The police officer’s dilemma: Using ethnicity to disambiguate potentially threatening individuals. Journal of Personality and Social Psychology, 83, 1314–1329. Correll, J., Park, B., Judd, C. M., Wittenbrink, B., Sadler, M. S., & Keesee, T. (2007). Across the thin blue line: Police officers and racial bias in the decision to shoot. Journal of Personality and Social Psychology, 92, 1006–1023. Cunningham, W. A., Preacher, K. J., & Banaji, M. R. (2001). Implicit attitude measures: Consistency, stability, and convergent validity. Psychological Science, 121, 163–170. Dasgupta, N., & Greenwald, A. G. (2001). On the malleability of automatic attitudes: Combating automatic prejudice with images of admired and disliked individuals. Journal of Personality and Social Psychology, 81, 800–814. De Houwer, J. (2003). The Extrinsic Affective Simon Task. Experimental Psychology, 50, 77–85. Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of Personality and Social Psychology, 56, 5–18. Devine, P. G., Plant, E. A., Amodio, D. M., HarmonJones, E., & Vance, S. L. (2002). The regulation of

174

II. METHODS AND PROCEDURES

explicit and implicit race bias: The role of motivations to respond without prejudice. Journal of Personality and Social Psychology, 82, 835–848. Dovidio, J. F., & Fazio, R. H. (1992). New technologies for the direct and indirect assessment of attitudes. In J. M. Tanur (Ed.), Questions about questions: Inquiries into the cognitive bases of surveys (pp.€204–237). New York: Russell Sage Foundation. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobstrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Ferguson, M. J., & Bargh, J. A. (2007). Beyond the attitude object: Implicit attitudes spring from objectÂ�centered contexts. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp.€ 216–246). New York: Guilford Press. Fiske, S. T., Lin, M., & Neuberg, S. L. (1999). The continuum model: Ten years later. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€231–254). New York: Guilford Press. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. Gawronski, B., Deutsch, R., LeBel, E. P., & Peters, K. R. (2008). Response interference as a mechanism underlying implicit measures: Some traps and gaps in the assessment of mental associations with experimental paradigms. European Journal of Psychological Assessment, 24, 218–225. Gawronski, B., & LeBel, E. P. (2008). Understanding patterns of attitude change: When implicit measures show change, but explicit measures do not. Journal of Experimental Social Psychology, 44, 1355–1361. Glaser, J., & Knowles, E. D. (2007). Implicit motivation to control prejudice. Journal of Experimental Social Psychology, 44, 164–172. Gonsalkorale, K., Allen, T. J., Sherman, J. W., & Klauer, K. C. (2009). Mechanisms of exemplar exposure effects on implicit attitudes. Unpublished manuscript. Gonsalkorale, K., Sherman, J. W., & Klauer, K. C. (2009). Aging and prejudice: Diminished regulation of automatic race bias among older adults. Journal of Experimental Social Psychology, 45, 410–414. Gonsalkorale, K., von Hippel, W., Sherman, J. W., & Klauer, K. C. (2009). Bias and regulation of bias in intergroup interactions: Implicit attitudes toward Muslims and interaction quality. Journal of Experimental Social Psychology, 45, 161–166. Govan, C. L., & Williams, K. D. (2004). Changing the affective valence of the stimulus items influences the IAT by re-Â�defining the category labels. Journal of Experimental Social Psychology, 40, 357–365. Govorun, O., & Payne, B. K. (2006). Ego depletion and prejudice: Separating automatic and controlled components. Social Cognition, 24, 111–136. Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. New York: Wiley. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480.

Greenwald, A. G., Oakes, M. A., & Hoffman, H. G. (2003). Targets of discrimination: Effects of race on responses to weapons holders. Journal of Experimental and Social Psychology, 39, 399–405. Huntsinger, J. R., Sinclair, S., & Clore, G. (2009). Affective regulation of implicitly measured attitudes and stereotypes: Automatic and controlled processes. Journal of Experimental Social Psychology, 45, 560–566. Jacoby, L. L. (1991). A process-Â�dissociation framework: Separating automatic from intentional uses of memory. Journal of Memory and Language, 30, 513–541. Jacoby, L. L., Kelley, C. M., & McElree, B. D. (1999). The role of cognitive control: Early selection versus late correction. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€ 383– 402). New York: Guilford Press. Jacoby, L. L., Toth, J. P., & Yonelinas, A. P. (1993). Separating conscious and unconscious influences of memory: Measuring recollection. Journal of Experimental Psychology: General, 122, 139–154. Kawakami, K., & Dovidio, J. F. (2001). The reliability of implicit stereotyping. Personality and Social Psychology Bulletin, 27, 212–225. Kawakami, K., Dovidio, J. F., Moll, J., Hermsen, S., & Russin, A. (2000). Just say no (to stereotyping): Effects of training in the negation of stereotypic associations on stereotype activation. Journal of Personality and Social Psychology, 78, 871–888. Kim, D. Y. (2003). Voluntary controllability of the Implicit Association Test (IAT). Social Psychology Quarterly, 66, 83–96. Klauer, K. C. (2009). Spontaneous evaluations. In F. Strack & J. Förster (Eds.), Social cognition: The basis of human interaction (pp.€ 199–217). New York: Psychology Press. Klauer, K. C., & Voss, A. (2008). Effects of race on responses and response latencies in the weapon identification task: A test of six models. Personality and Social Psychology Bulletin, 34, 1124–1140. Klauer, K. C., Voss, A., Schmitz, F., & Teige-Â�Mocigemba, S. (2007). Processing components of the Implicit Association Test: A diffusion-model analysis. Journal of Personality and Social Psychology, 93, 353–368. Lambert, A. J., Payne, B. K., Jacoby, L. L., Shaffer, L. M., Chasteen, A. L., & Khan, S. R. (2003). Stereotypes as dominant responses: On the “social facilitation” of prejudice in anticipated public contexts. Journal of Personality and Social Psychology, 84, 277–295. Lindsay, D. S., & Jacoby, L. L. (1994). Stroop processÂ�dissociations: The relationship between facilitation and interference. Journal of Experimental Psychology: Human Perception and Performance, 20, 219–234. Luce, R. D. (1995). Four tensions concerning mathematical modeling in psychology. Annual Review of Psychology, 46, 1–26. Luce, R. D. (1999). Where is mathematical modeling in psychology heading? Theory and Psychology, 9, 723–737. Macmillan, N. A., & Creelman, C. D. (1990). Response bias: Characteristics of detection theory, threshold theory, and “nonparametric” indexes. Psychological Bulletin, 107, 401–413. Maddux, W. W., Barden, J., Brewer, M. B., & Petty, R.



9. Mathematical Modeling

E. (2005). Saying no to negativity: The effects of context and motivation to control prejudice on automatic evaluative responses. Journal of Experimental Social Psychology, 41, 19–35. Mitchell, J. P., Nosek, B. A., & Banaji, M. R. (2003). Contextual variations in implicit evaluation. Journal of Experimental Psychology: General, 132, 455–469. Monteith, M. J., Ashburn-Nardo, L., Voils, C. I., & Czopp, A. M. (2002). Putting the brakes on prejudice: On the development and operation of cues for control. Journal of Personality and Social Psychology, 83, 1029–1050. Moskowitz, G. B., Gollwitzer, P. M., Wasel, W., & Schaal, B. (1999). Preconscious control of stereotype activation through chronic egalitarian goals. Journal of Personality and Social Psychology, 77, 167–184. Myung, I. J. (2000). The importance of complexity in model selection. Journal of Mathematical Psychology, 44, 190–204. Myung, I. J., & Pitt, M. (2002). Mathematical modeling. In H. Pashler & J. Wixted (Eds.), Stevens’ handbook of experimental psychology (3rd ed., pp.€429–460). New York: Wiley. Nosek, B. A., & Banaji, M. R. (2001). The Go/No-Go association task. Social Cognition, 19, 625–666. Nosek, B. A., Banaji, M., & Greenwald, A. G. (2002). Harvesting implicit group attitudes and beliefs from a demonstration web site. Group Dynamics: Theory, Research, and Practice, 6, 101–115. Payne, B. K. (2001). Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon. Journal of Personality and Social Psychology, 81, 181–192. Payne, B. K. (2005). Conceptualizing control in social cognition: How executive functioning modulates the expression of automatic stereotyping. Journal of Personality and Social Psychology, 89, 488–503. Payne, B. K. (2008). What mistakes disclose: A Process Dissociation approach to automatic and controlled processes in social psychology. Social and Personality Psychology Compass, 2, 1073–1092. Payne, B. K., Burkley, M., & Stokes, M. B. (2008). Why do implicit and explicit attitude tests diverge?: The role of structural fit. Journal of Personality and Social Psychology, 94, 16–31. Payne, B. K., Lambert, A. J., & Jacoby, L. L. (2002). Best laid plans: Effects of goals on accessibility bias and cognitive control in race-based misperceptions of weapons. Journal of Experimental Social Psychology, 38, 384–396. Petty, R. E., & Wegener, D. T. (1999). The Elaboration Likelihood Model: Current status and controversies.

175

In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€41–72). New York: Guilford Press. Plant, E. A., Peruche, B. M., & Butz, D. A. (2005). Eliminating automatic racial bias: Making race nonÂ�diagnostic for responses to criminal suspects. Journal of Experimental Social Psychology, 41, 141–156. Ranganath, K. A., Smith, C. T., & Nosek, B. A. (2008). Distinguishing automatic and controlled components of attitudes from direct and indirect measurement. Journal of Experimental Social Psychology, 44, 386–396. Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59–108. Roediger, H. L. (1990). Implicit memory: Retention without remembering. American Psychologist, 9, 1043–1056. Rosano, C., Aizenstein, H., Cochran, J., Saxton, J., De Kosky, S., Newman, A., et al. (2005). Functional neuroimaging indicators of successful executive control in the oldest old. NeuroImage, 4, 881–889. Rydell, R. J., & McConnell, A. R. (2006). Understanding implicit and explicit attitude change: A systems of reasoning analysis. Journal of Personality and Social Psychology, 91, 995–1008. Sherman, J. W., Gawronski, B., Gonsalkorale, K., Hugenberg, K., Allen, T. J., & Groom, C. J. (2008). The selfÂ�regulation of automatic associations and behavioral impulses. Psychological Review, 115, 314–335. Snodgrass, J. G., & Corwin, J. (1988). Pragmatics of measuring recognition memory: Applications to dementia and amnesia. Journal of Experimental Psychology: General, 117, 34–50. Stahl, C., & Degner, J. (2007). Assessing automatic Â�activation of valence: A multinomial model of EAST€ performance. Experimental Psychology, 54, 99–112. Stewart, B. D., von Hippel, W., & Radvansky, G. A. (2009). Age, race, and implicit prejudice: Using process dissociation to separate the underlying components. Psychological Science, 20, 164–168. Stroop, J. R. (1935). Studies on the interference in serial verbal reactions. Journal of Experimental Psychology, 59, 239–245. Wegner, D. M. (1994). Ironic processes of mental control. Psychological Review, 101, 34–52. Wilson, T. D., & Hodges, S. D. (1992). Attitudes as temporary constructions. In L. Martin & A. Tesser (Eds.), The construction of social judgment (pp.€ 37– 65). Hillsdale, NJ: Erlbaum. Zajonc, R. B. (1965). Social facilitation. Science, 149, 269–274.

C h a p t e r 10

Implicit Measures Similarities and Differences Jan De Houwer and Agnes Moors

D

uring the past 15 years, an increasingly large number of procedures and effects have been referred to as implicit measures. These include affective priming (e.g., Fazio, Jackson, Dunton, & Williams, 1995), the name-Â�letter effect (e.g., Koole, Dijksterhuis, & van Knippenberg, 2001; Nuttin, 1985), semantic priming (Wittenbrink, Judd, & Park, 1997), the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998), the affective Simon effect (De Houwer & Eelen, 1998), the Go/No-Go Association Task (Nosek & Banaji, 2001), stereotypic explanatory bias (Sekaquaptewa, Espinoza, Thompson, Vargas, & von Hippel, 2003), the Single-Target IAT (Karpinski & Steinman, 2006; Wigboldus, 2001), the Extrinsic Affective Simon Task effect (De Houwer, 2003a), the stimulus–Â�response compatibility task (Mogg, Bradley, Field, & De Houwer, 2003), the Implicit Association Procedure (Schnabel, Banse, & Asendorpf, 2006), the affect misattribution procedure (AMP; Payne, Cheng, Govorun, & Stewart, 2005), the single association test (Blanton, Jaccard, Gonzales, & Christie, 2006), the word association test (Stacy, Ames, & Grenard, 2007), the approach–avoid task (Rinck & Becker, 2007), the implicit relational assessment procedure (e.g., Barnes-Â�Holmes, Murtagh, & BarnesÂ�Holmes, in press), the Sorting Paired Features



Task (Bar-�Anan, Nosek, & Vianello, 2009), and the brief IAT (Sriram & Greenwald, 2009). Several of these implicit measures are described in other chapters of this volume (see Sekaquaptewa, Vargas, & von Hippel, Chapter 8, Teige-�Mocigemba, Klauer, & Sherman, Chapter 7, and Wentura & Degner, Chapter 6). At present, so many implicit measures are available that it becomes difficult to come to grips with why a particular procedure or effect qualifies as an implicit measure and how it differs from other (implicit) measures. Recently, we have provided a detailed analysis of what it means to say that something is an implicit measure (De Houwer, 2006; De Houwer & Moors, 2007; De Houwer, Teige-�Mocigemba, Spruyt, & Moors, 2009a). In the first part of the present chapter, we briefly summarize the results of this analysis. In the second and main part of this chapter, we focus on classification criteria that could be used to clarify how different implicit measures differ from each other (also see De Houwer, 2009). Making these criteria explicit not only has a heuristic value for organizing different implicit measures but might also help us to improve the measures themselves and the way in which they are applied. In the third and final part, we propose a number of meta-�criteria that can be used to evaluate the merits of classification criteria. 176



10. Implicit Measures

WHAT ARE IMPLICIT MEASURES? In line with the ideas of Borsboom, Mellenbergh, and van Heerden (2004, p.€1061) and De Houwer (2006; De Houwer & Moors, 2007), De Houwer and colleagues (2009a) defined an implicit measure as the outcome of a measurement procedure that is causally produced by psychological attributes in an automatic manner. Figure 10.1 provides a graphic representation of this definition. The definition has several important implications (see De Houwer et al., 2009a, for a more in-depth discussion). First, a measure is the outcome of a measurement procedure that is applied to a certain person. For instance, a racial IAT score (i.e., the difference in performance during the black–Â�positive task and the white–Â�positive task of a racial IAT) is derived from how a person responds when submitted to the racial IAT task. The outcome is assumed to be a measure of the attribute (e.g., racial attitudes) in that variations in the outcome are supposed to capture differences in this attribute (top arrow in Figure 10.1). The measurement procedure, on the other hand, is simply an objective list of instructions on how to obtain a measurement outcome. It specifies, among other things, the way in which stimuli should be presented, responses registered, and the outcome derived from the responses. Second, the definition clarifies that research on the validity of a measure should focus on obtaining evidence for the causal relation between the

177

to-be-Â�measured attribute and the measure. Ideally, this research should not only reveal that variations in the attribute cause variations in the measurement outcome but also uncover the processes by which the attribute has this causal effect (see also Wentura & Rothermund, 2007). As Borsboom and colleagues (2004, p.€1067) point out, in exact sciences “nobody starts constructing measurement instruments without the faintest idea of the processes that lead to measurement outcomes.” Although correlational research can provide useful information for detecting possible causal relations, experimental research generally offers more guarantees for the validity of causal inferences. Hence, experimental studies should be an essential part of validation research. Finally, whether a measure is implicit is determined by whether the processes by which the to-be-Â�measured attribute causes the measurement outcome are automatic in a certain manner (see Figure 10.1). In line with a decompositional view of automaticity (Bargh, 1992; Moors & De Houwer, 2006; see Moors, Spruyt, & De Houwer, Chapter 2, this volume), De Houwer and colleagues (2009a) assumed that processes can have different features of automaticity that do not always co-occur. For instance, some processes are automatic in the sense that they still operate even when participants do not have particular goals (e.g., the goal to engage in the process). Other processes are automatic in that they operate even when participants are unaware of the stimulus that

Inference about attribute

Situation --------------ATTRIBUTE

-------------- Responses ------ Outcome AUTOMATIC PROCESSES

Person

FIGURE 10.1.╇ A schematic representation of the definition of an implicit measure. Certain aspects of the responses that a person shows when submitted to a certain situation are assumed to be mediated by a mental attribute of the person (e.g., an attitude) by means of automatic mental processes (arrows). The responses are used to determine an outcome (i.e., the measure), which is then used to make inferences about the attributes of the person.

178

II. METHODS AND PROCEDURES

instigates the process. Different implicit measures can thus be implicit (i.e., automatic) in different ways. It, therefore, makes little sense to simply say that a measure is implicit. It is always necessary to specify the automaticity features that characterize the (processes underlying the) measure (see De Houwer, 2006; De Houwer & Moors, 2007, for a detailed analysis; see also Moors et al., Chapter 2, this volume). Like all definitions, the definition of implicit measures that is provided by De Houwer and colleagues (2009a) is a matter of convention and thus, to a certain degree, arbitrary. Nevertheless, the definition does have a number of advantages. First, it has a strong conceptual basis in the work of Borsboom and colleagues (2004; Borsboom, 2006) and De Houwer (2006; De Houwer & Moors, 2007). Second, it is broad enough to allow for a variety of opinions about the nature of the attributes that are captured by implicit measures, the nature of the processes that underlie implicit measures, and the way in which measures are implicit. Third, it is detailed enough to clarify the abstract core of what all implicit measures have in common and to specify the properties that an ideal implicit measure should have. With regard to the latter point, De Houwer et al. specified three normative criteria: (1) the “what” criterion: It should be clear which attributes causally produce the measurement outcome; (2) the “how” criterion: It should be clear by which processes the attributes cause the measurement outcome; and (3) the implicitness criterion: The way in which the processes underlying a measure are automatic needs to be specified and demonstrated empirically. Whereas De Houwer and colleagues focused on what it is that implicit measures (should) have in common, in the remainder of this chapter, we focus on how implicit measures can differ from each other. Of course, implicit measures differ with regard to many superficial characteristics. The challenge is not to find differences as such but to find patterns in all these differences, that is, commonalities in how measures can differ. We use the definition of implicit measures as a guideline to find such commonalities.

CRITERIA TO DIFFERENTIATE IMPLICIT MEASURES The definition of implicit measures as provided by De Houwer and colleagues (2009a) refers to elements internal to the person who is being mea-

sured (what is inside the box in Figure 10.1, i.e., mental attributes and processes) and elements external to the individual (what is outside the box in Figure 10.1, i.e., the measurement procedure). It leaves many degrees of freedom with regard to the precise properties of these internal and external elements. The outcome of the measurement procedure needs to be influenced by attributes of the individual, and the processes by which this occurs need to be automatic in some sense, but apart from that implicit measures can involve any type of measurement procedure, attribute, or process. Implicit measures can thus be compared with regard to the properties of these internal (attribute and process) and external (measurement procedure) elements. This insight provides the first step toward a heuristic system for classifying implicit measures. The second step involves making explicit how implicit measures can differ with regard to internal and external properties. This involves the specification of criteria that some implicit measures meet but others do not. In the following sections, we propose a number of criteria that are related to the internal and external properties of implicit measures. An overview of these criteria can be found in Table€10.1.

Differences with Regard to Internal Properties On the basis of their definition of implicit measures, De Houwer and colleagues (2009a) formulated three normative criteria that an ideal implicit measure should meet: (1) It should be clear which attributes the measure reflects (the what criterion); (2) the nature of the processes by which the attributes cause variations in the measure should be known (the how criterion); and (3) it should be clear that the underlying processes are automatic in a certain manner (the implicitness criterion). These criteria not only set the ultimate standards against which each implicit measure can be compared, but they also clarify the manner in which internal properties of the measures can differ. Implicit measures can differ with regard to (1) the attributes that cause the measure (e.g., attitudes, stereotypes), (2) the nature of the processes by which the attributes cause the measure (e.g., spreading of activation, response competition), and (3) the way in which these processes are automatic (e.g., unintentional, unconscious). As such, the what, how, and implicitness criteria allow for a classification of implicit measures. We now discuss each of these three criteria in more detail.



10. Implicit Measures

179

TABLE 10.1.╇Overview of Different Criteria That Can Be Used to Classify Implicit Measures Name

Criterion

Types

What

What is the to-be-measured attribute?

Racial attitudes Gender stereotypes .â•›.â•›.

How

By which process does the to-be-measured attribute cause the outcome?

Spreading of activation Response conflict .â•›.â•›.

Implicitness

Which automaticity features characterize the process by which the to-be-measured attribute causes the outcome?

Unintentional Unconscious Efficient .â•›.â•›.

Self-assessment

Is the measure derived from a self-assessment of the to-bemeasured attribute?

No: Direct Yes: Indirect

Response system

Is the measure derived from behavioral, physiological, or neurological responses?

Behavioral Physiological Neurological

Response content

Is the measure derived from the symbolic or physical properties of responses?

Symbolic Nonsymbolic

SRC

Is the measure derived from a comparison of trials that differ with regard to a type of SRC?

Relevant SRC Irrelevant SRC S-S .â•›.â•›.

Internal criteria

External criteria

Note. Ellipses (.â•›.â•›.) signify that other types are possible; SRC, stimulus–response compatibility.

The What Criterion Classifying measures on the basis of what they are supposed to measure is perhaps the most common and intuitively appealing manner of classification. Many researchers and practitioners are inclined to characterize measures on the basis of their face validity, that is, on whether the measurement procedure looks suitable for capturing a certain attribute. One should be aware, however, that classification of a measure on the basis of the what criterion cannot be done on the basis of superficial, objective features of the measurement procedure. For instance, saying that racial IAT scores measure racial attitudes is a theoretical claim rather than something that can be verified by looking at the measurement procedure or the measurement outcome. Validity is not a property of the measurement procedure or the measurement outcome; it is a property of the claims about the measurement outcome (i.e., about what the outcome captures). Like all theoretical claims, claims about the validity of a measure need to be backed up by basic research (see De Houwer, Teige-�Mocigemba, Spruyt, & Moors, 2009b). A measure can be considered as valid for measuring an attribute only when there is sufficient experimental and correlation evidence

to support the conclusion that a measure indeed captures the to-be-�measured attribute (see Borsboom et al., 2004; De Houwer et al., 2009a, 2009b). Hence, classification on the basis of the what criterion depends on the outcome of empirical research rather than on an inspection of the properties of the measurement procedure. As we noted, conceptual analysis is also important. It makes little sense to characterize and classify a measure on the basis of the to-be-�measured attribute if it is not clear what the attribute actually entails. The risks of relying on face validity for deciding what a measure captures are well known (Anastasi, 1988). Nevertheless, even in recent studies on implicit measures, researchers sometimes rely exclusively on superficial properties of the measurement procedure when interpreting a measure. For instance, several implicit measures have been proposed in which participants are asked to approach or avoid stimuli in some way (e.g., move a manikin or a joystick toward or away from a stimulus; see Mogg et al., 2003; Rinck & Becker, 2007). Because the measure is derived from the manner in which stimuli influence approach and avoidance responses, researchers tend to interpret the measures as indices of the motivational properties of stimuli.

180

II. METHODS AND PROCEDURES

There is, however, little if any direct evidence to support this interpretation. It might well be that all the effects are driven exclusively by the valence of the stimuli independent of their motivational properties (De Houwer, 2003b, pp.€ 236–237; see Eder & Rothermund, 2008, for evidence supporting this hypothesis). At the conceptual level, it is also not entirely clear what is unique about the motivational properties of a stimulus and thus how one should assess these properties. Until more empirical and conceptual work on this matter has been completed, researchers should be very careful in interpreting approach–Â�avoidance measures as indices of motivational properties. Even when there are good conceptual and empirical arguments to classify measures on the basis of the attribute that they reflect, one should not conclude that measures that are supposed to capture the same attribute always produce the same results. Different measures of the same attribute can, for instance, differ with regard to the impact of other attributes, the way in which the to-beÂ�measured attribute influences the measure, and the conditions under which it does so. There are thus plenty of potential reasons for why measures that are assumed to capture the same attribute often do not correlate (e.g., Bosson, Swann, & Pennebaker, 2000).

The How Criterion Measures can be classified also on the basis of the processes by which the to-be-�measured attribute causes the measure. A prototypical example of a psychological process is spreading of activation (e.g., Collins & Loftus, 1975). It is assumed to operate on concepts represented as nodes in a semantic network. In this network, the representations of semantically similar concepts are connected by associations through which activation can spread. Activation of a concept that is due to the presentation of one specific stimulus can thus spread to other, semantically related concepts. This could facilitate the subsequent (semantic) processing of stimuli related to those concepts. It is beyond the scope of this chapter to give an overview of all the possible processes that could operate in the various implicit measures that have been proposed so far (see De Houwer et al., 2009a, for a recent review). Nevertheless, we do want to emphasize that the how criterion is likely to be an important criterion for a number of reasons. First, knowing the processes underlying a measure can provide important information about the

validity of a measure. As Borsboom and colleagues (2004) noted, it is important that we have a good understanding of the processes that underlie a measure because this provides important information about the validity of a measure: If we know how a psychological attribute produces measurement outcomes, this provides more certainty about the fact that the attribute causes the measurement outcome. It could also provide hints for optimizing the validity of the measure. Once a process has been identified through which the to-be-�measured attribute can influence the measure, steps can be taken to strengthen the process and thus the impact of the attribute on the measures. Second, measures that are based on different processes are also likely to produce different results. Hence, applying the how criterion could provide an important insight into why different implicit measures of the same psychological attribute are often uncorrelated (e.g., Bosson et al., 2000). Third, merely acknowledging that implicit measures can differ with regard to the nature of the processes on which they are based is important for interpreting the results of (implicit) measures. It clarifies that all measures provide only an indirect reflection of psychological attributes. As depicted in Figure 10.1, a psychological attribute of the person can influence behavior only by virtue of certain processes (represented by the arrows inside the box in Figure 10.1). Many factors other than the to-be-�measured psychological attribute can influence the responses from which the measure is derived. Because of this, it is difficult to be certain about the interpretation of a particular measurement outcome. When a measure provides evidence for the presence of a psychological attribute, this could be due to the effect of other (correlated) attributes. For instance, it has been argued that IAT effects do not reflect associations in memory (i.e., attitudes and stereotypes) but differences in the salience of concepts (e.g., Rothermund & Wentura, 2004). When a measure does not provide evidence for the presence of a psychological attribute, this could be due to other attributes or processes that counter the impact of the to-be-�measured attribute on the measurement outcome. Before we can have certainty about how to interpret measures, it is thus vital that we learn more about the underlying processes. Until we have this knowledge, we should always keep in mind that a measure is the product not only of the to-be-�measured psychological attribute but also of the processes intervening between the attribute and the behavior from



10. Implicit Measures

which the outcome is derived (see Moors et al., Chapter 2, this volume, and Gawronski, Deutsch, LeBel, & Peters, 2008, for an in-depth discussion of this point).

The Implicitness Criterion In our previous work (De Houwer, 2006; De Houwer & Moors, 2007; Moors & De Houwer, 2006), we equated the concept implicit with the concept automatic and defined the latter according to a decompositional point of view (e.g., Bargh, 1992). From this perspective (see Moors et al., Chapter 2, this volume), automaticity is not an all-or-none property that processes either possess completely or not at all. Rather, it is an umbrella concept that refers to a variety of automaticity features that do not necessarily co-occur. Each automaticity feature concerns a certain condition on which the operation of the process might depend. Many features such as uncontrolled, unintentional, goal independent, autonomous, and purely stimulus driven refer to the fact that the process can operate in the absence of certain goals. For instance, a process can be called unintentional if it can operate even when participants do not have the goal to engage in this process. Other automaticity features refer to the need of awareness (of the instigating stimulus, the process itself, or the output of the process), processing resources, and time. Features do not always co-occur. Some processes might be automatic in the sense that they operate in the absence of the goal to engage in the process at hand but nonautomatic in that they still require processing resources and awareness of the instigating stimulus. Other processes might be automatic in that they do not require substantial processing resources but nonautomatic in that they operate only when participants have the goal to engage in the process at hand. Because processes can be automatic in different ways, it does not make sense to merely say that a process is automatic. Rather, one always needs to specify the way in which a process is automatic, that is, which automaticity features do and do not apply to the process. One can also not simply infer the presence of one automaticity feature from the presence of another automaticity feature. Instead, each feature needs to be examined separately. Based on this empirical work, measures can be classified according to the automaticity features of the processes on which they are based. We refer to this criterion of classification as the implicitness criterion.

181

Given that there are so many automaticity features, the number of possible combinations of features is very large. Also, many processes will have one or more automaticity feature and will thus be automatic in some way and nonautomatic in another. One might wonder how useful it is to classify measures on the basis of the implicitness criterion if there is a different box in the classification system for almost each different measure. We believe that there are at least four reasons why the implicitness criterion nevertheless might prove to be useful. First, at the very least, it offers a vocabulary for describing implicit measures. That is, it allows one to make explicit and examine similarities and differences among measures. Second, the same vocabulary can be used to describe behaviors in daily life. In fact, one could say that both the measurement outcome and the real-life behavior have certain automaticity features (i.e., the to-be-�measured attribute influences behavior under certain conditions). It is possible that the predictive value of the measure depends on the extent to which the automaticity features of the processes underlying that measure are the same as the automaticity features of the processes underlying the behavior that one wants to predict (see also Perugini, Richetin, & Zogmaister, Chapter 14, this volume). For instance, real-life behavior that is influenced by attitudes when people do not have the time to evaluate stimuli in the environment (e.g., buying products in a supermarket under time pressure) might be related most strongly to measurement outcomes that are observed when participants are under time pressure. The closer the overlap between the conditions under which the measurement outcome is observed and the conditions under which to-be-�predicted behavior occurs, the more the measurement outcome might be able to predict the behavior (see also De Houwer, 2006, 2009; Vargas, 2004). Third, the fact that there are many different possible combinations of automaticity features does not exclude the possibility that only a few features or combinations of features will turn out to be important for the added value of an implicit measure. The main reason for the introduction of implicit measures was that they might explain variance of to-be-�predicted behaviors on top of what can be predicted on the basis of traditional, explicit measures. Future research might show that a measure has incremental predictive validity only when (apart from being reliable and valid) it possesses some automaticity features (e.g., uncontrolled) but not others (e.g., unaware). The only way to exam-

182

II. METHODS AND PROCEDURES

ine this issue is by making explicit for each measure what automaticity features it possesses. Finally, the automaticity features of a measure specify the conditions under which the processes underlying the measure operate. If it is true that different types of processes operate under different conditions, knowledge about these conditions provides information about the type of processes that are at stake. Applying the implicitness criterion can increase understanding of the processes underlying a measure and thus help construct the measurement theory that is necessary to establish the validity of a measure (Borsboom et al., 2004; De Houwer et al., 2009a).

Differences with Regard to External Properties The external properties of a measure are related to the measurement procedure from which the measurement outcome is derived. As we pointed out earlier, a measurement procedure specifies which stimuli must be presented in what manner, which responses should be registered in what manner, and how a measurement outcome must be derived from those responses. This definition of a measurement procedure clarifies two important points. First, a distinction needs to be made between the responses that are observed (i.e., the raw data) and the outcome that is derived from these responses (see Figure 10.1). Responses are multifeatured entities. For instance, pressing a key during an IAT can be characterized by the time at which it was executed (i.e., reaction time) as well as by its accuracy or even the pressure with which it was exercised. In principle, an outcome (e.g., IAT score) can be de-

rived from each response feature or each combination of features. Moreover, several algorithms or statistical techniques can be used to derive the measurement outcome from the raw response data (e.g., Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005; Greenwald, Nosek, & Banaji, 2003; see also Sherman, Klauer, & Allen, Chapter 9, this volume). How the outcome is derived from observed responses is a crucial and integral part of the measurement procedure. All of the external classification criteria that we discuss in this section deal with differences in the features of the responses that are used to derive the measurement outcome. Second, as De Houwer (2006) pointed out, there is nothing implicit about a measurement procedure. Hence, it does not make sense to use the term implicit measure to refer to a measurement procedure. Only measurement outcomes can qualify as implicit measures. Measurement outcomes and thus implicit measures can, however, be characterized and compared with regard to the nature of the measurement procedure that is used to obtain the outcome. We now discuss a number of possible criteria that can be used to classify measures on the basis of the measurement procedure. An overview of these criteria can be found in Table 10.1. The relations between the various external criteria are depicted in Figure 10.2.

The Self-�Assessment Criterion: Direct versus Indirect Measures A first criterion relates to whether the measurement procedure requires the participant to self�assess the to-be-�measured attribute. Many measures are based on such a measurement procedure.

SELF-ASSESSMENT?

RESPONSE CONTENT?

RESPONSE SYSTEM?

Yes: Direct

Symbolic

Behavioral *

No: Indirect

Symbolic

Behavioral *

Nonsymbolic

Behavioral

TYPE OF SRC?

Relevant S-R Irrelevant S-R S-S€.€.€.

Physiological Neurological

FIGURE 10.2.╇ A graphic representation of how the different extrinsic criteria are related. SRC, stimulus– response compatibility; S-R, stimulus–response; S-S, stimulus–stimulus. See Note 3 for a discussion of whether all symbolic measures are behavioral. Additional (combinations of) SRC types are possible. *Self-report measures include all direct and indirect symbolic behavioral measures that are derived from self-assessments of attributes.



10. Implicit Measures

For instance, one can measure racial attitudes by asking participants to report the degree to which they hold a positive or negative attitude toward white and black people. Such self-Â�assessments are often registered using Likert scales on which the participant selects a numerical value that expresses his or her assessment of the degree to which he or she possesses the attribute. We refer to these measures as direct measures. More formally, direct measures are characterized by two properties: (1) The measurement outcome is derived from a self-Â�assessment by the participant, and (2) the target of the self-Â�assessment is the attribute that the measurement outcome is assumed to capture. If a measure does not have both of these properties, it can be called indirect. We have chosen the qualifications “direct” and “indirect” because they convey information about the nature of the relation between the measurement outcome and the responses on which it is based. In direct measurement procedures, the outcome is based directly on the responses of the participant (i.e., his or her self-Â�assessment of the to-be-Â�measured attribute) without the need for further interpretation of the responses. In indirect measurement procedures, on the other hand, there is an additional step of interpretation by the researcher. The outcome is based not on the responses as such but on how the responses are interpreted by the researchers.1 Take the example of a measurement procedure in which participants are asked to self-Â�assess for each letter of the alphabet how much they like or dislike that letter. When these self-Â�assessments are used to construct a measurement outcome that aims to assess attitudes toward letters, the outcome can be described as a direct measure of attitudes toward letters. In this case, the measurement outcome is derived directly from self-Â�assessments of the to-be-Â�measured attributes (i.e., attitudes toward letters). The same self-Â�assessments of attitudes toward letters can, however, also be used to indirectly measure self-Â�esteem. Typically, this has been done by calculating name-Â�letter effects, that is, the difference between the mean evaluation of letters that are part of the name of the participant and the mean evaluation that these letters receive from participants who do not have those letters in their name (see Koole et al., 2001; Nuttin, 1985). Such name-Â�letter effects are indirect measures of self-Â�esteem because they are not based directly on a self-Â�assessment of self-Â�esteem but rather are inferred indirectly by the researcher from another behavior, in this case, the self-Â�assessment of attitudes toward letters.

183

The example of the name-Â�letter ratings also clarifies the difference between direct measures and self-Â�report measures. Whereas a direct measure is based on a self-Â�assessment of the to-beÂ�measured attribute (i.e., the specific attribute that the outcome is supposed to capture), a self-Â�report measure is based on a self-Â�assessment of any kind of attribute, the to-be-Â�measured one or another one. Direct measures are thus a subclass of selfÂ�report measures (also see Figure 10.2). This analysis clarifies that self-Â�report measures can also be indirect measures. De Houwer (2006) illustrated this point by referring to the Minnesota Multiphasic Personality Inventory (MMPI), a widely used questionnaire designed to measure various personality traits (e.g., Butcher, Derksen, Sloore, & Sirigatti, 2003). The procedure of administering the MMPI does not involve asking participants to answer questions about the extent to which they believe they possess a certain personality trait. Rather, they are asked to indicate whether statements about feelings and behaviors apply to them. For instance, people who endorse the item “I have a good appetite” will receive a lower score on the Depression scale. Although the MMPI is a selfÂ�report measure (because participants are asked to self-Â�assess attributes), it is an indirect measure of personality. The self-Â�assessment criterion does not overlap with the internal classification criteria that we discussed previously. Regarding the what criterion, many if not all attributes can be measured directly or indirectly. Hence, knowing whether a measure is direct or indirect provides little or no information about what attribute is being measured. Regarding the how criterion, it is likely that all direct measures are at least in part based on similar processes because all involve the common goal of self-Â�assessing a psychological attribute. Nevertheless, direct measures can differ in the conditions under which the self-Â�assessment is registered and can thus differ with regard to some of the underlying processes. For instance, self-Â�assessment under time pressure is likely to involve different processes than self-Â�assessment without time pressure. Hence, simply classifying a measure as direct does not allow one to conclude how the attribute causes the measurement outcome. This is even more true for indirect measures. Indirect measures can be derived from self-Â�assessments of attributes other than the to-be-Â�measured attribute as well as from a variety of other responses and response features. Classifying a measure as indirect, therefore, provides little if any information about the underlying processes.

184

II. METHODS AND PROCEDURES

Finally, the self-Â�assessment criterion does not overlap with the implicitness criterion. A first issue to emphasize is that the distinction between direct and indirect measures differs from the distinction between explicit and implicit measures. Whereas the qualification “direct” or “indirect” refers to a property of the measurement procedure on which the measure is based, “explicit” and “implicit” describe the conditions under which the psychological processes underlying the measure operate. This implies that direct measures are not necessarily explicit and that indirect measures are not necessarily implicit. Whether a measure is implicit needs to be examined empirically (see De Houwer, 2006; De Houwer & Moors, 2007; Moors et al., Chapter 2, this volume). It is true, however, that indirect measures are more likely to be implicit in certain ways than direct measures. Direct measurement procedures by definition include the instruction to adopt the conscious goal to self-Â�assess the to-be-Â�measured attribute in a certain manner. These instructions do not ensure that participants will actually adopt the goal or that the goal will have any impact on performance, but they do increase the likelihood. Indirect measurement procedures do not encourage the adoption of a conscious goal to express attributes, rendering it less likely that indirect measures depend on such a goal. Asking participants to adopt the goal to self-Â�assess the to-be-Â�measured attribute also renders it highly likely that participants will become aware of the crucial stimuli, the attribute that is being measured, and the fact that the attribute influences performance. Hence, it is unlikely that direct measures will be implicit in the sense of unaware. It is more likely that indirect measures will be implicit in the sense of unaware, but this should be examined empirically. Whereas the procedural distinction between direct and indirect measures is relevant for whether a measure is likely to be implicit in the sense of dependent on goals and awareness, it is less relevant for whether a measure is likely to be implicit in the sense of minimally dependent on resources (i.e., efficient) and time (i.e., fast). Direct measures can be implicit in that expression of the attribute occurs quickly (e.g., self-Â�assessment under time pressure) or independently of the presence of considerable cognitive resources (e.g., self-Â�assessments while performing a difficult secondary task). Hence, it is important to realize that direct measurement procedures can generate outcomes that are implicit in some sense of the word (see Ranganath, Smith, & Nosek, 2008, for evidence supporting this hypothesis).

The Response System Criterion: Behavioral, Physiological, and€Neurological Measures In principle, measurement outcomes can be derived from any type of response. Typically, a distinction is made among (1) behavioral responses (e.g., spoken or written answers, key press responses), (2) physiological responses (e.g., skin conductance), and (3) neurological responses (e.g., brain activity as registered by electroencephalogram and functional magnetic resonance imaging [fMRI]). Both currently available and yet-to-be-�introduced measures can be classified by examining whether the responses from which the measurement outcome is derived belong to the behavioral, physiological, or neurological response system. As is the case with the self-�assessment criterion, it is important to realize that the response system criterion refers to a more or less objective external property, namely a feature of the measurement procedure that is used to arrive at a measurement outcome. As such, it clearly differs from and does not overlap with the internal criteria that we discussed earlier. First, many if not all attributes can be measured on the basis of behavioral, physiological, and neurological responses. Hence, classifying a measure on the basis of the response system criterion does not allow for a classification on the basis of the what criterion. Second, although some processes are likely to be unique to certain response systems, knowing the type of response from which the measurement outcome is derived says little about how the attribute causes the outcome. Hence, the response system criterion does not overlap with the how criterion. The response system criterion also does not overlap with the implicitness criterion. One might be tempted to believe that measures derived from physiological and neurological responses are, by definition, fully implicit. Such a belief could result from the fact that (some) physiological and neurological responses appear to arise independently of goals, awareness, substantial processing resources, or substantial time. This line of reasoning is not entirely valid. Most importantly, the implicitness of measures does not depend only on the automaticity features of the responses themselves. What also matters are the automaticity features of the processes by which the attributes influence the responses. Imagine a measurement procedure involving a device that provides a continuous and highly accurate index of the activity of the amygdala. Because the amygdala has been implicated in the processing of threatening stim-



10. Implicit Measures

uli (e.g., Öhman, Carlsson, Lundqvist, & Ingvar, 2007), the extent to which a stimulus activates the amygdala of an individual could be seen as a measure of how threatening the stimulus is for that individual. It is reasonable to assume that, without special training, people have little voluntary control over the activation of the amygdala. That is, the actual level of activity of the amygdala will most likely be independent of goals to promote or counteract activity in the amygdala. People are probably also unaware of the activation level of their amygdala and whether a stimulus activates the amygdala. This does not imply, however, that amygdala activity provides a measure of threat that is fully implicit. For instance, people might be able to exert indirect control over amygdala activity by avoiding processing of the (threat value of the) stimulus or engaging in other voluntary emotion regulation strategies (e.g., Cunningham, Van Bavel, & Johnsen, 2008). As is the case with behavioral measures, empirical research is needed before it can be concluded that a physiological or neurological measure is an implicit measure. In sum, neurological (and physiological) measures are not, by definition, more implicit than behavioral measures. We would also like to note that physiological and neurological measures are not, by definition, more valid measures than behavioral measures. As De Houwer and colleagues (2009a) pointed out, the validity of a measure of psychological attributes can go only as far as the validity of the assumptions about the to-be-Â�measured attributes. Detailed conceptual analyses and empirical research are needed to validate physiological and neurological measures also. Let us return to the example of amygdala activation as a measure of the threat value of stimuli. This idea is based on research showing that the presentation of threatening stimuli leads to a higher activation of the amygdala (e.g., Öhman et al., 2007). More recent research has shown, however, that also highly relevant positive stimuli (e.g., erotic pictures or rewards) lead to an increase in amygdala activation, giving rise to the hypothesis that threatening pictures activate the amygdala not because of their threat value but because of their relevance (e.g., Sander, Grafman, & Zalla, 2003). If this new hypothesis would turn out to be correct, it would invalidate amygdala activation as a measure of threat value. One could, of course, reinterpret the measure as an index of relevance, but the validity of this interpretation would also depend on the validity of the empirical evidence on which this interpretation is based. Moreover, this would

185

raise important conceptual questions about what it actually means to say that a stimulus is relevant. In sum, as is the case with behavioral measures, the validity of neurological (and physiological) measures depends on the outcome of conceptual analyses and empirical research.

The Response Content Criterion: Symbolic and Nonsymbolic Measures All responses, whether behavioral, physiological, or neurological, have several physical features. Examples of such physical features are the time at which a behavioral response is emitted (which provides the basis for reaction time measures), the amount and time course of sweating (which provides the basis for skin conductance measures), and the location of increased blood flow in the brain (which provides the basis for fMRI measures). In principle, measurement outcomes can be derived from any physical feature of any type of response. Some responses not only have physical features, but they also have a meaning. Such symbolic responses represent certain objects, concepts, or ideas. In the case of symbolic responses, measures can be based on the physical features of responses (e.g., the speed with which they are emitted, the pitch with which words are spoken), but they can also be derived from the symbolic properties of responses, that is, from their representational content. According to the response content criterion, a measure can be classified as symbolic if it is derived from the meaning of symbolic responses (e.g., the content of what someone says) and as nonsymbolic if it is derived merely from the physical properties of (symbolic or nonsymbolic) responses (e.g., the speed or amplitude with which something is said). To determine whether a measure is symbolic or nonsymbolic, it does not suffice to check whether it is derived from a symbolic response or a nonsymbolic response. This is because nonsymbolic measures can be derived also from the nonsymbolic properties of symbolic responses (see Figure 10.2). For instance, one could imagine a measure of racial attitudes that is derived not from what someone says about black people (i.e., the symbolic meaning of the description) but from the intonation with which the person speaks about black people. Because the intonation with which words are spoken is a physical, nonsymbolic property of words, measures that are based on this property are nonsymbolic. Well-known examples of symbolic measures are the Rorschach test (Exner, 2003) and the Thematic Apperception Test (Tuerlinckx, De Boeck,

186

II. METHODS AND PROCEDURES

& Lens, 2002), in which psychological traits are inferred from the content of the description that participants give of pictorial stimuli. A more recent example is the AMP (see Payne et al., 2005), in which the attitude toward a prime stimulus is derived from what people convey about how much they like a target stimulus that rapidly follows the (briefly presented) prime stimulus. The response content criterion does not overlap entirely with the other external criteria that we have considered so far (see Figure 10.2). First, indirect measures can be both symbolic and nonsymbolic. AMP measures, for instance, are indirect and symbolic. They are indirect because attitudes toward the prime stimulus are derived from what people say about the target stimuli. They are based on the meaning of what people say about those targets and thus are symbolic. IAT measures, on the other hand, are indirect and nonsymbolic. They are indirect because the attributes are estimated on the basis of categorization responses rather than self-�assessment. They are nonsymbolic because the estimate is derived from the speed rather than the meaning of the responses.2 All direct (and self�report) measures are, however, symbolic because the measurement outcome is based on the meaning of a self-�assessment. Second, the response content criterion does not overlap with the response system criterion (see Figure 10.2). Whereas the latter refers to the type of response (behavioral, physiological, or neurological), the former refers to the specific property of the response from which the measurement outcome is derived (physical or symbolical). The independence of the two criteria is most clear when considering behavioral responses. Measures based on behavioral responses can be either symbolic (if the measures are based on the meaning of the responses) or nonsymbolic (if the measures are based merely on the physical properties of the responses). It is less clear whether physiological and neurological responses can have symbolic properties and thus give rise to symbolic measures.3 The response content criterion also does not overlap entirely with the internal classification criteria that we have discussed. First, it is likely that many attributes can be measured on the basis of both symbolic and nonsymbolic responses. Hence, knowing the symbolic nature of a measure does not reveal what attribute is being measured (what criterion). Nevertheless, symbolic measures might be more suitable to capture symbolic attributes. Many of the psychological attributes that psychologists want to measure can be regarded as symbolic in that the attributes represent beliefs about events in

the world (e.g., attitudes, stereotypes). It is possible that such symbolic attributes are expressed more accurately in the meaning of symbolic responses than in the physical properties of (symbolic or nonsymbolic) responses. Second, there may be processes that are common to all symbolic measures, but it is also likely that different symbolic measures differ with regard to some underlying processes. Nonsymbolic measures are so diverse that classifying them as nonsymbolic says little or nothing about the underlying processes (how criterion). Finally, the symbolic nature of the measure says little about its implicitness (implicitness criterion). One exception is that many symbolic measures are based on responses that can be controlled (e.g., describing a picture; but see Note 3). This creates room for influences of factors other than the to-be-�measured attribute (e.g., social desirability concerns). Nevertheless, regardless of whether a measure is symbolic or nonsymbolic, the only way to arrive at definite conclusions about its validity and implicitness is by conducting research.

The Stimulus–Â�Response Compatibility€Criterion Many of the implicit measures that have been introduced during the past 20 years are based on reaction time tasks. The best known examples of these reaction time measures are affective priming effects (e.g., Fazio et al., 1995) and IAT effects (e.g., Greenwald et al., 1998). On the basis of the criteria specified so far, most reaction time-based measures can be classified as indirect, behavioral, and nonsymbolic: The psychological attribute is estimated not on the basis of a self-Â�assessment of the to-be-Â�measured attribute but rather the speed with which participants respond to certain stimuli (see Figure 10.2). This important subset of implicit measures can be classified further using the stimulus–Â�response compatibility (SRC) criterion, first described by De Houwer (2003b). He pointed out that reaction time measures are most often based on a comparison of trials that differ with regard to a certain type of SRC. Take the example of an affective priming task in which participants respond on the basis of the valence of positive and negative target stimuli that are preceded by positive and negative prime stimuli. On some trials, the task-Â�irrelevant valence of the prime and the task-Â�relevant valence of the target are compatible (e.g., happy–Â�summer); on other trials they are incompatible (e.g., happy–Â�cancer). Hence, the procedure involves a manipulation of stimulus–Â�stimulus (S-S) compatibility, that is,



10. Implicit Measures

the match between an irrelevant and a relevant aspect of the stimuli presented on a trial. The affective priming task also involves a manipulation of irrelevant stimulus–Â�response (S-R) compatibility. On some trials, the irrelevant valence of the prime and the valence of the response are compatible (e.g., say “good” to the target summer that is preceded by the prime happy), whereas on other trials they are incompatible (e.g., say “bad” to the target cancer that is preceded by the prime happy). In the affective priming task, there is even a confound between the manipulation of S-S compatibility and irrelevant S-R compatibility. Because participants respond to the valence of the target, whenever the valence of the prime and the target match (S-S compatible), the valence of the prime and the correct response also match (irrelevant S-R compatible). When the valence of the prime and target differ (S-S incompatible), the valence of the prime and the correct response also differ (irrelevant S-R compatible). Hence, the affective priming task can be classified on the basis of the SRC criterion as a task in which S-S compatibility and irrelevant S-R compatibility are manipulated in a confounded manner (De Houwer, 2003b). Other reaction time tasks involve a manipulation of other types of compatibility (e.g., relevant S-R compatibility) or different combinations of types of compatibility. Reaction time measures can thus be classified on the basis of the types of compatibility that are manipulated in the procedure from which the measure is derived (see De Houwer, 2003b). The SRC criterion does, in part, overlap with the other external criteria that we have put forward. Knowing that a measure can be classified according to the SRC criterion allows one to infer that the measure is likely to be an indirect, nonsymbolic, behavioral measure. On the other hand, knowing that a measure is indirect, symbolic, or behavioral does not allow one to decide whether the SRC criterion applies (because it is possible in principle to create indirect, nonsymbolic, and behavioral measures that are not based on reaction times) or which type of compatibility is manipulated (because reaction time measures can be based on different types of compatibility). The overlap between the SRC criterion and the internal classification criteria is also not perfect. First, knowing the type of compatibility on which a reaction time measure is based does not allow one to infer which attribute is being measured (the what criterion). Second, different types of compatibility can have an effect because of different types of processes (the how criterion). Nev-

187

ertheless, information about the SRC criterion can provide clues about the processes by which the to-be-Â�measured attribute causes the measurement outcome. Again, take the example of the affective priming task. In many applications of the affective priming task, the measurement outcome corresponds to a difference in reaction times between trials on which a particular class of prime stimuli (e.g., faces of black persons) is followed by a positive target and trials on which the same prime stimuli are followed by a negative target. On the basis of the SRC criterion, we know that these trials differ not only in the compatibility between the prime and the target stimuli (S-S compatibility) but also in the compatibility between the primes and the responses (irrelevant S-R compatibility). Hence, the measurement outcome (i.e., the difference in reaction times) could be due not only to the fact that the primes influence the processing of the targets but also to the fact that the primes influence the selection of the target responses. Analyses such as these have inspired theories and research about the processes underlying various reaction time measures (e.g., De Houwer, 2001; Gawronski & Bodenhausen, 2005; Olson & Fazio, 2003). Nevertheless, a classification on the basis of the SRC criterion at best provides only clues about the underlying processes. These clues need to be verified with empirical studies. The SRC criterion can also provides clues about the implicitness criterion. Most if not all measures to which the SRC criterion applies are reaction time measures. Effects in reaction time measures arise in a very short period of time (often just a few hundred milliseconds) and seem to be difficult to control. For instance, the well-known Stroop color–word effect has long been considered to be the prototypical example of an automatic effect. Naming the color of an incongruent Stroop stimulus (e.g., the word red in blue letters) takes much longer than naming the color of a congruent Stroop stimulus (e.g., the word blue in blue letters), even when participants try not to be influenced by the meaning of the words and when the words are presented only very briefly (see MacLeod, 1991, for a review). More recent research, however, has shown that even the Stroop color–word effect is not impervious to control (e.g., Kane & Engle, 2003). Hence, measuring psychological attributes by looking at their effects on reaction times does not guarantee that the measure is implicit in all possible ways. Moreover, little is known about whether certain types of compatibility are associated with particular features of automaticity. Empirical research about implicitness is, therefore,

188

II. METHODS AND PROCEDURES

necessary also for measures that can be classified according to the SRC criterion.

WHAT CRITERIA SHOULD WE USE TO CLASSIFY MEASURES? The criteria that we have discussed are only a subset of all possible criteria that could be used to differentiate between implicit measures. For instance, all the external criteria that we put forward focus on just one aspect of measurement procedures, namely the type of responses or response features that are used to determine the measurement outcome. Undoubtedly, other external classification criteria can be formulated on the basis of other aspects of measurement procedures (e.g., the kind of stimuli that are used). This raises the interesting question of how to determine which criteria are the best and should be used for classifying measures. In other words, what meta-Â�criteria can we use to evaluate the classification criteria? In our opinion, at least two meta-Â�criteria are important. The first concerns the applicability of the classification criterion. A classification criterion is useful only if there are ways to determine whether it applies to a certain measure. The extent to which a classification criterion meets this meta-Â�criterion depends on how easy it is to apply it and on how many measures it can be applied to. The second meta-Â�criterion concerns the functionality of the classification criterion. The main raison d’être of implicit measures is that they might allow one to predict variability in behavior above what can be predicted on the basis of other, explicit measures (see Perugini et al., Chapter 14, this volume). An ideal classification criterion would be one that (1) distinguishes between implicit measures that do and do not have incremental predictive validity or (2) indicates the conditions under which certain measures will have incremental predictive validity. Having such classification criteria is important because it could lead to the discovery of variables that determine whether implicit measures will have added value. This could help researchers to optimize their measures. We now evaluate the extent to which both meta-Â�criteria apply to the external and internal classification criteria that we discussed earlier. Regarding the meta-Â�criterion of applicability, it seems safe to conclude that external criteria are generally more easily applicable than internal criteria. Applying the external criteria requires only an analysis of the objective properties of the measurement procedure. For instance, to determine

whether a measure is a direct measure, it suffices to see whether the measurement outcome is derived from the responses that participants are asked to use to express their self-Â�assessment of the to-beÂ�measured attribute. There is no reason to do research about this; it can simply be verified by looking at the measurement procedure. Most external criteria can also be applied to a variety of different measures. Although the internal criteria can, in principle, be applied to most, if not all, measures, they are more difficult to operationalize. Internal representations and processes cannot be observed directly. They are hypothetical constructs that psychologists have invented as a way to think about what happens in the black box that mediates the relation between the environment and behavior (see Figure 10.1). Although it seems more than reasonable to assume that there are representations and processes that determine behavior, their nature and properties can be inferred only indirectly from (behavioral, physiological, or neurological) responses that are observable. The applicability of internal criteria thus depends on the degree to which they can be made objective. This can be done by linking them to observable responses in objective situations. The chapter by Moors and colleagues (Chapter 2, this volume) discusses in detail ways to verify whether processes possess certain automaticity features. For instance, in order to examine whether the processes underlying a measure are uncontrolled (in the sense of the goal to alter the outcome of the process), one can set up a situation in which participants are asked to try to fake a certain measurement outcome. If the instruction does not alter the validity of the measurement outcome, this provides evidence for the conclusion that the measure is uncontrolled. As becomes clear from Moors and colleagues’ Chapter 2 (this volume), there are many problems and pitfalls when trying to link automaticity features to observable responses. This complicates the application of the implicitness criterion. Applying the how criterion poses an even more daunting task. At present, we know little, if any, observable responses or other phenomena for which there is strong evidence that they unambiguously reveal the operation of a particular process.4 For instance, despite the popularity of the idea of spreading of activation, we do not know an unambiguous observable indicator of this process. To infer the operation of a particular process on the basis of a particular observable phenomenon, the process needs to be a necessary and sufficient condition of that phenomenon. This is often, if not always, an



10. Implicit Measures

implausible assumption. Even if one could find a particular observable phenomenon that currently can be explained in terms of only one kind of process, there is no guarantee that the same phenomenon cannot result from other processes that have not yet been considered. For instance, whereas the phenomenon of priming (e.g., faster responses to targets in the context of a related prime) was long regarded as an objective indicator of spreading of activation, it has become clear that priming effects can be due to a host of other processes (e.g., Ratcliff & McKoon, 1994). Attempts have been made to delineate when priming effects do provide evidence for spreading of activation, but the past has taught that specific process explanations of observable phenomena rarely remain unchallenged. The uncertainty about future theoretical developments constitutes another problem for the applicability of internal criteria of implicit measures. Theories about the exact nature of psychological processes and representations change constantly (also see Note 4). If measures would be classified on the basis of current theories about the processes underlying the measures, this classification would become useless when theories change. In sum, the applicability of internal classification criteria is endangered not only by the lack of objective criteria for establishing the presence of certain processes but also by the (expected) lack of stability in theories of the processes that might underlie the measures. External classification criteria are by far superior to internal classification criteria in terms of the meta-�criterion of applicability. The opposite is likely to be true when considering the meta-�criterion of functionality. We have argued that implicit measures differ from explicit measures with regard to the automaticity or nature of the processes by which psychological properties influence the measurement outcome. Hence, the added value of implicit measures probably depends on the automaticity or nature of the processes that underlie the measure. In other words, it seems reasonable to expect that classifying measures on the basis of their automaticity features or the nature of the underlying processes will provide more information about the incremental predictive validity of the measures than classification criteria based on objective properties of the measurement procedure. Nevertheless, at least some external criteria are also likely to be functional. The properties of the measurement procedure do not give a perfect indication of the processes that produce the outcome, but they can put strong restrictions on the processes that can operate. For instance, if stimuli

189

are presented so briefly that participants are not aware of their presence, then processes that require awareness of the instigating stimuli are unlikely to operate. Likewise, if the measurement outcome is based on a comparison of reaction times on trials that differ with regard to the similarity of a relevant and irrelevant stimulus, it is at least possible that the outcome is produced by processes akin to spreading of activation. Assuming that the added value of implicit measures depends on the processes underlying the measure, external classification criteria are likely to have at least some functional value in identifying which measures have predictive validity under which conditions. Ideally, there should be an interplay between the further development of external and internal classification criteria. Examining empirically which processes are likely to operate in which measurement procedures could provide new information not only about which measurement procedures are likely to produce implicit measures with added predictive validity (which would increase the functionality of external classification criteria) but also about what observable phenomena are good indicators of specific processes (which would increase the applicability of internal classification criteria). Ultimately, external criteria are probably the most crucial ones. Because psychological processes are not observable, internal classification criteria will always have to be linked to external, observable phenomena. Once this has been done, the internal classification criteria de facto become external criteria because they can be defined solely in terms of observable phenomena. Another way to deal with the potential problems of external and internal classification criteria is simply forget about ways to classify measures. One could adopt a purely pragmatic approach that focuses merely on assessing the reliability and (incremental) validity of individual measures without much consideration for regularities in the measurement procedures or processes underlying the measures (see Nosek & Greenwald, 2009). On the one hand, we agree that, in order to better understand and predict behavior, it would suffice to know whether a measure allows one to predict variability in behavior that cannot be predicted by other measures. On the other hand, we also realize that a purely pragmatic approach does have serious limitations. In the absence of useful classification criteria, there are no means for comparing various measures or for understanding why some measures do and other measures do not have incremental validity. It also becomes difficult to predict when a new measure will be related to a particular be-

190

II. METHODS AND PROCEDURES

havior. Progress in obtaining evidence for relations between measures and behavior will thus proceed slowly and haphazardly. Likewise, there will be little guidance for attempts to improve the quality of the measures. In the end, a purely pragmatic approach might be less efficient than a conscientious conceptual, experimental, and theoretical approach to understanding implicit measures of psychological attributes (see De Houwer et al., 2009b). The criteria put forward in this chapter could, in fact, provide a guiding framework for research on the psychometric properties of implicit measures. For instance, by characterizing various measures on the basis of the various criteria, one could uncover those properties that moderate the relation between the various measures (including reliability, i.e., the relation of a measure with itself). Our criteria could also provide guidance for uncovering those properties that determine the predictive validity of implicit measures. Importantly, analyses that are guided by this framework could already be conducted using the data that have been gathered in the past.

SUMMARY AND CONCLUSION A large number of implicit measures have been introduced during the past 20 years. In line with De Houwer and colleagues (2009a), we argued that an implicit measure can be defined as the outcome of a measurement procedure that is caused by the to-be-�measured psychological attribute (e.g., an attitude or stereotype) by means of automatic processes. In the present chapter, we focused on the ways in which implicit measures can differ. On the basis of our definition of implicit measures, we proposed that measures can differ with regard to internal properties (i.e., the properties of the attributes and processes underlying the measure) and external properties (i.e., the properties of the measurement procedure). With regard to the internal properties, we put forward the what criterion (i.e., What attributes influence the measure?), the how criterion (i.e., What is the nature of the processes underlying the measure?), and the implicitness criterion (i.e., What are the automaticity features of the processes underlying the measure?). With regard to the external properties, we discussed the self-�assessment criterion (i.e., Is the measurement outcome derived from a self-�assessment by the participant of the to-be-�measured attribute?), the response system criterion (i.e., Does the observed response belong to the behavioral, physiological,

or neurological response system?), the response content criterion (i.e., Is the measurement outcome based on the physical properties or on the meaning of the responses?), and the SRC criterion (i.e., How are different aspects of the stimulus display related to each other and to the responses?). Discussing these criteria also allowed us to make explicit our understanding of the terms implicit, explicit, direct, indirect, self-�report, symbolic, and nonsymbolic measures and how these different terms relate to each other. The classification criteria that we put forward are certainly not the only, and perhaps not even the best, criteria. We argued that the quality of classification criteria can be determined on the basis of two meta-�criteria: (1) applicability (Can the classification criterion easily be applied to various measures?) and (2) functionality (To what extent do measures that meet the same classification criterion have the same incremental predictive validity under the same set of conditions?). Whereas external classification criteria score high on applicability and poor on functionality, the reverse is true for internal classification criteria. Future research could help improve both kinds of criteria by examining empirically which processes are likely to operate given the presence of certain measurement procedures. By making explicit various criteria that can be used to classify implicit measures and by specifying meta-�criteria to evaluate these classification criteria, we hope to have provided new conceptual tools for improving communication and future research on implicit measures. Acknowledgments Preparation of this chapter was supported by GOA Grant No. BOF/GOA2006/001 and Methusalem Grant No. BOF09/01M00209 from Ghent University.

Notes 1. As Malte Friese pointed out to us, the terms direct and indirect are somewhat ambiguous in that they could also be understood as referring to other properties of measures. For instance, they could be interpreted as referring to the complexity of the processes underlying the expression of the to-be-Â�measured attribute, with “direct” implying less or less complex processes than “indirect.” Nevertheless, in the remainder of the chapter, we continue to use the terms direct measure and indirect measure but only in the sense of whether the measurement outcome is derived from a selfÂ�assessment of the to-be-Â�measured attribute.



10. Implicit Measures

2. Reaction time–based measures such as IAT effects can also be calculated on the basis of whether responses are correct or incorrect. Like response latency, response accuracy can be regarded as a physical response property. It can be determined solely on the basis of whether certain relevant physical properties of the responses (e.g., location of the key press) match the physical properties of the response that should have been emitted according to the instructed response rules. Hence, even outcomes that are (co-)determined by response accuracy can be regarded as nonsymbolic measures. Nevertheless, like any other physical response property, response accuracy can be given a meaning. Assume that participants are instructed to self-Â�assess the to-be-Â�measured attribute and express this self-Â�assessment via the accuracy of their responses. For instance, participants could be informed about response rules but then asked to press the incorrect key whenever they see an item they like and the correct key whenever they see an item they dislike. Attitudes toward the items can then be derived from calculating the number of correct and incorrect responses. Such a measure would be a direct attitude measure because the to-be-Â�measured attribute is estimated on the basis of behavior (i.e., accuracy of responses) that participants were asked to use to self-Â�assess the tobe-Â�measured attribute. Importantly, this measure would qualify as symbolic because the measurement outcome depends on the fact that an incorrect response is interpreted as symbolizing “I like” and a correct response is interpreted as symbolizing “I dislike.” The measurement outcome would thus be derived from the meaning assigned to the accuracy of the responses, not from the accuracy as such. 3. To convey a meaning via a certain response intentionally, a person must have at least some degree of conscious control over that response. Because (most) physiological and neurological responses are probably difficult to control in this manner, it seems unlikely that meaning can be conveyed through these responses in an intentional manner. However, it is possible that responses can convey meaning in an unintentional manner, that is, in a manner that is not due to a goal to convey meaning. For instance, the behavioral response of avoiding eye contact with a superior at work can be interpreted as a sign of submissiveness even though the person does not have the intention to communicate his or her submissiveness via the direction of gaze. These and other types of body language can indeed be regarded as symbolic responses (hence the phrase “body language”) that are unintentional. In a similar vein, certain physiological responses (e.g., increased sweating when seeing a superior at the workplace) or neurological responses (e.g., increased activation of the amygdala) can be seen as conveying a meaning.

191

4. Neurological activity does not provide a direct reflection of psychological processes and representations. As with the interpretation of behavioral and physiological responses, assumptions need to be made about which aspects of neurological activity are linked to which psychological processes and representations. The example of amygdala activity as an indicator of relevance rather than threat (see The Response System Criterion section and Sander et al., 2003) illustrates that theoretical assumptions can change over time. The validity of these assumptions is also limited by the validity of the available theories about the nature of processes and representations. Although progress has been made, one would be hard-�pressed to argue that current psychological theories provide the ultimate truth about the nature of psychological processes and representations. Identifying specific neurological responses with specific processes would even hamper theoretical development because it would detract attention from possible alternative explanations of those neurological responses.

References Anastasi, A. (1988). Psychological testing. New York: Macmillan. Bar-Anan, Y., Nosek, B. A., & Vianello, M. (2009). The Sorting Paired Features Task: A measure of association strengths. Experimental Psychology, 56, 329–343. Bargh, J. A. (1992). The ecology of automaticity. Toward establishing the conditions needed to produce automatic processing effects. American Journal of Psychology, 105, 181–199. Barnes-Â�Holmes, D., Murtagh, L., Barnes-Â�Holmes, Y., & Stewart, I. (in press). Using the Implicit Association Test and the implicit relational assessment procedure to measure attitudes towards meat and vegetables in vegetarians and meat-Â�eaters. Psychological Record. Blanton, H., Jaccard, J., Gonzales, P. M., & Christie, C. (2006). Decoding the Implicit Association Test: Implications for criterion prediction. Journal of Experimental Social Psychology, 42, 192–212. Borsboom, D. (2006). The attack of the psychometricians. Psychometrika, 71, 425–440. Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2004). The concept of validity. Psychological Review, 111, 1061–1071. Bosson, J. K., Swann, W. B., & Pennebaker, J. W. (2000). Stalking the perfect measure of implicit self-Â�esteem: The blind men and the elephant revisited? Journal of Personality and Social Psychology, 79, 631–643. Butcher, J., Derksen, J., Sloore, H., & Sirigatti, S. (2003). Objective personality assessment of people in diverse cultures: European adaptations of the MMPI-2. Behavior Research and Therapy, 41, 819–840. Collins, A. M., & Loftus, E. F. (1975). A spreading activation theory of semantic processing. Psychological Review, 82, 407–428.

192

II. METHODS AND PROCEDURES

Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K., & Groom, C. (2005). Separating multiple processes in implicit social cognition: The quadmodel of implicit task performance. Journal of Personality and Social Psychology, 89, 469–487. Cunningham, W. A., Van Bavel, J. J., & Johnsen, I. R. (2008). Affective flexibility: Evaluative processing goals shape amygdala activation. Psychological Science, 19, 152–159. De Houwer, J. (2001). A structural and process analysis of the Implicit Association Test. Journal of Experimental Social Psychology, 37, 443–451. De Houwer, J. (2003a). The Extrinsic Affective Simon Task. Experimental Psychology, 50, 77–85. De Houwer, J. (2003b). A structural analysis of indirect measures of attitudes. In J. Musch & K. C. Klauer (Eds.), The psychology of evaluation: Affective processes in cognition and emotion (pp.€ 219–244). Mahwah, NJ: Erlbaum. De Houwer, J. (2006). What are implicit measures and why are we using them? In R. W. Wiers & A. W. Stacy (Eds.), The handbook of implicit cognition and addiction (pp.€11–28). Thousand Oaks, CA: Sage. De Houwer, J. (2009). Comparing measures of attitudes at the procedural and functional level: Analysis and implications. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€361–390). New York: Psychology Press. De Houwer, J., & Eelen, P. (1998). An affective variant of the Simon paradigm. Cognition and Emotion, 12, 45–61. De Houwer, J., & Moors, A. (2007). How to define and examine the implicitness of implicit measures. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp.€179–194). New York: Guilford Press. De Houwer, J., Teige-Â�Mocigemba, S., Spruyt, A., & Moors, A. (2009a). Implicit measures: A normative analysis and review. Psychological Bulletin, 135, 347–368. De Houwer, J., Teige-Â�Mocigemba, S., Spruyt, A., & Moors, A. (2009b). Theoretical claims necessitate basic research. Psychological Bulletin, 135, 377–379. Eder, A. B., & Rothermund, K. (2008). When do motor behaviors (mis)match affective stimuli?: An evaluative coding view of approach and avoidance reactions. Journal of Experimental Psychology: General, 137, 262–281. Exner, J. E., Jr. (2003). The Rorschach: A comprehensive system: Vol. 1. Basic foundations (4th ed.). New York: Wiley. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Gawronski, B., & Bodenhausen, G. V. (2005). Accessibility effects on implicit social cognition: The role of€ knowledge activation and retrieval experiences. Journal of Personality and Social Psychology, 89, 672– 685. Gawronski, B., Deutsch, R., LeBel, E. P., & Peters, K. R. (2008). Response interference as a mechanism underlying implicit measures: Some traps and gaps

in the assessment of mental associations with experimental paradigms. European Journal of Psychological Assessment, 24, 218–225. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the Implicit Association Test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85, 197–216. Kane, M., J., & Engle, R. W. (2003). Working-Â�memory capacity and the control of attention: The contributions of goal neglect, response competition, and task set to Stroop interference. Journal of Experimental Psychology: General, 132, 47–70. Karpinski, A., & Steinman, R. B. (2006). The single category Implicit Association Test as a measure of implicit social cognition. Journal of Personality and Social Psychology, 91, 16–32. Koole, S. L., Dijksterhuis, A., & van Knippenberg, A. (2001). What’s in a name: Implicit self-Â�esteem and the automatic self. Journal of Personality and Social Psychology, 80, 669–685. MacLeod, C. M. (1991). Half a century of research on the Stroop effect: An integrative review. Psychological Bulletin, 109, 163–203. Mogg, K., Bradley, B. P., Field, M., & De Houwer, J. (2003). Eye movements to smoking-Â�related pictures in smokers: Relationship between attentional biases and implicit and explicit measures of stimulus valence. Addiction, 98, 825–836. Moors, A., & De Houwer, J. (2006). Automaticity: A conceptual and theoretical analysis. Psychological Bulletin, 132, 297–326. Nosek, B. A., & Banaji, M. R. (2001). The Go/No-Go Association Task. Social Cognition, 19, 625–666. Nosek, B. A., & Greenwald, A. G. (2009). (Part of) the case for a pragmatic approach to validity: Comment on De Houwer, Teige-Â�Mocigemba, Spruyt, and Moors (2009). Psychological Bulletin, 135, 373–376. Nuttin, J. M. (1985). Narcissism beyond gestalt and awareness: The name letter effect. European Journal of Social Psychology, 15, 353–361. Öhman, A., Carlsson, K., Lundqvist, D., & Ingvar, M. (2007). On the unconscious subcortical origin of human fear. Physiology and Behavior, 92, 180–185. Olson, M. A., & Fazio, R. H. (2003). Relations between implicit measures of prejudice: What are we measuring? Psychological Science, 14, 636–639. Payne, B. K., Cheng, C. M., Govorun, O., & Stewart, B. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. Journal of Personality and Social Psychology, 89, 277–293. Ranganath, K. A., Smith, C. T., & Nosek, B. A. (2008). Distinguishing automatic and controlled components of attitudes from direct and indirect measurement. Journal of Experimental Social Psychology, 44, 386–396. Ratcliff, R., & McKoon, G. (1994). Retrieving information from memory: Spreading-Â�activation theories versus compound-cue theories. Psychological Review, 101, 177–184.



10. Implicit Measures

Rinck, M., & Becker, E. (2007). Approach and avoidance in fear of spiders. Journal of Behavior Therapy and Experimental Psychiatry, 38, 105–120. Rothermund, K., & Wentura, D. (2004). Underlying processes in the Implicit Association Test (IAT): Dissociating salience from associations. Journal of Experimental Psychology: General, 133, 139–165. Sander, D., Grafman, J., & Zalla, T. (2003). The human amygdala: An evolved system for relevance detection. Reviews in the Neurosciences, 14, 303–316. Schnabel, K., Banse, R., & Asendorpf, J. (2006). Employing automatic approach and avoidance tendencies for the assessment of implicit personality selfÂ�concept: The Implicit Association Procedure (IAP). Experimental Psychology, 53, 69–76. Sekaquaptewa, D., Espinoza, P., Thompson, M., Vargas, P., & von Hippel, W. (2003). Stereotypic explanatory bias: Implicit stereotyping as a predictor of discrimination. Journal of Experimental Social Psychology, 39, 75–82. Sriram, N., & Greenwald, A. G. (2009). The brief Implicit Association Test. Experimental Psychology, 56, 283–294. Stacy, A. W., Ames, S. L., & Grenard, J. L. (2007). Word associations tests of associative memory and implicit processes: Theoretical and assessment issues. In R.

193

W. Wiers & A. W. Stacy (Eds.), The handbook of implicit cognition and addiction (pp.€ 75–90). Thousand Oaks, CA: Sage. Tuerlinckx, F., De Boeck, P., & Lens, W. (2002). Measuring needs with the Thematic Apperception Test: A psychometric study. Journal of Personality and Social Psychology, 82, 448–461. Vargas, P. T. (2004). On the relationship between implicit attitudes and behavior: Some lessons from the past, and directions for the future. In G. Haddock & G. R. Maio (Eds.), Contemporary perspectives on the psychology of attitudes (pp.€275–297). New York: Psychology Press. Wentura, D., & Rothermund, K. (2007). Paradigms we live by: A plea for more basic research on the Implicit Association Test. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp.€ 195–215). New York: Guilford Press. Wigboldus, D. (2001, September). The difference between relative and absolute implicit associations. Paper presented at the third meeting of the European Social Cognition Network, Houffalize, Belgium. Wittenbrink, B., Judd, C. M., & Park, B. (1997). Evidence for racial prejudice at the implicit level and its relationship with questionnaire measures. Journal of Personality and Social Psychology, 72, 262–274.

PA R T III

CROSS-CUTTING PERSPECTIVES

C h a p t e r 11

Consciousness, Introspection, and€the€Adaptive Unconscious Wilhelm Hofmann and Timothy D. Wilson

Consciousness is like the Trinity; if it is explained so that you understand it, it hasn’t been explained correctly. —Robert J. Joynt (1981, p.€108)

I

ration of mental processes outside of conscious awareness that nevertheless influence perception, judgments, feelings, and behavior. Together, these processes compose what we refer to as the adaptive unconscious (Wilson, 2002), adaptive in the sense that these processes are vital to everyday functioning. The adaptive unconscious is open to scientific investigations using experimental methods such as priming manipulations and indirect or implicit measures (see Section II, this volume), behavioral observations, neuropsychological data, and brain-�imaging methods (see Ito, Chapter 5, this volume). Just as the coastline of an island changes as the surrounding water rises or falls, so do changing views about the range of unconscious processing affect how much mental landscape is considered to be exclusively conscious territory. Faced with the progress in research on nonconscious social cognition in the past decades, one must admit that the sea level is rising fast. This development has sparked renewed interests in fundamental issues such as the functions of consciousness and the interplay between unconscious and conscious cognition. One particularly intriguing aspect of this interplay is whether, and to what extent, people may become consciously aware of the unconscious

n the history of psychology, the view that a great deal of mental operations is carried out in an unconscious manner certainly has had its highs and lows. As early as the second half of the 19th century, British physicians and philosophers such as William Carpenter (1874) argued for the existence of nonconscious processes that carry out a large range of everyday mental operations from perception to behavior, a view also advocated by William James (1890). In contrast, Sigmund Freud (1915/1960) presented a more narrow perspective on the unconscious, according to which irrational, unacceptable motives, drives, and feelings are repressed into the unconscious. The psychodynamic point of view dominated and perhaps hindered the scientific exploration of unconscious processing during the first half of the last century. When the behaviorist revolution overtook psychology and the interest in mental processes plummeted altogether, the all-time low of research on the unconscious was reached. Only with the advent of the cognitive revolution and more sophisticated methods to study mental processes has the tide turned again (e.g., Greenwald, 1992; Hassin, Uleman, & Bargh, 2005). Cognitive, personality, and brain researchers alike share a renewed interest in the explo-



197

198

III. CROSS-CUTTING PERSPECTIVES

underpinnings of their mental lives. On a more general level, this relates to the perennial question of how well we actually know ourselves (e.g., Wilson, 2002). The present chapter is divided into two main parts. In the first, we begin with a selective review of the scope of unconscious processing. We then ask what types of processes may be reserved for conscious processing and how consciousness may achieve these functions. This leads us to introduce the global workspace approach of consciousness (e.g., Baars, 1997; Dehaene & Naccache, 2001). We believe that this approach, which has been emerging from the interplay of cognitive, neuroscientific, and philosophical investigations, may offer a useful conceptual framework for understanding the interplay between unconscious and conscious social cognition. In the second part, we consider in detail whether and how introspective insight into the adaptive unconscious may be possible. To do so, we largely focus on the relationship between implicit and explicit dispositions (i.e., attitudes, self-Â�esteem, personality, self-Â�concept). We propose a self-Â�inference model that highlights the conditions under which more or less accurate explicit representations about nonconscious dispositions may be formed and organize the literature on implicit–Â�explicit consistency along this model. Our main conclusion is that, even though selfÂ�insight into implicit dispositions is often poor, it is not impossible to obtain. Rather, self-Â�insight into implicit dispositions will increase to the degree that valid mental or behavioral outcomes (such as gut feelings or nonverbal behaviors) are detected and used as a basis for self-Â�inference. Two conceptual issues need to be addressed at the outset. First, the distinction between unconscious versus conscious, which is in the focus of the present chapter, is typically part of dual-Â�system or dual-Â�process theories of the mind (Evans, 2008, for a review). In these models, the unconscious versus conscious distinction is generally viewed as one of several features associated with the broader distinction between automatic and controlled processing, alongside the features of unintentional versus intentional, effortless versus effortful, and uncontrollable versus controllable (Bargh, 1994). Whereas these features may often coincide, an all-or-none view of perfectly correlated features is clearly an oversimplification (see Moors & De Houwer, 2006, for a detailed analysis). Second, it should be noted that the term unconscious can refer to different features involved in a psychological process (e.g., Bargh, 1994; Gawronski, Hofmann, & Wilbur, 2006): (1) the conditions or stimuli that

set a process in motion, (2) the process itself (i.e., the processing steps and algorithms involved), (3) the output of the process, and (4) the consequences of the output. In the present chapter, we take a conditional view of automaticity by arguing that the output of an automatic process may, under certain circumstances, become consciously available. If it becomes consciously available, a large range of follow-up processes are possible (e.g., transformations, corrections, self-�inferences) that unconscious information cannot be subjected to. In other words, we view conscious availability as a highly consequential change of representational status that can (but often does not) happen with regard to the output of automatic processing, which would otherwise remain unconscious.

EVIDENCE FOR UNCONSCIOUS€PROCESSING Evidence for unconscious processing has accumulated in the domains of perceptual, affective, semantic, motor, and self-Â�regulatory processes. A variety of methods have been used, including subliminal priming, in which normal participants are presented with stimulus material for such short durations that it cannot be consciously perceived, and supraliminal manipulations, in which participants are aware of the stimulus material (e.g., scrambled sentences, hidden rules) but unaware of how it affects them. Evidence for unconscious processing is obtained if such manipulations reliably affect perception, feelings, judgments, or behavior in spite of participants’ reported unawareness. Further evidence for unconscious processing comes from studies on patients with brain lesion (Dietrich, 2007; Weisskrantz, 1997). Some of these lesions appear to wipe out aspects of conscious processing while leaving intact lower order unconscious processes of which patients are completely unaware. First and most importantly for the present purpose, unconscious processes have also been identified in affective processing (e.g., Murphy & Zajonc, 1993). This point has been made most prominently by the pioneering work of LeDoux (1996) on the brain’s fear circuit. LeDoux argued that the limbic system, and in particular the amygdala, is part of an automatic danger-Â�detection system. It has privileged access to incoming sensory information at a relatively crude level of perceptual analysis—Â�before the results of more accurate, but also more timeÂ�consuming, high-level perceptual analyses can enter conscious awareness. The amygdala quickly



11. Consciousness and Introspection

scans early perceptual processing outputs for signs of danger and can automatically trigger a fear response if it detects such signs. Because the analysis is fast and crude, however, errors can happen (such as when mistaking a tree trunk for a crocodile). Amygdala activation has been demonstrated in response to subliminally presented emotional stimuli, indicating that these affective responses do not require conscious awareness, identification, or additional cognitive processing (Whalen et al., 1998). In the domain of prejudice, Phelps and colleagues (2000) found that amygdala activation in response to outgroup members correlated with implicit attitude scores on the Implicit Association Test (Greenwald, McGhee, & Schwartz, 1998). Broadly, it has been argued that all kinds of simple affective processes involve a potentially unconscious contribution from subcortical processes (Berridge, 2003). This core affect (Russell, 2003) or, more colloquially, gut feeling may not become fully conscious (in the sense of access consciousness defined later) but may nevertheless influence behavior and decision making in ways that people do not consciously recognize (Bechara, Damasio, Tranel, & Damasio, 1997; Winkielman, Berridge, & Wilbarger, 2005). Recently, researchers argued that even specific emotions such as guilt can be primed nonconsciously to influence later behavior (Zemack-Rugar, Bettman, & Fitzsimons, 2007). Evidence for unconscious processing has also been obtained in the domains of perceptual processing (e.g., studies on blindsight; Weisskrantz, 1997), semantic processing (Devine, 1989; Marcel, 1983; Merikle, Joordens, & Stolz, 1995), implicit memory (e.g., Schacter, 1987), and with regard to the execution of overt behavior (e.g., the perception–Â�behavior link; Bargh, Chen, & Burrows, 1996; Chartrand & Bargh, 1999). Finally, even self-Â�regulatory processes, which have typically been attributed to be under the sole domain of conscious operations, may be carried out nonconsciously. Specifically, Bargh, Gollwitzer, LeeChai, Barndollar, and Troetschel (2001) argued that goals can be primed by external triggers and then guide self-Â�regulatory behavior without the individual consciously intending to do so (see Ferguson & Porter, Chapter 17, this volume). Taken together, an entire array of perceptual, affective, semantic, motor, and self-Â�regulatory processes has been shown to occur outside of conscious awareness. These findings make a strong case for views stressing the modularity of mind (e.g., Fodor, 1983), where the human brain is seen as a massive parallel processing system in which special subsystems, or “modules,” are dedicated to

199

specific computational purposes. As a result of the modular architecture of the mind, many mental operations and even sequences of interconnected operations (such as the perception–Â�behavior or the perception–Â�affect link) can proceed unconsciously. Clearly, the efficiency gained by delegating a great deal of brainwork to specialized mental compartments or subsystems that need no conscious awareness cannot be overestimated (Bargh, 2005; James, 1890). However, given that such a considerable amount of mental processing appears to occur nonconsciously, one is led to wonder what consciousness, the “cream on the cake of mentality” (Armstrong, 1980), is good for after all? Framed differently, what are the computational and evolutionary advantages enabled by conscious processing?

THE CASE FOR CONSCIOUS€PROCESSING The question about the functional utility of consciousness has been raised by neuroscientists, philosophers, and cognitive psychologists alike. Not surprisingly, quite divergent answers have been proposed. Still, asking the functional question is probably among the most fruitful ways to approach the thorny issue of consciousness (Dennett, 2001). From our reading of the literature, there seems to be some convergence in at least four classes of interrelated functions requiring consciousness (e.g., Baars, 1997; Dietrich, 2007; for an excellent overview, see Dehaene & Naccache, 2001): active information maintenance, flexible combination of information (including rule-based reasoning), the generation of intentional behavior, and the creation of a sense of “self.”1 These functions appear to build on each other in order to enable mental achievements of increasing complexity.

Active Information Maintenance First, many automatic processing modules appear to have their own domain-�specific memory buffer (e.g., iconic memory in the visual system). However, information in these buffers decays very quickly (Sperling, 1960). One primary purpose of consciousness, therefore, may be its capacity to maintain (selected) information in an active state so that it can be used for mental manipulations of all kinds (Dehaene & Naccache, 2001). The ability to bridge temporal gaps by maintaining active internal representations of objects, persons, and so on has been linked to working memory, especially

200

III. CROSS-CUTTING PERSPECTIVES

to the episodic buffer as a common storage system (Baddeley, 2007). One important implication of a common temporary store is that information may be represented in a shared representational format that is closely linked to thought and language (Dehaene & Naccache, 2001). Note that active information maintenance is distinct from longterm memory. The latter is probably best viewed as a modular subsystem contributing information to consciousness (Dehaene & Naccache, 2001). For instance, when asked “What is ‘Tiger’ Woods’s actual first name?” you have to hold the question temporarily in mind (active information maintenance) and then search your long-term memory in the hope for an answer.

Flexible Combination of Information A second type of conscious mental activity is the ability to combine information in a highly flexible way. This ability forms the basis of deliberate, rule-based reasoning, which requires the flexible selection, manipulation, and combination (e.g., weighting) of information. This idea lies at the heart of influential conceptualizations of working memory as involving not only an episodic buffer used for active information maintenance (see prior discussion) but also the ability to perform a large range of mental operations “on top of” the represented information (Baddeley, 2007; Kane, Bleckley, Conway, & Engle, 2001). Furthermore, the flexible manipulation of information is associated with a subjective feeling of mental effort. Take, for example, mental arithmetic (e.g., solve 24 × 13) or anagrams (e.g., find an anagram for “scones cousins”).2 In fact, abundant research attests to the limited capacity and resource dependence of executive operations such as switching between tasks, mental transformations, negations, or response inhibition (Deutsch, Gawronski, & Strack, 2006; Schmeichel, Vohs, & Baumeister, 2003). This limited capacity and resource dependence set considerable constraints on the scope and influence of conscious operations. We do not mean to suggest that consciousness is “smart” and unconscious processing is “dumb.” Recent research suggests that distracting people from consciously thinking about the information before letting them choose leads to higher quality decisions (Dijksterhuis, Bos, Nordgren, & van Baaren, 2006; but see Acker, 2008), at least for complex choice tasks involving large amounts of information (Payne, Samper, Bettman, & Luce, 2008). This may be the case because distraction may prevent people from consciously weighing

the presented information in an overly selective manner. Most researchers agree, however, that conscious thought is required to follow set rules when information has to be combined in a precise and selective manner (Dijksterhuis & Nordgren, 2006).

Generation of Intentional Behavior Third, consciousness seems to be strongly associated with the generation of willful, intentional behavior (Baars, 1997; Dehaene & Naccache, 2001). What we refer to here is the set of complex processes involved when people plan, initiate, and correct goal-Â�directed behavior, particularly those that involve long-term planning (e.g., Gilbert & Wilson, 2007; Wilson, 2002). These processes include (1) conscious deliberation about and simulation of the costs and benefits of future action, (2) the commitment to a particular action, (3) the ability to assemble action plans by combining subgoals into a goal hierarchy or sequence, ranging from concrete to abstract, and (4) the ability to adapt to discrepancies between actual and ideal states by inhibiting or overriding interfering behaviors during goal pursuit. Note that the research on nonconscious goal pursuit cited previously (Bargh et al., 2001) seems to call into question whether consciousness is necessary for intentional behavior to occur. There is more and more evidence that each of these stages can also be carried out nonconsciously (Hassin, Aarts, Eitam, Custers, & Kleiman, 2009). We believe that the essential difference between conscious and nonconscious goal pursuit lies in the flexibility enabled by conscious processing. Consciousness may not be needed for—and may even hamper—the initiation and performance of context-Â�appropriate action plans that, because of their frequent occurrence, have become consolidated into automatized routines (Baumeister, 1984; Cleeremans & Jiménez, 2002). However, consciousness may be needed to provide the “workplace” where action plans can be generated anew or significantly modified and corrected in response to an ever-Â�changing environment (Bargh, 2005; Bongers, Dijksterhuis, & Spears, 2008; Dehaene & Naccache, 2001; Tononi & Edelman, 1998). Somewhat ironically then, “one of the primary objectives of conscious processing may be to eliminate the need for itself in the future by making learned skills as automatic as possible” (Bargh, 2005, p.€53)—but to be back on the alert in case things go wrong. For an illustrative field experiment, simply exchange the coffee machine in your



11. Consciousness and Introspection

department by a completely different type and observe how people have to suppress their (now inappropriate) routine actions, use all their conscious attention to figure out the new operating rules, and after only a few days fetch their coffee whistling as absentmindedly as usual. Typically, our intentional behaviors are accompanied by a subjective feeling of conscious will or a sense of “agency.” Yet there are quite diverging views about whether this co-Â�occurrence should be interpreted in causal terms (i.e., conscious operations actually causing intentional behavior) or rather as a spurious correlation (i.e., conscious will and intentional behavior both caused by unconscious mechanisms, reducing conscious will to an epiphenomenon; Libet, Gleason, Wright, & Perl, 1983; Wegner, 2002). Some authors (e.g., Libet, 1999; Wilson, 2002) have adopted something like a middle position, assigning consciousness the role neither of an all-Â�controlling agent nor of a totally passive and utterly inconsequential recipient of fait accompli. A metaphor to describe this midline position is that of a chief executive presiding over a number of independent departments in a wellÂ�functioning company. The department officers set their own agendas and do not typically inform the executive about their every move. However, the executive still has a certain picture about what is and should be going on in the company. During times of crisis, he or she can use his or her power to veto (Libet, 1999) or modify important decisions under way if they do not conform to his or her vision of the company’s future.

Sense of Self Consciousness surely contributes to a sense of self. Each conscious state is typically experienced as a unity (Tononi & Edelman, 1998). The integrated, unitary nature of consciousness may provide the experiential basis for a feeling of selfhood. On a larger time scale, conscious experiences are condensed into an autobiographical self through the temporal integration of events into a coherent, personal narrative (Dietrich, 2007; Wilson, 2002). Thus, as a fourth function, consciousness may allow for the development of relatively stable explicit self-views, self-Â�concepts, beliefs, and attitudes. These explicit representations involving the self “bind” together what would otherwise be relatively meaningless reactions on the spur of the moment. Clearly, having a sense of self or “self model” (Vogeley, Kurthen, Falkai, & Maier, 1999) is a great organizing principle. It endows humans with the capacity to coordinate their activities

201

across long time spans (e.g., pursuing a university degree) and in accordance with basic orientations that have proven functional in the past.

CONTEMPORARY APPROACHES TO THE€NATURE OF CONSCIOUSNESS: CONSCIOUSNESS AS GLOBAL€WORKSPACE Having sketched a short list of functions that may be associated with consciousness, the next issue to address is obviously how these functions are achieved. That’s the million-Â�dollar question in consciousness research. It would be either naive or arrogant to claim that psychology has even come close to solving this riddle. However, the last decade or so has seen great progress in conceptualizing possible underlying mechanisms. This progress has been facilitated by the tools of cognitive neuroscience, which have made it possible to explore the neural architecture that supports consciousness. An extensive review of different approaches to the nature of consciousness is beyond the scope of this chapter (for a review, see Atkinson, Thomas, & Cleeremans, 2000). To sacrifice breadth for detail, we discuss only one basic idea toward which a number of scholars have been converging from quite different fields (Dennett, 2001): the idea of consciousness as a global workspace (Baars, 1997; Dehaene & Naccache, 2001). We then use the global workspace model as a background and elaborate a more specific model about the relation between implicit and explicit cognition and about possible ways by which accurate self-Â�inferences about the adaptive unconscious may be possible.

The Global Workspace as€a€Communication Platform Many cognitive theories share the assumption that conscious processing goes beyond the modularity of dedicated subsystems that operate in parallel along established neural pathways. Instead, it is supposed to be supported by a functional architecture that allows for a highly flexible exchange of information across participating processing units (e.g., Baddeley, 1986; Posner, 1994; Shallice, 1988). One idea that has received a great deal of attention in consciousness research (e.g., Baars, 1997) is that of a global workspace “with long-Â�distance connectivity that can potentially interconnect multiple specialized brain areas in a coordinated though variable manner” (Dehaene & Naccache,

202

III. CROSS-CUTTING PERSPECTIVES

2001, p.€ 13). The computational benefit enabled by such a global workspace is that modular systems that are not directly interconnected to each other nevertheless receive access to each other’s content, much like through a shared theater stage (Baars, 1997) or, to use our preferred metaphor, an internal news program.3 In other words, the global workspace provides a common communication platform onto which important “headlines” from a potentially great range of processing modules are broadcast. This global workspace seems to be highly interconnected with thought and language. Mental contents that enter the global workspace can be readily represented in the currency of “thought” (e.g., “The answer is ‘Eldrick’!”) or, to use a term applied by Strack and Deutsch (2004; see also Deutsch & Strack, Chapter 4, this volume), in a propositional format. These propositional representations can then be communicated via language by drawing on serial speech production centers (Dehaene & Naccache, 2001). Of course, people may sometimes lack the precise words for describing their internal states or be unwilling to report these states explicitly. Nevertheless, the connection between consciousness and language seems to be so close that many authors have made the verbal reportability a central defining feature of consciousness (e.g., Weisskrantz, 1997).

Access to the Global Workspace The next question to address, of course, is, which particular subsystems share a subscription to the common news channel and which do not? The assumption here is that brain modules not interconnected to the global workspace are permanently cut off from it and can, therefore, never participate in the internal broadcasting of conscious content (for instance, brain stem systems for controlling body functions). Dehaene and Naccache (2001) suggest that several classes of neural subsystems appear to participate in the workspace: (1) a working memory system used for active representation and manipulation of information, (2) perceptual systems that provide sensual information about the present state of the environment and the body, (3) longterm memory networks that retrieve condensed past workspace outputs (e.g., knowledge laid down in semantic memory) or reinstate past workspace states (e.g., episodic memory of past experiences), (4) evaluation circuits that provide valence in relation to previous experience, (5) motor circuits that are concerned with the preparation and execution of actions, and (6) special processing units dedi-

cated to extract and interpret information about the self (see also Damasio, 1999), allowing for the long-term temporal integration of conscious experiences into a personal narrative or autobiographical self. These systems can all contribute and exchange a wide range of potential contents within the global workspace through their mutual interconnections. This may account for the amazingly rich and differentiated spectrum of conscious states (Tononi & Edelman, 1998). However, only a tiny fraction of the information from subsystems that are potentially interconnected to the global workspace gains access at a given point in time (to determine each global workspace state). Whether the output of a given process is actually recruited into consciousness may depend on at least two dynamic parameters. First, the information stemming from nonconscious processing has to have a minimal amount of (bottom-up) activation that exceeds a certain threshold. Some processes may simply be too weak to yield sufficient degrees of ongoing activation in order for the represented information to be recruited into the global workspace (see Cleeremans & Jiménez, 2002, for a more differentiated view). Yet the same processes may still be strong enough to trigger further nonconscious processing and may eventually even produce significant behavioral output. This is presumably the case in the subliminal priming studies reviewed previously, where the neural activation triggered by the subliminal stimulus is assumed to fall in between a minimal threshold of information processing and a consciousness threshold, above which information can be recruited into the global workspace. Second, whether information becomes part of the current workspace is assumed to depend on a mechanism of top-down attentional amplification (Dehaene & Naccache, 2001; Lamme, 2003; Posner, 1994). Most theorists share the postulate that a selective attention system is responsible for gating access to and residence time in consciousness. Specifically, the orientation of attention, similar to a spotlight, is the “mechanism by which modular processes can be temporarily mobilized and made available to the global workspace, and therefore to consciousness” (Dehaene & Naccache, 2001, p.€ 14). In other words, ongoing activity is not sufficient for information to enter consciousness. It also has to be amplified and maintained over a sufficient amount of time in order to become available for other processes participating in the workspace.4 The capacity for what is represented in consciousness at a given point in time is severely



11. Consciousness and Introspection

limited. Therefore, at any given time, multiple modular processing outputs compete for access to the global workspace, or “fame in the brain” as Dennett (2001) has put it. Because access is limited and competitive, the contours of the global workspace are not constant (Dehaene & Naccache, 2001; Tononi & Edelman, 1998). Rather, global workspace states change dynamically as, depending on the context, different information gains temporary access and fades out again, giving rise to what James (1890) has called the “stream of consciousness.”

Three “Zones” of Consciousness In sum, the adopted framework of a global workspace leads to three different zones of consciousness, as illustrated in Figure 11.1. The first zone includes the class of information that is forever impervious to consciousness because it is part of lower order subroutines that cannot per se share their contents in the common communication protocol because of a lack of neural connectivity with the workspace (Dehaene & Naccache, 2001). Take, for instance, the “low-level” computations

203

performed by separate dedicated subsystems in your visual cortex. No matter how hard you try (without using a scalpel), you will never be able to perceive separately the intermediate results from the dorsal stream (the “where” pathway) and the ventral stream (the “what” pathway) before they are actually integrated on a higher order level of processing. The first zone may be referred to as the realm of unconscious processing in the strict sense. Even though impervious to consciousness, unconscious modular processing can be the source both of processing outputs from the following zone (phenomenal consciousness) and of behavioral output (e.g., Bargh et al., 1996). The second zone is given by the subclass of higher order processing outputs from modular subsystems that has the potential to be recruited into the workspace but does not (currently) gain access to it, either because it is too weakly represented or because it does not receive top-down attentional amplification. This zone refers to what has under different names and emphases been called fringe consciousness (James, 1890), preconscious (Freud, 1924/1968), experiential awareness (Strack & Deutsch, 2004), primary experience

ACCESS CONSCIOUSNESS Propositionally Represented Information in Global Workspace Selective

Verbal Reportability

Attention

PHENOMENAL CONSCIOUSNESS Internal Processing Outputs

Behavior

UNCONSCIOUS PROCESSING Modular Subsystems

Input

FIGURE 11.1.╇ Three “zones” of consciousness. At the bottom of the information-processing pyramid is the realm of unconscious processing delegated to modular subsystems. A portion of processing outputs from these modular subsystems may gain the status of phenomenal consciousness. Only a small subset of the rich spectrum of phenomenological experience enters the global workspace through mechanisms of selective attention, thereby gaining the status of access consciousness. Access-conscious information is assumed to be represented in a propositional format and is therefore, in principle, verbally reportable. Behavior can be generated via both unconscious processing and consciously formed action plans.

204

III. CROSS-CUTTING PERSPECTIVES

(Farthing, 1992), first-order experience (Lambie & Marcel, 2002), and, the term adopted here, phenomenal consciousness (Block, 1995). Phenomenal consciousness may encompass a vast range of sensations and experiences, including perceptions, feelings, and other bodily sensations such as pain that people are having without being aware (in the sense of knowing) that they are having them (Frijda, 2005; Lambie & Marcel, 2002). Importantly, information represented in this way may, under certain conditions (i.e., attentional amplification), become fully consciously accessible and gain the status of the third zone (Dehaene & Naccache, 2001; Lamme, 2003). The third zone is reserved for the elite information that has passed the gates of selective attention and, therefore, has become recruited into the global workspace. As part of the global workspace, it becomes accessible to a large range of different processing modules, including long-term memory, self-Â�processing units, and speech production centers that translate the information into a propositional format (see prior discussion). This zone represents what has been termed noetic awareness (Strack & Deutsch, 2004), reflective experience (Farthing, 1992), second-order experience (Lambie & Marcel, 2002), and access consciousness (Block, 1995). This transition to access consciousness is where an informational “quantum leap” takes place: Suddenly, experience is no more just experience. It has been tagged as such and becomes the object of higher order thoughts directed at it (Rosenthal, 1993; Schooler, 2002), such as when you eventually realize during an interesting sofa conversation that your foot has gone asleep. Access consciousness, therefore, refers to informational contents that people are aware of in the sense of knowing. These contents can be communicated, at least approximately, in the form of propositional statements about oneself and the world.

GETTING TO KNOW THE€ADAPTIVE UNCONSCIOUS The discovery that many processes take place outside of conscious awareness has raised the question about what people can actually know about their mental life (Wilson, 2002). In other words, to what degree can we gain conscious self-Â�insight into our adaptive unconscious? In the following, we consider two approaches to this question. A first line of research has demonstrated the limited nature of people’s introspective ability. Because this research has been summarized in great detail

elsewhere (Nisbett & Wilson, 1977b), we only provide a short overview of the major conclusions from this work. The second line of research, which we focus on in the present chapter, deals with implicit–Â�explicit consistency, that is, the match or mismatch between people’s explicit self-Â�reports about their attitudes, self-Â�esteem, self-Â�concept, or motives on the one hand and their scores in thematically corresponding implicit measures on the other. Completing a questionnaire about oneself (e.g., “Do I like green eggs and ham?”) presupposes some kind of introspective activity. Introspection is a very broad term and can involve quite different activities ranging from meditation to psychoanalytic therapy (Wilson, 2002). For the following analysis, we narrow the term down to those cases in which people reflect about their own dispositions or behaviors and define introspection as a conscious mental activity by which attention is directed toward one’s own phenomenal sensations and experiences in an attempt to form a self-Â�referential proposition about these experiences with the use of inferential rules. Introspection thus defined is a complex inference process by which mental experiences may become access conscious and become integrated into a coherent propositional judgment involving the self (e.g., “I do not like green eggs and ham; the mere sight of them makes me sick”).

Introspective Limits into the Causes of€Behavior A first wave of interest into people’s introspective abilities has been sparked by the general finding that people are often unaware of the effects of a given stimulus on their own behavior (for a review, see Nisbett & Wilson, 1977b). In a series of experiments, Nisbett and Wilson showed that participants’ verbal reports about the causes of their behavior often stand in stark contrast to the true state of affairs. For instance, in one study Nisbett and Wilson (1977a) manipulated the warmth or coldness of a foreign college teacher presented in a video interview. One group watched the lecturer answer a series of questions in an extremely warm and friendly manner. The second group saw the same person answer exactly the same questions in a cold and distant manner. They then rated the teacher on three attributes that were kept constant across conditions: physical attractiveness, mannerisms, and accent. Consistent with the so-Â�called halo effect, students who saw the warm version of the teacher rated his physical attractiveness, mannerisms, and accent as more appealing than



11. Consciousness and Introspection

did students who saw the cold version. However, when asked whether their liking for the teacher had influenced their ratings of the three attributes, participants in both groups denied any effect this might have had. Even more striking, those in the cold condition claimed that their impressions of the three attributes had influenced their overall liking for him. In other words, they had inverted the true causal relationship between the variables in this study. These and similar results across a wide range of domains (Nisbett & Wilson, 1977b; see also Eastwick & Finkel, 2008, for a recent extension to romantic partner preferences) support the idea that introspecting about the reasons for their behavior is a constructive process in which people have only limited conscious access to the true underlying processes. The true underlying processes, however, may often be produced by the adaptive unconscious (such as, for instance, the halo effect). When asked to provide causal explanations for their behavior, people may retrieve lay theories about themselves (i.e., chronic self-views or personal narratives) or about how certain events are caused and generate explanations that are consistent with these lay theories. However, people may often fail to recognize that the explanations thus generated are incomplete and inaccurate. As Nisbett and Wilson (1977b) implied, introspective reports should become more accurate to the extent that influential stimuli aspects are salient and plausible causes of behavior so that they are attended to and used as a basis for causal inference.

The Implicit–Â�Explicit Relationship: A€Self-Â�Inference Model The research program just described has provided evidence for limited introspective access into the nonconscious underpinnings of mental life by demonstrating discrepancies between people’s introspective reports and influential aspects in their stimulus environment that have been varied experimentally. However, the advances in the assessment of implicit representations such as implicit attitudes, self-Â�esteem, and personality selfÂ�concepts—Â�without which this Handbook would not have been written—have opened up a new way of studying the issue of introspective access to the adaptive unconscious. Specifically, by investigating the degree of correspondence (or consistency) between explicit and implicit measures of the same construct, new insights about the relationship between implicit and explicit cognition and the factors that moderate it may be gained.

205

Much of the early debate on implicit social cognition was sparked from empirical findings showing a dissociation between implicit and explicit measures of attitudes, self-Â�esteem, motives, and personality (Greenwald & Banaji, 1995; for a review, see Wilson, Lindsey, & Schooler, 2000). This lack of correspondence between implicit and explicit measures was often taken as evidence for the existence of implicit representations (with regard to objects, people, and the self) that were “introspectively unidentified (or inaccurately) identified” (e.g., Greenwald & Banaji, 1995, p.€ 5). However, the claim of complete independence may be too strong. That is, the relationship between implicit representations and explicit representations may be conditional rather than fixed. Such a view is theoretically consistent with the prior idea that access to consciousness is the result of a competition among many modular processes depending on features of the processes themselves (strength of activation) as well as on the surrounding context (attentional amplification). Hence, the result of unconscious processing may, at different times, become access-Â�conscious or not. Often, dissociations between implicit and explicit representations may emerge if implicit processing outputs are too weak, not attended to, misinterpreted, or suppressed for motivational reasons, and these dissociations may have important consequences for further information processing and well-being (Wilson & Dunn, 2004; Wilson et al., 2000). Given the right conditions, however, implicit processing outputs may enter access consciousness and factor into people’s explicit representations about themselves and the world. In these cases, at least gradual self-Â�insight into workings of the adaptive unconscious should be possible. The conditional view to implicit–Â�explicit consistency suggests a moderator approach by which changes in the correlation between implicit and explicit measures are investigated as a function of cognitive or motivational factors. To organize the empirical research that has adopted such an approach, we suggest a self-Â�inference model for implicit–Â�explicit consistency (Figure 11.2) that allows putting the observed correlation between explicit self-Â�reports and implicit measures (arrow i in Figure 11.2) into a broader theoretical perspective (see also Hofmann, Gschwendner, Nosek, & Schmitt, 2005).5 As a starting point, we adopt a dualÂ�representation view according to which implicit and explicit dispositions have distinct mental representations (e.g., Strack & Deutsch, 2004; Wilson et al., 2000). More specifically, most models

206

III. CROSS-CUTTING PERSPECTIVES

of implicit cognition propose that implicit dispositions are represented in an associative format (Gawronski & Bodenhausen, 2006; Strack & Deutsch, 2004). For instance, a negative implicit attitude toward obese people is understood as an associative link between the concept of obese and a negative evaluative node. Conscious, explicit dispositions are represented in a propositional format, that is, through a statement about concepts and their interrelations (e.g., “I do not like obese people”). In contrast to associative links, propositional statements carry a truth value and can, therefore, be accepted as true or rejected as false (e.g., Gawronski & Bodenhausen, 2006; Strack & Deutsch, 2004). Viewed this way, the issue of introspective access becomes the question of how information that is represented in an associative format can be translated into a propositional format. Clearly, distinct formats (i.e., associative vs. propositional) preclude direct introspective access in the sense of a direct explicit oversight of implicit representational structures (e.g., Nisbett & Wilson, 1977b). We cannot spot our associations directly. Any approach concerned with the transition from implicit to explicit representations must, therefore, specify how such a translation process may be brought about. We believe that much conceptual clarity can be gained by viewing this translation as a self-Â�inference process and by drawing on the logic of prominent models of social inference (e.g., Brunswik, 1956; Funder, 1999). That is, even though people cannot directly “take a look” at their implicit associations, they may be able to infer valid self-Â�insights about these properties by registering the perceivable processing outputs that these associations produce.6 Specifically, the activation of implicit representations may generate (1) inner phenomenal experiences or (2) behavioral cues, which may assist in drawing inferences about the nature of the underlying implicit representation. The accuracy of the self-Â�inference process will depend on the quality of three consecutive steps: First, implicit representations have to be reliably associated with phenomenal or behavioral cues in the first place (cue validity). Second, these valid cues have to be noticed (cue detection). Third, the information contained in these cues has to be integrated into the explicit self-Â�inference (cue utilization). As we will see, obstacles to accurate selfÂ�insights about implicit representations may lurk at each stage of this process. In the following, we first consider cue validity and cue detection for the two plausible routes to self-Â�knowledge about implicit representations, that

is, via phenomenal cues (path a × path b in Figure 11.2) or via the self-Â�observation of behavioral cues (path c × path e). If successfully taken, information contained in valid cues will become consciously available for self-Â�inference. We then discuss factors such as additional information retrieval and invalidation processes that may prevent consciously accessible information from having a traceable impact on the final explicit representation. These factors are represented by a feedback loop (arrow f in Figure 11.2) to indicate that conscious content is iteratively transformed through the additional processes involved. Finally, we highlight some issues of measurement (involving paths g and h in Figure 11.2) one should be aware of when drawing conclusions about self-Â�insight based on the correlation between implicit and explicit measures.

Route 1: Introspective Access to€Mental€Experiences According to the model, translation can work only if there are valid internal cues that are associated with implicit representations in the first place (path a in Figure 11.2). In principle, many different kinds of phenomenal experiences may be triggered by implicit representations and serve as valid cues, among them gut feelings or core affect (Russell, 2003) as well as experiences of fluency, familiarity, or confidence (e.g., Petty, Briñol, & Tormala, 2002; Reber & Schwarz, 2001; Schwarz & Clore, 1996; Topolinski & Strack, 2009). To illustrate our point, we focus on gut feelings as particularly relevant and likely cues for such selfÂ�inferences, especially in the domains of attitudes and self-Â�esteem (Fazio, Sanbonmatsu, Powell, & Kardes, 1986; Gawronski & Bodenhausen, 2006; Phelps et al., 2000). For other domains involving semantic processing, such as implicit stereotyping or the implicit self-Â�concept, knowledge-Â�related mental cues such as fluency, familiarity, or confidence may prove to be more relevant. In accordance with the prior approach to consciousness, such mental cues do not necessarily enjoy a conscious status. They reflect, in essence, an experiential, preconscious, or phenomenal state of awareness (Strack & Deutsch, 2004). These signals can be weak, ambiguous, or simply unattended to because attention is directed elsewhere. Often, therefore, these cues may fade into oblivion before ever becoming fully consciously available for self-Â�inferences. At the same time, they may still be strong enough to bias judgments and behavior in an unconscious manner (e.g., Winkielman et al., 2005). Following the global workspace



11. Consciousness and Introspection

207

ACCESS CONSCIOUSNESS f h

Explicit (Propositional) Representation

b

Introspective Access

e d Controlled Behavior

Selfperception

Implicit–Explicit Consistency

Uncontrolled

Phenomenal cues a

Self-report

c

Behavior

Implicit (Associative) Representation

g

i

(Accuracy)

Indirect Measure

ADAPTIVE UNCONSCIOUS

FIGURE 11.2.╇ A self-inference model for implicit–explicit consistency. According to the model, accurate explicit representations of implicit representations depend on whether valid phenomenal or behavioral cues are available (path a or path c, respectively) and whether these cues are detected (paths b and e, respectively). Furthermore, accuracy as assessed by implicit–explicit consistency (i) can be impaired by additional information integration or invalidation processes (arrow f) and by poor measurement (paths g and h).

approach, whether valid cues are introspectively accessible (path b) may hinge on at least two parameters: their strength of activation and whether they receive attentional top-down amplification (Dehaene & Naccache, 2001).

Activation Strength Given that access to consciousness is the result of a dynamic competition among processing outputs for “fame in the brain,” strong representations may yield outputs that have a higher potential for becoming available to access consciousness than weak representations. As a consequence, implicit and explicit representations may correspond more closely for strong representations. Nosek (2005) tested this hypothesis by using a combined strength factor of attitude importance, thought frequency, and familiarity as a moderator of implicit–Â�explicit consistency. Across 57 attitude domains, a multilevel analysis revealed that stronger representations were associated with greater consistency between implicit and self-Â�reported evaluations compared with weaker attitudes. Comparable effects have been observed for attitude importance as a proxy for representational strength (Hofmann, Gschwendner, & Schmitt, 2005; Karpinski, Steinman,

& Hilton, 2005). Although it is difficult to pinpoint the exact mechanism responsible for these findings, these data generally support the idea that strong implicit representations yield mental experiences that are more easily detectable than those stemming from weak representations. Is activation strength linearly related to better access into consciousness? Some authors have argued that strength (in terms of frequency of operation) may, rather, have an inverse U-shaped relationship with availability to access consciousness (Cleeremans & Jiménez, 2002). Novel representations may yet be too weak to become noticeable, whereas highly overlearned representations no longer attract attention (even though their representations may be very strong). This is clearly an area for future research and points to the interplay between representational properties and the modulating role of attention that are considered next.

Focus of Attention Whether attention is focused on valid diagnostic cues or directed somewhere else should have a decisive influence on the detection of these cues. For instance, Hofmann, Gschwendner, and Schmitt (2005) reasoned that, all else being equal, people

208

III. CROSS-CUTTING PERSPECTIVES

high in private self-Â�consciousness (Fenigstein, Scheier, & Buss, 1975) should direct their attention inward more often than people low in private self-Â�consciousness and become more sensitive with regard to inner mental experiences (especially affective and visceral signals). However, in an initial set of studies, private self-Â�consciousness did not by itself yield increased implicit–Â�explicit correspondence (Hofmann, Gawronski, Gschwendner, Le, & Schmitt, 2005; Hofmann, Gschwendner, & Schmitt, 2005). These first findings are consistent with the idea that introspection does not necessarily lead to greater accuracy. Rather, the focus at which attention is directed seems to be crucial. Often introspection may involve an extensive information search about why one holds a particular attitude or trait (Wilson, Dunn, Kraft, & Lisle, 1989). This may direct the spotlight of attention away from relevant phenomenal cues such as immediately experienced feelings (Gawronski & LeBel, 2008; Hixon & Swann, 1993; Storbeck & Clore, 2008). In a stringent test of this assumption, Gawronski and LeBel (2008) manipulated the focus of attention during introspection about their attitudes. Consistent with the assumed role of attentional focus, implicit–Â�explicit correspondence was close to zero when participants were instructed to focus on the reasons for their preferences; however, substantial implicit–Â�explicit correspondence emerged when participants were instructed to focus on their affective reactions instead. Similarly, Smith and Nosek (2007) showed that implicit and explicit attitudes formed a single factor when participants completed both measures under an affective focus manipulation. In contrast, implicit and explicit attitudes diverged when a cognitive focus was induced. These results are consistent with a meta-Â�analysis across many content domains in which IAT measures correlated more highly with self-Â�reports that implied an affective compared with a cognitive focus (Hofmann, Gawronski, et al., 2005).

Route 2: Self-Â�Observation of€Diagnostic Behavior Rather than “looking inward,” a person may take a “look-Â�outward” approach to infer something valid about his or her adaptive unconscious. In accordance with self-Â�perception theory (Bem, 1972), it has been suggested that individuals may gain conscious insights about implicit dispositions by selfÂ�observing diagnostic behaviors (path e in Figure 11.2) that are caused by implicit dispositions (path c) (e.g., Gawronski et al., 2006; Nosek, 2005; Wilson, 2002). Via this behavioral detour, individuals

may generate accurate self-Â�inferences about their implicit representations. What kinds of behavioral cues are potentially diagnostic with regard to underlying implicit dispositions (path c)? Accumulating research now shows that implicit dispositions related to social interactions, such as implicit prejudice, implicit anxiety, or implicit shyness, are particularly strongly reflected in nonverbal, relatively uncontrolled modes of behavior (e.g., Asendorpf, Banse, & Mücke, 2002; Dovidio, Kawakami, & Gaertner, 2002; Egloff & Schmukle, 2002). In contrast, explicit dispositions have been shown to primarily predict controlled behaviors (e.g., Asendorpf et al., 2002; Dovidio et al., 2002; indicated by path d in the model). During social interaction, people typically pay relatively little attention to the uncontrolled (e.g., nonverbal) aspects of their behavior, quite in contrast to what they say or what they intentionally do. Hence, asking individuals to adopt the visual perspective of an observer and to focus on these normally unattended behavioral cues may increase self-Â�perceivers’ accuracy about their underlying implicit dispositions. Plausible as the self-Â�perception route seems to be, the available evidence suggests that it is more like a bumpy road than a highway to the adaptive unconscious. Hofmann, Gschwendner, and Schmitt (2009) investigated whether selfÂ�perceivers can detect their own implicit dispositions such as implicit extraversion or implicit anxiety from nonverbal behavioral cues contained in video feedback. Across three studies, near-zero correlations between participants’ implicit dispositions and their behavioral self-Â�assessment on the respective nonverbal dimensions were consistently obtained. One may object that perhaps the behaviors under investigation were not diagnostic of implicit dispositions. This alternative interpretation can be ruled out by the fact that neutral observers reliably detected participants’ implicit dispositions from the videotapes (Hofmann et al., 2009). This suggests that the videos actually did contain valid information about implicit dispositions, which, however, remained undetected by self-Â�perceivers (see also Dovidio et al., 2002). The observed discrepancy between self and other perception suggests that people have a blind spot when it comes to the detection of their own implicit dispositions from behavioral information. Somewhat paradoxically then, observers making accurate inferences from behavioral cues may sometimes know a target person’s implicit dispositions better than the target him- or herself, at least when he or she has no privileged introspective access via route 1. A number of cognitive and



11. Consciousness and Introspection

motivational factors may account for such a blind spot (for a more detailed discussion, see Hofmann et al., 2009). For instance, individuals may harbor chronic self-Â�schemas (Markus, 1977), which may bias the perception and categorization of one’s own behavior, leading to less accurate self-Â�observations than observations made by unbiased observers. Such perceptual barriers may be overcome if accuracy takes precedence over consistency motivation, for instance when high incentives are provided or when people learn to view themselves through the eyes of others (Wilson, 2002; Wilson & Dunn, 2004).

Limits to Cue Utilization: Information Retrieval and Invalidation Processes So far we have argued that, at least under certain conditions, potentially diagnostic information about underlying implicit dispositions may become consciously available in a propositional format, but this is only half the story. For the self-�inference process to be completed, the potentially diagnostic information has to be integrated into the explicit representation (cue utilization). Consistent with the global workspace approach, conscious information may become subject to additional processing (arrow f in Figure 11.2) that leads to a flexible manipulation of the contents of consciousness in relation to other propositionally available information. Because of the large number of possible operations, these additional transformations of conscious content can take many forms. Here, we pinpoint two broad classes of operations that we believe are key when people form an explicit representation about their attitudes, self-�esteem, or self-�concept: further information retrieval and invalidation processes.

Further Information Retrieval and€Integration When forming an explicit representation of their dispositions, people are likely to retrieve information from long-term memory. Such information may include past behaviors, knowledge about the world, and knowledge about the self. Consistent with the basic tenets of information integration theory (Anderson, 1981), such additional information will dilute the residual weight or impact of the diagnostic cue for the resulting explicit representation. For instance, when introspecting about whether to buy a given car, a person’s explicit representation may be based on more than just the propositional translation of his or her gut feelings about the car, incorporating also judg-

209

ments derived from information about price, fuel consumption, and maintenance. Hence, the more an explicit representation includes additional propositions that are different from those implied by implicit representation, the more implicit and explicit representations will diverge. Consistent with the information integration hypothesis, implicit–Â�explicit consistency has been found to be lower for those individuals who are high in need for cognition (Florack, Scarabis, & Bless, 2001) or high in working memory capacity (Hofmann, Gschwendner, Friese, Wiers, & Schmitt, 2008, Study 2). Conversely, it has been found that the correspondence between explicit and implicit dispositions increases when explicit self-Â�reports are made spontaneously or under time pressure (Hofmann, Gawronski, et al., 2005; Koole, Dijksterhuis, & van Knippenberg, 2001; Ranganath, Smith, & Nosek, 2007). Taken together, factors that prevent additional information retrieval and integration may lead to self-Â�inferences that are more in line with implicit representations.

Invalidation Processes The process of information retrieval just described should yield gradual changes in implicit–Â�explicit consistency as more and more information is integrated. In contrast to the dilution effect, certain factors may lead to a complete invalidation of potentially diagnostic cues. As a result of invalidation, the information contained in these cues is not utilized for self-Â�inference at all. Specifically, information that would be potentially diagnostic of implicit representations may become tagged as false, inappropriate, or nondiagnostic and thus become rejected as a basis for an explicit judgment (e.g., Gawronski & Bodenhausen, 2006; Strack & Deutsch, 2004). If an invalidation process is directed at mental or behavioral cues stemming from implicit representations, explicit and implicit representations are likely to diverge. How are such invalidation processes brought about? Consistent (no pun intended) with theories of cognitive consistency (e.g., Festinger, 1957), invalidation may result if the propositional implication of an implicit representation (e.g., “I do not like obese people”) is logically inconsistent with other currently represented propositions. Of particular interest in this regard are chronically available self-views (e.g., Gawronski & Bodenhausen, 2006; Gawronski, Peters, Brochu, & Strack, 2008). For instance, it has often been reported that people high in motivation to control prejudiced reactions (MCPR) show decreased implicit–Â�explicit correspondence compared with

210

III. CROSS-CUTTING PERSPECTIVES

people low in MCPR (e.g., Fazio, Jackson, Dunton, & Williams, 1995; Hofmann, Gschwendner, & Schmitt, 2005; Payne, Cheng, Govorun, & Stewart, 2005). Assuming that both groups do not differ on their level of implicit prejudice (Devine, 1989), this pattern of findings can be interpreted as the result of an invalidation process in individuals who view themselves as unprejudiced (e.g., “I am an egalitarian person”). Specifically, chronic self-views may lead people to invalidate diagnostic mental experiences (e.g., becoming aware of negative gut feelings toward the prejudiced group) or behavioral self-Â�observations (e.g., becoming aware of the fact that one physically avoids members of the prejudiced group) that are inconsistent with these self-views. A second source of inconsistency may be given by lay theories about the diagnostic value of certain mental or behavioral events (Nisbett & Wilson, 1977b). A number of largely untested research hypotheses can be derived from this idea. Regarding mental experiences, for instance, people may question whether intuitions and gut reactions can be trusted (Epstein, Pacini, Denes-Raj, & Heier, 1996). All else being equal, low trust in intuition should, therefore, lead to a rejection of mental experiences stemming from implicit dispositions. Accordingly, higher correspondence between implicit and explicit self-Â�esteem has been obtained among people with high faith in their intuition (Jordan, Whitfield, & Zeigler-Hill, 2007). In the case of behavioral cues, people may need to be convinced that their nonverbal behavior may tell them something central about themselves and that the situation in which it is observed is valid enough to warrant a dispositional self-Â�attribution (Hofmann et al., 2009).7 Finally, some people may be more tolerable than others of inconsistencies between new potentially diagnostic information and existing top-down views about themselves (e.g., Cialdini, Trost, & Newsom, 1995; Kruglanski & Webster, 1996). It is possible that validation processes that involve the same constellations over and over again may themselves become so highly automatized that invalidation occurs outside of conscious awareness (i.e., before people become consciously aware of discordant implicit processing outputs) (e.g., Moskowitz, Gollwitzer, Wasel, & Schaal, 1999). This suggests the fascinating possibility of unconscious motivational barriers to introspection (Wilson & Dunn, 2004), similar to the Freudian idea of repression (Erdelyi, 1993). To our knowledge, these ideas are a still scientifically largely unchartered territory (see Northoff, Bermpohl, Schoeneich, &

Boeker, 2007, for a recently proposed neuroscientific approach).

A Final Note on Measurement When making inferences about the interplay of implicit and explicit representations, it is important to keep in mind that the measures used will only be proxies—never pure reflections—of the hypothetical constructs they are supposed to assess. Rather, both implicit and explicit measures inevitably suffer from method-Â�specific variance and reliability constraints that impinge on the quality of measurement (paths g and h in the model). Explicit self-Â�report measures are known to be sensitive to the way questions are posed (Schwarz, 1999). Also, self-Â�reports are generally more susceptible to faking than implicit measures (e.g., Asendorpf et al., 2002). Implicit measures likewise suffer from method-Â�specific sources of contamination (e.g., Mierke & Klauer, 2003). In addition, some implicit measures such as affective or sequential priming exhibit unsatisfactory reliabilities (Cunningham, Preacher, & Banaji, 2001; Olson & Fazio, 2003). Furthermore, implicit–Â�explicit correlations may be attenuated by a lack of structural or conceptual correspondence between measures (Gschwendner, Hofmann, & Schmitt, 2008; Hofmann, Gschwendner, Nosek, et al., 2005; Payne, Burkley, & Stokes, 2008) such as when using black and white faces in an Implicit Association Test and questions addressing perceived discrimination in the Modern Racism Scale (Brigham, 1993). Taken together, implicit–Â�explicit correlations can be attenuated because of method-Â�specific variance, measurement error, or lack of correspondence. Interpreting the size of implicit–Â�explicit correlations in an absolute manner may, therefore, lead to false conclusions about the “true” relationship at the level of representations. For this reason, approaches contrasting implicit–Â�explicit consistency as a function of specific experimental manipulations or personality dispositions in a relative manner are to be preferred. Nevertheless, researchers should use corresponding measures and make sure that the situational or dispositional moderators of interest are not confounded with sources of method-Â�specific variance, changes in the reliability of measurement, or both.

SUMMARY AND CONCLUSIONS Recent social cognition research has provided extensive support for a fundamental insight of



11. Consciousness and Introspection

early pioneers of psychology: that a vast amount of everyday human information processing occurs unconsciously. The amount of evidence attesting to the workings of the adaptive unconscious is so massive that it has become a challenging task to take up the cudgels for consciousness as a mental faculty that complements nonconscious processing in important ways. Drawing on global workspace theory (e.g., Baars, 1997; Dehaene & Naccache, 2001), we have argued that consciousness implies the global availability of information to a large range of participating modular systems that otherwise would not gain access to each other’s content. Information that has entered the global workspace enjoys the advantage of being actively represented for a longer duration so that it can be subject to all kinds of transformations and serve as the basis for the generation and execution of new, flexibly adjusted action plans. From this perspective, consciousness appears to be most needed when the organism encounters novel situations or problems that demand a careful analysis. Often, however, conscious processing may be superfluous and be effectively bypassed by powerful (but relatively inflexible) automatic processing, resulting in smooth, efficient, but potentially biased behavior. We have also argued that, without consciousness, there would be no sense of self, at least in the human sense with which we are all familiar. Only conscious processing appears to enable the long-term temporal integration of experience into a coherent narrative about oneself. Because this constructive process of self-Â�inference draws on a limited informational basis, explicit self-views may often be dissociated from the contents of the adaptive unconscious, rendering people strangers to themselves (Wilson, 2002). These dissociations become particularly striking when people are explicitly instructed to provide explanations for their often unconsciously driven feelings, judgments, and behaviors. Are people always ignorant about their adaptive unconscious? We believe that self-Â�insight into implicit dispositions may be best viewed as a gradual thing. Using the relationship between implicit and explicit measures as a criterion, we proposed a model according to which self-Â�insight into implicit dispositions may be the result of a self-Â�inference process. The accuracy of this process will be highest if valid cues for implicit dispositions exist (cue validity), are detected (cue detection), and are used (cue utilization) as a basis for an explicit inference about one’s attitude or self. Although there is evidence that valid cues do exist in the form of internal phenomenal experiences such as gut feelings or

211

self-�observed nonverbal behaviors, cue detection and cue utilization may be hampered by a host of variables related to attentional focus, information integration, and invalidation processes. Perhaps most centrally, chronic self-views and false lay theories may reduce the accuracy of self-�inferences. Because introspection can go awry so easily, limited but at the same time variable self-�insight into the adaptive unconscious can be expected. As the interplay between implicit and explicit cognition becomes increasingly well understood, so will be the conditions under which people can increase their self-�knowledge about the inner workings of their minds. Whether such self-�insight is always beneficial to the individual is a different story about which it may be worthwhile to introspect a little more. Acknowledgments We thank Axel Cleeremans, Malte Friese, Nico Frijda, Regina Krieglmeyer, Robert Kordts, Katie Lancaster, Justin Storbeck, Sascha Topolinski, and Reinout Wiers for valuable comments.

Notes 1. For a more fine-Â�grained list, see, for instance, Jack and Shallice (2001). Also, for reasons of space, we do not delve into the complex relationship between consciousness and learning (e.g., Cleeremans & Jiménez, 2002). 2. The correct answer to the anagram can be found in the title of this chapter. 3. As a reviewer pointed out, the theater metaphor seems more open to Dennett’s (1991) criticism of the “Cartesian theater” in which he asks “But who is the audience?” The newscast metaphor seems somewhat less open to this kind of criticism because it suggests some modules broadcasting information that other modules might access, so there is no homuncular audience but only modules as both generators and consumers of information. 4. Whether a given process receives top-down amplification may depend on a complex interplay of the current state of affairs. Rather than invoking some kind of homunculus or Cartesian master who decides what is and what is not attended to at will, Dennett (2001) and others warn against taking the term top-down too literally. Instead, attentional amplification should be seen as a competitive process that is heavily constrained by the activation of currently active processors representing current states, processing goals, rewards, and needs of the organism. Their combined influ-

212

III. CROSS-CUTTING PERSPECTIVES

ence on the emerging net pattern of activation may be lumped together as a top-down influence for convenience, but such a view is surely quite different from that of an internal observer sitting “on top” of everything (Dennett, 2001). 5. The present framework shares many commonalities with the associative–Â�propositional evaluation (APE) model proposed by Gawronski and Bodenhausen (2006). Because of its focus on introspective access, the present framework is only concerned with the bottom-up link from implicit to explicit representations and does not incorporate possible reverse, top-down influences from explicit to implicit cognition (Gawronski & Bodenhausen, 2006). Moreover, whereas the APE model is exclusively concerned with evaluations, the present framework provides a background for other types of implicit representations (e.g., implicit self-Â�concept) and other types of processes (e.g., behavioral causation and behavioral observation) as well. Finally, whereas the APE model assumes no introspective limits to associative evaluations, the present framework explicitly takes into account access-Â�related factors. 6. The assumption here is that implicit associations have already been formed by previous encounters with the attitude object. However, even in the case of novel objects, gut feelings may accumulate more quickly than explicit evaluations and, if detected, be used for conscious processing (see Wagar & Dixon, 2006). 7. In fact, the inclusion of a state-Â�inference measure in the work on self-Â�perception discussed previously (Hofmann et al., 2009, Study 3) revealed that self-Â�perceivers made state inferences in accordance with their nonverbal behavior but were hesitant to use these state inferences as a basis for a more general trait inference about themselves. Neutral observers, in contrast, readily turned their state inferences into corresponding trait inferences about the target persons. Thus, a change in perspective (i.e., viewing oneself from the outside) was not sufficient here to overcome the strong tendency of actors to interpret their own behavior in situational terms (Jones & Nisbett, 1972), even though doing so could have resulted in novel insights about themselves.

References Acker, F. (2008). New findings on unconscious versus conscious thought in decision making: Additional empirical data and meta-Â�analysis. Judgment and Decision Making, 3, 292–303. Anderson, N. H. (Ed.). (1981). Foundations of information integration theory. New York: Academic Press. Armstrong, D. M. (1980). What is consciousness? In D. M. Armstrong (Ed.), The nature of mind and other essays (pp.€ 55–67). Ithaca, NY: Cornell University Press.

Asendorpf, J. B., Banse, R., & Mücke, D. (2002). Double dissociation between implicit and explicit personality self-Â�concept: The case of shy behavior. Journal of Personality and Social Psychology, 83, 380–393. Atkinson, A., Thomas, M., & Cleeremans, A. (2000). Consciousness: Mapping the theoretical landscape. Trends in Cognitive Sciences, 4, 372–382. Baars, B. J. (1997). In the theater of consciousness: The workspace of the mind. New York: Oxford University Press. Baddeley, A. D. (1986). Working memory. Oxford, UK: Clarendon Press. Baddeley, A. D. (2007). Working memory, thought, and action. Oxford, UK: Oxford University Press. Bargh, J. A. (1994). The four horsemen of automaticity: Awareness, intention, efficiency, and control in social cognition. In J. R. S. Wyer, Jr. & T. K. Srull (Eds.), Handbook of social cognition (Vol. 2, pp.€1–40). Hillsdale, NJ: Erlbaum. Bargh, J. A. (2005). Bypassing the will: Toward demystifying the nonconscious control of social behavior. In R. R. Hassin, J. S. Uleman, & J. A. Bargh (Eds.), The new unconscious (pp.€ 37–58). Oxford, UK: Oxford University Press. Bargh, J. A., Chen, M., & Burrows, L. (1996). Automaticity of social behavior: Direct effects of trait construct and stereotype activation on action. Journal of Personality and Social Psychology, 71, 230–244. Bargh, J. A., Gollwitzer, P. M., Lee-Chai, A., Barndollar, K., & Troetschel, R. (2001). The automated will: Nonconscious activation and pursuit of behavioral goals. Journal of Personality and Social Psychology, 81, 1014–1027. Baumeister, R. F. (1984). Choking under pressure: SelfÂ�consciousness and paradoxical effects of incentives on skillful performance. Journal of Personality and Social Psychology, 46, 610–620. Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 275, 1293–1295. Bem, D. J. (1972). Self-Â�perception theory. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 6, pp.€1–62). New York: Academic Press. Berridge, K. C. (2003). Pleasures of the brain. Brain and Cognition, 52, 106–128. Block, N. (1995). On a confusion about a function of consciousness. Behavioral and Brain Sciences, 18, 227–287. Bongers, K. C. A., Dijksterhuis, A., & Spears, R. (2008). On the role of consciousness in goal-Â�pursuit. Unpublished manuscript. Brigham, J. C. (1993). College students’ racial attitudes. Journal of Applied Social Psychology, 23, 1933–1967. Brunswik, E. (1956). Perception and the representative design of psychological experiments. Berkeley: University of California Press. Carpenter, W. B. (1874). Principles of mental physiology, with their applications to the training and discipline of the mind, and the study of its morbid conditions. New York: Appleton. Chartrand, T. L., & Bargh, J. A. (1999). The chameleon effect: The perception-Â�behavior link and social interaction. Journal of Personality and Social Psychology, 76, 893–910. Cialdini, R. B., Trost, M. R., & Newsom, J. T. (1995).



11. Consciousness and Introspection

Preference for consistency: The development of a valid measure and the discovery of surprising behavioral implications. Journal of Personality and Social Psychology, 69, 318–328. Cleeremans, A., & Jiménez, L. (2002). Implicit learning and consciousness: A graded, dynamic perspective. In R. M. French & A. Cleeremans (Eds.), Implicit learning and consciousness (pp.€1–40). Hove, UK: Psychology Press. Cunningham, W. A., Preacher, K. J., & Banaji, M. R. (2001). Implicit attitude measurement: Consistency, stability, and convergent validity. Psychological Science, 12, 163–170. Damasio, A. R. (1999). The feeling of what happens. Body and emotion in the making of consciousness. San Diego, CA: Harcourt. Dehaene, S., & Naccache, L. (2001). Towards a cognitive neuroscience of consciousness: Basic evidence and a workspace framework. Cognition, 79, 1–37. Dennett, D. C. (1991). Consciousness explained. Boston: Little, Brown. Dennett, D. C. (2001). Are we explaining consciousness yet? Cognition, 79, 221–237. Deutsch, R., Gawronski, B., & Strack, F. (2006). At the boundaries of automaticity: Negation as reflective operation. Journal of Personality and Social Psychology, 91, 385–405. Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of Personality and Social Psychology, 56, 5–18. Dietrich, A. (2007). Introduction to consciousness. Houndmills, UK: Palgrave. Dijksterhuis, A., Bos, M. W., Nordgren, L. F., & van Baaren, R. B. (2006). On making the right choice: The deliberation-Â�without-Â�attention effect. Science, 311, 1005–1007. Dijksterhuis, A., & Nordgren, L. F. (2006). A theory of unconscious thought. Perspectives on Psychological Science, 1, 95–109. Dovidio, J. F., Kawakami, K., & Gaertner, S. L. (2002). Implicit and explicit prejudice and interracial interaction. Journal of Personality and Social Psychology, 82, 62–68. Eastwick, P. W., & Finkel, E. J. (2008). Sex differences in mate preferences revisited: Do people know what they initially desire in a romantic partner? Journal of Personality and Social Psychology, 94, 245–264. Egloff, B., & Schmukle, S. C. (2002). Predictive validity of an Implicit Association Test for assessing anxiety. Journal of Personality and Social Psychology, 83, 1441–1455. Epstein, S., Pacini, R., Denes-Raj, V., & Heier, H. (1996). Individual differences in intuitive-Â�experiential and analytical-Â�rational thinking styles. Journal of Personality and Social Psychology, 71, 390–405. Erdelyi, M. H. (1993). Repression: The mechanism and the defense. In D. M. Wegner & J. W. Pennebaker (Eds.), Handbook of mental control (pp.€ 126–148). Englewood-Â�Cliffs, NJ: Prentice Hall. Evans, J. S. B. T. (2008). Dual-Â�processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, 255–278. Farthing, G. W. (1992). The psychology of consciousness. Englewood Cliffs, NJ: Prentice Hall.

213

Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F., R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. Fenigstein, A., Scheier, M. F., & Buss, A. H. (1975). Public and private self-Â�consciousness: Assessment and theory. Journal of Consulting and Clinical Psychology, 42, 522–527. Festinger, L. (Ed.). (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press. Florack, A., Scarabis, M., & Bless, H. (2001). When do associations matter?: The use of automatic associations towards ethnic groups in person judgments. Journal of Experimental Social Psychology, 37, 518–524. Fodor, J. A. (1983). Modularity of mind: An essay on faculty psychology. Cambridge, MA: MIT Press. Freud, S. (1960). Das Unbewusste [The unconscious]. In S. Freud (Ed.), Das Ich und das Es und andere metapsychologische Schriften (pp.€73–104). Frankfurt: Fischer. (Original work published 1915) Freud, S. (1968). A general introduction to psychoanalysis (J. Riviere, Trans.). New York: Washington Square Press. (Original work published 1924) Frijda, N. H. (2005). Emotion experience. Cognition and Emotion, 19, 473–497. Funder, D. C. (1999). Personality judgment: A realistic approach to person perception. San Diego, CA: Academic Press. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. Gawronski, B., Hofmann, W., & Wilbur, C. (2006). Are implicit attitudes unconscious? Consciousness and Cognition, 15, 485–499. Gawronski, B., & LeBel, E. P. (2008). Understanding patterns of attitude change: When implicit measures show change, but explicit measures do not. Journal of Experimental Social Psychology, 44, 1355–1361. Gawronski, B., Peters, K. R., Brochu, P. M., & Strack, F. (2008). Understanding the relations between different forms of racial prejudice: A cognitive consistency perspective. Personality and Social Psychology Bulletin, 34, 648–665. Gilbert, D. T., & Wilson, T. D. (2007). Prospecting: Experiencing the future. Science, 317, 1351–1354. Greenwald, A. G. (1992). New Look 3: Unconscious cognition reclaimed. American Psychologist, 47, 766– 779. Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-Â�esteem, and stereotypes. Psychological Review, 102, 4–27. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Gschwendner, T., Hofmann, W., & Schmitt, M. (2008). Convergent and predictive validity of implicit and explicit anxiety measures as a function of specificity

214

III. CROSS-CUTTING PERSPECTIVES

similarity and content similarity. European Journal of Psychological Assessment, 24, 254–262. Hassin, R. R., Aarts, H., Eitam, B., Custers, R., & Kleiman, T. (2009). Non-Â�conscious goal pursuit and the effortful control of behavior. In J. A. Bargh & E. Morsella (Eds.), The psychology of action (Vol. 2, pp.€549–568). New York: Oxford University Press. Hassin, R. R., Uleman, J. S., & Bargh, J. A. (2005). The new unconscious. New York: Oxford University Press. Hixon, J. G., & Swann, W. B. (1993). When does introspection bear fruit?: Self-Â�reflection, self-Â�insight, and interpersonal choices. Journal of Personality and Social Psychology, 64, 35–43. Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., & Schmitt, M. (2005). A meta-Â�analysis on the correlation between the Implicit Association Test and explicit self-Â�report measures. Personality and Social Psychology Bulletin, 31, 1369–1385. Hofmann, W., Gschwendner, T., Friese, M., Wiers, R. W., & Schmitt, M. (2008). Working memory capacity and self-Â�regulation: Toward an individual differences perspective on behavior determination by automatic versus controlled processes. Journal of Personality and Social Psychology, 95, 962–977. Hofmann, W., Gschwendner, T., Nosek, B. A., & Schmitt, M. (2005). What moderates implicit-Â�explicit consistency? European Review of Social Psychology, 16, 335–390. Hofmann, W., Gschwendner, T., & Schmitt, M. (2005). On implicit-Â�explicit consistency: The moderating role of individual differences in awareness and adjustment. European Journal of Personality, 19, 25–49. Hofmann, W., Gschwendner, T., & Schmitt, M. (2009). The road to the implicit self not taken: Discrepancies between self- and observer-Â�inferences about implicit dispositions from nonverbal behavioral cures. European Journal of Personality, 23, 343–366. Jack, A. I., & Shallice, T. (2001). Introspective physicalism as an approach to the science of consciousness. Cognition, 79, 161–196. James, W. (1890). The principles of psychology. New York: Holt. Jones, E. E., & Nisbett, R. E. (1972). The actor and the observer: Divergent perception of the causes of behavior. In E. E. Jones, R. E. Nisbett, S. Valins, & B. Weiner (Eds.), Attribution: Perceiving the causes of behavior (pp.€79–94). Morristown, NJ: General Learning Press. Jordan, C. H., Whitfield, M., & Zeigler-Hill, V. (2007). Intuition and the correspondence between implicit and explicit self-Â�esteem. Journal of Personality and Social Psychology, 93, 1067–1079. Joynt, R. J. (1981). Are two heads better than one? Behavioral and Brain Sciences, 4, 108–109. Kane, M. J., Bleckley, M. K., Conway, A. R. A., & Engle, R. W. (2001). A controlled-Â�attention view of workingÂ�memory capacity. Journal of Experimental Psychology: General, 130, 169–183. Karpinski, A., Steinman, R. B., & Hilton, J. L. (2005). Attitude importance as a moderator of the relationship between implicit and explicit attitude measures. Personality and Social Psychology Bulletin, 31, 949– 962.

Koole, S. L., Dijksterhuis, A., & van Knippenberg, A. (2001). What’s in a name: Implicit self-Â�esteem and the automatic self. Journal of Personality and Social Psychology, 80, 669–685. Kruglanski, A. W., & Webster, D. M. (1996). Motivated closing of the mind: “Seizing” and “freezing.” Psychological Review, 103, 263–283. Lambie, J. A., & Marcel, A. J. (2002). Consciousness and the varieties of emotion experience: A theoretical framework. Psychological Review, 109, 219–259. Lamme, V. A. F. (2003). Why visual attention and awareness are different. Trends in Cognitive Sciences, 7, 12–18. LeDoux, J. (1996). The emotional brain: The mysterious underpinnings of emotional life. New York: Simon & Schuster. Libet, B. (1999). Do we have free will? Journal of Consciousness Studies, 6, 47–55. Libet, B., Gleason, C. A., Wright, E. W., & Perl, D. K. (1983). Time of conscious intention to act in relation to onset of cerebral activity (readiness potential): The unconscious initiation of a freely voluntary act. Brain, 106, 623–642. Marcel, A. J. (1983). Conscious and unconscious perception: Experiments on visual masking and word recognition. Cognitive Psychology, 15, 197–237. Markus, H. (1977). Self-Â�schemata and processing information about the self. Journal of Personality and Social Psychology, 35, 63–78. Merikle, P. M., Joordens, S., & Stolz, J. A. (1995). Measuring the relative magnitude of unconscious influences. Consciousness and Cognition, 4, 422–439. Mierke, J., & Klauer, K. C. (2003). Method-Â�specific variance in the Implicit Association Test. Journal of Personality and Social Psychology, 85, 1180–1192. Moors, A., & De Houwer, J. (2006). Automaticity: A theoretical and conceptual analysis. Psychological Bulletin, 132, 297–326. Moskowitz, G. B., Gollwitzer, P. M., Wasel, W., & Schaal, B. (1999). Preconscious control of stereotype activation through chronic egalitarian goals. Journal of Personality and Social Psychology, 77, 167–184. Murphy, S. T., & Zajonc, R. B. (1993). Affect, cognition, and awareness: Affective priming with optimal and suboptimal stimulus exposures. Journal of Personality and Social Psychology, 64, 723–739. Nisbett, R. E., & Wilson, T. D. (1977a). The halo effect: Evidence for unconscious alternation of judgments. Journal of Personality and Social Psychology, 35, 250–256. Nisbett, R. E., & Wilson, T. D. (1977b). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84, 231–259. Northoff, G., Bermpohl, F., Schoeneich, F., & Boeker, H. (2007). How does our brain constitute defense mechanisms?: First-Â�person neuroscience and psychoanalysis. Psychotherapy and Psychosomatics, 76, 141–153. Nosek, B. A. (2005). Moderators of the relationship between implicit and explicit evaluation. Journal of Experimental Psychology: General, 134, 565–584. Olson, M. A., & Fazio, R. H. (2003). Relations between implicit measures of prejudice: What are we measuring? Psychological Science, 14, 636–639.



11. Consciousness and Introspection

Payne, B. K., Burkley, M. A., & Stokes, M. B. (2008). Why do implicit and explicit attitude tests diverge?: The role of structural fit. Journal of Personality and Social Psychology, 94, 16–31. Payne, B. K., Cheng, C. M., Govorun, O., & Stewart, B. D. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. Journal of Personality and Social Psychology, 89, 277–293. Payne, J. W., Samper, A., Bettman, J. R., & Luce, M. F. (2008). Boundary conditions on unconscious thought in complex decision making. Psychological Science, 19, 1118–1123. Petty, R. E., Briñol, P., & Tormala, Z. L. (2002). Thought confidence as a determinant of persuasion: The selfÂ�validation hypothesis. Journal of Personality and Social Psychology, 82, 722–741. Phelps, E. A., O’Connor, K. J., Cunningham, W. A., Funayama, E. S., Gatenby, J. C., & Gore, J. C. (2000). Performance on indirect measures of race evaluation predicts amygdala activation. Journal of Cognitive Neuroscience, 12, 729–738. Posner, M. I. (1994). Attention: The mechanisms of consciousness. Proceedings of the National Academy of Sciences USA, 91, 7398–7403. Ranganath, K., Smith, C., & Nosek, B. (2007). DisÂ� tinguishing automatic and controlled components of€ attitudes from direct and indirect measurement. Journal of Experimental Social Psychology, 44, 386– 396. Reber, R., & Schwarz, N. (2001). The hot fringes of consciousness: Perceptual fluency and affect. Consciousness and Emotion, 2, 223–231. Rosenthal, D. (1993). Thinking that one thinks. In M. Davis & G. Humphreys (Eds.), Consciousness (pp.€15–36). Oxford, UK: Blackwell. Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110, 145–172. Schacter, D. L. (1987). Implicit memory: History and current status. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 501–518. Schmeichel, B. J., Vohs, K. D., & Baumeister, R. F. (2003). Intellectual performance and ego depletion: Role of the self in logical reasoning and other information processing. Journal of Personality and Social Psychology, 85, 33–46. Schooler, J. W. (2002). Re-Â�representing consciousness: Dissociations between experience and metaÂ�consciousness. Trends in Cognitive Sciences, 6, 339– 344. Schwarz, N. (1999). Self-Â�reports: How the questions shape the answers. American Psychologist, 54, 93– 105. Schwarz, N., & Clore, G. L. (1996). Feelings and phenomenal experiences. In E. T. Higgins & A. W. Kruglanski (Eds.), Social psychology: Handbook of basic principles (pp.€ 433–465). New York: Guilford Press. Shallice, T. (1988). From neuropsychology to mental structure. Cambridge, MA: Cambridge University Press. Smith, C. T., & Nosek, B. A. (2007). Affective focus in-

215

creases the concordance between implicit and explicit attitudes. Unpublished manuscript. Sperling, G. (1960). The information available in brief visual presentation. Psychological Monographs, 74, 1–29. Storbeck, J., & Clore, G. L. (2008). Affective arousal as information: How affective arousal influences judgments, learning, and memory. Social and Personality Psychology Compass, 2, 1824–1843. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Tononi, G., & Edelman, G. M. (1998). Consciousness and complexity. Science, 282, 1846–1851. Topolinski, S., & Strack, F. (2009). The architecture of intuition: Fluency and affect determine intuitive judgments of semantic and visual coherence and judgments of grammaticality in artificial grammar learning. Journal of Experimental Psychology: General, 138, 39–63. Vogeley, K., Kurthen, M., Falkai, P., & Maier, W. (1999). Essential functions of the human self model are implemented in the prefrontal cortex. Consciousness and Cognition, 8, 343–363. Wagar, B. M., & Dixon, M. (2006). Affective guidance in the Iowa Gambling Task. Cognitive, Affective, and Behavioural Neuroscience, 6, 277–290. Wegner, D. M. (2002). The illusion of conscious will. Cambridge, MA: MIT Press. Weisskrantz, L. (1997). Consciousness lost and found: A neurophysiological explanation. New York: Oxford University Press. Whalen, P. J., Rauch, S. L., Etcoff, N. L., McInterney, S. C., Lee, M. B., & Jenike, M. A. (1998). Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge. Journal of Neuroscience, 18, 411–418. Wilson, T. D. (2002). Strangers to ourselves: Discovering the adaptive unconscious. Cambridge, MA: Harvard University Press. Wilson, T. D., & Dunn, E. (2004). Self-Â�knowledge: Its limits, value, and potential for improvement. Annual Review of Psychology, 55, 493–518. Wilson, T. D., Dunn, D. S., Kraft, D., & Lisle, D. J. (1989). Introspection, attitude change, and attitudeÂ�behavior consistency: The disruptive effects of explaining why we feel the way we do. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 22, pp.€287–343). New York: Academic Press. Wilson, T. D., Lindsey, S., & Schooler, T. Y. (2000). A model of dual attitudes. Psychological Review, 107, 101–126. Winkielman, P., Berridge, K. C., & Wilbarger, J. L. (2005). Unconscious affective reactions to masked happy versus angry faces influence consumption behavior and judgments of value. Personality and Social Psychology Bulletin, 31, 121–135. Zemack-Rugar, Y., Bettman, J. R., & Fitzsimons, G. J. (2007). The effects of nonconsciously priming emotion concepts on behavior. Journal of Personality and Social Psychology, 93, 927–939.

C h a p t e r 12

Formation, Change, and Contextualization of Mental Associations Determinants and Principles of Variations in€Implicit€Measures Bertram Gawronski and Rajees Sritharan

S

tarting with the development of a new class of indirect measurement procedures in the mid1990s, research using these procedures has produced a plethora of remarkable findings that has stimulated wide interest far beyond the traditional boundaries of social psychology.1 People who consciously endorse egalitarian values tend to be quite astonished when they learn that their responses assessed by indirect procedures show racial bias (Nosek et al., 2007; see also Amodio, & Mendoza, Chapter 19, and Trawalter & Shapiro, Chapter 20, this volume); spontaneous behaviors that are difficult to predict with self-�report measures revealed strong relations to implicit measures (Friese, Hofmann, & Schmitt, 2008; see also Perugini, Richetin, & Zogmaister, Chapter 14, this volume); and deliberate decisions with important real-life implications have been found to be predictable by response latency differences in the range of milliseconds obtained by indirect procedures (e.g., Galdi, Arcuri, & Gawronski, 2008; Green et al., 2007; von Hippel, Brener, & von Hippel, 2008; see also Bodenhausen & Todd, Chapter 15, this volume). Given the ubiquity of such findings, researchers became interested in potential sources of the mental associations assessed by indirect procedures, in particular the principles underlying their formation, change, and contextualization.

The present chapter reviews the literature on the formation, change, and contextualization of the mental associations assessed by indirect procedures. For this purpose, we first review the core assumptions of four prominent attitude theories, including their implications regarding the formation, change, and contextualization of mental associations. Expanding on this theoretical synopsis, the second part provides a comprehensive overview of the currently available evidence, which is used to evaluate the range and limits of the reviewed theories and to identify potential directions for future research.

THEORETICAL MODELS The huge set of empirical findings in the literature on indirect procedures is certainly a challenge for any model that aims at explaining the formation, change, and contextualization of the associations assessed by these procedures. However, the more challenging task is to explain, and ideally predict, converging and diverging effects on explicit and implicit measures. For instance, whereas some studies have found effects on explicit but not implicit measures (e.g., Gawronski & Strack, 2004; Gregg, Seibt, & Banaji, 2006; Grumm, Nestler, & 216



12. Formation, Change, and Contextualization

von Collani, 2009), other studies report effects on implicit but not explicit measures (e.g., Dasgupta & Greenwald, 2001; Gawronski & LeBel, 2008; Gibson, 2008; Grumm et al., 2009; Karpinski & Hilton, 2001; Olson & Fazio, 2006). In addition, several studies have found converging effects on explicit and implicit measures (e.g., Gawronski & LeBel, 2008; Gawronski, Walther, & Blank, 2005; Grumm et al., 2009; Olson, & Fazio, 2001; Richeson & Nussbaum, 2004), whereas others have found antagonistic effects (e.g., Castelli, Tomelleri, & Zogmaister, 2008; Rydell, McConnell, Mackie, & Strain, 2006). In the following sections, we review four prominent attitude theories and their respective implications for the formation, change, and contextualization of attitudes. Even though these models have originally been designed for evaluative associations, many of their assumptions are equally applicable to nonevaluative domains, such as self-�concepts and stereotypes.

The MODE Model One of the earliest attitude theories addressing the difference between direct and indirect procedures is Fazio’s MODE model (for recent reviews, see Fazio, 2007; Olson & Fazio, 2009). The theoretical core of the MODE model is the definition of attitude as the mental association between an object and one’s summary evaluation of that object (Fazio, 2007). To the degree that this association is sufficiently strong, the evaluation associated with the object may be activated automatically when encountering that object (i.e., without intention to evaluate the object). According to the MODE model, automatically activated attitudes typically provide the basis for overt behaviors that are further downstream, such as the verbal report of an evaluation. However, the influence of automatically activated attitudes on downstream behaviors may be reduced when people have the motivation and the opportunity to engage in effortful processing. (MODE is the shortcut for Motivation and Opportunity as DEterminants.) Such effortful processes may involve a deliberation about specific attributes of the object or deliberate attempts to control for unwanted influences of automatically activated attitudes on downstream behavior. Thus, a central prediction of the MODE model is that downstream behaviors are influenced by automatically activated attitudes when either the motivation or the opportunity to engage in effortful processing is low. However, the impact of automatically activated attitudes will often be reduced when both motivation and opportunity are high.

217

Applied to the distinction between direct and indirect procedures, the MODE model argues that indirect procedures, such as evaluative priming (Fazio, Jackson, Dunton, & Williams, 1995; see Wentura & Degner, Chapter 6, this volume) or the Implicit Association Test (Greenwald, McGhee, & Schwartz, 1998; see Teige-Â�Mocigemba, Klauer, & Sherman, Chapter 7, this volume), tend to reduce participants’ opportunity to engage in effortful processing. As such, participants’ responses on these procedures provide a good proxy for their automatically activated attitudes. Moreover, verbally reported evaluations assessed by direct procedures can be regarded as a particular kind of behavior that is further downstream. Thus, to the degree that participants lack the motivation and the opportunity to engage in effortful processing, explicit measures should reflect the same automatically activated attitudes that are reflected in implicit measures. If, however, participants have the motivation and the opportunity to engage in effortful processing, explicit measures may reflect whatever evaluation is implied by a person’s deliberate inferences. These assumptions have been confirmed in a large number of studies showing that the correspondence between explicit and implicit measures is relatively high under conditions of impaired processing but tends to be low when both the motivation and the opportunity to engage in effortful processing are high (for a review, see Hofmann, Gschwendner, Nosek, & Schmitt, 2005; see also Hofmann & Wilson, Chapter 11, and Rydell & McConnell, Chapter 16, this volume). Even though the MODE model was originally designed to explain attitude–Â�behavior relations rather than the formation, change, and contextualization of attitudes, it has a number of important implications for converging versus diverging effects on explicit and implicit measures. First, experimentally induced differences on implicit measures can be expected whenever a given factor changes a person’s object–Â�evaluation association in memory, as it is implied in attitude formation or attitude change. If either the motivation or the opportunity to engage in effortful processing is low, the differences in automatically activated attitudes should also be reflected further downstream in verbally reported evaluations assessed by direct procedures. In this case, explicit and implicit measures should show corresponding effects (e.g., Gawronski & LeBel, 2008; Grumm et al., 2009; Olson, & Fazio, 2001; Richeson & Nussbaum, 2004). If, however, both motivation and opportunity are high, the impact of automatically activated attitudes on verbal selfÂ�reports may be reduced. In this case, explicit and

218

III. CROSS-CUTTING PERSPECTIVES

implicit measures should show a dissociation, such that implicit measures reflect the newly formed or recently changed attitude, whereas explicit measures reflect whatever evaluation is implied by a person’s deliberate inferences (e.g., Gawronski & LeBel, 2008; Gibson, 2008; Grumm et al., 2009; Olson & Fazio, 2006). Second, there may be cases in which a given manipulation influences participants’ motivation and opportunity to deliberate about specific attributes of the object or to control for unwanted influences of automatically activated attitudes. In such cases, explicit and implicit measures will also show dissociations, such that explicit measures reveal the aforementioned variations arising from differences in motivation and opportunity, whereas implicit measures still show the original automatically activated attitude (e.g., Gregg et al., 2006). Finally, there may be combinations of the two influences that produce antagonistic effects on explicit and implicit measures (e.g., Castelli et al., 2008; Rydell et al., 2006), such that implicit measures show effects in one direction as a result of changes in automatically activated attitudes and explicit measures show opposite effects as a result of motivation and opportunity to engage in effortful processing. What is important in these three cases is that genuine changes of attitudes, defined as object–Â�evaluation associations in memory (Fazio, 2007), should always be reflected in implicit measures. In contrast, variations in explicit measures may or may not reflect genuine changes in attitudes because they could also be driven by variations in participants’ motivation and opportunity to engage in effortful processing. Over and above these explanations for processes of attitude formation and change, it is important to note that the MODE model conceptualizes attitudes as associative knowledge structures in memory that are relatively stable over time and across contexts. Thus, according to the MODE model, variations in automatically activated attitudes as a function of contextual factors are rather unlikely. Nevertheless, the MODE model implies a number of assumptions that explain contextual variations in implicit measurement scores (e.g., Barden, Maddux, Petty, & Brewer, 2004; Blair, Ma, & Lenton, 2001; Dasgupta & Greenwald, 2001; Wittenbrink, Judd, & Park, 2001). First, the MODE model assumes that the particular attitude that is activated in response to a given stimulus depends on how that stimulus is categorized (Fazio, 2007). For instance, a black athlete may elicit a more favorable evaluation when he or she is categorized in terms of occupation rather than race (e.g., Mitch-

ell, Nosek, & Banaji, 2003). Thus, variations in implicit measurement scores may not necessarily indicate the storage of different attitudes toward the same person in memory but rather differing attitudes toward two different attitude objects (i.e., athletes vs. blacks). Second, the MODE model assumes that certain types of procedures are contaminated by extrapersonal associations (Olson & Fazio, 2004). Such extrapersonal associations are described as evaluative knowledge in a person’s memory that does not contribute to that person’s attitude. Even though some researchers question the feasibility of a clear definition of extrapersonal associations (Gawronski, Peters, & LeBel, 2008), Han, Olson, and Fazio (2006) have shown that some contextually induced variations in implicit measurement scores occur for some procedures but not others. In their study, evaluations that had been endorsed by other individuals in a brief video clip influenced participants’ scores on a standard variant of the Implicit Association Test (Greenwald et al., 1998), even though a personalized variant of the Implicit Association Test (Olson & Fazio, 2004) and an evaluative priming task (Fazio et al., 1995) remained unaffected and in line with participants’ self-Â�reported evaluations. Thus, from the perspective of the MODE model, many of the frequently obtained context effects may not reflect a high context sensitivity of automatically activated attitudes but rather a high context sensitivity of particular measurement procedures.

The Dual-Â�Attitudes Model Another prominent model addressing the distinction between direct and indirect procedures is Wilson, Lindsey, and Schooler’s (2000) dual-Â�attitudes model. Basically, this model assumes that people often have two attitudes toward the same object stored in memory: an implicit attitude that is activated automatically and a second, explicit attitude that requires cognitive effort to be retrieved from memory. Similar to the MODE model, the dual-Â�attitude model states that automatic, implicit attitudes generally influence responses on indirect procedures, whereas the impact of automatic, implicit attitudes on verbal self-Â�reports depends on whether a person engages in the effortful process of retrieving an explicit attitude from memory. In the latter case, self-Â�reported evaluations should primarily reflect a person’s explicit attitude, which may not necessarily be in line with his or her automatic, implicit attitude. A second central assumption of the dualÂ�attitude model concerns the origin of explicit and



12. Formation, Change, and Contextualization

implicit attitudes. According to Wilson and colleagues (2000), implicit attitudes can be described as highly overlearned, relatively robust memory structures that have their roots in repeated longterm experiences with an attitude object. Explicit attitudes, in contrast, are more recently acquired memory structures that have not erased the old, implicit attitude from memory. Put differently, the dual-�attitude model assumes that, when attitudes change, the old, implicit attitude still remains in memory, thereby influencing judgments and behavior when people are not able or motivated to engage in the effortful process of retrieving their new, explicit attitude from memory. These assumptions have a number of implications for the formation, change, and contextualization of attitudes. First, the dual-�attitude model states that the associations reflected in implicit measures are highly overlearned and stable. This assumption is in line with research showing relations between long-term socialization experiences and variations in implicit measures (e.g., Rudman, Phelan, & Heppen, 2007). However, it is at odds with other findings showing variations on implicit measures resulting from very little descriptive information (e.g., Gawronski, Walther, & Blank, 2005; Gregg et al., 2006). Second, the dual-�attitude model implies that variations in explicit but not implicit measures should be the default case because old, implicit attitudes tend to be more robust than newly acquired, explicit attitudes. Again, this assumption is consistent with several studies showing effects on explicit but not implicit measures (e.g., Gawronski & Strack, 2004; Gregg et al., 2006; Grumm et al., 2009). However, it stands in contrast with several studies showing effects on implicit but not explicit measures (e.g., Gawronski & LeBel, 2008; Gibson, 2008; Grumm et al., 2009; Karpinski & Hilton, 2001; Olson & Fazio, 2006). Finally, the dual-�attitudes model implies that simple context effects should be more likely for explicit measures because responses on these measures may vary as a function of whether people engage in the effortful process of retrieving their explicit attitude from memory. By contrast, contextual variations should be less likely for implicit measures, which presumably assess highly stable, implicit attitudes. These assumptions are supported by the large body of research on context effects on verbal self-�reports (for a review, see Schwarz & Strack, 1991). However, they are inconsistent with the accumulating number of studies showing context effects on implicit measures (e.g., Barden et al., 2004; Blair et al., 2001; Dasgupta & Greenwald, 2001; Wittenbrink et al., 2001).

219

The Metacognitive Model A relatively recent model addressing the distinction between direct and indirect procedures is Petty and Briñol’s metacognitive model (MCM) of attitudes (Petty & Briñol, 2006; Petty, Briñol, & DeMarree, 2007). Drawing on Fazio’s (2007) definition, the MCM conceptualizes attitudes as object–Â�evaluation associations in memory. Moreover, in line with Wilson and colleagues’ (2000) dual-Â�attitudes model, the MCM assumes that old attitudes are not erased from memory but often coexist with newly formed attitudes. If the validity of an old attitude is challenged by a new attitude, the old attitude will be tagged as “false” or “wrong” with a negation tag. However, because associative links to negation tags are assumed to be weaker (at least initially) compared with the links between attitude objects and their evaluations, the impact of negation tags on judgments and behavior depends on whether people are motivated and able to engage in the effortful process of retrieving the negation tag from memory. Yet the old attitude and the new attitude may both be activated automatically, which may lead to neutral evaluations at the implicit level (e.g., de Liver, van der Pligt, & Wigboldus, 2007). Such neutral evaluations resulting from simultaneously activated, antagonistic attitudes tend to produce a state of implicit ambivalence, which often leads to enhanced elaboration of attitude-Â�relevant information to reduce ambivalence (e.g., Petty, Tormala, Briñol, & Jarvis, 2006; Rydell, McConnell, & Mackie, 2008). In addition to conflict between old and new attitudes, implicit ambivalence can also result when people have opposite evaluative associations to an attitude object, such as when one’s endorsed view conflicts with cultural associations that have never been endorsed but are nonetheless present (Petty & Briñol, 2009). These assumptions have a number of implications for attitude formation and change. First, the MCM implies that explicit and implicit measures should show converging effects when all available information has the same evaluative implication. In this case, there are no negation tags that need to be stored or retrieved, and both explicit and implicit measures directly reflect a person’s object–Â� evaluation associations in memory. These conditions are characteristic of situations of attitude formation where the available information typically implies a particular evaluation of an attitude object. Second, converging effects may be expected when the available information is conflicting but both positive and negative information is regarded

220

III. CROSS-CUTTING PERSPECTIVES

as accurate. In this case, it is unlikely that one of the two evaluations is tagged as false, which, according to the MCM, is the primary cause of dissociations between explicit and implicit measures. In the MCM, this situation is referred to as one of explicit ambivalence. Third, corresponding effects can also be expected when the available information is conflicting and one piece of information leads to a rejection of the other, but people do not engage in the effortful process of retrieving the newly created negation tag from memory. In this case, both explicit and implicit measures should reflect a blend of the new and the unqualified old associations in memory. Fourth, the MCM implies the possibility of asymmetrical effects on explicit and implicit measures when an old attitude is qualified by new information and people engage in the effortful process of retrieving the newly created negation tag from memory when they verbally report an evaluation. In this case, verbal self-Â�reports will be jointly determined by the negated old and the affirmed new associations, which both imply the same evaluative response. However, implicit measures will reflect a blend of the new association and the unqualified old association. In such cases, the overall valence of a person’s response depends on the relative strength of the two associations. If the old association is stronger than the new one, the implicit measure will primarily reflect the valence of the old association. If, however, the new association is stronger than the old association, the implicit measure will primarily reflect the valence of the new association. Finally, if the two associations are equal in strength, the implicit measure will reflect a neutral evaluation, even though this evaluation will show the just-Â�mentioned characteristics of implicit ambivalence (Petty et al., 2006). As for context effects, the MCM shares the assumption of the MODE model that attitudes, defined as object–Â�evaluation associations in memory, are relatively stable over time and across contexts. From this perspective, contextual variations in implicit measures seem rather unlikely. Instead, context effects should be more likely for explicit measures, where contextual factors may influence whether people engage in the effortful process of retrieving negation tags from memory. Nevertheless, Petty and colleagues (2007) explicitly addressed the possibility of contextual variations in implicit measures when the associative representation of an attitude object is heterogeneous and different context cues activate different subsets of stored associations (e.g., Barden et al., 2004; Dasgupta & Greenwald, 2001; Wittenbrink et al., 2001).

The Associative–Â�Propositional Evaluation Model Gawronski and Bodenhausen’s (2006a, 2006b, 2007) associative–Â�propositional evaluation (APE) model was designed specifically to integrate heterogeneous findings in the literature on the formation, change, and contextualization of implicit evaluations. The theoretical core of the APE model is the distinction between associative and propositional processes. Associative processes are defined as the activation of mental associations in memory, whereas propositional processes are defined as the validation of the information implied by momentarily activated associations. The critical difference between the two processes is their dependency on subjective truth or accuracy (see also Strack & Deutsch, 2004). Whereas the activation of associations in memory is independent of whether the information implied by these associations is considered accurate or inaccurate, processes of propositional validation are inherently concerned with assessing the validity of this information. Drawing on this distinction, implicit measures can be regarded as a proxy for the activation of associations in memory, unqualified by subjective truth or falsity. Explicit measures, in contrast, can be equated with the outcome of propositional validation processes, in that direct measurement procedures typically ask participants to indicate their endorsement of or agreement with a propositional statement (e.g., “Please rate how much you agree with the statement .â•›.â•›. ”). Another central assumption of the APE model concerns the operating principles of associative and propositional processes. According to the APE model, the activation of associations is guided by principles of similarity and contiguity, which determine the particular pattern of associations that is activated in response to a given stimulus (see also Smith & DeCoster, 2000). Two central determinants of this process are (1) the preexisting structure of associations in memory and (2) the momentary set of input stimuli. The information implied by activated associations is further assumed to enter a propositional validation process, which is based on principles of logical consistency (Gawronski, Strack, & Bodenhausen, 2009). If the information implied by a given association is consistent with all momentarily considered information, this information will likely be regarded as valid and thus serve as a basis for a corresponding judgment. If, however, the information implied by a given association is inconsistent with other information, this inconsistency needs to be resolved



12. Formation, Change, and Contextualization

in order to avoid aversive feelings of cognitive dissonance (Festinger, 1957). Importantly, such inconsistency-Â�related “negations” do not necessarily deactivate the association that underlie a rejected proposition (e.g., a rejection of the statement “Old people are bad drivers” does not necessarily deactivate the concepts of old people and bad drivers; see Deutsch, Gawronski, & Strack, 2006; Gawronski, Deutsch, Mbirkou, Seibt, & Strack, 2008). Thus, according to the APE model, inconsistency within the momentarily considered set of information functions as the primary determinant of potential dissociations between explicit and implicit measures (e.g., Gawronski, Peters, Brochu, & Strack, 2008). Moreover, given that the likelihood of inconsistency increases as a function of the amount of information that is considered, dissociations between explicit and implicit measures should increase as a function of cognitive elaboration (e.g., Hofmann, Gawronski, Gschwendner, Le, & Schmitt, 2005). Another theoretical assumption that seems important in the present context concerns the mutual interplay between associative and propositional processes. According to the APE model, activated associations typically serve as the basis for explicit judgments, unless the information implied by these associations is rejected because of its inconsistency with other momentarily considered information. This assumption implies an influence of associative processes on propositional processes. Moreover, propositional validation processes may sometimes activate new associations in memory, for instance via intentional retrieval processes (e.g., Blair et al., 2001). This assumption implies an influence of propositional processes on associative processes. According to Gawronski and Bodenhausen (2006a), the first case should be reflected in an indirect effect on explicit measures that is mediated by implicit measures. In contrast, the second case implies an indirect effect on implicit measures that is mediated by explicit measures. These assumptions have a number of implications for the formation, change, and contextualization of the associations assessed by indirect procedures. First, the formation of new associations in memory may occur via two different learning mechanisms: (1) associative learning, which is driven by the mere co-Â�occurrence of objects or events, and (2) propositional learning, which is based on conscious insights into the validity of observed relations (Gawronski & Bodenhausen, 2009). These two processes may run simultaneously or in isolation, leading to different outcomes in each of the possible combinations (Gawronski

221

& Bodenhausen, 2006a). For instance, if associative learning creates a new association in memory and this association is regarded as valid, implicit and explicit measures should show corresponding effects, with the effect on the explicit measure being mediated by the implicit measure (e.g., Grumm et al., 2009; Olson & Fazio, 2001; Whitfield & Jordan, 2009; see Gawronski & Bodenhausen, 2006a). Conversely, if a new association results from propositional learning, implicit and explicit measures should again show corresponding effects. However, in this case, the effect on the implicit measure should be mediated by the explicit measure (e.g., Gawronski & Walther, 2008; Whitfield & Jordan, 2009). Moreover, if associative learning creates a new association in memory but this association is rejected as invalid because of its inconsistency with other information, effects should emerge only on the implicit measure and not on the explicit measure (e.g., Gawronski & LeBel, 2008; Gibson, 1998; Grumm et al., 2009; Karpinski & Hilton, 2001; Olson & Fazio, 2006). Finally, if new information produces inconsistency in the momentarily considered set of information and this inconsistency leads to a rejection of a stored association, effects should emerge only on the explicit measure and not on the implicit measure (e.g., Gawronski & Strack, 2004; Gregg et al., 2006; Grumm et al., 2009). The aforementioned principles integrate a wide range of empirical findings on the formation and change of the associations assessed by indirect procedures (Gawronski & Bodenhausen, 2006a). Over and above these principles, the APE model has a number of implications for context effects. Specifically, it assumes that a given stimulus does not necessarily activate all mental associations pertaining to that stimulus. Instead, the pattern of associations that is activated in response to a given stimulus depends on the overall set of input stimuli, which also includes context cues and other types of contextual information (e.g., Barden et al., 2004; Dasgupta & Greenwald, 2001; Wittenbrink et al., 2001). What is critical in such cases is the contingency of a given context cue and the particular type of information during the formation of an association (see Schmajuk & Holland, 1998). These assumptions can be illustrated by the findings of Rydell and Gawronski (2009). In their study, newly formed associations generalized across different contexts when the available information about the attitude object was homogeneous. However, when this information was subsequently challenged by evaluatively incongruent information, evaluative responses became context

222

III. CROSS-CUTTING PERSPECTIVES

sensitive such that they reflected the contingency between the valence of the available information and the context in which this information had been acquired. In other words, after the representation of the attitude object had become evaluatively heterogeneous, context cues determined which subset of associations became activated in response to that object, thereby leading to contextual variations in implicit evaluations.

EMPIRICAL EVIDENCE Many of the assumptions made by the reviewed theories have been empirically confirmed, whereas others are more difficult to reconcile with the available evidence. In the reminder of this chapter, we provide a comprehensive review of the available literature on the formation, change, and contextualization of mental associations as assessed by indirect procedures. Over and above construct-�related effects, this section also addresses the possibility of method-�related effects, which implies that some experimentally created effects may be driven by particular features of the measurement procedures rather than genuine variations in the underlying construct. Because of space constraints, we refrain from in-depth discussions of methodological details of the reviewed studies. Instead, we focus on the comprehensive nature of this review by providing brief summaries of the basic findings.

Formation Socialization Experiences Starting with the development of indirect procedures, there has been a strong intuitive belief that these procedures may tap memory traces that have their roots in long-term socialization experiences (Greenwald & Banaji, 1995; Wilson et al., 2000; see also Olson & Dunham, Chapter 13, this volume). A number of studies provide support for this assumption. For example, Rudman and colleagues (2007) showed that implicit evaluations of smoking and body weight were uniquely predicted by early childhood experiences, whereas explicit evaluations were uniquely predicted by recent experiences. Investigating the relationship between parental racial attitudes and implicit prejudice in children, Sinclair, Dunn, and Lowery (2005) found a strong correspondence among attitudes for children who strongly identified with their parents but not for those who weakly identified with their parents. Similar results are reported by Sherman, Chassin,

Presson, Seo, and Macy (2009), who provided evidence for an intergenerational transmission of implicit smoking evaluations from mothers to their children, with transmitted smoking evaluations in children predicting smoking initiation 18 months later. Applying the notion of socialization experiences to implicit self-Â�esteem, DeHart, Pelham, and Tennen (2006) found that children’s levels of implicit self-Â�esteem was positively related to nurturing parenting styles and negatively to overprotective parenting styles. Along similar lines, Kim, Sarason, and Sarason (2006) found that young Koreans in the United States showed higher levels of implicit positivity toward their ethnic ingroup as a function of positive self-Â�reported parent–child relationships, which, in turn, predicted lower levels of self-Â�reported distress.

Social Contact Closely related to the notion of socialization experiences is the contact hypothesis (Allport, 1954), which states that enhanced contact between social groups reduces intergroup conflict, at least when certain boundary conditions are met (for a meta-Â�analysis, see Pettigrew & Tropp, 2006). Investigating the effects of contact on implicit group evaluations, Henry and Hardin (2006) showed that friendly intergroup contact reduced implicit prejudice of blacks toward whites and Muslims toward Christians. However, they did not find any evidence for contact-Â�related prejudice reductions in whites’ attitudes toward blacks and Christians’ attitudes toward Muslims. Turner, Hewstone, and Voci (2007) provided more encouraging evidence, showing that exposure to South Asians was related to more favorable implicit evaluations of South Asians in white elementary school children. Investigating implicit group biases in targets of prejudice, Livingston (2002) found that black participants showed higher levels of implicit negativity toward their ingroup as a function of increased contact with whites. Moreover, this relation was mediated by perceived negativity from whites toward blacks, such that black participants’ implicit negativity toward their ingroup increased as a function of greater perceived negativity in whites. Interestingly, the opposite was true for explicit evaluations, such that perceived negativity in whites was associated with increased (rather than decreased) positivity toward the ingroup among blacks. Challenging the idea that implicit measures may reflect early socialization experiences, TowlesÂ�Schwen and Fazio (2001) found that implicit racial prejudice of whites against blacks was reduced by



12. Formation, Change, and Contextualization

positive interaction experiences only when these experiences were recent. Expanding on these findings, Shook and Fazio (2008) showed that white students who had been randomly assigned to share a dormitory room with a black roommate showed lower levels of implicit prejudice compared with white students who had been assigned to share a room with a white student. Interestingly, these effects emerged even though students in interracial rooms reported less satisfaction and less involvement with their roommates compared with students in same-race rooms.

Descriptive Information Even though the empirical findings reviewed thus far largely support the assumption that implicit measures are influenced by long-term socialization experiences (but see Towles-Â�Schwen & Fazio, 2001), the reverse conclusion—that variations in implicit measures generally reflect such experiences—is not necessarily correct (Gawronski, 2009). In fact, several studies show that implicit measures are influenced by a number of relatively simple factors that do not require repeated or long-term experiences. One example is research that has used verbal descriptions to create positive or negative implicit evaluations of individuals or social groups (e.g., Gawronski, Walther, & Blank, 2005; Gregg et al., 2006; Rydell & McConnell, 2006; Rydell, McConnell, Strain, Claypool, & Hugenberg, 2007). Some of these studies showed large effect sizes with as few as three statements (e.g., Gawronski, Walther, & Blank, 2005) or mere suppositions instead of factual descriptions (e.g., De Houwer, 2006a; Gregg et al., 2006). Drawing on the notion of cognitive balance (Heider, 1958), Gawronski, Walther, and Blank (2005) found that descriptive information about the relationship between two individuals created implicit evaluations that can be described as balanced when participants formed a positive or negative attitude toward one of the two individuals before they learned about their relationship. However, newly created implicit evaluations tended to be imbalanced when participants first learned about the relationship between two individuals and then formed a positive or negative attitude toward one of them afterward.

223

with either a positive or a negative unconditioned stimulus (US). As a result, the CS tends to acquire the valence of the US, such that CSs that are repeatedly paired with positive USs acquire a positive valence and CSs that are repeatedly paired with negative USs acquire a negative valance (for a review, see De Houwer, Thomas, & Baeyens, 2001). Such EC effects have also been demonstrated for implicit measures (e.g., Boschen, Parker, & Neumann, 2007; Hermans, Baeyens, Lamote, Spruyt, & Eelen, 2005; Hermans, Vansteenwegen, Crombez, Baeyens, & Eelen, 2002; Mitchell, Anderson, & Lovibond, 2003; Olson & Fazio, 2001, 2002; Petty et al., 2006; Woud, Becker, & Rinck, 2008). Relating the notion of EC to the development of racial prejudice, Livingston and Drwecki (2007) found that white participants who do not show any racial bias on implicit measures were significantly less likely to acquire negative associations in a standard EC paradigm, suggesting that EC-related mechanisms may be at least partially responsible for the high levels of implicit prejudice obtained in earlier studies (e.g., Nosek et al., 2007).

Self-�Anchoring Expanding the notion of EC to self-�associations, Walther, Nagengast, and Trasselli (2005) argued that the self may often function as a US, such that objects that become associated with the self acquire the valence of the self (see also Greenwald & Banaji, 1995). Thus, given that most people show a positive evaluation of themselves (e.g., Bosson, Swann, & Pennebaker, 2000; Greenwald & Farnham, 2000; Koole, Dijksterhuis, & van Knippenberg, 2001), any object that becomes associatively linked with the self may acquire a positive valence (see also Greenwald, Banaji, et al., 2002). Consistent with this assumption, Gawronski, Bodenhausen, and Becker (2007) showed that implicit evaluations of newly acquired objects become more positive as a function of ownership, and the size of this effect was positively related to implicit evaluations of the self. These effects were obtained regardless of whether ownership resulted from a choice decision or a random procedure (see also Gawronski & LeBel, 2008; Prestwich, Perugini, Hurling, & Richetin, 2010).

Evaluative Conditioning

Category Membership

Another set of studies have used procedures that are commonly used in research on evaluative conditioning (EC). In a typical EC study, a neutral conditioned stimulus (CS) is repeatedly paired

Similar considerations can be applied to self�related associations resulting from group membership. Several studies showed that mere categorization as a member of an unfamiliar, meaningless

224

III. CROSS-CUTTING PERSPECTIVES

group (see Tajfel, Billig, Bundy, & Flament, 1971) is sufficient to create an implicit preference for ingroups over outgroups (e.g., Ashburn-Nardo, Voils, & Monteith, 2001; Castelli, Zogmaister, Smith, & Arcuri, 2004; DeSteno, Dasgupta, Bartlett, & Cajdric, 2004; Otten & Wentura, 1999; Paladino & Castelli, 2008; see also Van Bavel & Cunningham, 2009). According to Walther and colleagues’ (2005) theorizing, these effects may be due to an associative transfer of self-Â�evaluations to the new ingroup (see also Cadinu & Rothbart, 1996; Gramzow & Gaertner, 2005; Otten & Wentura, 2001). Going beyond explicit categorization, a number of studies by Greenwald and colleagues (Greenwald, Pickrell, & Farnham, 2002; Pinter & Greenwald, 2004) showed that merely studying the names of the members of a hypothetical group enhanced implicit positivity toward the group. These effects were associated with parallel increases in groupÂ�related associations to the self, suggesting that the formation of implicit group evaluations is due to an associative transfer of positive self-Â�evaluations to the group. Investigating the interplay between group and exemplar evaluations, Ranganath and Nosek (2008) found that evaluative information about a given exemplar quickly generalized to the exemplar’s social group at the implicit level, whereas generalization at the explicit level occurred only after a delay of several days. According to the authors, these findings indicate that simple associative links between an exemplar and a social group are sufficient for attitude generalization at the implicit level, and that memory-based monitoring processes can reduce association-Â�related generalizations at the explicit level. Similar findings were obtained by Castelli and colleagues (2004), who showed that implicit group evaluations generalize to implicit evaluations of the members of these groups even when participants failed to remember the exemplars’ group membership.

Salient Cues Further evidence that implicit evaluations may be the product of quickly activated associative links is provided by several studies showing that implicit evaluations of unfamiliar individuals vary as a function of salient facial cues that are associated with either a positive or a negative valence. Van Leeuwen and Macrae (2004), for example, found that unfamiliar attractive faces elicited more favorable implicit evaluations than unfamiliar unattractive faces, despite the absence of any other

information about these faces (see also Olson & Marshuetz, 2005). In a similar vein, Richetin, Croizet, and Huguet (2004) showed that female faces elicited more favorable implicit evaluations when they were wearing makeup than when they were not. Investigating potential conflicts between facial cues and descriptive information, McConnell, Rydell, Strain, and Mackie (2008) found that associative cues related to attractiveness, obesity, and race outweighed the impact of verbally presented behavioral information, such that these group�associated cues influenced implicit evaluations despite the availability of alternative information of the opposite valence. Similar effects of attractiveness cues and verbal information about ambition were obtained by Sritharan, Heilpern, Wilbur, and Gawronski (in press) in an online dating context.

Summary Even though there is evidence that early socialization experiences can contribute to variations in implicit measures, there is accumulating evidence that the associations assessed by indirect procedures can be formed rather quickly and with relatively little effort. Findings of the latter kind challenge theorizing that implicit measures reflect highly overlearned associations that require longterm experiences for their formation (cf. Wilson et al., 2000). Aside from this inconsistency with a particular type of model, the reviewed theories are doing fairly well in explaining the available evidence on the formation of mental associations. However, most of these explanations have the status of post-hoc interpretations rather than a priori predictions. Future research investigating the applicability of core principles in the learning literature (e.g., Rescorla & Wagner, 1972) would be a useful avenue that could provide deeper insights into the formation of mental associations assessed by indirect procedures as well as potential differences to self-�reports.

Change Going beyond the formation of mental associations, many researchers have become interested in whether and how associations can be changed once they are formed. Interestingly, this research is much more heterogeneous than the reviewed evidence on formation in that some manipulations turned out to be more effective in producing changes on explicit compared with implicit measures, whereas other manipulations were more



12. Formation, Change, and Contextualization

effective in producing changes in implicit compared with explicit measures (see also Gawronski & Bodenhausen, 2006a).

Evaluative Conditioning Drawing on earlier evidence for EC effects in the formation of implicit evaluations, a number of studies have shown that repeated pairings of CSs and USs can also be used to change implicit evaluations. For instance, using self-�related words as CSs in a subliminal EC paradigm, Dijksterhuis (2004) found higher levels of implicit self-�esteem when self-�related words were repeatedly paired with positive words than when they were repeatedly paired with neutral words (see also Grumm et al., 2009). Along the same lines, Baccus, Baldwin, and Packer (2004) obtained EC-related variations in implicit self-�esteem when self-�relevant words were repeatedly paired with pictures of smiling, frowning, or neutral faces in a computer game. Similar effects have been found for various other attitude objects, including young and old people (Karpinski & Hilton, 2001), black and white faces (Olson & Fazio, 2006), different continents (Gawronski & LeBel, 2008), and consumer brands (Gibson, 2008). Interestingly, all of these studies found EC effects on implicit measures, even though explicit measures were unaffected. These findings stand in contrast to research on attitude formation, where EC effects have typically been demonstrated for both explicit and implicit measures (e.g., Olson & Fazio, 2001). To our knowledge, only two studies have found parallel EC effects on both explicit and implicit measures in a context of attitude change: one by Gawronski and LeBel (2008) and the other by Grumm and colleagues (2009). In both of these studies, EC effects emerged on both explicit and implicit measures when participants were asked to introspect on their feelings before they completed the self-�report measure. However, EC-related pairings influenced only implicit but not explicit measures when participants were asked to introspect on their knowledge about the attitude object (Gawronski & LeBel, 2008; Grumm et al., 2009).

Approach–Â�Avoidance Some researchers have argued that repeated pairings of a neutral stimulus with either positive or negative motor actions (e.g., approach vs. avoidance movements) can be used to induce EC-related variations in implicit evaluations (e.g., Woud et al., 2008). This idea has also been applied to

225

the context of attitude change. In a series of studies, Kawakami, Phills, Steele, and Dovidio (2007) found significant reductions in implicit prejudice against blacks when participants had to respond repeatedly with an approach action to black faces and with an avoidance action to white faces. In line with Walther and colleagues’ (2005) speculations about EC effects resulting from self-Â�associations, a follow-up study by Kawakami, Steele, Cifa, Phills, and Dovidio (2008) showed that these effects accompanied enhanced associations between the attitude object and the self. Other research using a similar rationale found that participants who were surreptitiously induced to smile while viewing photographs of black people showed lower levels of implicit prejudice compared with participants who viewed white faces while engaging in the same facial expression and control participants who were not induced to smile (Ito, Chiao, Devine, Lorig, & Cacioppo, 2006).

US Revaluation Drawing on the idea of US revaluation in EC (Baeyens, Eelen, Van den Bergh, & Crombez, 1992; Rescorla, 1974), Walther, Gawronski, Blank, and Langer (2009) demonstrated that subsequent changes in the valence of a positive or negative US led to corresponding changes in implicit evaluations of a previously associated CS. In their study, neutral faces (CS) were repeatedly paired with either positive or negative faces (US). Immediately afterward, the original valence of the USs was reversed by means of descriptive statements of the opposite valence; participants in a control condition were presented with neutral statements about the US faces. Results showed that both explicit and implicit evaluations of the CSs changed in line with the new evaluations of the USs, even though the CSs had never been presented with any new information.

Persuasion Given that most indirect measurement procedures have been developed by social psychologists, and given that social psychological research on attitude change in the past decades has been dominated by the persuasive communication paradigm (e.g., Chen & Chaiken, 1999; Kruglanski & Thompson, 1999; Petty & Wegener, 1999), it seems somewhat surprising that hardly any research has investigated changes in implicit evaluations from a persuasion point of view (for a discussion of several unpub-

226

III. CROSS-CUTTING PERSPECTIVES

lished studies, see Petty & Briñol, Chapter 18, this volume). A well-Â�replicated finding in persuasion research is that under conditions of low cognitive elaboration attitudes tend be influenced more strongly by peripheral/heuristic cues of the persuasive message (e.g., source attractiveness, source expertise, consensus information) rather than central/systematic features (i.e., argument strength). Conversely, under high cognitive elaboration attitudes tend to be more influenced by central/systematic features, whereas the impact of peripheral/ heuristic is often attenuated (but see Kruglanski & Thompson, 1999). To our knowledge, only two published studies have investigated changes in implicit evaluations in a standard persuasion paradigm (Briñol, Horcajo, Becerra, Falces, & Sierra, 2002; Tormala, Briñol, & Petty, 2004). These studies showed that strong arguments resulted in more favorable implicit evaluations compared with weak arguments; explicit evaluations were unaffected by argument strength. Neither of these two studies included a manipulation of cognitive elaboration or peripheral/heuristic cues. Information about the potential impact of peripheral/heuristic cues can be derived from two studies that investigated effects of celebrity voiceovers (Forehand & Perkins, 2005) and consensus information (Sechrist & Stangor, 2001). Using a paradigm similar to commercial advertising, Forehand and Perkins (2005) found that celebrity endorsement of a product influenced implicit product evaluations in line with participants’ attitudes toward the celebrity. Explicit product evaluations showed similar effects unless participants were able to identify the celebrity. In fact, when participants were able to identify the celebrity, the originally positive correlation between celebrity attitude and explicit product evaluation turned into a negative correlation. Investigating the effects of consensus information on implicit stereotypes, Sechrist and Stangor (2001) found that preexisting implicit stereotypes were enhanced when participants learned that other individuals shared that stereotype than when the stereotype was not shared by other individuals.

Cognitive Dissonance Using Festinger and Carlsmith’s (1959) induced compliance paradigm, Gawronski and Strack (2004) investigated the differential effects of cognitive dissonance on explicit and implicit evaluations arising from counterattitudinal behavior. In line with a conceptualization of cognitive consistency as an inherently propositional phenomenon

(Gawronski et al., 2009), their results showed that dissonance influenced explicit but not implicit evaluations (for similar findings, see Wilson et al., 2000). Moreover, explicit and implicit evaluations were significantly correlated under control conditions and when participants had a situational explanation for their counterattitudinal behavior. However, correlations tended to be negative, albeit nonsignificant, when participants did not have a situational explanation and, therefore, changed their explicit evaluations as a result of cognitive dissonance (see also Gawronski, Peters, Brochu, & Strack, 2008).

Descriptive Information Drawing on the idea that descriptive verbal information can provide a basis for newly formed implicit evaluations, several studies have investigated the range and the limits of descriptive information in changing implicit evaluations. For instance, Petty and colleagues (2006) showed that descriptive information is capable of reversing newly formed explicit evaluations that have been created by means of an EC manipulation. However, implicit evaluations seemed to integrate the information of both EC-related pairings and descriptive information, such that subsequent implicit evaluations were only neutralized rather than reversed. Similar effects were reported by Rydell and colleagues (2007), who showed that counterattitudinal information quickly reversed explicit evaluations, whereas implicit evaluations displayed rather slow, incremental changes as a function of increasing counterattitudinal information (see also Rydell & McConnell, 2006). One of the most interesting studies in this context has been conducted by Rydell and colleagues (2006), who combined CS–US pairings in a subliminal EC paradigm with descriptive information about the CS that was opposite to the valence of the US. Their results showed that implicit evaluations of the CS were uniquely influenced by the valence of the US with which it was paired, but not by the descriptive information about the CS. In contrast, explicit evaluations of the CS were uniquely influenced by the descriptive information, but not by EC-related pairings to positive or negative USs.

Negation Testing the effectiveness of a training paradigm to reduce implicit stereotyping, Kawakami, Dovidio, Moll, Hermsen, and Russin (2000) presented their participants with pairings of faces and adjectives



12. Formation, Change, and Contextualization

that formed either stereotypical or counterstereotypical pairs (e.g., a black face combined with either a stereotypically black or a stereotypically white trait). Participants were asked to press a no key each time they saw a stereotype-Â�congruent combination and a yes key each time they saw a stereotype-Â�incongruent combination. Over a series of several hundred trials, Kawakami and colleagues found that implicit stereotyping was significantly reduced over the course of the task. However, drawing on earlier findings showing counterintentional effects of negations (e.g., Deutsch et al., 2006; Gilbert, 1991), Gawronski, Deutsch, Mbirkou, Seibt, and Strack (2008) showed that reductions in implicit stereotyping are primarily driven by the affirmation of counterstereotypes rather than the negation of stereotypes. In fact, repeated negations of a stereotype resulted in ironic effects, such that negation training increased rather than decreased implicit stereotyping. Similar ironic effects have been obtained by Payne, Lambert, and Jacoby (2002), who found that instructions to ignore race as a cue in a sequential priming measure of race bias in weapon identification (Payne, 2001) increased (rather than reduced) the impact of race. From a general point of view, these results suggest that affirming alternative associations may be more effective in producing the intended outcome than negating unwanted associations. This conclusion is in line with findings by both Sassenberg and Moskowitz (2005), who showed that a procedural priming manipulation to “think different” effectively reduced implicit stereotyping, and Stewart and Payne (2008), who demonstrated similar effects for implementation intentions to think in a counterstereotypical manner. The differential effectiveness of negation in qualifying explicit and implicit evaluations is also reflected in a study by Gregg and colleagues (2006). In their study, participants received verbal descriptions of two groups, one of which was described as positive and the other as negative. After participants completed measures of explicit and implicit group evaluations, they were told that the experimenter had mistakenly mixed up the conditions and that the information about the two groups should have been reversed. Participants were then asked to mentally reverse the information they have seen before and to complete the two measures again. Results showed that reversal instructions effectively reversed explicit evaluations, whereas implicit evaluations still reflected the valence of the original descriptions about the two groups.

227

Media Influences Addressing influences in real-life settings, a number of studies have investigated potential effects of TV clips on mental associations. For instance, Dal Cin, Gibson, Zanna, Shumate, and Fong (2007) found that watching movie clips featuring a cigarette-Â�smoking protagonist enhanced self–Â� smoking associations and self-Â�reported intentions to smoke, and this effect increased as a function of identification with the protagonist. Evaluating the effectiveness of anti-Â�marijuana and antiÂ�tobacco TV advertisements, Czyzewska and Ginsburg (2007) found that the campaigns evaluated in their study effectively increased implicit negativity toward tobacco and marijuana. However, the anti-Â�marijuana campaigns produced ironic effects at the explicit level, in that participants who watched them showed the most favorable attitudes toward marijuana. Lincoln, Arens, Berger, and Rief (2008) investigated the effects of different kinds of anti-Â�stigma campaigns, showing that implicit schizophrenia stereotypes can be effectively reduced by campaigns that emphasize either biogenetic or psychosocial causes of schizophrenia.

Interventions Adopting a similar focus on real-life settings, several studies have used indirect procedures to evaluate various kinds of interventions. For instance, Teachman and Woody (2003) found that cognitive-�behavioral therapy effectively reduced implicit fear associations in spider phobics, and these reductions were associated with parallel effects at the behavioral level. Similar results were obtained by Grumm and colleagues (2008) for the impact of cognitive-�behavioral psychotherapy on implicit pain associations in patients suffering from chronic pain. Across two quasi-�experiments, Rudman, Ashmore, and Gary (2001) reported evidence for the effectiveness of a diversity education seminar in reducing explicit and implicit prejudice. Along the same lines, Dasgupta and Asgari (2004) found that academic environments with enhanced exposure to female leaders effectively reduced implicit gender stereotyping. Evaluating the effectiveness of a social competence training program in reducing aggressive behavior, Gollwitzer, Banse, Eisenbach, and Naumann (2007) obtained no differences between intervention and control groups immediately after the training program, with both groups showing a significant decrease from pretest to posttest. However, control participants showed a significant rebound in implicit and explicit ag-

228

III. CROSS-CUTTING PERSPECTIVES

gressiveness 4 months after the intervention, whereas the intervention group did not. Plant and Peruche (2005) evaluated the effectiveness of a training program designed to reduce racial bias in police officers’ tendency to shoot at unarmed suspects, showing that a training task in which race was unrelated to the presence of a weapon effectively reduced racial bias (see also Plant, Peruche, & Butz, 2005).

Summary The available evidence shows that, once formed, the associations assessed by indirect procedures can indeed be changed. However, different manipulations seem to vary in their relatively effectiveness, with some being more effective in producing changes on explicit rather than implicit measures (e.g., Gawronski & Strack, 2004; Gregg et al., 2006; Grumm et al., 2009) and others being more effective in producing changes on implicit rather than explicit measures (e.g., Gawronski & LeBel, 2008; Gibson, 2008; Grumm et al., 2009; Karpinski & Hilton, 2001; Olson & Fazio, 2006). The first outcome—Â�changes in explicit but not implicit measures—can be easily explained by all of the reviewed theories, albeit with nontrivial differences in their particular interpretations (Fazio, 2007; Gawronski & Bodenhausen, 2006a; Petty et al., 2007; Wilson et al., 2000). The latter outcome—Â�changes in implicit but not explicit measures— seems difficult to reconcile with theories claiming that implicit measures reflect old, highly overlearned associations that have not been replaced by more recently acquired associations (cf. Wilson et al., 2000). However, the results can be explained by the majority of other theories, although again with nontrivial differences in their particular interpretations (Fazio, 2007; Gawronski & Bodenhausen, 2006a; Petty et al., 2007). In addition, it seems worth noting that some studies showing parallel effects on explicit and implicit measures provided evidence for particular mediation patterns (e.g., Galdi et al., 2008; Gawronski & Walther, 2008; Whitfield & Jordan, 2009; see also Gawronski & Bodenhausen, 2006a). Even though some of these mediations can be explained by the MODE model (i.e., changes in object–Â� evaluation associations that are subsequently used for self-Â�reported evaluations should produce an indirect effect on explicit measures that is mediated by implicit measures), the prediction of indirect effects on implicit measures that are mediated by explicit measures is a unique implication of the APE model. So far, the particular conditions of

the obtained mediation patterns are in line with the assumptions of APE model, providing a preliminary advance of this theory in accounting for the available data.

Context Effects Even though most of the studies reviewed thus far did not include follow-up assessments at a later point (for notable exceptions, see Gollwitzer et al., 2007; Kawakami et al., 2000; Olson & Fazio, 2006; Walther et al., 2009), the general assumption in these studies is that the obtained results reflect stable effects that remain robust over time. Deviating from the focus on long-term changes, a number of studies have investigated effects that may be regarded as momentary shifts as a function of the particular context. The assumption underlying these studies is that variations in the particular context influence the type of associations that are momentarily activated, which, in turn, should influence performance on indirect procedures.

Accessible Information The most representative set of studies in this context has investigated the impact of momentarily accessible information on implicit measures. In one of the first studies on context effects, Dasgupta and Greenwald (2001) showed that exposure to liked and disliked exemplars can shift implicit prejudice scores (see also Dasgupta & Rivera, 2008). Blair and colleagues (2001) showed similar effects for self-�generated information. In their study, participants who were instructed to vividly imagine a counterstereotypical woman showed reduced levels of implicit gender stereotyping compared with participants instructed to imagine a stereotypical woman and participants in a control condition. Similar results are reported by Sassenberg and Wieber (2005), who found less favorable implicit evaluations of an ingroup category when participants were asked to recall a situation when they were angry about their ingroup versus a situation when they were happy about their ingroup. However, qualifying the generality of such effects, Gawronski and Bodenhausen (2005) found decreased levels of implicit stereotyping only when the retrieval of counterstereotypical information was easy (i.e., a low number of examples) but not when the retrieval task was difficult (i.e., a high number of examples). This finding resembles the well-�replicated ease-of-�retrieval effect (Schwarz et al., 1991), showing that explicit judgments are influenced by the experienced ease of retrieving



12. Formation, Change, and Contextualization

information from memory rather than the actual content of that information. Note, however, that such ease-of-�retrieval effects were obtained only for a particular type of procedure, whereas other procedures showed the expected effects of mere accessibility; we return to these findings in the context of method-�related effects. Investigating potential effects of momentarily accessible information on implicit self-�esteem, Glen and Banse (2004) did not find any evidence for variations resulting from interviews focusing on personal deficits versus personal strengths. Similar null effects are reported by Grumm and colleagues (2009) for a directed-�thinking task that involved the retrieval of positive or negative personal characteristics, which influenced only explicit, and not implicit, self-�esteem. A manipulation by Stapel and Blanton (2004) appears to be more effective in shifting implicit self-�esteem scores. These authors found significant variations in implicit self-�esteem as a function of subliminally primed comparison standards.

Context Cues Another set of studies investigated whether the presence of simple context cues can produce variations in implicit measures. For instance, Wittenbrink and colleagues (2001) have shown that implicit evaluations of blacks vary as a function of the background context against which the target is presented (e.g., family barbecue vs. graffiti wall). Expanding on these findings, Barden and colleagues (2004) showed that it is not the context per se but rather the social role within that context that influences implicit evaluations (see also Maddux, Barden, Brewer, & Petty, 2005). In their study, implicit evaluations of the same black target presented in a prison context varied as a function of whether the target’s clothing suggested the role of prisoner or lawyer. Investigating the role of acoustic cues, Rudman and Lee (2002) found higher levels of implicit prejudice against African Americans when participants were exposed to violent or misogynistic rap music. Moreover, Schaller, Park, and Mueller (2003) showed that ambient darkness led to higher levels of implicit prejudice against black people for participants with chronic beliefs in a dangerous world, but not for participants who did not believe in a dangerous world. Studying the importance of context cues in the domain of eating behavior, Roefs and colleagues (2006) showed that momentary associations of high-fat foods (i.e., palatable vs. unhealthy) depended on primed interpretation foci (i.e., restaurant vs. health). How-

229

ever, in contrast to these findings, Huijding, de Jong, Wiers, and Verkooijen (2005) did not find any differences in implicit evaluations of smoking as a function of whether evaluations were assessed in a smoking or a nonsmoking setting. Investigating effects of context cues on implicit ingroup favoritism, Zogmaister, Arcuri, Castelli, and Smith (2008) found that loyalty primes enhanced ingroup favoritism, whereas equality primes decreased ingroup favoritism. Along the same lines, Castelli and colleagues (2008) reported that ingroup members who showed ingroup bias were evaluated more favorably on an implicit measure but less favorably on an explicit measure. Even though the reviewed findings suggest that implicit measures are highly variable across contexts, a recent study by Gschwendner, Hofmann, and Schmitt (2008) provided evidence for relatively high stability of implicit measures when the context is specified and kept constant. In their study, measures of implicit anxiety and implicit prejudice showed higher levels of stability over a period of 2 weeks when the procedure included construct�relevant backgrounds than when the relevant target stimuli were presented by themselves.

Categorization Further evidence for the context-Â�sensitivity of implicit measures is implied by research on category salience. For instance, Kühnen and colleagues (2001) found that increasing the salience of the categories East German and West German increased implicit ingroup bias in West German participants but decreased implicit ingroup favoritism in East Germans. Similar findings were obtained by Smith, Dijksterhuis, and Chaiken (2008), who found higher levels of implicit prejudice against African Americans when they subliminally primed white participants with white faces. Along the same lines, Steele and Ambady (2006) showed that female participants displayed a stronger implicit preference for arts over math when the salience of gender categories was high than when it was low. Investigating the flexibility of ingroup-Â�related evaluations in participants with dual national identity, Bohner, Siebler, González, Haye, and Schmidt (2008) found that identity priming influenced men’s, but not women’s, implicit ingroup evaluations. Manipulating the relative salience of an intergroup context, Pratto and Shih (2000) found enhanced levels of implicit ingroup bias for participants high, but not for those low, in social dominance orientation (see Pratto, Sidanius, Stallworth, & Malle, 1994). Examining

230

III. CROSS-CUTTING PERSPECTIVES

the effects of categorization, Mitchell and colleagues (2003) found that implicit evaluations of the same familiar individual (e.g., Michael Jordan) depended on whether this individual was categorized in terms of race or occupation.

Category Interpretation Even though these results suggest a powerful role of momentarily salient categories on implicit evaluation, a number of studies have shown that the particular impact of salient categories also depends on the momentary interpretation of these categories. For instance, Foroni and Mayr (2005) demonstrated a significant reduction in the well-Â�replicated implicit preference for flowers over insects when participants were asked to read a fictional postnuclear war scenario in which all flowers were contaminated and insects were the only harmless food available. Similar effects were obtained by Govan and Williams (2004), who showed a reversal of participants’ implicit preference for flowers over insects when the particular stimuli used in the task (e.g., butterfly, skunkweed) suggested a reversed evaluation of the two categories (i.e., flowers as negative and insects as positive). Applying similar considerations to racial prejudice, Richeson and Nussbaum (2004) found lower levels of implicit race bias among white participants when they read a message advocating a multicultural approach to reducing racial conflict than when they read a message advocating a colorblind approach. To the degree that a multicultural approach implies a favorable construal of ethnic categories whereas a color-blind approach aims at ignoring ethnic categories, these findings are consistent with the assumption that momentary construals of a given category influence implicit evaluations of that category.

Social Roles Investigating the role of salient categories in an interactive context, Richeson and Ambady (2001, 2003) conducted a series of studies showing that superior or subordinate roles in dyadic interactions influence implicit prejudice. In one study, white participants showed higher levels of implicit racial bias when they anticipated being in a superior role versus an inferior role during an interaction with a black participant (Richeson & Ambady, 2003). Similar results were obtained for implicit gender bias when male participants anticipated superior versus inferior roles in a dyadic interaction with a female participant (Richeson & Ambady, 2001).

Expanding on these findings, McCall and Dasgupta (2007) showed that these effects are associated with increased levels of implicit self-Â�stereotyping. Applying similar ideas to social roles in computer games, Uhlmann and Swanson (2004) demonstrated that playing a violent video game increased participants’ associations between the self and aggressive traits.

Social Tuning Going beyond anticipated social roles, a number of studies have investigated dynamic influences resulting from actual social interactions. Drawing on earlier research on social tuning (McCann & Higgins, 1992), these studies demonstrated that people’s implicit evaluations tend to move closer to the presumed attitude of their interaction partner. In one study, Lowery, Hardin, and Sinclair (2001) found that white participants showed reduced levels of implicit race bias after they interacted with a black experimenter than when they interacted with a white experimenter. However, the generality of these findings was recently qualified by several follow-up studies showing that social tuning effects on implicit evaluations are limited to conditions under which participants have a motivation to affiliate with their interaction partner (Sinclair, Lowery, Hardin, & Colangelo, 2005) or an epistemic desire to acquire knowledge about the interaction partner (Lun, Sinclair, Whitchurch, & Glenn, 2007).

Motivational States Further highlighting the significance of motivational processes, several studies have shown that implicit evaluations of goal-Â�relevant objects vary as a function of goal pursuit (see also Ferguson & Porter, Chapter 17, this volume). In one of the first studies in this domain, Ferguson and Bargh (2004) showed that implicit evaluations of neutral words became more favorable when these words were relevant for the outcome in an achievement-Â�related task. Along the same lines, Seibt, Häfner, and Deutsch (2007) found that implicit evaluations of food stimuli became more positive as a function of food deprivation. Investigating the effects of nicotine deprivation in smokers, Sherman, Rose, Koch, Presson, and Chassin (2003) obtained more favorable implicit evaluations of smoking-Â�related stimuli in heavy smokers, whereas light smokers showed more favorable implicit evaluations when they had just smoked a cigarette than when they were nicotine deprived (see also Payne, McCler-



12. Formation, Change, and Contextualization

non, & Dobbins, 2007; Waters et al., 2007). Investigating similar influences on implicit alcohol evaluations in heavy drinkers, Schoenmakers, Wiers, and Field (2008) found no differences as a function of whether participants received a low dose of alcohol or a placebo drink, even though correlations between implicit alcohol evaluations and a measure of attentional bias to alcohol stimuli increased in the alcohol condition compared with the placebo condition.

Emotional States A related set of studies have investigated the impact of affective or emotional states on implicit measures. For instance, Gemar, Segal, Sagrati, and Kennedy (2001) found that recently recovered depressed participants showed less favorable implicit self-�evaluations following an induction of sad mood compared with control conditions. Using a longitudinal design with multiple measurements, DeHart and Pelham (2007) showed a strong relation between negative life events and implicit self-�esteem for participants with low explicit self�esteem and low self-�concept clarity. Studying emotional effects on intergroup attitudes, DeSteno and colleagues (2004) showed that anger, but not sadness, enhanced negative implicit evaluations of a meaningless outgroup. Investigating the effects of personal threat, Frantz, Cuddy, Burnett, Ray, and Hart (2004) obtained higher scores of implicit prejudice in an Implicit Association Test (Greenwald et al., 1998) when the task was introduced as a diagnostic measure of racism versus a measure of cultural stereotypes. Similar effects are reported by Rudman, Dohn, and Fairchild (2007), who found higher levels of implicit prejudice under conditions of personal threat, and by Gonsalkorale, Carlisle, and von Hippel (2007), who showed enhanced levels of implicit stereotyping in response to collective threat by the stereotyped group. Exploring the potential interplay between emotion and motivation, Birch and colleagues (2008) found that enhancement-�motivated, but not coping�motivated, drinkers showed more favorable implicit alcohol associations after positive compared with negative mood induction.

Determinants of Contextualization Even though the organization of our review may suggest that formation, change, and contextualization are independent, a recent study by Rydell and Gawronski (2009) provides evidence for systematic relations among the three. Using an impres-

231

sion formation paradigm with verbal statements as evaluative information and background colors as contextual cues, they showed that newly formed implicit evaluations generalized across different contexts when information about the attitude object was homogeneous. However, when prior information about the attitude object was subsequently challenged by evaluatively incongruent information, implicit evaluations became contextsensitive, such that they reflected the contingency between the valence of prior information and the context in which this information was acquired. Moreover, when the available information about the attitude object was heterogeneous across different contexts, novel contexts elicited implicit evaluations that reflected the valence of the initial experiences with the attitude object, indicating a superiority of earlier compared with later acquired information. These results provide an integration of the notions of formation, change, and contextualization by specifying how each is related to the other two. Specifically, Rydell and Gawronski’s (2009) findings show that initially formed associations tend to be context-Â�independent, at least as long as the available information is homogeneous. If, however, the validity of these associations is later challenged by novel information, this information does not erase the old associations from memory. Instead, the resulting changes in implicit evaluations are often context-Â�dependent in that they are limited to the particular context in which the novel information had been learned. The result is a contextualized activation of early versus later formed associations, which depends on the contingency between context cues and type of information during the formation of old and new associations.

Summary Deviating from the notion of enduring effects in research on formation and change, there is strong evidence for context effects on the activation of associations assessed by indirect procedures. Such context effects pose a challenge to models that limit the possibility of contextual variations to self-�report measures (e.g., Wilson et al., 2000). Moreover, models that explicitly address context effects on implicit measures differ considerably in their proposed explanations for these effects, with some attributing context effects to the impact of extrapersonal associations on particular kinds of measurement procedures (e.g., Fazio, 2007) and others explaining the same effects with the dynam-

232

III. CROSS-CUTTING PERSPECTIVES

ic nature of associative processes (e.g., Gawronski & Bodenhausen, 2006a). Notwithstanding these differences, several theories share the assumption that different categorizations of the same stimulus can influence what type of associations get activated in response to that stimulus (e.g., Fazio, 2007; Gawronski & Bodenhausen, 2006a), which accounts for at least a subset of the reviewed findings. In our view, the most important task for future research is to go beyond mere demonstrations of context effects and to investigate principles of contextualization versus generalization, ideally by integrating the available evidence on formation and change (e.g., Rydell & Gawronski, 2009).

Method-Â�Related Effects A common assumption in the literature on formation, change, and context effects is that experimentally induced changes in measurement scores reflect meaningful variations in the underlying associations. However, it is important to note that implicit measures do not provide a direct reflection of these associations. Instead, every procedure is based on task-Â�specific mechanisms that mediate between the to-be-Â�assessed associations and participants’ performance in the task (Gawronski, Deutsch, LeBel, & Peters, 2008). Thus, it is possible that some experimentally induced effects are due to variations in the task-Â�specific mechanism rather than the underlying associations. A number of studies support this concern.

Strategic Influences A first set of studies that can be subsumed under the category of method-Â�related effects tested the effectiveness of faking instructions on implicit measures. Even though earlier studies did not find any evidence for variations in measurement scores as a function of faking instructions (e.g., Banse, Seise, & Zerbes, 2001; Kim, 2003), more recent research showed small but significant influences for Greenwald and colleagues’ (1998) Implicit Association Test (e.g., Czellar, 2006; De Houwer, Beckers, & Moors, 2007; Fiedler & Blümke, 2005; Lowery et al., 2001; Steffens, 2004; Verschuere, Prati, & De Houwer, 2009) and Fazio and colleagues’ (1995) affective priming task (e.g., Degner, 2009; Klauer & Teige-Â�Mocigemba, 2007; Teige-Â�Mocigemba & Klauer, 2008). Needless to say, these variations in measurement scores do not necessarily reflect variations in the underlying associations but rather variations that are related to the mechanisms underlying a given measurement procedure.

Impaired Control Further evidence for task-Â�related variations in measurement scores comes from research using the Quad model (Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005). The Quad model is a multinomial model that allows researchers to quantify the relative contributions of four distinct processes that all contribute to a participant’s performance on an indirect procedure (see also Sherman, Klauer, & Allen, Chapter 9, this volume). Aside from automatic associations, the most important of these processes is participants’ success at overcoming the response tendencies resulting from automatic associations. Analyzing existing and new data sets with the Quad model, Sherman and colleagues (2008) found that some experimentally induced effects on implicit measurement scores are indeed due to variations in automatic associations, whereas others are due to variations in overcoming bias. For instance, Bartholow, Dickter, and Sestir (2006) found increased scores of implicit race bias as a result of alcohol consumption, which, according to Sherman and colleagues’ reanalysis, are exclusively driven by participants’ impaired ability to overcome their associationÂ�related responses. Using an adaptation of Jacoby’s (1991) process-Â�dissociation procedure, similar effects of impaired executive control are reported by Govorun and Payne (2006) for ego-Â�depletion (Muraven & Baumeister, 2000) and by Lambert and colleagues (2003) for enhanced arousal resulting from anticipated public contexts. These findings have important implications for at least some of the reviewed findings. For instance, to the degree that personal threat impairs executive function (e.g., via increased levels of arousal), it seems possible that increased scores of implicit prejudice (e.g., Frantz et al., 2004; Rudman et al., 2007), implicit stereotyping (Gonsalkorale et al., 2007), and even implicit self-Â�esteem (Rudman et al., 2007) resulting from personal threat are due to participants’ reduced ability to control associationÂ�related response tendencies rather than genuine variations in the underlying associations. Given this alternative interpretation, researchers should be cautious in drawing potentially premature conclusions from threat-Â�related effects on implicit measures.

Procedural Variations Another important issue in this context is the fact that not all procedures are based on the same task�specific mechanism. Thus, a given effect obtained



12. Formation, Change, and Contextualization

with one procedure may not necessarily generalize to another procedure that is based on a different mechanism. Needless to say, such cases indicate that the obtained effect is most likely driven by task-Â�specific mechanisms rather than by the underlying associations, which should be equal for two otherwise equivalent procedures. However, determining the correct interpretation for a given effect seems much more difficult when two procedures produce opposite effects for the same manipulation. To our knowledge, at least two studies have demonstrated such antagonistic effects. One example is a series of studies by Gawronski and Bodenhausen (2005), who showed that generating a high (versus low) number of counterstereotypical exemplars decreased scores of implicit stereotyping on procedures that are based on stimulus–Â�stimulus compatibility but increased implicit stereotyping scores on procedures that are based stimulus–Â� response compatibility (see De Houwer, 2003). The latter finding resembles earlier evidence for ease-of-Â�retrieval effects on social judgments (Schwarz et al., 1991), whereas the former is in line with spreading activation models of associative activation, suggesting that the activation level of associations in memory should increase as a function of increasing stimulation. A second example is a series of studies by Deutsch and Gawronski (2009), who found that two sequentially presented prime stimuli produced contrast effects in Fazio and colleagues’ (1995) affective priming task (see also Gawronski, Deutsch, & Seidel, 2005), but additive effects in an otherwise identical version of Payne, Cheng, Govorun, and Stewart’s (2005) affect misattribution procedure. Taken together, these results indicate that researchers should be cautious in interpreting experimentally induced differences in measurement scores as direct reflections of variations in the underlying associations, given that differences in measurement scores can also be driven by effects on task-Â�specific mediators (for a more detailed discussion, see Gawronski, Deutsch, LeBel, & Peters, 2008).

SUMMARY The main goal of the present chapter was to review theoretical models and the empirical evidence regarding the formation, change, and contextualization of the associations assessed by indirect procedures. The currently available data suggest a wide range of factors that may contribute to the formation of mental associations. The same is true for the factors producing changes in preex-

233

isting associations. In addition to these findings, an accumulating body of research has investigated the context dependency of implicit measures. However, recent studies showing method-Â�related effects suggest that researchers should be cautious in quickly interpreting experimentally induced variations in measurement scores as direct evidence for variations in the underlying associations, and this concern applies equally to research on formation, change, and contextualization. The majority of construct-Â�related findings are well explained by current models of attitudes, which to a large extent are applicable to nonevaluative representations as well. Nevertheless, there are some considerable asymmetries in the power of the reviewed models in integrating the available evidence. A critical challenge for all of these theories is to move from developing post-hoc explanations for existing findings to generating new hypotheses that could help to establish each theory’s predictive power. In addition, it would be useful to supplement future studies on formation, change, and contextualization with behavioral measures. A common assumption in past and current research is that observed changes in the assessed constructs will lead to corresponding changes in behavior (see Perugini et al., Chapter 14, this volume). Even though this assumption seems quite plausible, it is not trivial and has not been tested empirically. Thus, to the degree that previously obtained correlations between implicit measures and overt behavior are driven by a common third variable, experimentally induced variations in implicit measures may not necessarily be related to corresponding changes in behavior if the common third variable is unaffected. Given the increasing use of indirect measurement procedures in applied contexts (see Nosek, Graham, & Hawkins, Chapter 29, Perkins & Forehand, Chapter 28, Snowden & Gray, Chapter 27, Teachman, Cody, & Clerkin, Chapter 26, and Wiers et al., Chapter 25, this volume), we propose that this question should be regarded as one of the next major steps in research on the formation, change, and contextualization of the associations assessed by indirect procedures. Acknowledgments Preparation of this chapter was supported by grants from the Canada Research Chairs Program and the Social Sciences and Humanities Research Council of Canada. We are grateful to Galen Bodenhausen, Russell Fazio, Keith Payne, and Richard Petty for helpful comments on an earlier version of this chapter.

234

III. CROSS-CUTTING PERSPECTIVES

Note 1. Following De Houwer (2006b), we use the terms direct and indirect to describe the nature of measurement procedures and the terms explicit and implicit to refer to the outcomes or measurement scores obtained by these procedures. The term measure is used to describe measurement scores, whereas measurement procedures are always identified as such. Please note that the terms explicit and implicit are simply used for illustrative purposes without any empirical claims about the automatic/unconscious or controlled/conscious nature of the assessed constructs (see Moors, Spruyt, & De Houwer, Chapter 2, this volume).

References Allport, G. W. (1954). The nature of prejudice. Cambridge, MA: Addison-Â�Wesley. Ashburn-Nardo, L., Voils, C. I., & Monteith, M. J. (2001). Implicit associations as seeds of intergroup bias: How easily do they take root? Journal of Personality and Social Psychology, 81, 789–799. Baccus, J. R., Baldwin, M. W., & Packer, D. J. (2004). Increasing implicit self-Â�esteem through classical conditioning. Psychological Science, 15, 498–502. Baeyens, F., Eelen, P., Van den Bergh, O., & Crombez, G. (1992). The content of learning in human evaluative conditioning: Acquired valence is sensitive to US revaluation. Learning and Motivation, 23, 200–224. Banse, R., Seise, J., & Zerbes, N. (2001). Implicit attitudes towards homosexuality: Reliability, validity, and controllability of the IAT. Zeitschrift für Experimentelle Psychologie, 48, 145–160. Barden, J., Maddux, W. W., Petty, R. E., & Brewer, M. B. (2004). Contextual moderation of racial bias: The impact of social roles on controlled and automatically activated attitudes. Journal of Personality and Social Psychology, 87, 5–22. Bartholow, B. D., Dickter, C. L., & Sestir, M. A. (2006). Stereotype activation and control of race bias: Cognitive control of inhibition and its impairment by alcohol. Journal of Personality and Social Psychology, 90, 272–287. Birch, C. D., Stewart, S. H., Wiers, R. W., Klein, R. M., MacLean, A. D., & Berish, M. J. (2008). The moodÂ�induced activation of implicit alcohol cognition in enhancement and coping motivated drinkers. Addictive Behaviors, 33, 565–581. Blair, I. V., Ma, J. E., & Lenton, A. (2001). Imagining stereotypes away: The moderation of implicit stereotypes through mental imagery. Journal of Personality and Social Psychology, 81, 828–841. Bohner, G., Siebler, F., González, R., Haye, A., & Schmidt, E. A. (2008). Situational flexibility of ingroup-Â�related attitudes: A Single Category IAT study of people with dual national identity. Group Processes and Intergroup Relations, 11, 301–317. Boschen, M. J., Parker, I., & Neumann, D. L. (2007). Changes in implicit associations do not occur simul-

taneously to Pavlovian conditioning of physiological anxiety responses. Journal of Anxiety Disorders, 21, 788–803. Bosson, J. K., Swann, W. B., & Pennebaker, J. W. (2000). Stalking the perfect measure of implicit self-Â�esteem: The blind men and the elephant revisited? Journal of Personality and Social Psychology, 79, 631–643. Briñol, P., Horcajo, J., Becerra, A., Falces, C., & Sierra, B. (2002). Cambio de actitudes implicitas [Implicit attitude change]. Psicothema, 14, 771–775. Cadinu, M. R., & Rothbart, M. (1996). Self-Â�anchoring and differentiation processes in the minimal group setting. Journal of Personality and Social Psychology, 70, 661–677. Castelli, L., Tomelleri, S., & Zogmaister, C. (2008). Implicit ingroup meta-Â�favoritism: Subtle preference for ingroup members displaying ingroup bias. Personality and Social Psychology Bulletin, 34, 807–818. Castelli, L., Zogmaister, C., Smith, E. R., & Arcuri, L. (2004). On the automatic evaluation of social exemplars. Journal of Personality and Social Psychology, 86, 373–387. Chen, S., & Chaiken, S. (1999). The Heuristic Systematic model in its broader context. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€73–96). New York: Guilford Press. Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K., & Groom, C. (2005). Separating multiple processes in implicit social cognition: The quadmodel of implicit task performance. Journal of Personality and Social Psychology, 89, 469–487. Czellar, S. (2006). Self-Â�presentational effects in the Implicit Association Test. Journal of Consumer Psychology, 16, 92–100. Czyzewska, M., & Ginsburg, H. J. (2007). Explicit and implicit effects of anti-Â�marijuana and anti-Â�tobacco TV advertisements. Addictive Behaviors, 32, 114–127. Dal Cin, S., Gibson, B., Zanna, M. P., Shumate, R., & Fong, G. (2007). Smoking in movies, implicit associations with the self, and intentions to smoke. Psychological Science, 18, 559–563. Dasgupta, N., & Asgari, S. (2004). Seeing is believing: Exposure to counterstereotypic women leaders and its effect on the malleability of automatic gender stereotyping. Journal of Experimental Social Psychology, 40, 642–658. Dasgupta, N., & Greenwald, A. G. (2001). On the malleability of automatic attitudes: Combating automatic prejudice with images of admired and disliked individuals. Journal of Personality and Social Psychology, 81, 800–814. Dasgupta, N., & Rivera, L. M. (2008). When social context matters: The influence of long-term contact and short-term exposure to admired outgroup members on implicit attitudes and behavioral intentions. Social Cognition, 26, 112–123. De Houwer, J. (2003). A structural analysis of indirect measures of attitudes. In J. Musch & K. C. Klauer (Eds.), The psychology of evaluation: Affective processes in cognition and emotion (pp.€219–244). Mahwah, NJ: Erlbaum. De Houwer, J. (2006a). Using the Implicit Association Test does not rule out an impact of conscious



12. Formation, Change, and Contextualization

propositional knowledge on evaluative conditioning. Learning and Motivation, 37, 176–187. De Houwer, J. (2006b). What are implicit measures and why are we using them? In R. W. Wiers & A. W. Stacy (Eds.), The handbook of implicit cognition and addiction (pp.€11–28). Thousand Oaks, CA: Sage. De Houwer, J., Beckers, T., & Moors, A. (2007). Novel attitudes can be faked on the Implicit Association Test. Journal of Experimental Social Psychology, 43, 972–978. De Houwer, J., Thomas, S., & Baeyens, F. (2001). Associative learning of likes and dislikes: A review of 25 years of research on human evaluative conditioning. Psychological Bulletin, 127, 853–869. de Liver, Y., van der Pligt, J., & Wigboldus, D. (2007). Positive and negative associations underlying ambivalent attitudes. Journal of Experimental Social Psychology, 43, 319–326. Degner, J. (2009). On the (un)controllability of affective priming: Strategic manipulation is feasible but can possibly be prevented. Cognition and Emotion, 23, 327–354. DeHart, T., & Pelham, B. W. (2007). Fluctuations in state implicit self-Â�esteem in response to daily negative events. Journal of Experimental Social Psychology, 43, 157–165. DeHart, T., Pelham, B. W., & Tennen, H. (2006). What lies beneath: Parenting style and implicit self-Â�esteem. Journal of Experimental Social Psychology, 42, 1–17. DeSteno, D. A., Dasgupta, N., Bartlett, M. Y., & Cajdric, A. (2004). Prejudice from thin air: The effect of emotion on automatic intergroup attitudes. Psychological Science, 15, 319–324. Deutsch, R., & Gawronski, B. (2009). When the method makes a difference: Antagonistic effects on “automatic evaluations” as a function of task characteristics of the measure. Journal of Experimental Social Psychology, 45, 101–114. Deutsch, R., Gawronski, B., & Strack, F. (2006). At the boundaries of automaticity: Negation as reflective operation. Journal of Personality and Social Psychology, 91, 385–405. Dijksterhuis, A. (2004). I like myself but I don’t know why: Enhancing implicit self-Â�esteem by subliminal evaluative conditioning. Journal of Personality and Social Psychology, 86, 345–355. Fazio, R. H. (2007). Attitudes as object-Â�evaluation associations of varying strength. Social Cognition, 25, 603–637. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Ferguson, M. J., & Bargh, J. A. (2004). Liking is for doing: The effects of goal pursuit on automatic evaluation. Journal of Personality and Social Psychology, 87, 557–572. Festinger, L. (1957). A theory of cognitive dissonance. Evanston, IL: Row Peterson. Festinger, L., & Carlsmith, J. M. (1959). Cognitive consequences of forced compliance. Journal of Abnormal and Social Psychology, 58, 203–210.

235

Fiedler, K., & Blümke, M. (2005). Faking the IAT: Aided and unaided response control on the Implicit Association Tests. Basic and Applied Social Psychology, 27, 307–316. Forehand, M. R., & Perkins, A. (2005). Implicit assimilation and explicit contrast: A set/reset model of response to celebrity voice-overs. Journal of Consumer Research, 32, 435–441. Foroni, F., & Mayr, U. (2005). The power of a story: New, automatic associations from a single reading of a short scenario. Psychonomic Bulletin and Review, 12, 139–144. Frantz, C. M., Cuddy, A. J. C., Burnett, M., Ray, H., & Hart, A. (2004). A threat in the computer: The race Implicit Association Test as a stereotype threat experience. Personality and Social Psychology Bulletin, 30, 1611–1624. Friese, M., Hofmann, W., & Schmitt, M. (2008). When and why do implicit measures predict behaviour? Empirical evidence for the moderating role of opportunity, motivation, and process reliance. European Review of Social Psychology, 19, 285–338. Galdi, S., Arcuri, L., & Gawronski, B. (2008). Automatic mental associations predict future choices of undecided decision makers. Science, 321, 1100–1102. Gawronski, B. (2009). Ten frequently asked questions about implicit measures and their frequently supposed, but not entirely correct answers. Canadian Psychology, 50, 141–150. Gawronski, B., & Bodenhausen, G. V. (2005). Accessibility effects on implicit social cognition: The role of knowledge activation versus retrieval experiences. Journal of Personality and Social Psychology, 89, 672– 685. Gawronski, B., & Bodenhausen, G. V. (2006a). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. Gawronski, B., & Bodenhausen, G. V. (2006b). Associative and propositional processes in evaluation: Conceptual, empirical, and meta-Â�theoretical issues. Reply to Albarracín, Hart, and McCulloch (2006), Kruglanski and Dechesne (2006), and Petty and Briñol (2006). Psychological Bulletin, 132, 745–750. Gawronski, B., & Bodenhausen, G. V. (2007). Unraveling the processes underlying evaluation: Attitudes from the perspective of the APE model. Social Cognition, 25, 687–717. Gawronski, B., & Bodenhausen, G. V. (2009). Operating principles versus operating conditions in the distinction between associative and propositional processes. Behavioral and Brain Sciences, 32, 207–208. Gawronski, B., Bodenhausen, G. V., & Becker, A. (2007). I like it, because I like myself: Associative self-Â�anchoring and post-Â�decisional change of implicit evaluations. Journal of Experimental Social Psychology, 43, 221–232. Gawronski, B., Deutsch, R., LeBel, E. P., & Peters, K. R. (2008). Response interference as a mechanism underlying implicit measures: Some traps and gaps in the assessment of mental associations with experimental paradigms. European Journal of Psychological Assessment, 24, 218–225.

236

III. CROSS-CUTTING PERSPECTIVES

Gawronski, B., Deutsch, R., Mbirkou, S., Seibt, B., & Strack, F. (2008). When “just say no” is not enough: Affirmation versus negation training and the reduction of automatic stereotype activation. Journal of Experimental Social Psychology, 44, 370–377. Gawronski, B., Deutsch, R., & Seidel, O. (2005). Contextual influences on implicit evaluation: Additive versus contrastive effects of evaluative context stimuli in affective priming. Personality and Social Psychology Bulletin, 31, 1226–1236. Gawronski, B., & LeBel, E. P. (2008). Understanding patterns of attitude change: When implicit measures show change, but explicit measures do not. Journal of Experimental Social Psychology, 44, 1355–1361. Gawronski, B., Peters, K. R., Brochu, P. M., & Strack, F. (2008). Understanding the relations between different forms of racial prejudice: A cognitive consistency perspective. Personality and Social Psychology Bulletin, 34, 648–665. Gawronski, B., Peters, K. R., & LeBel, E. P. (2008). What makes mental associations personal or extraÂ�personal?: Conceptual issues in the methodological debate about implicit attitude measures. Social and Personality Psychology Compass, 2, 1002–1023. Gawronski, B., & Strack, F. (2004). On the propositional nature of cognitive consistency: Dissonance changes explicit, but not implicit attitudes. Journal of Experimental Social Psychology, 40, 535–542. Gawronski, B., Strack, F., & Bodenhausen, G. V. (2009). Attitudes and cognitive consistency: The role of associative and propositional processes. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€85–117). New York: Psychology Press. Gawronski, B., & Walther, E. (2008). The TAR effect: When the ones who dislike become the ones who are disliked. Personality and Social Psychology Bulletin, 34, 1276–1289. Gawronski, B., Walther, E., & Blank, H. (2005). Cognitive consistency and the formation of interpersonal attitudes: Cognitive balance affects the encoding of social information. Journal of Experimental Social Psychology, 41, 618–626. Gemar, M. C., Segal, Z. V., Sagrati, S., & Kennedy, S. J. (2001). Mood-Â�induced changes on the Implicit Association Test in recovered depressed patients. Journal of Abnormal Psychology, 110, 282–289. Gibson, B. (2008). Can evaluative conditioning change attitudes toward mature brands?: New evidence from the Implicit Association Test. Journal of Consumer Research, 35, 178–188. Gilbert, D. T. (1991). How mental systems believe. American Psychologist, 46, 107–119. Glen, I. S., & Banse, R. (2004). Probing the malleability of implicit and explicit self-Â�esteem: An interview approach. Current Psychology of Cognition, 22, 133– 158. Gollwitzer, M., Banse, R., Eisenbach, K., & Naumann, A. (2007). Effectiveness of the Vienna social competence training on explicit and implicit aggression: Evidence from an aggressiveness-IAT. European Journal of Psychological Assessment, 23, 150–156. Gonsalkorale, K., Carlisle, K., & von Hippel, W. (2007).

Intergroup threat increases implicit stereotyping. International Journal of Psychology and Psychological Therapy, 7, 189–200. Govan, C. L., & Williams, K. D. (2004). Changing the affective valence of the stimulus items influences the IAT by re-Â�defining the category labels. Journal of Experimental Social Psychology, 40, 357–365. Govorun, O., & Payne, B. K. (2006). Ego-Â�depletion and prejudice: Separating automatic and controlled components. Social Cognition, 24, 111–136. Gramzow, R. H., & Gaertner, L. (2005). Self-Â�esteem and favoritism toward novel in-Â�groups: The self as an evaluative base. Journal of Personality and Social Psychology, 88, 801–815. Green, A. R., Carney, D. R., Pallin, D. J., Ngo, L. H., Raymond, K. L., Iezzoni, L. I., et al. (2007). Implicit bias among physicians and its prediction of thrombolysis decisions for black and white patients. Journal of General Internal Medicine, 22, 1231–1238. Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-Â�esteem, and stereotypes. Psychological Review, 102, 4–27. Greenwald, A. G., Banaji, M. R., Rudman, L. A., Farnham, S. D., Nosek, B. A., & Mellott, D. S. (2002). A unified theory of implicit attitudes, stereotypes, selfÂ�esteem, and self-Â�concept. Psychological Review, 109, 3–25. Greenwald, A. G., & Farnham, S. D. (2000). Using the Implicit Association Test to measure self-Â�esteem and self-Â�concept. Journal of Personality and Social Psychology, 79, 1022–1038. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Greenwald, A. G., Pickrell, J. E., & Farnham, S. D. (2002). Implicit partisanship: Taking sides for no reason. Journal of Personality and Social Psychology, 83, 367–379. Gregg, A. P., Seibt, B., & Banaji, M. R. (2006). Easier done than undone: Asymmetry in the malleability of implicit preferences. Journal of Personality and Social Psychology, 90, 1–20. Grumm, M., Erbe, K., von Collani, G., & Nestler, S. (2008). Automatic processing of pain: The change of implicit pain associations after psychotherapy. Behaviour and Research Therapy, 46, 701–714. Grumm, M., Nestler, S., & von Collani, G. (2009). Changing explicit and implicit attitudes: The case of self-Â�esteem. Journal of Experimental Social Psychology, 45, 327–335. Gschwendner, T., Hofmann, W., & Schmitt, M. (2008). Differential stability: The effects of acute and chronic construct accessibility on the temporal stability of the Implicit Association Test. Journal of Individual Differences, 29, 70–79. Han, H. A., Olson, M. A., & Fazio, R. H. (2006). The influence of experimentally created extrapersonal associations on the Implicit Association Test. Journal of Experimental Social Psychology, 42, 259–272. Heider, F. (1958). The psychology of interpersonal relations. New York: Wiley. Henry, P. J., & Hardin, C. D. (2006). The contact hy-



12. Formation, Change, and Contextualization

pothesis revisited: Status bias in the reduction of implicit prejudice in the United States and Lebanon. Psychological Science, 17, 862–868. Hermans, D., Baeyens, F., Lamote, S., Spruyt, A., & Eelen, P. (2005). Affective priming as an indirect measure of food preferences acquired through odor conditioning. Experimental Psychology, 52, 180–186. Hermans, D., Vansteenwegen, D., Crombez, G., Baeyens, F., & Eelen, P. (2002). Expectancy-Â�learning and evaluative learning in human classical conditioning: Affective priming as an indirect and unobtrusive measure of conditioned stimulus valence. Behaviour Research and Therapy, 40, 217–234. Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., & Schmitt, M. (2005). A meta-Â�analysis on the correlation between the Implicit Association Test and explicit self-Â�report measure. Personality and Social Psychology Bulletin, 31, 1369–1385. Hofmann, W., Gschwendner, T., Nosek, B. A., & Schmitt, M. (2005). What moderates explicit-Â�implicit consistency?: European Review of Social Psychology, 16, 335–390. Huijding, J., de Jong, P. J., Wiers, R. W., & Verkooijen, K. (2005). Implicit and explicit attitudes toward smoking in a smoking and a nonsmoking setting. Addictive Behaviors, 30, 949–961. Ito, T. A., Chiao, K. W., Devine, P. G., Lorig, T. S., & Cacioppo, J. T. (2006). The influence of facial feedback on race bias. Psychological Science, 17, 256–261. Jacoby, L. L. (1991). A process-Â�dissociation framework: Separating automatic from intentional uses of memory. Journal of Memory and Language, 30, 513–541. Karpinski, A., & Hilton, J. L. (2001). Attitudes and the Implicit Association Test. Journal of Personality and Social Psychology, 81, 774–788. Kawakami, K., Dovidio, J. F., Moll, J., Hermsen, S., & Russin, A. (2000). Just say no (to stereotyping): Effects of training in the negation of stereotypic associations on stereotypic activation. Journal of Personality and Social Psychology, 78, 871–888. Kawakami, K., Phills, C., Steele, J., & Dovidio, J. (2007). (Close) distance makes the heart grow fonder: Improving implicit attitudes and interracial interactions through approach behaviors. Journal of Personality and Social Psychology, 92, 957–971. Kawakami, K., Steele, J. R., Cifa, C., Phills, C. E., & Dovidio, J. F. (2008). Approaching math increases math = me and math = pleasant. Journal of Experimental Social Psychology, 44, 818–825. Kim, D. (2003). Voluntary controllability of the Implicit Association Test (IAT). Social Psychology Quarterly, 66, 83–96. Kim, D., Sarason, B. R., & Sarason, I. G. (2006). Implicit social cognition and culture: Explicit and implicit psychological acculturation, and distress of KoreanÂ�American young adults. Journal of Social and Clinical Psychology, 25, 1–32. Klauer, K. C., & Teige-Â�Mocigemba, S. (2007). Controllability and resource dependence in automatic evaluation. Journal of Experimental Social Psychology, 43, 648–655. Koole, S. L., Dijksterhuis, A., & van Knippenberg, A. (2001). What’s in a name: Implicit self-Â�esteem and

237

the automatic self. Journal of Personality and Social Psychology, 80, 669–685. Kruglanski, A. W., & Thompson, E. P. (1999). Persuasion by a single route: A view from the unimodel. Psychological Inquiry, 10, 83–109. Kühnen, U., Schiessl, M., Bauer, N., Paulig, N., Pöhlmann, C., & Schmidthals, K. (2001). How robust is the IAT?: Measuring and manipulating implicit attitudes of East- and West-Â�Germans. Zeitschrift für Experimentelle Psychologie, 48, 135–144. Lambert, A. J., Payne, B. K., Jacoby, L. L., Shaffer, L. M., Chasteen, A. L., & Khan, S. R. (2003). Stereotypes as dominant responses: On the “social facilitation” of prejudice in anticipated public contexts. Journal of Personality and Social Psychology, 84, 277–295. Lincoln, T. M., Arens, E., Berger, C., & Rief, W. (2008). Can antistigma campaigns be improved?: A test of the impact of biogenetic vs. psychosocial causal explanations on implicit and explicit attitudes toward schizophrenia. Schizophrenia Bulletin, 34, 984–994. Livingston, R. W. (2002). The role of perceived negativity in the moderation of African Americans’ implicit and explicit racial attitudes. Journal of Experimental Social Psychology, 38, 405–413. Livingston, R. W., & Drwecki, B. B. (2007). Why are some individuals not biased?: Susceptibility to affective conditioning predicts nonprejudice toward African Americans. Psychological Science, 18, 816–823. Lowery, B. S., Hardin, C. D., & Sinclair, S. (2001). Social influence effects on automatic racial prejudice. Journal of Personality and Social Psychology, 81, 842– 855. Lun, J., Sinclair, S., Whitchurch, E. R., & Glenn, C. (2007). (Why) do I think what you think?: Epistemic social tuning and implicit prejudice. Journal of Personality and Social Psychology, 93, 957–972. Maddux, W. W., Barden, J., Brewer, M. B., & Petty, R. E. (2005). Saying no to negativity: The effects of context and motivation to control prejudice on automatic evaluative responses. Journal of Experimental Social Psychology, 41, 19–35. McCall, C., & Dasgupta, N. (2007). The malleability of men’s gender self-Â�concept. Self and Identity, 6, 173–188. McCann, C. D., & Higgins, E. T. (1992). Personal and contextual factors in communication: A review of the “communication game.” In G. R. Semin & K. Fiedler (Eds.), Language, interaction, and social cognition (pp.€144–172). Newbury Park, CA: Sage. McConnell, A. R., Rydell, R. J., Strain, L. M., & Mackie, D. M. (2008). Forming implicit and explicit attitudes toward individuals: Social group association cues. Journal of Personality and Social Psychology, 94, 792–807. Mitchell, C. J., Anderson, N. E., & Lovibond, P. F. (2003). Measuring evaluative conditioning using the Implicit Association Test. Learning and Motivation, 34, 203–217. Mitchell, J. P., Nosek, B. A., & Banaji, M. R. (2003). Contextual variations in implicit evaluation. Journal of Experimental Psychology: General, 132, 455–469. Muraven, M., & Baumeister, R. F. (2000). Self-Â�regulation and depletion of limited resources: Does self-Â�control

238

III. CROSS-CUTTING PERSPECTIVES

resemble a muscle?: Psychological Bulletin, 126, 247– 259. Nosek, B. A., Smyth, F. L., Hansen, J. J., Devos, T., Lindner, N. M., Ranganath, K. A., et al. (2007). Pervasiveness and correlates of implicit attitudes and stereotypes. European Review of Social Psychology, 18, 36–88. Olson, I. R., & Marshuetz, C. (2005). Facial attractiveness is appraised at a glance. Emotion, 5, 186–201. Olson, M. A., & Fazio, R. H. (2001). Implicit attitude formation through classical conditioning. Psychological Science, 12, 413–417. Olson, M. A., & Fazio, R. H. (2002). Implicit acquisition and manifestation of classically conditioned attitudes. Social Cognition, 20, 89–104. Olson, M. A., & Fazio, R. H. (2004). Reducing the influence of extra-Â�personal associations on the Implicit Association Test: Personalizing the IAT. Journal of Personality and Social Psychology, 86, 653–667. Olson, M. A., & Fazio, R. H. (2006). Reducing automatically activated racial prejudice through implicit evaluative conditioning. Personality and Social Psychology Bulletin, 32, 421–433. Olson, M. A., & Fazio, R. H. (2009). Implicit and explicit measures of attitudes: The perspective of the MODE model. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€19–63). New York: Psychology Press. Otten, S., & Wentura, D. (1999). About the impact of automaticity in the minimal group paradigm: Evidence from affective priming tasks. European Journal of Social Psychology, 29, 1049–1071. Otten, S., & Wentura, D. (2001). Self-Â�anchoring and in-group favoritism: An individual profiles analysis. Journal of Experimental Social Psychology, 37, 525– 532. Paladino, M.-P., & Castelli, L. (2008). On the immediate consequences of intergroup categorization: Activation of approach and avoidance motor behavior toward ingroup and outgroup members. Personality and Social Psychology Bulletin, 34, 755–768. Payne, B. K. (2001). Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon. Journal of Personality and Social Psychology, 81, 181–192. Payne, B. K., Cheng, S. M., Govorun, O., & Stewart, B. D. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. Journal of Personality and Social Psychology, 89, 277–293. Payne, B. K., Lambert, A. J., & Jacoby, L. L. (2002). Best laid plans: Effects of goals on accessibility bias and cognitive control in race-based misperceptions of weapons. Journal of Experimental Social Psychology, 38, 384–396. Payne, B. K., McClernon, J. F., & Dobbins, I. G. (2007). Automatic affective responses to smoking cues. Experimental and Clinical Psychopharmacology, 15, 400–409. Pettigrew, T. F., & Tropp, L. R. (2006). A meta-Â�analytic test of intergroup contact theory. Journal of Personality and Social Psychology, 90, 751–783. Petty, R. E., & Briñol, P. (2006). A meta-Â�cognitive approach to “implicit” and “explicit” evaluations: Com-

ment on Gawronski and Bodenhausen (2006). Psychological Bulletin, 132, 740–744. Petty, R. E., & Briñol, P. (2009). Implicit ambivalence: A meta-Â�cognitive approach. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€119–164). New York: Psychology Press. Petty, R. E., Briñol, P., & DeMarree, K. G. (2007). The meta-Â�cognitive model (MCM) of attitudes: Implications for attitude measurement, change, and strength. Social Cognition, 25, 657–686. Petty, R. E., Tormala, Z. L., Briñol, P., & Jarvis, W. B. G. (2006). Implicit ambivalence from attitude change: An exploration of the PAST model. Journal of Personality and Social Psychology, 90, 21–41. Petty, R. E., & Wegener, D. T. (1999). The elaboration likelihood model: Current status and controversies. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€41–72). New York: Guilford Press. Pinter, B., & Greenwald, A. G. (2004). Exploring implicit partisanship: Enigmatic (but genuine) group identification and attraction. Group Processes and Intergroup Relations, 7, 283–296. Plant, E. A., & Peruche, B. M. (2005). The consequence of race for police officers’ responses to criminal suspects. Psychological Science, 16, 180–183. Plant, E. A., Peruche, B. M., & Butz, D. A. (2005). Eliminating automatic racial bias: Making race nonÂ�diagnostic for responses to criminal suspects. Journal of Experimental Social Psychology, 41, 141–156. Pratto, F., & Shih, M. (2000). Social dominance orientation and group context in implicit group prejudice. Psychological Science, 11, 515–518. Pratto, F., Sidanius, J., Stallworth, L. M., & Malle, B. F. (1994). Social dominance orientation: A personality variable predicting social and political attitudes. Journal of Personality and Social Psychology, 67, 741– 763. Prestwich, A., Perugini, M., Hurling, R., & Richetin, J. (2010). Using the self to change implicit attitudes. European Journal of Social Psychology, 40, 61–71. Ranganath, K. A., & Nosek, B. A. (2008). Implicit attitude generalization occurs immediately, explicit attitude generalization takes time. Psychological Science, 19, 249–254. Rescorla, R. A. (1974). Effect of inflation of the unconditioned stimulus value following conditioning. Journal of Comparative and Physiological Psychology, 86, 101–106. Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II: Current research and theory (pp.€64–99). New York: Appleton. Richeson, J. A., & Ambady, N. (2001). Who’s in charge?: Effects of situational roles on automatic gender bias. Sex Roles, 44, 493–512. Richeson, J. A., & Ambady, N. (2003). Effects of situational power on automatic racial prejudice. Journal of Experimental Social Psychology, 39, 177–183. Richeson, J. A., & Nussbaum, R. J. (2004). The impact



12. Formation, Change, and Contextualization

of multiculturalism versus color-Â�blindness on racial bias. Journal of Experimental Social Psychology, 40, 417–423. Richetin, J., Croizet, J. C., & Huguet, P. (2004). Facial make-up elicits positive attitudes at the implicit level: Evidence from the Implicit Association Test. Current Research in Social Psychology, 9, 145–164. Roefs, A., Quaedacjers, L., Werrij, M. Q., Wolters, G., Havermans, R., Nederkoorn, C., et al. (2006). The environment influences whether high-fat foods are associated with palatable or with unhealthy. Behaviour Research and Therapy, 44, 715–736. Rudman, L. A., Ashmore, R. D., & Gary, M. L. (2001). “Unlearning” automatic biases: The malleability of implicit prejudice and stereotypes. Journal of Personality and Social Psychology, 81, 856–868. Rudman, L. A., Dohn, M. C., & Fairchild, K. (2007). Implicit self-Â�esteem compensation: Automatic threat defense. Journal of Personality and Social Psychology, 93, 798–813. Rudman, L. A., & Lee, M. R. (2002). Implicit and explicit consequences of exposure to violent and misogynous rap music. Group Processes and Intergroup Relations, 5, 133–150. Rudman, L. A., Phelan, J. E., & Heppen, J. B. (2007). Developmental sources of implicit attitudes. Personality and Social Psychology Bulletin, 33, 1700–1713. Rydell, R. J., & Gawronski, B. (2009). I like you, I like you not: Understanding the formation of contextÂ�dependent automatic attitudes. Cognition and Emotion, 23, 1118–1152. Rydell, R. J., & McConnell, A. R. (2006). Understanding implicit and explicit attitude change: A systems of reasoning analysis. Journal of Personality and Social Psychology, 91, 995–1008. Rydell, R. J., McConnell, A. R., & Mackie, D. M. (2008). Consequences of discrepant explicit and implicit attitudes: Cognitive dissonance and increased information processing. Journal of Experimental Social Psychology, 44, 1526–1532. Rydell, R. J., McConnell, A. R., Mackie, D. M., & Strain, L. M. (2006). Of two minds: Forming and changing valence-Â�inconsistent implicit and explicit attitudes. Psychological Science, 17, 954–958. Rydell, R. J., McConnell, A. R., Strain, L. M., Claypool, H. M., & Hugenberg, K. (2007). Implicit and explicit attitudes respond differently to increasing amounts of counterattitudinal information. European Journal of Social Psychology, 37, 867–878. Sassenberg, K., & Moskowitz, G. B. (2005). Don’t stereotype, think different!: Overcoming automatic stereotype activation by mindset priming. Journal of Experimental Social Psychology, 41, 506–514. Sassenberg, K., & Wieber, F. (2005). Don’t ignore the other half: The impact of ingroup identification on implicit measures of prejudice. European Journal of Social Psychology, 35, 621–632. Schaller, M., Park, J. J., & Mueller, A. (2003). Fear of the dark: Interactive effects of beliefs about danger and ambient darkness on ethnic stereotypes. Personality and Social Psychology Bulletin, 29, 637–649. Schmajuk, N. A., & Holland, P. C. (1998). Occasion setting: Associative learning and cognition in animals.

239

Washington, DC: American Psychological Association. Schoenmakers, T., Wiers, R. W., & Field, M. (2008). Effects of a low dose of alcohol on cognitive biases and craving in heavy drinkers. Psychopharmacology, 197, 169–178. Schwarz, N., Bless, H., Strack, F., Klumpp, G., RittenauerÂ�Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61, 195–202. Schwarz, N., & Strack, F. (1991). Context effects in attitude surveys: Applying cognitive theory to social research. European Review of Social Psychology, 2, 31–51. Sechrist, G. B., & Stangor, C. (2001). Perceived consensus influences intergroup behavior and stereotype accessibility. Journal of Personality and Social Psychology, 80, 645–654. Seibt, B., Häfner, M., & Deutsch, R. (2007). Prepared to eat: How immediate affective and motivational responses to food cues are influenced by food deprivation. European Journal of Social Psychology, 37, 359–379. Sherman, J. W., Gawronski, B., Gonsalkorale, K., Hugenberg, K., Allen, T. A., & Groom, C. (2008). The selfÂ�regulation of automatic associations and behavioral impulses. Psychological Review, 115, 314–335. Sherman, S. J., Chassin, L., Presson, C., Seo, D.-C., & Macy, J. T. (2009). The intergenerational transmission of implicit and explicit attitudes toward smoking. Journal of Experimental Social Psychology, 45, 313–319. Sherman, S. J., Rose, J. S., Koch, K., Presson, C. C., & Chassin, L. (2003). Implicit and explicit attitudes toward cigarette smoking: The effects of context and motivation. Journal of Social and Clinical Psychology, 22, 13–39. Shook, N. J., & Fazio, R. H. (2008). Interracial roommate relationships: An experimental field test of the contact hypothesis. Psychological Science, 19, 717–723. Sinclair, S., Dunn, E., & Lowery, B. S. (2005). The relationship between parental racial attitudes and children’s implicit attitudes. Journal of Experimental Social Psychology, 41, 283–289. Sinclair, S., Lowery, B. S., Hardin, C. D., & Colangelo, A. (2005). Social tuning of automatic racial attitudes: The role affiliative motivation. Journal of Personality and Social Psychology, 89, 583–592. Smith, E. R., & DeCoster, J. (2000). Dual-Â�process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4, 108–131. Smith, P. K., Dijksterhuis, A., & Chaiken, S. (2008). Subliminal exposure to faces and racial attitudes: Exposure to whites makes whites like blacks less. Journal of Experimental Social Psychology, 44, 50–64. Sritharan, R., Heilpern, K., Wilbur, C. J., & Gawronski, B. (in press). I think I like you: Spontaneous and deliberate evaluations of potential romantic partners in an online dating context. European Journal of Social Psychology.

240

III. CROSS-CUTTING PERSPECTIVES

Stapel, D. A., & Blanton, H. (2004). From seeing to being: Subliminal comparisons affect implicit and explicit self-Â�evaluations. Journal of Personality and Social Psychology, 87, 468–481. Steele, J. R., & Ambady, N. (2006). “Math is hard!”: The effect of gender priming on women’s attitudes. Journal of Experimental Social Psychology, 42, 428–436. Steffens, M. C. (2004). Is the Implicit Association Test immune to faking?: Experimental Psychology, 51, 165– 179. Stewart, B. D., & Payne, B. K. (2008). Bringing automatic stereotyping under control: Implementation intentions as efficient means of thought control. Personality and Social Psychology Bulletin, 34, 1332–1345. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Tajfel, H., Billig, M. G., Bundy, R. P., & Flament, C. (1971). Social categorization and intergroup behaviour. European Journal of Social Psychology, 1, 149– 178. Teachman, B. A., & Woody, S. R. (2003). Automatic processing in spider phobia: Implicit fear associations over the course of treatment. Journal of Abnormal Psychology, 112, 100–109. Teige-Â�Mocigemba, S., & Klauer, K. C. (2008). “Automatic” evaluation?: Strategic effects on affective priming. Journal of Experimental Social Psychology, 44, 1414–1417. Tormala, Z. L., Briñol, P., & Petty, R. E. (2004). Hidden effects of persuasion. Advances in Consumer Research, 31, 75–76. Towles-Â�Schwen, T., & Fazio, R. H. (2001). On the origins of racial attitudes: Correlates of childhood experience. Personality and Social Psychology Bulletin, 27, 162–175. Turner, R. N., Hewstone, M., & Voci, A. (2007). Reducing explicit and implicit outgroup prejudice via direct and extended contact: The mediating role of self-Â�disclosure and intergroup anxiety. Journal of Personality and Social Psychology, 93, 369–388. Uhlmann, E., & Swanson, J. (2004). Exposure to violent video games increases automatic aggressiveness. Journal of Adolescence, 27, 41–52.

Van Bavel, J. J., & Cunningham, W. A. (2009). SelfÂ�categorization with a novel mixed-race group moderates automatic social and racial biases. Personality and Social Psychology Bulletin, 35, 321–335. Van Leeuwen, M. L., & Macrae, C. N. (2004). Is beautiful always good?: Implicit benefits of facial attractiveness. Social Cognition, 22, 637–649. Verschuere, B., Prati, V., & De Houwer, J. (2009). Cheating the lie detector: Faking in the autobiographical IAT. Psychological Science, 20, 410–413. von Hippel, W., Brener, L., & von Hippel, C. (2008). Implicit prejudice toward injecting drug users predicts intentions to change jobs among drug and alcohol nurses. Psychological Science, 19, 7–11. Walther, E., Gawronski, B., Blank, H., & Langer, T. (2009). Changing likes and dislikes through the backdoor: The US revaluation effect. Cognition and Emotion, 23, 889–917. Walther, E., Nagengast, B., & Trasselli, C. (2005). Â�Evaluative conditioning in social psychology: Facts and speculations. Cognition and Emotion, 19, 175– 196. Waters, A. J., Carter, B. L., Robinson, J. D., Wetter, D. W., Lam, C. Y., & Cinciripini, P. M. (2007). Implicit attitudes to smoking are associated with craving and dependence. Drug and Alcohol Dependence, 91, 178–186. Whitfield, M., & Jordan, C. H. (2009). Mutual influences of explicit and implicit attitudes. Journal of Experimental Social Psychology, 45, 748–759. Wilson, T. D., Lindsey, S., & Schooler, T. Y. (2000). A model of dual attitudes. Psychological Review, 107, 101–126. Wittenbrink, B., Judd, C. M., & Park, B. (2001). Spontaneous prejudice in context: Variability in automatically activated attitudes. Journal of Personality and Social Psychology, 81, 815–827. Woud, M. L., Becker, E. S., & Rinck, M. (2008). Implicit evaluation bias induced by approach and avoidance. Cognition and Emotion, 22, 1187–1197. Zogmaister, C., Arcuri, L., Castelli, L., & Smith, E. R. (2008). The impact of loyalty and equality on implicit ingroup favoritism. Group Processes and Intergroup Relations, 11, 493–512.

C h a p t e r 13

The Development of Implicit Social Cognition Kristina R. Olson and Yarrow Dunham

T

he near-Â�complete helplessness of the human infant is one of the most striking features of our species, clearly differentiating us from most other mammals. Compare, for example, a human infant with a horse’s foal, up and walking almost imÂ�mediately after birth. Our total dependence in early life is even more striking when placed Â�alongside the mature human adult, who accomplishes so many cognitive feats otherwise unknown in the animal kingdom. As many have convincingly argued, our eventual cognitive prowess is in large part the result of our unique capacity for social interactions, such as our ability to acquire language rapidly and through language vast amounts of cultural knowledge and normative behavioral repertoires (e.g., Sperber, 1996; Tomasello, 2001). This process of enculturation begins in infancy and continues throughout the life span, but it clearly is at its most dizzyingly rapid during childhood, when children acquire the lion’s share of the knowledge and skills necessary to operate in the adult world. Social psychologists have long studied the mechanisms underlying human social interaction, behavior, and thought, concerning themselves with why we do what we do when we do it and how we navigate our rich, multifaceted social worlds. Yet in the context of our opening paragraph a limitation swims into focus: This inquiry has almost exclusively involved the examination of

young adults (i.e., the university undergraduate), despite the fact that the constructs we care about (e.g., attitudes, stereotypes, self-Â�esteem, identity) have their natural genesis in early childhood. Are not these processes, as they manifest themselves in adults, a snapshot in a time course of developmental change? With these questions in mind, we open our contribution to this volume by directly addressing the question of “Why development?”, that is, why should researchers interested primarily in implicit social cognition in adults be interested in the minds of children? We contend that a developmental approach does not constitute a special interest only relevant to those concerned with children or those with appointments in developmental areas any more than a social neuroscience approach is a special interest only relevant to those concerned with the brain and residing in neuroscience departments. Rather, like neuroscience, it can be considered another valuable method that allows specific types of questions to be addressed in novel ways, providing a perspective that simply cannot be provided by behavioral research with adults alone. Within the broader fabric of social cognition research then, developmentalists are members of the “ingroup,” providing data directly relevant to our attempts to delineate all aspects of social functioning. In what follows, we expand on these thoughts in the context of implicit social cognition research. 241

242

III. CROSS-CUTTING PERSPECTIVES

WHY DEVELOPMENT? At the most basic level, a developmental approach to the study of implicit social cognition is important because the initial form of a given mental representation and the path it follows after its emergence provide crucial evidence that can be used to adjudicate between alternative construals of that representation in adulthood, ultimately leading to more accurate theories of what we study. To flesh out this rather abstract idea more clearly, we walk through this argument with several examples. Individual researchers in implicit social cognition have their favorite ways of characterizing their topic of study. For example, influential accounts have made distinctions between control and awareness, associative and rule-based processing, or goal dependence and goal independence (e.g., Bargh, 1994; Moors & De Houwer, 2006; Smith & DeCoster, 2000). Yet the empirical support for any one of these distinctions is not grounded in the psychological investigation of a particular organism, as we might naively assume, but something narrower: a particular organism at a particular stage of development (adulthood, or what usually stands in for it, the university undergraduate). If we are interested only in understanding how this particular creature behaves at this particular stage of development, this limitation is not relevant, and research can continue uninterrupted. On the other hand, if what we are interested in is something broader, for example the characterization of implicit or automatic social processes writ large or the conceptual basis of this very distinction, then this limitation suddenly becomes pertinent because findings so narrowly situated are a shaky ground on which to base broader conclusions. Reviewing the implicit social cognition literature, one might be tempted to say that there are two kinds of implicit cognitions. The first, one might suggest, is a class that is intrinsically implicit, a result of a dual-Â�process human representational system. That is, things are implicit because our representational system contains a lower level associative encoding alongside a higher level propositional encoding. Implicit attitudes and stereotypes or implicit goal activation might suggest themselves as examples. One might further argue for a second class of automatic or implicit behaviors that cannot be implicit in this sense; rather, they are implicit because they have been overlearned, automatized (Anderson, 1992; Logan, 1992), or “modularized” (i.e., have, through practice, come to take on the properties of an independent cognitive module;

see Karmiloff-Smith, 1996). In short, we might say that some aspects of cognition are implicit by virtue of their intrinsic representational structure, whereas others are made implicit through sufficient expertise. But how would we decide if there were indeed these two classes of implicit cognitions? How would we decide whether a given cognitive construct is one or the other? We might look at patterns of variability and so conclude that parsing positions on a chessboard or tying shoelaces is of the second sort (after all, some people cannot do it at all and those who can required extensive practice to do so initially), while the ability for statistical learning or covariation detection is of the first sort (after all, everyone has these capabilities irrespective of learning histories). However, as long as we are basing decisions about which types of constructs belong in which category solely on the study of adults, our conclusions are little more than articles of faith. After all, we are ignoring the entire natural history of the organism. Is it not highly plausible that buried in ontogeny are numerous instances of automatization, of explicit becoming implicit? And can we rule out the converse type of change, of initially implicit processes being made explicit as metacognitive control strategies develop and acquire new scope? Recall that one of the major developmental accomplishments, extended over the first dozen or so years of life (and some would argue continuing into the early 20s; e.g., Shaw et al., 2008), involves the development of the prefrontal cortex and the concomitant rise of executive function and control. To our eyes, this looks like the emergence and development of the explicit system, of a metarepresentational, propositional system for reflecting on and evaluating one’s own (and others’) mental content. Thus, much that is implicit in the infant is explicit in the toddler, who can report on his or her desires; much more is explicit in the preschooler, who has finally acquired an adult-like theory of mind in which mental states like belief are understood as embedded in one’s own (or others’) mental space (a so-Â�called representational theory of mind; see Leslie, 1987); and more still is explicit in the elementary school student, who strategically shares (and does not share) his or her own mental state for varied and sundry aims. This is the explicit system laying claim to the “implicit’s” territory, exerting increasing control over previously automatic processes. However, this is also very likely the driving force behind the dissociation between implicit and explicit processes because self-Â�reflection appears to drive the revision



13. The Development of Implicit Social Cognition

of explicit attitudes to a greater extent than implicit attitudes (e.g., see Rutland, Cameron, Milne, & McGeorge, 2005). Thus, the rise of the metacognitive, increasingly “explicit” child is also the rise of the dual-Â�process child. Furthermore, the child will laboriously acquire new skills and competencies and practice them until they become automatic; highly controlled, conscious processing can guide these repetitive efforts until they become overlearned and thus implicit. Imagine how we would respond if a theorist claimed that the ability to shift gears in a manual transmission was a paradigmatic case of an implicit process (after all, large segments of the population can readily accomplish it without conscious control and with little effort). We would surely respond by pointing out that, although such a skill might well be implicit in some senses, it is the result of a protracted period of extensive practice, that its implicitness, such as it is, is highly contingent. A developmental perspective cautions us to remember that many other seemingly paradigmatic cases of implicit processing in adults could be more like this than we may first realize, the direct result of the child’s repetitive learning efforts. The broader point here is that by examining social cognition throughout the life span we can actually see progress along these different paths: implicit becoming explicit and explicit becoming implicit. These aspects of our social cognition will be crucial to truly understanding the phenomena of interest but can be invisible in adult “endstate” processing. Put another way, at each point in development the implicit–Â�explicit distinction may be drawn differently. Different constructs and different processes will fall on different sides of the divide, and understanding how and when this boundary changes will provide an invaluable resource for theory building, for adequately characterizing precisely what we mean by implicit and explicit in the first place. In addition to contributing to discussions about the emergence, interplay, and the best conceptual characterization of implicit and explicit processes, a developmental approach to the study of implicit social cognition also allows us to test specific developmental predictions derived from theoretical claims in the literature. Are implicit attitudes slow-Â�learned maps of statistical regularities read off the environment (e.g., Smith & DeCoster, 2000), or are they approach–avoid orientations that, to do their work, must be generated online, even in the absence of a learning history (Duckworth, Bargh, Garcia, & Chaiken, 2002; Dunham,

243

Baron, & Banaji, 2008)? Enter development; a slow-�learning view implies that implicit attitudes ought to emerge gradually, in lockstep with increasing social experience, increasing exposure to the environmental regularities on which they are based. A rapid-�emergence view makes a different kind of prediction, instead implying that implicit attitudes might emerge as soon as new social targets are encountered. We return to data bearing on this particular example later, but our point here is simply that developmental inquiry is uniquely positioned to address these sorts of questions because it provides direct evidence of change (or lack thereof) over time and thus indirect evidence as to the fitness of conflicting theories. In addition to contributing to current questions and debates, a developmental approach to the study of implicit social cognition will raise new avenues entirely. For example, researchers of implicit social cognition have been interested in how people create new implicit attitudes (e.g., Fazio, Eiser, & Shook, 2004; Sherman, 1996). Presumably, the interest of this work stems in large part from its ability to shed light on the origin of implicit social attitudes. Yet many entrenched social attitudes have their actual genesis in early childhood. Can we assume that the way an adult forms new attitudes is identical to the way a child does? This represents an unspoken and unproven hypothesis. Given the considerable amount of brain growth and related cognitive changes during childhood, there is considerable reason to doubt that the mechanisms would necessarily be identical. If one were to find that children and adults do not form new attitudes in identical ways, then theories of the formation of social attitudes would need to be revised, and tests of the development of social attitudes in adulthood would need to be understood as merely tests of the emergence of attitudes in adulthood, not as contributing to discussions of the origins of that large class of social attitudes that emerge before the age of 18. One final reason for studying development is that young children provide a more unadulterated human mind for investigation. The very complexity of adult knowledge structures works against our goal of understanding strictly how the mind works. Adults are plagued by concerns such as social desirability; they have years of exposure to formal education, religion, media, and social norms; and they have large quantities of experience spent interacting with other, equally complex minds. Of course, some research questions have everything to do with the impact of these forces on implicit

244

III. CROSS-CUTTING PERSPECTIVES

social cognition, but even here an illuminating test will be comparing humans before, after, or at varying levels of exposure to these forces. Yet at the same time, as researchers we are often interested not just in the impact of various factors on implicit social cognition but in the cognition itself: how the mind works. The best subjects to answer our basic questions about social cognition may very well be children. In summary, adult social cognition is in an important sense “end-state” cognition and thus the result of developmental processes. The same way that comparative research with primates and other species reveals what is provided by our evolutionary endowment and what must be somehow constructed by human-Â�specific capacities and contexts, developmental research can reveal what aspects of our cognitive system are contingent on cultural constructions and what aspects are common across all members of our species. In writing for an audience that may not be familiar with developmental approaches, we have chosen to begin with the “Why development?” question. Ideally, we would now summarize the ways in which the extant literature illustrates our claims. However, we must (unfortunately) acknowledge that in the area of implicit social cognition the field is only beginning to provide these illustrations. That is, although we argue there are many important theoretical questions one can answer using developmental approaches to implicit social cognition, and that the extant literature has begun to answer some of them, the interface between developmental and social cognitive research is, to risk a bad pun, still in its infancy. “Big picture” theoretical questions are just beginning to come to the forefront because the majority of what has been done is best considered methodological and conceptual groundwork, which must precede the testing of more specific theoretical hypotheses. Some may find this dissatisfying, and certainly we are sympathetic to such a response. However, we hope we can provide reason to be optimistic about what the developmental approach will contribute in the coming years. More specifically, the work we review next has largely focused on what kinds of implicit measures are suitable for children and what modifications need to be made; how children’s results compare with their adult counterparts’ at the mean level, in the patterns between groups and across measures; and what role socializing factors such as parents and exposure play in the development of attitudes. We believe there is much of interest here, even if

some of the broader possibilities we outlined previously must remain a promissory note.

THE EXTANT LITERATURE To date, fewer than 30 published reports have involved a developmental approach (defined loosely as any study with an implicit measure in a population of participants younger than 18) to the study of implicit social cognition. The work that has been conducted has primarily focused on two areas reflecting somewhat the trends in the adult implicit social cognition literature: intergroup attitudes and clinical applications. Next, we briefly describe the work to date.

The Development of Implicit Intergroup Attitudes What Counts as Implicit? The largest area of developmental implicit social cognition research has focused on children’s intergroup attitudes. These studies have emerged from several labs with a diverse set of goals ranging from assessing the feasibility of implicit measures with children and comparing the levels of implicit attitudes in children and adults to more theoretically oriented questions, such as whether children, like their adult counterparts, show evidence of balanced identity in their implicit evaluations. Before turning to these studies, however, we must emphasize that definitions of explicit and implicit have been and can be used in a great variety of ways in the developmental literature. The mappings between these usages and those employed in contemporary social cognition are not always clear, making it difficult to determine which developmental studies are relevant to this chapter. For example, infant work on intergroup preferences presents one potential problem. As we pointed out previously, almost everything infants do is implicit: They are unlikely to be consciously considering any of their attitudes; therefore, we might be tempted to include this work under the title “developmental approaches to the study of implicit social cognition.” Of course, researchers in this area seldom specify these preferences as implicit, probably because that is so obvious. The findings to date in this area are consistent with a claim that infants primarily prefer people who are familiar and secondarily prefer unfamiliar people who look like familiar people. That is, infants prefer their mothers



13. The Development of Implicit Social Cognition

to unknown other females (Pascalis, de Schonen, Morton, Deruelle, & Fabre-Â�Grenet, 1995), preferentially look toward faces of their caregiver’s gender (Quinn, Yahr, Kuhn, Slater, & Pascalis, 2002), preferentially look at faces of familiar racial groups (Bar-Haim, Ziv, Lamy, & Hodes, 2006), and look longer at faces of people speaking a familiar language (Kinzler, Dupoux, & Spelke, 2007). Clearly, these preferential looks, observed in infants as young as 3 months, cannot be considered evidence for an explicit preference. Yet it is also not clear that this should be interpreted as an implicit preference in the sense that we usually mean. Preferentially looking may, for example, be based on attraction to a small subset of features that do not correspond to an adult conception of race or gender; therefore, these may not be attitudes toward the same categories that we have as adults (to provide an example, infants selectively orient toward and prefer looking at faces from minutes after birth, but careful experimental work has revealed that what constitutes a face for an infant can be as simple as three dots arranged in a two dots over one dot— two eyes over one mouth—Â�configuration; see Pascalis & Slater, 2003). There are additional thorny issues associated with determining whether an actual attitude underlies looking time rather than something more basic like perceptual fluency. Establishing that these are indeed indices of implicit attitudes as such would require, among other things, demonstrating substantial category overlap between infants and adults and showing, ideally with a single measure, some continuity of effect through infancy and early childhood. Significant methodological challenges will need to be overcome before this effort can succeed. We thus suggest caution when interpreting findings based on the infant record, although this will undoubtedly be an area of future interest. During the preschool years, many studies used measures that are not strictly based on self-Â�report and, therefore, might be considered implicit. We call this set of measures indirect, rather than implicit, largely because the responses are verbal and, therefore, the role of controlled processing could clearly come into play (and probably would in adults), yet the attitude itself is inferred rather than measured directly. An illustrative example comes from the work of McGlothlin, Killen, and colleagues (McGlothlin & Killen, 2006; McGlothlin, Killen, & Edmonds, 2005), who showed children ambiguous scenes in which one person could be interpreted as, for example, an aggressor or a helper to a second person. Varying the race of the

245

two characters revealed that, at least in the case of white children in homogenous schools, interpretations of the actions of black characters tended to be more negative than parallel scenes involving white characters (McGlothlin & Killen, 2006). Other examples of indirect measures include memory tasks, in which differential recall of, for example, stereotypically consistent versus inconsistent information is compared (Davis, Leman, & Barrett, 2007; Signorella, Bigler, & Liben, 1997), or drawing tasks, in which children’s drawings of ingroup and outgroup targets are coded for stereotypical content (e.g., Teichmen, 2001). These tasks may well depend on implicit representations and thus may relate closely to implicit measures as more commonly considered in the adult literature, but that remains an open empirical question. Because this work represents a huge body of research spanning several decades, we do not rehash it here (for reviews, see Aboud, 1988; Bigler & Liben, 2006); instead, we focus on more recent studies that stem more directly from theories and methods derived from adult implicit social cognition research. In our review we focus on studies using measures that most closely align with those in the literature on adult implicit social cognition, such as the Implicit Association Test (IAT), the Extrinsic Affective Simon Task (EAST), and priming measures. Although there is considerable debate regarding whether these measures and the results of these measures should be termed implicit or not (De Houwer, 2006), for sake of simplicity and lack of a clearer term to refer to this subset of research, we identify the measures and the constructs expressed on these measures as implicit. That said, it is important to acknowledge that none of these studies independently assessed criteria identified by researchers as the hallmark of implicit or automatic processing (e.g., John Bargh’s, 1994, “four horsemen”). That is, questions of whether children are aware of the attitudes or beliefs they are expressing, whether they intended to make the responses they made, whether their responses were efficient, or whether the participants could exert control over their responses have not been directly investigated in children. As we discuss later, it will surely be the case that in children, as in adults, the responses to these measures will turn out to reflect varying degrees of both automatic and controlled influences (e.g., Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005; Payne, 2001; Stahl & Degner, 2007). The degree to which a given response at a given age reflects varying degrees of these influences will be an important topic for future inquiry.

246

III. CROSS-CUTTING PERSPECTIVES

Gender As with adults, the most commonly used measure of implicit social cognition in children is the IAT (Greenwald, McGhee, & Schwartz, 1998). To date, most of the studies using this measure have focused on assessing mean differences or similarities in the IAT effect across development as well as differences between known groups. The first reported use of the IAT with children comes from an investigation of implicit gender attitudes in fifth-grade children and in adults, which found that both children and adults tended toward implicit pro-Â�female attitudes, with some evidence for increasing female preference with age (Skowronski & Lawrence, 2001). A more recent investigation of children’s implicit gender attitudes, which included a broader age range and the revised scoring method for the IAT (Greenwald, Nosek, & Banaji, 2003), found in-Â�gender preference among 6-yearolds, gradually shifting with age in the direction of more pro-Â�female implicit attitudes for both boys and girls (Baron, Dunham, & Banaji, 2009). Thus, convergence on the adult norm of pro-Â�female implicit preferences (Rudman & Goodwin, 2004) appears to happen over a protracted period. More recent work suggests that, in contrast to changes in gender attitudes throughout childhood, there is relative consistency in spontaneous, or what we might think of as implicit, gender stereotyping throughout a similar age range, as measured by a different reaction time measure, the action inference paradigm (Banse, Gawronski, Rebetez, Gutt, & Morton, in press).

Race and Ethnicity As in the adult literature, perhaps the most prolific area of research has been work on children’s implicit racial and ethnic attitudes. The first studies of implicit race attitudes investigated whether children’s implicit attitudes differed across development. These studies found similar means on two variations of the IAT across childhood and into adulthood in samples of white British (Rutland et al., 2005) and white American (Baron & Banaji, 2006) children, despite the fact that explicit preference declined during this same period. The study by Rutland and colleagues (2005) also demonstrated that children’s explicit but not implicit attitudes are impacted by self-Â�presentation concerns, providing indirect support for the claim that implicit measures may be less controllable in children than explicit measures.

More recently, two studies have contrasted the IAT with evaluative priming (Degner & Wentura, in press; Williams, Steele, & Durante, 2009). Both found that these measures do yield somewhat different results at some ages. For example, white Canadian 6-year-olds showed in-race preference on both the IAT and evaluative priming, whereas 9-year-olds showed in-race preference only on the IAT (Williams et al., 2009). Examining somewhat older children, Degner and Wentura (in press) found that white German 9-year-olds showed implicit preference for white Germans over Turks on an IAT but not on an evaluative priming measure; ingroup preference on the priming measure emerged only in early adolescence. These findings suggest that these two measures may diverge in interesting ways in childhood, perhaps because the IAT assesses category-level preferences while evaluative priming assesses exemplar-level preferences (e.g., Olson & Fazio, 2003). Still, these two patterns of results are in some tension: Why do younger but not older Canadian children and older but not younger German children show preferences on a priming measure? Additional research will be required to identify the age-Â�related, cultural, or methodological differences driving these results. As we describe in more depth later, one limitation of the IAT for developmental researchers is that it is simply too difficult for young children to perform. Therefore, Dunham and Banaji (2009) have modified another implicit task (Hugenberg & Bodenhausen, 2004), which has been shown to correlate with IAT performance in adults, for use with children. In this task, individuals as young as age 3 through adulthood were asked to categorize faces as either black or white. The faces were racially ambiguous and were smiling or angry. Past work with adults demonstrated a bias such that faces that are angry are more likely to be seen as black. Dunham and Banaji found that this bias exists in children as young as 3, and that the level of bias was invariant through adulthood, suggesting that children’s implicit race bias emerges early and at levels nearly identical to that of adults in the same society. By establishing when implicit measures can be used, that they yield interpretable patterns of results in children, and by charting population means across ages, these studies of intergroup attitudes have laid the groundwork for additional inquiry. Two studies assessed the role of social status on implicit attitudes while using more diverse samples. In a cross-Â�cultural comparison of whites in the United States and Japanese in Japan, researchers



13. The Development of Implicit Social Cognition

found that children from both countries showed the same level of implicit ingroup preference, and that the level of ingroup bias was the same irrespective of the status of the outgroup (white/Japanese vs. black), despite the fact that adults in both cultures showed a sensitivity to the outgroup’s status on the same implicit measures (Dunham, Baron, & Banaji, 2006). In other words, children’s implicit attitudes appeared to reflect a more basic ingroup–Â�outgroup dichotomy rather than the nuanced representation of status of multiple groups that adults demonstrated. A related study (Dunham, Baron, & Banaji, 2007) investigated Hispanic children (and adults’) implicit attitudes toward white (high-Â�status) and black (low-Â�status) outgroups. When blacks were the outgroup, Hispanic children showed an ingroup preference on the IAT; however, when whites were the outgroup, they demonstrated no overall preference, suggesting that at least some aspects of status sensitivity may emerge earlier in minority compared with majority children (who, as noted previously, did not display status sensitivity in their implicit attitudes until sometime after age 10). However, verifying this conclusion will require unconfounding status and familiarity, which are difficult to disentangle in majority–Â� minority interactions in the United States, where it is plausible that children are more familiar with the majority, high-Â�status group than other minority groups. This study additionally assessed implicit ethnic identification and self-Â�esteem; the relationships between these tasks provided some, albeit weak, evidence of balanced identity theory (Greenwald et al., 2002) in children.

Impact of Social Context A second question addressed by several studies of implicit attitudes in children has been the role of social experiences on implicit attitudes. For example, consistent with intergroup contact theory (Allport, 1954; Pettigrew, 1998), Turner, Hewstone, and Voci (2007) found that increased intergroup contact was related to less implicit ethnic bias in 8- to 11-year-old white children in the United Kingdom. Studies have also assessed the impact of parental attitudes on children’s attitudes. Prior research focusing on explicit attitudes has generally failed to find such relationships (e.g., Aboud & Doyle, 1996), but Sinclair, Dunn, and Lowery (2005) found that the implicit attitudes of children were related to parents’ (mostly mothers’) explicit racial attitudes, but only for those children

247

who were highly identified with their parents. A similar study (Castelli, Zogmaister, & Tomelleri, 2009) revealed a connection between mothers’ implicit race attitudes and preschoolers’ explicit race attitudes.

Retrospective Accounts Finally, although not directly investigating developmental attitudes, retrospective studies with adults also suggest an important role of early experience in affecting implicit attitudes. For example, smokers’ implicit attitudes toward smoking were correlated with the valence associated with their earliest smoking-Â�related memories (Rudman & Heppen, 2001), and early (retrospective) attachment to maternal caregivers was associated with more pro-Â�female implicit attitudes (Rudman & Goodwin, 2004). This retrospective approach has shown similar results in the domains of selfÂ�esteem, obesity, and dreams (Rudman, 2004; Rudman, Phelan, & Heppen, 2007).

Common Intergroup Findings The burgeoning study of the development of implicit attitudes is certain to be one of the most exciting topics for study in the development of implicit social cognition, much as the adult study of implicit attitudes has been one of the most productive and prolific areas of research in social cognition as a whole. These initial studies suggest a few very interesting conclusions. First, implicit attitudes appear to emerge early—by age 5 or 6 and perhaps as early as age 3 (Dunham & Banaji, 2009). Second, there is relative stability in the degree of implicit bias from childhood into adulthood (although alternative accounts are possible; see later discussion), in stark contrast to the degree of explicit bias, which undergoes a developmental decline in majority group children (Aboud, 1988). When changes were observed over time, these often involved an indication that what might have initially been a simple ingroup–Â� outgroup distinction differentiated into high- and low-Â�status outgroups over time (Dunham et al., 2006, 2008). Work in this area is now poised to address several important topics, such as the development of self-Â�presentation concerns and executive control, the ability to inhibit prejudicial responses, and the early origins of intergroup biases more broadly. Thus, it is a safe bet that it will continue to be an active area of research.

248

III. CROSS-CUTTING PERSPECTIVES

Clinical–Â�Developmental Uses of Implicit Social Cognition The second domain using implicit measures across development has been clinical psychology, in this case to assess mental representations related to mental and physical disorders and conditions. This approach has been most popular within the study of fears, suicidal and self-Â�injurious behavior, and obesity, although a handful of other studies have also used this approach. Across these studies, the IAT has been the most common measure of implicit cognition, although other measures, including the EAST (De Houwer, 2003) and affective priming (Fazio, Sanbonmatsu, Powell, & Kardes, 1986), have been used. We review these studies briefly because the literature on clinical uses of implicit measures is summarized elsewhere in this volume (Teachman, Cody, & Clerkin, Chapter 26).

Fears A group of researchers have begun to use implicit measures to study fears in childhood. Two studies have been designed to test whether learning information from another person can induce new implicit “fears” (the distinction between fears and what social psychologists would call “attitudes” is ambiguous) in young children and adolescents. Both studies involved an informant telling the child negative, positive, or no information about either a novel animal (Field & Lawson, 2003) or an unusual social situation (Lawson, Banerjee, & Field, 2007). Both studies found that children’s implicit attitudes as assessed by the IAT (Field & Lawson, 2003) or affective priming (Lawson et al., 2007) toward these animals and situations were affected by this learning, in that they preferred the animals and situations associated with positive information and disfavored the animals and situations associated with negative information. The authors argue that these studies are clinically useful because they demonstrate that children can learn fears from information provided by another person (rather than firsthand experience), and that the assessment of these fears via implicit measures is important because it rules out experimental demand (but see De Houwer, Beckers, & Moors, 2007, for work suggesting that implicit measures can be faked). These findings are also interesting to researchers of implicit social cognition, in that they demonstrate that implicit attitudes toward novel things can be developed in a matter of seconds

and based only on word of mouth (for a related point in adults, see Gregg, Seibt, & Banaji, 2006). As with the broad pattern of developmental stability in children’s implicit attitudes, these findings at least generally favor models of implicit attitude formation that do not rely on a protracted learning history.

Suicide and Self-�Injury A second clinical area benefiting from the use of implicit measures has been the study of suicide and self-�injury. The findings in this area are particularly interesting to implicit social cognition researchers because these represent the only cases of predictive validity with child participants. Additionally, clinically this is an important domain for studying implicit cognition because people who are suicidal or self-�injurious often will not report these tendencies to clinicians, making it difficult for clinicians to distinguish individuals who are ready to be released (no longer suicidal) from those who just want to be released (but are still suicidal). Researchers have demonstrated that self-�injurers and non-self-�injurers (Nock & Banaji, 2007a) as well as nonsuicidal people, suicidal ideators, and recent suicide attemptors (Nock & Banaji, 2007b) are categorically distinguishable using a self-�injury IAT (demonstrating known-�groups validity). Demonstrating the predictive validity of these measures, implicit attitudes toward self-�injury improved the prediction of self-�injurious behavior over and above the predictive power of psychiatric and demographic variables. The self-�injury IAT significantly predicted suicidal thoughts as well as attempts at the time of the initial study and at a 6-month follow-up and predicted suicide ideation at the follow-up even after controlling for baseline ideation and a range of other demographic and psychiatric factors. Perhaps most provocatively, the self-�injury IAT scores of the two individuals who attempted suicide during the 6 months after the study showed significantly higher IAT scores than the rest of the sample, providing a potentially powerful avenue for identifying particularly highrisk patients.

Obesity and Related Phenomena The third main area in which implicit measures have been applied to clinical questions is in the study of obesity. Mietje Craeynest and colleagues have conducted a series of studies examining implicit and explicit cognition concerning food and exercise in children and adolescents with and



13. The Development of Implicit Social Cognition

without obesity. Two studies found that obese and nonobese children and adolescents do not differ in their explicit attitudes about fat-free versus fatty foods, yet they do differ in their implicit food-�related cognition (Craeynest et al., 2005; Craeynest, Crombez, De Houwer, Deforche, & De Bourdeaudhuij, 2006). Using the EAST (De Houwer, 2003), the researchers found that children and adolescents with obesity showed a stronger general preference for food (irrespective of whether it was healthy or unhealthy) than their normal-�weight peers (Craeynest et al., 2005). Additionally, children and adolescents without obesity identified themselves with nonfat food on the IAT, whereas obese children and adolescents implicitly identified equally with fat and nonfat foods (Craeynest et al., 2006). Obesity was not associated with differences in implicit or explicit cognitions (attitudes or identification) concerning strenuous or sedentary exercise in either study. In a longitudinal follow-up to their 2005 study, assessing the impact of a residential treatment program on explicit and implicit food and exercise attitudes in children and adolescents with obesity, Craynest, Crombez, Deforche, Tanghe, and De Bourdeaudhuij (2008) found that increasingly positive implicit attitudes toward healthy food across treatment were associated with losing weight, but they found no relationship between explicit attitudes and weight loss.

Other Clinical Applications Two other clinically relevant developmental studies have used implicit measures. Rosen, Milich, and Harris (2007) found that more frequent selfÂ�reported victimization and self-Â�reported distress in discussing victimization were associated with a stronger implicit association between the self and victimization. Sandstrom and Jordan (2008) demonstrated the use of implicit measures not only as a clinically relevant dependent variable but as an independent variable as well. They investigated whether self-Â�reported high self-Â�esteem included two clinically distinct subtypes—those with “genuine” high self-Â�esteem and those with “defensive” self-Â�esteem—or a tendency to mask low implicit self-Â�esteem with high explicit self-Â�esteem. The authors found that the latter, but not the former, were more likely to be aggressive.

Common Clinical Findings and€Open€Questions A few themes emerged from the developmental clinical work on implicit cognition. Perhaps most

249

importantly, these findings suggested the importance of assessing both implicit and explicit constructs. For example, it was only the union of high explicit self-�esteem with low implicit self-�esteem that predicted increased aggression in adolescents (Sandstrom & Jordan, 2008), such that only measuring one of these factors would not have predicted aggression at all. A second theme was the often unique contribution of implicit measures. For example, changes in implicit food attitudes predicted changes in weight loss over time in children with obesity better than explicit food attitudes. Additionally, performance on the self-�injury identification IAT predicted self�injurious and suicidal ideation over and above traditional predictors such as psychiatric symptoms and demographics, suggesting that it did not merely replicate other predictors of suicidal ideation. A final theme is that implicit attitudes can emerge in children quite rapidly, following a single experimental manipulation such as a story about dangerous animals. As noted, this places some limits on theories that characterize implicit attitudes as the result of slow-�learned cultural associations culled from environmental regularities.

The Far Side of Development: Older€Adults Although nearly all of the work described so far has concerned implicit attitudes in children, a chapter on development would be remiss if it excluded mention of implicit social cognition in older adults. The work to date with older adults suggests the critical importance of understanding the role of self-Â�regulatory abilities on implicit measures because slowed responding and a reduced ability to control responses can lead to artificially inflated effects. To our knowledge, three studies have compared the implicit attitudes of younger and older adults. The first (Hummert, Gartska, O’Brien, Greenwald, & Mellott, 2002) examined older and younger adults’ age attitudes, identity, and self-Â�esteem as well as attitudes toward age-Â�neutral stimuli, flowers, and insects. The critical contribution of this work was the demonstration that older adults’ implicit attitudes, identity, and self-Â�esteem were inflated compared with their younger counterparts, an effect the authors felt was likely due to their lower inhibition abilities. Importantly, Hummert and colleagues suggested that simple statistical corrections (z-score transformation) could result in more interpretable results. The general finding that older adults show an inflated IAT score was

250

III. CROSS-CUTTING PERSPECTIVES

also found by Greenwald and Nosek in an analysis of data collected on the Web (2001). Advancing on this first investigation, two recent studies provide quantitative evidence that older adults’ observed greater automatic race bias (relative to their younger adult counterparts) is the result of their limited ability to regulate automatic associations rather than their actually having stronger race bias. One of these studies (Stewart, von Hippel, & Radvansky, 2009) employed Jacoby’s (1991, 1998) process dissociation procedure, which uses error rates to dissociate automatic and controlled responding, and concluded that, although underlying associations did not appear to increase in strength with age, control did decline with age, thus leading to larger IAT scores in older adults. In a similar vein, the second study in this area (Gonsalkorale, Sherman, & Klauer, 2009) used the quadruple process model (Conrey et al., 2005) to assess independently several hypothesized automatic and controlled components of IAT responding. This procedure also revealed that the primary age-Â�related change driving higher increased IAT scores was the ability to overcome or regulate bias, not the strength of underlying associations. Taken together, these new analytic techniques offer exciting possibilities for developmental research, a topic to which we return later.

LESSONS IN PROGRESS Can Effects across Age Be Compared? As we have suggested throughout this chapter, one primary concern, and one echoed by the older adult work, is what we can conclude from previous findings of age invariance in these implicit measures across development. There is reason to believe that children, like older adults, have more difficulty inhibiting responses and, therefore, that their implicit effects could possibly be significantly inflated. Consider that children undergo considerable change with respect to inhibitory control and task-�switching ability (e.g., Dibbets & Jolles, 2006; Zelazo, 2000), skills that are directly involved in most methods of implicit measurement. There are at least a few ways to address this potential problem. A stop-gap strategy is to attempt to statistically correct for differential response latencies or error rates that may be associated with age (as Hummert at al., 2002, did). However, a better strategy would likely involve modeling the relative contributions of automatic and controlled processes using a strategy such as process dissociation (Jacoby 1991, 1998; Payne, 2001) or the Quad model

(Conrey et al., 2005), which can provide independent estimates of the contribution of automatic and controlled components of responding. Efforts to reanalyze existing developmental data to model these processes are currently under way (Dunham, Skorek, & Sherman, 2009).

Methods To make implicit measures possible for use with child participants, many methodological innovations have been used. Here we primarily discuss changes that have been made to the IAT to make it more child friendly because the vast majority of work with children has involved that measure. Perhaps the problem that emerges first in attempting to conduct research with young children is their varying levels of literacy. Because most implicit measures involve reading and categorizing words, this introduces the possibility that a generally unmeasured variable (reading ability) would influence reaction times. To deal with this problem, a few approaches have been developed, including the audio presentation of stimuli words and the pictorial presentation of category words (e.g., Baron & Banaji, 2006) and an all-Â�pictorial presentation of stimuli (e.g., Rutland et al., 2005; Williams et al., 2009). The latter has been used in cross-Â�cultural studies in which not all participants are literate or have the same native language (Olson, Shutts, Spelke, & Banaji, 2008). To date, no studies have directly compared these procedures or attempted to assess which method is superior. Decisions regarding stimulus modality have important consequences for data analysis. Because there are different processing speeds associated with visual and auditory stimuli, the data from each modality essentially create two overlapping distributions of response latencies. The data analytic strategy used with these data has involved computing and averaging two effect sizes, one for auditory stimuli and one for visual stimuli (Baron & Banaji, 2006; Dunham et al., 2006; Dunham, Baron, & Banaji, 2007). The potential disadvantage of additional statistical complexity should be considered alongside the potentially greater flexibility of the auditory approach for measuring more diverse constructs. For example, it is not obvious what pictorial stimuli can represent the self–other words in a self-Â�esteem or identity IAT; aural presentation can easily accommodate the standard stimuli in these tests. Still, it is clear that additional work is needed to evaluate the psychometric properties following from these methodological decisions.



13. The Development of Implicit Social Cognition

Another question that is often raised when converting the IAT to a more developmentally appropriate measure concerns the number of trials one should use with children. Initial intuitions suggested that children would need more trials because their data are likely to be noisier. However, children also have difficulty with sustained attention tasks, providing some reason to believe that fewer trials will result in a more accurate IAT effect. That said, in the literature, it is most common for researchers to use the typical adult-�length IAT, although some efforts are underway in our labs to develop a shorter IAT modeled after the shorter adult procedure (Sriram & Greenwald, 2009). Here, too, what we would really want is careful work comparing different methodological decisions with specific criteria to more definitively establish evidence for or against different IAT and other implicit measure variants. Perhaps the largest methodological problem, and one that remains unsolved, concerns the development of measures for children younger than 5 or 6 years. If the studies to date have taught us one thing, it is that attitudes develop early. Across many studies in many different populations, researchers observed that by age 5 or 6 children already had clear implicit attitudes, often as strong and in the same direction as adults in their populations. This indicates that attitudes emerge before this age, yet because of cognitive and executive function limitations children younger than this age cannot perform the typical measures we have relied on in adult implicit social cognition research (e.g., the IAT). Therefore, if we are ever to answer questions about when attitudes, stereotypes, and other social cognitive constructs emerge in the natural development of children, one of the first agenda items must be to develop better measures. Some attempts toward this end are in progress (i.e., the ambiguous-race face task described previously; Dunham & Banaji, 2009), but more stringent tests will need to be conducted to assess whether these measures and others that will be developed meet the necessary criteria of reliability and validity. Finally, different research teams have tried different response options. For example, Rutland and colleagues (2005) have used a design in which children move a mouse forward and backward to assign stimuli to categories, while Baron, Dunham, and colleagues (e.g., Baron & Banaji, 2006; Dunham et al., 2007) have had children use large colored buttons to respond. These colored buttons correspond to colored columns on the two sides of the screen within which the category labels reside. Finally, one of the authors has found that simply

251

putting a sticker on two keys of a regular laptop keyboard as reminders of which keys to press works well with children as young as 5 years (as well as with nonliterate adults and adults and children with limited or no exposure to computers; Olson et al., 2008). Again, the differences between these methods have not been empirically examined.

Reliability and Validity As we previously alluded to, one question that has barely been discussed in the developmental literature concerns reliability and validity of implicit measures in children. Although the IAT in adults has been shown to exhibit moderate test–Â�retest reliability (Bosson, Swann, & Pennebaker, 2000), acceptable convergent and discriminant validity (Banse, Seise, & Zerbes, 2001), high internal consistency (Steffens, 2005), and significant predictive validity (Greenwald, Poehlman, Uhlmann, & Banaji, 2009), it cannot be assumed that there would be similar rates of reliability and validity in these measures with children. Indeed, most measures rely on reaction time as well as some degree of response inhibition (e.g., to “unlearn” a response–key pairing in the second critical block in the IAT). Also as alluded to, we have every reason to believe these processes undergo substantial developmental change (Levin et al., 1991), raising the specter of a potential confound in developmental research. The only studies relevant to reliability and validity include the self-Â�injury IAT’s predictive validity described previously (Nock & Banaji, 2007a, 2007b) and an (as yet) unpublished study demonstrating a test–Â�retest reliability of r = .42 and an internal consistency estimate of r = .74 on a child all-Â�picture IAT (Williams & Steele, 2009). Until more studies exploring this issue are completed, pressing questions will remain.

CONCLUSION A developmental approach to the study of implicit social cognition will ultimately allow us to better understand the phenomena we care about and create theories that are better able to account for existing data (see also Dunham & Olson, 2008). This is accomplished by considering evidence not only from the end state but from across the ages that marks the emergence and maturation of constructs of interest. We see perhaps the most important question to be the one we opened with: What can development can tell us about the ontogenetic history of implicit constructs? For example, what

252

III. CROSS-CUTTING PERSPECTIVES

can development tell us about the interaction between implicit and explicit processes throughout development, and how does that interaction relate to the emergence, trajectory, and malleability of these constructs as they appear in adulthood? These are promising questions for future research. Thus, we strongly encourage those looking to pursue questions such as these to find developmental colleagues with whom to collaborate in order to design the best measures, make age-Â�appropriate conclusions, and accrue relevant background knowledge and encourage social psychology students to receive training in developmental methodologies. In so doing, we believe a developmental approach can fit squarely in the domain of social psychology, joining methods such as functional magnetic resonance imaging, psychophysiology, and reaction time measures in the social psychologists’ toolbox. Most important to the present discussion, we see development as uniquely important in the building of theories concerning the distinction between implicit and explicit processes and representations. This is surely one of the most interesting directions in both developmental and social psychology. References Aboud, F. E. (1988). Children and prejudice. New York: Blackwell. Aboud, F. E., & Doyle, A. B. (1996). Parental and peer influences on children’s racial attitudes. International Journal of Intercultural Relations, 20, 371–383. Allport, G. W. (1954). The nature of prejudice. Cambridge, MA: Perseus Books. Anderson, J. R. (1992). Automaticity and the ACT* theory. American Journal of Psychology, 105, 165– 180. Banse, R., Gawronski, B., Rebetez, C., Gutt, H., & Morton, J. B. (in press). The development of spontaneous gender stereotyping in childhood: Relations to stereotype knowledge and stereotype flexibility. Developmental Science. Banse, R., Seise, J., & Zerbes, N. (2001). Implicit attitudes towards homosexuality: Reliability, validity, and controllability of the IAT. Zeitschrift fur Experimentelle Psychologie, 48, 145–160. Bar-Haim, Y., Ziv, T., Lamy, D., & Hodes, R.M. (2006). Nature and nurture in own-race face processing. Psychological Science, 17, 159–163. Bargh, J. A. (1994). The four horsemen of automaticity: Awareness, intention, efficiency, control in social cognition. In R. S. Wyer, Jr. & T. K. Srull (Eds.), Handbook of social cognition, Vol. 1: Basic processes (pp.€1–40). Hillsdale, NJ: Erlbaum. Baron, A. S., & Banaji, M. R. (2006). The development of implicit attitudes: Evidence of race evaluations from ages 6 and 10 and adulthood. Psychological Science, 17, 53–58.

Baron, A. S., Dunham, Y., & Banaji, M. R. (2009). Asymmetry in the development of implicit and explicit gender attitudes. Manuscript in preparation. Bigler, R. S., & Liben, L. S. (2006). A developmental intergroup theory of social stereotypes and prejudice. In R. V. Kail (Ed.), Advances in child development and behavior (Vol. 34, pp.€ 39–89). San Diego, CA: Elsevier. Bosson, J. K., Swann, W. B., Jr., & Pennebaker, J. W. (2000). Stalking the perfect measure of implicit self-Â�esteem: The blind men and the elephant revisited. Journal of Personality and Social Psychology, 79, 631–643. Castelli, L., Zogmaister, C., & Tomelleri, S. (2009). The transmission of racial attitudes within the family. Developmental Psychology, 45, 586–591. Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K., & Groom, C. (2005). Separating multiple processes in implicit social cognition: The quadmodel of implicit task performance. Journal of Personality and Social Psychology, 89, 469–487. Craeynest, M., Crombez, G., De Houwer, J., Deforche, B., & De Bourdeaudhuij, I. (2006). Do children with obesity implicitly identify with sedentariness and fat food? Journal of Behavior Therapy and Experimental Psychiatry, 37, 347–357. Craeynest, M., Crombez, G., De Houwer, J., Deforche, B., Tanghe, A., & De Bourdeaudhuij, I. (2005). Explicit and implicit attitudes towards food and physical activity in childhood obesity. Behaviour Research and Therapy, 43, 1111–1120. Craeynest, M., Crombez, G., Deforche, B., Tanghe, A., & De Bourdeaudhuij, I. (2008). The role of implicit attitudes towards food and physical activity in the treatment of youth obesity. Eating Behaviors, 9, 41–51. Davis, S. C., Leman, P. J., & Barrett, M. (2007). Children’s implicit and explicit ethnic group attitudes, ethnic group identification, and self-Â�esteem. International Journal of Behavioral Development, 31, 514–525. De Houwer, J. (2003). The Extrinsic Affective Simon Task. Experimental Psychology, 50, 77–85. De Houwer, J. (2006). What are implicit measures and why are we using them? In R. W. Wiers & A. W. Stacy (Eds.), Handbook of implicit cognition and addiction (pp.€11–28). Thousand Oaks, CA: Sage. De Houwer, J., Beckers, T., & Moors, A. (2007). Novel attitudes can be faked on the Implicit Association Test. Journal of Experimental Social Psychology, 43, 972–978. Degner, J., & Wentura, D. (in press). Automatic prejudice in childhood and early adolescence. Journal of Personality and Social Psychology. Dibbets, P., & Jolles, J. (2006). The switch task for children: Measuring mental flexibility in young children. Cognitive Development, 21, 60–71. Duckworth, K. L., Bargh, J. A., Garcia, M., & Chaiken, S. (2002). The automatic evaluation of novel stimuli. Psychological Science, 13, 513–519. Dunham, Y., & Banaji, M. R. (2009). The invariance of intergroup bias across the lifespan. Manuscript submitted for publication. Dunham, Y., Baron, A., & Banaji, M. R. (2006). From American city to Japanese village: A cross-Â�cultural



13. The Development of Implicit Social Cognition

investigation of implicit race attitudes. Child Development, 77, 1268–1281. Dunham, Y., Baron, A., & Banaji, M. R. (2007). The person and the group: A developmental analysis of implicit consistency among Hispanic-Â�Americans. Self and Identity, 6, 238–255. Dunham, Y., Baron, A. S., & Banaji, M. R. (2008). The development of implicit intergroup cognition. Trends in Cognitive Sciences, 12, 248–253. Dunham, Y., & Olson, K. R. (2008). The importance of origins: Why cognitive development is central to a mature understanding of social cognition. The Open Psychology Journal, 1, 59–65. Dunham, Y., Skorek, M., & Sherman, J. (2009). The independent development of automatic and controlled components of implicit race bias. Manuscript in preparation. Fazio, R. H., Eiser, J. R., & Shook, N. J. (2004). Attitude formation through exploration: Valence asymmetries. Journal of Personality and Social Psychology, 87, 293–311. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. Field, A. P., & Lawson, J. (2003). Fear information and the development of fears during childhood: Effects on implicit fear responses and behavioural avoidance. Behaviour Research and Therapy, 41, 1277–1293. Gonsalkorale, K., Sherman, J. W., & Klauer, K. C. (2009). Aging and prejudice: Diminished regulation of automatic race bias among older adults. Journal of Experimental Social Psychology, 45, 410–414. Greenwald, A. G., Banaji, M. R., Rudman, L. A., Farnham, S. D., Nosek, B. A., & Mellott, D. S. (2002). A unified theory of implicit attitudes, stereotypes, selfÂ�esteem, and self-Â�concept. Psychological Review, 109, 3–25. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Greenwald, A. G., & Nosek, B. A. (2001). Health of the Implicit Association Test at age 3. Zeitschrift für Experimentelle Psychologie, 48, 85–93. Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the Implicit Association Test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85, 197–216. Greenwald, A. G., Poehlman, T. A., Uhlmann, E., & Banaji, M. R. (2009). Understanding and using the Implicit Association Test: III. Meta-Â�analysis of predictive validity. Journal of Personality and Social Psychology, 97, 17–41. Gregg, A. P., Seibt, B., & Banaji, M. R. (2006). Easier done than undone: Asymmetry in the malleability of implicit preferences. Journal of Personality and Social Psychology, 90, 1–20. Hugenberg, K., & Bodenhausen, G. V. (2004). Ambiguity in social categorization: The role of prejudice and facial affect in race categorization. Psychological Science, 15, 342–345. Hummert, M. L., Gartska, R. A., O’Brien, L. S., Greenwald, A. G., & Mellott, D. S. (2002). Using the im-

253

plicit association test to measure age differences in implicit social cognitions. Psychology and Aging, 17, 482–495. Jacoby, L. L. (1991). A process dissociation framework: Separating automatic from intentional uses of memory. Journal of Memory and Language, 30, 513–541. Jacoby, L. L. (1998). Invariance in automatic influÂ� ences€ of memory: Towards a user’s guide for the process-Â�dissociation procedure. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 3–26. Karmiloff-Smith, A. (1996). Beyond modularity: A developmental perspective on cognitive science. Cambridge, MA: MIT Press. Kinzler, K. D., Dupoux, E., & Spelke, E. S. (2007). The native language of social cognition. Proceedings of the National Academy of Sciences USA, 104, 12577– 12680. Lawson, J., Banerjee, R., & Field, A. P. (2007). The effects of verbal information on children’s fear beliefs about social situations. Behaviour Research and Therapy, 45, 21–37. Leslie, A. (1987). Pretense and representation: The origins of theory of mind. Psychological Review, 94, 412–426. Levin, H. S., Culhane, K. A., Hartmann, J., Evankovich, K., Mattson, A. J., Harward, H., et al. (1991). Developmental changes in performance on tests of purported frontal lobe functioning. Developmental Neuropsychology, 7, 377–395. Logan, G. D. (1992). Attention and preattention in theories of automaticity. American Journal of Psychology, 105, 317–339. McGlothlin, H., & Killen, M. (2006). Intergroup attitudes of European American children attending ethnically homogenous schools. Child Development, 77, 1375–1386. McGlothlin, H., Killen, M., & Edmonds, C. (2005). European-Â�American children’s intergroup attitudes about peer relationships. British Journal of Developmental Psychology, 23, 227–249. Moors, A., & De Houwer, J. (2006). Automaticity: A theoretical and conceptual analysis. Psychological Bulletin, 132, 297–326. Nock, M. K., & Banaji, M. R. (2007a). Assessment of self-Â�injurious thoughts using a behavioral test. American Journal of Psychiatry, 164, 820–823. Nock, M. K., & Banaji, M. R. (2007b). Prediction of suicide ideation and attempts among adolescents using a brief performance-based test. Journal of Consulting and Clinical Psychology, 75, 707–715. Olson, K. R., Shutts, K., Spelke, E. S., & Banaji, M. R. (2008, February). Implicit intergroup attitudes in South Africa. Poster presented at the annual conference of the Society for Personality and Social Psychology, Albuquerque, NM. Olson, M. A., & Fazio, R. H. (2003). Implicit measures in social cognition research: Their meaning and uses. Annual Review of Psychology, 54, 297–327. Pascalis, O., de Schonen, S., Morton, J., Deruelle, C., & Fabre-Â�Grenet, M. (1995). Mother’s face recognition by neonates: A replication and an extension. Infant Behavior and Development, 18, 79–85. Pascalis, O., & Slater, A. O. (2003). The development of

254

III. CROSS-CUTTING PERSPECTIVES

face processing in infancy and early childhood: Current perspectives. Hauppauge, NY: Nova Science. Payne, B. K. (2001). Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon. Journal of Personality and Social Psychology, 81, 181–192. Pettigrew, T. F. (1998). Intergroup contact: Theory, research and new perspectives. Annual Review of Psychology, 49, 65–85. Quinn, P. C., Yahr, J., Kuhn, A., Slater, A. M., & Pascalis, O. (2002). Representation of the gender of human faces by infants: A preference for female. Perception, 31, 1109–1121. Rosen, P. J., Milich, R., & Harris, M. J. (2007). Victims of their own cognitions: Implicit social cognitions, emotional distress, and peer victimization. Journal of Applied Developmental Psychology, 28, 211–236. Rudman, L. A. (2004). Sources of implicit attitudes. Current Directions in Psychological Science, 13, 79– 82. Rudman, L. A., & Goodwin, S. A. (2004). Gender differences in automatic in-group bias: Why do women like women more than men like men? Journal of Personality and Social Psychology, 87, 494–509. Rudman, L. A., & Heppen, J. (2001). The smoking gun: Implicit and explicit attitudes towards smoking. Unpublished manuscript. Rudman, L. A., Phelan, J. E., & Heppen, J. B. (2007). Developmental sources of implicit attitudes. Personality and Social Psychology Bulletin, 33, 1700–1713. Rutland, A., Cameron, L., Milne, A., & McGeorge, P. (2005). Social norms and self-Â�presentation: Children’s implicit and explicit intergroup attitudes. Child Development, 76, 451–466. Sandstrom, M. J., & Jordan, R. (2008). Defensive selfÂ�esteem and aggression in childhood. Journal of Research in Personality, 42, 506–514. Shaw, P., Kabani, N. J., Lerch, J. P., Eckstrand, K., Lenroot, R., Gogtay, N., et al. (2008). Neurodevelopmental trajectories of the human cerebral cortex. Journal of Neuroscience, 28, 3586–3594. Sherman, J. W. (1996). Development and mental representation of stereotypes. Journal of Personality and Social Psychology, 70, 1126–1141. Signorella, M. L., Bigler, R. S., & Liben, L. S. (1997). A meta-Â�analysis of children’s memories for own-sex and other-sex information. Journal of Applied Developmental Psychology, 18, 429–445.

Sinclair, S., Dunn, E., & Lowery, B. S. (2005). The relationship between parental racial attitudes and children’s implicit prejudice. Journal of Experimental Social Psychology, 41, 283–289. Skowronski, J. J., & Lawrence, M. A. (2001). A comparative study of the implicit and explicit gender attitudes of children and college students. Psychology of Women Quarterly, 25, 155–165. Smith, E. R., & DeCoster, J. (2000). Dual-Â�process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4, 108–131. Sperber, D. (1996). Explaining culture: A naturalistic approach. London: Wiley-Â�Blackwell. Sriram, N., & Greenwald, A. G. (2009). The brief Implicit Association Test. Experimental Psychology, 56, 283–294. Stahl, C., & Degner, J. (2007). Assessing automatic activation of valence: A multinomial model of EAST performance. Experimental Psychology, 54, 99–112. Steffens, M. (2005). Implicit and explicit attitudes towards lesbians and gay men. Journal of Homosexuality, 49, 39–66. Stewart, B. D., von Hippel, W., & Radvansky, G. A. (2009). Age, race, and implicit prejudice: Using process dissociation to separate the underlying components. Psychological Science, 20, 164–168. Teichmen, Y. (2001). The development of Israeli children’s images of Jews and Arabs and their expression in human figure drawings. Developmental Psychology, 37, 749–761. Tomasello, M. (2001). The cultural origins of human cognition. Cambridge, MA: Harvard University Press. Turner, R. N., Hewstone, M., & Voci, A. (2007). Reducing explicit and implicit outgroup prejudice via direct and extended contact: The mediating role of self-Â�disclosure and intergroup anxiety. Journal of Personality and Social Psychology, 93, 369–388. Williams, A., & Steele, J. R. (2009, February). The reliability of a child-Â�friendly race-IAT. Poster presented at the annual meeting convention of the Society for Personality and Social Psychology, Tampa, FL. Williams, A., Steele, J. R., & Durante, S. (2009). The development of implicit racial bias among 6- and 9-yearolds: Is forced categorization by race a precondition for bias? Manuscript submitted for publication. Zelazo, P. (2000). The development of executive function in early childhood. New York: Wiley-Â�Blackwell.

C h a p t e r 14

Prediction of Behavior Marco Perugini, Juliette Richetin, and Cristina Zogmaister

O

ne of the hallmarks of a good measure is predictive validity. Ultimately, to paraphrase an often-cited quote, measuring is for doing. Among the infinite measures that could be taken of any kind of psychological process or phenomenon, the ones that will spread most and stand the judgment of time are those that best capture the phenomenon, help to understand its functioning, and, especially, help to predict its consequences. Therefore, establishing the predictive validity of a psychological measure assumes a paramount importance. In this chapter we review research related to the predictive validity of implicit–Â�indirect measures. At the onset we clarify a few points that characterize the logic and purposes of this chapter. First, we aim to provide a conceptual framework through which the issue of predictive validity of implicit measures can be understood. We do so by initially briefly summarizing two major comprehensive models of implicit and explicit cognitions, then specifying simple general patterns of predictive validity, and finally reviewing the accumulated empirical evidence. We do not aim at an empirical quantification of the predictive validity of implicit measures. Readers interested in this aspect should consult, for instance, the meta-Â�analysis of the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) by Greenwald, Poehlman, Uhlmann, and Banaji (2009; see also Rooke, Hine, &



Thorsteinsson, 2008, for a meta-�analysis on implicit measures and substance use). Second, the empirical evidence is reviewed mainly in terms of conditions under which implicit measures predict behavior. In this sense, this chapter has a similar logic to the recent review by Friese, Hofmann, and Schmitt (2008). However, the review by Friese and colleagues is organized around a specific theoretical model, whereas the current chapter is organized around factors that have been shown to have a moderating influence. Third, this chapter aims also at discussing a few methodological issues that can be important in research establishing the predictive validity of implicit measures. Although probably there is little entirely new or groundbreaking, methodological aspects are usually sparsely reported or neglected altogether in the relevant literature (but see Nosek, Greenwald, & Banaji, 2005, 2007). Finally, the underlying logic of this chapter is not to review literature to establish whether implicit measures predict behavior. The empirical evidence is already very clear in this sense: They do predict behavior. However, the issues are what type of behavior, under what conditions, for whom, and with what measure. In other words, we wholeheartedly take a conditional approach (Greenwald, Pratkanis, Leippe, & Baumgardner, 1986) to the issue of predictive validity and try to 255

256

III. CROSS-CUTTING PERSPECTIVES

highlight some important qualifying conditions. We first review two of the most influential theoretical contributions—Fazio’s (1990) motivation and opportunity as determinants (MODE) model and Strack and Deustch’s (2004) reflective and impulsive model (RIM)—by outlining the aspects that are most relevant for the issue at hand. Then we set out a simple framework of patterns of predictive validity that can help both to organize the different ways in which the predictive validity of implicit measures can be established and to understand the implications of different results. We then review studies that are relevant for the issue of predictive validity and finally address some methodological issues that are particularly relevant for implicit measures.

THEORETICAL MODELS Theoretical models tackling the relationship between cognition and behavior can be classified into two basic types, unitary and dual construct models, the most prominent of the first probably being Fazio’s MODE model (1990, 2007). The MODE model conceptualizes attitudes as associations in memory between an object and a summary evaluation of the object. These associations can vary in terms of strength and, consequently, in their accessibility from memory. The MODE model postulates that attitudes can be activated automatically from memory upon encountering the object, and thus they can prompt attitudinally consistent behavior in a spontaneous way, by biasing the perception of the object in the immediate situation. This process can occur without intentional reflections of the individuals about their attitudes and without any awareness of the influence of the attitude on behavior. According to Fazio’s model, attitude–Â�behavior consistency varies and the strength of the association is one important moderator of this relationship. Thus, all circumstances that strengthen or weaken object–Â� evaluation associations, and hence influence accessibility, should impact on the predictive validity of attitudes. Fazio (2007) emphasizes that attitudes, like any other mental content, cannot be directly observed. Responses on explicit measures (e.g., questionnaires) can be considered as verbal expressions of the attitudes that may be influenced by many factors in addition to the evaluative associations in memory (e.g., self-Â�presentation concerns, question comprehension, use of appropriate standards of comparison). Explicit measures may reflect rela-

tively spontaneous processes involving the direct influence of the evaluative association automatically activated by the object. On the other hand, when motivation and opportunity are sufficient, they may also reflect highly deliberative analyses of the object representation or a blend of automatic and deliberative processes. Implicit measures, in contrast, do not require direct reports and hence should provide less biased estimates of associations in memory. However, implicit measures are not considered as tapping into a different construct than explicit measures. The difference is in the respective degrees of controllability of the behaviors on which explicit and implicit measures are based. Strack and Deutsch’s (2004) RIM explains behavior, like the MODE model, as a joint function of deliberative and automatic processes. Differently from the MODE model, however, it postulates the presence of two separate systems of information processing: reflective and impulsive. In the reflective system, behavior follows a decision process and the subsequent activation of appropriate behavioral schemata through intention. In the impulsive system, behavioral schemata are triggered through automatic spread of activation and without the need of the individual’s intention. When an object is perceived, the impulsive system always processes it, whereas the reflective system is engaged only when the object receives a sufficient degree of attention. In this second case, the two systems operate in parallel and interact at various stages of processing, and their outputs may determine behavior in a synergistic or antagonistic fashion. Similar to the object–Â�evaluation association links in Fazio’s model, Strack and Deutsch’s model postulates that the connections between elements in the impulsive system may bias perception and influence behavior when they are activated. This may be true both for object–Â�evaluation associations that may bias perception and for object–Â�behavior associations that may directly influence behavior (and for other types of associations as well). The reflective system, in contrast, represents knowledge of the world in a propositional format, which is subject to syllogistic rules of inference to generate new knowledge. The RIM postulates that both the reflective and the impulsive systems affect behavior by activating common behavioral schemata, the core assumption being that if a certain behavioral schemata is activated above a certain threshold, behavior is enacted. If the two systems activate incompatible schemata, conflict may arise (e.g., between lighting another cigarette following a habitual behavior



14. Prediction of Behavior

and throwing away the half-full pack following the intention to quit smoking), and the specific circumstances (e.g., characteristics of the situation, of the behavior, available cognitive resources) will determine which behavioral schemata will prevail. According to RIM, explicit measures tap into the reflective system and are an expression of individuals’ knowledge and beliefs, whereas implicit measures are the expression of the associative structures that characterize the impulsive system. Based on these two main different theoretical frameworks about explicit and implicit attitudes and their relation with behaviors, it is then possible to articulate different patterns of prediction.

PATTERNS OF PREDICTION Establishing predictive validity in its simplest form entails a significant association between a measure and a validity criterion. However, this simple associative pattern deals with predictive validity in isolation and does not take into account the broader theoretical context within which implicit measures are better understood. In fact, given the underlying social cognitive theoretical framework (see Deutsch & Strack, Chapter 4, this volume), properly establishing the predictive validity of implicit measures requires considering them alongside germane explicit measures (e.g., semantic differentials, self-�report questionnaires) and analyzing different validity patterns in predicting relevant criteria (e.g., judgments, behaviors). Perugini (2005) proposed a framework in which he described three main validity patterns (additive, double dissociation, and interactive) of explicit and implicit attitudes in predicting behavior. We expand that framework so to provide a more comprehensive overview of patterns of prediction that allow better positioning on the issue of predictive validity of implicit measures.

Simple Association Pattern The simple association corresponds to the simplest pattern with a single measure predicting a single behavior (Figure 14.1a). It is probably the most common pattern tested in research on predictive validity of implicit measures for many behaviors (e.g., Arcuri, Castelli, Galdi, Zogmaister, & Amadori, 2008; Czopp, Monteith, Zimmerman, & Lynam, 2004; Egloff & Schmukle, 2002), and many examples can be found in the section “Conditions under Which Implicit Measures Predict Behavior.”

257

Moderation Pattern The simple association pattern can be qualified by looking for conditions under which predictive validity is enhanced, with the added consequence that there is an increased theoretical understanding. The main strategy is to identify a theoretical€ moderator and measure it (or manipulate it experimentally) to ascertain its amplifying (or suppressing) effects on the simple association (Figure 14.1b). The whole logic of this chapter revolves around this idea of conditional predictive validity, and the section “Conditions under Which Implicit Measures Predict Behavior” contains several examples of moderation effects of a simple association between an implicit measure and a relevant criterion (e.g., Hofmann, Rauch, & Gawronski, 2007; Perugini, O’Gorman, & Prestwich, 2007).

Additive Pattern According to the additive pattern, an implicit measure explains a unique portion of variance of a criterion in addition to what is predicted by an explicit measure (Figure 14.1c). This pattern is important to elucidate the specific contribution of implicit measures in the crucial sense of incremental validity over explicit measures. In fact, one could argue that for many practical purposes an implicit measure is as good as its incremental validity over an explicit measure (Perugini & Banse, 2007). Explicit measures have a pragmatic advantage (e.g., easier to design, administer, validate, and interpret on the basis of normative scoring); therefore, the additional costs of including an implicit measure should be offset by some tangible benefits, one of the most important being that it allows prediction of variance in behavior over and above what is already predicted by an explicit measure. A number of studies have found additive patterns (e.g., Asendorpf, Banse, & Mücke, 2002; Egloff & Schmukle, 2002; Houben & Wiers, 2006; Roccato & Zogmaister, in press; Schnabel, Banse, & Asendorpf, 2006a). For instance, Richetin, Perugini, Prestwich, and O’Gorman (2007) obtained an additive pattern with unique contributions from both a fruit/snack IAT and an explicit attitudinal measure for predicting a spontaneous choice between a snack and a fruit. An additive pattern can be easily explained in a dual-Â�system perspective as evidence of the existence and distinction between the two systems. However, a single-Â�system account can explain it as well by focusing on the different properties of the measures that tap into

258

III. CROSS-CUTTING PERSPECTIVES 1a. Single Association Pattern Implicit Measure

Behavior

1b. Moderation Pattern Implicit Measure

Behavior

Moderator

1c. Additive Pattern

1d. Interactive/Multiplicative Pattern

Implicit Measure

Implicit Measure Behavior

Explicit Measure

Behavior Explicit Measure

1e. Double-Dissociation Pattern

1f. Partial-Dissociation Pattern

Implicit Measure

Spontaneous Behavior

Implicit Measure

Explicit Measure

Controlled Behavior

Explicit Measure

a

Spontaneous Behavior

b

1g. Double Additive Pattern Implicit Measure

Spontaneous Behavior

Explicit Measure

Controlled Behavior

Controlled Behavior

Note: Either the dotted line (a) or the dotted line (b) is significant depending on the direction of the asymmetry.

FIGURE 14.1.╇ Patterns of predictive validity.

different processes at different stages of cognitive processing of the same stored attitude.

Interactive or Multiplicative Pattern In the multiplicative pattern, implicit and explicit measures interact synergistically to predict a relevant behavioral criterion (Figure 14.1d). The interactive hypothesis, although directly connected

with the theoretical framework proposed by Strack and Deutsch (2004), is compatible with both a dual- and a single-�system account of attitudes. Empirically, this should be reflected in a significant interaction term between an implicit and an explicit attitudinal measure over and above their individual contributions (Brunel, Tietje, & Greenwald, 2004; Maison, Greenwald, & Bruin, 2004; Perugini, 2005, Study 1; Roccato & Zogmaister,



14. Prediction of Behavior

2009; Schröder-Abé, Rudolph, Wiesner, & Schütz, 2007). For example, using the IAT as a measure of implicit self-Â�esteem, Jordan, Spencer, Zanna, Hoshino-Â�Browne, and Correll (2003) have shown that the interplay between implicit and explicit measures of self-Â�esteem predicted defensive behaviors and narcissism, and similar results were shown in relation to attribution styles and health by Schröder-Abé, Rudolph, and Schütz (2007). Similarly, the discrepancy between implicit and explicit self-Â�concept predicted greater elaboration of inconsistent information (Briñol, Petty, & Wheeler, 2006). It is useful to note here that a multiplicative pattern may not necessarily be reflected in a significant interactive term in a specific study. Theoretically, it is conceivable that the discordance between automatic and controlled responses is an indication of incongruence and conflict between cognitive contents or between different aspects of cognition. One implication of this discordance could be that the influence of each on behavior should be mutually hampered and, therefore, they should both be less predictive and behavior execution should be less fluent. Conversely, when the behavioral implications of the two types of cognitive contents are congruent with each other, they should act synergistically and behavior should be facilitated; in this case, both explicit and implicit measures should be more predictive of behavior. These considerations are in line with the recent meta-Â�analysis by Greenwald and colleagues (2009) on the predictive validity of the IAT where it emerged that the correlation between IAT and explicit measures was the most important moderator of predictive validity. More specifically, predictive validities of both IAT and self-Â�report were higher in those studies in which implicit and explicit measures were highly correlated. These meta-Â�analytic results, coupled with the failure to achieve significant interaction terms in a number of studies, open the possibility that this synergistic effect can be reflected either directly in a significant interaction term or indirectly in two stronger main effects of implicit and explicit measures without a significant interaction term. We can speculate that the crucial issue is whether the congruence (incongruence) exerts its effects contextually, in which case a significant interactive term should be expected, or chronically. In the latter case, congruence (incongruence) between the two levels has already exerted its influence on the underlying cognitive structures, and this influence is reflected in the measures of implicit and explicit attitudes: therefore, one can expect increased main effects also in the absence of a significant interaction. Empirical

259

tests of this possibility are needed in future studies.

Double-Â�Dissociation Pattern The double-Â�dissociation pattern consists of implicit measures predicting spontaneous behavior and explicit measures predicting deliberate behavior and not vice versa (Figure 1e). This pattern has received some empirical support in a number of studies (e.g., Asendorpf et al., 2002; Dovidio, Kawakami, & Gaertner, 2002; Egloff & Schmukle, 2002; Friese, Hofmann, & Wänke, 2008; Perugini, 2005, Study 2). For example, Asendorpf and colleagues (2002) showed that a shyness IAT uniquely predicted spontaneous but not deliberate behavior, whereas self-Â�reports uniquely predicted deliberate but not spontaneous behavior. Research designs that allow for testing double-Â�dissociation patterns are theoretically very informative. They allow investigation of incremental validity and embed it neatly within a dual-Â�system theoretical framework. Note that all patterns reviewed so far are compatible with both a single- and a dual-Â�system theory. However, whereas any evidence of dissociation is directly compatible with a dual-Â�system theory, a single-Â�system account needs to reinterpret the dissociation as a consequence of some processes (e.g., for the MODE, motivation and opportunity) that differentially affects both types of measures. Moreover, any evidence of dissociation that cannot be easily explained in terms of motivation and opportunity provides a further challenge to a singleÂ�system account such as the MODE.

Partial-�Dissociation Pattern The partial- (or asymmetrical, weak) dissociation pattern (Figure 14.1f) refers to situations in which one of the two types of measures (i.e., implicit or explicit) predicts one type of behavior (i.e., more spontaneous or more deliberate) and the other predicts both behaviors (e.g., Dovidio et al., 2002; Fazio, Jackson, Dunton, & Williams, 1995; McConnell & Leibold, 2001; Richetin, Perugini, Adjali, & Hurling, 2007; Spalding & Hardin, 1999). For example, Richetin, Perugini, Hurling, and Adjali (2007) used a soft drinks Single-Target (ST) IAT and an explicit measure to predict a more spontaneous behavior (i.e., amount of soft drink consumed in a taste-and-rate test) and a more deliberate behavior (i.e., self-�reported consumption of soft drinks). The results showed that the ST-IAT significantly predicted the spontaneous behavior, whereas the explicit attitude predicted both delib-

260

III. CROSS-CUTTING PERSPECTIVES

erate and spontaneous behaviors. Note that this pattern could also be labeled as a partial (asymmetrical, weak) double additive pattern.

Double Additive Pattern The double additive pattern implies that both implicit and explicit measures predict both spontaneous and deliberate behavior (Figure 14.1g). Although this pattern has been demonstrated in only a few studies, logically it represents a theoretically plausible option. These two last patterns offer some challenges to both mainstream theoretical accounts (single vs. dual systems). However, they can be explained by considering that behaviors and processes vary in their level of spontaneity and deliberation (cf. Moors & De Houwer, 2006) and, therefore, it is possible that they contain, in fact, a minor but significant proportion of deliberation when mostly spontaneous and of spontaneity when mostly deliberate. This would be reflected statistically in a validity pattern in which both measures add significantly to the prediction of both behaviors, therefore both showing evidence of incremental validity. Note that this pattern would hold also if, in a given sample, for some people the same behavior and process are more spontaneous whereas for others they are more deliberative. In other words, the same results (partial or double additive pattern) would be obtained both if, across individuals, they reflect this mixed composition of unique processes within a person and if they reflect a mixed composition within a person that is common across persons.

Moderated and Mediated Patterns Each of the previous validity patterns can be complicated further by considering moderating factors or a mediated pattern (or moderated–Â�mediated and mediated–Â�moderated patterns; see Edwards & Lambert, 2007). Many of the results reviewed in the next section refer to moderation effects. With a significant moderator effect it is possible to test a dissociation pattern also with a single behavior. For instance, a partial-Â�dissociation moderated pattern has been found by Hofmann, Gschwendner, Castelli, and Schmitt (2008). In two studies, these authors found that the implicit measure was more predictive of behavior when attentional resources were depleted, whereas explicit attitudes were slightly more predictive when resources were full (see also Hofmann et al., 2007). In two studies, Conner, Perugini, O’Gorman, Ayres, and Prestwich (2007) have provided evidence of a more

complex validity pattern, a double dissociation of moderators, in which one individual difference variable (need for cognition) moderated the predictive validity of the explicit but not the implicit measure, whereas conversely another individual difference variable (self-Â�reported habit index [SRHI]) moderated the validity of implicit but not explicit measures. Although rarely tested, evidence for mediated patterns can be evinced in all studies in which the implicit measure significantly correlates with both the explicit measure and a criterion, but in a multiple regression the explicit measure predicts the criterion, whereas the implicit measure has a much reduced (compared with the bivariate correlation) predictive validity or does not predict anymore (e.g., Friese, Bluemke, & Wänke, 2007). Studies that adopt longitudinal designs are ideally suited for testing mediated patterns (cf. Galdi, Arcuri, & Gawronski, 2008).

Summary and Theoretical Implications So far, empirical evidence has accumulated that supports the various patterns discussed. What implications can we draw from the picture just outlined? Given their validity in predicting behaviors, implicit measures are an expression of relevant cognitive contents (simple association model). Empirical results of incremental validity of implicit measures alone are not sufficient to demonstrate that implicit measures tap different types of contents, as compared with the corresponding explicit measures, because they could be easily dismissed as an indication that the explicit measure used in the study is not sensitive enough in that specific domain, that it may be otherwise biased (e.g., by social desirability) or that it reflects additional processes further down the processing stream. However, dissociation and additive results strongly suggest that they provide valid and/or qualitatively different information, and the predictive validity of the implicit measure is not just a sign of a lack of validity of the corresponding explicit measure (and vice versa). The critical question, then, is, what meaning should be attributed to the differences between implicit and explicit measures? Do they provide information about the same underlying constructs, but the intervening processes when these constructs are expressed in open response in questionnaires are different from the processes that cause performance in indirect measures (e.g., Fazio & Olson, 2003)? Or do humans hold different forms of knowledge for the same concept, either as separate representations (e.g., Strack & Deutsch, 2004; Wilson, Lindsey, & Schooler, 2000) or as



14. Prediction of Behavior

the result of separate processes (e.g., Gawronski & Bodenhausen, 2006)? In other words, do unitary or dual constructs underlie the two different sets of measures? As discussed elsewhere (Greenwald & Nosek, 2009; Perugini, 2005), whereas it is possible to associate loosely a specific theoretical model and some specific predictive models, both dual�representation and unitary representation models are able to accommodate results from the different predictive patterns. For instance, a double�dissociation pattern clearly mirrors the logic of a dual system, yet it can be explained from a unitary construct perspective by assuming that this pattern concerns the relation between measures and behaviors and not between constructs and behaviors. In other words, it is always possible to reinterpret the evidence for dissociation as indicative of different processes underlying the measures themselves rather than reflecting different forms of knowledge as such (cf. Olson & Fazio, 2009). Hence, the theoretical debate about the unitaryversus dual-�concept status of cognitions tapped, respectively, by implicit and explicit measures will probably not be settled on the empirical ground of predictive validity alone (for a similar view, see Greenwald & Nosek, 2009). It seems more promising to us to tackle this issue from the perspective of attitude formation and functioning. For instance, evidence of independence in attitude formation as reflected in differential effects on implicit versus explicit measures (e.g., Rydell & McConnell, 2006) is harder to be reinterpreted as only indicative of different processes underlying the measures. As an example, it is unclear how the MODE model can explain that explicit attitude change mediates implicit attitude change (Whitfield & Jordan, 2009). One could speculate that if the evidence for dissociation, especially in attitude formation but also in attitude functioning, will accumulate substantially, a unitary construct representation will eventually become less attractive.

Conditions under Which Implicit Measures Predict€Behavior According to several theoretical models (e.g., Epstein, 1990; Gawronski & Bodenhausen, 2006; Strack & Deutsch, 2004; Wilson et al., 2000), associative processes are assumed to influence spontaneous responses, whereas propositional processes are assumed to influence deliberate responses. If we further add that implicit measures tap mostly into associative processes whereas explicit measures

261

reflect primarily propositional processes, it follows that implicit measures should predict spontaneous behaviors whereas explicit measures should predict deliberate behaviors. However, more recent research shows that the relative dominance of one of the two systems of information processing is determined by various factors, and consequentially the variance in the predictive validity of implicit measures is remarkable (cf. Greenwald et al., 2009). It is not surprising, therefore, that recent research has started to focus on moderators rather than main effects of implicit measures (for a recent review, see Friese, Hofmann, & Schmitt, 2008). We next review the evidence concerning different types of behaviors and then look at factors (situational, personal, and methodological) that influence the predictive validity of implicit measures.

Types of Behaviors Spontaneous versus More Deliberate€Behavior Because nonverbal behaviors are more strongly influenced by spontaneous processes compared with verbal behaviors, implicit measures should be better at predicting nonverbal than verbal behaviors (e.g., Dovidio, Kawakami, Johnson, Johnson, & Howard, 1997, Study 3; Dovidio et al., 2002). For example, in a study on spider phobia (Huijding & de Jong, 2006), an Extrinsic Affective Simon Task (EAST; De Houwer, 2003) predicted largely spontaneous eyeblink-Â�startle responses to pictures of spiders but not a more deliberate behavioral measure. Similarly, an IAT predicted a spontaneous choice between a fruit and a snack but not the selfÂ�reported consumption of fruits and snacks (Perugini, 2005, Study 2). However, this simple dichotomy may be an oversimplification. As Friese, Hofmann, and Schmitt (2008) argued, the spontaneous–Â� deliberate classification may vary depending on the context and might not take into account the continuum of control on which behaviors could be classified. Moreover, behavior can be classified in different ways (e.g., operant vs. respondent) that cut across the spontaneous–Â�deliberate continuum and can provide different predictions (cf. Perugini & Leone, 2009).

Criterion Domain In their meta-�analysis, Greenwald and colleagues (2009) showed that the IAT criterion effect sizes (predictive and incremental validity) varied substantially for different domains. The effect sizes

262

III. CROSS-CUTTING PERSPECTIVES

for predictive validity were relatively large for consumer/political preferences and for clinical/ addiction and relatively small for gender/sexual orientation and for close relationships. However, when considering incremental validity, the most promising domains were black/white and other intergroup topics and self-�concepts, whereas little incremental validity was evident for consumer/political preferences and for clinical/addiction. If we consider also the relative incremental validity of explicit measures, only in the racial and intergroup domains did the IAT actually predict more variance in behavior than an explicit measure. Probably the main reason for this is the sensitivity of the topic that affects impression management and social desirability processes. In fact, social sensitivity was also found to have a substantial moderation effect for explicit measures (lower validity for socially sensitive topics) but a much weaker effect for implicit measures.

Situational Moderators In line with a body of research that demonstrates the sensitivity to context of implicit measures (see Blair, 2002, for a review), numerous contextual features of the situation can moderate the predictive validity of implicit measures. Most of these situational effects, reviewed next, can be roughly summarized in a main assumption according to which all moderators that temporarily weaken central executive control and increase the influence of impulsive processes in the guidance of behavior lead to increased predictive validity of implicit measures.

Situational Encouragement to Form Deliberate Intentions/Act upon Gut€Reactions Marsh, Johnson, and Scott-�Sheldon (2001) suggested that, compared with having sex with a steady partner, the situation of having sex with a casual partner leads to undermining the motivation to form deliberate intentions to use condoms. Marsh and colleagues (2001) showed that an affective priming procedure (Fazio, Sanbonmatsu, Powell, & Kardes, 1986) was unrelated to condom use with a steady partner and tended to correlate negatively with condom use with a casual partner, a self�concept IAT was not correlated with condom use in both situations, and an attitudinal IAT tended to correlate with condom use with a casual partner but not with condom use with a steady partner.

Cognitive Capacity Because temporarily reduced cognitive capacity decreases the influence of reflective processes on judgments and behavior while increasing the influence of impulsive processes, implicit measures should be more strongly related to behavior that occurs under reduced cognitive capacity compared with conditions of full capacity (see Friese, Hofmann, & Schmitt, 2008, for a more detailed argument). For example, Friese, Hofmann, and Wänke (2008, Study 1) showed that a chocolate/fruit IAT predicted choice behavior for participants with reduced cognitive capacity (i.e., keeping in mind an eight-digit number) but not for those with full cognitive capacity (i.e., keeping in mind a one-digit number). However, with similar attitude objects, Scarabis, Florack, and Gosejohann (2006) obtained no manipulation effects of cognitive capacity on the predictive validity of the IAT. Possible reasons for this inconsistency could be due to the method (see Friese, Hofmann, & Schmitt, 2008). As another example of this line of evidence, because alcohol impairs executive functions, such as attentional processes, abstract reasoning, selfÂ�monitoring, and working memory skills, implicit measures should better predict behavior for people who have consumed alcohol than for sober people. Hofmann and Friese (2008) showed that a candy Single-Â�Category (SC) IAT predicted candy consumption in a product test for participants who had consumed alcohol but not for sober participants.

Processing Time Time pressure leads people to consider less information, to rely more on categorical thinking and stereotypes, to use simpler decision strategies, and to make more use of easily available cues in judgments, decisions, and behavior than under unconstrained conditions (e.g., Dijker & Koomen, 1996; Kruglanski & Freund, 1983). Implicit measures should, therefore, predict behavior especially under processing time constraints. Friese, Wänke, and Plessner (2006) demonstrated that, among participants with inconsistency in their implicit and explicit preferences, nearly all followed their explicitly measured attitude when they had time to make their decision, whereas more than 60% followed their implicit preference when under time pressure.

Situational Regulatory Focus Promotion focus leads people to endorse a lower order processing style such as reliance on affective



14. Prediction of Behavior

responses, whereas prevention focus leads people to endorse a higher order processing style such as reliance on substantive external cues. Implicit measures should thus predict behavior better in the first than in the second situation, as demonstrated by Florack, Ineichen, and Bieri (2009). Participants were instructed either to count their correct responses in a speeded concentration task and informed that they would win a cup if they met the condition (promotion condition) or to count their errors and informed that they would give back the cup if they did not meet the condition (prevention condition). Results indicated that a potato chips SC-IAT predicted consumption of potato chips only for those participants in the promotion condition, and not for participants in the prevention condition.

Self-Â�Regulatory Resources or€Ego€Depletion Baumeister and colleagues (e.g., Muraven & Baumeister, 2000) assume that self-Â�control depletes energy and leads to an increased influence of impulsive tendencies on behavior. Implicit measures should predict behavior better for participants depleted of self-Â�regulatory resources than when resources are plentiful. For example, a candy SC-IAT predicted candy consumption in a taste-and-rate task with depleted self-Â�regulatory resources (emotion-Â�suppression task; Baumeister, Bratslavsky, Muraven, & Tice, 1998) but not in the control condition (Hofmann et al., 2007; see also Friese, Hofmann, & Wänke, 2008).

Affective/Cognitive Focus When people focus on their affective reactions to target objects, impulsive processes gain influence in guiding behavior (e.g., Shiv & Fedorikhin, 2002; Wilson & Schooler, 1991). Implicit measures should better predict behavior that is based on affective compared with cognitive aspects. For example, both an evaluative and a self-�concept fruit/ chocolate IAT predicted choice behavior between a chocolate and a piece of fruit better for participants who were instructed to think about which option would make their mouth water more (affective focus condition) than for participants who were asked to analyze their choice and list several arguments for their preferred option (cognitive focus condition) (Scarabis et al., 2006). Importantly, motivation and opportunity to reflect did not differ between conditions.

263

Self-Â�Activation Activation of one part of the self-Â�concept can lead to a heightened accessibility of other parts of the self, such as self-Â�related knowledge structures in the associative network, without requiring conscious awareness. This heightened accessibility can influence information processing and behavior (e.g., Fazio & Towles-Â�Schwen, 1999; Strack & Deutsch, 2004). Perugini, O’Gorman, and Prestwich (2007) concluded that an IAT assessed directly after a self-Â�activation manipulation should reflect more “valid variance” of the associative network, which should, in turn, increase its predictive validity. In fact, it was argued that the IAT would be more likely to reflect personal knowledge structure than extrapersonal associations in a somehow similar manner as the personalized IAT (Olson & Fazio, 2004) although via a different mechanism and with different implications (cf. Perugini et al., 2007). Three studies showed that IATs taken directly after a manipulation of self-Â�activation were able to predict behaviors significantly (drinking alcohol, choice of arts vs. sciences faculties, and consumption of junk vs. healthy food). This effect was further moderated in another study in the domain of racial preferences by the chronic motivation to reduce prejudiced actions because it held only for participants low in this motivation.

Mood Individuals in a positive mood process information in a more top-down fashion than individuals in a negative mood (e.g., Bless et al., 1996). Importantly, individuals in a positive mood also rely more heavily on their associative network in information processing (e.g., Bolte, Goschke, & Kuhl, 2003). Therefore, implicit measures should better predict behavior for people in a positive mood than for people in a negative mood. In a first study (Hermsen, Holland, & van Knippenberg, 2006, Study 1), for participants in a positive mood, but not for those in a negative mood, an IAT on political parties predicted the seating distance that participants left between themselves and two posters of the respective parties in a waiting room. The same series of studies found a similar effect for the predictive validity of the SC-IAT and a personalized IAT.

Mortality Salience Mortality salience leads generally to suppression thoughts about death or redirection of the atten-

264

III. CROSS-CUTTING PERSPECTIVES

tion to other things and, therefore, requires self�regulatory resources leading to an increased impact of impulsive tendencies on behavior regulation. As a consequence, implicit measures should better predict behavior. Friese and Hofmann (2008) tested this hypothesis and showed that a chocolate SC-IAT predicted chocolate consumption in a taste-and-rate task for participants who were asked to think about their own death (mortality salience condition) but not for participants who were asked to think about something else (control condition).

Priming Perugini and Prestwich (2007) proposed the gatekeeper model, the main point of which is that perception can trigger action automatically only if there is a link between the two in memory at the individual level. This assumption furthermore implies that priming can increase (assimilation effect) or decrease (contrast effect) the likelihood of performing a congruent action for a given individual depending on the direction and strength of the idiosyncratic association between the concept and its valence. Priming can make these associations more accessible and, therefore, act as a moderator of the predictive validity of implicit measures. For example, a more positive implicit attitude toward Americans predicted a more positive evaluation of an essay written with American spelling (e.g., behavior instead of behaviour), but only when the American concept had been previously primed (Perugini & Prestwich, 2007). The gatekeeper model can, therefore, provide a theoretical explanation for a number of subsequently identified similar effects of increased predictive validity following a priming manipulation (e.g., Richetin, Richardson, & Mason, 2010; Smeesters, Yzerbyt, Corneille, & Warlop, 2009; Wheeler & Berger, 2007; Wheeler, DeMarree, & Petty, 2007).

Personal Moderators Similarly to situational moderators, we may assume that the great majority of the moderators operating at the individual level influence predictive validity of implicit measures by causing chronic individual differences in the reliance upon reflective versus impulsive processes and/or in the strength of the intervention of control mechanisms. Specifically, they will be more predictive when people rely preferentially on impulsive processes and when they do not exercise control upon behavior execution.

Need for Cognition Need for cognition (NFC) is the dispositional motivation to engage in and enjoy effortful cognitive processing (for an overview, see Cacioppo, Petty, Feinstein, & Jarvis, 1996). In a study by Florack, Scarabis, and Bless (2001), a Turks/Germans IAT predicted judgments toward a fictitious Turkish offender, but only for participants low in NFC. However, in two studies, NFC did not affect the predictive validity of implicit measures (IAT and EAST), only of explicit attitudinal measures (Conner et al., 2007).

Motivation to Control Prejudiced€Reactions The motivation to control prejudiced reactions (MCPR; Dunton & Fazio, 1997) is the motivation to avoid prejudiced judgments and behaviors. Olson and Fazio (2004) showed that an evaluative priming procedure assessing attitudes toward blacks successfully predicted the impressions formed of black target persons, but only for participants scoring low on the restraint to avoid dispute factor of the MCPR. More negative automatically activated attitudes were associated with more positive judgments for participants who scored high on the restraint to avoid dispute factor (see also Towles-Â�Schwen & Fazio, 2003). In contrast, a study by Gawronski, Geschke, and Banse (2003) found that implicit attitudes toward the target’s category, assessed through an IAT, influenced the interpretation of ambiguous behaviors, which, in turn, affected perceivers’ dispositional inferences about the target without a moderating effect of participants’ motivation to control prejudiced reactions.

Working Memory Capacity Working memory capacity (WMC) refers to the ability to maintain information in short-term memory and to use attention to shield this information in working memory against distracting internal or external influences. Individuals high in WMC are more successful in enacting controlled, goal-�directed processing, whereas controlled processing breaks down more easily in individuals with low WMC (Barrett, Tugade, & Engle, 2004). Implicit measures should better predict behavior for participants with low versus high WMC. For example, a candy SC-IAT predicted candy consumption in the taste-and-rate task, but only for participants low in WMC as assessed with a WMC



14. Prediction of Behavior

task (Hofmann, Gschwendner, Friese, Wiers, & Schmitt, 2008). Grenard and colleagues (2008) obtained similar results with the IAT in the addiction domain.

Trait Self-�Control Self-�control refers to the ability to change automatic responses or to interrupt behavioral tendencies (Tangney, Baumeister, & Boone, 2004). People who are low in trait self-�control follow through more often on their impulsive tendencies and have more difficulty living up to personal standards and goals (e.g., Gailliot & Baumeister, 2007). Consequently, implicit measures should predict behavior better for individuals low in trait self-�control. Friese and Hofmann (2009) showed that a potato chips SC-IAT predicted consumption behavior in a later taste-and-rate task better for participants low in trait self-�control than those high in trait self-�control as assessed with the self-�control scale (Tangney et al., 2004). Relatedly, Hofmann, Friese, and Roefs (2009) showed that three aspects of impulse control (namely executive attention, inhibitory control, and affect regulation) moderated the relationship between automatic affective reactions toward candy and subsequent consumption.

Preference for Intuition Preference for intuition describes the dispositional tendency to rely on affective inner reactions in decision making (Betsch, 2004; Epstein, Pacini, Denes-Raj, & Heier, 1996). In other words, individuals with a high preference for intuition by default rely more heavily on their affect over and above other factors. Implicit measures should then predict behavior better for individuals high in preference for intuition. Hofmann and Baumert (in press) showed that a moral affect misattribution procedure (AMP) predicted the probability of rejection of the unfair proposal for participants high in preference for intuition but not for those low in preference for intuition. Similarly, a soft drinks STIAT better predicted sensory evaluation of a soft drink in a taste test for people higher in intuition (Richetin, Perugini, Adjali, & Hurling, 2007).

Habitual Behavior A habitual behavior is more spontaneous than other types of behaviors and, consequently is less deliberate and needs less effort for action regulation (e.g., Aarts & Dijksterhuis, 2000; Verplanken & Aarts, 1999). Most behaviors can, in principle,

265

become habitual, but the extent to which this happens can vary reliably from person to person. The implication is that the very same behavior can be spontaneous for some people and deliberate for others, therefore qualifying the simple dichotomy spontaneous versus deliberate behavior as a function of individual differences. Following this logic, implicit measures should predict a specific behavior better for those people who do it habitually as opposed to those who exert greater deliberations when performing it. Conner and colleagues (2007) showed in two different studies that the predictive validity of an EAST for self-�reported eating behavior and an IAT for self-�reported consumption and a fruit and a chocolate bar choice was greater for participants with higher habitualness in the target behavior, as measured by the SRHI (Verplanken & Orbell, 2003).

Summary We have reviewed several moderators of the predictive validity of implicit measures. The bottom line is that there is robust evidence of predictive validity, but most of it is conditional to some specific moderator, be it type of behavior or situational and personal factors. Most of the time the effects of the moderators have been examined separately in the literature. However, as suggested by Friese, Hofmann, and Schmitt (2008), several hypotheses about effects of different moderators enhancing or counteracting each other can also be drawn. Some recent research has started to examine synergistic (e.g., Gabriel, Banse, & Hug, 2007) or disjunctive (e.g., Conner et al., 2007) effects of more moderators at once. This issue may well represent a new frontier of future studies with implicit and explicit measures. From a theoretical perspective, it is important to emphasize that the conditional approach to the relationship between implicit measures and behaviors reviewed here, while responding to the different questions of which behaviors are predicted under what conditions and for whom, does not support a specific theoretical model (the why question). In particular, both the unitary- and dual-�construct theories outlined earlier postulate the existence of automatic contents and/or processes that influence behavior in a spontaneous way, and that can be contrasted only under certain circumstances by controlled contents/processes, depending on the level of engagement of controlled processes. They both assume facilitating or inhibitory factors that can moderate the relation between implicit measures and behavior. In short, both theoretical

266

III. CROSS-CUTTING PERSPECTIVES

views can accommodate the results. However, as argued previously, a unitary-�construct perspective has more difficulties in explaining dissociation results, and if they will accumulate substantially, it may become increasingly attractive to start from the assumption that there are two distinct knowledge systems or processes.

METHODOLOGICAL ISSUES In this final section, we deal with some methodological and psychometric topics that are particularly relevant for the issue of predictive validity of implicit measures. A number of these topics are common to virtually every psychological measure, whereas others are more specific to implicit measures.

The Need for Reliable Measures Reliability refers to the precision of a measurement. Reliability of implicit measures is usually estimated as interitem consistency and, less often, as test–Â�retest stability. Typically, estimates of internal consistency are higher than those of test–Â� retest reliability, but low reliability is sometimes a troublesome issue in implicit measures (e.g., Bosson, Swann, & Pennebaker, 2000; Cunningham, Preacher, & Banaji, 2001). Although important for every measurement purpose, reliability is particularly crucial for predictive studies. To appreciate its importance, it is useful to focus on what happens when the internal consistency is low. Low internal consistency of measures of unitary constructs implies that a high proportion of variability in observed scores is caused by random error (e.g., reactions to the specific stimuli that are presented, external influences like a sudden noise in the environment, differential sequential effects), and only a small part is caused by systematic sources, among them the interindividual differences in the construct of interest. Even in the presence of a measure that is valid in the sense that its variability is influenced by the construct of interest (internal validity), a high proportion of random error means that individual scores are highly unstable, scantly informative, and only loosely related to the construct. Moreover, given that correlations reflect joint individual rankings in the variables, everything else being equal, low internal consistency in a measure leads to underestimation of its true correlation value with a criterion. Finally, low internal consistency means that any kind of applied (e.g., diagnostic) decisions using scores cannot be

made. Suppose that John has been administered an implicit measure based on 60 critical trials. Low internal consistency means that his score, and consequently his ranking in the sample (or in the population in reference to a normative score), in the first 30 trials can be substantially different (unrelated) from his score in the last 30 trials. Given that there are no reasons to trust the first or the last 30 trials and no valid reasons why the trials should be 60 instead than 40 or 80, in practice one would not be able to say anything about John’s relative standing and, consequently, would be unable to make practical decisions based on his score. The reasoning is somewhat different for test–Â� retest estimates of reliability. Besides random error and changes in the construct being measured, estimates of test–Â�retest stability may also be decreased by the malleability often observed in implicit measures (see Blair, 2002; Gawronski & Sritharan, Chapter 12, this volume). Therefore, a test–Â�retest reliability noticeably lower than internal consistency may also be an indication of high malleability of the construct being measured and may prompt the researcher to pay particular attention to the congruence between contexts of measurement and, subsequently, the context in which the behavior is enacted. We address this issue later in the Principle of Correspondence section. If, as we argue, reliability is so important, then it seems worthwhile to briefly mention some strategies that could be helpful to raise it. A general rule is to diminish the variation resulting from features that are unrelated to the construct of interest. The use of rigid and standardized instructions with very specific response goals is one way to do this (cf. Buchner & Wippich, 2000). Another way to increase internal consistency (and predictive validity) is by presenting the stimuli in a fixed random order identical for all participants so as to minimize irrelevant (from the perspective of the construct) differential effects caused by the order in which the stimuli are presented. A third way involves the use of robust statistics (Erceg-Hurn & Mirosevich, 2008) for the computation of individual scores. Indeed, standard statistical parameters like the mean are heavily influenced by nonnormality of the distribution (which is a common feature for reaction times) and by the presence of outliers. In these cases, robust statistics (e.g., trimmed means at the individual level; cf. Borkenau & Mauer, 2007) should provide an estimate of the value of interest (e.g., of the central tendency) that is less biased and therefore, everything else being equal, more reliable and more valid. A fourth way is to keep a



14. Prediction of Behavior

paradigm as simple as possible, so as to reduce the likelihood that resulting scores would also reflect individual differences not related to the task at hand (e.g., differences in general capacity, in the strategies used to accomplish the task, or in the interpretation of the instruction provided). We must highlight the potential dangers of two common ways to increase internal consistency. First, psychometric wisdom would suggest that an obvious stratagem to enhance internal consistency is to increase the number of trials. Although this is, in general, correct (e.g., see Marsh et al., 2001, Study 2), when the measure contains too many trials, its validity may decrease as a result of a number of factors (e.g., habituation, fatigue, learning, task-�recoding strategies). Therefore, the choice of the optimal number of trials is not easy because it should balance increased internal consistency with the risks of a decreasing validity. Research on this specific issue would be highly beneficial to help find the best trade-off. Finally, internal consistency may be increased by minor changes in the paradigms such as the introduction of response windows and the use of images as stimuli. It is possible that these changes can increase internal consistency (Spruyt, Hermans, De Houwer, Vandekerckhove, & Eelen, 2007), but it is far from obvious that this would be reflected in increased validity. Indeed, predictive validity may even decrease to the extent to which these changes introduce systematic variance in individual differences with respect to features that are construct irrelevant. For instance, if a certain change in the paradigm increases the internal consistency of a measure by increasing the impact on the score of individual differences in general speed of processing (and general speed of processing is not related to the construct), the resulting measure will be more reliable, but it will reflect mainly individual differences in cognitive speed rather than in the construct of interest and hence it will be less valid.

The Need for Procedural Formats with€Large Effects At a first analysis, effect sizes of the difference between conditions (e.g., positive/white–Â�negative/ black) for an implicit measure appear to be irrelevant for the prediction of behavior: One might argue that, because prediction of behavior is based on correlation, only rank orders are important and effect sizes are irrelevant for rank orders (cf. Perugini & Banse, 2007). Moreover, effect sizes can be artificially inflated by features that are irrelevant for the measurement of the construct. However,

267

there are indirect reasons why effect sizes are important in predictive studies. First, everything else being equal, a larger size of the difference between conditions indicates higher sensitivity of the measure to systematic effects shared among individuals compared with other irrelevant interindividual sources of variability. Second, everything else being equal, measures characterized by substantial effect sizes are more likely to be valid given that power is affected by the variability range of the variables. It is important to emphasize that large effect sizes are valuable when they are supplemented by a substantive degree of specificity, by which we mean the proportion of true variance in the measure or, in other words, variance that is caused by the construct that one wants to measure. If, for instance, we would develop a measure of attitudes toward smoking that is more strongly related to the cultural and normative view of smoking than to the personal attitude of the individual, we would probably end up with large effect sizes but low sensitivity and low power in the prediction of interindividual differences in behavior. In sum, although statistically individual differences in ranking orders are independent of effect sizes, smaller effect sizes may indirectly reduce predictive validity because they restrict the range of variability in the measure. Therefore, a measure with a larger effect size cannot be claimed per se to be better than a measure with a smaller effect size—it all depends on why the effect size is larger—but, given a certain measure with a certain degree of validity, if it is also associated to a large effect size, it is more likely to be valid: A valid measure should be both sensitive and specific.

Order Effects Order effects, by which we mean differential variance introduced systematically by sequential influences, can represent a threat to predictive validity. In fact, these effects can have a distorting influence on the rankings in the scores and hence affect predictive validity. There are at least three types of order effects worth addressing: the order of the stimuli within a block of a task, the order of the blocks within a task, and the order of the tasks within a study. For the first type, as we have argued previously, from a predictive perspective the best strategy is to use a fixed random order of stimuli presentation within a block. Concerning order effects resulting from the blocks within a task, it is usually believed that counterbalanced presentation is an effective procedure to counteract possible effects of order (e.g., Bosson et al., 2000;

268

III. CROSS-CUTTING PERSPECTIVES

Greenwald et al., 1998), but counterbalancing alone is not sufficient. Many implicit measurement procedures involve two separate blocks, and the score is based on the difference in performance in the two blocks. In these procedural formats, order effects may arise in the form of an influence of the block administered first on the second block. Evidence for such effects has been reported by Klauer and Mierke (2005), who investigated aftereffects of the compatible and incompatible tasks of the IAT and found that they are asymmetrical: Only the incompatible block of the IAT caused subsequent interference with the access to the construct being measured. If establishing predictive validity is the main goal of a given study, when one of the two blocks is thought to be normatively compatible, a simple solution to the problem is to administer the task to all participants in the same order starting with the compatible block first. If, however, the focus is not on predictive validity and there is no clear normatively compatible block, counterbalancing the blocks may be advisable, but statistical analyses are necessary to uncover and eliminate at least some of the effects of order. A systematic framework for statistically assessing and controlling for order effects is order analysis (Gallucci & Perugini, 2009). Briefly, in order analysis two possible order effects are distinguished: offset, which implies an asymmetry between the measure and a criterion, and attenuation, which implies an underestimation of the true relation between the measure and the criterion that would be obtained if there were no order effects. By using order analysis, which in its simplest form requires only to covariate the blocks order, Richetin, Perugini, Prestwich, and O’Gorman (2007) were able to detect an otherwise not significant relation between IAT and a criterion. Finally, when designing a study with multiple measures, researchers need to decide in which order the measures will be administered. Although there is evidence for all kinds of reciprocal effects between measures as a function of their order of presentation (e.g., Klauer & Mierke, 2005; Monteith, Voils, & Ashburn-Nardo, 2001), the metaÂ�analysis by Greenwald and colleagues (2009) failed to detect a significant effect on predictive validity across studies. Note that this result does not imply that it is an irrelevant issue because it is conceivable that there are different effects in different directions that perhaps cancel out each other across studies but may exert a significant effect within a specific study performed under specific conditions. In principle, a strategy similar to the one advocated previously for within-task order effects could

be used for between-tasks order effects as well, by varying the order of the measures and statistically investigating their order effects. However, complexity and required sample size would substantially increase when both orders within and between tasks are considered statistically within the same study. Moreover, there may be specific order combinations of tasks that are undesirable for theoretical reasons in a given study.

Principle of Correspondence The principle of correspondence in the prediction of behavior was brought forward by Ajzen and Fishbein (1977). The main idea is that the more a construct measure shares relevant features with a behavioral measure, the stronger the relationship between the two. For instance, measures of general attitudes toward sport will probably be poorly predictive of whether an individual will go jogging during next week, whereas more specific attitudes toward going jogging in the near future will probably be much more predictive. Scarce observance of this general principle may explain why at times measures of implicit preferences failed to predict relevant behaviors (e.g., Karpinski & Hilton, 2001), while similar studies characterized by higher levels of correspondence between measure and behavior were successful in predicting similar contexts (Perugini, 2005; Richetin, Perugini, Prestwich, & O’Gorman, 2007). Direct support of the hypothesis that implicit measures will be more predictive when the principle of correspondence is satisfied was recently provided by Gschwendner, Hofmann, and Schmitt (2008a). Among other measures, the authors assessed participants’ implicit general anxiety, implicit spider anxiety, and implicit speech anxiety through the IAT. It emerged that only implicit speech anxiety significantly predicted the level of anxiety manifested by participants while giving a speech some weeks later. The recent meta-Â�analysis conducted by Greenwald and colleagues (2009) on the predictive validity of the IAT provides further support for this general principle. The features that may be important in the correspondence between implicit measures and behavior are numerous. Based on the malleability of implicit cognitions (see Blair, 2002; Gawronski & Sritharan, Chapter 12, this volume) and recent theoretical models (e.g., Conrey & Smith, 2007; Gawronski & Bodenhausen, 2006), the matching between the mental contents that are accessible during measurement and during overt behavior seems particularly relevant (Schwarz, 2007;



14. Prediction of Behavior

Schwarz & Bohner, 2001). For instance, according to both the associative–Â�propositional evaluation (Gawronski & Bodenhausen, 2006) and the connectionist (e.g., Conrey & Smith, 2007) models of attitudes, the activation of the associations depends on both the preexisting structure of associations in memory and the specific pattern of activation that is determined by the stimulus and the context. An identical object may activate different spontaneous knowledge, depending on the particular context in which it is encountered (e.g., Barden, Maddux, Petty, & Brewer, 2004; Wittenbrink, Park, & Judd, 2001). In line with this reasoning, two recent studies investigating personality and racial attitudes showed that the test–Â�retest reliability of two IATs administered 2 weeks apart increased when the same construct-Â�relevant content was activated at both measurement occasions (Gschwendner, Hofmann, & Schmitt, 2008b). This activation supposedly caused the measurement of the same facets of the construct in both occasions compared with an IAT in which no domain-Â�specific content was activated. In line with these findings, we can further hypothesize that, when similar contents are activated in relation to the implicit knowledge expression and in relation to spontaneous overt behavior, there will be greater cognition–Â�behavior consistency and hence greater predictive validity. This general matching principle may be particularly relevant when the representation of the object of interest is heterogeneous because in this case similar input stimuli may activate different patterns of association, causing different automatic reactions depending on the specific context (see Rydell & Gawronski, 2009). Another feature that may enhance or decrease the correspondence between implicit measures and overt behavior is the absolute or relative nature of the tasks. If the goal is to predict a behavior that is inherently relative (e.g., a choice between two options), relativity may be a strength of the measure because the relative context in which the spontaneous expression of relevant knowledge is triggered is similar to the relative context of choice. In line with this reasoning, Greenwald and colleagues’ (2009) recent meta-Â�analysis on predictive validity of the IAT, which is an intrinsically comparative measure, revealed that IAT scores were most predictive in situations in which complementary categories were involved (e.g., political attitudes toward opposing candidates predicting vote). In other words, the principle of correspondence would suggest that inherently comparative measures such as the standard IAT may fare better

269

in predicting dichotomous choices, whereas noncomparative measures such as the SC-IAT may have an advantage in predicting behaviors based on frequency counts of a single choice (e.g., candy consumption).

Principle of Structural Fit Payne, Burkley, and Stokes (2008) recently pointed out the confounding between test features and the concepts they are meant to measure that is ubiquitous in research on implicit cognition. The main idea, extensively addressed in relation to implicit and explicit memory measures (e.g., Merikle & Reingold, 1991; Schacter, Bowers, & Booker, 1989), is that often two different tests are used to investigate implicit and explicit cognitions, and these tests differ not only in the measured concepts (e.g., spontaneous vs. deliberate reactions to members of outgroups) but also in the types of stimuli that are administered, the response format, and even the specific content (e.g., a more abstract evaluation of a problem in the explicit measure and a more specific reaction to an individual stimulus in the implicit measure). Consequently, differences in the results cannot be attributed with certainty to the nature of the processes supposedly underlying the tests unless all other aspects of the test are matched, and they differ only in the relevant aspect (i.e., the cognitive content influences responses in a spontaneous vs. deliberate way). From a predictive perspective, gathering knowledge about the nature of the processes and how they are related to criteria means to pave the way for refined designs with increased predictive validity.

Measurement Operation and Scores There has been debate on the relative nature of the IAT and on the meaning of IAT scores (e.g., Blanton & Jaccard, 2006; Blanton, Jaccard, Gonzales, & Christie, 2006; Nosek & Sriram, 2007). Without going into details about the debate, we articulate some implications of one issue that has emerged, namely the relative nature of scores. One of the features of the IAT is that it requires a contrast category and, therefore, scores are inherently comparative, whereas the SC-IAT, for example, relies on only the focal category. It would seem straightforward, therefore, that this latter measure logically provides a less contaminated and more absolute score and henceforth greater predictive validity. However, whether this is true depends critically on factors such as whether a paradigm

270

III. CROSS-CUTTING PERSPECTIVES

of absolute construct activation works better than one of relative activation. In fact, one could argue that both the IAT and the SC-IAT scores produce a relative metrics but they do so differentially by relying on a paradigm of relative (IAT) or absolute (SC-IAT) concept activation. Moreover, one could argue that the IAT, by using a contrast category, can provide a context within which the focal category is interpreted. Given the substantial evidence for the contextual nature of human decision making (Smith & Semin, 2004: Stewart, Brown, & Chater, 2005), under some conditions providing a context can increase the validity of the scores. More generally, the issue here is that there is no one-to-one mapping between the scores resulting from a task and the construct that the task is meant to measure because the measurement operation fundamentally mediates this correspondence (for similar considerations applied to implicit attitude change, see Deutsch & Gawronski, 2009). Applied to the issue at hand, this implies that whether the scores contain more valid variance (and, therefore, have greater predictive validity) depends on whether a measurement operation relying on an absolute activation of the concept is better in some sense than one relying on a relative activation. Therefore, the issue becomes an empirical one, and head-to-head comparisons will be needed to establish which paradigm works best under what conditions (cf. Richetin & Perugini, 2008).

Types of Measures As reviewed in Part II of this volume, several implicit measures have been developed in the last decades. They differ in terms of not only procedural details and theoretical mechanisms but also reliability and performance in predicting behaviors, as we briefly review next.

The Implicit Association Test and€Its€Variants Currently, the most widely used implicit measure is the IAT (Greenwald et al., 1998), whose internal consistency is satisfactory according to psychometric standards (in their meta-Â�analysis, Hofmann, Gawronski, Gschwendner, Le, & Schmitt, 2005, reported a mean reliability of .79 across 50 studies). Test–Â�retest reliability is lower (according to Hofmann et al.’s meta-Â�analysis, the average value across 11 studies is .51), but as discussed previously, this may be at least partly related to the

malleability of implicit measures. In line with its satisfactory reliability, predictive and incremental validities of the IAT are well established. The meta-�analysis by Greenwald and colleagues (2009) revealed an average predictive validity of r = .27 based on a sample of 184 independent studies and an incremental predictive validity of r = .18 based on a subsample of 155 studies that included both implicit and explicit measures. Mainly to overcome the intrinsically bipolar nature of the IAT, some studies have used IATs with a neutral (Czopp et al., 2004) or a negated (Zayas & Shoda, 2005) category as a contrast category to successfully predict behavior. Several variants have been proposed that do not require the use of a contrast category, such as the Single-Attribute IAT (SA-IAT; Penke, Eichstaedt, & Asendorpf, 2006), the ST-IAT (Wigboldus, Holland, & van Knippenberg, 2004), and the SC-IAT (Karpinski & Steinman, 2006). Other variants of the IAT have focused on aspects other than the contrast category, like the personalized IAT proposed by Olson and Fazio (2004), the Implicit Association Procedure (IAP) developed by Schnabel, Banse, and Asendorpf (2006b), and the Single-Block IAT (SB-IAT) created by Teige-�Mocigemba, Klauer, and Rothermund (2008). Overall, these variants of the IAT show acceptable levels of internal consistency, albeit sometimes slightly lower than levels usually observed with the traditional IAT. In terms of predictive validity, so far no study has shown the ability of the SA-IAT to predict behavior, whereas the ST-IAT (e.g., Bluemke & Friese, 2008; Dotsch & Wigboldus, 2008: Richetin, Perugini, Adjali, & Hurling, 2007) and the SC-IAT (e.g., Steinman & Karpinski, 2008; von Hippel, Brener, & von Hippel, 2008; but see Richetin & Perugini, 2008) have been shown to predict various criteria. The personalized IAT (e.g., Houben & Wiers, 2007a, 2007b), the IAP (e.g., Schnabel et al., 2006a), and the SB-IAT (Teige-�Mocigemba et al., 2008) also have been shown to have some predictive validity. However, there is a strong need for additional studies that provide more robust evidence as well as for head-to-head comparisons between these variants and the IAT.

The Go/No-Go Association Task Go/No-Go Association Task (GNAT; Nosek & Banaji, 2001) has been used to assess automatic evaluations for target concepts in a variety of contextual situations (e.g., Mitchell, Nosek, & Banaji, 2003). To our knowledge, there is little evidence of



14. Prediction of Behavior

predictive validity of the GNAT (but see Spence & Townsend, 2007; Zogmaister, Arcuri, Castelli, & Smith, 2008), perhaps because its reliability tends to be low (Nosek & Banaji, 2001, report an average split-half reliability of .20).

The Extrinsic Affective Simon Task and€Its Variants The EAST (De Houwer, 2003) has been shown to predict behavior (e.g., Ellwart, Becker, & Rinck, 2005; Houben, Gijsen, Peterson, de Jong, & Vlaeyen, 2005; Huijding & de Jong, 2005). However, some results have been mixed, and the low reliability and validity have raised doubts about whether it can provide a valid measure of individual differences (De Houwer, 2008; De Houwer & De Bruycker, 2007a; Schmukle & Egloff, 2006; Teige, Schnabel, Banse, & Asendorpf, 2004). Also for its variant, the identification-EAST (De Houwer & De Bruycker, 2007b), problematic results have emerged (Richetin, Perugini, Adjali, & Hurling, 2007), and it has been outperformed by the IAT in head-to-head comparisons (De Houwer & De Bruycker, 2007b; Nosek et al., 2007).

Affective or Evaluative Priming Supraliminal and subliminal affective priming (Bargh, Chaiken, Raymond, & Hymes, 1996; Fazio et al., 1986, 1995) have been used in a number of domains to predict different behaviors (e.g., Degner, Wentura, Gniewosz, & Noack, 2007; Dovidio, Gaertner, Kawakami, & Hodson, 2002; Fazio et al., 1995; Spalding & Hardin, 1999; Wilson et al., 2000; for a review, see Fazio & Olson, 2003; see also Degner, Wentura, & Rothermund, 2006), but their reliability is often low.

Semantic or Concept Priming Semantic priming (Wittenbrink, Judd, & Park, 1997) has not often been used to predict behavior. However, Bessenoff and Sherman (2000) showed that a priming procedure of attitudes toward obese people predicted how far away participants seat themselves from an obese confederate. The weapon identification task (Payne, 2001) is a perceptual identification paradigm, structurally similar to priming tasks, that can used to investigate implicit associations between concepts (e.g., between blacks and weapons), with the advantage that it allows disentangling the effect of automatic associations from the operation of control processes by

271

way of the process dissociation procedure (e.g., Jacoby, 1991; see Lambert et al., 2003). Unfortunately, no data are available on its reliability, but its predictive validity has been shown in relation to judgments of black individuals (Lambert, Payne, Ramsey, & Shaffer, 2005).

The Affect Misattribution Procedure Recently proposed by Payne, Cheng, Govorun, and Stewart (2005), the AMP is reported to have good levels of internal consistency, comparable to those of the IAT, and was shown to be predictive in various domains such as alcohol use (Payne, Govorun, & Arbuckle, 2008) and smoking behavior and cognitions (Payne, McClernon, & Dobbins, 2007) as well as in the political (Payne et al., 2005, Study 5) and moral (Hofmann & Baumert, in press) domains.

Paper-and-�Pencil Implicit Measures Paper-and-�pencil and low-tech implicit measures such as the linguistic intergroup bias measure (Maass, Salvi, Arcuri, & Semin, 1989) and the name-�letter task (Nuttin, 1985; see LeBel & Gawronski, 2009) are less known and used, probably because of their usually poor reliability. However, some measures have been successfully used to predict behavior (see Sekaquaptewa, Vargas, & von Hippel, Chapter 8, this volume; Vargas, Sekaquaptewa, & von Hippel, 2004, for a review).

Physiological Measures In the last years, various physiological approaches have also been used as implicit measures of attitudes, such as electromyography (e.g., Ito, Thompson, & Cacioppo, 2004), functional magnetic resonance imaging (e.g., Hart et al., 2000), event�related brain potentials (e.g., Cacioppo, Crites, & Gardner, 1996), skin conductance level (e.g., Dotsch & Wigboldus, 2008), electrodermal activity, and eyeblink-�startle modification (e.g., Amodio, Harmon-Jones, & Devine, 2003; see Ito & Cacioppo, 2007, for a review). However, most of the research has focused on the assessment of the attitudes as such and much less on the prediction of specific behaviors (but, for instance, see Dotsch & Wigboldus, 2008). Although this may be a promising direction for the future, the paradigms need still to show whether they provide reliable measures, and there is a need to accumulate evidence of predictive validity.

272

III. CROSS-CUTTING PERSPECTIVES

CONCLUSIONS We have reviewed the evidence concerning the predictive validity of implicit measures. The gist of this review is that implicit measures do predict behavior, but they do so especially in some conditions for some people for some behaviors with some tasks. We have seen that there are different patterns of predictive validity that can help to provide a framework for understanding the implications of some results and for guiding the design of studies concerned with predictive validity. Furthermore, we have addressed some methodological issues concerning implicit measurement that can have an important influence on predictive validity. Although the knowledge and understanding on the predictive validity of implicit measures have grown exponentially during the last few years, there is still much that needs to be addressed and clarified by future research. References Aarts, H., & Dijksterhuis, A. (2000). Habits as knowledge-Â�structures: Automaticity in goal-Â�directed behavior. Journal of Personality and Social Psychology, 78, 53–63. Ajzen, I., & Fishbein, M. (1977). Attitude-Â�behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84, 888–918. Amodio, D. M., Harmon-Jones, E., & Devine, P. G. (2003). Individual differences in the activation and control of affective race bias as assessed by startle eyeblink response and self-Â�report. Journal of Personality and Social Psychology, 84, 738–753. Arcuri, L., Castelli, L., Galdi, S., Zogmaister, C., & Amadori, A. (2008). Predicting the vote: Implicit attitudes as predictors of the future behavior of decided and undecided voters. Political Psychology, 29, 369–387. Asendorpf, J. B., Banse, R., & Mücke, D. (2002). Double dissociation between implicit and explicit personality self-Â�concept: The case of shy behavior. Journal of Personality and Social Psychology, 83, 380–393. Barden, J., Maddux, W. W., Petty, R. E., & Brewer, M. B. (2004). Contextual moderation of racial bias: The impact of social roles on controlled and automatically activated attitudes. Journal of Personality and Social Psychology, 87, 5–22. Bargh, J. A., Chaiken, S., Raymond, P., & Hymes, C. (1996). The automatic evaluation effect: Unconditional automatic attitude activation with a pronunciation task. Journal of Experimental Social Psychology, 32, 104–128. Barrett, L. F., Tugade, M. M., & Engle, R. W. (2004). Individual differences in working memory capacity and dual process theories of mind. Psychological Bulletin, 130, 553–573. Baumeister, R. F., Bratslavsky, E., Muraven, M., & Tice,

D. M. (1998). Ego depletion: Is the active self a limited resource? Journal of Personality and Social Psychology, 74, 1252–1265. Bessenoff, G. R., & Sherman, J. W. (2000). Automatic and controlled components of prejudice toward fat people: Evaluation versus stereotype activation. Social Cognition, 18, 329–353. Betsch, C. (2004). Präferenz für Intuition und Deliberation (PID): Inventar zur Erfassung von affekt- und kognitionsbasiertem Entscheiden [Preference for intuition and deliberation (PID): An inventory for assessing affect- and cognition based decision-Â�making]. Zeitschrift für Differentielle und Diagnostische Psychologie, 25, 179–197. Blair, I. V. (2002). The malleability of automatic stereotypes and prejudice. Personality and Social Psychology Review, 6, 242–261. Blanton, H., & Jaccard, J. (2006). Arbitrary metrics in psychology. American Psychologist, 61, 27–41. Blanton, H., Jaccard, J., Gonzales, P., & Christie, C. (2006). Decoding the Implicit Association Test: Perspectives on criterion prediction. Journal of Experimental Social Psychology, 42, 192–212. Bless, H., Clore, G. L., Schwarz, N., Golisano, V., Rabe, C., & Wölk, M. (1996). Mood and the use of scripts: Does being in a happy mood really lead to mindlessness? Journal of Personality and Social Psychology, 71, 665–679. Bluemke, M., & Friese, M. (2008). Reliability and Â�validity of the Single-Â�Target IAT (ST-IAT): Assessing automatic affect toward multiple political groups.€Â�European Journal of Social Psychology, 95, 962–977. Bolte, A., Goschke, T., & Kuhl, J. (2003). Emotion and intuition: Effects of positive and negative mood on implicit judgments of semantic coherence. Psychological Science, 14, 416–421. Borkenau, P., & Mauer, N. (2007). Well-being and the accessibility of pleasant and unpleasant concepts. European Journal of Personality, 21, 169–189. Bosson, J. K., Swann, W. B., Jr., & Pennebaker, J. W. (2000). Stalking the perfect measure of implicit self-Â�esteem: The blind man and the elephant revisited?: Journal of Personality and Social Psychology, 79, 631–643. Briñol, P., Petty, R. E., & Wheeler, S. C. (2006). Discrepancies between explicit and implicit self-Â�concepts: Consequences for information processing. Journal of Personality and Social Psychology, 91, 154–170. Brunel, F. F., Tietje, B. C., & Greenwald, A. G. (2004). Is the Implicit Association Test a valid and valuable measure of implicit consumer social cognition?: Journal of Consumer Psychology, 14, 385–404. Buchner, A., & Wippich, W. (2000). On the reliability of implicit and explicit memory measures. Cognitive Psychology, 40, 227–259. Cacioppo, J. T., Crites, S. L., & Gardner, W. L. (1996). Attitudes to the right: Evaluative processing is associated with lateralized late positive event-Â�related brain potentials. Personality and Social Psychology Bulletin, 22, 1205–1219. Cacioppo, J. T., Petty, R. E., Feinstein, J. A., & Jarvis, W. B. G. (1996). Dispositional differences in cognitive motivation: The life and times of individuals vary-



14. Prediction of Behavior

ing in need for cognition. Psychological Bulletin, 119, 197–253. Conner, M., Perugini, M., O’Gorman, R., Ayres, K., & Prestwich, A. (2007). Relations between implicit and explicit measures of attitudes and measures of behavior: Evidence of moderation by individual difference variables. Personality and Social Psychology Bulletin, 33, 1727–1740. Conrey, F. R., & Smith, E. R. (2007). Attitude representation: Attitudes as patterns in a distributed, connectionist representational system. Social Cognition, 25, 718–735. Cunningham, W. A., Preacher, K. J., & Banaji, M. R. (2001). Implicit attitude measures: Consistency, stability, and convergent validity. Psychological Science, 12, 163–170. Czopp, A. M., Monteith, M. J., Zimmerman, R. S., & Lynam, D. R. (2004). Implicit attitudes as potential protection from risky sex: Predicting condom use with the IAT. Basic and Applied Social Psychology, 26, 227–237. De Houwer, J. (2003). The Extrinsic Affective Simon Task. Experimental Psychology, 50, 77–85. De Houwer, J. (2008). Comparing measures of attitudes at the procedural and functional level. In R. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€361–390). Mahwah, NJ: Erlbaum. De Houwer, J., & De Bruycker, E. (2007a). The IAT outperforms the EAST as a measure of attitudes. British Journal of Social Psychology, 46, 401–421. De Houwer, J., & De Bruycker, E. (2007b). The identification-EAST as a valid measure of implicit attitudes toward alcohol-Â�related stimuli. Journal of Behavior Therapy and Experimental Psychiatry, 38, 133–143. Degner, J., Wentura, D., Gniewosz, B., & Noack, P. (2007). Hostility-Â�related prejudice against Turks in adolescents: Masked affective priming allows for a differentiation of automatic prejudice. Basic Applied Social Psychology, 29, 245–256. Degner, J., Wentura, D., & Rothermund, K. (2006). Indirect assessment of attitudes with response-timebased measures. Chances and problems. Zeitschrift für Sozialpsychologie, 37, 131–139. Deutsch, R., & Gawronski, B. (2009). When the method makes a difference: Antagonistic effects on “automatic evaluations” as a function of task characteristics of the measure. Journal of Experimental Social Psychology, 45, 101–114. Dijker, A., & Koomen, W. (1996). Stereotyping and attitudinal effects under time pressure. European Journal of Social Psychology, 26, 61–74. Dotsch, R., & Wigboldus, D. (2008). Virtual prejudice. Journal of Experimental Social Psychology, 44, 1194– 1198. Dovidio, J. F., Gaertner, S. L., Kawakami, K., & Hodson, G. (2002). Why can’t we just get along?: Interpersonal biases and interracial distrust. Cultural Diversity and Ethnic Minority Psychology, 8, 88–102. Dovidio, J. F., Kawakami, K., & Gaertner, S. L. (2002). Implicit and explicit prejudice and interracial interaction. Journal of Personality and Social Psychology, 82, 62–68. Dovidio, J. F., Kawakami, K., Johnson, C., Johnson, B.,

273

& Howard, A. (1997). On the nature of prejudice: Automatic and controlled processes. Journal of Experimental Social Psychology, 33, 510–540. Dunton, B. C., & Fazio, R. H. (1997). An individual difference measure of motivation to control prejudiced reactions. Personality and Social Psychology Bulletin, 23, 316–326. Edwards, J. R., & Lambert L. S. (2007). Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis. Psychological Methods, 12, 1–22. Egloff, B., & Schmukle, S. C. (2002). Predictive validity of an Implicit Association Test for assessing anxiety. Journal of Personality and Social Psychology, 83, 1441–1455. Ellwart, T., Becker, E., & Rinck, M. (2005). Activation and measurement of threat associations in fear of spiders: An application of the Extrinsic Affective Simon Task. Journal of Behavior Therapy and Experimental Psychiatry, 36, 281–299. Epstein, S. (1990). Cognitive-Â�experiential self-Â�theory. In L. Pervin (Ed.), Handbook of personality: Theory and research (pp.€165–192). New York: Guilford Press. Epstein, S., Pacini, R., Denes-Raj, V., & Heier, H. (1996). Individual differences in intuitive experiential and analytical-Â�rational thinking styles. Journal of Personality and Social Psychology, 71, 390–405. Erceg-Hurn, D. M., & Mirosevich, V.M. (2008). Modern robust statistical methods: An easy way to maximize the accuracy and power of your research. American Psychologist, 63, 591–601. Fazio, R. H. (1990). Multiple processes by which attitudes guide behaviour: The MODE model as an integrative framework. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 23, pp.€75–109). San Diego, CA: Academic Press. Fazio, R. H. (2007). Attitudes as object-Â�evaluation associations of varying strength. Social Cognition, 25, 603–637. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Fazio, R. H., & Olson, M. A. (2003). Implicit measures in social cognition research: Their meaning and use. Annual Review of Psychology, 54, 297–327. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. Fazio, R. H., & Towles-Â�Schwen, T. (1999). The MODE model of attitude-Â�behavior processes. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€97–116). New York: Guilford Press. Florack, A., Ineichen, S., & Bieri, R. (2009). The impact of regulatory focus on two-sided advertising. Social Cognition, 27, 37–56. Florack, A., Scarabis, M., & Bless, H. (2001). When do associations matter?: The use of automatic associations toward ethnic group in person judgments. Journal of Experimental Social Psychology, 37, 518–524. Friese, F., Bluemke, M., & Wänke, M. (2007). Predicting voter behavior with implicit attitude measures: The

274

III. CROSS-CUTTING PERSPECTIVES

2002 German parliamentary election. Experimental Psychology, 54, 247–255. Friese, M., & Hofmann, W. (2008). What would you have as a last supper?: Thoughts about death influence evaluation and consumption of food products. Journal of Experimental Social Psychology, 44, 1388– 1394. Friese, M., & Hofmann, W. (2009). Control me, or I will control you: Impulses, trait self-Â�control, and the guidance of behavior. Journal of Research in Personality, 43, 795–805. Friese, M., Hofmann, W., & Schmitt, M. (2008). When and why do implicit measures predict behavior?: Empirical evidence for the moderating role of opportunity, motivation, and process reliance. European Review of Social Psychology, 19, 285–338. Friese, M., Hofmann, W., & Wänke, M. (2008). When impulses take over: Moderated predictive validity of explicit and implicit attitude measures in predicting food choice and consumption behavior. British Journal of Social Psychology, 47, 397–419. Friese, M., Wänke, M., & Plessner, H. (2006). Implicit consumer preferences and their influence on product choice. Psychology and Marketing, 23, 727–740. Gabriel, U., Banse, R., & Hug, F. (2007). Predicting private and public helping behaviour by implicit attitudes and the motivation to control prejudiced reactions. British Journal of Social Psychology, 46, 365–382. Gailliot, M. T., & Baumeister, R. F. (2007). SelfÂ�regulation and sexual restraint: Dispositionally and temporarily poor self-Â�regulatory abilities contribute to failures at restraining sexual behavior. Personality and Social Psychology Bulletin, 33, 173–186. Galdi, S., Arcuri, L., & Gawronski, B. (2008). Automatic mental associations predict future choices of undecided decision makers. Science, 321, 1100–1102. Gallucci, M., & Perugini, M. (2009). Order analysis in counterbalanced methods and experimental designs. Unpublished manuscript. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. Gawronski, B., Geschke, D., & Banse, R. (2003). Implicit bias in impression formation: Associations influence the construal of individuating information. European Journal of Social Psychology, 33, 573–589. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Greenwald, A. G., & Nosek, B. A. (2009). Attitudinal dissociation: What does it mean?: In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€65–82). New York: Psychology Press. Greenwald, A. G., Poehlman, T. A., Uhlmann, E., & Banaji, M. R. (2009). Understanding and using the Implicit Association Test: III. Meta-Â�analysis of predictive validity. Journal of Personality and Social Psychology, 97, 17–41. Greenwald, A. G., Pratkanis, A. R., Leippe, M. R., & Baumgardner, M. H. (1986). Under what conditions

does theory obstruct research progress? Psychological Review, 93, 216–229. Grenard, J. L., Ames, S. L., Wiers, R. W., Thush, C., Sussman, S., & Stacy, A. W. (2008). Working memory moderates the predictive effects of drug-Â�related associations on substance use. Psychology of Addictive Behaviors, 22, 426–432. Gschwendner, T., Hofmann, W., & Schmitt, M. (2008a). Convergent and predictive validity of implicit and explicit anxiety measures as a function of specificity similarity and content similarity. European Journal of Psychological Assessment, 24, 254–262. Gschwendner, T., Hofmann, W., & Schmitt, M. (2008b). Differential stability: The effects of acute and chronic construct accessibility on the temporal stability of the Implicit Association Test. Journal of Individual Differences, 29, 70–79. Hart, A. J., Whalen, P. J., Shin, L. M., McInerney, S. C., Fischer, H., & Rauch, S. L. (2000). Differential response in the human amygdala to racial outgroup vs. ingroup face stimuli. NeuroReport, 11, 2351–2355. Hermsen, B., Holland, R. W., & van Knippenberg, A. (2006). The happy act on impulse, the sad think twice: Mood as a moderator of the impact of implicit and explicit attitudes on behavior. Unpublished manuscript. Hofmann, W., & Baumert, A. (in press). Immediate affect as a basis for intuitive moral judgment: An adaptation of the affect misattribution procedure. Cognition and Emotion. Hofmann, W., & Friese, M. (2008). Impulses got the better of me: Alcohol moderates the influence of immediate affective reactions to food cues on eating behavior. Journal of Abnormal Psychology, 117, 420–427. Hofmann, W., Friese, M., & Roefs, A. (2009). Three ways to resist temptation: The independent contributions of executive attention, inhibitory control, and affect regulation on the impulse control of eating behavior. Journal of Experimental Social Psychology, 45, 431–435. Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., & Schmitt, M. (2005). A meta-Â�analysis on the correlation between the Implicit Association Test and explicit self-Â�report measures. Personality and Social Psychology Bulletin, 31, 1369–1385. Hofmann, W., Gschwendner, T., Castelli, L., & Schmitt, M. (2008). Implicit and explicit attitudes and interracial interaction behavior: The moderating role of situationally available control resources. Group Processes and Intergroup Relations, 11, 69–87. Hofmann, W., Gschwendner, T., Friese, M., Wiers, R. W., & Schmitt, M. (2008). Working memory capacity and self-Â�regulatory behavior: Towards an individual differences perspective on behavior determination by automatic versus controlled processes. Journal of Personality and Social Psychology, 95, 962–977. Hofmann, W., Rauch, W., & Gawronski, B. (2007). And deplete us not into temptation: Automatic attitudes, dietary restraint, and self-Â�regulatory resources as determinants of eating behavior. Journal of Experimental Social Psychology, 43, 497–504. Houben, K., & Wiers, R. W. (2006). Assessing implicit alcohol associations with the IAT: Fact or artifact? Addictive Behaviors, 31, 1346–1362.



14. Prediction of Behavior

Houben, K., & Wiers, R. W. (2007a). Are drinkers implicitly positive about drinking alcohol?: Personalizing the alcohol-IAT to reduce negative extrapersonal contamination. Alcohol and Alcoholism, 42, 301–307. Houben, K., & Wiers, R. W. (2007b). Personalizing the alcohol-IAT with individualized stimuli: Relationship with drinking behavior and drinking-Â�related problems. Addictive Behaviors, 32, 2852–2864. Houben, R., Gijsen, A., Peterson, J., de Jong, P., & Vlaeyen, J. (2005). Do health care providers’ attitudes towards back pain predict their treatment recommendations?: Differential predictive validity of implicit and explicit attitude measures. Pain, 114, 491–498. Huijding, J., & de Jong, P. J. (2005). A pictorial version of the Extrinsic Affective Simon Task: Sensitivity to generally affective and phobia-Â�relevant stimuli in high and low spider fearful individuals. Experimental Psychology, 52, 289–295. Huijding, J., & de Jong, P. S. (2006). Specific predictive power of automatic spider related affective associations for controllable and uncontrollable fear responses toward spiders. Behaviour Research and Therapy, 44, 161–176. Ito, T. A., & Cacioppo, J. T. (2007). Attitudes as mental and neural states of readiness: Using physiological measures to study implicit attitudes. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp.€125–158). New York: Guilford Press. Ito, T. A., Thompson, E., & Cacioppo, J. T. (2004). Tracking the timecourse of social perception: The effects of racial cues on event-Â�related brain potentials. Personality and Social Psychology Bulletin, 30, 1267–1280. Jacoby, L. L. (1991). A process dissociation framework: Separating automatic from intentional uses of memory. Journal of Memory and Language, 30, 513–541. Jordan, C. H., Spencer, S. J., Zanna, M. P., HoshinoÂ�Browne, E., & Correll, J. (2003). Secure and defensive high self-Â�esteem. Journal of Personality and Social Psychology, 85, 969–978. Karpinski, A., & Hilton, J. L. (2001). Attitudes and the Implicit Association Test. Journal of Personality and Social Psychology, 81, 774–788. Karpinski, A., & Steinman, R. B. (2006). The single category Implicit Association Test as a measure of implicit social cognition. Journal of Personality and Social Psychology, 91, 16–32. Klauer, K. C., & Mierke, J. (2005). Task-set inertia, attitude accessibility, and compatibility-order effects: New evidence for a task-set switching account of the IAT effect. Personality and Social Psychology Bulletin, 31, 208–217. Kruglanski, A., & Freund, T. (1983). The freezing and unfreezing of lay-Â�inferences: Effects on impressional primacy, ethnic stereotyping, and numerical anchoring. Journal of Experimental Social Psychology, 19, 448–468. Lambert, A. J., Payne, B. K., Ramsey, S., & Shaffer, L. M. (2005). On the predictive validity of implicit attitude measures: The moderating effect of perceived group variability. Journal of Experimental Social Psychology, 41, 114–128. Lambert, A. J., Payne, B. K., Shaffer, L. M., Jacoby, L.

275

L., Chasteen, A., & Khan, S. (2003). Stereotypes as dominant responses: On the “social facilitation” of prejudice in anticipated public contexts. Journal of Personality and Social Psychology, 84, 277–295. LeBel, E. P., & Gawronski, B. (2009). How to find what’s in a name: Scrutinizing the optimality of five scoring algorithms for the name-Â�letter task. European Journal of Personality, 23, 85–106. Maass, A., Salvi, C., Arcuri, L., & Semin, G. R. (1989). Language use in intergroup contexts: The linguistic intergroup bias. Journal of Personality and Social Psychology, 57, 981–993. Maison, D., Greenwald, A. G., & Bruin, R. H. (2004). Predictive validity of the Implicit Association Test in studies of brands, consumer attitudes and behavior. Journal of Consumer Psychology, 14, 405–415. Marsh, K. L., Johnson, B. T., & Scott-Â�Sheldon, L. A. J. (2001). Heart versus reason in condom use: Implicit versus explicit attitudinal predictors of sexual behavior. Zeitschrift für Experimentelle Psychologie, 48, 161–175. McConnell, A. R., & Leibold, J. M. (2001). Relations among the Implicit Association Test, discriminatory behavior, and explicit measures of racial attitudes. Journal of Experimental Social Psychology, 37, 435–442. Merikle, P. M., & Reingold, E. M. (1991). Comparing direct (explicit) and indirect (implicit) measures to study unconscious memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17, 224–233. Mitchell, J. P., Nosek, B. A., & Banaji, M. R. (2003). Contextual variations in implicit evaluation. Journal of Experimental Psychology: General, 132, 455–469. Monteith, M. J., Voils, C. I., & Ashburn-Nardo, L. (2001). Taking a look underground: Detecting, interpreting and reacting to implicit racial biases. Social Cognition, 19, 395–417. Moors, A., & De Houwer, J. (2006). Automaticity: A conceptual and theoretical analysis. Psychological Bulletin, 132, 297–326. Muraven, M., & Baumeister, R. F. (2000). Self-Â�regulation and depletion of limited resources: Does self-Â�control resemble a muscle? Psychological Bulletin, 126, 247– 259. Nosek, B., & Sriram, N. (2007). Faulty assumptions: A comment on Blanton, Jaccard, Gonzales, and Christie (2006). Journal of Experimental Social Psychology, 43, 393–398. Nosek, B. A., & Banaji, M. R. (2001). The Go/No-Go Association Task. Social Cognition, 19, 625–666. Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2005). Understanding and using the Implicit Association Test: II. Method variables and construct validity. Personality and Social Psychology Bulletin, 31, 166–180. Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2007). The Implicit Association Test at age 7: A methodological and conceptual review. In J. A. Bargh (Ed.), Automatic processes in social thinking and behavior (pp.€265–292). New York: Psychology Press. Nuttin, J. M. (1985). Narcissism beyond gestalt and awareness: The name letter effect. European Journal of Social Psychology, 15, 353–361. Olson, M. A., & Fazio, R. H. (2004). Reducing the in-

276

III. CROSS-CUTTING PERSPECTIVES

fluence of extrapersonal associations on the Implicit Association Test: Personalizing the IAT. Journal of Personality and Social Psychology, 86, 653–667. Olson, M. A., & Fazio, R. H. (2009). Implicit and explicit measures of attitudes: The perspective of the MODE model. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€19–63). New York: Psychology Press. Payne, B. K. (2001). Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon. Journal of Personality and Social Psychology, 81, 181–192. Payne, B. K., Burkley, M., & Stokes, M. B. (2008). Why do implicit and explicit attitude tests diverge?: The role of structural fit. Journal of Personality and Social Psychology, 94, 16–31. Payne, B. K., Cheng, C. M., Govorun, O., & Stewart, B. D. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. Journal of Personality and Social Psychology, 89, 277–293. Payne, B. K., Govorun, O., & Arbuckle, N. L. (2008). Automatic attitudes and alcohol: Does implicit liking predict drinking? Cognition and Emotion, 22, 238–271. Payne, B. K., McClernon, J. F., & Dobbins, I. G. (2007). Automatic affective responses to smoking cues. Experimental and Clinical Psychopharmacology, 15, 400–409. Penke, L., Eichstaedt, J., & Asendorpf, J. B. (2006). Single Attribute Implicit Association Tests (SAIAT) for the assessment of unipolar constructs: The case of sociosexuality. Experimental Psychology, 53, 283–291. Perugini, M. (2005). Predictive models of implicit and explicit attitudes. British Journal of Social Psychology, 44, 29–45. Perugini, M., & Banse, R. (2007). Editorial: Personality, implicit self-Â�concept, and automaticity. European Journal of Personality, 21, 257–261. Perugini, M., & Leone, L. (2009). Implicit self-Â�concept and moral action. Journal of Research in Personality, 43, 747–754. Perugini, M., O’Gorman, R., & Prestwich, A. (2007). An ontological test of the IAT: Self-Â�activation can increase predictive validity. Experimental Psychology, 54, 134–147. Perugini, M., & Prestwich, A. (2007). The gatekeeper: Individual differences are key in the chain from perception to behavior. European Journal of Personality, 21, 303–317. Richetin, J., & Perugini, M. (2008). When temporal contiguity matters: A moderator of the predictive validity of implicit measures. European Journal of Psychological Assessment, 25, 246–253. Richetin, J., Perugini, M., Adjali, I., & Hurling, R. (2007). The moderator role of intuitive versus deliberative decision making for the predictive validity of implicit and explicit measures. European Journal of Personality, 21, 529–546. Richetin, J., Perugini, M., Prestwich, A., & O’Gorman, R. (2007). The IAT as a predictor of spontaneous food choice: The case of fruits versus snacks. International Journal of Psychology, 42, 166–173. Richetin, J., Richardson, D. S., & Mason, G. (2010).

Predictive validity of implicit and explicit measures of direct and indirect aggression in the context of provocation. Social Psychology, 41, 27–34. Roccato, M., & Zogmaister, C. (in press). Predicting the vote through implicit and explicit attitudes: A field research. Political Psychology. Rooke, S. A., Hine, D. W., & Thorsteinsson, E. B. (2008). Implicit cognition and substance use: A meta-Â�analysis. Additive Behaviors, 33, 1314–1328. Rydell, R. J., & Gawronski, B. (2009). I like you, I like you not: Understanding the formation of contextÂ�dependent automatic attitudes. Cognition and Emotion, 23, 1118–1152. Rydell, R. J., & McConnell, A. R. (2006). Understanding implicit and explicit attitude change: A systems of reasoning analysis. Journal of Personality and Social Psychology, 91, 995–1008. Scarabis, M., Florack, A., & Gosejohann, S. (2006). When consumers follow their feelings: The impact of affective or cognitive focus on the basis of consumers’ choice. Psychology and Marketing, 23, 1015–1034. Schacter, D. L., Bowers, J., & Booker, J. (1989). Intention, awareness, and implicit memory: The retrieval intentionality criterion. In S. Lewandowsky, K. Kirsner, & J. Dunn (Eds.), Implicit memory: Theoretical issues (pp.€47–65). Hillsdale, NJ: Erlbaum. Schmukle, S. C., & Egloff, B. (2006). Assessing anxiety with extrinsic Simon tasks. Experimental Psychology, 53, 149–160. Schnabel, K., Banse, R., & Asendorpf, J. B. (2006a). Assessment of implicit personality self concept using the Implicit Association Test (IAT): Concurrent assessment of anxiousness and angriness. British Journal of Social Psychology, 45, 373–396. Schnabel, K., Banse, R., & Asendorpf, J. B. (2006b). Employing automatic approach and avoidance tendencies for the assessment of implicit personality selfÂ�concept: The Implicit Association Procedure (IAP). Experimental Psychology, 53, 69–76. Schröder-Abé, M., Rudolph, A., & Schütz, A. (2007). High implicit self-Â�esteem is not necessarily advantageous: Discrepancies between explicit and implicit self-Â�esteem and their relationship with anger expression and psychological health. European Journal of Personality, 21, 319–339. Schröder-Abé, M., Rudolph, A., Wiesner, A., & Schütz, A. (2007). Self-Â�esteem discrepancies and defensive reactions to social feedback. International Journal of Psychology, 42, 174–183. Schwarz, N. (2007). Attitude construction: Evaluation in context. Social Cognition, 25, 638–656. Schwarz, N., & Bohner, G. (2001). The construction of attitudes. In A. Tesser & N. Schwarz (Eds.), Blackwell handbook of social psychology: Intraindividual processes (pp.€436–457). Malden, MA: Blackwell. Shiv, B., & Fedorikhin, A. (2002). Spontaneous versus controlled influences of stimulus-based affect on choice behaviour. Organizational Behavior and Human Decision Processes, 87, 342–370. Smeesters, D., Yzerbyt, V. Y., Corneille, O., & Warlop, L. (2009). When do primes prime?: The moderating role of the self-Â�concept in individuals’ susceptibility to priming effects on social behavior. Journal of Experimental Social Psychology, 45, 211–216.



14. Prediction of Behavior

Smith, E. R., & Semin, G. R. (2004). Socially situated cognition: Cognition in its social context. Advances in Experimental Social Psychology, 36, 53–117. Spalding, L. R., & Hardin, C. D. (1999). Unconscious€ unease and self-Â�handicapping: Behavioral consequences of individual differences in implicit and explicit self-Â�esteem. Psychological Science, 10, 535–539. Spence, A., & Townsend, E. (2007). Predicting behaviour towards genetically modified (GM) food using implicit and explicit attitudes. British Journal of Social Psychology, 46, 437–457. Spruyt, A., Hermans, D., De Houwer, J., Vandekerckhove, J., & Eelen, P. (2007). On the predictive validity of indirect attitude measures: Prediction of consumer choice behaviour on the basis of affective priming in the picture-Â�picture naming task. Journal of Experimental Social Psychology, 43, 599–610. Steinman, R. B., & Karpinski, A. (2008). The single category Implicit Association Test (SC-IAT) as a measure of implicit consumer attitudes. European Journal of Social Sciences, 7, 32–42. Stewart, N., Brown, G. D. A., & Chater, N. (2005). Absolute identification by relative judgment. Psychological Review, 112, 881–911. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High self-Â�control predicts good adjustment, less pathology, better grades, and interpersonal success. Journal of Personality, 72, 271–322. Teige, S., Schnabel, K., Banse, R., & Asendorpf, J. B. (2004). Assessment of multiple implicit self-Â�concept dimensions using the Extrinsic Affective Simon Task. European Journal of Personality, 18, 495–520. Teige-Â�Mocigemba, S., Klauer, K. C., & Rothermund, K. (2008). Minimizing method-Â�specific variance in the IAT: A single block IAT. European Journal of Psychological Assessment, 24, 237–245. Towles-Â�Schwen, T., & Fazio, R. H. (2003). Choosing social situations: The relation between automaticallyÂ�activated racial attitudes and anticipated comfort interacting with African Americans. Personality and Social Psychology Bulletin, 29, 170–182. Vargas, P. T., Sekaquaptewa, D., & von Hippel, W. (2004). It’s not just what you think, it’s also how you think: Prejudice as biased information processing. In J. D. Williams, W.-N. Lee, & C. P. Haugtvedt (Eds.),

277

Diversity in advertising: Broadening the scope of research direction (pp.€93–120). Mahwah, NJ: Erlbaum. Verplanken, B., & Aarts, H. (1999). Habit, attitude and planned behaviour: Is habit an empty construct or an interesting case of automaticity? European Review of Social Psychology, 10, 101–134. Verplanken, B., & Orbell, S. (2003). Reflections on past behavior: A self-Â�report index of habit strength. Journal of Applied Social Psychology, 33, 1313–1330. von Hippel, W., Brener, L., & von Hippel, C. (2008). Implicit prejudice toward injecting drug users predicts intentions to change jobs among drug and alcohol nurses. Psychological Science, 19, 7–11. Wheeler, S. C., & Berger, J. A. (2007). When the same prime leads to different effects. Journal of Consumer Research, 34, 357–368. Wheeler, S. C., DeMarree, K. G., & Petty, R. E. (2007). Understanding the role of the self in prime-toÂ�behavior effects: The active-self account. Personality and Social Psychology Review, 11, 234–261. Whitfield, M., & Jordan, C. H. (2009). Mutual influences of explicit and implicit attitudes. Journal of Experimental Social Psychology, 45, 748–759. Wigboldus, D. H. J., Holland, R. W., & van Knippenberg, A. (2004). Single target implicit associations. Unpublished manuscript. Wilson, T. D., Lindsey, S., & Schooler, T. Y. (2000). A model of dual attitudes. Psychological Review, 107, 101–126. Wilson, T. D., & Schooler, J. W. (1991). Thinking too much: Introspection can reduce the quality of preferences and decisions. Journal of Personality and Social Psychology, 60, 181–191. Wittenbrink, B., Judd, C. M., & Park, B. (1997). Evidence for racial prejudice at the implicit level and its relationship with questionnaire measures. Journal of Personality and Social Psychology, 72, 262–274. Wittenbrink, B., Park, B., & Judd, C. M. (2001). Evaluative versus conceptual judgments in automatic stereotyping and prejudice. Journal of Experimental Social Psychology, 37, 244–252. Zayas, V., & Shoda, Y. (2005). Do automatic reactions elicited by thoughts of romantic partner, mother, and self relate to adult romantic attachment? Personality and Social Psychology Bulletin, 31, 1011–1025. Zogmaister, C., Arcuri, L., Castelli, L., & Smith, E. R. (2008). The impact of loyalty and equality on implicit ingroup favoritism. Group Processes and Intergroup Relations, 11, 495–514.

C h a p t e r 15

Automatic Aspects of Judgment and€Decision Making Galen V. Bodenhausen and Andrew R. Todd

I

f asked to introspect about how they make decisions, many people would describe a thoughtful process of deliberating about the available options and weighing the pros and cons before selecting the most logical course of action. For them, decision making seems to be a conscious, intentional, language-based process. However, for others, decision making consists of an entirely different phenomenal experience, largely an intuitive process that simply involves selecting the option that “feels right.” In this case, the decision seems to be based on something other than deliberation, something more like a visceral gut reaction that arises spontaneously and for which only the resulting preference, and not the process(es) giving rise to it, is consciously accessible and verbalizable. Although they seem to represent dramatically different approaches, presumably most people can relate to both of these ways of making decisions, even if they favor one over the other (Richetin, Perugini, Adjali, & Hurling, 2007). Still, the most dominant cultural prototypes of the decision maker emphasize conscious reasoning rather than merely following intuitive hunches. High-Â�quality decisions, it is assumed, require thoughtful deliberation. One important kind of evidence for the power of nondeliberative processes in decision making is the fact that, across the vast spectrum of animal species, individuals (and collections of individuals) are capable of making adaptive decisions on



a regular basis (e.g., Balci, Freestone, & Gallistel, 2009; McFarland, 1977). Presumably, only humans have the capacity for language and its associated potential for rational deliberation, yet even invertebrates make adaptive decisions (e.g., Detrain, Deneubourg, & Pasteels, 1999). It might be tempting to dismiss nonhuman decision making as “merely” innate behavior or as behavior acquired via extremely simple learning algorithms such as instrumental conditioning. Even if such accounts were adequate, they would only attest to the power of automatic mechanisms to produce adaptive decision outcomes; moreover, they would not preclude the likelihood that human decision making can also rely on similar mechanisms. In fact, these explanations do not appear to be sufficient to explain the sophistication of many kinds of animal decision making. For example, capuchin monkeys learn to decide systematically between novel food options in a manner that is far too rapid for standard instrumental conditioning accounts (PadoaÂ�Schioppa, Jandolo, & Visalberghi, 2006). Preverbal human infants also have been shown to engage in evaluative information processing (Hamlin, Wynn, & Bloom, 2007), further evidence that a developed capacity for language, reflection, and rational analysis is not necessary for the execution of many kinds of judgment. Given that so many members of the animal kingdom possess powerful tools for effective de278



15. Automatic Decision Making

cision making—tools that do not rely on laborious, rational deliberation—and given that human beings are, in fact, part of this kingdom, it seems uncontroversial to assume that human cognition comes equipped with similar tools. At the same time, it also seems self-Â�evident that human decision-Â�making processes have certain especially powerful capacities that far outpace those of our phylogenetic cousins, tools that derive from language and the capacity for conscious reflection. One approach to reconciling these two seemingly disparate ways of making choices is to postulate the existence of two distinct mental systems that undergird judgment and decision making (Evans, 2008; Kahneman, 2003; see also Deutsch & Strack, Chapter 4, this volume). From this perspective, effortful deliberation constitutes a separate, potentially anatomically distinct (Lieberman, 2007) system from more automatic forms of judgment, a system that requires more time and mental resources for its operation. A common assumption is that a speed–Â�accuracy trade-off underlies the functioning of these systems. The automatic system produces rapid reactions that provide relatively crude first approximations for judgment or choice, whereas the deliberative system produces more finely tuned and “optimal” decisions, albeit at the cost of time and mental energy. In many circumstances, automatic responses do a sufficiently good job of generating adaptive responses, rendering deliberation entirely optional. Without sufficient motivation and opportunity for deliberation, individuals are unlikely to trade speed for accuracy. Dual-Â�process models of reasoning and judgment have proliferated at an impressive rate (Chaiken & Trope, 1999). Although there is a fair degree of coherence to the way the deliberative system is characterized across different models, the hypothesized nature and operating characteristics of the automatic system vary in noteworthy ways across specific approaches (Evans, 2008; see also Shah & Oppenheimer, 2008). There have been some impressive efforts to produce an integrative, canonical dual-Â�process model (e.g., Smith & DeCoster, 2000; Strack & Deutsch, 2004), although the prospects for completely avoiding controversy in such endeavors are clearly slim. In this chapter, we survey some of the principal aspects of automatic decision making and then consider how the automatic systems can interact with the deliberative system. In particular, we consider implicit aspects of ostensibly deliberative decision making as well as the contribution of explicit processes to ostensibly automatic decision making. We conclude the

279

chapter by considering the possibility that, rather than conferring cognitively costly accuracy, conscious deliberation may actually undermine effective decision making under some circumstances by leading decision makers to disregard more optimal implicit reactions.

VARIETIES OF AUTOMATIC DECISION MAKING A useful starting point is to consider what is meant by the term automatic decision making. Bargh (1994), Moors and De Houwer (2006), and others have enumerated several conceptually separable criteria that characterize automatic mental processes, including implicitness, spontaneity, rapidity, efficiency, and inevitability (in the presence of triggering cues). Automaticity is typically considered to be a matter of degree, and these features of automaticity do not necessarily covary in a systematic manner; thus, a decision-�making process can be considered automatic to the degree that it is characterized by some or all of these features. Implicitness is the criterion that is often of greatest interest. Indeed, to the extent that effective judgment and decision making can occur without conscious awareness, it would constitute a particularly compelling counterpoint to the notion of rational choice based on deliberation. Nisbett and Wilson (1977) provided a seminal analysis of the role of consciousness in choice behavior. They documented numerous situations in which judgments and decisions were influenced by variables that decision makers either had never consciously considered (e.g., whether an object was on the left or right side of a choice array) or had deemed to be completely irrelevant (e.g., whether or not a job candidate had spilled a little coffee). Such demonstrations confirm that important aspects of decision processes fall outside the boundaries of conscious deliberation. More recently, Pessiglione and colleagues (2008) showed that choice behavior could be influenced via subliminally presented reinforcement and punishment cues. Using functional neuroimaging, Pessiglione et al. found that the same brain regions involved in the conscious processing of reward value were also active when reinforcers were presented in a strictly subliminal manner. Given that many sophisticated forms of information acquisition occur in a nonconscious manner (Lewicki, Hill, & Czyzewska, 1992), it is virtually guaranteed that decision makers will have a substantially incomplete sense of the factors that influence their choices.

280

III. CROSS-CUTTING PERSPECTIVES

The implicitness of decision making is not limited to a lack of awareness of factors that influence a given decision but also includes lack of awareness of the processes through which the influential factors are combined to produce a discrete choice. Individuals sometimes may not even be aware of the fact that they have made a decision. That is, they select a course of action from among a set of contending possibilities without ever realizing that they have done so. This occurs, for example, when decision making is so routinized that it requires no conscious attention. Perhaps somewhat more provocatively, there may be instances in which strong motivational forces result in self-Â�deception about choices one has made (or failed to make). The selective exposure literature (for a review, see Bryant & Davies, 2006), for instance, suggests that people strategically decide to avoid potentially threatening information, but they may at least sometimes be unaware of the fact that they have done so. Finally, even when people are aware of making a choice, they may remain unaware of some of the psychological consequences of this decision. For example, they may not consciously appreciate that choosing an item can often cause elevated value to be ascribed to that object (Brehm, 1956; Sharot, De Martino, & Dolan, 2009), including at the level of automatic mental associations (Gawronski, Bodenhausen, & Becker, 2007). The features of automaticity that characterize judgment processes have been considered in several other influential research programs. For example, Zajonc (1980) showed that preferences can develop in ways that are independent of conceptual analysis, in a very spontaneous and unintended manner. The rapidity of some kinds of judgment processes has been emphasized in research on “thin slices,” which reveals that people can form seemingly instantaneous judgments of others based on very limited behavioral evidence (see Ambady & Skowronski, 2008). To understand the nature of automatic decision making, it may be most useful to specify some of the distinct forms it takes, irrespective of which particular features of automaticity are implicated in the supporting research.

Stimulus Affordances The concept of stimulus affordances developed in Gibson’s (1977) ecological model of perception describes one important basis for automatic decision making. Affordances are the “action possibilities” inherent in a given stimulus. Stimuli that possess a given affordance are assumed to elicit a corresponding kind of action tendency on the part of

perceivers. One account for the development of this kind of triggering relationship between a stimulus and the perceiver is the notion that adaptively appropriate reactions to significant, commonly recurring environmental cues facing a given species (e.g., avian predators) may become an innate characteristic of that species. This happens because individuals who possess impulses to react in adaptive ways to these environmental cues (e.g., running for cover whenever any bird-like stimulus looms from above) stand a better chance of reproducing. Appreciation of adaptively significant stimulus affordances can undoubtedly also be acquired in the course of ontogeny. With respect to human judgment, the implication is that there may be some kinds of cues—cues that bear in an important way on one’s well-being—that trigger rapid decisions when they are encountered. Research on rapid impressions of faces provides an interesting application of the notion of affordances in social decision making (e.g., Zebrowitz, 2006). When making potentially risky decisions about whether to trust a stranger, for example, it may be that we have an ability to make very rapid appraisals that can inform such decisions almost instantaneously. This idea lies at the heart of Oosterhof and Todorov’s (2008) model of face evaluation. Many diverse research streams have converged on the notion that the two key dimensions that characterize interpersonal relations are affiliation and dominance (see Abele, Cuddy, Judd, & Yzerbyt, 2008). The affiliation dimension bears on whether or not others can be trusted (i.e., how they would likely impact our well-being), and the dominance dimension bears on the person’s competence and power (i.e., how much they could potentially impact our well-being). Because of the fundamental significance of these issues, Oosterhof and Todorov propose that we have a capacity to extract rapid impressions of others on the basis of their physiognomic features; as a result, we can immediately size up strangers in terms of these dimensions. A person’s degree of trustworthiness is signaled by subtle similarities of the person’s basic (neutral) facial features to displays that are characteristic of happiness versus anger. Inferences of dominance are determined by the degree to which facial features signal masculinity and maturity. Because the degree of facial masculinity varies within each gender group, and facial maturity varies within age groups, these inferences respond to specific facial features and not to category membership per se. Evidence suggests that these inferences are essentially overgeneralizations of associations that have a crude kernel of truth (e.g., masculine faces



15. Automatic Decision Making

go with dominant behavior) but that are certainly far from universally applicable (Zebrowitz, Fellous, Mignault, & Andreoletti, 2003). They are nevertheless valuable because they provide a basis for rapid, nonrandom first impressions that can guide initial decisions when other information is lacking and the time and resources required to acquire it are not available. Research confirms that people do rely on such automatic inferences when making consequential decisions. For example, van ’t Wout and Sanfey (2008) showed that investment decisions made by players in the frequently studied trust game were guided by implicit inferences of trustworthiness derived from facial appearance cues. Even when other relevant information is available, facial affordances may still be influential in shaping decisions. Zebrowitz and McDonald (1991) showed systematic effects of litigants’ facial physiognomy on the legal decisions that were made about their cases. Todorov, Mandisodza, Goren, and Hall (2005) provided evidence suggesting that voting decisions are influenced by facial cues conveying competence, over and above explicit information about the candidates’ positions and credentials. Thus, facial affordances can be consequential in providing an immediate (and presumably often implicit) basis for making socially significant decisions.

Learned Associations A more flexible and potentially idiosyncratic basis for automatic judgments resides in learned associations that cannot plausibly be regarded as having a basis in innately recognized stimulus affordances. Some kinds of associations are descriptively specific in their content and would be relevant only for a relatively narrow range of decisions. For example, if one possesses an automatic association linking a particular ethnic group with sophisticated culinary skills, this association might influence decisions regarding selecting a restaurant for dinner or hiring a chef but probably not much else. In contrast, when associations involve global evaluations or heavily evaluation-laden traits, they provide a basis for making a much wider array of decisions. Documenting the potentially idiosyncratic and arbitrary nature of such associations, Lewicki (1985) showed that, after exposure to a single rude individual, participants choose to avoid a different person who had a similar hairstyle. At an unconscious level, the participants presumably associated the hairstyle with rudeness and made subsequent decisions on the basis of this

281

association. Although learned associations can be idiosyncratic in this way, they need not be. For example, ubiquitous cultural representations consistently depict particular social groups as possessing certain characteristics, producing widely shared automatic associations. Racial prejudice is often cited as a prime example of this phenomenon. If African Americans are commonly portrayed in negative contexts in the mass media, then it would not be surprising if consumers of the mass media tend to have automatic negative associations regarding this group. A boundary condition of this phenomenon was identified in recent research by Livingston and Drwecki (2007), who showed that individuals low in susceptibility to affective conditioning were unlikely to show racial biases. Thus, many people come to acquire automatic racial associations on the basis of commonly encountered representations of minority groups, but this form of socialization appears to be limited to those who are susceptible to affective conditioning processes. Does implicit racial bias of this sort have any important consequences for decision making? Some scholars have argued that implicit prejudice is primarily likely to influence spontaneous expressive behavior, such as nonverbal rapport, but may not influence explicit, intentional behavior and decision making (Dovidio, Kawakami, Johnson, Johnson, & Howard, 1997; Neumann, Hülsenbeck, & Seibt, 2004). However, evidence is growing that explicit decisions can indeed be shaped by automatic biases. Green and colleagues (2007) investigated medical decision making in a sample of 220 physicians. They assessed self-Â�reported racial attitudes as well as automatic racial associations (using the Implicit Association Test [IAT]). The physicians made treatment decisions about a series of clinical vignettes that systematically varied the race of the patient. Explicit racial attitudes were unrelated to treatment decisions, but implicit associations did predict differential decisions. Specifically, although they showed no evidence of preferential treatment for white patients in an absolute sense, physicians who held relatively more negative associations regarding blacks (both in terms of general evaluations and descriptive stereotypes about their cooperativeness) were less likely to recommend a therapeutic intervention (thrombolysis) for the cardiac symptoms presented by black versus white patients. Other research showing that automatic associations predict explicit judgments was provided by Gawronski, Ehrenberg, Banse, Zukova, and Klauer (2003). They demonstrated that targets in an impression formation task were ascribed stereotypic traits on the basis of their gen-

282

III. CROSS-CUTTING PERSPECTIVES

der only by individuals who had relatively strong automatic gender stereotypes, as assessed with the IAT; among individuals for whom these gender associations were relatively weak, judgments were based on individuating information rather than gender category membership. Studies like these indicate that there is a potential for strong automatic associations to influence judgments and decisions. Understanding the conditions in which such influence is or is not likely to emerge is a topic to which we return later.

Habit Although habitual action is sometimes contrasted with choice, it can be viewed as constituting an automatic form of decision making. Aarts, Verplanken, and van Knippenberg (1998; see also Ronis, Yates, & Kirscht, 1989) argued that choices in a new domain are likely to be based on a deliberative cost–Â�benefit analysis, as described in prominent theories of the attitude–Â�behavior relation (e.g., Ajzen, 1991; Fishbein & Ajzen, 1975); however, with sufficient repetition, decisions in the domain need not recruit any elaborate decision process. Instead, the habitual choice is simply automatically selected (Triandis, 1977). Aarts et al. provided empirical confirmation of this possibility in the case of decisions regarding travel mode.

Affective Reactions The impact of affective states on the process of making decisions has been frequently demonstrated. For example, Lerner and Keltner (2001) showed that anger can facilitate risky decision making, whereas fear inhibits it. Affective reactions typically carry many of the features of automaticity, including rapidity, spontaneity, and efficiency. Moreover, people often experience difficulty controlling emotional reactions (Wegner & Pennebaker, 1992). On the other hand, affective reactions are commonly accessible to conscious awareness, but, as we consider momentarily, some research suggests that they need not always be. How might these substantially automatic reactions influence decision making? One approach emphasizes the role of anticipated affect in choice (e.g., Knutson & Greer, 2008). When contemplating choice options, such as considering a product that one might purchase, decisions are guided substantially by whether anticipatory positive or negative affect arises. Knutson and colleagues have examined how this kind of anticipatory affect arises in the brain and how it relates to subsequent deci-

sions. The brain’s reward circuitry, in the nucleus accumbens, shows an activation spike in the period before approach-Â�related decisions are enacted (e.g., purchasing a product), while negative arousal, localized in the anterior insula, precedes avoidance (e.g., rejecting a product). Reviewing research involving a variety of economic decisions (purchases, investments, gambles), Knutson and Greer (2008) showed that functional magnetic resonance imaging measures of activity in these brain regions, in the period before actually making the decision, can predict eventual choices at levels well above chance. Anticipatory affect has also been shown to be important in more social kinds of decision making. For example, Kim, Adolphs, O’Doherty, and Shimojo (2008) recorded brain activity as participants viewed photos of strangers’ faces (without an explicit judgment task). The level of spontaneous activity observed in the nucleus accumbens predicted later preference judgments about these same faces. On the negative affect side, Sanfey, Rilling, Aronson, Nystrom, and Cohen (2003) showed that individuals’ decisions to defect when their partners had behaved unfairly in the ultimatum game were preceded by activation of the anterior insula. The extent to which anticipatory affect is independent of cognitive deliberation is an issue warranting further investigation, but there are good reasons to believe it can arise quite spontaneously, both independently of cognitive appraisals (see Zajonc, 1984) as well as in the course of thoughtful cost–Â�benefit analysis (see Fishbein & Ajzen, 1975). A provocative perspective on the relative importance of automatic affect versus “cold” cognitive cost–Â�benefit approaches to decision making is provided by the somatic marker hypothesis (Bechara & Damasio, 2005). A central claim of this hypothesis is that effective decision making relies heavily on spontaneous emotional signals. When such signals are compromised (e.g., in cases of brain damage specific to emotional processes; Bechara, 2004), individuals are forced to rely on “cold” reasoned analysis. As a result, they typically take a very long time to reach often inferior decisions. The term somatic marker refers to the entire ensemble of emotional signals arising in the body, which may or may not be consciously accessible. In essence, the somatic marker hypothesis proposes that emotional systems provide “valuable implicit or explicit knowledge for making fast and advantageous decisions” (Bechara & Damasio, 2005, p.€ 336). The neurophysiology underlying somatic markers provides a mechanistic account for the emergence of gut reactions, which can often shape judgment.



15. Automatic Decision Making

Although research in the somatic marker tradition implies that normal processing of affective cues is a critical ingredient in high-Â�quality decision making, there is an interesting exception. Specifically, Shiv, Loewenstein, and Bechara (2005) showed that individuals with impaired emotional processing (specifically, patients with focal brain lesions in neural tissue subserving emotional responses) made more personally advantageous decisions in an iterated investment task than did normal control participants. An explanation for this pattern lies in the fact that the anxiety experienced by the control participants in the context of risky investment decisions led them to adopt a suboptimal, conservative strategy. Because the lesion patients experienced much less anxiety given the nature of their brain damage, they were not as reluctant to invest at a higher level, and they earned higher payoffs as a result. Thus, the enduring notion that emotions can be at odds with effective decision making is not entirely without merit. More research is needed to clarify the contexts in which emotional responses facilitate versus undermine high-Â�quality decision making. The complexity of the role of affect in decision making is also reflected by research showing that incidental affective states having no connection or logical bearing on a given decision process can nevertheless bias its outcome. Schwarz and Clore (2003) provided influential demonstrations of this phenomenon, arguing that incidental affect serves as informational input into a straightforward “how-do-I-feel-about-it?” assessment. Unless an affective state is explicitly attributed to something other than the to-be-Â�judged stimulus, judgments of it are likely to be assimilated toward the evaluative tone of the experienced affect. Knutson, Wimmer, Kuhnen, and Winkielman (2008) provided an interesting demonstration of this phenomenon. They exposed participants to irrelevant positively arousing (e.g., erotic scenes) or negatively arousing (e.g., dangerous animals) images before making gambling decisions; the pictures were described as irrelevant distractors. Viewing positive images increased the likelihood of choosing a high-risk gamble, and this effect was mediated by activation of the nucleus accumbens following exposure to the pictures. As we have noted, it is sometimes unclear whether or not individuals are consciously aware of the affective signals that can shape their choices (see also Hofmann & Wilson, Chapter 11, this volume). Explicitly instructing individuals to monitor their feelings in the course of decision making has been shown to result in faster,

283

more consistent judgments compared with making reason-based assessments (e.g., Pham, Cohen, Pracejus, & Hughes, 2001). On the other hand, preventing individuals from monitoring their affect, by inducing the affect unconsciously, does not prevent the influence of the affective cue on judgments. For example, Zemack-Rugar, Bettman, and Fitzsimons (2007) showed that subliminal inductions of guilt (vs. sadness) resulted in less selfÂ�indulgent purchase decisions, even though the decision makers showed no conscious awareness of the guilt. Winkielman, Berridge, and Wilbarger (2005) likewise showed that subliminal exposure to happy (vs. angry) faces resulted in more positive judgments of a beverage even though conscious affective states were not influenced by the subliminal stimuli; however, this effect was limited to thirsty individuals, showing that motivational relevance can be an important moderator determining the impact of unconscious affect. Another kind of evidence bearing on awareness of emotional influences arises from the examination of the prospects for controlling these influences. If a particular emotional reaction is unconscious, then people should not be able to make use of explicit instructions for using the reaction to make good decisions. Along these lines, Stocco and Fum (2008) used the Iowa Gambling Task, in which participants must choose between card decks that vary in their likelihood of payoff. Over time, individuals in this task develop a preference for the advantageous decks that appears to be mediated by aversive physiological responses to the disadvantageous ones; these responses, in effect, may constitute an implicit affective early warning system. To test whether their participants were consciously aware of the affective influence that was presumably guiding their choices, Stocco and Fum stopped providing participants with feedback on their choices after the bias toward the “good” deck was established. Participants were instructed to make their subsequent “blind” choices in one of two ways. Half of the participants were told simply to continue making their choices as they had before, and the other half were explicitly told that the “bad” deck had turned “good” and vice versa. If these latter participants had been consciously aware of their aversive reaction to one of the decks, then they could capitalize on this information by explicitly choosing that deck. However, they generally failed to do so. Thus, the aversive responses that guide behavior so effectively in this task appear to do so without conscious awareness. So far, we have considered the role of innate and learned automatic associations, habits, and af-

284

III. CROSS-CUTTING PERSPECTIVES

fective reactions in decision making. Many other kinds of specific decision heuristics have been proposed over the years. Shah and Oppenheimer (2008) noted that the term heuristic has been used in such disparate ways that its fundamental, defining characteristics have become somewhat elusive. In an effort to remedy this situation, Shah and Oppenheimer selected “effort reduction” as the key hallmark of heuristics. From this perspective, heuristics constitute a relatively more automatic form of decision making, at least in terms of the efficiency criterion. However, one may ask whether such strategies are still essentially deliberative in nature, but just to a lesser degree than a full-scale cost–Â�benefit analysis. Some types of heuristics do seem to imply at least some degree of conscious deliberation, whereas others do not. The broad classes of decision mechanisms we have reviewed previously are ones that appear to be substantially automatic in terms of multiple criteria, but our list is surely not exhaustive. However, in many if not most circumstances, decision making is likely to represent a mixture of automatic and deliberative processes, and we now turn our attention to how automatic and deliberative mechanisms might interact in judgment and choice.

IMPLICIT ASPECTS OF€OSTENSIBLY DELIBERATIVE DECISION MAKING One seemingly radical position taken by some scholars is that it is not necessary to consider subjective psychological processes in order to account for adaptive decision making (e.g., Gul & Pesendorfer, 2005). Researchers studying animal decision making have developed models of choice based on the aggregation of stimulus-�driven patterns of neural activity (evidence accumulation) and subsequent attractor dynamics (response selection; see Beck et al., 2008), presumably in the complete absence of anything like reflective deliberation. To a radical behaviorist, such evidence conforms to the expectation that the conscious mind is merely the perceiver (and not the enactor) of decisions (Wegner & Wheatley, 1999). Soon, Brass, Heinze, and Haynes (2008) reported evidence suggesting that decisions arise in the human brain as much as 10 sec before awareness of the decision. In a similar vein, Bechara, Damasio, Tranel, and Damasio (1997) showed that physiological and behavioral responses reveal a nonconscious appreciation for the relative riskiness of different choice options, well before decision makers become consciously

aware of the relevant contingencies. Although provocative, evidence of this sort cannot rule out the possibility that conscious, deliberative processes may, in turn, influence the psychological and neural processes underlying judgment and decision making, at least under some conditions. To investigate the interplay between automatic and deliberative aspects of decision making, we begin by considering ways in which seemingly deliberate decision making is influenced by automatic processes, and we then consider the converse possibility that explicit processes can also shape automatic aspects of judgment and choice. To start with the obvious, it is indisputable that all deliberated decisions rest on a base of multiple automatic processes. For example, basic processes of sensation and perception are involved in discerning the evidence that is relevant to a given decision, and a complex and sophisticated array of implicit processes govern these phenomena. Lewicki and colleagues (1992) reviewed evidence that many important kinds of information used in decision making are acquired nonconsciously (see also Reber, 1989). They argue, for example, that patterns of covariation far too complex for the conscious mind to handle can be discerned and effortlessly used to guide behavior thanks to nonconscious mechanisms of information acquisition. The basic processes of attention, encoding, and memory that are essential to deliberative decision making are, in their fundamental character, automatic (see Weber & Johnson, 2009). Of greater concern here, however, is the question of how the varieties of automatic input into decisions described previously, such as automatic mental associations and spontaneous emotional reactions, relate to processes of deliberation. By virtue of their rapidity and spontaneity, it is likely that automatic reactions provide an initial starting point for reflective deliberation, assuming that they become accessible to awareness (and perhaps even if they do not). Some dual-�process models incorporate the idea that automatic reactions are likely to bias subsequent cognitive elaboration and deliberation, particularly when there is limited motivation for accuracy or objectivity (Chaiken, Liberman, & Eagly, 1989; Petty & Cacioppo, 1986). Many kinds of evidence suggest that when people have an initial hypothesis, they tend to engage in confirmatory strategies for testing that hypothesis (e.g., Klayman & Ha, 1987; Mussweiler, 2003). In the domain of stereotyping, Bodenhausen (1988; Bodenhausen & Wyer, 1985) proposed that stereotypic mental associations provide the basis for a rapid initial judgment or



15. Automatic Decision Making

hypothesis in decision-Â�making situations; subsequent deliberation and evidence appraisals tend to be guided by this snap judgment, producing discriminatory biases in both the decision itself and memory for relevant evidence. The specific role of automatic associations in this process of biased assimilation was documented more directly in research by Gawronski, Geschke, and Banse (2003). In this research, the authors measured automatic prejudice (toward Turks in Germany) using the IAT, and they gave participants an impression formation task in which they read somewhat ambiguous descriptions of a Turkish or ethnic German target. In this context, biased assimilation would be evident if judgments of the Turkish target were harsher than judgments of an ethnic German target given the same factual information because the behavioral information was interpreted in a more negative way. This was, in fact, the case, but only among participants who had relatively strong automatic prejudice, consistent with the notion that automatic associations biased the construal of the relevant evidence. Similarly, Hugenberg and Bodenhausen (2003) showed that participants’ interpretations of African Americans’ ambiguous facial displays were biased in a stereotypic direction, but only among individuals who were high in implicit prejudice as assessed by a race IAT. Findings of this sort exemplify the self-Â�perpetuating encoding biases described by Hill, Lewicki, Czyzewska, and Boss (1989); once a particular covariation (association) is established in the cognitive system, ambiguous cases are likely to be coded as confirming instances. One interesting extension of this line of thinking is the possibility that when a choice situation is characterized by conflict or ambivalence, the outcome of this conflict (i.e., the decision that ultimately wins out) may be predictable on the basis of association strength. Given that strong associations can bias evidence gathering, interpretation, and deliberation, they should have a leg up in guiding the subsequent course of decision making. This possibility was supported in recent research by Galdi, Arcuri, and Gawronski (2008), who showed that, among individuals who were undecided on a political issue, subsequent decisions could be predicted significantly above chance on the basis of knowing which option activated more positive automatic associations (as assessed using a variant of the IAT) at the earlier point in time. The extent to which automatic reactions bias deliberation is moderated by several factors. One important consideration is the “stop rule” governing the deliberation process. Decision makers must

285

determine when they have given an issue sufficient thought and can proceed with a decision. For example, Chaiken and colleagues’ (1989) heuristicÂ�systematic model incorporates a sufficiency principle, which states that the observed degree of deliberation will vary as a function of the individual’s desired level of decision confidence. When this criterion is low (i.e., when little deliberation is considered necessary), the likelihood of automatic biases dictating the final decision should be greater, compared with when the criterion is high and a wider array of information ends up being considered. Presumably, the more information one considers, the more likely it becomes that at least some of the information cannot be construed as supporting the choice implied by dominant automatic reactions. Similar predictions could be developed on the basis of Fazio’s motivation and opportunity as determinants (MODE) model (e.g., Fazio, 2007) as well as the associative–Â�propositional evaluation (APE) model (Gawronski & Bodenhausen, 2006). According to the APE model, automatic affective reactions form the starting point for evaluative judgments, but when competing propositional knowledge is encountered or retrieved from memory, it can result in the invalidation of the automatic affective reaction, in effect overriding it. Evidence consistent with this idea was provided by Florack, Scarabis, and Bless (2001), who showed that social judgments were more closely aligned with implicit associations among people who are lower in the need for cognition. Because they intrinsically enjoy the process of deliberation, individuals high in the need for cognition are likely to set a higher threshold for decision confidence and as a result are more likely to consider a broader range of information, some of which is likely to contradict the judgment implied by their implicit associations. As a result, these associations are less influential in the final judgment. Other kinds of motivational forces can exert analogous effects. For example, Schuette and Fazio (1995) showed that strong attitudinal associations were more aligned with relevant judgments when fear of invalidity was low, but when fear of invalidity was high judgments were likely to reflect a wider range of considerations other than just the activated evaluative associations. In addition to motivational forces, cognitive resources also moderate the degree of correspondence between rapid, automatic reactions and final judgments (e.g., Bodenhausen, 1990; Gilbert & Malone, 1995). When individuals are depleted or under cognitive load, they are likely to set a lower criterion for decision validity; as a result, they may

286

III. CROSS-CUTTING PERSPECTIVES

be satisfied with a decision that is largely based on the implications of their automatic reactions rather than more extensive deliberation. Using a process dissociation procedure, Ferreira, GarciaÂ�Marques, Sherman, and Sherman (2006) showed that cognitive load selectively interfered with propositional reasoning but not automatic (heuristic) reasoning; consequently, decisions made under a load were more likely to be based on a salient simple heuristic. With respect to depletion, Hofmann, Rauch, and Gawronski (2007) showed that candy consumption decisions were predicted by automatic candy evaluations (assessed via a variant of the IAT), but only among participants in a state of self-Â�regulatory depletion. When in full possession of self-Â�regulatory resources, candy consumption decisions were predicted by explicit dietary standards and not automatic associations. Both motivation and cognitive resources are needed for thoughtful deliberation, and when either of these factors is compromised, automatic reactions stand a substantially greater chance of controlling judgment and behavior. The example of candy consumption highlights the fact that, beyond the possibility of invoking a wider range of potentially contradictory considerations in the course of deliberation, controlled thought processes can also be strategically used in an attempt to override or rein in automatic tendencies (for a review, see Sherman, 2009). Many decision-Â�making situations are characterized by a tempting automatic response that is nevertheless considered inappropriate upon reflection (see Deutsch & Strack, Chapter 4, this volume). Relevant cases include hedonically appealing choice options (like candy) that hold the promise of immediate gratification, albeit at a cost to long-term well-being, impulsive reactions that are considered socially unacceptable (such as racial prejudice), and superstitious reactions that one explicitly recognizes as irrational (Kramer & Block, 2008). Stereotypic decisions and prejudice are often more evident under a cognitive load (see Bodenhausen, Macrae, & Sherman, 1999). For example, Hofmann, Gschwendner, Castelli, and Schmitt (2008) showed that a race IAT predicted actual interracial behavior to a greater extent when participants were under a cognitive load than when their cognitive resources were unencumbered. Similar findings have emerged with respect to depletion manipulations. Using a process dissociation procedure, Govorun and Payne (2006) showed that, although automatic biases were unaffected by ego depletion, controlled processes were compromised when participants’ self-Â�regulatory resources had

been temporarily diminished by a prior Stroop color-Â�naming task. The potential for controlling automatic biases is a topic of burgeoning interest in the literature. Some evidence suggests that automatic biases can be difficult to control (Bargh, 1999). In line with this notion, Gawronski, Geschke, and Banse’s (2003) study of prejudice toward Turkish people in Germany found that self-Â�reported motivation to control prejudiced responses did not moderate discrimination against Turks; that is, when individuals had negative automatic associations about Turks relative to ethnic Germans, they formed more negative impressions of a Turkish target completely irrespective of their motivation to control this kind of prejudice. It may be that, under some circumstances, people are unaware of the fact that they are being biased by their automatic associations, and thus they feel no need to make any corrections. This may be one important difference between implicit and explicit forms of racial prejudice (see Gawronski, Hofmann, & Wilbur, 2006). In general, though, the perceiver’s goals are known to be quite influential in decision making (e.g., Heath, Larrick, & Wu, 1999), and recent research has emphasized the idea that goals can operate nonconsciously (Bargh, Gollwitzer, LeeChai, Barndollar, & Troetschel, 2001; see Förster, Liberman, & Friedman, 2007, for a review). Thus, the pursuit of self-Â�regulatory goals such as avoiding unhealthy food choices or squelching prejudicial reactions could potentially operate with relative automaticity in decision making. At the very least, it has become apparent that efforts at controlling racial biases are evident even in highly speeded tasks (Sherman, 2009), and the success of these efforts appears to hinge crucially on the strength of general executive control skills (Payne, 2005). Powerful methodologies for separating out automatic bias and efforts to control this bias, such as the process dissociation procedure (Jacoby, 1991) and the Quad model (Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005), are providing new leverage on these issues (see Sherman, Klauer, & Allen, Chapter 9, this volume). In this section, we have considered several ways in which deliberative decision making responds to automatic decision inputs (i.e., automatic cognitive associations and affective reactions). One final perspective on the interaction of deliberative and automatic processes emphasizes the idea that controlled cognitive process and conscious deliberation are useful primarily for resolving conflict among competing automatic reactions (Frith & Singer, 2008). In essence, controlled processing



15. Automatic Decision Making

provides a top-down mechanism for prioritizing conflicting impulses. Frith and Singer use the example of players making decisions in economic games who simultaneously experience impulses to be prosocial and to be selfish. Cognitive control is needed to break the stalemate. Analogously, if individuals experience automatic activation of both prejudice and the goal to be egalitarian, cognitive control can be deployed to make a contextually appropriate decision as to which impulse is more acceptable. Such a mechanism clearly implies that controlled processing can influence (inhibit or reinforce) automatic tendencies, and in the next section we turn our attention in this direction.

EXPLICIT ASPECTS OF€OSTENSIBLY AUTOMATIC€REACTIONS As we previously noted, some automatic reactions may be relatively universal and, for all intents and purposes, innate, such as reactions to emotional faces, cheating/defection behavior, and disgusting substances. However, many kinds of automatic reactions are learned, and conscious processes can play a central role in the formation of such associations. The literature on skill acquisition incorporates this idea prominently. For example, Anderson (1982) proposed that acquiring a skill begins with declarative knowledge that is explicit and applied in a resource-Â�dependent manner but ends with procedural knowledge that can function quite automatically. This idea could certainly apply to decision making, in that when a certain type of decision is made repeatedly (e.g., deciding how to move in chess games), components of the decision-Â�making process become highly automated even though they start out quite deliberate (see Charness, Tuffiash, Krampe, Reingold, & Vasyukova, 2005). Research on intuitive decision making strongly underscores the importance of initially explicit processing in the relevant domain, perhaps quite extensive in nature, as a necessary precursor to high-Â�quality intuitive judgments (Hogarth, 2001). In a recent review of the literature on the role of intuition in judgment, Dane and Pratt (2007) stipulated as one of their key propositions the idea that “explicit learning will positively influence the effectiveness of intuitive decision making through the formation of complex, domain-Â�relevant schemas” (p.€43). Many lines of research show that deliberate cognitive processes have the ability to modulate automatic responses. For example, Blair, Ma, and Lenton (2001) showed that imagining counterste-

287

reotypic exemplars was sufficient to modify the momentary automatic associations that are detected by the IAT, and Dasgupta and Greenwald (2001) showed that thinking about particular admired or disliked members of a racial outgroup significantly influenced IAT scores. Similarly, automatic associations can be markedly modified simply by reading a relatively short passage describing the to-be-�evaluated categories in a counterstereotypical manner (Foroni & Mayr, 2005). Correll, Park, Judd, and Wittenbrink (2007) showed that reading newspaper stories about black criminals increased automatic racial bias in a simulated police decision�making task, in which participants must decide whether or not to shoot potential criminals. Research by Fazio, Blascovich, and Driscoll (1992) also provides a relevant window on the possibility that conscious processes can influence how automatic processes guide decisions. In their studies, participants either did or did not consciously rehearse their attitudes toward a range of stimuli about which they subsequently were required to make decisions. In so doing, they were presumably strengthening the automatic evaluative response they experienced regarding each stimulus. The result of this intentional strengthening of the automatic evaluative response was greater decision ease (reflected in reductions in an autonomic indicator of task demands, namely diastolic blood pressure) as well as higher decision quality. Kawakami, Dovidio, Moll, Hermsen, and Russin (2000) showed that, through conscious, intentional practice negating stereotypic associations and affirming counterstereotypic associations (see also Gawronski, Deutsch, Mbirkou, Seibt, & Strack, 2008), participants were able to reduce the subsequent amount of automatic stereotype activation they evinced in the presence of a relevant stimulus. In a follow-up study, Kawakami, Dovidio, and van Kamp (2005) documented a marked reduction in incidents of sex discrimination in hiring decisions as a result of this training procedure. Studies such as these indicate that people can modify their automatic reactions through intentional cognitive activities of one kind or another.

THE RELATIVE QUALITY OF AUTOMATIC VERSUS DELIBERATED DECISIONS One last issue of interest concerns the relative quality of decisions that rely on automatic versus deliberated cognitive processes. As we noted at the outset, one common perspective has been the idea

288

III. CROSS-CUTTING PERSPECTIVES

of a speed–Â�accuracy trade-off, which views automatic reactions as a rapid but crude initial basis for response. Such reactions may be sufficient in many conditions, but their accuracy or optimality could theoretically be improved if the person were to devote conscious thought to the matter, perhaps collecting more evidence and weighing the pros and cons systematically (Janis & Mann, 1977). This kind of pattern has been shown in prior research, even in domains that one would expect to be characterized by excellent automatic appraisals. For example, Tracy and Robins (2008) showed that judgments of facial emotion were generally accurate even when formed very rapidly and under cognitive load, but they also found that (modest) gains in accuracy resulted from deliberating about the faces before making a judgment. Although seemingly widely endorsed, the idea that deliberation can often result in better decisions than relying solely on one’s automatic reactions has been challenged in noteworthy ways. An illustrative example of such a challenge comes from research showing that creativity and problem solving can be facilitated by shifting one’s attention away from the task at hand for a period of time—an incubation period (Wallas, 1926). Although incubation effects have, at times, proven to be elusive (Olton & Johnson, 1976), a recent meta-Â�analysis supports their existence (Sio & Ormerod, 2009), noting several ways in which various unconscious processes that operate during incubation periods may contribute to problem solution. Another challenge to the assumed superiority of conscious thought comes from research documenting the disruptive effects of conscious deliberation on both the objective and subjective quality of decisions. For instance, there is evidence that engaging in careful analysis of the reasons for one’s preferences can lead to decisions that show little correspondence with those of experts (e.g., Halberstadt & Green, 2008; Wilson & Schooler, 1991). In one study, Halberstadt and Green (2008) had participants judge the quality of a set of Olympic dives and then compared these judgments with those of actual Olympic judges. They found that participants who were instructed to think carefully about why they thought each dive was good or bad were less accurate than participants who were left to their own devices in formulating their judgments. Other studies have shown that conscious deliberation can likewise be disruptive to the subjective quality of decisions. Wilson and colleagues (1993; see also Dijksterhuis & van Olden, 2006) had participants think about and list their reasons

for liking or disliking several posters. Afterward, participants chose the poster they wanted to keep. Wilson and colleagues found that postchoice satisfaction suffered as a result of having engaged in the reasons analysis. Because consciousness is limited in the number of pieces of information it can consider concurrently (Miller, 1956), it stands to reason that conscious deliberation can restrict decision makers’ attention to specific (and perhaps suboptimal) features of decision-Â�relevant stimuli at the expense of other, more relevant attributes. Wilson and colleagues (Wilson & Schooler, 1991; Wilson et al., 1993) argue that conscious deliberation can cause decision makers to place too much weight on those stimulus characteristics that are most accessible, plausible, and easily verbalized; consequently, they place too little weight on other characteristics, thereby diminishing the quality of their decisions. For instance, taking a rational and analytic approach to decision making may lead decision makers to discount affective reactions to decisionÂ�relevant stimuli as valid bases for their decisions (Halberstadt & Hooten, 2008) in favor of more easily verbalized, nonaffective reasons that are nonetheless misguided. Dijksterhuis and his colleagues (Dijksterhuis, 2004; Dijksterhuis, Bos, Nordgren, & van Baaren, 2006; Dijksterhuis & Nordgren, 2006) take the argument of Wilson and colleagues a step further by asserting not only that conscious thought is poor at weighting the relative importance of various attributes of decision-Â�relevant stimuli but also that unconscious thought, which they define as “object-Â�relevant or task-Â�relevant cognitive or affective thought processes that occur while conscious attention is directed elsewhere” (Dijksterhuis & Nordgren, 2006, p.€96), is particularly good at it. In part because of its greater information-Â�processing capacity and its ability to integrate vast amounts of information into a coherent representation, unconscious thought, they argue, should be especially adept at making complex decisions. This proposition was supported in a series of studies (Dijksterhuis et al., 2006). Specifically, when the circumstances surrounding a consumer decision were relatively complex (e.g., deciding among four cars that differ along multiple dimensions), a predecisional period of unconscious thought (i.e., distraction from the decision task) produced more optimal decisions than a period of conscious deliberation of equal duration. Under simpler circumstances (e.g., when those same four cars differ along only a few dimensions), however, it seems that



15. Automatic Decision Making

conscious deliberation led to more optimal decisions. This “deliberation-Â�without-Â�attention” effect has since been replicated outside the domain of consumer decisions. Zhong, Dijksterhuis, and Galinsky (2008), for example, showed that a period of unconscious thought increased accessibility of the correct solutions to a difficult set of items assessing creative problem solving. Additionally, Ham, Van den Bos, and Van Doorn (2009) found that unconscious thought led to more accurate judgments regarding the justness of a set of complex job application procedures. Dijksterhuis and colleagues conceive of unconscious thought as an active process wherein information regarding different decision alternatives is organized and integrated in a holistic and evaluatively polarized manner. In this way, their conception of unconscious thought bears a resemblance to processes proposed by certain “unconscious-work” explanations of incubation effects in problem solving (e.g., Yaniv & Meyer, 1987). In other words, they contend that the optimality of decisions made following unconscious thought cannot be attributed simply to the disruption of counterproductive conscious thought processes. Bos, Dijksterhuis, and van Baaren (2008) provided evidence supporting this contention. Specifically, they compared the quality of decisions made by distracted participants who learned (before being distracted) that they would later have to make a decision (i.e., their standard unconscious thought condition) and decisions by participants who were distracted but never given a decision-Â�making goal. Consistent with their predictions, Bos and colleagues found that the quality of decisions made by the former group far exceeded that of the latter. The work of Dijksterhuis and his colleagues thus offers evidence supporting the notion that, under certain circumstances (i.e., when making complex decisions), both the objective and subjective quality of decision making may benefit from a period of active yet unconscious deliberation. Despite its appeal, this work has received some criticism on both theoretical and methodological grounds (e.g., Acker, 2008; González-Â�Vallejo, Lassiter, Bellezza, & Lindberg, 2008; Newell, Wong, Cheung, & Rakow, 2009; Payne, Samper, Bettman, & Luce, 2008; Shanks, 2006). One such criticism comes from a recent meta-Â�analysis of 17 studies using procedures similar to those of Dijksterhuis and his colleagues (Acker, 2008). Perhaps surprisingly, there was little support for the claim that unconscious thought produces more optimal decisions than conscious thought. In fact, there was virtually no

289

difference in the quality of decisions as a function of whether they were made following a fixed period of conscious deliberation, a period of unconscious thought of equal duration, or immediately. Beyond these difficulties in replicating the deliberation-Â�without-Â�attention effect, Payne and colleagues (2008) raised the concern that forcing participants to deliberate for an artificially fixed amount of time (4 minutes; Dijksterhuis et al., 2006), as opposed to allowing them to deliberate at their own pace, is a less than ideal instantiation of conscious decision analysis. Consistent with this argument, they found that participants in a self-paced, conscious thought condition always performed at least as well as (and sometimes better than) participants in an unconscious thought condition. Interestingly, self-paced participants always outperformed participants in a fixed-time, conscious thought condition despite deliberating for a considerably shorter amount of time (median of 18 seconds), which suggests that forcing decision makers to deliberate consciously for an extended period of time may encourage them to shift their attention to irrelevant information, information that they would not have considered otherwise. Taken together, this research paints a rather complicated picture regarding the merits of unconscious thought on the quality of decisions. More generally, if it were the case that conscious, deliberate thought commonly undermines the quality of decision making relative to response preferences that emerge more automatically, then we might expect that individuals who possess a stronger inclination to deliberate about a decision, or who possess more resources for engaging in such deliberation (e.g., higher working memory capacity), would tend to produce inferior decisions relative to persons who respond more impulsively or who lack the capacity for more extensive deliberation. In fact, however, the opposite is true (Cokely & Kelley, 2009). Cognitive impulsivity is negatively related to decision quality in decision making under risk, whereas working memory capacity is positively related. Interestingly, the research of Cokely and Kelley (2009) indicates that these gains in decision quality do not arise from greater reliance on normative expectancy–value calculations but instead derive from metacognitive dynamics and elaborative heuristic search processes. Taken as a whole then, the decision-Â�making literature supports the conclusion that, although conscious deliberation can undermine decision quality under some circumstances, this is by no means a general principle.

290

III. CROSS-CUTTING PERSPECTIVES

CONCLUSION The contributions of automatic processes to decision making are clearly complex and multifaceted, with some theoretical perspectives praising their role and others impugning it. There are many compelling reasons to view automatic reactions as adaptive and even essential to judgment and choice. Rapid, spontaneous affective reactions provide a basis for preferences that efficiently guide decision making; in their absence, even the simplest kinds of choices can be difficult to navigate. Unconscious information acquisition algorithms provide a basis for detecting and using complex covariation information to an extent that would be daunting to the limited processing capacity of the conscious mind. The weighting and integration of multiple considerations in multiattribute choice situations can also, at least sometimes, unfold in a relatively effortless manner, even when conscious attention is directed elsewhere. In respects such as these, automatic processes seem to be superior to conscious deliberation. At the same time, the unconscious mind is also commonly depicted as the repository of irrational biases. Countless studies document the existence and operation of automatic stereotyping and prejudice. Automatic impulses for immediate gratification often trump rational, long-term thinking and planning, resulting in choices that have cumulatively detrimental consequences for personal and societal well-being. Emotions like anger, fear, and sadness can run amok, producing irrational and ultimately self-Â�defeating tendencies. In respects such as these, automatic processes often seem to pose problems that the conscious mind must then attempt to address and remedy. These dueling perspectives each capture important truths. The lesson is that simplistic characterizations of automatic decision making as either a panacea or a poison are clearly inadequate. Instead, we need to work toward a conceptual synthesis that carefully delineates the multiple ways that automatic processes interact with conscious thinking to produce human decision making in all of its gradations of quality. Although there are noteworthy difficulties in assessing decision quality objectively (Edwards, Kiss, Majone, & Toda, 1984; Kruglanski, 1989), descriptive models of decision making will necessarily have to grapple with the interaction of automatic and deliberative processes in order to provide a compelling picture of the realities of human judgment and choice (cf. Glöckner & Betsch, 2008).

In this chapter, we have considered cases in which automatic reactions bias subsequent deliberations as well as cases in which conscious reasoning processes attempt to override automatic biases. These reasonably well-Â�explored ideas are just a starting point. Many basic questions about the dynamic interplay of automatic and deliberative processes remain to be investigated, and boundary conditions and moderating variables remain to be specified. Concerted efforts of conscious analysis (and perhaps also unconscious incubation) will be required to illuminate these issues in more satisfying detail. References Aarts, H., Verplanken, B., & van Knippenberg, A. (1998). Predicting behavior from actions in the past: Repeated decision making or a matter of habit? Journal of Applied Social Psychology, 28, 1355–1374. Abele, A. E., Cuddy, A. J. C., Judd, C. M., & Yzerbyt, V. Y. (Eds.). (2008). Fundamental dimensions of social judgment [Special issue]. European Journal of Social Psychology, 38. Acker, F. (2008). New findings on unconscious versus conscious thought in decision making: Additional empirical data and meta-Â�analysis. Judgment and Decision Making, 3, 292–303. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. Ambady, N., & Skowronski, J. J. (Eds.). (2008). First impressions. New York: Guilford Press. Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369–406. Balci, F., Freestone, D., & Gallistel, C. R. (2009). Risk assessment in man and mouse. Proceedings of the National Academy of Sciences USA, 106, 2459–2463. Bargh, J. A. (1994). The four horsemen of automaticity: Awareness, intention, efficiency, and control in social cognition. In R. S. Wyer, Jr. & T. K. Srull (Eds.), Handbook of social cognition (2nd ed., pp.€ 1–40). Hillsdale, NJ: Erlbaum. Bargh, J. A. (1999). The cognitive monster: The case against the controllability of automatic stereotype effects. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€301–382). New York: Guilford Press. Bargh, J. A., Gollwitzer, P. M., Lee-Chai, A. Y., Barndollar, K., & Troetschel, R. (2001). The automated will: Nonconscious activation and pursuit of behavioral goals. Journal of Personality and Social Psychology, 81, 1014–1027. Bechara, A. (2004). The role of emotion in decisionÂ�making: Evidence from neurological patients with orbitofrontal damage. Brain and Cognition, 55, 30–40. Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 52, 336–372.



15. Automatic Decision Making

Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 275, 1293–1295. Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., et al. (2008). Probabilistic population codes for Bayesian decision making. Neuron, 60, 1142–1152. Blair, I. V., Ma, J. E., & Lenton, A. P. (2001). Imagining stereotypes away: The moderation of implicit stereotypes through mental imagery. Journal of Personality and Social Psychology, 81, 828–841. Bodenhausen, G. V. (1988). Stereotypic biases in social decision making and memory: Testing process models of stereotype use. Journal of Personality and Social Psychology, 55, 726–737. Bodenhausen, G. V. (1990). Stereotypes as judgmental heuristics: Evidence of circadian variations in discrimination. Psychological Science, 1, 319–322. Bodenhausen, G. V., Macrae, C. N., & Sherman, J. W. (1999). On the dialectics of discrimination: Dual processes in social stereotyping. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€271–290). New York: Guilford Press. Bodenhausen, G. V., & Wyer, R. S., Jr. (1985). Effects of stereotypes on decision making and informationÂ�processing strategies. Journal of Personality and Social Psychology, 48, 267–282. Bos, M. W., Dijksterhuis, A., & van Baaren, R. B. (2008). On the goal-Â�dependency of unconscious thought. Journal of Experimental Social Psychology, 44, 1114–1120. Brehm, J. W. (1956). Postdecision changes in the desirability of alternatives. Journal of Abnormal and Social Psychology, 52, 384–389. Bryant, J., & Davies, J. (2006). Selective exposure processes. In J. Bryant & P. Vorderer (Eds.), Psychology of entertainment (pp.€19–34). Mahwah, NJ: Erlbaum. Chaiken, S., Liberman, A., & Eagly, A. H. (1989). Heuristic and systematic information processing within and beyond the persuasion context. In J. S. Uleman & J. A. Bargh (Eds.), Unintended thought (pp.€ 212– 252). New York: Guilford Press. Chaiken, S., & Trope, Y. (Eds.). (1999). Dual-Â�process Â�theories in social psychology. New York: Guilford Press. Charness, N., Tuffiash, M., Krampe, R., Reingold, E., & Vasyukova, E. (2005). The role of deliberate practice in chess expertise. Applied Cognitive Psychology, 19, 151–165. Cokely, E. T., & Kelley, C. M. (2009). Cognitive abilities and superior decision making under risk: A protocol analysis and process model evaluation. Judgment and Decision Making, 4, 20–33. Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K., & Groom, C. J. (2005). Separating multiple processes in implicit social cognition: The quad model of implicit task performance. Journal of Personality and Social Psychology, 89, 469–487. Correll, J., Park, B., Judd, C. M., & Wittenbrink, B. (2007). The influence of stereotypes on decisions to shoot. European Journal of Social Psychology, 37, 1102–1117.

291

Dane, E., & Pratt, M. G. (2007). Exploring intuition and its role in managerial decision making. Academy of Management Review, 32, 33–54. Dasgupta, N., & Greenwald, A. G. (2001). On the malleability of automatic attitudes: Combatting automatic prejudice with images of admired and disliked individuals. Journal of Personality and Social Psychology, 81, 800–814. Detrain, C., Deneubourg, J. L., & Pasteels, J. M. (Eds.). (1999). Information processing in social insects. Boston: Birkhäuser. Dijksterhuis, A. (2004). Think different: The merits of unconscious thought in preference development and decision making. Journal of Personality and Social Psychology, 87, 586–598. Dijksterhuis, A., Bos, M. W., Nordgren, L. F., & van Baaren, R. B. (2006). On making the right choice: The deliberation-Â�without-Â�attention effect. Science, 311, 1005–1007. Dijksterhuis, A., & Nordgren, L. F. (2006). A theory of unconscious thought. Perspectives on Psychological Science, 1, 95–109. Dijksterhuis, A., & van Olden, Z. (2006). On the benefits of thinking unconsciously: Unconscious thought can increase post-Â�choice satisfaction. Journal of Experimental Social Psychology, 42, 627–631. Dovidio, J. F., Kawakami, K., Johnson, C., Johnson, B., & Howard, A. (1997). On the nature of prejudice: Automatic and controlled processes. Journal of Experimental Social Psychology, 33, 510–540. Edwards, W., Kiss, I., Majone, G., & Toda, M. (1984). What constitutes “a good decision”? Acta Psychologica, 56, 5–27. Evans, J. S. (2008). Dual-Â�processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, 255–278. Fazio, R. H. (2007). Attitudes as object-Â�evaluation associations of varying strength. Social Cognition, 25, 603–637. Fazio, R. H., Blascovich, J., & Driscoll, D. M. (1992). On the functional value of attitudes: The influence of accessible attitudes on the ease and quality of decision making. Personality and Social Psychology Bulletin, 18, 388–401. Ferreira, M. B., Garcia-Â�Marques, L., Sherman, S. J., & Sherman, J. W. (2006). Automatic and controlled components of judgment and decision making. Journal of Personality and Social Psychology, 91, 797–813. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Â�Wesley. Florack, A., Scarabis, M., & Bless, H. (2001). When do associations matter?: The use of automatic associations toward ethnic groups in person judgments. Journal of Experimental Social Psychology, 37, 518–524. Foroni, F., & Mayr, U. (2005). The power of a story: New, automatic associations from a single reading of a short scenario. Psychonomic Bulletin and Review, 12, 139–144. Förster, J., Liberman, N., & Friedman, R. S. (2007). Seven principles of goal activation: A systematic approach to distinguishing goal priming from priming

292

III. CROSS-CUTTING PERSPECTIVES

of non-goal constructs. Personality and Social Psychology Review, 11, 211–233. Frith, C. D., & Singer, T. (2008). The role of social cognition in decision making. Philosophical Transactions of the Royal Society B, 363, 3875–3886. Galdi, S., Arcuri, L., & Gawronski, B. (2008). Automatic mental associations predict future choices of€Â�undecided decision-Â�makers. Science, 321, 1100– 1102. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. Gawronski, B., Bodenhausen, G. V., & Becker, A. P. (2007). I like it, because I like myself: Associative self-Â�anchoring and post-Â�decisional change of implicit attitudes. Journal of Experimental Social Psychology, 43, 221–232. Gawronski, B., Deutsch, R., Mbirkou, S., Seibt, B., & Strack, F. (2008). When “just say no” is not enough: Affirmation versus negation training and the reduction of automatic stereotype activation. Journal of Experimental Social Psychology, 44, 370–377. Gawronski, B., Ehrenberg, K., Banse, R., Zukova, J., & Klauer, K. C. (2003). It’s in the mind of the beholder: The impact of stereotypic associations on categorybased and individuating impression formation. Journal of Experimental Social Psychology, 39, 16–30. Gawronski, B., Geschke, D., & Banse, R. (2003). Implicit bias in impression formation: Associations influence the construal of individuating information. European Journal of Social Psychology, 33, 573–589. Gawronski, B., Hofmann, W., & Wilbur, C. J. (2006). Are “implicit” attitudes unconscious? Consciousness and Cognition, 15, 485–499. Gibson, J. J. (1977). The theory of affordances. In R. E. Shaw & J. Bransford (Eds.), Perceiving, acting, and knowing (pp.€67–82). Hillsdale, NJ: Erlbaum. Gilbert, D. T., & Malone, P. S. (1995). The correspondence bias. Psychological Bulletin, 117, 21–30. Glöckner, A., & Betsch, T. (2008). Modeling option and strategy choices with connectionist networks: Towards an integrative model of automatic and deliberative decision making. Judgment and Decision Making, 3, 215–228. González-Â�Vallejo, C., Lassiter, G. D., Bellezza, F. S., & Lindberg, M. J. (2008). “Save angels perhaps”: A critical examination of unconscious thought theory and the deliberation-Â�without-Â�attention effect. Review of General Psychology, 12, 282–296. Govorun, O., & Payne, B. K. (2006). Ego-Â�depletion and prejudice: Separating automatic and controlled components. Social Cognition, 24, 111–136. Green, A. R., Carney, D. R., Pallin, D. J., Ngo, L. H., Raymond, K. L., Iezzoni, L. I., et al. (2007). Implicit bias among physicians and its prediction of thrombolysis decisions for black and white patients. Journal of General Internal Medicine, 22, 1231–1238. Gul, F., & Pesendorfer, W. (2005). The case for mindless economics. Princeton, NJ: Princeton University Press. Halberstadt, J., & Green, J. (2008). Carryover effects of analytic thought on preference quality. Journal of Experimental Social Psychology, 44, 1199–1203.

Halberstadt, J., & Hooten, K. (2008). The affect disruption hypothesis: The effect of analytic thought on the fluency and appeal of art. Cognition and Emotion, 22, 964–976. Ham, J., Van den Bos, K., & Van Doorn, E. A. (2009). Lady justice thinks unconsciously: Unconscious thought can lead to more accurate justice judgments. Social Cognition, 27, 509–521. Hamlin, J. K., Wynn, K., & Bloom, P. (2007). Social evaluation by preverbal infants. Nature, 450, 557– 559. Heath, C., Larrick, R. P., & Wu, G. (1999). Goals as reference points. Cognitive Psychology, 38, 79–109. Hill, T., Lewicki, P., Czyzewska, M., & Boss, A. (1989). Self-Â�perpetuating development of encoding biases in person perception. Journal of Personality and Social Psychology, 57, 373–387. Hofmann, W., Gschwendner, T., Castelli, L., & Schmitt, M. (2008). Implicit and explicit attitudes and interracial interaction: The moderating role of situationally available control resources. Group Processes and Intergroup Relations, 11, 69–87. Hofmann, W., Rauch, W., & Gawronski, B. (2007). And deplete us not into temptation: Automatic attitudes, dietary restraint, and self-Â�regulatory resources as determinants of eating behavior. Journal of Experimental Social Psychology, 43, 497–504. Hogarth, R. M. (2001). Educating intuition. Chicago: University of Chicago Press. Hugenberg, K., & Bodenhausen, G. V. (2003). Facing prejudice: Implicit prejudice and the perception of facial threat. Psychological Science, 14, 640–643. Jacoby, L. L. (1991). A process dissociation framework: Separating automatic from intentional uses of memory. Journal of Memory and Language, 30, 513–541. Janis, I. L., & Mann, L. (1977). Decision making: A psychological analysis of conflict, choice, and commitment. New York: Free Press. Kahneman, D. (2003). A perspective on judgment and choice: Mapping bounded rationality. American Psychologist, 58, 697–720. Kawakami, K., Dovidio, J. F., Moll, J., Hermsen, S., & Russin, A. (2000). Just say no (to stereotyping): Effects of training in the negation of stereotypic associations on stereotype activation. Journal of Personality and Social Psychology, 78, 871–888. Kawakami, K., Dovidio, J. F., & van Kamp, S. (2005). Kicking the habit: Effects of nonstereotypic association training and correction processes on hiring decisions. Journal of Experimental Social Psychology, 41, 68–75. Kim, H., Adolphs, R., O’Doherty, J. P., & Shimojo, S. (2008). Temporal isolation of neural processes underlying face preference decisions. Proceedings of the National Academy of Sciences USA, 104, 18253–18258. Klayman, J., & Ha, Y.-W. (1987). Confirmation, disconfirmation, and information in hypothesis testing. Psychological Review, 94, 211–228. Knutson, B., & Greer, S. M. (2008). Anticipatory affect: Neural correlates and consequences for choice. Philosophical Transactions of the Royal Society B, 363, 3771–3786. Knutson, B., Wimmer, G. E., Kuhnen, C. M., & Winkielman, P. (2008). Nucleus accumbens activation medi-



15. Automatic Decision Making

ates the influence of reward cues on financial risk taking. NeuroReport, 19, 509–513. Kramer, T., & Block, L. (2008). Conscious and nonconscious components of superstitious beliefs in judgment and decision making. Journal of Consumer Research, 34, 783–793. Kruglanski, A. W. (1989). The psychology of being “right”: The problem of accuracy in social perception and cognition. Psychological Bulletin, 106, 395–409. Lerner, J. S., & Keltner, D. (2001). Fear, anger, and risk. Journal of Personality and Social Psychology, 81, 146–159. Lewicki, P. (1985). Nonconscious biasing effects of single instances on subsequent judgments. Journal of Personality and Social Psychology, 48, 563–574. Lewicki, P., Hill, T., & Czyzewska, M. (1992). Nonconscious acquisition of information. American Psychologist, 47, 796–801. Lieberman, M. D. (2007). The X- and C-systems: The neural basis of automatic and controlled social cognition. In E. Harmon-Jones & P. Winkielman (Eds.), Social neuroscience: Integrating biological and psychological explanations for social behavior (pp.€290–315). New York: Guilford Press. Livingston, R. W., & Drwecki, B. B. (2007). Why are some individuals not racially biased?: Susceptibility to affective conditioning predicts nonprejudice toward blacks. Psychological Science, 18, 816–823. McFarland, D. J. (1977). Decision making in animals. Nature, 269, 15–21. Miller, G. A. (1956). Information theory. Scientific American, 195, 42–46. Moors, A., & De Houwer, J. (2006). Automaticity: A theoretical and conceptual analysis. Psychological Bulletin, 132, 297–326. Mussweiler, T. (2003). Comparison processes in social judgment: Mechanisms and consequences. Psychological Review, 110, 472–489. Neumann, R., Hülsenbeck, K., & Seibt, B. (2004). Attitudes toward people with AIDS and avoidance behavior: Automatic and reflective bases of behavior. Journal of Experimental Social Psychology, 40, 543–550. Newell, B. R., Wong, K. Y., Cheung, J. C. H., & Rakow, T. (2009). Think, blink or sleep on it?: The impact of modes of thought on complex decision making. Quarterly Journal of Experimental Psychology, 62, 707–732. Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84, 231–259. Olton, R. M., & Johnson, D. M. (1976). Experimental studies of incubation in creative problem solving. American Journal of Psychology, 89, 617–630. Oosterhof, N. N., & Todorov, A. (2008). The functional basis of face evaluation. Proceedings of the National Academy of Sciences USA, 105, 11087–11092. Padoa-Â�Schioppa, C., Jandolo, L., & Visalberghi, E. (2006). Multi-stage mental process for economic choice in capuchins. Cognition, 99, B1–B13. Payne, B. K. (2005). Conceptualizing control in social cognition: How executive functioning modulates the expression of automatic stereotyping. Journal of Personality and Social Psychology, 89, 488–503.

293

Payne, J. W., Samper, A., Bettman, J. R., & Luce, M. F. (2008). Boundary conditions on unconscious thought in complex decision making. Psychological Science, 19, 1118–1123. Pessiglione, M., Petrovic, P., Daunizeau, J., Palminteri, S., Dolan, R. J., & Frith, C. D. (2008). Subliminal instrumental conditioning demonstrated in the human brain. Neuron, 59, 561–567. Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 19, pp.€123–205). New York: Academic Press. Pham, M. T., Cohen, J. B., Pracejus, J. W., & Hughes, G. D. (2001). Affect monitoring and the primacy of feelings in judgment. Journal of Consumer Research, 28, 167–188. Reber, A. S. (1989). Implicit learning and tacit knowledge. Journal of Experimental Psychology: General, 118, 219–235. Richetin, J., Perugini, M., Adjali, I., & Hurling, R. (2007). The moderator role of intuitive versus deliberative decision making for the predictive validity of implicit measures. European Journal of Personality, 21, 529–546. Ronis, D. L., Yates, J. F., & Kirscht, J. P. (1989). Attitudes, decision, and habits as determinants of repeated behavior. In A. R. Pratkanis, S. J. Breckler, & A. G. Greenwald (Eds.), Attitude structure and function (pp.€213–239). Hillsdale, NJ: Erlbaum. Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The neural basis of economic decision-Â�making in the ultimatum game. Science, 300, 1755–1758. Schooler, J. W., Ohlsson, S., & Brooks, K. (1993). Thoughts beyond words: When language overshadows insight. Journal of Experimental Psychology: General, 122, 166–183. Schuette, R. A., & Fazio, R. H. (1995). Attitude accessibility and motivation as determinants of biased processing: A test of the MODE model. Personality and Social Psychology Bulletin, 21, 704–710. Schwarz, N., & Clore, G. L. (2003). Mood as information: 20 years later. Psychological Inquiry, 14, 296– 303. Shah, A. K., & Oppenheimer, D. M. (2008). Heuristics made easy: An effort-Â�reduction framework. Psychological Bulletin, 134, 207–222. Shanks, D. R. (2006). Complex choices better made unconsciously? Science, 313, 760–761. Sharot, T., De Martino, B., & Dolan, R. J. (2009). How choice reveals and shapes expected hedonic outcome. Journal of Neuroscience, 29, 3760–3765. Sherman, J. W. (2009). Controlled influences on implicit measures: Confronting the myth of process-Â�purity and taming the cognitive monster. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new wave of implicit measures (pp.€391–428). New York: Psychology Press. Shiv, B., Loewenstein, G., & Bechara, A. (2005). The dark side of emotion in decision making: When individuals with decreased emotional reactions make more advantageous decisions. Cognitive Brain Research, 23, 85–92. Sio, U. N., & Ormerod, T. C. (2009). Does incubation

294

III. CROSS-CUTTING PERSPECTIVES

enhance problem solving?: A meta-Â�analytic review. Psychological Bulletin, 135, 94–120. Smith, E. R., & DeCoster, J. (2000). Dual-Â�process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4, 108–131. Soon, C. S., Brass, M., Heinze, H.-J., & Haynes, J.-D. (2008). Unconscious determinants of free decisions in the human brain. Nature Neuroscience, 11, 543– 545. Stocco, A., & Fum, D. (2008). Implicit emotional biases in decision making: The case of the Iowa Gambling Task. Brain and Cognition, 66, 253–259. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Todorov, A., Mandisodza, A. N., Goren, A., & Hall, C. C. (2005). Inferences of competence from faces predict election outcomes. Science, 308, 1623–1626. Tracy, J. L., & Robins, R. W. (2008). The automaticity of emotion recognition. Emotion, 8, 81–95. Triandis, H. C. (1977). Interpersonal behavior. Monterey, CA: Brooks/Cole. van ’t Wout, M., & Sanfey, A. G. (2008). Friend or foe: The effect of implicit trustworthiness judgments in social decision-Â�making. Cognition, 108, 796–803. Wallas, G. (1926). The art of thought. London: Cape. Weber, E. U., & Johnson, E. J. (2009). Mindful judgment and decision making. Annual Review of Psychology, 60, 53–85. Wegner, D. M., & Pennebaker, J. W. (Eds.). (1992). Handbook of mental control. Englewood Cliffs, NJ: Prentice Hall. Wegner, D. M., & Wheatley, T. (1999). Apparent mental causation: Sources of the experience of will. American Psychologist, 54, 480–492. Wilson, T. D., Lisle, D., Schooler, J. W., Hodges, S. D., Klaaren, K. J., & LaFleur, S. J. (1993). Introspecting about reasons can reduce post-Â�choice satisfac-

tion. Personality and Social Psychology Bulletin, 19, 331–339. Wilson, T. D., & Schooler, J. W. (1991). Thinking too much: Introspection can reduce the quality of preferences and decisions. Journal of Personality and Social Psychology, 60, 181–192. Winkielman, P., Berridge, K. C., & Wilbarger, J. (2005). Unconscious affective reactions to masked happy versus angry faces influence consumption behavior and judgments of value. Personality and Social Psychology Bulletin, 31, 121–135. Yaniv, I., & Meyer, D. E. (1987). Activation and metaÂ�cognition of inaccessible stored information: Potential bases for incubation effects in problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 187–205. Zajonc, R. B. (1980). Feeling and thinking: Preferences need no inferences. American Psychologist, 35, 151– 175. Zajonc, R. B. (1984). On the primacy of affect. American Psychologist, 39, 117–123. Zebrowitz, L. A. (2006). Finally, faces find favor. Social Cognition, 24, 657–701. Zebrowitz, L. A., Fellous, J. M., Mignault, A., & Andreoletti, C. (2003). Trait impressions as overgeneralized responses to adaptively significant facial qualities: Evidence from connectionist modeling. Personality and Social Psychology Review, 7, 194–215. Zebrowitz, L. A., & McDonald, S. (1991). The impact of litigants’ babyfacedness and attractiveness on adjudications in small claims courts. Law and Human Behavior, 15, 603–623. Zemack-Rugar, Y., Bettman, J. R., & Fitzsimons, G. J. (2007). The effects of nonconsciously priming emotion concepts on behavior. Journal of Personality and Social Psychology, 93, 927–939. Zhong, C. B., Dijksterhuis, A., & Galinsky, A. D. (2008). The merits of unconscious thought in creativity. Psychological Science, 19, 912–918.

C h a p t e r 16

Consistency and Inconsistency in Implicit Social Cognition The Case of Implicit and Explicit Measures of Attitudes Robert J. Rydell and Allen R. McConnell

E

veryone experiences mixed or conflicted feelings from time to time. Does the divorcée “get back on the horse” and start dating again even when the sting of a disastrous marriage still lingers? At the restaurant, how does a diabetic patron ponder whether or not to order the tasty cheesecake, knowing that it is laden with so much sugar, fat, and calories? In a faculty meeting, how does one balance a tenure decision involving a productive colleague who is a complete jerk to students and other faculty alike? So many everyday decisions are fraught with a cacophony of feelings. In social psychological research, we understand that holding mixed feelings about attitude objects (e.g., dating, cheesecake, a colleague) is attitudinal ambivalence. In fact, the study of the consequences of holding inconsistent attitudes has long been acknowledged as important in the field (Kaplan, 1972; Petty & Briñol, 2009). However, it is interesting that social psychological thinking about inconsistent attitudes has focused almost exclusively on the implications of holding conflicting attitudes that people can verbalize. Yet what happens when evaluative inconsistencies operate at different levels (i.e., implicit and explicit measures of attitudes)? In the current chapter, we explore how discrepancies between implicit and explicit measures

of attitudes develop, how a variety of theoretical perspectives and psychological processes can account for them, and what important implications result from holding them. When considering these issues, it is important to specify a number of constructs that, at times, seem poorly defined in the literature. For example, there is much debate and discord about the definition of an attitude (Gawronski, 2007). In general, attitudes are viewed as a perceiver’s evaluation of some object. In other words, is the object liked or disliked? More formally, attitudes are “a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor” (Eagly & Chaiken, 1993, p.€ 1). Yet our understanding of attitudes is complicated by the fact that attitudes are not directly observable, and thus their existence is only manifested by measuring people’s responses, directly or indirectly (Eagly & Chaiken, 2007; Fazio, 2007). That is, attitudes have to be understood by the measures used to assess them, and as a result, attitude measures at best provide estimates of attitudes.1 As attitude theories develop, attitude conceptualization and measurement evolve as well. During the past decade, the explosion of research featuring implicit attitude measures has led research295

296

III. CROSS-CUTTING PERSPECTIVES

ers to reconsider attitude theory, leading some to retool existent ideas and others to advance new theories about attitudes. Although implicit measures of attitudes have been around for some time (e.g., Fazio, Sanbonmatsu, Powell, & Kardes, 1986), the proliferation of research using newer, indirect attitude measures (see Wittenbrink & Schwarz, 2007, for a review) has led many psychologists to reconsider their conceptualization of attitudes. One reason why many researchers have extended or modified their theoretical perspectives on attitudes is that there are a number of demonstrations that implicitly and explicitly measured attitudes are often only weakly related and can be responsive to different manipulations (see Gawronski & Bodenhausen, 2006, for reviews of divergence; see Hofmann, Gawronski, Gschwendner, Le, & Schmitt, 2005, and Nosek, 2005, for reviews on correspondence). In addition, implicit attitude measures are sometimes altered quite easily with subtle manipulations (e.g., Karpinski & Hilton, 2001; Wittenbrink, Judd, & Park, 2001) and at other times they take Herculean effort to change (e.g., Gregg, Seibt, & Banaji, 2006; Rydell & McConnell, 2006). Although explicit attitude measures show equivalent variation in response to attitude change manipulations (e.g., Petty & Wegener, 1998), some conditions reveal distinct and dissociated effects on implicit and explicit attitude measures (see also Gawronski & Sritharan, Chapter 12, this volume). How do attitude theorists explain these divergent outcomes, and what are their consequences for cognition and behavior? To address these issues, we first review the impact that such discrepancies have had on theorizing about attitudes. Specifically, we detail how major theories of attitudes have been modified, refocused, or created to accommodate implicit–Â� explicit attitude measure discrepancies. Then we examine the processes that can impact implicit and explicit attitude measures differently, leading to discrepancy on these measures. Finally, we examine the consequences of discrepancies between implicit and explicit attitude measures.

THEORETICAL CONCEPTUALIZATIONS OF IMPLICIT–EXPLICIT ATTITUDE MEASURE DISCREPANCIES To explore why and how consistency and inconsistency occurs for implicit and explicit attitude measures, we examine how several different theories explain these discrepancies. Understanding these

theoretical treatments of consistency and discrepancy serves to highlight underlying similarities and differences between the theories’ explanations for consistency and inconsistency between implicit and explicit attitude measures.

The MODE Model Research on the motivation and opportunity as determinants (MODE) model (Fazio, 1995, 2007; Fazio & Olson, 2003) was the first to integrate findings from reaction time measures of implicitly measured attitudes and paper-and-Â�pencil measures of explicitly measured attitudes. From the perspective of the MODE model, attitudes are simply object–Â�evaluation associations that are stored in memory, which can vary in their strength of association. The stronger the associative link between the object and its evaluation in memory, the greater that attitude’s accessibility. From the perspective of the MODE model, implicit attitude measures capture attitudes at an earlier time in their expression than do explicit measures, and they better reflect the associative strength of the attitude object and its evaluation in memory. Explicit attitude measures, on the other hand, can be inconsistent with implicit attitude measures because reporting explicit attitudes is itself a deliberative behavior, which can be impacted by deception, self-Â�presentational concerns, or inconsistency of the accessible attitude with previously expressed beliefs or ideals (Fazio, 2007). According to the MODE model, implicit and explicit attitudes diverge when people have the motivation and ability to engage in deception, self-Â�presentation, or comparison of behavior and thought over time. That is, divergence should be seen when people monitor their explicit attitude expression and deploy the cognitive resources required to alter their expression. Convergence, however, should be seen when motivation or opportunity is low and attitudes accessed from memory guide the expression of evaluation on explicit attitude measures. In the MODE model, attitude change (i.e., the change of the association between the attitude object and its evaluation in memory) occurs following repeated pairing of the attitude object with counterattitudinal information (e.g., Rudman, Ashmore, & Gary, 2001; Rydell, McConnell, Strain, Claypool, & Hugenberg, 2007). However, how does the MODE model account for quick implicit attitude change (e.g., Wittenbrink et al., 2001)? As Fazio (2007) points out, this quick change can be due to manipulations that impact the difficulty of the response-Â�mapping task posed



16. Consistency and Inconsistency

by many implicit attitude measures (e.g., Han, Olson, & Fazio, 2006; Olson & Fazio, 2004) or to changes in the categorization of an attitude object (e.g., Smith, Fazio, & Cejka, 1996). For example, decreased prejudice toward African Americans in church as opposed to those in a jail occurs not because implicit attitudes have changed but because African Americans are not the same attitude object in jail as in church. As another example, motivational states such as hunger (Seibt, Häfner, & Deutsch, 2007) and thirst (Ferguson & Bargh, 2004) affect the construal of these attitude objects to be so different that, for instance, a cheesecake is not the same attitude object to a diabetic when he or she is hungry versus full (Fazio, 2007, p.€626). The MODE model assumes that studies showing quick change among implicitly measured attitudes reflect the relative influence of current concerns, context, or construal (categorization) on attitude object instantiation. This temporary change is adaptive in the moment but does not impact the attitude object evaluation held in memory.

The Associative–Â�Propositional Evaluation Model The associative–Â�propositional evaluation (APE) model (Gawronski & Bodenhausen, 2006) assumes that implicit and explicit attitude measures tap into two different types of evaluative processes: associative and propositional. In this model, associative processes determine affective reactions automatically activated when one encounters an attitude object (e.g., feelings of negativity when walking by an obnoxious colleague’s office). The APE model proposes that implicit attitudes capture the process of pattern activation (see Smith, 1996), wherein a subset of the information associated with an attitude object is activated based on learning history, contextual information, motivational states, and other stimuli. Thus, the evaluative information activated in memory by the attitude object is the best fit between these factors and the information connected in memory at that moment. In another moment when these factors change, other associations with the attitude object will be active, changing implicit attitude measure responses. Because of rapid changes in pattern activation, implicit attitude measures are able to change quickly. On the other hand, propositional processes underlie the expression of evaluations on explicit attitude measures. According to Gawronski and Bodenhausen (2006), “Evaluations resulting from propositional processes can be characterized as

297

evaluations that are based on syllogistic inferences derived from any kind of propositional information that is considered relevant for a given judgment” (p.€694). These propositions can include information extracted from the outcome of associative processes (e.g., “I like X” or “I dislike X”; see Strack & Deutsch, 2004). All propositions are then subject to logical inference that assesses which propositions are valid and, therefore, used in explicit attitude measure responses. For example, if a department chair has an automatic negative reaction to the obnoxious but productive professor, implicit attitude measures will reflect this negativity. However, if the department chair also knows that this professor receives substantial amounts of grant support, has prestigious publications, and attracts high-Â�quality graduate students, explicit attitudes toward this professor will likely be more positive. In this latter case, the propositional information about the utility of the obnoxious professor for the department will result in greater positivity being expressed on explicit measures of attitudes. In other words, even though the department chair’s automatic affective reaction is negative, the proposition formed in response to this reaction will be invalidated because of its inconsistency with other meaningful information about the professor and will thus be rejected. This propositional process of validating or invalidating automatic evaluations is how the APE model explains consistency and inconsistency between implicit and explicit attitude measures (see also Gawronski, Strack, & Bodenhausen, 2009). Thus, when the information automatically activated from memory is consistent with propositional information about the attitude object, implicit and explicit attitude measures should be consistent. However, when information automatically activated from memory is inconsistent (i.e., does not logically fit) with propositional information, implicit and explicit attitude measures should be discrepant. As such, the truth value (or validity) of the propositional output determines whether discrepancies occur (Gawronski & Bodenhausen, 2006). Discrepancies between implicit and explicit measures can also result from changing the propositions available or seen as applicable to the situation. Of course, consistency between implicit and explicit attitude measures also occurs when associative information changes to be in line with propositional information or propositional information changes to be in line with the output of associative processes. Thus, a change in either process can lead to consistency or inconsistency of implicit and explicit attitude measures; however,

298

III. CROSS-CUTTING PERSPECTIVES

the propositional process ultimately determines whether there is consistency or inconsistency between measures.

The Metacognitive Model The metacognitive model (MCM; Petty, Briñol, & DeMarree, 2007; Petty, Tormala, Briñol, & Jarvis, 2006), like the MODE model, assumes that attitudes are evaluations of objects that are stored in memory. However, the MCM posits that attitude objects can (sometimes) be associated simultaneously with positive and negative valence. When attitude objects are not simultaneously associated with positivity and negativity, this model is very similar to the MODE model (e.g., Fazio, 1995, 2007). However, the MCM model goes beyond the MODE model because it assumes people tag their associations (e.g., true–false, confident–doubt, accept–Â�reject, valid–Â�invalid). These metacognitive tags are stored in memory to help people determine the validity or confidence with which the association is held. Because of its predictions about simultaneous linkages to positivity and negativity (bivalent associations) and its assumptions about metacognitive tags, the MCM makes several predictions about when consistency and inconsistency will arise. When these bivalent associations exist and one association is tagged as invalid, the metacognitive tags are not easily detected by implicit attitude measures. For example, the negativity automatically activated in response to the obnoxious professor could be tagged as invalid because of his or her productivity. In this situation, implicit attitude measures would reflect the automatic negativity and not the tag of its invalidity; however, explicit attitude measures would account for the invalidity tag. Therefore, unless the tag is highly accessible, it will not impact implicit measures because of the default assumption that associations are true (Gilbert, 1991). Only when people devote time and resources are these tags utilized; this need for elaboration in utilizing tags is exacerbated because metacognitive tags are assumed to be associated with the positivity or negativity in memory and not the attitude object itself. According to the MCM, implicit and explicit attitude measures will be consistent when (1) only positive or negative information is associated with an attitude object in memory and that information is “tagged” or accepted as true; and (2) when a metacognitive tag automatically negates either the linkage to positivity or negativity and this same negation is made after some deliberation. According to the MCM, implicit and explicit attitude

measures will diverge when positive or negative information is not properly tagged as “false” or this tag is not detected by implicit measures, but the tag is used when completing explicit measures (see Petty & Briñol, 2009; Petty et al., 2006).

The Systems of Evaluation Model The systems of evaluation model (SEM; McConnell, Rydell, Strain, & Mackie, 2008; Rydell & McConnell, 2006; Rydell, McConell, Mackie, & Strain, 2006) assumes that implicit and explicit measures tap into different mental systems that correspond to an associative system (implicit attitude measures) or a rule-based system (explicit attitude measures). Specifically, we (Rydell & McConnell, 2006; Rydell et al., 2006) have proposed that there are two somewhat or partially independent systems of evaluation that differ both in what information they use and in how they act on it (Sloman, 1996; Smith & DeCoster, 2000; Strack & Deutsch, 2004). The associative system of evaluation is relevant to our understanding of how implicit attitudes form and function because implicit attitude measures are posited to follow the basic principles of similarity and association (Smith & DeCoster, 2000). The rule-based system fits with a conceptualization of explicit attitude measures as evaluations based on conscious deliberation or syllogistic reasoning, which can reveal quick but resource-�dependent changes on explicit attitude measures (Fazio, 1995, 2007). According to the SEM, implicit attitude measures are more sensitive to associative forms of information such as subliminal primes (Rydell et al., 2006) or associative cues (e.g., race, obesity, physical attractiveness; McConnell et al., 2008). For example, eating cheesecake brings about pleasurable feelings associated with rich, satisfying foods. On the other hand, explicit attitude measures are more sensitive to symbolic forms of information (quite often verbal in nature, although other forms exist as well, such as musical notation and mathematical symbols that exist in a framework governed by rules), which are often used in logic and symbolic reasoning. For instance, one can use reasoning and deduction to understand that eating cheesecake, because it can lead to high blood sugar, could lead to blindness or death.2 However, these systems do interact when no (or a relatively small amount of) associative or verbal information is available. For instance, subliminal evaluative priming has been shown to impact explicit attitude measures (e.g., Olson & Fazio, 2001; Petty et al., 2006), and a large amount of verbal informa-



16. Consistency and Inconsistency

tion can influence implicit attitude measures (e.g., Rydell & McConnell, 2006; Rydell et al., 2007). Thus, the SEM predicts that implicit and explicit attitude measures will be consistent when the valence of associative information (e.g., subliminal primes, cues with strong evaluative associations) and verbal information is equivalent or when a considerable amount of verbal information contradicts the valence of previous associative learning. Implicit and explicit measures will be inconsistent when the valence of the associative information and verbal information becomes divergent through a variety of means, including different evaluative implications of rule-based (e.g., verbal) and associative-based (e.g., associative cues) information (e.g., McConnell et al., 2008; Rydell et al., 2006) or when a relatively small amount of new verbal information revises rule-based evaluations but is insufficient to alter association-based evaluations (e.g., Rydell et al., 2007) One major difference between the SEM and the theories reviewed previously is that it posits that there are distinct systems as opposed to distinct processes that can lead to discrepancies between implicit and explicit attitude measures. In the SEM, systems refer to aspects of the mind that use particular types of information and operating principles, actively or passively, to render evaluations. Essentially, each system can be conceived of as an interrelated group of mechanisms that are dedicated to evaluation. Because the two systems differ in their inputs and style of information processing, their outputs can differ. A system approach assumes that two broad groups of processes and mechanisms operate and serve to disambiguate the attitude object, its features, and its value to (or probable impact on) the perceiver given current environmental constraints and perceiver goals. Instead of viewing these mechanisms as part of one process (e.g., activation of evaluations stored in memory—Fazio, 2007; Petty et al., 2007; pattern activation in a connectionist network—Â� Gawronski & Bodenhausen, 2006), a systems approach assumes that, although related mechanisms operate on similar input, even similar mechanisms may produce inconsistent outputs.

The Constructivist Perspective Finally, a constructivist perspective views all attitudes as momentarily constructed evaluations that are created each time an object is encountered (Schwarz, 2007; Schwarz & Bohner, 2001). As such, the term attitude does not refer to stable evaluative information stored in memory but rath-

299

er to the process of constructing evaluations based on the information that is available at the time and the demands of the attitude measure. In this conceptualization, attitudes are epiphenomena and are a by-Â�product of the construction process. Thus, the notion that people possess attitudes is, conceptually, inherently flawed. According to a constructionist perspective, implicit and explicit attitude measures will converge when the same information is culled from memory to construct an attitude. This could result from context making accessible the same information or the measures themselves eliciting the same information (e.g., “What was my last experience with cheesecake?”). To the extent that the context or any momentary stimuli impact the information recruited from memory (e.g., a divorcée is at a romantic movie vs. talking to the ex-Â�spouse), implicit and explicit attitude measures will be inconsistent with past measurements. In addition, if the methods used to assess implicit and explicit attitudes differ in what they access from memory or demand different responses based on framing (Schwarz, 1999), then implicit and explicit attitude measures will differ. From this perspective, the consistency of implicit and explicit attitude measures is a consequence of how attitudes are constructed in the moment, which can be impacted by how attitudes are assessed (cf. Fazio, 2007, for a strong challenge to this perspective). Finally, the timing of responses may lead to discrepancies between implicit and explicit attitudes according to a constructivist perspective. Implicit attitude measures assess quick responses, and explicit attitude measures might reflect different evaluations to the extent that they take longer to answer. That is, when completing explicit attitude measures, people could recruit additional information from memory that they were unable to retrieve when responding quickly on implicit attitude measures that might change their construal of the attitude object.

MECHANISMS OF CONSISTENCY AND INCONSISTENCY In this section, we review several key mechanisms that have been studied in research examining discrepancies and consistencies between implicit and explicit attitude measures. The mechanisms are organized (in order) based on whether they tend to (1) have a larger impact on automatically activated evaluations or (2) be based on further elaboration and integration of automatically acti-

300

III. CROSS-CUTTING PERSPECTIVES

vated evaluations with other information relevant for rendering evaluations. By highlighting these mechanisms, we provide an overview of how the aforementioned attitude theories differ in their explanation of these different processes, processes that must be explained by a comprehensive theory of attitudes. In our assessment, we examine integration of counterattitudinal information, balanced identities, extrapersonal associations, dissonance and balance, and “downstream” processes. In addition, we discuss how the simultaneous automatic activation of positivity and negativity, which is an associative process, is accounted for by different models of attitudes.

Integration of Counterattitudinal Information The most straightforward way that automatic affective reactions can be changed is by repeatedly encountering counterattitudinal information (e.g., Karpinski & Hilton, 2001; Rudman et al., 2001; Rydell & McConnell, 2006). For example, Rydell and colleagues (2007) had participants learn 100 positive pieces of information about a novel individual. After this initial learning phase, participants were presented with 0, 20, 40, 60, 80, or 100 pieces of additional, counterattitudinal information (i.e., negative information). Implicit attitude measures assessing evaluations of the novel individual became increasingly negative in proportion to the amount of counterattitudinal information encountered. Furthermore, Sherman and colleagues (2008) showed through multinomial modeling (see Sherman, Klauer, & Allen, Chapter 9, this volume) that changes in implicit attitude measures in this paradigm were due to changes in association between the attitude object and valence in memory as opposed to other possible processes (i.e., overcoming bias, discrimination of stimuli, guessing). Most models of attitudes predict that implicit attitude measures should change when enough counterattitudinal information is encountered (see Gawronski & Sritharan, Chapter 12, this volume). The MODE model, MCM, and SEM specify that this should occur through the alteration of object–Â�evaluation links in memory. The APE model assumes that this change occurs because a larger subset of heterogeneous (i.e., positive and negative) information about the attitude object is stored in memory and, therefore, associations that are of the opposite valence of originally learned information should be more likely to be used (and connected to other attitude relevant information) in associative processes.

Balanced Identities Research examining Greenwald and colleagues’ (2002) unified model of social cognition provides considerable evidence that implicit attitude measures can be changed via balance principles. As an illustration, Greenwald and colleagues assessed female participants’ self-Â�esteem, gender identification, and attitudes toward women with implicit and explicit measures. The results showed that the interaction of any two of the implicit measures predicted the third implicit measure. Most relevant to the current discussion, women who had greater implicitly measured self-Â�esteem and implicitly measured gender identification showed relatively more positive implicitly measured attitudes toward women. However, consider a situation where women have been exposed to extremely negative female exemplars. Based on past research, this exposure to negative female exemplars should make implicitly measured attitudes toward women relatively more negative (Dasgupta & Greenwald, 2001). However, a balanced identity perspective predicts that implicitly measured attitudes toward women should be relatively more positive to the extent that women have greater implicitly measured gender identification and greater implicitly measured self-Â�esteem. Implicitly measured attitudes toward women could be influenced by the exemplar manipulation, but because this association is inconsistent with (or “pressured” by) greater implicitly measured self-Â�esteem and implicitly measured gender identification, implicitly measured attitudes toward women should be relatively positive (see Greenwald et al., 2002, Principle 2). Presumably, negative evaluations of women would be not activated or may even be inhibited to maintain balance among implicit measures of self-Â�esteem, gender identification, and attitudes toward women. It should be noted that there are two alternatives to these predictions about the impact of a negative female exemplar and inhibition. First, the exemplar manipulation could, in fact, lead to less positive implicit evaluations of women, but this change in the mean level of implicitly measured attitudes would not impact the correlations to gender identity and self-Â�esteem. Second, selfÂ�esteem could be reduced via spreading activation from evaluations of women overall. This change in self-Â�esteem would lead to balance but at a cost to the self (e.g., Walther, Nagengast, & Trasselli, 2005). To our knowledge, these interesting possibilities have not been examined empirically. How would models of attitudes explain the tendency for balance in the face of information that



16. Consistency and Inconsistency

has, in past research, changed implicit attitude measures? Based on the MODE model, balance could be achieved through changing categorization of women when implicitly measured attitudes toward women are pressured. Thus, women could be recategorized in some other fashion (e.g., highÂ�achieving women), and this would produce balance among implicit measures. The APE model would explain this balance among implicit measures by changes in pattern activation in response to information that the self is positive and that the self is a woman. A likely output, given this set of inputs, is that implicitly measured attitudes toward women would be positive. Through processes like pattern completion, a pattern of activity would be “settled” on indicating that implicitly measured attitudes toward women are relatively positive through a passive process of constraint satisfaction. The SEM has a more difficult time explaining balanced identities. It would predict that the critical factor would be whether the exemplar manipulation constituted associative or verbal information. To the extent that this information is verbal, implicitly measured self-Â�esteem and implicitly measured gender identification could impact implicitly measured attitudes toward women by influencing the valence of the evaluations that are activated in memory via facilitation of associations between positivity and women and inhibition of associations between negativity and women. However, if the negative information is associative in nature, implicitly measured attitudes toward women should be less positive and not balanced with implicitly measured self-Â�esteem and gender identification. Thus, the SEM predicts that manipulations that are associative in nature can counteract the processes underlying balanced identities. It is worth pointing out that the impact of balanced identities is less likely on explicit measures of self-Â�esteem, gender identity, and attitudes toward women because balance on explicit measures can be achieved in many more ways (e.g., adding concepts, splitting the pressured concept) than on implicit measures, and it is, therefore, not a necessity that explicit attitude measures converge to maintain overall balance (e.g., Greenwald et al., 2002).

Extrapersonal Information Another way associations could impact the consistency or discrepancy between implicit and explicit attitude measures is if an implicit attitude measure is contaminated by “extrapersonal associations” (Fazio, 2007; Fazio & Olson, 2003; Han et al., 2006;

301

Karpinski & Hilton, 2001; Olson & Fazio, 2004). Extrapersonal associations are environmental associations that come from one’s experiences in the greater culture or from other individuals rather than reflecting one’s own attitude (Fazio, 2007). It has been suggested that implicit attitude measures, such as the Implicit Association Test (IAT), are affected by extrapersonal associations unrelated to one’s personally held attitude, and thus, discrepancies result when implicit measures of attitudes are unduly influenced by extrapersonal information. In an illustrative study, Han and colleagues (2006) had participants initially learn that one Pokemon character was objectively superior to another. Next, they exposed participants to a video that had children express beliefs about the Pokemon characters that were consistent or inconsistent with initial learning. The children’s views were seen as silly and illogical when they were inconsistent with initial learning (i.e., they were rejected as a valid indicator of the object’s positivity or negativity). Despite the perceived invalidity of the information given by the children, their information impacted a traditional IAT measure of implicit attitudes. Interestingly, information given by the children did not impact a personalized IAT (where the category labels were marked “I like” vs. “I dislike” instead of “pleasant” vs. “unpleasant”; Olson & Fazio, 2004) or an evaluative priming measure (Fazio et al., 1986). Because the latter two measures are assumed to measure personal attitudes rather than momentarily accessible knowledge, they were perceived to be better measures of attitudes assessing an individual’s, and not societal, beliefs. The main point of this work is that implicit and explicit measures of attitudes can sometimes diverge because extrapersonal information impacts implicit attitude measures, but these extrapersonal associations are invalidated and not used when explicit measures are completed. The role of extrapersonal information in implicit attitude measures is a point of debate in the literature (Conrey & Smith, 2007; Gawronski & Bodenhausen, 2006; Nosek, 2005; Nosek & Hansen, 2008). Indeed, from several perspectives (e.g., APE, MCM, SEM), people should not be able, at an associative level, to distinguish intrapersonal from extrapersonal knowledge. That task must occur via controlled processes in which one can assess the extent to which an evaluation is personal. Indeed, recent research has challenged the claim that implicit attitude measures, including the IAT, are influenced by cultural knowledge (Nosek & Hansen, 2008). Furthermore, the impact that cultural knowledge has on implicit attitude measures

302

III. CROSS-CUTTING PERSPECTIVES

is accounted for by its impact on explicit attitude measures. As these authors note, “Endorsement, especially in the context of implicit cognition, is irrelevant for information to be a measure of individual attitude and predict individual behavior.€.â•›.â•›. Associative representations reflect accumulated experience with attitude objects regardless of whether those experiences are accepted or rejected as true” (Nosek & Hansen, 2008, p.€549). Because introspective access to the causes or circumstances involving associations in memory is poor and may not even be stored in memory, extrapersonal influences should not impact implicit attitude measures. If this is true, why did Han and colleagues (2006) show an impact of extrapersonal associations on the IAT if cultural knowledge does not impact this measure? One possibility is that people actually do tag evaluations as “mine” or “not mine.” The MCM’s process of metacognitive tagging could be used to tag either positivity or negativity stored in memory as “not mine.” Under most circumstances, this tag would not impact implicit attitude measures. However, if the tag is relatively salient or strongly associated with evaluative information, it could potentially impact an implicit attitude measure (see Gawronski, Peters, & LeBel, 2008, for a similar argument where personal associations could be interpreted as the stored outcomes of propositional inferences in associative memory). Another, more likely possibility for Han and colleagues’ results is based on the fact that implicit attitude measures are not process pure (e.g., Sherman et al., 2008) and operate through different cognitive mechanisms (e.g., De Houwer, 2003). For the personalized IAT and the traditional IAT, which share the same response-Â�interference mechanisms, the differences are likely due to how the labels (“I like” vs. “pleasant”) impact controlled processes that influence responding to the IAT. Moreover, because the paradigm used in the traditional IAT (unlike the personalized IAT) requires that participants make correct responses on each trial (and that they receive error feedback on trials in which incorrect responses are made), it is possible that additional processes are involved in the personalized IAT (e.g., overcoming bias; Sherman et al., 2008) that do not apply in the same way to the traditional IAT. Turning to explicit attitude measures, personal endorsement is important for determining people’s attitudes; however, it remains to be seen whether this is true for implicit attitude measures. Further research will need to address this issue more directly to determine the relation between cultural or extrapersonal knowledge and

implicit attitude measures (see Nosek & Hansen, 2008, for a review).

Cognitive Consistency An important aspect of understanding consistencies and discrepancies between implicit and explicit attitude measures is elucidating the role of cognitive consistency on these measures. Research on cognitive consistency has adopted two frameworks regarding implicit and explicit attitude measures: dissonance theory and balance theory (which is distinct from and potentially broader than the balanced identities perspective outlined previously). Thus, we consider each of these areas of research separately.

Dissonance Theory In early research on dissonance theory and implicit and explicit attitude measures, researchers found that dissonance only led to changes in explicitly measured attitudes (e.g., Wilson, Lindsey, & Schooler, 2000). Gawronski and Strack (2004) showed that an induced compliance paradigm led to changes in explicit attitude measures when participants were given choice but not when no choice was given. Implicit attitude measures did not show any impact of choice, but they were positively correlated to explicit attitude measures in the noÂ�choice condition and uncorrelated to explicit attitude measures in the high-Â�choice condition. Thus, the dissonance created by “freely chosen” counterattitudinal behavior had an impact only on explicit attitude measures. The APE, MCM, and SEM models predict, consistent with what was found, that only explicit measures changed. The APE model comes to this conclusion because logical relations between propositions (e.g., “I disliked a boring task,” “I told someone else the task was fun”) can only be tested by propositional processes. If these propositions are logically inconsistent, explicit attitude measures changed to resolve this logical inconsistency. The MCM would predict that the automatically activated negativity to the task would be tagged as invalid and, therefore, explicit attitudes would show the impact of this tag, unless the tag were to become strongly associated with negativity (which is extremely unlikely in an induced compliance paradigm). The SEM would predict that dissonance would be dealt with by the rule-based system of evaluation, where the logical inference and consistency concerns can be assessed. The MODE model would predict one of two accounts for these results. First, implicit atti-



16. Consistency and Inconsistency

tude measures assess evaluation at an earlier point when dissonance arousal is not a concern, and thus implicit attitude measures do not change. Second, implicit attitude measures did not change because they were measured with a standard IAT (rather than the personalized IAT or affective priming). That is, the impact of extrapersonal associations may have concealed changes on implicit attitude measures in a forced-�choice paradigm. A problem with this second account is that explicit attitude measures that are completed under time pressure (presumably an implicit attitude measure) do not reveal attitude change in a forced-�choice paradigm (Wilson et al., 2000), and this null result cannot be explained by extrapersonal associations impacting implicit attitude measurement. However, there is evidence that in other dissonance paradigms implicit attitudes can be changed. In a postdecisional attitude change study, Gawronski, Bodenhausen, and Becker (2007) showed that implicit attitude measures can change. Specifically, participants chose between one of two equally attractive pictures. In the usual spreading of alternatives study, the dissonance created by the positive features of the unselected picture lead to a devaluing of the picture and the features on which it was seen as positive. In the Gawronski and colleagues study, implicit attitude measures for each picture were assessed before and after participants chose. The results showed that implicitly measured attitudes were more positive toward the chosen picture and more negative toward the rejected picture, suggesting that choice decisions led to changes in implicit attitude measures. Given that such attitude changes are typically explained by the operation of postdecisional dissonance, this outcome seems inconsistent with the models discussed previously. However, a second study found that these differences were not due to dissonance but to self-�anchoring. Specifically, in a second study, the chosen picture became more strongly associated with the self than the rejected picture. Importantly, implicit measures of self�esteem were positively correlated to implicit attitude measures toward the chosen picture (Greenwald et al., 2002). Thus, dissonance paradigms can lead to changes in implicit attitude measures via different mechanisms than dissonance reduction that are not always propositional in nature. How do these postdecisional attitude change studies fit with the models we discussed in the first section? All of the models would assume that as the attitude object becomes more associated with the self, and the self is viewed positively either through spreading activation or pattern activa-

303

tion, the attitude object would be more positive on implicit measures (see also Greenwald et al., 2002). The strength of this association and the positivity of the self should be the main determinants of implicitly measured attitude change. Especially because this association was just created in an experimental setting and does not have a long history of coactivation, the models’ predictions do not differ.

Balance Theory In a similar vein to studies on balanced identities, research has examined the impact of simple balance principles on attitudes assessed by implicit and explicit measures (e.g., Gawronski, Walther, & Blank, 2005). This research showed that when participants learned positive or negative information about a novel individual and then learned whether this individual liked or disliked a second, novel individual, balance was observed on implicit and explicit attitude measures. Take the example of learning that person A is positive. If person A acted positively and participants learned that person A liked person B, person B was assessed positively on implicit and explicit attitude measures. If person A acted positively and participants learned that person A disliked person B, person B was assessed negatively on implicit and explicit attitude measures. Interestingly, when participants learned about the relationship between person A and person B (whether they like or dislike one another) and then learned about person A, this balance effect did not occur on either implicit or explicit attitude measures. As stated by Gawronski and colleagues (2005), these findings “suggest that cognitive balance influences the encoding of social information rather than the retroactive construal of evaluative judgments” (p.€625). This finding is interesting because Gawronski and Strack (2004) showed that cognitive dissonance led to “retroactive construal of evaluative judgments” or changes on an explicit attitude measure after a freely chosen counterattitudinal behavior. Why was this change absent in the balance paradigm? Gawronski and colleagues (2005) propose that this difference could be due to the fact that “source valence [evaluation of person A] and observed sentiment [person A’s feeling toward person B] may be stored independently in memory when source valence is encoded after observed sentiments” (p.€ 625). This explanation assumes that when source valence is encoded after observed sentiments, it is less likely that both pieces of information will be retrieved from memory and

304

III. CROSS-CUTTING PERSPECTIVES

associated with one another. Thus, similar to research on the sleeper effect, the relation between person A and person B is “lost” or unused when followed by the qualifying information about the valence of person A (Kumkale & Albarracín, 2004). If the relation between person A and person B is not accessible, there may be no inconsistency altogether because inconsistency at the explicit level requires that the relevant information is accessible and subjectively valid in order to produce inconsistency (see Gawronski, Peters, & Strack, 2008). Nevertheless, this lack of balance on explicit measures, unlike that seen in the balanced identity work, seems much more problematic for theories of attitudes. In this research, there are not, for instance, additional cognitions available to resolve the imbalance on explicit attitude measures.

“Downstream” Consequences Implicit and explicit attitudes can also be discrepant because additional information, as opposed to automatic affective reactions, affects responding to explicit attitude measures. Indeed, there is much work showing that implicit and explicit attitudes are more likely to diverge when people have the opportunity and ability to change or “correct” the information activated automatically (Fazio & Olson, 2003). People can change their explicitly measured attitude because of self-Â�presentational concerns, social norms, and motives to hold certain beliefs (e.g., Dunton & Fazio, 1997). All of the models that have been reviewed assume that explicit attitudes can be changed with motivation and ability. As Fazio (2007) noted, these downstream consequences can be a mixture of automatically activated evaluations and “corrective” processes, or they can be based solely on controlled processing. However, the APE model (Gawronski & Bodenhausen, 2006) assumes that these changes are all based on controlled processing of propositional pieces of information about the attitude object because the automatically activated affective reaction is transformed into a proposition itself (see Strack & Deutsch, 2004). Thus, there are differences between models as to whether mixed processes can lead to changes in explicit attitudes, but these differences are hard to distinguish empirically. For instance, according to the APE model, cognitive dissonance can be reduced only via propositional processes (Gawronski & Strack, 2004). In other models, when implicit and explicit attitudes themselves are inconsistent, this should lead to tension or ambivalence because the associative and rule-based systems are discor-

dant (Rydell, McConnell, & Mackie, 2008) or because positivity and negativity are concurrently activated from memory (Petty & Briñol, 2009). Regardless, most models of attitudes assume that downstream consequences occur through very similar deliberative processes (e.g., syllogistic reasoning, logic) and need motivation and ability to be instantiated. For instance, the MODE model and the SEM predict that implicit and explicit attitude measures can diverge only through elaborative information processing and that greater divergence would be associated with more elaborative information processing (e.g., Hofmann et al., 2005). Although controlled thought is likely necessary for discrepancies between implicit and explicit attitude measures, the type of thought matters (Gawronski & Bodenhausen, 2006). If thoughts that are contradictory to automatically activated affective reactions are considered during conscious deliberations, then implicit and explicit attitudes should diverge with more elaborative processing. However, if thoughts supporting the automatically activated affective reaction are utilized during elaborative processing, implicit and explicit attitude measures could show greater convergence as more elaboration occurs. Because the impact of downstream consequences on implicit attitude measures has been extensively examined elsewhere (Fazio, 1995; Gawronski & Bodenhausen, 2006; Sloman, 1996), we do not discuss them further. However, it should be noted that perceivers could use an almost infinite number of strategies to modulate automatically activated evaluations of an attitude object when responding to explicit attitude measures. Future work should address when certain strategies are used and how they impact the consistency between implicit and explicit attitude measures and the cognitive and behavioral outcomes of these discrepancies.

SIMULTANEOUS AUTOMATIC ACTIVATION OF POSITIVITY AND NEGATIVITY Not all inconsistency has to come from differences in implicit and explicit attitude measures or differences in explicit attitude measures. Namely, de Liver, van der Pligt, and Wigboldus (2007) showed that both positive and negative information can be simultaneously activated from memory and impact implicit attitude measures. Specifically, de Liver and colleagues had participants generate objects that they believed were positive, negative, neutral, or ambivalent (i.e., both positive and neg-



16. Consistency and Inconsistency

ative). Their work showed (1) strong associations between positivity and objects that were described as positive; (2) strong associations between negativity and objects that were described as negative; (3) no associations between positivity or negativity for objects that were described as neutral; and (4) strong associations between the objects and both positivity and negativity when objects were described as ambivalent. Thus, it seems that a single attitude object can simultaneously activate both positivity and negativity in memory (see also Petty et al., 2007). How is this finding explained with the models of attitudes outlined previously? As discussed earlier, the MCM (Petty et al., 2007) explicitly makes this assumption about attitude representation and indeed uses this simultaneous activation along with metacognitive tagging to explain discrepancies between implicit and explicit attitude measures and as the basis for implicit ambivalence (i.e., implicit ambivalence is caused by this simultaneous activation along with a metacognitive tag negating the relation between either positivity or negativity and the attitude object in memory). According to the APE model (Gawronski & Bodenhausen, 2006), simultaneous, spontaneous activation of positivity and negativity in response to an attitude object can occur when both positive and negative information is stored in memory, activated from memory, and is the output of pattern activation and constraint satisfaction. That is, if the pattern of activation that best fits the current inputs from information in memory, the environment, and motivation is indeed mixed in valence, then implicit attitude measures will be able to detect this pattern of activation. It is interesting to think about how the output of associative processes is transferred into propositional information when both positivity and negativity are automatically activated. It could be that two inconsistent propositions are created (i.e., “I like X” and “I dislike X”) or that a single proposition is created that includes both positivity and negativity (“I like and dislike X”; “I am unsure how I feel about X”; or even “X makes me feel uncomfortable”). Research on this question may be important for incorporating the MCM and APE model. The MODE model (Fazio, 2007) asserts that activating both positive and negative information can occur when attitudes are less accessible and evaluations need to be constructed. However, when attitudes are more accessible or have already been summarized in memory, then there should not be the automatic activation of both positivity and negativity in response to an at-

305

titude object. As Fazio (2007) states, “The essence of more reasoned forms of attitude development is an integration of any such conflicting information into a summary evaluation .â•›.â•›. the summary evaluation effectively resolves the ambivalence” (p.€627). Of course, positive or negative summary evaluations can be automatically activated from memory depending on how an object is categorized (Smith et al., 1996). Perhaps asking participants to think of attitudes for which they are ambivalent leads to categorization of the attitude object into both a category for which there is positivity and a category for which there is negativity. In this way, the MODE model could explain the findings of de Liver and colleagues (2007). The SEM (Rydell & McConnell, 2006) would explain the simultaneous spontaneous activation of positivity and negativity in response to an attitude object that can occur when heterogeneous associative information is stored in memory and the situation and attitude object activates both this positive and negative information at the same time.

CONSEQUENCES OF DISCREPANCIES ON IMPLICIT AND EXPLICIT ATTITUDE€MEASURES Research and theorizing on when and how implicit and explicit attitude measures differ or converge is extremely helpful in defining and understanding the attitude concept. However, another key question is, what are the consequences of implicit and explicit attitude measures diverging? The only research examining the psychological consequences of divergent implicit and explicit attitude measures (outside of work on self-Â�esteem; see Zeigler-Hill & Jordan, Chapter 21, this volume) or beliefs has shown that increased discrepancies lead to greater implicit ambivalence (a stronger association between the attitude object and doubt in memory) and increased information processing of attitude-Â�relevant information (Petty et al., 2006; see Petty & Briñol, 2009, for an extensive review). This research showed that once attitudes formed they were not completely replaced when attitudes changed and increased implicit ambivalence accompanied attitude change. In conditions in which attitudes had changed and implicit and explicit measures were divergent, information processing increased. Briñol, Petty, and Wheeler (2006) showed that the greater the discrepancy between standardized measures of explicit and implicit self-Â�beliefs (e.g., one’s own shyness), the more

306

III. CROSS-CUTTING PERSPECTIVES

extensive the processing of persuasive messages related to the domain of discrepancy (e.g., arguments favoring shyness). This result, however, did not occur when information available for subsequent information processing was unrelated to the attitude object. As discrepancies between implicit and explicit attitude measures increased, people were motivated to consider carefully subsequently presented relevant information. Yet why does this outcome occur, and what phenomenology occurs with this increased information processing? Rydell and colleagues (2008) showed that when explicit–Â�implicit attitude discrepancies were greater, more negative arousal (i.e., dissonance; Festinger, 1957) was evoked. This negative arousal, in turn, induced greater information processing of attitude object-Â�relevant information. The results are consistent with the idea that discrepancies between implicit and explicit attitude measures can create aversive feelings that people attempt to reduce through learning more about the attitude object. This research also showed that a previously used measure of implicit ambivalence (i.e., IAT measure used by Petty et al., 2006) could not account for increased information processing when implicit and explicit attitude measures diverged, but reports of dissonance arousal could. However, both Rydell and colleagues (2008) and Petty and Briñol (2009) note that inconsistent cognitions (here attitudinal information assessed by implicit and explicit measures) are uncomfortable and lead to increased information processing. The dissonance referred to in Rydell and colleagues was similar to Festinger’s (1957) conceptualization: negative arousal felt in response to two inconsistent cognitions. Dissonance theory now recognizes several conditions that must be met for inconsistency to lead to dissonance (e.g., Cooper & Fazio, 1984). At this point, the theoretical conceptualizations of a dissonance or an ambivalence view are relatively similar because ambivalence and dissonance as conceptualized by Rydell and colleagues (consistent with Festinger, 1957) are simply inconsistent cognitions that are uncomfortable, and people are motivated to avoid such feelings (see Petty & Briñol, 2009, for the same prediction). Although the implicit ambivalence IAT measure used by Petty and colleagues (2006) did not predict the extent of information processing for these inconsistencies, the main underlying point of both these lines of research is the same and can probably best be described as felt ambivalence, a subjective state of discomfort or tension that is due to inconsistent evaluations, because it

does not require all of the conditions necessary for cognitive dissonance to occur. The next step for this research is to examine which behaviors that are engaged to reduce felt ambivalence are effective in resolving the discrepancy between implicit and explicit attitude measures and actually reducing felt ambivalence. It seems prudent to look at self-�regulatory models to examine when this effort will and will not be successful. For instance, in terms of convergence between measures, models of self-�regulation suggest that the velocity (or speed) with which inconsistencies can be resolved reduces negative affect (e.g., Carver & Scheier, 2002). Therefore, it is likely that changes in explicit attitude measures may be more likely in many circumstances. Because explicit attitude measures generally show change more quickly (e.g., Rydell et al., 2007) and implicit attitude measures may change because of contextual moderation of activated associations or changes in categorization (which may be ephemeral), explicit attitude change may be the most likely route to provide faster relief from discomfort (i.e., consonant cognitions). However, changes in the associations stored in memory between an attitude object and valence (whether this is through an associative network or the amount of strongly weighted information available for pattern activation) may be more likely to reduce the frequency of felt ambivalence in the future. Thus, moderation of implicit measures of attitudes is possible too. Regardless, the fewer self-�regulatory resources needed to reconcile discrepancies between implicit and explicit attitude measures, the more resources that can be applied to other issues that people face. Indeed, the experience of discomfort caused by discrepancies between the mental residue revealed by implicit and explicit attitude measures may be functional in that it serves as a signal that action is needed and self-�regulatory resources should be brought to bear on the attitude object (e.g., Harmon-Jones & Harmon-Jones, 2002). Furthermore, whether implicitly or explicitly measured attitudes change in response to discrepancies may be related to the accessibility of the attitude. If an attitude is highly accessible because of recent or frequent use (Fazio, 1995) and the attitude object elicits discrepancy between implicit and explicit attitude measures, it may be more imperative to alter evaluations to attain convergence between measures because feelings of discomfort should be more accessible as well. In addition, to the extent that there is a positive relation between implicit and explicit attitude measure discrepan-



16. Consistency and Inconsistency

cies and discomfort (see Rydell et al., 2008), highly accessible attitudes that are widely discrepant on implicit and explicit measures should be especially pronounced, attracting greater attentional resources in the service of reducing attitude discrepancy. And it may be that lasting changes on implicit attitude measures may be more likely to reduce discomfort over a longer period of time than changes on explicit attitude measures (on average) because changing the information automatically activated in memory, in many theories the “seed” of the attitude concept (Fazio, 2007; Gawronski & Bodenhausen, 2006), should be more likely to reduce discrepancies consistently.

CONCLUSIONS Understanding discrepancies and consistencies between explicit and implicit attitudes has been at the forefront of contemporary social cognition research. Delineating when implicit and explicit attitude measures are impacted similarly or differently by a manipulation not only is important for models of attitudes but speaks to meaningful debates regarding (1) the processes through which automatic and controlled cognition diverge (e.g., Gawronski & Bodenhausen, 2006), (2) the consequences of their divergence (Briñol et al., 2006), and (3) exactly how to conceptualize attitude representation (Fazio, 2007; Schwarz, 2007). From a functional perspective, it seems that having inconsistent scores on implicit and explicit attitude measures is aversive and induces one to expend cognitive resources in the service of understanding the attitude object better, suggesting that such circumstances merit the individual’s attention and thus have psychological value. How people get to that point is, at present, not fully understood but is necessary for correctly predicting behavior (see Perugini, Richetin, & Zogmaister, Chapter 14, this volume). People experience conflicted feelings toward the self, others, groups, and consumer products. Social psychologists have long understood and studied this phenomenon by examining discrepancies between explicit attitude measures under the auspices of attitude ambivalence. Although this approach has yielded many insights (Petty & Briñol, 2009, for a review), the current chapter examined how discrepancies between implicit and explicit attitude measures occur (in terms of both attitude models and cognitive processes) and what their impact is on cognition and behavior.

307

Let us return to the examples that we opened the chapter with: the divorcée thinking about dating again, the diabetic patron deciding about whether to order the cheesecake, the faculty member making a tenure decision about an obnoxious but productive junior colleague. Each of these examples could be examined by looking at ambivalent attitudes (e.g., does the sweetness of the cheesecake outweigh its fat and sugar content?). However, we believe that by examining discrepancies between implicit and explicit measures, social psychologists gain further insight into how people experience and render decisions. How does a spontaneous negative reaction to an obnoxious junior colleague and a more positive evaluation based on consideration of several positive features (e.g., grant support, prestigious publications) occur? Different models of attitudes answer this question very differently. What processes bring these measures closer together or pull them farther apart and why? Again, the answer depends on the model of attitudes adopted. Finally, what is the psychological impact of these discrepancies for the perceiver and for making decision strategies? When do these discrepancies help versus hinder action or impact versus not impact behavior? Understanding attitudes has proven difficult time and time again. However, we believe that work on discrepancies between implicit and explicit attitudes can provide useful insights for understanding why a divorcée starts dating someone who is obviously wrong for her or why diabetics find it difficult to resist the foods that wreak havoc on their health. It is our hope that by understanding how these discrepancies operate we can examine how to manage them in ways that promote beneficial outcomes. Acknowledgments The research described in this chapter was supported by Grant No. BCS 0601148 from the National Science Foundation and by the Lewis Endowed Professorship.

Notes 1. In this chapter we use the terms implicit attitude and explicit attitude to refer to measures of attitudes that are indirect (e.g., infer evaluations from response latency tasks that involve the attitude object or from evaluations of novel stimuli associated with the attitude object) or direct (e.g., asking people for their evaluations of an attitude object on a scale), respectively. Here, implicit at-

308

III. CROSS-CUTTING PERSPECTIVES

titude measures are conceptualized as measures that better assess evaluations activated without the intent of the perceiver, whose expression is difficult to control, and are less affected by higher order cognitive processes. Explicit attitude measures are conceptualized as measures that better assess evaluations whose expression can be controlled by the perceiver and for which people can utilize higher order cognitive processes. These relatively broad definitions are used in the current work to facilitate comparisons between and among the different models of attitudes described in this chapter that examine consistency and inconsistency between implicit and explicit attitude measures (yet disagree about, e.g., the antecedents of these measures). It is also important to note that implicit and explicit measures are not process pure (e.g., Sherman et al., 2008), meaning that a strict dichotomy between processes and operations assessed by implicit and explicit attitude measures is not warranted. 2. In this model, the input into the rule-based system is referred to as “verbal,” at least in part, because the way social psychologists usually present information in attitudes research is in terms of language (there are some exceptions in research on evaluative conditioning; e.g., Olson & Fazio, 2001). We emphasize that what makes this system distinct is its rule-based underpinnings, not its ties to language. For example, other forms of rules operate in music (e.g., musical notation) and math (e.g., laws of algebra or trigonometry) that are not purely verbal. Although these examples are certainly verbalizable, they are symbolic and rule governed.

References Briñol, P., Petty, R. E., & Wheeler, S. C. (2006). Discrepancies between explicit and implicit self-Â�concepts: Consequences for information processing. Journal of Personality and Social Psychology, 91, 154–170. Carver, C. S., & Scheier, M. F. (2002). Control processes and self-Â�organization as complementary principles underlying behavior. Personality and Social Psychology Review, 6, 304–315. Conrey, F. R., & Smith, E. R. (2007). Attitude representation: Attitudes as patterns in a distributed, connectionist representational system. Social Cognition, 25, 718–735. Cooper, J., & Fazio, R. H. (1984). A new look at dissonance theory. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 17, pp.€229–266). Orlando, FL: Academic Press. Dasgupta, N., & Greenwald, A. G. (2001). On the malleability of automatic attitudes: Combating automatic prejudice with images of admired and disliked individuals. Journal of Personality and Social Psychology, 81, 800–814. De Houwer, J. (2003). A structural analysis of indirect

measures of attitudes. In J. Musch & K. C. Klauer (Eds.), The psychology of evaluation: Affective processes in cognition and emotion (pp.€219–244). Mahwah, NJ: Erlbaum. de Liver, Y., van der Pligt, J., & Wigboldus, D. (2007). Positive and negative associations underlying ambivalent attitudes. Journal of Experimental Social Psychology, 43, 319–326. Dunton, B. C., & Fazio, R. H. (1997). An individual difference measure of motivation to control prejudiced reactions. Personality and Social Psychology Bulletin, 23, 316–326. Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth, TX: Harcourt, Brace, Jovanovich. Eagly, A. H., & Chaiken, S. (2007). The advantages of an inclusive definition of attitude. Social Cognition, 25, 582–602. Fazio, R. H. (1995). Attitudes as object-Â�evaluation associations: Determinants, consequences, and correlates of attitude accessibility. In R. E. Petty & J. A. Krosnick (Eds.), Attitude strength: Antecedents and consequences (pp.€247–282). Mahwah, NJ: Erlbaum. Fazio, R. H. (2007). Attitudes as object-Â�evaluation associations of varying strength. Social Cognition, 25, 603–637. Fazio, R. H., & Olson, M. A. (2003). Implicit measures in social cognition research: Their meaning and uses. Annual Review of Psychology, 54, 297–327. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. Ferguson, M. J., & Bargh, J. A. (2004). Liking is for doing: The effects of goal pursuit on automatic evaluation. Journal of Personality and Social Psychology, 87, 557–572. Festinger, L. (1957). A theory of cognitive dissonance. Palo Alto, CA: Stanford University Press. Gawronski, B. (2007). Attitudes can be measured! But what is an attitude? Social Cognition, 25, 573–581. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. Gawronski, B., Bodenhausen, G. V., & Becker, A. P. (2007). I like it, because I like myself: Associative self-Â�anchoring and post-Â�decisional change of implicit evaluations. Journal of Experimental Social Psychology, 43, 221–232. Gawronski, B., Peters, K. R., & LeBel, E. P. (2008). What makes mental associations personal or extraÂ�personal?: Conceptual issues in the methodological debate about implicit attitude measures. Social and Personality Psychology Compass, 2, 1002–1023. Gawronski, B., Peters, K. R., & Strack, F. (2008). CrossÂ�cultural differences vs. universality in cognitive dissonance: A conceptual reanalysis. In R. M. Sorrentino & S. Yamaguchi (Eds.), Handbook of motivation and cognition across cultures (pp.€297–314). New York: Elsevier. Gawronski, B., & Strack, F. (2004). On the propositional nature of cognitive consistency: Dissonance



16. Consistency and Inconsistency

changes implicit but not explicit attitudes. Journal of Experimental Social Psychology, 40, 535–542. Gawronski, B., Strack, F., & Bodenhausen, G. V. (2009). Attitudes and cognitive consistency: The role of associative and propositional processes. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€86–117). New York: Psychology Press. Gawronski, B., Walther, E., & Blank, H. (2005). Cognitive consistency and the formation of interpersonal attitudes: Cognitive balance affects the encoding of social information. Journal of Experimental Social Psychology, 41, 618–626. Gilbert, D. T. (1991). How mental systems believe. American Psychologist, 46, 107–119. Greenwald, A. G., Banaji, M. R., Rudman, L. A., Farnham, S. D., Nosek, B. A., & Mellott, D. S. (2002). A unified theory of implicit attitudes, stereotypes, selfÂ�esteem, and self-Â�concept. Psychological Review, 109, 3–25. Gregg, A. P., Seibt, B., & Banaji, M. R. (2006). Easier done than undone: Asymmetry in the malleability of implicit preferences. Journal of Personality and Social Psychology, 90, 1–20. Han, H. A., Olson, M. A., & Fazio, R. H. (2006). The influence of experimentally created extrapersonal associations on the Implicit Association Test. Journal of Experimental Social Psychology, 42, 259–272. Harmon-Jones, E., & Harmon-Jones, C. (2002). Testing the action-based model of cognitive dissonance: The effect of action orientation on post-Â�decisional attitudes. Personality and Social Psychology Bulletin, 28, 711–723. Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., & Schmitt, M. (2005). A meta-Â�analysis on the correlation between the Implicit Association Test and explicit self-Â�report measures. Personality and Social Psychology Bulletin, 31, 1369–1385. Kaplan, M. F. (1972). Interpersonal attraction as a function of relatedness of similar and dissimilar attitudes. Journal of Experimental Research in Personality, 6, 17–21. Karpinski, A., & Hilton, J. L. (2001). Attitudes and the Implicit Association Test. Journal of Personality and Social Psychology, 81, 774–788. Kumkale, G. T., & Albarracín, D. (2004). The sleeper effect in persuasion: A meta-Â�analytic review. Psychological Bulletin, 130, 143–172. McConnell, A. R., Rydell, R. J., Strain, L. M., & Mackie, D. M. (2008). Forming implicit and explicit attitudes toward individuals: Social group association cues. Journal of Personality and Social Psychology, 94, 792–807. Nosek, B. A. (2005). Moderators of the relationship between implicit and explicit evaluation. Journal of Experimental Psychology: General, 134, 565–584. Nosek, B. A., & Hansen, J. J. (2008). The associations in our heads belong to us: Searching for attitudes and knowledge in implicit evaluation. Cognition and Emotion, 22, 553–594. Olson, M. A., & Fazio, R. H. (2001). Implicit attitude formation through classical conditioning. Psychological Science, 12, 413–417.

309

Olson, M. A., & Fazio, R. H. (2004). Reducing the influence of extra-Â�personal associations on the Implicit Association Test: Personalizing the IAT. Journal of Personality and Social Psychology, 86, 653–667. Petty, R. E., & Briñol, P. (2009). Implicit ambivalence: A meta-Â�cognitive approach. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€119–164). New York: Psychology Press. Petty, R. E., Briñol, P., & DeMarree, K. G. (2007). The meta-Â�cognitive model (MCM) of attitudes: Implications for attitude measurement, change, and strength. Social Cognition, 25, 657–686. Petty, R. E., Tormala, Z. L., Briñol, P., & Jarvis, W. B. G. (2006). Implicit ambivalence from attitude change: An exploration of the PAST model. Journal of Personality and Social Psychology, 90, 21–41. Petty, R. E., & Wegener, D. T. (1998). Attitude change: Multiple roles for persuasion variables. In D. T. Gilbert, S. T., Fiske, & G. Lindzey (Eds.), Handbook of social psychology (4th ed., Vol. 1, pp.€323–390). New York: McGraw-Hill. Rudman, L. A., Ashmore, R. D., & Gary, M. L. (2001). “Unlearning” automatic biases: The malleability of implicit prejudice stereotypes. Journal of Personality and Social Psychology, 81, 856–868. Rydell, R. J., & McConnell, A. R. (2006). Understanding implicit and explicit attitude change: A systems of reasoning analysis. Journal of Personality and Social Psychology, 91, 995–1008. Rydell, R. J., McConnell, A. R., & Mackie, D. M. (2008). Consequences of discrepant explicit and implicit attitudes: Cognitive dissonance and increased information processing. Journal of Experimental Social Psychology, 44, 1526–1532. Rydell, R. J., McConnell, A. R., Mackie, D. M., & Strain, L. M. (2006). Of two minds: Forming and changing valence inconsistent attitudes. Psychological Science, 17, 954–958. Rydell, R. J., McConnell, A. R., Strain, L. M., Claypool, H. M., & Hugenberg, K. (2007). Implicit and explicit attitudes respond differently to increasing amounts of counterattitudinal information. European Journal of Social Psychology, 37, 867–878. Schwarz, N. (1999). Self-Â�reports: How the questions shape the answers. American Psychologist, 54, 93–105. Schwarz, N. (2007). Attitude construction: Evaluation in context. Social Cognition, 25, 638–655. Schwarz, N., & Bohner, G. (2001). The construction of attitudes. In A. Tesser & N. Schwarz (Eds.), Blackwell handbook of social psychology: Intraindividual processes (pp.€436–457). Malden, MA: Blackwell. Seibt, B., Häfner, M., & Deutsch, R. (2007). Prepared to eat: How immediate affective and motivational responses to food cues are influenced by food deprivation. European Journal of Social Psychology, 37, 359–379. Sherman, J. W., Gawronski, B., Gonsalkorale, K., Hugenberg, K., Allen, T. J., & Groom, C. J. (2008). The selfÂ�regulation of automatic associations and behavioral impulses. Psychological Review, 115, 314–335. Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119, 3–22.

310

III. CROSS-CUTTING PERSPECTIVES

Smith, E. R. (1996). What do connectionism and social psychology offer each other? Journal of Personality and Social Psychology, 70, 893–912. Smith, E. R., & DeCoster, J. (2000). Dual-Â�process Â�models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4, 108–131. Smith, E. R., Fazio, R. H., & Cejka, M. A. (1996). Accessible attitudes influence categorization of multiply categorizable objects. Journal of Personality and Social Psychology, 71, 888–898. Strack, F., & Deutsch, R. (2004). Reflective and impul-

sive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Walther, E., Nagengast, B., & Trasselli, C. (2005). Evaluative conditioning in social psychology: Facts and speculations. Cognition and Emotion, 19, 175–196. Wilson, T. D., Lindsey, S., & Schooler, T. Y. (2000). A model of dual attitudes. Psychological Review, 107, 101–126. Wittenbrink, B., Judd, C. M., & Park, B. (2001). Spontaneous prejudice in context: Variability in automatically activated attitudes. Journal of Personality and Social Psychology, 81, 815–827. Wittenbrink, B., & Schwarz, N. (Eds.). (2007). Implicit measures of attitudes. New York: Guilford Press.

C h a p t e r 17

What Is Implicit about Goal Pursuit? Melissa J. Ferguson and Shanette C. Porter

M

any of the iconic topics in social psychology—Â�attitudes, stereotypes, decisions, moods, and emotions—have been reconceptualized over the last few decades from an implicit cognition perspective. This perspective assumes that mental phenomena and behavior operate according to processes that the perceiver is unable to identify or recognize (e.g., Bargh, 2007; Greenwald & Banaji, 1995; Nisbett & Wilson, 1977; Wilson, 2002; Wilson & Dunn, 2004). For example, cognitive psychological research has shown that people cannot accurately introspect on how certain memories or answers spring to mind. When asked who the Democratic vice presidential candidate was in the 1984 U.S. election, for instance, people may be able to report the answer but have limited or no access into the processes that gave rise to it. As applied to social psychological phenomena, this perspective has introduced a focus on how mental processes as well as behavior can occur without the person’s intention or awareness. Practically, this focus has taken two directions in terms of empirical work. First, social psychologists have examined the unintentional “downstream” effects of intentionally activated constructs. Second, researchers have studied whether a construct, such as an attitude or stereotype, can itself be activated in memory without the person’s intention or awareness. This implicit cognition perspective has ushered in a new wave of research on how

human social behavior can proceed nonconsciously, without the traditionally assumed components of conscious control and guidance (for reviews, see Bargh, 2007; Bargh & Ferguson, 2000; Greenwald & Banaji, 1995; Hassin, Uleman, & Bargh, 2005), as the assortment of chapters in this current volume attests. Especially over the last 10 to 15 years, the implicit cognition perspective has extended to the topic of motivation, which is in many ways a quintessential example of what is assumed to be a consciously mediated, intentional phenomenon. According to the traditional social psychological literature, one of the hallmark characteristics of goal pursuit is its inherent intentional and conscious planning, execution, and monitoring (e.g., Bandura, 1986; Carver & Scheier, 1998; Deci & Ryan, 1985; Gollwitzer, 1990; Locke & Latham, 1990; see also Mischel, Cantor, & Feldman, 1996, for a review). For example, much of the early and even some of the recent work on motivation emphasized how people intentionally set a goal, deliberately invest effort to pursue it, and then consciously and reflectively assess the progress and trajectory of the pursuit. In this chapter, we instead review research on goal pursuit from an implicit cognition perspective. Much of this work has occurred over the last two decades, although there are important and influential earlier traces of relevant ideas and empirical evidence. One characteristic, and strength, of the 311

312

III. CROSS-CUTTING PERSPECTIVES

research conducted over the last two decades has been its reliance on more sophisticated methodology geared toward measuring implicit influence and more refined theory on how human cognition works (e.g., see Ferguson, Hassin, & Bargh, 2008). The two lines of empirical work in implicit social cognition mentioned earlier also exist with regard to the topic of motivation. The first line of work—that a conscious and intentional state, in this case a goal, might lead to a variety of implicit or unintended effects—was clearly evident in the “New Look” research in the 1940s and 1950s (e.g., Bruner, 1957; Bruner & Goodman, 1947) as well as in the work on need for achievement by McClelland and colleagues (e.g., De Charms, Morrison, Reitman, & McClelland, 1955; McClelland, Atkinson, Clark, & Lowell, 1953; McClelland, Koestner, & Weinberger, 1989). People may consciously want something and strive for it and not realize or intend for that goal to subtly influence the way in which they perceptually decode an ambiguous stimulus in the environment. Surely, this kind of effect on perception is both unintended and also largely escapes conscious detection. We do not realize that the drawing of the elderly woman we are looking at from across the room could also have been interpreted as a young woman, for instance. We review recent work showing how conscious or explicit goal pursuit yields a variety of implicit effects in the first section of the present chapter. The second line of work on how goals can be unintentionally activated was prefaced by early theoretical traditions espousing the possibility of nonconscious motivations, such as Freud’s writings (e.g., Freud, 1901, 1920, 1923/1960). Although there are important differences between the Freudian notion of the nonconscious and the contemporary social cognitive notion of the nonconscious (e.g., see Wilson, 2002), this theoretical work was an important forerunner to empirically based social cognitive work on the same topic that has emerged over the last two decades. We review this contemporary work in the second section of the current chapter. Implicit motivation is a burgeoning area of research within social psychology (see Shah & Gardner, 2008), especially within social cognition, and at times the empirical findings have jumped ahead of the theoretical perspectives to explain or predict them. In the third and final section of this chapter, we raise what we consider to be some of the most pressing theoretical questions in this area. Many of these questions can be applied to implicit social cognitive phenomena in general, but some are specific to motivation and goal pursuit.

Finally, it seems necessary to discuss briefly the terminology we use in the chapter, especially because there are some differences in the way in which key terms are used across areas of social psychology as well as across social and cognitive psychology. First, we adopt a fairly uncontroversial social cognitive definition of a goal as a cognitive representation of a desired end state that can influence evaluations, emotions, and behaviors (see Fishbach & Ferguson, 2007). However, we discuss this definition in more detail and raise some questions about it in the third section of the chapter. Second, we define the term implicit as a characteristic of a process or behavior that occurs unintentionally and at times without conscious awareness. This definition is largely accepted across social cognition, but it should be noted that cognitive psychologists and some social psychologists tend to define the term in a slightly different manner. Namely, an implicit memory is a memory that is influenced by experiences of which the perceiver is unaware (e.g., Greenwald & Banaji, 1995; Roediger, 1990; Schacter, 1987). This means that the cognitive definition assumes both a lack of intention and a lack of awareness. In contrast, the social cognition definition does not insist on the trace or influence being incapable of being recognized by the perceiver; it simply depends on the lack of intention. This means, for example, that an attitude might be classified as implicit simply because it was generated spontaneously, even if the perceiver is, or can easily become, aware of it once it has been activated (see Gawronski, LeBel, & Peters, 2007). Obviously, when a phenomenon or process is intentional, it will also be conscious. When a phenomenon is conscious, however, it does not necessarily mean that it was intentional (e.g., mood or emotional states or obsessive thought; see Loewenstein, 1996). Another way to state this is that an unintended phenomenon can be conscious or unconscious. We attempt to point out when social cognitive researchers assume a lack of awareness in addition to a lack of intention when using the term implicit to describe a phenomenon.

THE UNINTENTIONAL EFFECTS OF INTENTIONAL GOALS A growing body of research shows that motivational states lead to a variety of implicit effects during goal pursuit. These effects include, but are not limited to, effects on perception, knowledge accessibility, attitudes, and behaviors. All of the following work relied on dependent measures that



17. What Is Implicit about Goal Pursuit?

could capture such unintended effects. Often, the measures were ostensibly about a task that seemed unrelated to the construct of interest (in these cases, a conscious goal).

Implicit Effects on Perception As a forerunner to contemporary social cognitive research, Bruner and colleagues investigated the influence of internal (e.g., needs, beliefs) factors on what one perceives in the external world (e.g., Bruner & Goodman, 1947). These New Look theorists argued that one’s reality does not simply exist as a given, but rather is constructed by many top-down factors, such as one’s values. In a classic test of this hypothesis, Bruner and Goodman (1947) asked children to estimate the size of different coins using a light cast into a box that they could adjust accordingly. The researchers found that poor children tended to overestimate the size of the coins more than rich children. One explanation for these findings is that poor children valued money more than rich children (i.e., perhaps having a stronger goal to acquire money), and this difference in valuation resulted in a corresponding difference in the perceived size of the coins. In this way, the children may have been conscious and intentional about such a goal but would have presumably not realized or intended that goal to influence their estimation of coin size. In other words, their conscious goal had an unintended effect on their perception of money-Â�related stimuli in the environment. McClelland and colleagues further tested the effects of internal factors on perception (e.g., De Charms et al., 1955; McClelland et al., 1953, 1981, 1989). Like Bruner and colleagues, these researchers argued that perceptions depend on both sensory inputs from the environment and a priori beliefs and knowledge. For example, a word that is visually partially obscured will, in many cases, still be perceived as the word in its entirety because knowledge of the word influences perception. More to the point, however, is that included in the repertoire of a priori influences on perception are chronic motives. McClelland and colleagues tested this idea largely through the use of projective test methodologies, such as the Thematic Apperception Test, in which participants are asked to interpret ambiguous images and stories. From this work, the researchers concluded that internal motives, especially the need for achievement, could reliably be derived from individuals’ perceptions on projective tests. Furthermore, behaviors consistent with these motives could reliably be predicted from

313

performance on projective tests. These results suggest that people’s achievement needs, which may at times (but not always) be consciously accessible and intentional, have unintended effects on the interpretation of ambiguous visual scenes and narratives. In a set of experiments that represents a contemporary reexamination of the New Look thesis, Balcetis and Dunning (2006) demonstrated that temporary motivational states influence individuals’ perceptions. Over the course of five studies, the researchers found that participants’ current motivational states guided how they disambiguated visual stimuli. In one experiment, participants were told that their ultimate task would be to drink either a desirable or an undesirable beverage, depending on the random assignment of the computer. Half of the participants were told that if the computer produced a letter, they would drink the desirable drink. The other half were told that if the computer produced a number, they would drink the undesirable drink. In fact, all participants were shown an ambiguous figure that could easily be interpreted as either an uppercase B or the number 13. This ambiguous figure was shown only briefly before the computer ostensibly crashed. The critical measure was what participants reported seeing before the computer malfunctioned. A large majority of the participants reported seeing the image that would have allowed them to drink the desirable beverage. In a series of follow-up experiments, the researchers established the generality of this effect and ruled out alternative explanations for these findings, such as participants’ dishonesty. As Balcetis and Dunning point out, these experiments are a quite literal illustration of the assertion that we see what we want to see and suggest, more generally, that our (conscious and intentional) wants can unintentionally influence our lower level perceptions (e.g., visual, auditory).

Implicit Effects on Knowledge Accessibility A number of early cognitive-Â�motivational theories and models have posited that a relationship exists between motivational states and concomitant goal-Â�related knowledge activation. Specifically, these theories claim that patterns of knowledge accessibility during goal pursuit are in the service of doing—that is, that the relationship between goal activation and knowledge accessibility is functional in nature (e.g., Ach, 1935; Bruner, 1957; Kuhl, 1983). For example, Bruner (1957) suggested that need states direct attention toward

314

III. CROSS-CUTTING PERSPECTIVES

goal-Â�facilitating objects, which, in turn, prepares one for action and enhances one’s ability to attain a given goal. Likewise, Kuhl and colleagues (1983, 1987; Goshke & Kuhl, 1993) suggested that successful action-Â�oriented goal pursuit is characterized by activation of goal-Â�relevant memories. Indeed, contemporary work in social cognition has supported these contentions. Findings show that goal states lead to increased accessibility of goalÂ�related knowledge and decreased accessibility (i.e., inhibition) of goal-Â�irrelevant and goal-Â�hindering knowledge (e.g., Aarts, Dijksterhuis, & De Vries, 2001; Fishbach, Friedman, & Kruglanski, 2003; Förster, Liberman, & Higgins, 2005; Moskowitz, 2002). Moreover, there is also evidence that the strength of activation of goal-Â�related knowledge is related to the strength of the goal (Förster et al., 2005). Aarts and colleagues (e.g., Aarts & Dijksterhuis, 2000; Aarts et al., 2001) were among the first to show empirically that there is heightened goal-Â�related knowledge accessibility while in motivational states. In one experiment conducted by Aarts and colleagues (2001), some participants were made to feel thirsty during an initial task, while others were not. In an ostensibly unrelated task, all participants were then asked to complete a lexical decision task in which they were presented with a series of letter strings and for each had to decide quickly whether or not the letter string formed a real word or was nonsense. Some targets were drink-Â�related words. As predicted, thirsty participants correctly classified drink-Â�related stimuli as words significantly more quickly than participants in the control conditions. Thus, drinkÂ�related memories were more accessible for thirsty participants. In a second experiment, thirsty participants and control participants were placed in a room with several items, eight of which were related to quenching thirst. In a later surprise recall task, conducted in a different room, thirsty participants were better able to recall drink-Â�related items than were control participants. In another set of studies, Moskowitz (2002) found that, even when a task required participants to ignore goalÂ�relevant objects, attention was directed toward goal-Â�relevant information when the respective goal was activated. Taken together, these studies suggest that when in a conscious need state people are more inclined to perceptually search for, and attend to, goal-Â�related objects, even if it interferes with a focal task. These findings offer strong support for the contention that intentional motivational states at times unintentionally lead to increased accessibility of goal-Â�related knowledge,

which then guides attention toward goal-Â�related objects. There is converging evidence that conscious goals not only increase the accessibility of goalÂ�relevant knowledge but can also reduce the accessibility of goal-Â�irrelevant and goal-Â�hindering knowledge. Shah, Friedman, and Kruglanski (2002, Study 1), for example, found that the conscious generation of one important goal inhibited the activation of other important, but interfering alternative goals. Shah and colleagues also found that level of commitment to the focal goal, as well as reported tenacity to reach the goal, moderated this effect, such that those with high commitment and tenacity showed a stronger inhibition effect. Importantly, participants were not trying to inhibit other goals; their focal task was to generate an important goal, and an unintended consequence of this generation was the inhibition of competing goals. It has been suggested that, unlike knowledge activation due to other sources (e.g., semantic priming), this pattern of activation while in a conscious goal state (i.e., enhanced accessibility of goal-Â�related knowledge and reduced accessibility of goal-Â�irrelevant knowledge) endures only until one has completed the goal (e.g., Förster et al., 2005; Goschke & Kuhl, 1993). Förster and colleagues (2005) conducted a series of six experiments in which they found that the accessibility of goal-Â�related information was enhanced while conscious and intentional goal pursuit was ongoing but inhibited immediately following goal attainment. Additionally, both goal-Â�related knowledge activation during goal pursuit and postcompletion goal-Â�related knowledge inhibition were related to the strength of the intentional goal, as measured by the expectation for successful goal completion as well as the value that the goal held. One reason that goal-Â�related knowledge is inhibited postfulfillment may be that in many cases another goal will become activated after the completion of a particular goal (e.g., Fishbach, Dhar, & Zhang, 2006), thus rendering knowledge related to the original focal goal irrelevant.

Implicit Effects on Attitudes Intentional and conscious goal states lead not just to the activation (or inhibition) of goal-�related knowledge but also specifically to the spontaneous (i.e., unintended) activation of evaluatively positive goal-�related memories as well as the spontaneous inhibition of evaluatively negative goal-�related memories (e.g., Brendl, 2001; Brendl,



17. What Is Implicit about Goal Pursuit?

Markman, & Messner, 2003; Ferguson & Bargh, 2004; Markman & Brendl, 2000; Moors & De Houwer, 2001; Moors, De Houwer, Hermans, & Eelen, 2005; Payne, McClernon, & Dobbins, 2007; Seibt, Haefner, & Deutsch, 2007; Sherman, Rose, Koch, Presson, & Chassin, 2003; Waters, Carter, Robinson, Wetter, Lam, & Cinciripini, 2007). As in the case of the selective accessibility of goal-Â�related knowledge while in a conscious goal state (see Bruner, 1957; Shah et al., 2002), there is a functional explanation for the net evaluative positivity of memories associated with the goal (as well as the net evaluative negative appraisal of objects that might interfere with the goal) while in a goal state (e.g., Ferguson & Bargh, 2004; Lewin, 1936). Indeed, evaluative appraisals of stimuli (i.e., evaluations or attitudes) serve to characterize the corresponding stimuli as either desirable potential rewards when evaluatively positive or as undesirable potential threats when evaluatively negative (e.g., Fazio, 1989; see also Ferguson & Bargh, 2004, 2008). In the former case one will tend to approach those stimuli, while in the latter case one will tend to avoid them. Conscious goal states, then, lead to functional (yet unintended) evaluations of goal-Â�relevant stimuli, which should then guide and shape behavior toward those stimuli in a goal-Â�consistent manner. As one example of this work, Ferguson and Bargh (2004) manipulated participants’ thirst such that half the participants were thirsty and half were not. The researchers then used a sequential evaluative priming task (e.g., Fazio, Jackson, Dunton, & Williams, 1995; Fazio, Sanbonmatsu, Powell, & Kardes, 1986) to determine participants’ implicit attitudes toward goal-Â�relevant versus goalÂ�irrelevant objects. In the task, participants briefly saw a prime consisting of either a goal-Â�relevant word (e.g., water) or a goal-Â�irrelevant word (e.g., trees). The prime was followed by a target word that was either positive or negative. Participants were instructed to categorize the target word as either positive or negative as quickly as possible. If they evaluated the prime as positive, then they should be quicker to categorize correctly a positive versus a negative target word. If they evaluated the prime as negative, they should be quicker to correctly categorize a negative versus a positive target word. As predicted, thirsty participants evaluated goal-Â�relevant primes more positively than goalÂ�irrelevant primes, whereas nonthirsty participants did not show this pattern. In addition, thirsty participants showed the greatest implicit positivity toward highly goal-Â�relevant objects (e.g., water), followed by weakly goal-Â�relevant objects (e.g., coffee)

315

and then goal-Â�irrelevant objects (e.g., trees). This last finding suggests that people’s goal-Â�driven attitudes are sensitive to the extent to which objects could be helpful to the goal. Research on the effect of intentional, conscious goals on unintended attitudes also demonstrates that the effects exist only as long as the goal is active, such as is the case when participants are in a state of temporary deprivation (Ferguson & Bargh, 2004; Payne et al., 2007; Sherman et al., 2003; Waters et al., 2007). In the thirst study just described, the predicted effects emerged in thirsty participants in comparison to those who had been thirsty minutes earlier but who had sated their thirst by drinking water and juice before the implicit attitude measure. These findings complement research on addiction, which has shown that habitual heavy smokers who had not recently smoked, as well as those who explicitly reported feeling withdrawal symptoms, implicitly evaluated smoking-Â�related items (e.g., a pack of cigarettes) more positively than smokers who had recently smoked or who were not experiencing withdrawal symptoms (Sherman et al., 2003; see also Payne et al., 2007; Waters et al., 2007). Recent findings have established that the activation of a conscious goal will not necessarily always lead to more positive implicit attitudes toward stimuli that might foster goal pursuit. Instead, only those who have some level of skill at the goal, and some motivation to approach the goal, will exhibit such an effect (Ferguson, 2008). This means that when the goal is easy to attain (e.g., sating thirst), most people should have plenty of experience succeeding at the goal and should demonstrate more positive implicit attitudes toward goal-Â�facilitating stimuli whenever the goal becomes active. However, when the goal is more difficult (e.g., academic achievement, dieting), only those who are generally successful at the goal will show the effect on implicit attitudes. For example, when participants had the conscious goal to academically achieve (vs. not), they implicitly evaluated school-Â�related stimuli as more positive only if they had a relatively high grade point average (Ferguson, 2008). It is important to note that one assumption of the functional argument here is that any goal-Â�driven increase in the positivity of implicit attitudes toward goal-Â�facilitating stimuli will actually lead to behavioral changes. That is, implicit attitudes are assumed to possess predictive validity for behavior, a claim that is supported by a burgeoning literature (e.g., Wittenbrink & Schwarz, 2007). Yet it seems reasonable to expect that one’s implicit attitudes toward certain goal-Â�relevant stimuli are more pre-

316

III. CROSS-CUTTING PERSPECTIVES

dictive under some circumstances than others. For example, recent work shows that when people are under cognitive fatigue or deficit (e.g., from alcohol, ego depletion), their implicit attitudes toward temptations are more likely to predict their behavior compared with when they are not under such duress (e.g., Friese, Hofmann, & Wänke, 2008; Hofmann & Friese, 2008; Hofmann, Rauch, & Gawronski, 2007). In one study, participants either were or were not under the influence of alcohol and were asked to sample some candy (Hofmann & Friese, 2008). Interestingly, alcohol-Â�impaired participants’ implicit attitudes toward candy strongly predicted their consumption, whereas control participants’ implicit attitudes toward candy did not. Of course, one critical question is whether control participants’ behavior might have been predicted by their implicit attitudes toward some other type of stimuli (perhaps related to their long-term dieting goals). This line of work raises intriguing issues concerning the types of implicit versus explicit attitudes that ultimately drive behavior depending on circumstances, especially those related to selfÂ�control resources.

Implicit Effects on Behavior If conscious goals lead to unintended effects on attitudes toward goal-�related stimuli, then goal states should also influence unintended approach and avoidance behaviors in a similar fashion. A number of studies have used variations of a lever-based tool for measuring approach and avoidance behaviors (e.g., De Houwer, 2003; Fishbach & Shah, 2006; Fitzsimons & Shah, 2008; Markman & Brendl, 2005; Seibt et al., 2007). These measures share several methodological procedures. For instance, in all cases participants are presented with stimuli, typically on a computer screen. In response to these stimuli, participants are asked to either pull a lever (or joystick) toward them or push it away. To the extent that pulling is done more quickly, one is said to have an approach orientation toward that object or construct.1 Likewise, to the extent that pushing is done more quickly, one is said to have an avoidance orientation relative to that object or construct. This measure captures behavioral effects that are subtle, in that participants, although obviously aware that they are pushing and pulling the lever, are presumably not aware of pushing (or pulling) the lever slightly faster in response to some stimuli in some situations, for instance. This work suggests ways in which a conscious goal can subtly alter the force or duration of an intended behavior without the person realizing it (see also Aarts, Custers, & Marien, 2008).

As one example of this work, in a study by Fishbach and Shah (2006) in which a variant of this lever task was used, participants first listed both a chronic goal and a temptation relative to that goal. Next, participants were presented with strings of letters that were one of the following: the goalÂ�related word, the temptation-Â�related word, one of several control words, or one of several nonword strings of letters. Half of the participants were asked to pull the lever if the string of letters formed a word, and the other half were asked to push the lever if the string of letters formed a word, as quickly as possible. The researchers found that participants asked to pull the lever did so more quickly for the goal word than the temptation word, whereas the reverse was true for participants asked to push the lever. These effects show that simply perceiving a stimulus that is consistent with one’s conscious goal facilitates approach arm movements, just as perceiving a disruption to that goal facilitates avoidance arm movements. If these behavioral movements are due to goal states, then one should expect that they are strongest when there is an active goal. Conceivably, in the Fishbach and Shah (2006) experiments described previously, participants’ chronic goals were activated by the individual words, thus producing the behavioral results. However, Seibt and colleagues (2007) explicitly tested the extent to which behavioral orientations (as indicated by arm movements) vary by goal state. In one experiment, they measured participants’ behavioral orientations toward food-Â�related and food-Â�unrelated stimuli before and after eating lunch. The results showed that individuals in an unsatisfied need state (those who had not yet eaten lunch) pulled the lever more quickly than they pushed it for food-Â�related stimuli. Those who were no longer in a hunger need state also showed this effect, although to a smaller degree. These results suggest that conscious goal states do indeed influence approach and avoidance behaviors in a goal-Â�consistent fashion. It seems reasonable to expect that conscious goals yield unintended modifications of many different behaviors. Even while people may be at some level aware of a given behavior, they may not be aware of or intend all aspects of that behavior (e.g., variations, duration, frequency, strength). This suggests that once a conscious goal has been activated, people who are knowingly pursuing a goal-Â�consistent behavior may nevertheless unintentionally enact that behavior in a more versus less goal-Â�consistent manner. This raises the issue of the many levels at which a behavior might be identified and the variation in the (conscious) accessibility of those levels of behavioral identifi-



17. What Is Implicit about Goal Pursuit?

cation for the person (e.g., Vallacher & Wegner, 1989). For instance, a person may be consciously trying to socialize with a new acquaintance, and this conscious attempt at an abstract behavior may also entail more specific, unintentional nonverbal behaviors such as eye gazing, body orientation, and voice inflection. To study the potential effects of a conscious goal on unintended behaviors of this kind, it would be necessary to first identify which unintentional aspects of any given intentional behavior are goal consistent and then test whether the activation of a goal increases their enactment.

Conclusions In this section, we have reviewed evidence that intentional, conscious goals can lead to a range of effects that are either partially or largely unintended (i.e., spontaneous, implicit). It is important to note that many of the effects described previously emerged despite participants’ engagement in a non-goal-Â�related focal task. For example, the participants in the studies by Moskowitz (2002) were intentionally trying to ignore the goal-Â�relevant distractors and were less able to do so when the respective goal was (consciously) active. Also, the participants in the studies on implicit attitudes (Ferguson & Bargh, 2004; Sherman et al., 2003) were not evaluating the goal-Â�relevant words themselves; rather, they were trying to evaluate the goal-unrelated target words, and their speed in doing so was unintentionally influenced by the goal-Â�relevant primes that preceded those targets. These kinds of effects are easily classified as implicit in that they were unintentional and spontaneous, but they may have been available to consciousness if participants had been asked (e.g., see Gawronski et al., 2007). In contrast, in other research, such as in the studies by Balcetis and Dunning (2006), participants were presumably not aware of the effect of their conscious goal on their perception of the ambiguous figures, which also, of course, means they did not intend such effects. In this case, implicit, therefore, indicates a lack of intention and awareness.

EVIDENCE FOR UNINTENTIONAL GOAL€ACTIVATION In the first section, we demonstrated that goals that are activated consciously and intentionally elicit effects that are themselves implicit and likely exist, at times, outside of the person’s awareness. Such effects clearly illustrate how an implicit cog-

317

nitive perspective is pertinent to even conscious goal pursuit. Since the early 1990s, however, social cognition researchers have also revealed that goals can be nonconsciously and unintentionally activated from memory and influence downstream behaviors. This work is reviewed next. It should be noted, however, that even though a given goal can be activated in memory largely without the person’s intention or conscious awareness of that activation, this does not, of course, mean that conscious processing is irrelevant or unrelated to the activation. Participants are typically consciously engaged in the implicit priming technique, whatever it is, but simply do not report any increased awareness or importance of the goal state or that they have been primed in some way. Thus, even though participants may be consciously and actively reading and processing words related to the goal within a crossword puzzle, for instance, they do not consciously realize or experience the effects of those words on their motivational level or behavior or attitude. In each of the following areas, we consider the empirical evidence suggesting the implicit nature of the goal whenever possible.

Evidence from Information Processing Chartrand and Bargh (1996) authored the first empirical study in the social cognition literature on implicit goal activation. In this work, the authors tested whether an impression formation goal can be activated using standard implicit priming techniques. They conceptually replicated two classic studies on conscious impression formation. These earlier studies tested whether people with the goal of forming an impression of another person on the basis of a series of behaviors (vs. to memorize the behaviors) would exhibit greater organizational processing of the behaviors. Instead of consciously activating the goal to form an impression, Chartrand and Bargh asked participants to unscramble groups of words into sentences, and some of the sentences contained words related to forming an impression (e.g., interpret, judge), whereas for other participants some of the words were related to memorization (e.g., memory, retain). The results across two studies showed that, even though participants in the impression condition did not realize that they had been primed with an impression formation goal, they showed greater organizational processing of the behaviors compared with those in the memorization condition. For example, they showed significantly greater clustering around the personality traits that corresponded to the behaviors of the target person, a trademark of impression formation.

318

III. CROSS-CUTTING PERSPECTIVES

This first evidence of implicit goal activation demonstrated that a subtle prompt from a perceiver’s immediate environment can shift the way in which the perceiver absorbs and organizes social information about another person. Although participants were consciously processing the primes themselves (e.g., judge, interpret), they did not report any awareness of those primes on the way in which they completed the person impression task. Recent work has again examined an implicit impression formation goal (McCulloch, Ferguson, Kawada, & Bargh, 2008). A series of experiments tested more closely which mental operations relevant to impression formation emerged for those implicitly primed with impression formation or not. These findings showed that those who had been implicitly primed with impression formation (vs. not) were significantly faster at analyzing the trait implications of behaviors and were also more likely to form associations in memory between behaviors and the traits that corresponded with them. Other evidence from this series of studies demonstrated that those who had been implicitly primed showed more sensitivity to whether incongruent trait information was applicable versus inapplicable to the actor’s underlying personality. The research on implicit impression formation reveals the mental processes that commence when a goal has been activated implicitly, but what about actual social behavior? Research published since Chartrand and Bargh (1996) has focused on this and other theoretically relevant questions: What kinds of stimuli might activate a goal without the perceiver’s awareness? How do we know a goal per se is being activated rather than a semantic construct? What is the evidence that the goal is operating outside of conscious awareness and intentions? We review next some of the pertinent literature on these questions.

Evidence from Behavior The next empirical piece to be published on implicit goal activation was by Bargh, Gollwitzer, Lee-Chai, Barndollar, and Troetschel (2001). The authors presented a series of studies that examined the effects of priming a goal on actual behavior. These studies also, importantly, identified the distinction between the activation of a motivational versus perceptual or semantic construct. Earlier literature on motivation revealed certain signatures of motivated behavior, including persistence at the goal, resumption of the goal after an interruption, and an increase in the strength of the goal over time until it is met (Atkinson & Birch,

1970; Gollwitzer & Moskowitz, 1996; Lewin, 1936; McClelland et al., 1953). Bargh and colleagues showed that an implicitly activated goal led to behavior that met these classic signs of motivation. For example, in one study, participants were primed with achievement or not and then worked on a word puzzle for a few minutes, at which point the overhead projector that was displaying the puzzle information was rigged to break down. The experimenter explained that because of the time lost from the projector breaking down participants had to choose whether to continue with the word puzzle (an achievement-Â�related task) or move on to the other task in the experiment: judging the humor of cartoons (not particularly achievement related). Those who had been primed with achievement (vs. not) were significantly more likely to continue working on the puzzle rather than the normatively more enjoyable cartoon task. Most convincingly, another experiment showed that the strength of the implicitly primed achievement goal increased over several minutes, an effect that is inconsistent with the typical decay rates of semantic priming (see Higgins, Bargh, & Lombardi, 1985; Srull & Wyer, 1979). These experiments together demonstrated persistence at the goal, resumption after an interruption, and an increase in strength over time (for a recent review of how goal priming is distinguished from nongoal priming, see Förster, Liberman, & Friedman, 2007). The evidence in Bargh and colleagues (2001) that the activation was implicit comes from the debriefing data. Participants did not report any awareness of any relation or relevance between the initial priming task and the later crossword puzzles. However, one might ask whether the level of participants’ conscious awareness of the goal predicted their goal-Â�related behavior. Bargh and colleagues tested this in a different experiment in which participants were implicitly primed with cooperation or not and were consciously primed with cooperation or not. The dependent variable was the degree to which participants behaviorally considered the greater good in a common resources dilemma game (i.e., a fishing game where the lake had to be kept stocked). Those who were primed implicitly showed more cooperation than those in the control group, and those who were primed explicitly also showed the same effect. Importantly, however, participants were also asked at the end of the experiment how much they had cooperated. The level of cooperation reported by those in the explicit goal condition significantly predicted their cooperative behavior, whereas the level of cooperation reported by those in the implicit goal condi-



17. What Is Implicit about Goal Pursuit?

tion was completely unrelated to their cooperative behavior. This evidence suggests that participants were not aware of the implicit goal (see also Aarts, Gollwitzer, & Hassin, 2004; Eitam, Schul, & Hassin, 2008; Ferguson, 2008). Multiple articles have since been published on implicit goal activation effects on behavior. These articles have focused largely on the different kinds of cues in the environment that can activate goals implicitly. Fitzsimons and Bargh (2003) and Shah (2003) have examined how relationship partners can trigger the goals that a perceiver associates with them. Shah, for example, showed that when participants were primed with close relationship partners (vs. not), they tended to pursue the goals that those partners had for them. Interestingly, this effect was moderated by participants’ reported closeness to those partners. Thus, the closer participants reported being with their mother, for instance, the stronger the effect of being implicitly primed with their mother on their reported commitment to the goals their mother had for them. Shah also showed that the perceived value of the goal to the relationship partner also mattered for the nature of the priming effect such that for those who were close to their father, only those who perceived a goal as being of high value to their father showed an increased commitment to it after being primed with their father. Those who perceived the goal as being of low value to their father showed a decrease in commitment after being primed. Participants did not report any awareness of the priming measures or any relation between the priming measures and the dependent variables. There are different ways in which one’s relationship partners might be associated with relevant goals. One way is when a partner wants the person to pursue a goal, and this is what Shah (2003) examined. However, another way is when one tends to pursue a certain goal with a specific partner, and this is what Fitzsimons and Bargh (2003) examined. They showed that participants who had a goal in the context of a certain relationship partner were more likely to pursue that goal after being subtly primed with that partner. For example, participants who reported a goal of trying to achieve in order to impress their mothers performed better on a verbal task after being implicitly reminded of their mother compared with those who were not primed as well as those who did not report having such a goal in the context of their relationship with their mother. In another experiment, importantly, Fitzsimons and Bargh demonstrated that the goals that one tends to pursue within certain interpersonal relationships can be orthogonal

319

to the partner’s own standards or goals. In all of this work, participants did not report an awareness of any relation between the priming task and the central dependent measures, suggesting that they had been unknowingly and unintentionally influenced by the partner primes. Beyond one’s relationship partners, what other kinds of cues in the environment might implicitly activate goals? In addition to reading highly goalÂ�relevant words within puzzles or sentence tasks and reading about one’s relationship partners, research has shown that reading about a stranger’s goal pursuit can unknowingly influence one’s own goal pursuit (Aarts et al., 2004). In one experiment, participants read a vignette about a guy trying to pick up a woman in a bar. The authors expected that this would activate among the male participants the goal to seek causal sex. Participants were then asked to provide feedback on a computer task to either a male or female experimenter. Those who were primed with the sex vignette provided significantly more help to the female (but not male) experimenter compared with those who were not primed. Critically, there was no difference between those in the primed condition and control condition in terms of their awareness of a helping intention, suggesting that the goal was influencing their behavior toward the female experimenter in a subtle, nonconscious manner. Aarts and colleagues (2004) also tested whether an undesirable goal can be activated implicitly. Participants read the same casual sex vignette as described previously, or one in which the information that the guy had a new baby at home was added to the story, making it normatively undesirable (according to pilot testing). Other participants did not read any sex-Â�related vignette. The results showed that whereas those who read the original sex vignette showed the same goalÂ�relevant behavior, those in the undesirable condition did not, compared with the control condition. This suggests a boundary condition for implicit goal activation: Undesirable goals may not be able to be activated implicitly. As a final note, it is worth mentioning that recent work even suggests that reading about another’s effortful goal pursuit can lead to depletion effects in the perceiver (Ackerman, Goldstein, Shapiro, & Bargh, 2009), also a presumably unintentional effect (see also Dik & Aarts, 2007). Beyond examining the types of cues that might lead to implicit goal effects on behavior, recent research has addressed the mental contents involved in a goal representation that might underlie such effects. That is, what is it about a goal representa-

320

III. CROSS-CUTTING PERSPECTIVES

tion that allows it to direct behavior without one’s awareness or intention? Aarts, Custers, and colleagues have argued that in addition to information about the relevant behaviors, the underlying mental representations must also include positive affect, which can serve as a reward cue for performing those behaviors (see Aarts, Custers, & Holland, 2007; Custers & Aarts, 2005). Aarts and colleagues (2008) tested this claim by asking participants to squeeze a handgrip whenever the word squeeze appeared on a computer screen. However, participants first completed a computer paradigm in which the word squeeze was subliminally presented by itself, subliminally presented and paired with positive (unrelated) words, or not presented at all. Those participants for whom the behavioral instruction had been subliminally paired with positive (reward) cues differed from the other two groups when subsequently squeezing the handgrip: They squeezed more quickly and also exerted their maximum force more quickly. This work shows that positive affective information may be a crucial ingredient in goals that allows them to direct and shape behavior implicitly (see also Ferguson, 2007). Researchers have also examined whether implicit goal activation might, in fact, explain effects that have been assumed to emerge as a result of nonmotivational mechanisms (Cesario, Plaks, & Higgins, 2006). Many studies have demonstrated that priming participants with a behavior (e.g., rudeness), stereotype (e.g., the older adults), norm (e.g., quiet in a library), or even smell (e.g., lemon cleaning scent) leads to corresponding effects on behavior (e.g., Aarts & Dijksterhuis, 2003; Bargh, Chen, & Burrows, 1996; Dijksterhuis & van Knippenberg, 1998; Holland, Hendriks, & Aarts, 2005). Typically, these effects have been interpreted as resulting from the perception of the construct, whatever it is, having direct effects on the likelihood of behaving in line with that construct (i.e., an ideomotor account; see Dijksterhuis, Chartrand, & Aarts, 2007). However, Cesario and colleagues (2006) argued that at least in some of these cases, especially when the prime invokes a group of people, the perception of the group triggers one’s motivation to interact with that group, which can differentially influence the likelihood of behaving in line with that group’s stereotypical behaviors. For example, they reasoned that people who show an implicit positivity toward older adults are motivated to interact with them. Thus, when primed with that group, such people should walk more slowly. Those who show implicit negativity toward the group, on the other hand, should

be motivated to avoid interacting with them and should walk more quickly after being primed with the group. Their results supported this claim. (They also argued that even though people may have a different motivation toward a given group, they should all nevertheless have the same kinds of stereotypes and information about them, which rules out the possibility of an ideomotor account of their findings.)

Evidence from Judgments of Others How else might we detect the implicit activation of a goal? Kawada, Oettingen, Gollwitzer, and Bargh (2004) examined whether people who have been implicitly primed with a goal are more likely to see that goal in other people’s behavior, which the authors interpret as an implicit form of transference (e.g., see also Andersen & Chen, 2002). In one study, participants were implicitly or explicitly primed with the goal of competitiveness, or not primed, and then asked to read about two fictitious characters playing a prisoner’s dilemma game and rate how likely the characters were to play competitively. The results showed that those who had been primed either implicitly or explicitly with the goal rated the characters as significantly more likely to behave competitively compared with those in the control condition (there was no difference between the two goal conditions). Importantly, however, how do we know that a goal was being primed and not simply the construct of competitiveness? Previous work in the conscious goal literature suggests that the strength of a goal may increase, especially when the pursuit becomes difficult (e.g., Brehm & Self, 1989; Brunstein & Gollwitzer, 1996; Förster et al., 2007; Gollwitzer & Kirchhof, 1998; Gollwitzer & Wicklund, 1985; Wright, 1996). In their next study, participants were primed implicitly, explicitly, or not at all with the goal of competitiveness and then played a game that was slightly competitive (a wordÂ�guessing game) and received either success or failure feedback about their performance. Participants then completed the same ratings task concerning the two fictitious characters in the prisoner’s dilemma vignette. The results showed an interaction between priming condition and type of feedback received. Participants in the two priming conditions who received failure feedback rated the characters as significantly more likely to be competitive compared with those in the control condition and all of those who received success feedback. This is suggestive that a goal construct, whose strength should be influenced by goal-Â�relevant feedback,



17. What Is Implicit about Goal Pursuit?

was likely activated initially rather than merely the trait of competitiveness, for example.

Evidence from Knowledge Accessibility Just as researchers have shown that an intentional, conscious goal can influence the accessibility in memory of knowledge related to the goal, some work has tested how an implicitly activated goal leads to similar knowledge accessibility effects. For example, Aarts and Dijksterhuis (2000) tested whether the activation of a goal increases the accessibility of habitual behaviors related to that goal. In one study, habitual and nonhabitual bicycle riders were subtly primed, or not, with the goal of traveling (around town). They then completed a task that measured the accessibility of the word bicycle. The results demonstrated that habitual bike riders who had first been implicitly primed with a travel goal showed enhanced accessibility for bikes compared with nonhabitual bike riders. Interestingly, habitual and nonhabitual bike riders did not differ in the accessibility of bike when the travel goal had not been primed. This demonstrates that for people who have repeatedly used a particular means to meet a goal, such as riding one’s bike in order to get from point A to point B, information can become more accessible in memory after the goal has been primed. It should be noted, however, that these studies did not differentiate between a goal in particular being primed versus a nonmotivational construct (e.g., semantic knowledge, procedural knowledge). Another line of work related to implicitly activated goals and effects on knowledge accessibility was presented by Shah and colleagues (2002). Across multiple studies, these authors showed that the implicit (in this case, subliminal) activation of one important goal inhibited the activation of other important, but interfering, alternative goals. In addition, participants’ reported commitment to the goal and tenacity to reach the goal mattered, such that those high on both dimensions showed a stronger inhibition effect (as mentioned earlier, Shah et al. also found this effect when the goal was activated consciously). This work shows that the implicit activation of a goal can lead to inhibition effects that serve to facilitate the focal goal, in that potential distractions automatically (i.e., spontaneously) become less noticeable. From a slightly different perspective, Fishbach and colleagues (2003) examined how various distractions and temptations themselves might influence the accessibility of the goal they would undermine. They tested whether the perception

321

of short-term temptations (e.g., TV) that would undermine a longer term goal (e.g., doing well in school) automatically activated the goal itself. In one study, within a sequential priming paradigm, participants were first subliminally primed with a temptation or goal-Â�related word. They then had to make a lexical decision about a subsequent target word, which was a temptation- or goal-Â�related word. The main finding was that subliminally perceived temptation primes facilitated relevant goal words, whereas the opposite was not true. That is, when someone perceives the word TV, the goal with which TV might interfere—Â�academics—Â� automatically becomes more accessible in memory. However, when someone sees a word related to academics, the temptations that might interfere with that goal do not seem to spring to mind automatically. One way to interpret this work within the perspective of this chapter is that the heightened accessibility of the goal is evidence that the goal was activated, and that the triggers themselves (the temptations) were implicit. Another important finding from this work was that this effect was most evident among those participants who most valued the long-term goal and those who were the most successful at the long-term goal. This provides suggestive evidence that the effects were due to a goal being activated, however, it is always possible that those who most value a goal and who are successful at it have different kinds of semantic knowledge structures concerning the goal. More generally, Fishbach and colleagues have conducted a comprehensive line of research on the implicit processes underlying self-Â�control dilemmas, which can be thought of as a special type of goal pursuit. Namely, self-Â�control involves a conflict between an attractive immediate goal that interferes with a longer term goal. This work has identified the variety of ways in which people handle such dilemmas implicitly, at times according to their skill level, commitment to the goal, and conscious motivation to achieve the goal (see also, e.g., Fishbach et al., 2006; Fishbach & Shah, 2006; Fishbach & Zhang, 2008; Koo & Fishbach, 2008). This research is beyond the purview of the current chapter but is reviewed in detail elsewhere (see Fishbach & Converse, in press; Fishbach & Ferguson, 2007).

Evidence from Attitudes How else might researchers detect whether a goal has been activated implicitly? Work on the effect of intentional goals on implicit attitudes, reviewed earlier, shows that a goal leads to more positive

322

III. CROSS-CUTTING PERSPECTIVES

implicit attitudes (Ferguson & Bargh, 2004; Seibt et al., 2007; Sherman et al., 2003) and sometimes explicit attitudes (Cabanac, 1971; Fitzsimons & Shah, 2008) toward those stimuli that might best facilitate the goal (see also Brendl, 2001; Brendl et al., 2003; Markman & Brendl, 2000). As discussed, this kind of effect should be functional given that those who regard such facilitating objects in a more positive light should be more likely to approach them and potentially reach the goal. Also, critically, this work has shown that such effects are due to goal activation rather than mere semantic priming. When the goal had been met, even minutes earlier, the effect on implicit attitudes disappeared (see Ferguson & Bargh, 2004; Sherman et al., 2003). If a semantic construct had been activated, meeting the goal should have either made the effect stronger or left it unchanged (see also Förster et al., 2007). Does this type of goal-Â�driven reappraisal of the stimuli in our environment occur even when the goal is activated outside of awareness and intentions? Recent work examined this issue (Ferguson, 2008). Across a series of experiments, participants were implicitly primed with words relevant to a goal (e.g., achievement, dieting, cooperation) and then completed an implicit attitude measure. The implicit attitude measure consisted of attitude objects that were subliminally presented. The results showed that those primed with the goal exhibited significantly more positive implicit attitudes toward those stimuli that were highly relevant and helpful to the goal. For example, those implicitly primed with the goal to be thin displayed significantly more positive implicit attitudes toward gym and salads. This effect was most likely to emerge for those with some chronic success at the goal. For example, the effect just described emerged only for those who were at least moderately successful at the goal to be thin, and not for those who had the goal but who were largely unsuccessful at meeting it. Furthermore, the effects also tended to emerge for those for whom the primed goal was most currently important or relevant. For instance, in one study, participants were implicitly primed with the goal to eat and then completed a task measuring their implicit attitudes toward the words food, eat, and snacks. However, the time that had passed since participants had last eaten moderated the effect of the goal-Â�priming condition. Among those who were primed, the more time that had passed, the more they displayed highly positive attitudes toward the food-Â�related stimuli. It is also important to note that across all of these studies the par-

ticipants who were implicitly primed with a goal did not report any increased awareness or interest in the goal compared with those in the control condition, suggesting that the goal was not more consciously available for these participants. Recent work has also examined the effect of implicit goals on the (explicit) positivity toward partners within interpersonal relationships. Fitzsimons and Shah (2008) have shown that when a relationship partner can no longer instrumentally facilitate a currently activated, implicit goal, that partner is devalued on a variety of measures. Across a series of experiments, this work suggests the intriguing possibility that our regard and affection for relationship partners may, at least in some circumstances, be contingent on the degree to which those partners are seen as being able to facilitate our currently active goals, even if we are unaware of the goals themselves. Beyond the effect of a temporarily activated implicit goal on implicit attitudes, research has also examined whether chronic implicit goals can be measured with implicit attitudes (e.g., Ferguson, 2007; Glaser & Knowles, 2008; see also Moskowitz, Gollwitzer, Wasel, & Schaal, 1999). For example, Glaser and Knowles (2008) reasoned that people who have a chronic implicit motive to be egalitarian might, in fact, possess greater implicit negative attitudes toward prejudice. Indeed, for those with such negative implicit attitudes, their stereotypes about race did not guide or shape their race-Â�related behavior. Only those participants who did not have such negative implicit attitudes toward prejudice showed evidence of allowing their race-based stereotypes to guide their behavior. This work suggests that people’s implicit attitudes may reflect not only recently activated goals but also chronic motivational tendencies.

Conclusions There is a substantial and growing social cognitive literature on the implicit activation of goals. This work has started to identify the many ways in which goals might be activated implicitly from the environment—Â�whether by words, relationship partners, or a stranger’s behavior—as well as the types of evidence that might be used to detect goal activation, from behavior to knowledge accessibility to attitudes to person impressions. Still, given the range of findings on the implicit effects of intentional and conscious goals, there would seem to be many interesting avenues for detecting implicit goal activation. For example, implicit goal activation should lead to effects on perception. Such evi-



17. What Is Implicit about Goal Pursuit?

dence would suggest that not only does what people consciously want influence how they parse ambiguously related percepts, but also what people nonconsciously want similarly shapes and guides their perceptions. Additionally, although some work has addressed how implicitly activated goals influence knowledge accessibility (Aarts & Dijksterhuis, 2000; Fishbach et al., 2003; Shah, Kruglanski, & Friedman, 2002), it would be helpful if we knew that such effects corresponded to the established trajectories of motivated knowledge accessibility, such as those we reviewed earlier in the chapter (see Förster et al., 2005, 2007). We now turn to a more specific discussion of various outstanding questions that arise when considering goal pursuit from an implicit cognition perspective.

OUTSTANDING THEORETICAL€QUESTIONS One of the most pressing theoretical concerns within the area of implicit goal pursuit is definitional in nature, namely, what is a goal? Because recent work in implicit cognition has obviated what used to be central components of the definition of a goal—Â�conscious intention and drive—it is necessary to specify a definition of a goal that is not dependent on conscious or intentional processing. Of course, this assumes that what we talk about when we talk about goals is operating at both the explicit and implicit level. However, such a definition would not necessarily imply that implicit and explicit goals are exactly alike, apart from the differential role of conscious processing. It could be that both implicit and explicit goals meet the minimal criteria we identify as being crucial to the goal construct but nevertheless differ in some additional characteristics. This topic has yet to be addressed comprehensively within the social cognitive literature.

What Is a Goal? As mentioned at the beginning of this chapter, early research on motivation tended to define goal pursuit as behavior that is consciously and intentionally directed toward a desired end point (for a review, see Elliot & Fryer, 2008). Much of the emphasis from this perspective is on the conscious setting of a goal and then the conscious monitoring of its progression. There has historically been less emphasis on how a goal might be represented in memory, although certainly even early work acknowledged that it was (e.g., Hull, 1931; Tolman,

323

1932). We mentioned at the outset of the chapter that we adopt a contemporary, social cognitive definition of a goal as a cognitive representation of a desired end point that impacts evaluations, emotions, and behaviors (e.g., see also Bargh, 1990; Ferguson & Porter, 2009; Fishbach & Ferguson, 2007; Gollwitzer & Moskowitz, 1996; Higgins & Kruglanski, 2000; Moskowitz, Li, & Kirk, 2004; Sorrentino & Higgins, 1986). Both structural as well as content characteristics of a goal have been identified and are discussed in more detail elsewhere (Ferguson & Porter, 2009; Fishbach & Ferguson, 2007), but are briefly summarized here. In terms of structural characteristics, a goal is assumed to fluctuate in accessibility across time, person, and situation, a goal is assumed to consist of a diverse array of interconnected memories (e.g., evaluative, procedural, episodic, and semantic knowledge); and the memories related to a goal are assumed to operate according to the basic information-Â�processing principles of facilitative and inhibitory processes. In terms of content characteristics, a goal is assumed to consist of information about the end point, information about means to reach the end point, and evaluative or affective information. It is useful to note that the evaluative information associated with a goal is what presumably gives it its motivational force and influence (Carver & Scheier, 1981; Custers & Aarts, 2005; Kruglanski et al., 2002; Peak, 1955; Pervin, 1989; Shah et al., 2002; Young, 1961). But what exactly is it about affective information that accomplishes this? Interestingly, Berridge and colleagues have identified two kinds of reward-based brain circuitry in rats: “liking” and “wanting” (Berridge, 2003; Berridge & Kringelbach, 2008; Berridge & Robinson, 2003; Winkielman & Berridge, 2004). Liking refers to behavioral expressions of pleasure common across many mammals, such as pursing and protrusions of the lips and tongue when experiencing a bitter or disgusting taste and licking of the lips when experiencing a sweet or sugary taste. Wanting, on the other hand, refers to effortful attempts to acquire something, such as food pellets. Berridge and colleagues have found that these two reward-based behaviors can be dissociated in rats and appear to be based on at least partially independent neural substrates in the nucleus accumbens. After the administration of region-Â�specific lesions or drug inhibitors, rats show the behavioral manifestations of liking but are incapable of wanting behavior, as well as the reverse. It is important to note, however, that such dissociations are probably rare in natural circumstances, and it is unclear how such a

324

III. CROSS-CUTTING PERSPECTIVES

dissociation might map onto human behavior (but see Aharon et al., 2001; Robinson & Berridge, 2008). Perhaps the positive evaluative or affective information involved in goal pursuit is more closely dependent on the wanting type of reward processing, but we reiterate the potential difficulty of experimentally investigating this in humans. For example, it would not be sufficient to demonstrate that pleasure ratings of some stimulus are dissociated from effortful behavior to acquire that stimulus, given that differences between method and response scale can introduce variability and dissociation that does not reflect different underlying processes. Still, this work suggests interesting future research directions for understanding exactly how affect is involved in goal representations, goal pursuit, and the associated underlying neural circuitry.

Mechanism The topic of mechanism for many of the effects we have thus far reviewed is, of course, related to the previous discussion of what we consider to be the minimal criteria for a goal. However, the language of the previous discussion was admittedly underspecified in terms of cognitive architecture and process. In part, such generality seems necessary so that goal-�related phenomena can be appreciated (and tested) by researchers without them committing to the assumptions, terminology, and methods of a particular model of cognition. In this way, scholars who have different allegiances to cognitive models can attempt to explain and test various hypothesized components of a goal using the language and methodology of her or his model of choice. What are the possible cognitive models that might be used to explain goal pursuit? We view this as one of the most pressing theoretical questions in this area. As with much early and even contemporary social cognition work, many goal-�related phenomena are assumed to operate according to classic associative networks (e.g., Collins & Loftus, 1975), although there have not been many attempts to model phenomena formally. Dual-�process models, which have been successfully applied to many classic social psychological constructs (e.g., see Gawronski & Bodenhausen, 2006), have seldom been applied to goal pursuit. Standard dual-�process models assume that the brain operates according to two different processes: one primarily based on an associative network guided by the temporal and spatial contiguity and semantic similarity among

stimuli, and the other a propositional knowledge base that operates by logical analysis and reasoning (e.g., Deutsch & Strack, 2006; Gawronski & Bodenhausen, 2006; Rydell & McConnell, 2006; Sloman, 1996; Smith & DeCoster, 2000; Strack & Deutsch, 2004). The latter process is assumed to enable conscious endorsement and logical analysis (e.g., computing the truth of a proposition). Goal pursuit is an especially interesting phenomenon because it classically involves what is assumed to be some type of executive functioning and often involves conscious and intentional decision making and reasoning (e.g., willfully overcoming temptations). At the same time, as we have argued in this chapter, goal pursuit (always) operates according to a variety of implicit mechanisms and processes, which have often been explained in the context of dual-Â�process models by associative processes. There are, therefore, many interesting questions about how two such different types of goal-Â�relevant processes might interact and the role of consciousness in each (see Hassin, 2005). One of us has argued elsewhere that additional models of cognition may be useful in trying to explain goal-Â�related phenomena (Ferguson & Wojnowicz, in press; see also Wojnowicz, Ferguson, Dale, & Spivey, 2009). A dynamic systems approach assumes one underlying “process” whereby any mental state is assumed to reflect one of many possible (nonlinear) trajectories through a high dimensional state space (e.g., see Spivey, 2007). Rather than a stage-based process whereby initial representations are discretely discarded or endorsed, this perspective assumes that any thought, action, utterance, and so on is the end result of a continuous (and nonlinear) process of competition. Research has shown that this perspective can explain lowlevel perceptual and cognitive processing, such as word analysis and visual attention (e.g., Abrams & Balota, 1991; Gold & Shadlen, 2000; McClelland & Rogers, 2003) as well as high-level cognition, such as decision making (McKinstry, Dale, & Spivey, 2008; Roe, Busemeyer, & Townsend, 2001) and evaluation (Wojnowicz et al., 2009). Given that this perspective can be applied to high-level, seemingly discrete decisions, it might be helpful in addressing the mechanisms that enable people to resolve choices between two goal-Â�relevant alternatives (e.g., self-Â�control dilemmas). A dynamic systems perspective would also suggest a different way of thinking about a person’s free will, which is a construct regularly invoked in research on goal pursuit. Namely, rather than assuming an executive controller that hierarchically



17. What Is Implicit about Goal Pursuit?

controls lower order subprocesses, a dynamic systems perspective is consistent with self-�organizing principles, which are commonly espoused across the natural sciences (e.g., Kauffman, 1993). Such principles can explain how many different interacting components of a system can organize into a stable pattern or structure without invoking an orthogonal executive controller. We argue that, in the search for an underlying cognitive model for goal pursuit, inclusion and consideration of multiple cognitive models and perspectives would be useful and productive, just as such a comparative analysis has been generative in other areas of cognitive science.

Development The developmental trajectory of goal pursuit has traditionally been conceptualized in terms of the question of when humans first show signs of understanding goal pursuit, at least in terms of being able to infer it in others. Classic work in social psychology shows that people are remarkably ready to infer goal states even in animated geometrical figures, as well as in other people (e.g., Heider, 1944; Heider & Simmel, 1944; see also Hassin, Aarts, & Ferguson, 2005). For example, developmental psychologists have argued that humans infer goal states in others starting at least by 12 months of age (e.g., Csibra, Gergely, Biro, Koos, & Brockbank, 1999; Kuhlmeier, Wynn, & Bloom, 2003; Premack & Premack, 1997). This implies that some understanding of goal pursuit seems to emerge very early on, at least to the extent that an understanding of goal pursuit can be assumed to emerge as soon as one shows a readiness to infer goal pursuit in others. How exactly does a particular goal develop? Although there has been scarce research on this question, some work suggests that behavioral activities can become more goal-relevant when paired with positive affect. Aarts and colleagues have found that when behaviors (e.g., puzzle) are paired repeatedly and subliminally with various, semantically unrelated positive affective cues (e.g., sunshine, happy), participants exert more effort to begin the behavior (see Aarts et al., 2008; Custers & Aarts, 2005), even though they report no awareness of having done so. These authors argue that the inclusion of positive affective information in the representation of an end point is a crucial determinant of whether someone will nonconsciously strive toward that end point. Whereas multiple researchers have argued that positive affect is a

325

driving force for (conscious) goal pursuit generally (Carver & Scheier, 1981; Custers & Aarts, 2005; Kruglanski et al., 2002; Peak, 1955; Pervin, 1989; Shah et al., 2002; Young, 1961), Aarts and colleagues believe it explains how one might demonstrate classic motivated behavior toward an end point without conscious intention or guidance. The implicit detection of the positive connotation of a stimulus can provoke (and predict) effortful and persistent behavior toward that stimulus, perhaps in a way that one’s conscious estimation of that positivity cannot (see also Ferguson, 2007). One direction for future research might be to utilize further the method of conditioning a stimulus in order to identify the components necessary to elicit classic, motivated behavior toward that stimulus. For example, instead of adding positive affect to known and highly familiar behaviors or events (e.g., puzzle) that may themselves already be associated with procedural, semantic, and reward-Â�related information in memory, one could condition a novel stimulus with different kinds and degrees of affective, semantic, behavioral, and procedural (means) information. This would consist of a comprehensive building process whereby the sufficiency and necessity of various types of knowledge for motivated behavior could be causally and individually (and interactively) tested. Such an approach would not only help to reveal the critical ingredients of a goal, but it could be harnessed to develop strategies to improve the influence of some (desirable) goals while dampening or mollifying the force of other (undesirable) goals.

Dissociation and Boundaries Another obvious and still largely unanswered question in this area is the degree to which intentional goals differ from unintentional ones. In other words, of what use is conscious intention in the arena of motivation? The consensus at this point seems to be that, beyond the obvious phenomenological difference, there are no functional or process differences. This has been concluded based on a handful of studies showing that an implicitly activated goal leads to the same kinds of downstream effects, in nature and degree, as does an explicitly activated goal (e.g., Bargh et al., 2001; Chartrand & Bargh, 1996; Kawada et al., 2004; McCulloch et al., 2008). For example, McCulloch and colleagues (2008) found that an impression formation goal led to the same specific mental operations concerning the associations between traits and behaviors regardless of whether it was induced implicitly

326

III. CROSS-CUTTING PERSPECTIVES

or explicitly. However, such work represents only the preliminary empirical testing of this idea. Just as conscious versus nonconscious processing leads to considerably different outcomes across domains (e.g., Betsch, Plessner, Schwieren, & Gutig, 2001; Dijksterhuis, 2004), we argue here that the role of conscious and intentional activation and guidance in a given goal pursuit may introduce a host of implications that differ in kind and intensity from an unintentionally activated pursuit. If so, the question remains as to whether and where such differences emerge and, importantly, how influential they are on the person’s goal progress, reactions, flexibility, and so on. In other words, if such differences exist, in what ways are they influential with regard to the pursuit? This question, of course, also raises interesting connections with the cognitive science literature on the functionality of consciousness more generally (e.g., Baars, 1997, 2002; Damasio, 1999; Morsella, 2005). We also believe it is critical to stress the importance of continuing to finesse the methodological tools used to establish unintentional goal pursuit empirically. Any comparison of intentional versus unintentional goal activation and operation, of course, presupposes accurate measurement of each. In addition, as is the case with other social psychological constructs that have been deemed implicit (e.g., see Gawronski et al., 2007; Gawronski, Hofmann, & Wilbur, 2006), there is often some confusion in the meaning of the term, as we discussed at the outset of the chapter, as well as great variety in the methods that are used to ensure a lack of intention (and sometimes consciousness). At the least, this area of research would benefit from more rigorous measurement of people’s conscious awareness of a goal throughout a given experiment, from the priming phase through the dependent measure (see Uhlmann, Pizarro, & Bloom, 2008). Another important question concerns the boundaries and moderators of implicitly activated goals. One especially striking version of this question is, which goal-Â�relevant cue in the environment will win out over others and succeed in driving one’s motivated behavior (see Bargh, 2006)? The impetus for this question is undoubtedly the observation that any given environment contains a multitude of potentially goal-Â�relevant cues, and the literature has indeed shown that many different kinds of cues can implicitly trigger goal pursuit, as we reviewed earlier. However, it is useful to note that even though the environment seems bountiful and unlimited, people, of course, only process a small portion of the available stimuli and cues. For example, we selectively narrow down the

seemingly infinite cast of cues according to factors such as relevance, accessibility, salience, and convenience. A different version of the selectivity question is, how might implicitly triggered goal pursuits interact with consciously, intentionally chosen ones? The extant work mentioned earlier that has compared the same goal activated either implicitly or explicitly has not found strong evidence for additivity (e.g., Bargh et al., 2001), but these comparisons are rare and the findings may or may not be conclusive. Especially interesting is the issue of what happens when implicit goal pursuit conflicts with explicit goal pursuit. Which one will win out? There has been surprisingly little research on this question and yet it seems like one of the more interesting and relevant questions to explore. For example, what happens when we enter into a situation and are consciously trying to cooperate but are exposed to implicit competitive cues? When do the cues wash out and cancel one another? Is there is a distinct temporal profile of the influence of each? Does the conflict between the two increasingly mean that the person becomes aware of the competitive cues (i.e., consciousness is triggered by the conflict)? Another way to address the question of boundaries is to identify the moderators that matter for both the explicit and the implicit pursuit of a goal. There has been some progress on this front. For example, researchers have shown that a person’s explicitly reported commitment and valuing of a relationship partner should influence the extent to which that partner implicitly triggers any associated goals (e.g., see Shah, 2003; see also Shah et al., 2002). Researchers have also shown that one’s expectancy and valuing of a conscious goal will determine the nature of the unintended effects of that goal on the accessibility of knowledge (Förster et al., 2005). Additionally, it seems that a person’s expertise at a given goal moderates a variety of implicit effects of that goal (Ferguson, 2008; Fishbach et al., 2003; Fishbach & Shah, 2006). Finally, some work has shown that the desirability of a goal, whether normative (Aarts et al., 2004) or situational (Ferguson, 2008), influences whether that goal can be activated implicitly. Normatively or situationally undesirable goals are less likely to influence motivated behavior implicitly. This work has started to reveal the kinds of factors that predict implicit goal pursuit, and so far it suggests important limitations on when and how a goal can be activated and then influence downstream behaviors without conscious intention or awareness.



17. What Is Implicit about Goal Pursuit?

CONCLUSIONS We have reviewed how motivation is understood and empirically studied from an implicit social cognitive perspective. We organized the existing empirical literature into two lines. In the first, we summarized the work showing that an intentional, conscious goal state can lead to a variety of effects on perception, knowledge accessibility, behavior, and attitudes that are themselves unintended and sometimes nonconscious. In the second, we reviewed the evidence that goals can be activated unintentionally, and perhaps nonconsciously, according to a number of downstream consequences similar to the unintended effects reviewed in the first section. Finally, we raised a number of theoretical questions that remain unanswered, although we did not provide an exhaustive list. The bottom line of this literature unequivocally calls for a reconceptualization of the meaning or definition of goal pursuit. The construct is now divorced from its historical reliance on conscious intention and monitoring, and many exciting questions remain to be answered. Note 1. Work by Eder and Rothermund (2008) suggests that lever-based approach–Â�avoidance tools depend on the successful mapping of affective codes to motor behaviors, and thus similar effects can be produced with, for example, right and left lever movements when these movements are affectively labeled.

References Aarts, H., Custers, R., & Holland, R. W. (2007). The unconscious cessation of goal pursuit: When goals and negative affect are coactivated. Journal of Personality and Social Psychology, 92, 165–178. Aarts, H., Custers, R., & Marien, H. (2008). Preparing and motivating behavior outside of awareness. Science, 319, 1639. Aarts, H., & Dijksterhuis, A. (2000). Habits as knowledge structures: Automaticity in goal-Â�directed behavior. Journal of Personality and Social Psychology, 78, 53–63. Aarts, H., & Dijksterhuis, A. (2003). The silence of the library: Environment, situational norm, and social behavior. Journal of Personality and Social Psychology, 84, 18–28. Aarts, H., Dijksterhuis, A., & De Vries, P. (2001). On the psychology of drinking: Being thirsty and perceptually ready. British Journal of Psychology, 92, 631–642.

327

Aarts, H., Gollwitzer, P. M., & Hassin, R. R. (2004). Goal contagion: Perceiving is for pursuing. Journal of Personality and Social Psychology, 87, 23–37. Abrams, R. A., & Balota, D. A. (1991). Mental chronometry: Beyond reaction time. Psychological Science, 2, 153–157. Ach, N. (1935). Analyse des Willens [The analysis of willing]. In E. Abderhalden (Ed.), Handbuch der biologischen Arbeitsmethoden, Bd. VI. Berlin: Urban & Schwarzenberg. Ackerman, J. M., Goldstein, N. J., Shapiro, J. R., & Bargh, J. A. (2009). You wear me out: the vicarious depletion of self-Â�control. Psychological Science, 20, 326–332. Aharon, I., Etcoff, N., Ariely, D., Chabris, C. F., O’Connor E., & Breiter, H. C. (2001). Beautiful faces have variable reward value: fMRI and behavioral evidence. Neuron, 32, 537–551. Andersen, S. M., & Chen, S. (2002). The relational self: An interpersonal social-Â�cognitive theory. Psychological Review, 109, 619–645. Atkinson, J. W., & Birch, D. (1970). The dynamics of action. New York: Wiley. Baars, B. J. (1997). In the theatre of consciousness: Global workspace theory, a rigorous scientific theory of consciousness. Journal of Consciousness Studies, 4, 292–309. Baars, B. J. (2002). The conscious access hypothesis: Origins and recent evidence. Trends in Cognitive Sciences, 6, 47–52. Balcetis, E., & Dunning, D. A. (2006). See what you want to see: Motivational influences on visual perception. Journal of Personality and Social Psychology, 91, 612–625. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Upper Saddle River, NJ: Prentice Hall. Bargh, J. A. (1990). Auto-Â�motives: Preconscious determinants of social interaction. In E. T. Higgins & R. M. Sorrentino (Eds.), Handbook of motivation and cognition: Vol. 2. Foundations of social behavior (pp.€93–130). New York: Guilford Press. Bargh, J. A. (2006). Agenda 2006: What have we been priming all these years?: On the development, mechanisms, and ecology of nonconscious social behavior. European Journal of Social Psychology, 36, 147–168. Bargh, J. A. (2007). Social psychology and the unconscious: The automaticity of higher mental processes. New York: Psychology Press. Bargh, J. A., Chen, M., & Burrows, L. (1996). Automaticity of social behavior: Direct effects of trait construct and stereotype activation on action. Journal of Personality and Social Psychology, 71, 230–244. Bargh, J. A., & Ferguson, M. J. (2000). Beyond behaviorism: On the automaticity of higher mental processes. Psychological Bulletin, 126, 925–945. Bargh, J. A., Gollwitzer, P. M., Lee-Chai, A., Barndollar, K., & Troetschel, R. (2001). The automated will: Nonconscious activation and pursuit of behavioral goals. Journal of Personality and Social Psychology, 81, 1014–1027. Berridge, K. C. (2003). Pleasures of the brain. Brain and Cognition, 52, 106–128. Berridge, K. C., & Kringelbach, M. L. (2008). Affective

328

III. CROSS-CUTTING PERSPECTIVES

neuroscience of pleasure: Reward in humans and animals. Psychopharmacology, 199, 457–480. Berridge, K. C., & Robinson, T. E. (2003). Parsing reward. Trends in Neurosciences, 26, 507–513. Betsch, T., Plessner, H., Schwieren, C., & Gutig, R. (2001). I like it but I don’t know why: A value-Â�account approach to implicit attitude formation. Personality and Social Psychology Bulletin, 27, 242–253. Brehm, J. W., & Self, E. A. (1989). The intensity of motivation. Annual Review of Psychology, 40, 109–131. Brendl, C. M. (2001). Goals and the compatability principle in attitudes, judgment, and choice. In G. B. Moskowitz (Ed.), Cognitive social psychology (pp.€317– 322). Mahwah, NJ: Erlbaum. Brendl, C. M., Markman, A. B., & Messner, C. (2003). The devaluation effect: Activating need devalues unrelated objects. Journal of Consumer Research, 29, 463–473. Bruner, J. S. (1957). Going beyond the information given. In H. E. Gruber, K. H. Hammond, & R. Jessor (Eds.), Contemporary approaches to cognition (pp.€41– 69). Cambridge, MA: Harvard University Press. Bruner, J. S., & Goodman, C. C. (1947). Value and need as organizing factors in perception. Journal of Abnormal and Social Psychology, 42, 33–44. Brunstein, J. C., & Gollwitzer, P. M. (1996). Effects of failure on subsequent performance: The importance of self-Â�defining goals. Journal of Personality and Social Psychology, 70, 395–407. Cabanac, M. (1971). Physiological role of pleasure. Science, 173, 1103–1107. Carver, C. S., & Scheier, M. F. (1981). Attention and selfÂ�regulation: A control-Â�theory approach to human behavior. New York: Springer. Carver, C. S., & Scheier, M. F. (1998). On the selfÂ�regulation of behavior. New York: Cambridge University Press. Cesario, J., Plaks, J. E., & Higgins, E. T. (2006). Automatic social behavior as motivated preparation to interact. Journal of Personality and Social Psychology, 90, 893–910. Chartrand, T. L., & Bargh, J. A. (1996). Automatic activation of impression formation and memorization goals: Nonconscious goal priming reproduces effects of explicit task instructions. Journal of Personality and Social Psychology, 71, 464–478. Collins, A. M., & Loftus, E. F. (1975). A spreadingÂ�activation theory of semantic processing. Psychological Review, 82, 407–428. Csibra, G., Gergely, G., Biro, S., Koos, O., & Brockbank, M. (1999). Goal attribution without agency cues: The perception of “pure reason” in infancy. Cognition, 72, 237–267. Custers, R., & Aarts, H. (2005). Positive affect as implicit motivator: On the nonconscious operation of behavioral goals. Journal of Personality and Social Psychology, 89, 129–142. Custers, R., & Aarts, H. (2007). In search of the nonconscious sources of goal pursuit: Accessibility and positive affective valence of the goal state. Journal of Experimental Social Psychology, 43, 312–318. Damasio, A. R. (1999). The feeling of what happens: Body and emotion in the making of consciousness. Orlando, FL: Harcourt.

De Charms, R., Morrison, H. W., Reitman, W. R., & McClelland, D. C. (1955). Behavioral correlates of directly and indirectly measured achievement motivation. In D. C. McClelland (Ed.), Studies in motivation (pp.€414–423). New York: Appleton-Â�Century-Â�Crofts. De Houwer, J. (2003). The Extrinsic Affective Simon Task. Experimental Psychology, 50, 77–85. Deci, E. L., & Ryan, R. M. (1985). The General Causality Orientations Scale: Self-Â�determination in personality. Journal of Research in Personality, 19, 109–134. Deutsch, R., & Strack, F. (2006). Duality-Â�models in social psychology: From opposing processes to interacting systems. Psychological Inquiry, 17, 166–172. Dijksterhuis, A. (2004). Think different: The merits of unconscious thought in preference development and decision making. Journal of Personality and Social Psychology, 87, 586–598. Dijksterhuis, A., Chartrand, T. L., & Aarts, H. (2007). Effects of priming and perception on social behavior and goal pursuit. In J. A. Bargh (Ed.), Social psychology and the unconscious: The automaticity of higher mental processes (pp.€51–131). New York: Psychology Press. Dijksterhuis, A., & van Knippenberg, A. (1998). The relation between perception and behavior, or how to win a game of Trivial Pursuit. Journal of Personality and Social Psychology, 74, 865–877. Dik, G., & Aarts, H. (2007). Behavioral cues to others’ motivation and goal pursuits: The perception of effort facilitates goal inference and contagion. Journal of Experimental Social Psychology, 43, 727–737. Eder, A., & Rothermund, K. (2008). When do motor behaviors (mis)match affective stimuli?: An evaluative coding view of approach and avoidance reactions. Journal of Experimental Psychology: General, 137, 262–281. Eitam, B., Schul, Y., & Hassin, R. R. (2009). GoalÂ�relevance and artificial grammar learning. Quarterly Journal of Experimental Psychology, 62, 228–238. Elliot, A. J., & Fryer, J. W. (2008). The goal construct in psychology. In J. Y. Shah & W. L. Gardner (Eds.), Handbook of motivation science (pp.€ 235–250). New York: Guilford Press. Fazio, R. H. (1989). On the power and functionality of attitudes: The role of attitude accessibility. In A. R. Pratkanis, S. J. Breckler, & A. G. Greenwald (Eds.), Attitude structure and function (pp.€ 153–179). Hillsdale, NJ: Erlbaum. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. Ferguson, M. J. (2007). On the automatic evaluation of end-Â�states. Journal of Personality and Social Psychology, 92, 596–611. Ferguson, M. J. (2008). On becoming ready to pursue a goal you don’t know you have: Effects of nonconscious goals on evaluative readiness. Journal of Personality and Social Psychology, 95, 1268–1294.



17. What Is Implicit about Goal Pursuit?

Ferguson, M. J., & Bargh, J. A. (2004). Liking is for doing: Effects of goal-Â�pursuit on automatic evaluation. Journal of Personality and Social Psychology, 87, 557–572. Ferguson, M. J., & Bargh, J. A. (2008). Evaluative readiness: The motivational nature of automatic evaluation. In A. J. Elliot (Ed.), Handbook of approach and avoidance motivation (pp.€ 289–306). New York: Psychology Press. Ferguson, M. J., Hassin, R., & Bargh, J. A. (2008). Implicit motivation: Past, present, and future. In J. Y. Shah & W. L. Gardner (Eds.), Handbook of motivation science (pp.€150–168). New York: Guilford Press. Ferguson, M. J., & Porter, S. C. (2009). Goals and (implicit) attitudes: A social cognitive perspective. In G. B. Moskowitz & H. Grant (Eds.), The psychology of goals (pp.€447–479). New York: Guilford Press. Ferguson, M. J., & Wojnowicz, M. (in press). Goals change what we (implicitly) like: Empirical evidence and new theoretical directions. Social and Personality Psychology Compass. Fishbach, A., & Converse, B. A. (in press). Walking the line between goals and temptations: Asymmetric effects of counteractive control. In R. R. Hassin, Y. Trope, & K. Ochsner (Eds.), Self control in brain, mind, and society. New York: Oxford University Press. Fishbach, A., Dhar, R., & Zhang, Y. (2006). Subgoals as substitutes or complements: The role of goal accessibility. Journal of Personality and Social Psychology, 91, 232–242. Fishbach, A., & Ferguson, M. J. (2007). The goal construct in social psychology. In A. W. Kruglanski & E. T. Higgins (Eds.), Social psychology: Handbook of basic principles (2nd ed., pp.€ 490–515). New York: Guilford Press. Fishbach, A., Friedman, R., & Kruglanski, A. W. (2003). Leading us not unto temptation: Momentary allurements elicit automatic goal activation. Journal of Personality and Social Psychology, 84, 296–309. Fishbach, A., & Shah, J. Y. (2006). Self-Â�control in action: Implicit dispositions toward goals and away from temptations. Journal of Personality and Social Psychology, 90, 820–832. Fishbach, A., & Zhang, Y. (2008). Together or apart: When goals and temptations complement versus compete. Journal of Personality and Social Psychology, 94, 547–559. Fitzsimons, G. M., & Bargh, J. A. (2003). Thinking of you: Nonconscious pursuit of interpersonal goals associated with relationship partners. Journal of Personality and Social Psychology, 84, 148–163. Fitzsimons, G. M., & Shah, J. Y. (2008). How goal instrumentality shapes relationship evaluations. Journal of Personality and Social Psychology, 95, 319–337. Förster, J., Liberman, N., & Friedman, R. (2007). Seven principles of goal activation: A systematic approach to distinguishing goal priming from priming of nongoal constructs. Personality and Social Psychology Review, 11, 211–233. Förster, J., Liberman, N., & Higgins, E. T. (2005). Accessibility from active and fulfilled goals. Journal of Experimental Social Psychology, 41, 220–239. Freud, S. (1901). Psychopathology of everyday life. London: T. Fisher Unwin.

329

Freud, S. (1920). A general introduction to psycho-Â�analysis. Oxford, UK: Boni & Liveright. Freud, S. (1960). The ego and the id. New York: Norton. (Original work published 1923) Friese, M., Hofmann, W., & Wänke, M. (2008). When impulses take over: Moderated predictive validity of implicit and explicit attitude measures in predicting food choice and consumption behavior. British Journal of Social Psychology, 47, 397–419. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. Gawronski, B., Hofmann, W., & Wilbur, C. J. (2006). Are “implicit” attitudes unconscious? Consciousness and Cognition, 15, 485–499. Gawronski, B., LeBel, E. P., & Peters, K. R. (2007). What do implicit measures tell us?: Scrutinizing the validity of three common assumptions. Perspectives on Psychological Science, 2, 181–193. Glaser, J., & Knowles, E. D. (2008). Implicit motivation to control prejudice. Journal of Experimental Social Psychology, 44, 164–172. Gold, J. I., & Shadlen, M. N. (2000) Representation of a perceptual decision in developing oculomotor commands. Nature, 404, 390–394. Gollwitzer, P. M. (1990). Action phases and mindsets. In E. T. Higgins & R. M. Sorrentino (Eds.), Handbook of motivation and cognition: Vol. 2. Foundations of social behavior (pp.€53–92). New York: Guilford Press. Gollwitzer, P. M., & Kirchhof, O. (1998). In J. Heckhausen & C. S. Dweck (Eds.), Motivation and selfÂ�regulation across the life span (pp.€ 389–423). New York: Cambridge University Press. Gollwitzer, P. M., & Moskowitz, G. B. (1996). Goal effects on action and cognition. In E. T. Higgins & A. W. Kruglanski (Eds.), Social psychology: Handbook of basic principles (pp.€361–399). New York: Guilford Press. Gollwitzer, P. M., & Wicklund, R. A. (1985). SelfÂ�symbolizing and the neglect of others’ perspectives. Journal of Personality and Social Psychology, 48, 702–715. Goschke, T., & Kuhl, J. (1993). Representation of intentions: Persisting activation in memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 19, 1211–1226. Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-Â�esteem, and stereotypes. Psychological Review, 102, 4–27. Hassin, R. R. (2005). Non-Â�conscious control and implicit working memory. In R. R. Hassin, J. S. Uleman, & J. A. Bargh (Eds.), The new unconscious (pp.€196– 224). New York: Oxford University Press. Hassin, R. R., Aarts, H., & Ferguson, M. J. (2005). Automatic goal inferences. Journal of Experimental Social Psychology, 41, 129–140. Hassin, R. R., Uleman, J. S., & Bargh, J. A. (2005). The new unconscious. New York: Oxford University Press. Heider, F. (1944). Social perception and phenomenal causality. Psychological Review, 51, 358–374. Heider, F., & Simmel, M. (1944). An experimental study of apparent behavior. American Journal of Psychology, 57, 243–259.

330

III. CROSS-CUTTING PERSPECTIVES

Higgins, E. T., Bargh, J. A., & Lombardi, W. J. (1985). Nature of priming effects on categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11, 59–69. Higgins, E. T., & Kruglanski, A. W. (2000). Motivational science: Social and personality perspectives. New York: Psychology Press. Hofmann, W., & Friese, M. (2008). Impulses got the better of me: Alcohol moderates the influence of implicit attitudes toward food cues on eating behavior. Journal of Abnormal Psychology, 117, 420–427. Hofmann, W., Rauch, W., & Gawronski, B. (2007). And deplete us not into temptation: Automatic attitudes, dietary restraint, and self-Â�regulatory resources as determinants of eating behavior. Journal of Experimental Social Psychology, 43, 497–504. Holland, R. W., Hendriks, M., & Aarts, H. (2005). Smells like clean spirit: Nonconscious effects of scent on cognition and behavior. Psychological Science, 16, 689–693. Hull, C. L. (1931). Goal attraction and directing ideas conceived as habit phenomena. Psychological Review, 38, 487–506. Kauffman, S. A. (1993). The origins of order: SelfÂ�organization and selection in evolution. Oxford, UK: Oxford University Press. Kawada, C. L. K., Oettingen, G., Gollwitzer, P. M., & Bargh, J. A. (2004). The projection of implicit and explicit goals. Journal of Personality and Social Psychology, 86, 545–559. Koo, M., & Fishbach, A. (2008). Dynamics of selfÂ�regulation: How (un)accomplished goal actions affect motivation. Journal of Personality and Social Psychology, 94, 183–195. Kruglanski, A. W., Shah, J. Y., Fishbach, A., Friedman, R., Chun, W. Y., & Sleeth-Â�Keppler, D. (2002). A theory of goal-Â�systems. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 34, pp.€331–387). New York: Academic Press. Kuhl, J. (1983). Motivation, konflikt und handlungskontrolle [Motivation, conflict, control and treatment]. New York: Springer. Kuhl, J. (1987). Action control: The maintenance of motivational states. In F. Halisch & J. Kuhl (Eds.), Motivation, intention, and volition (pp.€279–291). New York: Springer. Kuhlmeier, V., Wynn, K., & Bloom, P. (2003). Attribution of dispositional states by 12-month-olds. Psychological Science, 14, 402–408. Lewin, K. (1936). Principles of topological psychology. New York: McGraw-Hill. Locke, E. A., & Latham, G. P. (1990). A theory of goal setting and task performance. Englewood Cliffs, NJ: Prentice Hall. Loewenstein, G. (1996). Out of control: Visceral influences on behavior. Organizational Behavior and Human Decision Processes, 65, 272–292. Markman, A. B., & Brendl, C. M. (2000). The influence of goals on value and choice. Psychology of Learning and Motivation, 39, 97–128. Markman, A. B., & Brendl, C. M. (2005). Constraining theories of embodied cognition. Psychological Science, 16, 6–10. McClelland, D. C., Atkinson, J. W., Clark, R. A., &

Lowell, E. L. (1953). The achievement motive. East Norwalk, CT: Appleton-Â�Century-Â�Crofts. McClelland, D. C., Koestner, R., & Weinberger, J. (1989). How do self-Â�attributed and implicit motives differ? Psychological Review, 96, 690–702. McClelland, J. L., & Rogers, T. T. (2003). The parallel distributed processing approach to semantic cognition. Nature Reviews Neuroscience, 4, 310–322. McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: Part I. An account of basic findings. Psychological Review, 88, 375–407. McCulloch, K. C., Ferguson, M. J., Kawada, C., & Bargh, J. A. (2008). Taking a closer look: On the operation of nonconscious impression formation. Journal of Experimental Social Psychology, 44, 514–523. McKinstry, C., Dale, R., & Spivey, M. J. (2008). Action dynamics reveal parallel competition in decision making. Psychological Science, 19, 22–24. Mischel, W., Cantor, N., & Feldman, S. (1996). Principles of self-Â�regulation: The nature of willpower and self-Â�control. In E. T. Higgins & A. W. Kruglanski (Eds.), Social psychology: Handbook of basic principles (pp.€329–360). New York: Guilford Press. Moors, A., & De Houwer, J. (2001). Automatic appraisal of motivational valence: Motivational affective priming and Simon effects. Cognition and Emotion, 15, 749–766. Moors, A., De Houwer, J., Hermans, D., & Eelen, P. (2005). Unintentional processing of motivational valence. Quarterly Journal of Experimental Psychology, 58A, 1043–1063. Morsella, E. (2005). The function of phenomenal states: Supramodular interaction theory. Psychological Review, 112, 1000–1021. Moskowitz, G. B. (2002). Preconscious effects of temporary goals on attention. Journal of Experimental Social Psychology, 38, 397–404. Moskowitz, G. B., Gollwitzer, P. M., Wasel, W., & Schaal, B. (1999). Preconscious control of stereotype activation through chronic egalitarian goals. Journal of Personality and Social Psychology, 77, 167–184. Moskowitz, G. B., Li, P., & Kirk, E. R. (2004). The implicit volition model: On the preconscious regulation of temporarily adopted goals. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 36, pp.€317–404). New York: Academic Press. Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84, 231–259. Payne, B. K., McClernon, J. F., & Dobbins, I. G. (2007). Automatic affective responses to smoking cues. Experimental and Clinical Psychopharmacology, 15(4), 400–409. Peak, H. (1955). Attitude and motivation. In M. R. Jones (Ed.), Nebraska symposium on motivation: 1955 (pp.€149–189). Lincoln: University of Nebraska Press. Pervin, L. A. (1989). Goal concepts in personality and social psychology. Hillsdale, NJ: Erlbaum. Premack, D., & Premack, A. J. (1997). Infants attribute value ± to the goal-Â�directed actions of self-Â�propelled objects. Journal of Cognitive Neuroscience, 9, 848– 856. Robinson, T. E., & Berridge, K. C. (2008). The incen-



17. What Is Implicit about Goal Pursuit?

tive sensitization theory of addiction: Some current issues. Philosophical Transactions of the Royal Society B, 363, 3137–3146. Roe, R. M., Busemeyer, J., & Townsend, J. T. (2001). Multialternative decision field theory: A dynamic artificial neural network model of decision making. Psychological Review, 108, 370–392. Roediger, H. L. (1990). Implicit memory: Retention without remembering. American Psychologist, 45, 1043–1056. Rydell, R. J., & McConnell, A. R. (2006). Understanding implicit and explicit attitude change: A systems of reasoning analysis. Journal of Personality and Social Psychology, 91, 995–1008. Schacter, D. L. (1987). Implicit memory: History and current status. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 501–518. Seibt, B., Haefner, M., & Deutsch, R. (2007). Prepared to eat: How immediate affective and motivational responses to food cues are influenced by food deprivation. European Journal of Social Psychology, 37, 359–379. Shah, J. (2003). The motivational looking glass: How significant others implicitly affect goal appraisals. Journal of Personality and Social Psychology, 85, 424–439. Shah, J. Y., Friedman, R., & Kruglanski, A. W. (2002). Forgetting all else: On the antecedents and consequences of goal shielding. Journal of Personality and Social Psychology, 83, 1261–1280. Shah, J. Y., & Gardner, W. L. (Eds.). (2008). Handbook of motivation science. New York: Guilford Press. Shah, J. Y., Kruglanski, A. W., & Friedman, R. (2002). A goal systems approach to self-Â�regulation. In M. P. Zanna, J. M. Olson, & C. Seligman (Eds.), The Ontario symposium on personality and social psychology (pp.€247–276). Mahwah, NJ: Erlbaum. Sherman, S. J., Rose, J. S., Koch, K., Presson, C. C., & Chassin, L. (2003). Implicit and explicit attitudes toward cigarette smoking: The effects of context and motivation. Journal of Social and Clinical Psychology, 22, 13–39. Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119, 3–22. Smith, E. R., & DeCoster, J. (2000). Dual-Â�process Â�models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4, 108–131.

331

Sorrentino, R. M., & Higgins, E. T. (Eds.). (1986). Handbook of motivation and cognition: Vol. 1. Foundations of social behavior. New York: Guilford Press. Spivey, M. J. (2007). The continuity of mind. New York: Oxford University Press. Srull, T. K., & Wyer, R. S. (1979). The role of category accessibility in the interpretation of information about persons: Some determinants and implications. Journal of Personality and Social Psychology, 37, 1660–1672. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Tolman, E. C. (1932). Purposive behavior in animals and men. London: Random House. Uhlmann, E. L., Pizarro, D. A., & Bloom, P. (2008). Varieties of unconscious social cognition. Journal for the Theory of Social Behaviour, 38, 293–322. Vallacher, R. R., & Wegner, D. M. (1987). What do people think they’re doing?: Action identification and human behavior. Psychological Review, 94, 3–15. Waters, A. J., Carter, B. L., Robinson, J. D., Wetter, D. W., Lam, C. Y., & Cinciripini, P. M. (2007). Implicit attitudes to smoking are associated with craving and dependence. Drug and Alcohol Dependence, 91, 178–186. Wilson, T. D. (2002). Strangers to ourselves: Discovering the adaptive unconscious. Cambridge, MA: Belknap Press/Harvard University Press. Wilson, T. D., & Dunn, E. W. (2004). Self-Â�knowledge: Its limits, value and potential for improvement. Annual Review of Psychology, 55, 493–518. Winkielman, P., & Berridge, K. C. (2004). Unconscious emotion. Current Directions in Psychological Science, 13, 120–123. Wittenbrink, B., & Schwarz, N. (Eds.). (2007). Implicit measures of attitudes. New York: Guilford Press. Wojnowicz, M., Ferguson, M. J., Dale, R., & Spivey, M. (2009). The self-Â�organization of deliberate evaluations. Psychological Science, 20, 1428–1435. Wright, R. A. (1996). Brehm’s theory of motivation as a model of effort and cardiovascular response. In P. M. Gollwitzer & J. A. Bargh (Eds.), The psychology of action: Linking cognition and motivation to behavior (pp.€ 424–453). New York: Guilford Press. Young, P. T. (1961). Motivation and emotion. New York: Wiley.

PA R T I V

DOMAIN-SPECIFIC PERSPECTIVES

C h a p t e r 18

Attitude Structure and Change Implications for Implicit Measures Richard E. Petty and Pablo Briñol

A

ttitudes refer to one’s likes and dislikes, what one favors or disfavors, supports or opposes, views positively or negatively. That is, attitudes refer to people’s evaluations of a wide variety of objects, issues, and people, including the self. In the contemporary literature, both explicit and implicit measures of attitudes are commonly used. Explicit measures are those that directly ask people to report what their evaluations are, such as: “Is Diet Coke good or bad?” In a recent review, Petty, Fazio, and Briñol (2009b) articulated three different meanings that have been applied to defining implicit attitude measures: indirect, automatic, and unconscious. In the first meaning, implicit measures are indirect in that they do not ask the individual to report his or her attitude like a direct measure does (Petty, Wheeler, & Tormala, 2003). In the second meaning, implicit measures are said to tap into an automatic evaluative reaction, one that comes to mind spontaneously on the mere presentation of the attitude object, rather than a more deliberative assessment that comes to mind only on some reflection (Fazio, Jackson, Dunton, & Williams, 1995). In the third meaning, implicit measures are said to tap into an attitude of which the person is unaware, an unconscious evaluation rather than a conscious one (Kihlstrom, 2004). Although these aspects are conceptually and empirically separable (De Houwer, 2009), we refer to implicit measures in this review as measures that



are both indirect and designed to tap into automatic evaluative reactions, whereas explicit measures are characterized by requiring at least some deliberative self-Â�report. We do not assume that implicit measures tap into unconscious reactions. Assessing a person’s automatic evaluative reactions is important because such measures can often bypass social desirability concerns and have been shown to have a pervasive influence on spontaneous information processing, judgment, and behavior (e.g., Fazio, 1995; see Petty, Fazio, & Briñol, 2009a, for a review). In contrast, deliberative attitudes are especially important in predicting behaviors that also are undertaken with some degree of thought (e.g., Dovidio, Kawakami, Johnson, Johnson, & Howard, 1997). Although implicit and explicit measures often yield the same outcome (e.g., both reveal that a person likes Diet Coke), sometimes these measures are discrepant. Because implicit and explicit measures of attitudes are useful in predicting behavior separately (e.g., Greenwald, Poehlman, Uhlmann, & Banaji, 2009) and in combination (e.g., Briñol, Petty, & Wheeler, 2006), it is helpful to understand how each is modified by various persuasion techniques. We use the term persuasion to refer to any situation in which the attitudes of a person are modified in a desired direction. After a long tradition of assessing the impact of persuasion treatments on attitudes solely with deliberative self-Â�reports (Eagly & Chaiken, 335

336

IV. DOMAIN-SPECIFIC PERSPECTIVES

1993; Petty & Wegener, 1998), more recent work has assessed attitude change with implicit measures that are designed to tap the more automatic evaluations associated with objects, issues, and people. After a brief discussion of attitude structure, we turn to our primary focus: understanding attitude change on implicit and explicit measures.

Attitude Structure: The€Metacognitive Model We assume that, in addition to associating attitude objects with general evaluative summaries (e.g., good/bad), people sometimes develop an attitude structure in which attitude objects are linked to both positivity and negativity separately (see also Cacioppo, Gardner, & Berntson, 1997). Furthermore, we assume that people can tag these evaluations as valid or invalid or hold them with varying degrees of confidence. Our framework for understanding attitude structure is called the metacognitive model (MCM; Petty & Briñol, 2006a; Petty, Briñol, & DeMarree, 2007). For many attitude objects, one evaluation is dominant and seen as valid. This evaluation would come to mind spontaneously upon encountering the attitude object (e.g., see Bargh, Chaiken, Raymond, & Hymes, 1996; Fazio et al., 1995). However, sometimes both the positive and negative evaluations are deemed to be valid, and people’s attitudes are best described as being explicitly ambivalent because both positive and negative associations come to mind and are endorsed (e.g., de Liver, van der Pligt, & Wigboldus, 2007; see left panel of Figure 18.1). At other times, however, people might have two opposite accessible evaluations but one is seen as valid and the other is rejected (see right panel of Figure 18.1). A denied evaluation can be a past attitude (e.g., “I used to like smoking, but now it is disgusting”) or an association that was never endorsed but nonetheless salient because of one’s culture (e.g., from continuous depictions in the

1. Explicit Ambivalence GOOD

media). In such cases, the MCM refers to the attitude structure as one of implicit ambivalence (Petty, Tormala, Briñol, & Jarvis, 2006). This kind of ambivalence is not explicit because people do not endorse opposite evaluations of the same attitude object. Nevertheless, people can feel uncomfortable about such attitude objects even though they might not know the specific source of the conflict (see Petty & Briñol, 2009). The MCM relates to explicit and implicit attitude measures in the following ways. First, implicit measures are sensitive to the strength of the evaluative associations without respect to validity tags. Second, explicit measures also consider the extent to which people endorse their evaluative associations. That is, just as overall attitudes held with high confidence are more likely to affect behavior than those held with doubt (e.g., Fazio & Zanna, 1978), automatic reactions that are trusted are more likely to affect deliberative measures of attitudes than those held with doubt or explicitly denied (see also Gawronski & Bodenhausen, 2006). Later in this chapter we show that online assessments of confidence or doubt can influence which specific beliefs or thoughts people incorporate into their general evaluations (e.g., Petty, Briñol, & Tormala, 2002).

FUNDAMENTAL PROCESSES OF€ATTITUDE CHANGE Over the past 50 years, researchers have developed numerous theories of attitude change. One of the earliest assumptions was that effective influence required a sequence of steps leading to absorption of the content of a message (e.g., exposure, attention, comprehension, learning, retention; see McGuire, 1985). However, subsequent research evidence showed that message learning could occur in the absence of attitude change and that attitudes could change without learning the specific information in the communication (Petty

2. Implicit Ambivalence YES

GOOD

NO

BAD

YES

Smoking

Smoking BAD

YES

FIGURE 18.1.╇ Illustration of the metacognitive model representation of explicit (left panel) and implicit (right panel) ambivalence toward smoking.



18. Attitude Structure and Change

& Cacioppo, 1981). Cognitive response theory (Greenwald, 1968; Petty, Ostrom, & Brock, 1981) was developed explicitly to account for the low correlation between message learning and persuasion observed in many studies. In contrast to the traditional learning view, the cognitive response approach contended that the external information was merely a stimulus for a person’s own thoughts, which, in turn, determined the extent of influence. According to this framework, an appeal that elicited issue-Â�relevant thoughts that were primarily favorable toward a particular recommendation would produce agreement, whereas an appeal that elicited issue-Â�relevant thoughts that were predominantly unfavorable would be ineffective in achieving attitude change. Although the cognitive response approach provided important insights into the persuasion process, it focused only on those situations in which people were active processors of the information provided to them. The theory did not account very well for persuasion that was obtained in situations where people were not actively thinking about the message content. In fact, persuasion was thought to be unlikely in such situations. Yet numerous studies have shown that attitudes can be changed when the likelihood of extensive thinking is low. The elaboration likelihood model of persuasion (ELM; Petty & Cacioppo, 1981) was proposed to correct this deficit by arguing that persuasion can occur when thinking is high or low, but the processes and consequences of persuasion are different in each situation (see Petty & Cacioppo, 1986). The ELM is an early example

Elaboration Likelihood: (amount of thinking)

337

of what became an explosion of dual-Â�process and dual-Â�system theories that distinguished thoughtful from nonthoughtful (or automatic) determinants of judgment (see Chaiken & Trope, 1999; Petty & Briñol, 2006b, 2008). According to the ELM, any persuasion variable (i.e., whether source, message, recipient, or context) can influence attitudes by affecting the key processes of persuasion. That is, variables can affect attitudes by (1) serving as simple cues or heuristics; (2) biasing the thoughts that are generated; (3) affecting one’s confidence in those thoughts (or other structural features of thoughts); (4) serving as persuasive arguments or evidence; and/or (5) affecting the amount of information processing that occurs. This is depicted schematically in Figure 18.2. As we discuss shortly, the likelihood that variables will serve in these different roles depends on a person’s overall motivation and ability to think in a given situation as well as factors such as the relevance of the variable to the topic of persuasion and the timing of the variable (e.g., does it occur before or after the persuasive message?). By identifying a finite set of persuasion processes and specifying when they operate, the ELM provides a useful guide to organize key findings in the growing literature on change in implicit attitude measures just as these processes have organized the literature on explicit change. We describe the implications of these processes for attitude strength, the extent to which any observed attitude change is consequential (Petty & Krosnick, 1995). Illustrative research on each of these processes is described next.

Elaboration Likelihood Mediating Psychological Processes

Weak Attitudes

Low Use of simple cues

Persuasion Variables Source Message Recipient Context

Medium

Use of arguments

Attitude Change

Direction of thinking High

Thoughts about thoughts (metacognition)

Strong Attitudes Attitudes that are consequential and impactful

FIGURE 18.2.╇ Fundamental processes by which persuasion variables influence explicit and implicit measures of attitude change.

338

IV. DOMAIN-SPECIFIC PERSPECTIVES

Serving as Cues As noted earlier, people do not think about all the information they receive. When conditions do not foster thinking, variables influence attitudes by serving as simple cues. Cues operate by a variety of low-Â�effort processes such as mere association (Cacioppo, Marshall-Â�Goodell, Tassinary, & Petty, 1992) or reliance on simple heuristics (Chaiken, 1987). The existence of cue processes is important because it suggests that attitude change does not always require effortful evaluation of information. Instead, when a person’s motivation or ability to process issue-Â�relevant information is low, numerous studies have shown that persuasion on explicit measures can occur by a peripheral route in which processes invoked by simple cues in the persuasion context influence attitudes (see Petty & Wegener, 1998). Although peripheral ways to change attitudes can be very powerful in the short term, research has shown that explicit attitude changes based on peripheral cues tend to be less accessible, enduring, and resistant to subsequent attacking messages than attitude changes based on careful processing of message arguments (see Petty, Haugtvedt, & Smith, 1995, for a review). Petty (1994) suggested that one reason why cue processes have little impact on explicit measures is that people explicitly reject this impact (e.g., an attractive source is not relevant to one’s judgment), or the weight of the argument processing can overwhelm the cue impact. However, because implicit measures typically require less thinking during attitude expression than explicit measures, the impact of simple cue processes might be more apparent on them just as these processes have greater impact on explicit measures when thinking during attitude formation is low rather than high. Indeed, the growing research on the malleability of implicit measures of attitudes has demonstrated that simple associative processes requiring little thinking can sometimes affect automatic evaluations even if there is no impact on an explicit measure. Some researchers have even suggested that implicit measures are influenced only by simple associative processes (e.g., Banaji, 2004; Rudman, Ashmore, & Gary, 2001; Rydell & McConnell, 2006; Rydell, McConnell, Strain, Claypool, & Hugenberg, 2007). For example, classical conditioning and mere exposure are two relatively low thought or automatic processes that rely on multiple exposures. Consistent with the idea that automatic attitudes can be changed with these mechanisms, Olson and Fazio (2001) showed that automatic evaluations were sensitive to classical

conditioning procedures that used 20 pairings of the target attitude objects and the conditioned stimulus. Using a similar paradigm, Dijksterhuis (2004) found that automatic evaluations of the self were affected by subliminal evaluative conditioning trials (15 pairings) in which the pronoun I was repeatedly associated with positive- or negativetrait terms (see also Petty et al., 2006; Walther, 2002). Also consistent with this approach, research on automatic prejudice has shown that implicit measures can be changed using paradigms that involve exposing individuals repeatedly to either positive or negative examples of outgroup members. For example, automatic evaluations of blacks have been shown to be affected by exposure to admired black individuals (Dasgupta & Greenwald, 2001; Dasgupta & Rivera, 2008), to a black professor (Rudman et al., 2001), to a black experimenter (Lowery, Hardin, & Sinclair, 2001), to a black partner who occupied a superior task role (Richeson & Ambady, 2003), or to other counterstereotypical group members (Dasgupta & Asgari, 2004; for reviews, see Blair, 2002; Fazio & Olson, 2003; Gawronski & Bodenhausen, 2006). Although some of these studies likely involve invoking a different attitude object (e.g., the subtype of a black professional rather than the general category of blacks; e.g., see Barden, Maddux, Petty, & Brewer, 2004) rather than attitude change, there are a sufficient number of studies that clearly demonstrate that automatic evaluations of the same attitude object are being modified to conclude that automatic attitudes can be changed by simple associative processes requiring little elaborative thinking. Thus, the accumulated research is generally consistent with the idea that implicit measures of attitudes can be affected by relatively low Â�thought processes just as explicit attitudes can be changed by these processes especially when thinking is low. Another illustration of this possibility comes from research on embodiment. That is, simple bodily responses of the recipient can affect not only explicit but also implicit measures of attitudes. For example, embodiment research conducted with explicit measures has shown that stimuli presented while performing an approach behavior (e.g., using one’s hands to pull up from underneath a table) is evaluated more positively than stimuli presented during an avoidance behavior (e.g., pushing down on a tabletop surface) (Cacioppo, Priester, & Berntson, 1993), especially when thinking is low (Priester, Cacioppo, & Petty, 1996). Similar findings have been found for a large number of behaviors, postures, and body movements (for a review, see Â�Â�Briñol



18. Attitude Structure and Change

& Petty, 2008) and hold for implicit measures as well. For example, Kawakami, Phills, Steele, and Dovidio (2007) found significant reductions in an implicit measure of prejudice toward blacks when participants had to respond repeatedly with an approach action to black faces and with an avoidance action to white faces (see also Ito, Chiao, Devine, Lorig, & Cacioppo, 2006; Kawakami, Steele, Cifa, Phills, & Dovidio, 2008).

Type or Direction of Thinking When motivation and ability to think are high, people will be engaged in careful thought about a message, but that thinking can be biased by other variables in the persuasion setting. Most importantly, variables can motivate or enable people to either support or derogate the content of the information provided. This is important, of course, because when elaboration is high, attitude change is a function of the number and valence of thoughts that come to mind (see reviews by Eagly & Chaiken, 1993; Petty & Cacioppo, 1986). When thinking is high, some factors such as being in a positive mood increase the likelihood of favorable thoughts being elicited (e.g., Petty, Schumann, Richman, & Strathman, 1993), but other factors such as providing a forewarning of persuasive intent increase the likelihood that unfavorable thoughts come to mind (Petty & Cacioppo, 1979b). One of the most powerful factors that produce a bias is the position the message takes. In general, any time a message takes a position opposed to one’s attitudes, values, or identity, people will be biased against it (Petty & Cacioppo, 1990). When a message takes a position supporting one’s views, people will be biased in favor of it. Nevertheless, if the likelihood of thinking is high, some variables are capable of producing thinking that is biased against one’s favored position or biased in favor of a disliked position (e.g., instilling reactance; see Petty & Cacioppo, 1979a). Perhaps the most direct way to bias the direction of the thoughts that come to mind is to ask people to think in a given direction (e.g., ask them to generate and write down only proarguments in favor of a specific issue or only counterarguments against it). Previous research on persuasion has shown that participants are able to comply with this kind of direct instruction, and that this is an effective way to create different profiles of thoughts and attitudes regarding an issue (see Killeya & Johnson, 1998). In line with the findings for explicit persuasion, other research has found that implicit measures are sensitive to simi-

339

lar forms of directed thinking. For example, Blair, Ma, and Lenton (2001) found that, compared with controls, participants who were asked to think about and visualize a counterstereotypical woman showed reduced levels of implicit gender stereotyping. Along with previous research on explicit persuasion, these findings suggest that the direction of the thoughts that come to mind in response to a treatment can influence both explicit and implicit measures of attitudes. Not only can self-Â�generated thoughts influence attitudes, but so too can thoughts generated in response to persuasive messages. There are, of course, numerous studies showing that explicit attitudes can change following exposure to persuasive messages. In one study examining implicit attitude change, Horcajo, Briñol, and Petty (2009) asked participants to read a communication composed of compelling arguments in favor of consuming vegetables. In a control condition, participants read a neutral message. An example argument in favor of vegetable consumption was that vegetables have more vitamins than most supplements on the market, making them particularly beneficial during exam and workout periods. The neutral topic was an editorial related to interior design in which the word vegetable was also mentioned explicitly to control for the accessibility of the attitude object itself. All participants were asked to think carefully about the message. After thinking about the message, participants had to complete an apparently unrelated task (Implicit Association Test [IAT]) that was designed to assess automatic evaluations relevant to the proposal of the message. In the IAT, participants classified target concepts (vegetable and animal) and attributes (good–bad). Consistent with the idea that persuasive messages processed under thoughtful conditions can influence implicit measures, we found that automatic evaluations of vegetables were more favorable in the message than in the control condition. Although this research demonstrates that automatic evaluations can be influenced by traditional persuasive messages, it is unclear what the psychological processes were underlying the obtained effects. In our initial study, because we asked participants to read the content of the arguments, we suspect that the observed changes on automatic evaluations were due to the careful consideration of their merits. However, it is also possible that participants just counted and relied on the number of arguments presented in favor of the proposal (e.g., Petty & Cacioppo, 1984), or they might have followed some other low-Â�effort process. For example, just by looking at the message superficially (i.e.,

340

IV. DOMAIN-SPECIFIC PERSPECTIVES

simply attending to the advocated position of the message without reading the content of the arguments), a person might have reasoned that the culture favors vegetables (e.g., Olson & Fazio, 2004). Because the findings of this study, like other research in this domain, do not allow us to examine whether (and how much) participants elaborated the information received, we conducted several additional studies in which the extent of thinking and argument quality were manipulated. In one study, Horcajo, Briñol, and Petty (2009) exposed participants to a persuasive message composed of strong or weak arguments in favor of including more vegetables in their diet (adapted from Briñol et al., 2006). Thinking about the strong message leads people to generate favorable thoughts associated with the proposal, whereas thinking about the weak message leads to unfavorable thoughts.1 In addition to varying the strength of the arguments, the extent of thinking was manipulated by making the message personally relevant or irrelevant (see Petty & Cacioppo, 1979b). Thus, the message was introduced as part of an article about personal habits with potential consequences for academic performance (personally relevant frame) or as part of an article about plant properties (personally irrelevant frame). After reading the message, participants were asked to list their thoughts about the proposal. After the thought listing, and as part of an ostensibly unrelated study, participants were then asked to complete an IAT to assess automatic evaluations of vegetables. The results of this study were consistent with the idea that automatic evaluations (as assessed by the IAT) can change as a result of processing persuasive messages. We first found that elaboration increased the impact of argument quality on automatic evaluations just as past research has shown this pattern for deliberative evaluations. That is, under high elaboration conditions, automatic evaluations of vegetables were more impacted by argument quality than they were under low elaboration conditions. More importantly, under high elaboration conditions, the obtained changes on automatic evaluations from argument quality were mediated by the valence of the thoughts (i.e., positive or negative) that participants generated in response to the message. Thus, this study provides preliminary evidence for thoughtful mediation of changes on implicit measures. According to Briñol, Petty, and McCaslin (2009), deliberation about message arguments can produce change in implicit measures because thoughts (like emotions and any other variable)

can serve in different roles depending on the circumstances. The most simplistic treatment of one’s thoughts would only consider their number and valence, two qualities that are relatively easy to extract (e.g., see Betsch, Plessner, & Schallies, 2004). When processing a persuasive message, a person generating mostly positive thoughts would rehearse mostly positive evaluative associations to the attitude object and a person generating mostly negative thoughts would rehearse mostly negative evaluative associations. This would lead strong arguments to show more positive evaluations on an implicit measure than weak arguments. Thus, when the measurement conditions involve low thinking, as is the case with measures of automatic evaluation, thoughts likely have an impact on attitudes because of the relatively low-Â�effort extraction of their evaluative information. Importantly, a person’s own thoughts can operate through different processes under different circumstances. Thus, when conditions foster more thinking, as is the case with deliberative measures, it is possible to extract additional information (besides number and valence) from one’s thoughts. For example, in addition to the desirability (valence) involved in a thought about a persuasive proposal, a person can consider other aspects of that thought, such as the likelihood of the consequence it implies (e.g., Fishbein & Ajzen, 1975) and the overall confidence one has in the thought (see Briñol, Petty, & Tormala, 2004; Petty et al., 2002). These additional features of thoughts should be less likely to emerge on an automatic measure of attitudes until these dimensions also become highly accessible or well integrated into the overall attitude structure. Thus, as described in the next section, when the measurement conditions involve high thinking, not only the number and valence of one’s thoughts but also other information associated with the thoughts is more likely to have an impact.

Structural Features of Thoughts The structural features of thoughts refer to dimensions of thoughts other than direction (favorable or unfavorable) and amount (high or low). Although there are several important structural features of thoughts, such as how quickly the thoughts come to mind, in this section we highlight metacognitive aspects of thinking, or thoughts about one’s thoughts (for a review, see Petty, Briñol, Tormala, & Wegener, 2007). When the amount of thinking is high, variables can affect metacognitive features of the thoughts that are generated, such as how



18. Attitude Structure and Change

much confidence people have in their thoughts or how biasing they are perceived to be. According to what we have called the self-Â�validation hypothesis, confidence in thoughts is important because when people have greater confidence in the validity of their thoughts, these thoughts are more likely to be used in forming judgments (Petty et al., 2002). On the other hand, if people doubt the validity of their thoughts, the thoughts will be less likely to have an impact on judgments. This may be one reason why some persuasion campaigns are unsuccessful. That is, the campaign might produce the appropriate amount of favorable thoughts, but these thoughts might not be held with sufficient confidence to affect judgments. Recent research on self-Â�validation has identified a large number of variables that have an impact on attitude change by affecting thought confidence (for a review, see Briñol & Petty, 2009). For example, in one of the initial studies on self-Â�validation, Petty and colleagues (2002) gave participants false feedback about the extent to which other people shared similar thoughts to the ones the participants just listed regarding a persuasive proposal. This social consensus affected thought confidence, increasing persuasion when the message recipients’ thoughts were mostly favorable and decreasing it when the thoughts were unfavorable. Importantly, social consensus can also validate automatic associations. In an illustration of this possibility, Stangor, Sechrist, and Jost (2001) found that preexisting implicit stereotypes were enhanced when participants learned that other individuals shared that stereotype than when the stereotype was said not to be shared by other individuals. As described later in this review, variables other than social validation can influence explicit and implicit measures by affecting thought confidence. In the domain of explicit attitudes, these metacognitive features of thoughts have been found to be most impactful when the amount of thinking at the time of attitude formation or change is high because it is only in such situations that people have a substantial number of issue-Â�relevant thoughts with the potential to shape attitudes and also have the motivation and the ability to think about their own thoughts. Just as it is important to consider the extent of thinking during the time of exposure to a persuasion treatment, it is also useful to consider the extent of thinking permitted during response to the attitude measure. In general, if attitudes are not well formed or practiced at the time of attitude measurement, we anticipate that an implicit measure is unlikely to reflect aspects other than evaluation. However, if the attitude is

341

well formed and practiced at the time of attitude measurement (i.e., people have already considered the confidence in their thoughts in developing their attitudes), we predict that implicit measures would be likely to reflect the same factors as explicit measures. Future research should explore this possibility.

Serving as Arguments When thinking is high, people assess the relevance of all of the information in the context and that comes to mind in order to determine the merits of the attitude object under consideration. That is, when thinking is high, people examine source, message, recipient, contextual, and internally generated information as possible arguments or reasons for favoring or disfavoring the attitude object. Individuals (and situations) can vary in what type of information serves as persuasive evidence for any given attitude object. Although there is less research looking at the influence of this process on implicit measures, we suspect that, similar to any other mechanism, a variable that serves as an argument can potentially influence both explicit and implicit measures.

Amount of Thinking We have already specified several roles that variables can play in producing persuasion depending on whether the amount of thinking is relatively low or high. Variables can also affect the amount of thinking itself. For expository purposes, we have described persuasion processes as if they can be neatly categorized into high versus low Â�thought mechanisms. However, it is important to note that the various persuasion processes fall along a thinking continuum (Petty & Cacioppo, 1986). The more motivated and able people are to think, the more their attitudes are determined by their valenced thoughts and thought confidence. Also, explicit attitudes based on high amounts of thinking are postulated to be stronger than attitudes based on little thought. That is, such attitudes are more accessible, stable, resistant to countermessages, and predictive of behavior (see Petty et al., 1995). Thus, considering the amount of thinking underlying attitude change is important because the overall goal of most persuasion attempts is to induce attitude change that has these features. A large number of variables have been examined that can influence explicit measures of attitudes by affecting people’s general motivation or ability

342

IV. DOMAIN-SPECIFIC PERSPECTIVES

to think about a message (see Petty & Wegener, 1998, for a review). For example, distraction in the situation reduces one’s ability to process a message so that distraction reduces persuasion if the arguments in a persuasive message are strong (because favorable thoughts are disrupted) but increases persuasion if the arguments are weak (because unfavorable thoughts are disrupted (Petty, Wells, & Brock, 1976). In contrast, repeating a message increases the ability to process it by providing greater opportunities to do so (Cacioppo & Petty, 1989). With respect to motivation, perhaps the most studied variable is the personal relevance of the communication. By increasing the personal relevance of a message, people become more motivated to scrutinize the evidence more carefully such that if the evidence is found to be strong, more persuasion results, but if the evidence is found to be weak, less persuasion occurs (Petty & Cacioppo, 1979b). In fact, linking the message to almost any aspect of the self such as one’s values, outcomes, self-Â�conception, identity, and so forth can enhance self-Â�relevance and thereby increase the extent of information processing (Fleming & Petty, 2000; Petty & Cacioppo, 1990). Most of the research on matching some aspect of a communication to the self has dealt with explicit characteristics of the recipient, dimensions of which people are aware (e.g., gender) and that can be assessed with deliberative, explicit selfÂ�reports (e.g., need for cognition, self-Â�monitoring, and so forth; for reviews, see Briñol & Petty, 2006; Petty, Wheeler, & Bizer, 2000). However, of most relevance to this chapter, individual differences can also be assessed with implicit measures. The importance of the distinction between explicit and implicit measures of individual differences is especially apparent when there is a discrepancy between them. We have suggested that such discrepancies can produce implicit ambivalence and have important consequences for information processing and attitude change (Petty & Briñol, 2009). Specifically, because internal inconsistencies that are explicit (e.g., endorsing both positive and negative evaluations of the self) are often associated with aversive feelings (e.g., Abelson et al., 1968) and enhanced information processing (e.g., Maio, Bell, & Esses, 1996), we argue that individuals with discrepancies between their automatic and deliberative self-Â�conceptions are similarly (implicitly) motivated to process discrepancy-Â�relevant information. We use the term implicit to refer to this ambivalence in the sense that when deliberative and automatic self-Â�conceptions conflict, people are endorsing their deliberative self-Â�conception

but not their automatic one (see right panel of Figure 18.1). Because the conflicting reactions are not endorsed, people do not claim to be ambivalent about themselves. To test the idea that implicit ambivalence exists and is consequential, in a series of studies Briñol and colleagues (2006) assessed various individual differences (e.g., self-Â�esteem, shyness) with both explicit and implicit measures and then examined the information-Â�processing consequences of the extent of discrepancy between the two. Across three studies, as the discrepancy between the implicit and explicit measures increased, so too did processing of a message relevant to that discrepancy. Consistent with our MCM of attitudes described earlier, this line of research reveals that when people reject their stored automatic associations (and, therefore, do not use them when deliberatively responding), those associations can still be impactful. Thus, although people might not be aware of any ambivalence associated with the trait or attitude object in question, they might still experience some discomfort associated with the trait or attitude object that motivates processing of discrepancy-Â�relevant information (see Petty & Briñol, 2009, for a review). In another series of studies, we examined the notion that discrepancies between automatic versus deliberative attitudes could lead to enhanced information processing in the domain of racial prejudice. In one experiment (Petty, Briñol, See, & Fleming, 2009), we assessed Ohio State University students’ attitudes toward African Americans using both automatic (a race IAT; Greenwald, McGhee, & Schwartz, 1998) and deliberative (agreement with pro- and anti-black statements; Katz & Hass, 1988) measures. After completing the implicit and explicit measures of racial attitudes, all of the students were exposed to a message advocating a new program to hire African American faculty at their university that was supported with either strong or weak arguments. Consistent with the idea that people with automatic–Â�deliberative discrepancies would act as if they were ambivalent, discrepancy interacted with argument quality to predict attitudes toward the program. That is, as the discrepancy between attitudes assessed with implicit and explicit measures increased, attitudes were more affected by argument quality.2 Finally, in another relevant line of research, we examined whether explicit attitude change (changing one’s endorsed attitude from one valence to another) can also produce explicit–Â�implicit discrepancy and enhanced information processing. If a new evaluative association is formed and an old one is rejected (e.g., tagged as false), the MCM



18. Attitude Structure and Change

predicts that the previous association is still present creating some implicit ambivalence (see right panel of Figure 18.1). In one study testing this idea (Petty et al., 2006, Study 2), participants first formed an initial positive or negative attitude toward another person via evaluative conditioning. This manipulation was pretested and shown to be effective in modifying both automatic (evaluative priming) and deliberative (semantic differential) measures of attitudes. Then the participants received information about the target individual’s attitudes on several important topics. The attitudinal information was designed to either get the person to like or dislike the target by having the target agree or disagree with the participant on several important issues. In some conditions, this information was in the same direction as the conditioning manipulation so that no attitude change would occur, but in other conditions the information was opposite in valence to the conditioning. In the latter situation, individuals rejected their earlier evaluations based on conditioning and adopted new evaluations based on the similarity information. Following these inductions, participants were told that the target person was a candidate for a job at their university. To evaluate the candidate, they were provided with either a strong or a weak resume to examine. The key result was that attitudes toward the target as a job candidate were more influenced by the quality of the candidate’s resume in the condition where attitudes were changed than in conditions where attitudes were not changed. That is, when attitudes were changed, people engaged in greater information processing as if they were attempting to resolve some ambivalence. In a conceptual replication of our procedure, Rydell, McConnell, and Mackie (2008) examined whether automatic–Â�deliberative discrepancies were associated with measures of reported discomfort and whether this was responsible for the enhanced information processing observed. In this research, participants were first exposed to either positive or negative associative information (subliminal word prime) about a target person. Then participants received positive or negative information about the behaviors of the target individual. As in the Petty and colleagues (2006) design, in some conditions, this information was in the same direction as the initial manipulation so that no attitude change would occur, and in other conditions the information was opposite in valence to the priming manipulation. After these inductions, participants were exposed to information relevant to the target person that was composed of either strong or weak arguments.

343

There were several notable findings from this study. First, it was found that a discrepancy in automatic versus deliberative evaluations was associated with increased information processing, replicating our previous findings. Furthermore, the automatic–Â�deliberative discrepancy was associated with reports of general discomfort regarding the attitude object. Perhaps of most interest, Rydell and colleagues (2008) showed that the measure of general discomfort mediated the information processing effect observed. Because previous investigators used the measure of discomfort to assess a state of cognitive dissonance (e.g., Elliot & Devine, 1994), Rydell and colleagues interpreted their results to suggest that implicit–Â�explicit discrepancies enhance information processing because of cognitive dissonance rather than implicit ambivalence. However, finding that discomfort is affected by an implicit–Â�explicit discrepancy and that it mediates information processing does not necessarily indicate that dissonance is involved because many psychological states other than dissonance can produce discomfort. In fact, we hypothesized that implicit ambivalence would do just that (see Petty et al., 2006; Petty & Briñol, 2009). That is, we argued that people with automatic–Â� deliberative discrepancies would be in a state of implicit ambivalence, which would produce discomfort that motivated information processing. As noted earlier, the ambivalence is implicit because individuals do not explicitly acknowledge endorsing contrary views about the object, although they might acknowledge some discomfort with respect to the object. However, the discomfort does not stem from freely choosing to bring about an aversive consequence (Cooper & Fazio, 1984) or some acknowledged self-Â�inconsistency (Aronson, 1969), conditions necessary for dissonance. Rather, the discomfort stems from the conflict between an endorsed and an unendorsed evaluation associated with the attitude object (see right panel of Figure 18.1).

THE INFLUENCE OF€COMMUNICATION VARIABLES ON EXPLICIT AND€IMPLICIT PERSUASION As we just reviewed, the ELM (Petty & Cacioppo, 1981, 1986) identifies the key processes by which variables can affect attitudes and highlights their role in producing attitude changes that are consequential or not. Thus, analyzing processes informs us of both the immediate and long-term consequences for persuasion. Whether variables are part

344

IV. DOMAIN-SPECIFIC PERSPECTIVES

of the message source, the recipient, the communication itself, or the persuasion context, they can affect attitudes by affecting the same key processes. A few examples should help to clarify the multiple roles that any variable can have in different situations for both explicit and implicit measures.

Source Factors Consider first the multiple processes by which source factors, such as expertise, attractiveness, race, or gender, can have an impact on persuasion. In research using explicit measures to assess attitude change, all of the postulated roles for source factors have been observed. Thus, when the likelihood of thinking was low, source factors have influenced persuasion by serving as a peripheral cue, affecting attitudes in the same direction as their valence (e.g., Petty, Cacioppo, & Goldman, 1981; see Chaiken, 1987). On the other hand, in several studies in which the elaboration likelihood was moderate, the source factors of expertise and attractiveness affected how much thinking people did about the message (e.g., Priester & Petty, 1995; Puckett, Petty, Cacioppo, & Fisher, 1983). When the likelihood of thinking is already set to be very high (e.g., high personal relevance of the message topic), source factors have taken on other roles. For example, if a source factor is relevant to the merits of a message, it can serve as a persuasive argument. Thus, an attractive endorser might provide persuasive visual evidence for the effectiveness of a beauty product (see Petty & Cacioppo, 1986). Under low Â�thinking conditions, where attractiveness serves as a simple cue, the relevance of attractiveness to the topic of the message is of little importance: All that matters is valence. Thus, under low Â�thinking conditions, source attractiveness would be just as effective in selling cars as beauty products, whereas under high thinking conditions attractiveness would be more impactful in the latter than the former case. Another role that sources can play under high thinking conditions is biasing information processing. For example, Chaiken and Maheswaran (1994) found that when recipients under highÂ�elaboration conditions received an ambiguous message (i.e., not clearly strong or weak), the expertise of the source biased message processing in a positive direction and produced more favorable attitudes. Finally, under high-Â�elaboration conditions, source factors have been found to influence persuasion by affecting people’s confidence in the validity of their thoughts in response to the message (Briñol et al., 2004). The effect of source cred-

ibility on thought confidence under high thinking conditions is most likely to occur when the source information follows, rather than precedes, the persuasive message (Tormala, Briñol, & Petty, 2007). When source credibility validates thoughts, it leads to more persuasion when the thoughts are favorable (such as if the arguments are strong) but to less persuasion when the thoughts are unfavorable (such as when the arguments are weak). In sum, the ELM shows how any one outcome for a source factor can be produced by different processes in different situations. For example, if a credible source led to more persuasion than a source that lacked credibility, it could be because credibility served as a simple cue under low thinking conditions or enhanced thinking about strong arguments under moderate thinking conditions. If thinking was high, then credibility could have enhanced persuasion because it served as a strong argument, biased thinking in a positive direction, or validated people’s positive thoughts. As noted earlier, the role that was operative under high thinking conditions would depend on factors such as the relevance of the source variable to the topic under consideration and the timing of the source factor (i.e., whether it was available before or after message presentation). Can the same source factors influence implicit measures by multiple processes as well? We think so. Perhaps the most obvious role for source factors is as a simple cue. For example, Forehand and Perkins (2005) exposed participants to an advertisement for a product that featured a liked celebrity. Some participants recognized the identity of the celebrity, whereas others did not. When the liked celebrity was not explicitly recognized, both implicit and explicit attitudes were affected positively. However, when the celebrity was explicitly identified, only the implicit measure was affected positively. In fact, under these conditions, a reversal effect emerged on the explicit measure, revealing more negative attitudes toward the liked source. When the celebrity was explicitly recognized, recipients presumably attempted to debias their judgments, not wanting them to be influenced by this presumably irrelevant factor. If people overcorrect their judgments, a reverse effect will be obtained (e.g., see Petty, Wegener, & White, 1998, for a reverse effect of source attractiveness resulting from correction). The Forehand and Perkins findings suggest that implicit measures are particularly sensitive to the valence of the source of the persuasive treatment but less so to correction processes.3 In another relevant line of research, McConnell, Rydell, Strain, and Mackie (2008) presented



18. Attitude Structure and Change

participants with positive or negative behavioral information about a target who also varied in some observable physical characteristic (i.e., overweight vs. normal, attractive vs. average vs. unattractive, black vs. white). They found that explicit evaluations of the target were affected by the explicit behavioral information but not by the physical characteristics. These results are analogous to classic persuasion studies in which substantive arguments impacted explicit attitudes, but simple issue-Â�irrelevant valence cues did not when people were thinking carefully (see Petty & Wegener, 1998, for a review). In contrast, implicit attitudes toward the target generally reflected only the observable physical characteristics of the person rather than the explicitly provided behavioral information (see also Rydell & McConnell, 2006). For example, when the source was unattractive, overweight, or black, implicit attitudes were negative regardless of whether the behavioral information was positive or negative. These results are also analogous to classic persuasion studies in which simple source cues impacted explicit attitudes, but substantive arguments did not when thinking was impaired (e.g., Chaiken, 1980; Petty et al., 1981). Interestingly, when physical appearance provided no particularly positive or negative valence cue (e.g., a white, normal-Â�weight target of average attractiveness), then the valence of the explicit behavioral information did affect implicit attitudes. This finding is similar to the research noted earlier where argument quality affected implicit measures in the absence of any simple peripheral cues (Horcajo, Briñol, & Petty, 2009; see Briñol, Petty, & McCaslin, 2009, for a review). Although the McConnell and colleagues (2008) research suggests that easily processed features of people (e.g., race, attractiveness) are especially likely to affect implicit measures by serving as simple valence cues, this does not mean that implicit measures cannot be affected by source variables under high thinking conditions. Under high thinking conditions, source factors could influence automatic measures, but they would do so by other more deliberative processes such as affecting the valence of the thoughts generated. Indeed, this may be what happened when McConnell and colleagues in some conditions presented participants with behavioral information that was ambiguous rather than clear-cut. Under these conditions, the target physical characteristics also affected implicit attitudes but may have done so by a different mechanism. If so, this finding would be similar to past persuasion research showing that simple cues are more likely to affect attitudes

345

under high thinking conditions if the substantive information is ambiguous because the cue biases interpretation of the information (see Chaiken & Maheswaran, 1994; Petty et al., 1993).

Message Factors Like source variables, message variables can serve in multiple roles. For example, think about the mere number of arguments that a persuasive message contains. This variable serves as a simple peripheral cue when people are either unmotivated or unable to think about the information. That is, people can simply count the arguments in a message or have some sense of a large quantity of arguments and agree more with the advocacy the more information that is presented, regardless of the cogency of that information. When motivation and ability to think are high, however, the informational items in a message are not simply counted as cues, but instead the information is processed for its quality. Thus, under low thinking conditions when the number of arguments in a message serves as a cue, adding weak reasons in support of a position enhances persuasion, but when the items in a message are processed as arguments, adding weak reasons reduces persuasion (e.g., Petty & Cacioppo, 1984). The mere number of arguments is only one of the many message factors that can influence persuasion by serving in different roles in different situations (see Petty & Wegener, 1998). Like explicit measures, implicit measures are also affected by message factors. For example, Klauer, Musch, and Eder (2004) found that just adding more information to a proposal can influence automatic evaluations. We suggest that this effect can result from a variety of low and high thinking processes. For example, people could have a general positive reaction to the many arguments or could have generated more positive thoughts as the amount of information increased. As described earlier in this chapter, implicit measures have proven to be sensitive to a number of different message variables, such as content, direction, and quality of the message (see Briñol et al., 2009; see also Gawronski & Sritharan, Chapter 12, this volume).

Recipient Factors Many recipient variables are relevant for persuasion, ranging from motives and abilities to individual differences in personality. For example, as noted earlier, bodily responses of the recipient

346

IV. DOMAIN-SPECIFIC PERSPECTIVES

can influence persuasion by different processes. Notably, research using both explicit and implicit measures has shown that information presented while performing a positive behavior (e.g., head nodding, arm flexion, smiling) is evaluated more positively than information presented during a negative behavior (e.g., head shaking, arm extension, frowning; e.g., see Wells & Petty, 1980). Although many researchers have speculated that the underlying mechanism for all of these effects was a relatively simple, automatic one (probably because of the nature of the variable and the measure), subsequent research on embodied persuasion has revealed that behaviors such as head movements can influence attitudes not only by relatively simple processes but also by deliberative ones, including metacognitive mechanisms (for a review, see Briñol & Petty, 2008). Thus, bodily responses, like other variables, are capable of affecting both explicit and implicit attitudes through high and low thinking processes. To illustrate the impact of bodily movements on an implicit measure by a high thinking process, DeMarree, Briñol, and Petty (2009) subliminally primed participants with words related to the black (vs. white) stereotype. Following this induction, participants were instructed to follow a ball moving vertically or horizontally on the screen with their heads. Participants then completed a measure of felt aggression adapted from prior research (i.e., DeMarree, Wheeler, & Petty, 2005), which was framed as a subliminal perception task. As part of this measure, participants were informed that a word would be subliminally presented on the screen and that they would then choose which of four answer choices had been presented. They were instructed that, although they would not be able to consciously process the stimulus, if they relied on the feelings they experienced during stimulus presentation, they would be able to choose the target word successfully. If participants were feeling more or less aggressive as a function of the prime, these feelings should be detected on this measure.4 Consistent with the self-Â�validation logic, DeMarree and colleagues found that the direction of the prime affected participants’ reports of aggressive feelings (as well as their deliberative ratings of closeness to African Americans) in a stereotype-Â�congruent fashion in the head-Â�nodding but not the head-Â�shaking condition. Thus, as was the case with head nodding affecting confidence in thoughts to a persuasive message (Briñol & Petty, 2003), so too did it appear to affect the validity and use of subtly activated mental content via priming.5

Another recipient factor that has been studied extensively is the emotions the target of persuasion is experiencing. In accord with the ELM, prior research has shown that a person’s emotions can serve in multiple roles (see Petty et al., 2003). First and most simply, when thinking is constrained to be low (e.g., as a result of many distractions), then emotions tend to serve as simple associative cues and produce evaluations consistent with their valence (e.g., Petty et al., 1993). When thinking is high, one’s emotions serve in other roles. First, emotions can be evaluated as evidence (e.g., negative emotions such as sadness or fear can lead to positive evaluations of a movie if these are the intended states; e.g., see Martin, 2000). Also, when thinking is high, emotions can bias the ongoing thoughts (e.g., positive consequences seem more likely when people are in a happy vs. a sad state; e.g., DeSteno, Petty, Wegener, & Rucker, 2000). Emotions can also affect the confidence people have in their thoughts but primarily when the emotions are experienced after rather than before thinking (Briñol, Petty, & Barden, 2007). Finally, when the likelihood of thinking is not constrained to be high or low by other variables, then emotions have been shown to affect the extent of thinking. For example, people might think about messages more when in a sad than a happy state because sadness either signals a problem to be solved (Schwarz, Bless, & Bohner, 1991) or conveys a sense of uncertainty (Tiedens & Linton, 2001). If people process a message more when in a sad than happy state, this means that they would be more persuaded by cogent arguments when sad than happy but less persuaded by specious arguments. To affect the extent of thinking, the experience of emotion should precede the presentation of the message. As was the case with other persuasion variables, recent research has revealed that the emotions experienced by a person can influence not only explicit but also implicit measures. For example, Sassenberg and Moskowitz (2005) found that asking individuals to think about a situation in which they were happy with their ingroup increased the evaluation of that group on an implicit measure relative to thinking about situations in which they were angry with their ingroup. Using different emotions, Gemar, Segal, Sagrati, and Kennedy (2001) studied formerly depressed individuals and found that an implicit measure of self-Â�esteem was affected in a negative way by an induction of sad (vs. control) mood (see also DeHart & Pelham, 2007). In another study conducted in the domain of intergroup attitudes, DeSteno, Dasgupta, Bartlett, and Cajdric (2004) found that anger, but



18. Attitude Structure and Change

not sadness, increased negativity toward outgroup members on an automatic measure. Although the impact of emotional manipulations on implicit measures is clear in these studies, there are numerous mechanisms by which these effects could have occurred. As described earlier for explicit measures, we argue that an induction of emotion can affect implicit measures by a number of different processes depending on the circumstances (e.g., acting as a simple valence cue; affecting the direction or amount of processing). Because the process involved is potentially consequential for the strength of the attitude, future work should pay more attention to the mechanism involved when emotions influence implicit attitudes.

Consequences of Deliberative and€Automatic Processes for€Implicit€Measures The research we have reviewed suggests that deliberative and automatic measures of attitudes can change through the operation of source, message, and recipient factors affecting both low and high Â�thought processes. As noted earlier, traditional research on explicit measures of change has shown that, although both high and low Â�thought attitude change processes are possible, the consequences of those processes are different. In particular, according to the ELM, attitudes formed or changed through low thinking processes are less durable and impactful than attitudes changed via high thinking processes (see Petty et al., 1995, for a review). Although considerable research has demonstrated that extensive thinking enhances the strength of explicit attitudes, there is relatively little research addressing this possibility with respect to automatic attitudes. Attitude strength can be demonstrated in many different ways, ranging from enhanced accessibility of the attitude to influence on related thought processes and behavior. We conducted several studies to test whether automatic attitudes might show some evidence of greater strength when changed through highÂ�versus low-Â�Â�elaboration processes (Horcajo, Petty, & Briñol, 2009). For example, we noted earlier that attitude change processes that require thinking deeply about the attitude object are likely to result in attitude representations that are well integrated and connected with other relevant material in memory (see, e.g., McGuire, 1981; Tesser, 1978). Because of the strong linkage among constructs associated with high thinking, activating one mental representation induced via high thinking should

347

more readily activate related cognitive elements. Indeed, within the literature on explicitly assessed attitudes, there is some suggestive evidence that it is easier to activate related constructs for high than for low need-for-Â�cognition individuals (Petty, DeMarree, Briñol, Horcajo, & Strathman, 2008; Smith, Haugtvedt, & Petty, 1994). An important issue to examine is the extent to which this argument holds for automatic attitudes induced via high thinking processes. We have recently used the classic paradigm on minority influence to address this issue. In this paradigm, participants receive persuasive information that is endorsed by either a numerical minority or a majority source. The traditional result for this paradigm is that, although minorities produce little change on explicit measures directly linked to the attitude object, they can sometimes produce change on explicit measures indirectly related to the proposal (e.g., changing on birth control when the message is on abortion; see Mugny & Perez, 1991). This finding has been interpreted in terms of elaboration differences, with minority sources leading to more deliberative processing of the information compared with majority sources (Baker & Petty, 1994; Moscovici, Mucchi-Faina, & Maass, 1994). If participants exposed to minority sources engage in greater message processing, then change on indirect topics becomes more likely as the implications of the information on the direct topic percolate through the cognitive system and impact related beliefs (e.g., see McGuire, 1981). To examine the implications of these findings for automatic attitudes, Horcajo, Petty, and Briñol (2009) told participants that they were helping out with research designed to assess possible changes in the institutional color representing their university. Following this cover story, participants were exposed to a strong or weak message in favor of the color green endorsed by either a majority or a minority status source. We measured indirect automatic change by using an IAT toward the brand Heineken, a product associated with the color green. That is, the logo of the brand is green and they use the slogan “Think in green” in many of their marketing campaigns. The results showed that only in the minority source condition were attitudes toward Heineken more favorable as a result of the message advocating the color green. If minority sources foster more thinking than majority sources, these findings provide some evidence suggesting that for implicit measures deliberative processes can lead to changes on automatic measures though a process of spreading activation (i.e., from green to Heineken).

348

IV. DOMAIN-SPECIFIC PERSPECTIVES

SUMMARY AND CONCLUSIONS By understanding the basic mechanisms of persuasion, we know that numerous variables are capable of affecting both explicit and implicit measures relevant to attitude change by a variety of different processes. Like explicit measures, implicit measures can change as the result of one or more of these mechanisms: (1) affecting the amount of information processing; (2) affecting the direction (valence) of the thoughts that are generated; (3) affecting confidence in one’s thoughts (or other structural features); (4) serving as persuasive arguments or evidence, or (5) serving as simple cues. We have reviewed some evidence that both explicit and implicit attitude measures are susceptible to these low and high thinking processes. We also emphasized the idea that just as high Â�thought processes can produce different consequences than low Â�thought processes when explicit measures are involved (e.g., greater spreading activation), the same appears to be true for implicit measures. Finally, we noted that explicit and implicit measures can show similar outcomes (e.g., when people endorse their automatic evaluative associations as valid) but also different outcomes in response to the same treatment (e.g., when people deny or reject one of their automatic evaluations). Notes 1. The argument quality technique is a procedure designed to assess mechanisms relevant to persuasion, such as the extent of information processing (Petty & Cacioppo, 1986). The arguments are typically pretested to produce the appropriate pattern of cognitive responding. That is, the strong arguments elicit mostly favorable thoughts and the weak arguments mostly unfavorable thoughts when people are instructed to think carefully about them. Notably, both the strong and weak arguments argue in favor of the proposal, but the strong arguments provide more compelling reasons than the weak arguments. Because the argument manipulation is used to assess how much thinking people are doing about the message, all arguments need to argue for the same position, but only with high or low convincingness. Because both sets of arguments are in favor of the issue, they would be equally persuasive if people do not think about their implications. Individuals not thinking about the message carefully could respond simply to the number of arguments presented or their initial gut reaction to the proposal (e.g., Petty & Cacioppo, 1984; see Petty & Wegener, 1998). The more attention paid to the

2. 3.

4.

5.

information provided, however, the greater the difference in subsequent attitudes to strong versus weak arguments. For a variable (e.g., source credibility) to affect the extent of processing, it should precede presentation of the message. Notably, the direction of the discrepancy did not further qualify the results. With enough repetition and practice, however, correction processes can become automatic and be evident on implicit measures (e.g., Maddux, Barden, Brewer, & Petty, 2005). We portray this as an implicit measure because participants’ reports presumably reflect their aggressive feelings rather directly without much reflection. It is important to highlight that even with subliminally induced thoughts and an unrecognized source of validation (head nodding), the validation processes examined in this research still might occur deliberatively; participants might simply be unaware of the origin of their conscious thoughts or why they feel valid.

References Abelson, R. P., Aronson, E., McGuire, W. J., Newcomb, T. M., Rosenberg, M. J., & Tannenbaum, P. H. (1968). Theories of cognitive consistency: A sourcebook. Chicago: Rand McNally. Aronson, E. (1969). Cognitive dissonance: A current perspective. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 4, pp.€ 1–34). New York: Academic Press. Baker, S. M., & Petty, R. E. (1994). Majority and minority influence: Source-Â�position imbalance as a determinant of message scrutiny. Journal of Personality and Social Psychology, 67, 5–19. Banaji, M. R. (2004). The opposite of a great truth is also true. In J. T. Jost, M. R. Banaji, & D. A. Prentice (Eds.), Perspectivism in social psychology: The yin and yang of scientific progress (pp.€127–140). Washington, DC: American Psychological Association. Barden, J., Maddux, W. W., Petty, R. E., & Brewer, M. B. (2004). Contextual moderation of racial bias: The impact of social roles on controlled and automatically activated attitudes. Journal of Personality and Social Psychology, 87, 5–22. Bargh, J. A., Chaiken, S., Raymond, P., & Hymes, C. (1996). The automatic evaluation effect: Unconditional automatic attitude activation with a pronunciation task. Journal of Experimental Social Psychology, 32, 104–128. Betsch, T., Plessner, H., & Schallies, E. (2004). The value-Â�account model of attitude formation. In G. R. Maio & G. Haddock (Eds.), Contemporary perspectives on the psychology of attitudes (pp.€252–273). Hove, UK: Psychology Press. Blair, I. V. (2002). The malleability of automatic stereotypes and prejudice. Personality and Social Psychology Review, 6, 242–261. Blair, I. V., Ma, J. E., & Lenton, A. P. (2001). Imagining



18. Attitude Structure and Change

stereotypes away: The moderation of implicit stereotypes through mental imagery. Journal of Personality and Social Psychology, 81, 828–841. Briñol, P., & Petty, R. E (2003). Overt head movements and persuasion: A self-Â�validation analysis. Journal of Personality and Social Psychology, 84, 1123–1139. Briñol, P., & Petty, R. E. (2006). Fundamental processes leading to attitude change: Implications for cancer prevention communications. Journal of Communication, 56, 81–104. Briñol, P., & Petty, R. E. (2008). Embodied persuasion: Fundamental processes by which bodily responses can impact attitudes. In G. R. Semin & E. R. Smith (Eds.), Embodiment grounding: Social, cognitive, affective, and neuroscientific approaches (pp.€ 184–207). Cambridge, UK: Cambridge University Press. Briñol, P., & Petty, R. E. (2009). Persuasion: Insights from self-Â�validation hypothesis. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 41, pp.€69–118). New York: Elsevier. Briñol, P., Petty, R. E., & Barden, J. (2007). Happiness versus sadness as determinants of thought confidence in persuasion: A self-Â�validation analysis. Journal of Personality and Social Psychology, 93, 711–727. Briñol, P., Petty, R. E., & McCaslin, M. J. (2009). Changing attitudes on implicit versus explicit measures: What is the difference? In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€285–326). New York: Psychology Press. Briñol, P., Petty, R. E., & Tormala, Z. L. (2004). The selfÂ�validation of cognitive responses to advertisements. Journal of Consumer Research, 30, 559–573. Briñol, P., Petty, R. E., & Wheeler, S. C. (2006). Discrepancies between explicit and implicit self-Â�concepts: Consequences for information processing. Journal of Personality and Social Psychology, 91, 154–170. Cacioppo, J. T., Gardner, W. L., & Berntson, G. G. (1997). Beyond bipolar conceptualizations and measures: The case of attitudes and evaluative space. Personality and Social Psychology Review, 1, 3–25. Cacioppo, J. T., Marshall-Â�Goodell, B. S., Tassinary, L. G., & Petty, R. E. (1992). Rudimentary determinants of attitudes: Classical conditioning is more effective when prior knowledge about the attitude stimulus is low than high. Journal of Experimental Social Psychology, 28, 207–233. Cacioppo, J. T., & Petty, R. E. (1989). Effects of message repetition on argument processing, recall, and persuasion. Basic and Applied Social Psychology, 10, 3–12. Cacioppo, J. T., Priester, J. R., & Berntson, G. G. (1993). Rudimentary determinants of attitudes: II. Arm flexion and extension have differential effects on attitudes. Journal of Personality and Social Psychology, 65, 5–17. Chaiken, S. (1980). Heuristic versus systematic information processing in the use of source versus message quest in persuasion. Journal of Personality and Social Psychology, 39, 752–766. Chaiken, S. (1987). The heuristic model of persuasion. In M. P. Zanna, J. Olson, & C. P. Herman (Eds.), Social influence: The Ontario symposium (Vol. 5, pp.€3–39). Hillsdale, NJ: Erlbaum.

349

Chaiken, S., & Maheswaran, D. (1994). Heuristic processing can bias systematic processing: Effects of source credibility, argument ambiguity, and task importance on attitude judgment. Journal of Personality and Social Psychology, 66, 460–473. Chaiken, S., & Trope, Y. (1999). Dual-Â�process theories in social psychology. New York: Guilford Press. Cooper, J., & Fazio, R. H. (1984). A new look at dissonance theory. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 17, pp.€229–266). Orlando, FL: Academic Press. Dasgupta, N., & Asgari, S. (2004). Seeing is believing: Exposure to counterstereotypic women leaders and its effect on the malleability of automatic gender stereotyping. Journal of Experimental Social Psychology, 40, 642–658. Dasgupta, N., & Greenwald, A. G. (2001). On the malleability of automatic attitudes: Combating automatic prejudice with images of admired and disliked individuals. Journal of Personality and Social Psychology, 81, 800–814. Dasgupta, N., & Rivera, L. M. (2008). When social context matters: The influence of long-term contact and short-term exposure to admired outgroup members on implicit attitudes and behavioral intentions. Social Cognition, 26, 112–123. DeHart, T., & Pelham, B. W. (2007). Fluctuations in state implicit self-Â�esteem in response to daily negative events. Journal of Experimental Social Psychology, 43, 157–165. De Houwer, J. (2009). Comparing measures of attitudes at the functional and procedural level: Analysis and€ implications. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€361–390). New York: Psychology Press. de Liver, Y., van der Pligt, J., & Wigboldus, D. (2007). Positive and negative associations underlying ambivalent attitudes. Journal of Experimental Social Psychology, 43, 319–326. DeMarree, K. G., Briñol, P., & Petty, R. E. (2009). Embodied validation of subliminally primed mental contents. Unpublished manuscript. DeMarree, K. G., Wheeler, S. C., & Petty, R. E. (2005). Priming a new identity: Self-Â�monitoring moderates the effects of non-self primes on self-Â�judgments and behavior. Journal of Personality and Social Psychology, 89, 657–671. DeSteno, D., Dasgupta, N., Bartlett, M. Y., & Cajdric, A. (2004). Prejudice from thin air: The effect of emotion on automatic intergroup attitudes. Psychological Science, 15, 319–324. DeSteno, D., Petty, R. E., Wegener, D. T., & Rucker, D. D. (2000). Beyond valence in the perception of likelihood: The role of emotion specificity. Journal of Personality and Social Psychology, 78, 397–416. Dijksterhuis, A. (2004). I like myself but I don’t know why: Enhancing implicit self-Â�esteem by subliminal evaluative conditioning. Journal of Personality and Social Psychology, 86, 345–355. Dovidio, J., Kawakami, K., Johnson, C., Johnson, B., & Howard, A. (1997). The nature of prejudice: Automatic and controlled processes. Journal of Experimental Social Psychology, 33, 510–540.

350

IV. DOMAIN-SPECIFIC PERSPECTIVES

Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth, TX: Harcourt Brace Jovanovich. Elliot, A. J., & Devine, P. G. (1994). On the motivational nature of cognitive dissonance: Dissonance as psychological discomfort. Journal of Personality and Social Psychology, 67, 382–394. Fazio, R. H. (1995). Attitudes as object-Â�evaluation Â�associations: Determinants, consequences, and Â�correlates of attitude accessibility. In R. E. Petty & J. A. Krosnick (Eds.), Attitude strength: Antecedents and consequences (pp.€ 247–282). Mahwah, NJ: Erlbaum. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Fazio, R. H., & Olson, M. (2003). Implicit measures in social cognition research: Their meaning and uses. Annual Review of Psychology, 54, 297–327. Fazio, R. H., & Zanna, M. P. (1978). Attitudinal qualities relating to the strength of the attitude-Â�behavior relationship. Journal of Experimental Social Psychology, 14, 398–408. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior. Reading, MA: Addison-Â�Wesley. Fleming, M. A., & Petty, R. E. (2000). Identity and persuasion: An elaboration likelihood approach. In D. J. Terry & M. A. Hogg (Eds.), Attitudes, behavior, and social context: The role of norms and group membership (pp.€171–199). Mahwah, NJ: Erlbaum. Forehand, M. R., & Perkins, A. (2005). Implicit assimilation and explicit contrast: A set/reset model of response to celebrity voiceovers. Journal of Consumer Research, 32, 435–441. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. Gemar, M. C., Segal, Z. V., Sagrati, S., & Kennedy, S. J. (2001). Mood induced changes on the implicit association test in recovered depressed patients. Journal of Abnormal Psychology, 110, 282–289. Greenwald, A. G. (1968). Cognitive learning, cognitive response to persuasion, and attitude change. In A. Greenwald, T. Brock, & T. Ostrom (Eds.), Psychological foundations of attitudes (pp.€148–170). New York: Academic Press. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Greenwald, A. G., Poehlman, T. A., Uhlmann, E. L., & Banaji, M. R. (2009). Understanding and using the Implicit Association Test: III. Meta-Â�analysis of predictive validity. Journal of Personality and Social Psychology, 97, 17–41. Horcajo, J., Briñol, P., & Petty, R. E. (2009). Deliberative automatic attitude change in response to persuasive messages. Unpublished manuscript. Horcajo, J., Petty, R. E., & Briñol, P. (2009). The effects of minority influence on indirect automatic evaluations. Unpublished manuscript. Ito, T. A., Chiao, K. W., Devine, P. G., Lorig, T. S., &

Cacioppo, J. T. (2006). The influence of facial feedback on race bias. Psychological Science, 17, 256–261. Katz, I., & Hass, R. G. (1988). Racial ambivalence and American value conflict: Correlational and priming studies of dual cognitive structures. Journal of Personality and Social Psychology, 55, 893–905. Kawakami, K., Phills, C., Steele, J., & Dovidio, J. (2007). (Close) distance makes the heart grow fonder: Improving implicit attitudes and interracial interactions through approach behaviors. Journal of Personality and Social Psychology, 92, 957–971. Kawakami, K., Steele, J. R., Cifa, C., Phills, C. E., & Dovidio, J. F. (2008). Approaching math increases math = me and math = pleasant. Journal of Experimental Social Psychology, 44, 818–825. Kihlstrom, J. F. (2004). Implicit methods in social psychology. In C. Sansone, C. C. Morf, & A. T. Panter (Eds.), The Sage handbook of methods in social psychology (pp.€195–212). Thousand Oaks, CA: Sage. Killeya, L. A., & Johnson, B. T. (1998). Experimental induction of biased systematic processing: The directed-Â�thought technique. Personality and Social Psychology Bulletin, 24, 17–33. Klauer, K., Musch, C. J., & Eder, A. (2004, June). Response-Â�related priming and attitude-Â�related priming in the evaluative decision task. Paper presented at the Small Group Meeting of the European Association of Experimental Social Psychology on Conscious and Unconscious Attitudinal Processes, Madrid, Spain. Lowery, B. S., Hardin, C. D., & Sinclair, S. (2001). Social influence effects on automatic racial prejudice. Journal of Personality and Social Psychology, 81, 842– 855. Maddux, W. W., Barden, J., Brewer, M. B., & Petty, R. E. (2005). Saying no to negativity: The effects of context and motivation to control prejudice on automatic evaluative responses. Journal of Experimental Social Psychology, 41, 19–35. Maio, G. R., Bell, D. E., & Esses, V. M. (1996). Ambivalence and persuasion: The processing of messages about immigrant groups. Journal of Experimental Social Psychology, 32, 513–536. Martin, L. L. (2000). Moods do not convey information: Moods in context do. In J. P. Forgas (Ed.), Feeling and thinking: The role of affect in social cognition (pp.€153– 177). Cambridge, UK: Cambridge University Press. McConnell, A. R., Rydell, R. J., Strain, L. M., & Mackie, D. M. (2008). Forming implicit and explicit attitudes toward individuals: Social group association cues. Journal of Personality and Social Psychology, 94, 792–807. McGuire, W. J. (1981). The probabilogical model of cognitive structure and attitude change. In R. E. Petty, T. M. Ostrom, & T. C. Brock (Eds.), Cognitive responses in persuasion (pp.€291–307). Hillsdale, NJ: Erlbaum. McGuire, W. J. (1985). Attitudes and attitude change. In G. Lindzey & E. Aronson (Eds.), Handbook of social psychology (3rd ed., Vol. 2, pp.€233–346). New York: Random House. Moscovici, S., Mucchi-Faina, A., & Maass, A. (1994). Minority influence. Chicago: Nelson-Hall. Mugny, G., & Perez, J. A. (1991). The social psychology of minority influence. Cambridge, UK: Cambridge University Press.



18. Attitude Structure and Change

Olson, M. A., & Fazio, R. H. (2001). Implicit attitude formation through classical conditioning. Psychological Science, 12, 413–417. Olson, M. A., & Fazio, R. H. (2004). Reducing the influence of extra personal associations on the Implicit Association Test: Personalizing the IAT. Journal of Personality and Social Psychology, 86, 653–667. Petty, R. E. (1994). Two routes to persuasion: State of the art. In G. d’Ydewalle, P. Eelen, & P. Bertelson (Eds.), International perspectives on psychological science (Vol. 2, pp.€229–247). Hillsdale, NJ: Erlbaum. Petty, R. E., & Briñol, P. (2006a). A meta-Â�cognitive approach to “implicit” and “explicit” evaluations: Comment on Gawronski and Bodenhausen (2006). Psychological Bulletin, 132, 740–744. Petty, R. E., & Briñol, P. (2006b). Understanding social judgment: Multiple systems and processes. Psychological Inquiry, 17, 217–223. Petty, R. E., & Briñol, P. (2008). Persuasion: From single to multiple to meta-Â�cognitive processes. Perspectives on Psychological Science, 3, 137–147. Petty, R. E., & Briñol, P. (2009). Implicit ambivalence: A meta-Â�cognitive approach. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€ 119–161). New York: Psychology Press. Petty, R. E., Briñol, P., & DeMarree, K. G. (2007). The meta-cognitive model (MCM) of attitudes: Implications for attitude measurement, change, and strength. Social Cognition, 25, 657–686. Petty, R. E., Briñol, P., See, M., & Fleming, M. A. (2009). Watchdog motivation: A new look from the implicit ambivalence perspective. Unpublished manuscript. Petty, R. E., Briñol, P., & Tormala, Z. L. (2002). Thought confidence as a determinant of persuasion: The selfÂ�validation hypothesis. Journal of Personality and Social Psychology, 82, 722–741. Petty, R. E., Briñol, P., Tormala, Z. L., & Wegener, D. T. (2007). The role of meta-Â�cognition in social judgment. In E. T. Higgins & A. W. Kruglanski (Eds.), Social psychology: A handbook of basic principles (2nd ed., pp.€254–284). New York: Guilford Press. Petty, R. E., & Cacioppo, J. T. (1979a). Effects of forewarning of persuasive intent on cognitive responses and persuasion. Personality and Social Psychology Bulletin, 5, 173–176. Petty, R. E., & Cacioppo, J. T. (1979b). Issue-Â�involvement can increase or decrease persuasion by enhancing message-Â�relevant cognitive responses. Journal of Personality and Social Psychology, 37, 1915–1926. Petty, R. E., & Cacioppo, J. T. (1981). Attitudes and persuasion: Classics and contemporary approaches. Boulder, CO: Westview Press. Petty, R. E., & Cacioppo, J. T. (1984). The effects of involvement on response to argument quantity and quality: Central and peripheral routes to persuasion. Journal of Personality and Social Psychology, 46, 69–81. Petty, R. E., & Cacioppo, J. T. (1986). Communication and persuasion: Central and peripheral routes to attitude change. New York: Springer. Petty, R. E., & Cacioppo, J. T. (1990). Involvement and persuasion: Tradition versus integration. Psychological Bulletin, 107, 367–374.

351

Petty, R. E., Cacioppo, J. T., & Goldman, R. (1981). Personal involvement as a determinant of argumentbased persuasion. Journal of Personality and Social Psychology, 41, 847–855. Petty, R. E., DeMarree, K. G., Briñol, P., Horcajo, J., & Strathman, A. J. (2008). Need for cognition can magnify or attenuate priming effects in social judgment. Personality and Social Psychology Bulletin, 34, 900–912. Petty, R. E., Fazio, R. H., & Briñol, P. (Eds.). (2009a). Attitudes: Insights from the new implicit measures. New York: Psychology Press. Petty, R. E., Fazio, R. H., & Briñol, P. (2009b). The new implicit measures: An overview. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€3–18). New York: Psychology Press. Petty, R. E., Haugtvedt, C., & Smith, S. M. (1995). Elaboration as a determinant of attitude strength: Creating attitudes that are persistent, resistant, and predictive of behavior. In R. E. Petty & J. A. Krosnick (Eds.), Attitude strength: Antecedents and consequences (pp.€93–130). Mahwah, NJ: Erlbaum. Petty, R. E., & Krosnick, J. A. (Eds.). (1995). Attitude strength: Antecedents and consequences. Mahwah, NJ: Erlbaum. Petty, R. E., Ostrom, T. M., & Brock, T. C. (1981). Cognitive responses in persuasion. Hillsdale, NJ: Erlbaum. Petty, R. E., Schumann, D. W., Richman, S. A., & Strathman, A. J. (1993). Positive mood and persuasion: Different roles for affect under high and low elaboration conditions. Journal of Personality and Social Psychology, 64, 5–20. Petty, R. E., Tormala, Z. L., Briñol, P., & Jarvis, W. B. G. (2006). Implicit ambivalence from attitude change: An exploration of the PAST model. Journal of Personality and Social Psychology, 90, 21–41. Petty, R. E., & Wegener, D. T. (1998). Attitude change: Multiple roles for persuasion variables. In D. Gilbert, S. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (4th ed., Vol. 1, pp.€323–390). New York: McGraw-Hill. Petty, R. E., Wegener, D. T., & White, P. (1998). Flexible correction processes in social judgment: Implications for persuasion. Social Cognition, 16, 93–113. Pett, R. E., Wells, G. L., & Brock, T. C. (1976). Distraction can enhance or reduce yielding to propaganda: Thought disruption versus effort justification. Journal of Personality and Social Psychology, 34, 874–884. Petty, R. E., Wheeler, S. C., & Bizer, G. Y. (2000). Attitude functions and persuasion: An elaboration likelihood approach to matched versus mismatched messages. In G. R. Maio & J. M. Olson (Eds.), Why we evaluate: Functions of attitudes (pp.€ 133–162). Mahwah, NJ: Erlbaum. Petty, R. E., Wheeler, S. C., & Tormala, Z. L. (2003). Persuasion and attitude change. In T. Millon & M. J. Lerner (Eds.), Handbook of psychology: Volume 5. Personality and social psychology (pp.€353–382). Hoboken, NJ: Wiley. Priester, J. M., Cacioppo, J. T., & Petty, R. E. (1996). The influence of motor processes on attitudes toward novel versus familiar semantic stimuli. Personality and Social Psychology Bulletin, 22, 442–447.

352

IV. DOMAIN-SPECIFIC PERSPECTIVES

Priester, J. M., & Petty, R. E. (1995). Source attributions and persuasion: Perceived honesty as a determinant of message scrutiny. Personality and Social Psychology Bulletin, 21, 637–654. Puckett, J., Petty, R. E., Cacioppo, J. T., & Fisher, D. (1983). The relative impact of age and attractiveness stereotypes on persuasion. Journal of Gerontology, 38, 340–343. Richeson, J. A., & Ambady, N. (2003). Effects of situational power on automatic racial prejudice. Journal of Experimental Social Psychology, 39, 177–183. Rudman, L. A., Ashmore, R. D., & Gary, M. L. (2001). “Unlearning” automatic biases: The malleability of implicit prejudice and stereotypes. Journal of Personality and Social Psychology, 81, 856–868. Rydell, R. J., & McConnell, A. R. (2006). Understanding implicit and explicit attitude change: A systems of reasoning analysis. Journal of Personality and Social Psychology, 91, 995–1008. Rydell, R. J., McConnell, A. R., & Mackie, D. M. (2008). Consequences of discrepant explicit and implicit attitudes: Cognitive dissonance and increased information processing. Journal of Experimental Social Psychology, 44, 1526–1532. Rydell, R. J., McConnell, A. R., Strain, L. M., Claypool, H. M., & Hugenberg, K. (2007). Implicit and explicit attitudes respond differently to increasing amounts of counterattitudinal information. European Journal of Social Psychology, 37, 867–878. Sassenberg, K., & Moskowitz, G. B. (2005). Don’t stereotype, think different!: Overcoming automatic stereotype activation by mindset priming. Journal of Experimental Social Psychology, 41, 506–514. Schwarz, N., Bless, H., & Bohner, G. (1991). Mood and persuasion: Affective states influence the processing of persuasive communications. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 24, pp.€161–197). San Diego, CA: Academic Press.

Smith, S. M., Haugtvedt, C. P., & Petty, R. E. (1994). Need for cognition and the effects of repeated expression on attitude accessibility and extremity. Advances in Consumer Research, 21, 234–237. Stangor, C., Sechrist, G. B., & Jost, J. (2001). Social influence and intergroup beliefs: The role of perceived social consensus. In J. Forgas & K. D. Williams (Eds.), Social influence: Direct and indirect processes (pp.€235–252). Philadelphia: Psychology Press. Tesser, A. (1978). Self-Â�generated attitude change. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 11, pp.€289–338). New York: Academic Press. Tiedens, L. Z., & Linton, S. (2001). Judgment under emotional certainty and uncertainty: The effects of specific emotions on information processing. Journal of Personality and Social Psychology, 81, 973–988. Tormala, Z. L., Briñol, P., & Petty, R. E. (2007). Multiple roles for source credibility under high elaboration:€ It’s all in the timing. Social Cognition, 25, 536–552. Tormala, Z. L., Falces, C., Briñol, P., & Petty, R. E. (2007). Ease of retrieval effects in social judgment: The role of unrequested cognitions. Journal of Personality and Social Psychology, 93, 143–157. Walther, E. (2002). Guilty by mere association: Evaluative conditioning and the spreading attitude effect. Journal of Personality and Social Psychology, 82, 919–934. Wegener, D. T., & Petty, R. E. (1997). The flexible correction model: The role of naive theories of bias in bias correction. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 29, pp.€141–208). Mahwah, NJ: Erlbaum. Wells, G. L., & Petty, R. E. (1980). The effects of overt head movements on persuasion: Compatibility and incompatibility of responses. Basic and Applied Social Psychology, 1, 219–230.

C h a p t e r 19

Implicit Intergroup Bias Cognitive, Affective, and Motivational Underpinnings David M. Amodio and Saaid A. Mendoza

R

esearch on implicit race bias has led the surge in implicit social cognition research over the past 20 years, in part because it gives a distinctly social psychological face to an abstract cognitive construct. The domain of intergroup bias provides a unique context for the study of implicit social cognition that emphasizes the roles of cognition, affect, and motivation in coordinating social behavior. Furthermore, it connects the intrapersonal mechanisms of social cognition to dyadic, group, and societal level processes, thereby linking implicit cognition to social behavior. It is for this reason that studies of implicit race bias have been particularly influential in the development of theory and research in the field of implicit social cognition. Theories of intergroup relations have also benefited profoundly from implicit social cognition research. Several intergroup phenomena that had previously eluded theoretical explication, such as modern forms of racism, have been largely explained by models of implicit social cognition. In this way, theories and methods of implicit social cognition have contributed to our understanding of how prejudices and stereotypes are represented and expressed in behavior and how such behaviors are affected by intergroup dynamics. Hence, research on implicit race bias has sustained the interest of the field because, on one hand, it provides critical social context for the study of implicit pro

cesses and, on the other hand, it has provided an expanded theoretical approach to social behavior in intergroup relations. In this chapter, we review major findings and theoretical perspectives in the area of implicit intergroup bias. The structure of this chapter follows from the two major types of questions addressed by research on implicit racial bias: (1) How are implicit biases represented in the mind? and (2) How are implicit biases expressed in behavior? We begin our discussion of these questions with a brief review of the field’s theoretical origins and descriptions of some key terms used in the literature. Next, we review major findings in the contemporary literature on implicit race bias, focusing on how implicit biases are expressed in behavior and how these expressions may be changed. We then describe two major theoretical approaches to accounting for the phenomenon of implicit racial bias and conclude with a discussion of some remaining questions and controversies in the field. Our goal is to orient the reader to the basic findings in the literature on implicit race bias and to provoke thought on the larger theoretical issues and pressing challenges in this area of research. Finally, although we focus primarily on implicit biases regarding African Americans (the main historical target of intergroup discrimination in America), the processes discussed in this chapter refer to general mechanisms of cognition, affect, 353

354

IV. DOMAIN-SPECIFIC PERSPECTIVES

and motivation, and so the major themes we discuss should apply broadly to implicit cognitive processes concerning other social groups.

ORIGINS OF RESEARCH ON€IMPLICIT RACE BIAS Early interest in implicit racial bias grew out of concerns that self-Â�report questionnaires did not always capture people’s true attitudes toward members of racial outgroups. Although the mismatch of word and deed toward a social outgroup is a phenomenon that likely spans the ages, it has gained the attention of social scientists only recently with the emergence of social psychology (Allport, 1954; LaPierre, 1934). An early experimental demonstration of this phenomenon by Rankin and Campbell (1955) showed that, although white participants reported similarly positive attitudes toward the white and black experimenters in their study, their physiological responses revealed greater autonomic arousal when they were touched by the black experimenter (ostensibly to check their pulse) compared with the white experimenter. This early report of an implicit racial outgroup bias was followed by a series of studies showing a similar pattern of divergence between implicit and explicit responses (Crosby, Bromley, & Saxe, 1980). Why did the subjects’ self-Â�reported attitudes not match their physiological reaction to race? Some researchers suggested that post-civil rights era norms proscribing prejudice led respondents to conceal their biases (Crosby et al., 1980; Rankin & Campbell, 1955; Sigall & Page, 1971). Others proposed that people were simply unaware of their biases (Devine, 1989). The bottom line was that much of people’s intergroup behavior was not accounted for by their self-Â�reported attitudes and beliefs. This discordance between self-Â�reports and behavior raised a number of profound questions for social psychologists. Were people’s “true” racial attitudes residing somewhere in the unconscious, hidden from introspection? To others, it was a slightly different question: To what extent do explicit versus implicit forms of bias predict behaviors in different situations? At a more practical level, these developments highlighted the need for new methods capable of assessing implicit forms of bias, an endeavor that has had major implications for theoretical developments in this area of research. Like most great ideas in science, contemporary ideas about automatic and implicit processes emerged in the minds of several different scientists

working in different areas of psychology in the 1970s and 1980s. In particular, research on how concepts are learned and stored within semantically related categories suggested that the categorical processing of social information may operate automatically (e.g., Meyer & Schvaneveldt, 1971, 1976, but noted years earlier by Allport, 1954). Interest in category processing led to methodological innovations such as the sequential semantic priming technique, which allowed scientists to assess the strength of implicit associations without having to rely on people’s deliberative responses, such as with self-Â�reports (Meyer & Schvaneveldt, 1971; Neely, 1977). In a different literature, memory researchers had discovered dissociations between episodic (explicit) and procedural (implicit) forms of memory (Cohen & Squire, 1980; Graf & Schacter, 1985; Jacoby & Witherspoon, 1982), which suggested dissociable underlying systems for implicit and explicit processes. In yet another literature, research on human factors examined the degree to which a choice or motor response involved automatic (parallel) versus controlled (serial) processing (Posner & Snyder, 1975; Shiffrin & Schneider, 1977). Finally, Langer’s (1975; Langer & Abelson, 1972) research on the role of mindlessness in social behavior demonstrated how automatic responses could be triggered and implemented by situational cues with little conscious intervention. Although these incipient perspectives on implicit cognition (and implicit social cognition) had roots in much earlier theorizing (Allport, 1954; Freud, 1930; James, 1890), they represented a new age of theoretical and methodological sophistication. Together, these converging lines of research set the stage for the emergence of implicit social cognition, which, in turn, provided the backdrop for modern research on implicit intergroup bias. Social psychologists applied these early advances in implicit and automatic forms of cognition to questions about person memory, social judgments, and social behavior (Bargh & Pietromonaco, 1982; Smith & Miller, 1979; Srull & Wyer, 1980), including questions about social stereotypes (Gaertner & McLaughlin, 1983; Taylor, Fiske, Etcoff, & Ruderman, 1978). A seminal series of studies by Gaertner and McLaughlin (1983) first demonstrated the implicit priming of racial stereotypes, such that participants categorized African American stereotype words more quickly when they were paired with the group label “NEGRO” than the label “WHITE” (see also Dovidio, Evans, & Tyler, 1986; Perdue, Dovidio, Gurtman, & Tyler, 1990; Perdue & Gurtman, 1990). On the basis of these findings, researchers posited that stereotypic be-



19. Implicit Intergroup Bias

liefs were represented in the mind in a semantic network. Interestingly, however, the degree of bias on priming tasks was often unrelated to subjects’ self-Â�reported racial attitudes and beliefs. As evidence for implicit racial associations accumulated, researchers puzzled over their theoretical significance and struggled with the fact that implicit assessments were typically not correlated with self-Â�reported attitudes and beliefs. Devine’s (1989) landmark paper on the automatic and controlled components of stereotyping and prejudice provided an important theoretical solution to this puzzle. In it, she proposed that reaction time assessments reflected automatic processing of passively learned stereotypic associations, whereas self-Â�report measures typically reflected intentionally endorsed beliefs. In a set of three studies, Devine demonstrated that high- and low-Â�prejudice subjects held similar knowledge of African American stereotypes, and that, regardless of their explicit beliefs about blacks’ civil rights, subliminal priming of the stereotyped category would cause people to judge new individuals in a stereotypeÂ�consistent fashion. However, when subjects were aware that their responses could be influenced by race, they controlled their responses to reflect their explicit beliefs rather than their automatic stereotyping tendencies. That is, low-Â�prejudice subjects chose not to endorse racial stereotypes, whereas high-Â�prejudice subjects did. These findings supported the idea that shared cultural knowledge of stereotypes predisposed all members of a culture to automatic stereotyping tendencies, but that lowÂ�prejudice individuals will replace these tendencies with belief-based egalitarian responses when they have sufficient cognitive resources. With the theoretical scaffolding of Devine’s (1989) dissociation model in place, researchers began to develop new methods for assessing one’s degree of implicit racial bias (Fazio, Jackson, Dunton, & Williams, 1995; Greenwald, McGhee, & Schwartz, 1998). Much of this work focused on the circumstances in which implicit and explicit measures of racial bias did or did not correspond (Blair, 2001; Nosek et al., 2007). Other research examined the extent to which implicit measures predicted bias in social behavior, such as in anticipated or actual interracial interactions (Dovidio, Kawakami, Johnson, Johnson, & Howard, 1997; Fazio et al., 1995; McConnell & Leibold, 2001). In general, the domain of intergroup bias has provided a unique context for studying implicit processes because it examines these processes as they relate to social behavior, interpersonal interactions, and group dynamics. Hence, the findings from implicit

355

race bias research have addressed questions of intergroup bias while advancing our understanding of more general aspects of implicit social cognition.

DEFINITIONS AND USAGE Before proceeding with our review, it is worthwhile to define our terms. In particular, the terms implicit and explicit have been used to refer to a range of constructs, and they are sometimes confused with the constructs of automaticity and control. Similarly, “implicit” is often ascribed to different experimental tasks, yet it is sometimes unclear just how a task might be implicit. To clarify such issues at the outset, we provide our definitions of key terms (although we acknowledge that other researchers may prefer alternative definitions).

Implicit versus Explicit In line with the literature on learning and memory that forms the foundation of modern implicit social cognition, we use the terms implicit and explicit to refer to one’s level of awareness of a particular psychological process (Jacoby & Witherspoon, 1982; Schacter, 1987; Squire, 1986). That is, an explicit process can be consciously detected and reported (regardless of whether it was triggered spontaneously). Any process that is not explicit is referred to as implicit. Hence, “implicit” describes a process that cannot be directly inferred through introspective awareness (Greenwald & Banaji, 1995; Wilson, Lindsey, & Schooler, 2000).

Automatic versus Controlled The terms implicit and explicit are distinguishable from automatic and controlled. In line with classic work on automaticity and control, control refers to an intentional regulative process and automatic to an unintentional process (Posner & Snyder, 1975; Shiffrin & Schneider, 1977). Controlled processes are typically goal directed, whereas automatic processes may be triggered spontaneously by external cues (see Bargh, 1994, for a more detailed analysis of automaticity). The regulative nature of control refers to the process of overriding a prepotent tendency or favoring one particular response over another. Control does not relate to content per se, such as an explicit belief, but rather to the deliberate adjudication of an endorsed response over a different, undesired response. Importantly, the automatic–Â�controlled nature of a response is inde-

356

IV. DOMAIN-SPECIFIC PERSPECTIVES

pendent of its implicit–Â�explicit nature (although some features of automaticity and implicitness may tend to coincide).

“Implicit” Tasks A particular task may be designed to assess the expression of automatic (vs. controlled) or implicit (vs. explicit) processes in behavior. Responses on such tasks constitute an observable behavior from which an implicit or automatic process may be inferred, even if the response itself is explicit or involves control, as in the Implicit Association Test (IAT; Greenwald et al., 1998). Because a behavioral response reflects a combination of automatic and controlled processes, and the response can usually be perceived explicitly, it would be inaccurate to describe any particular task or behavior as implicit (Jacoby, 1991; Payne, 2001). Rather, it is the influence of an underlying association on behavior that may be implicit, and this influence is the critical inference made from such task responses (Payne, 2008). This issue arises later in this chapter as we discuss interpretations of behavioral tasks used to infer implicit forms of racial bias. In this chapter, we use the colloquialism of “implicit task” or “implicit measure” to describe a behavior-based procedure for inferring a pattern of implicitly biased behavior. When changes in performance are observed, it is important to refer to it as a change in the expression of implicit bias rather than a change in an underlying bias per se, given that a change in behavior may or may not reflect a change in underlying mental structures.

Implications of Usage At a broader level of analysis, the distinction between implicit–Â�explicit and automatic–Â�controlled processes has important implications for the psychological questions under investigation. The terms implicit and explicit describe the property of awareness, and thus these terms are particularly relevant to questions about attribution, mental representation, self-Â�reflection, and person perception, but not as relevant to issues concerning action. By contrast, the terms automatic and controlled describe a property of an action, which has particular relevance to questions about goals, motivation, and behavior, but with less direct relevance to mental representation and person perception. Indeed, a difference in emphasis can be seen in the research literature, where some research is focused on identifying and characterizing the mental representation of implicit bias (Gawronski & Bodenhausen,

2006; Sherman, 1996) and other research focuses on the role of implicit bias in behavior (Amodio & Devine, 2006; Dovidio, Kawakami, & Gaertner, 2002; Payne, 2005). Thus, precision in the use of these terms is necessary because they refer to different psychological questions.

THE PHENOMENON OF IMPLICIT RACE BIAS The seminal work of Gaertner and McLaughlin (1983) and Devine (1989) prompted an explosion of studies on the basic phenomenon of implicit race bias. Much of this work has been descriptive. That is, the idea that people could possess unconscious intergroup biases was novel and fascinating in itself. As a result, much attention turned to documenting this phenomenon using an array of “implicit” tasks (Blair, 2001). Throughout this work, the chief defining characteristic of implicit racial biases was a dissociation with explicit measures of racial attitudes and beliefs (e.g., Devine, 1989; Gaertner & McLaughlin, 1983; see also Greenwald & Banaji, 1995; Wilson et al., 2000). In this section, we provide a selective review of the major types of implicit bias phenomenon that have been studied in the literature.

Implicit Stereotyping Initial studies of implicit bias examined racial stereotypes, inspired by questions about the changing nature of stereotypes over time (Karlins, Coffman, & Walters, 1969). In the first demonstration of implicit stereotyping, described previously, Gaertner and McLaughlin (1983) found that African American stereotypic words were categorized more quickly when primed by labels of the social group. They interpreted this effect as evidence that the prime and target words were included within a common semantic network and used the degree of stereotype-Â�consistent response facilitation to estimate a particular subjects’ degree of bias. As personal computers became more common in the laboratory, researchers increasingly used sequentially primed lexical decision tasks, in which a prime word quickly preceded the presentation of the target word on the computer screen and responses were made on the computer keyboard (e.g., Macrae, Bodenhausen, & Milne, 1995; Macrae, Stangor, & Milne, 1994; Spencer, Fein, Wolfe, Fong, & Dunn, 1998; Wittenbrink, Judd, & Park, 1997). For example, Wittenbrink and colleagues (1997) used a primed lexical decision task to ex-



19. Implicit Intergroup Bias

amine positive and negative stereotypes of black and white Americans (see Wentura & Degner, Chapter 6, this volume). As with Gaertner and McLaughlin (1983), the logic was that if the prime and target were represented in the same mental category, activation of the prime should enhance accessibility of the target, thereby speeding one’s lexical judgment. The authors found that the black prime significantly speeded the categorization of negative African American stereotype words relative to all other targets, whereas the white prime speeded categorization of white positive stereotype words. An advantage of the lexical decision paradigm is that it appears to provide a relatively straightforward assessment of the strength of semantic associations. Dovidio and his colleagues (e.g., 1986, 1997) took a slightly different approach to assessing stereotype associations. In the general version of their paradigm, primes consisting of white or black faces or group labels are presented very quickly and then replaced by a target stimulus. Target stimuli consist of trait adjectives that could apply to either a person or a nonsocial object (e.g., a house), and subjects are told to categorize each target adjective according to whether it “could ever be true” or “is never true” of people (or of houses, in other blocks of trials). This task is notable because the instructions place subjects in the mindset of making social judgments, which may be more in line with real-life social situations than the relatively decontextualized word–Â�nonword judgments made in basic lexical decision paradigms. Several other variations of the semantic priming paradigm have been used to assess implicit stereotypes. Examples have included a primed word pronunciation task (Kawakami, Dion, & Dovidio, 1998); primed word fragment completion (Gilbert & Hixon, 1991; Spencer et al., 1998); stereotypeÂ�naming Stroop task (Kawakami, Dovidio, Moll, Hermsen, & Russin, 2000); and the IAT (see later discussion for more detail; Amodio & Devine, 2006; Rudman, Greenwald, & McGhee, 2001). A related set of paradigms have examined weapons bias, whereby a white American subject is quicker to identify a gun correctly and more likely to misidentify a hand tool as a gun when primed by a black face than a white face (Lambert et al., 2003; Payne, 2001, 2005; Payne, Lambert, & Jacoby, 2002). A variation on the weapons identification task is the Shooter task, in which subjects must quickly choose to “shoot” or “not shoot” male targets who are holding guns or innocuous objects (Correll, Park, Judd, & Wittenbrink, 2002, 2007). White and black Americans alike tend to show a

357

similar pattern of bias, such that they are quicker to “shoot” armed blacks than whites and more likely to erroneously shoot unarmed blacks than unarmed whites. The basic logic behind the range of implicit stereotyping tasks is the same, in that they assume that the racial prime activates elements of the stereotype in one’s mind, and that the heightened accessibility of the stereotype facilitates the processing of a stereotype-Â�related target (while inhibiting the processing of stereotype-Â�incongruent targets). Across studies and paradigms of American research subjects, a relatively consistent pattern of findings has demonstrated an association of black people with negative African American stereotypes. This association is considered to be implicit because responses on the task are either too fast for conscious deliberation or, in some cases, the group prime is presented so quickly that it cannot be consciously perceived. Stereotype-Â�facilitated responses in these studies may also be considered automatic because they are initiated without awareness or intent, and they appear to operate in the absence of intentional control. Finally, implicit responses tend to be uncorrelated with explicit racial attitudes and endorsed racial stereotypes, yet they are sometimes associated with knowledge of stereotypic beliefs held by one’s society (Correll et al., 2002; Devine, 1989).

Implicit Evaluative Bias Whereas implicit stereotyping research emerged from the traditional literature on intergroup stereotyping and prejudice, interest in implicit racial evaluations emerged primarily from the attitudes literature in social psychology. According to the traditional tripartite model of attitudes, an attitude (or evaluation) is a favorable/unfavorable assessment of an object that reflects cognitive, affective, and behavioral processes (Eagly & Chaiken, 1998). Importantly, the cognitive component may refer to a semantic association between the object and the concept of good or bad (much like a stereotypic association), whereas the affective component refers to the aroused affective response associated with the object. It is notable that attitudes research in social psychology has focused primarily on the cognitive component of attitudes and evaluations, in both its theoretical models and its measures (Breckler, 1984). This is especially true in the implicit social cognition literature, in which measures of implicit attitudes typically rely on semantic judgments, with little attention given to the measurement of aroused affective responses.

358

IV. DOMAIN-SPECIFIC PERSPECTIVES

For this reason, our review of implicit racial evaluation focuses on measures that appear to tap into the cognitive (or semantic) component of an attitude. Implicit affective forms of racial bias are then addressed in the following section. According to representational accounts, an implicit racial evaluation reflects a semantic association between an attitude object (e.g., a member of a racial group) and general concepts of good versus bad (Fazio, 2007) or, alternatively, the net valence of semantic associations with the attitude object (Gawronski & Bodenhausen, 2006). In both cases, the primed activation of an attitude object should increase the accessibility of associated good/bad concepts. Building on the principle of evaluative networks, Fazio and his colleagues developed a sequential priming technique to measure the degree to which an attitude object facilitates responses to positive versus negative words (Fazio, Chen, McDonel, & Sherman, 1982; Fazio, Powell, & Herr, 1983; see Wentura & Degner, Chapter 6, this volume). Faster categorizations of positive words compared with negative words following the presentation of the attitude object would suggest an implicit positive evaluation, or attitude. It is notable that alternative theoretical accounts have been proposed to explain evaluative priming effects on such measures (for a review, see Klauer & Musch, 2003). The representational account is that the prime increases accessibility of the target, via a semantic network, which speeds the mental processing of the target. By this account, priming of a negative attitude object, such as spider, would raise the accessibility of all negative attitude objects in one’s mind, making it easier to then process a negative target word than a positive word. An alternative explanation is that the prime activates a valence-Â�congruent response, which is in line with a valence-Â�consistent target word but inconsistent with a valence-Â�inconsistent word. By this account, priming of the word spider would set a negative categorization response in motion. The categorization of a negative target word would be facilitated because the congruent response was already activated. The difference between these accounts concerns whether priming effects occur at the level of mental representations or actions. To measure implicit responses to racial groups as the attitude objects, Fazio and colleagues (1995) designed a computerized priming task in which black or white faces were presented as primes for 315 msec, followed by a blank screen (135 msec) and then either a positive or negative adjective presented as the target. Subjects were instructed

to categorize target words as good or bad as quickly as possible via button press. Responses on this task were considered to be implicit because the short stimulus-onset asynchrony (450 msec) made it difficult to deliberate on the association between the prime and target. Fazio and colleagues found a pattern of race-Â�biased responses among both white and black subjects in their studies. White subjects responded most quickly to positive adjectives following white face primes, showing an implicit pro-Â�ingroup bias. Black subjects responded most quickly to negative targets following white face primes, showing an implicit anti-Â�outgroup bias. Importantly, among white subjects, the magnitude of bias was uncorrelated with responses on the Modern Racism Scale (MRS; McConahay, 1986), an explicit measure of prejudiced beliefs. However, Fazio and colleagues (Study 4) found that, among participants reporting low motivation to control prejudice, stronger implicit bias was correlated with more prejudiced racial attitudes. Since its introduction, the IAT has become a very popular method for assessing implicit evaluations (Greenwald et al., 1998). The IAT is a dualÂ�categorization task in which participants categorize words as pleasant or unpleasant and faces as either black or white by pressing one of two keys on the computer keyboard (see Teige-Â�Mocigemba, Klauer, & Sherman, Chapter 7, this volume). On bias-Â�compatible blocks of the IAT, participants must classify white faces and positive words with one response key and black faces and negative words with the other. A person with a strong antiblack or pro-white bias should find these trials easy and perform them quickly. On bias-Â�incompatible blocks, these pairings are reversed, such that white faces and negative words are classified with one key and black faces and positive words are classified with the other. A person with an anti-black or prowhite bias should find these trials to be difficult and perform them more slowly. Evaluative bias is characterized by faster responses on compatible blocks than incompatible blocks. The “IAT effect”—the difference in response latencies for incompatible minus compatible blocks—Â�reflects two processes: (1) the ease with which bias-Â�consistent responses are made (i.e., the strength of an automatic association) and (2) the difficulty with which a biasÂ�inconsistent response is made (i.e., the extent to which controlled processing is needed). Thus, the IAT effect represents a combination of automatic and controlled processing (see also Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005). However, because the source of automaticity and the need for control are presumably unconscious



19. Implicit Intergroup Bias

and unintentional, the IAT effect is considered to represent an implicit bias. Payne, Cheng, Govorun, and Stewart’s (2005) affect misattribution procedure (AMP) assesses implicit evaluative bias through self-Â�reported judgments, in contrast to the more typical reaction time–based procedures. In the AMP, subjects view a prime picture of the attitude object (e.g., a black vs. white face), which they are typically instructed to ignore. Next, an unfamiliar target picture (e.g., a Chinese pictograph) is presented. Participants must then evaluate the target picture as pleasant or unpleasant in a forced dichotomous choice. Payne and colleagues observed that, across trials, target pictures were evaluated more negatively following black face primes than white face primes. The AMP is unique because it assesses implicit evaluative bias using a self-Â�report format, which lends itself to much higher interitem reliability scores than reaction time assessments. The task may be considered implicit because subjects are unaware of exactly how their response to the prime might influence their evaluation of the target. As such, Payne and colleagues have used the AMP to underscore the theoretical point that “implicit” refers to awareness of how a bias influences a response rather than to the experience of bias or to the response itself. It is notable that the measures of evaluative bias reviewed here are sometimes described as assessing affect. However, it remains to be determined whether such measures are able to pick up on the affective component of an evaluation, which is typically marked by some degree of autonomic arousal, in addition to cognitive aspects of an evaluation. Additional research will be needed to determine the extent to which such measures of evaluative race bias are driven by aroused affective reactions or cognitive associations pertaining to emotional appraisals. This distinction becomes critical when considering the underlying neurocognitive mechanisms of bias measured by the task, described in the Memory Systems Model of Implicit Bias section.

Implicit Affective Bias Just as research on explicit intergroup bias suggests a distinction between cognitive and affective forms of bias (e.g., Dovidio, Brigham, Johnson, & Gaertner, 1996; Judd & Park, 1993), researchers have attempted to distinguish between semantic (or conceptual) and affective forms of implicit bias (Amodio & Devine, 2006; Wittenbrink, Judd, & Park, 1997, 2001). However, affect has been a dif-

359

ficult construct to capture in modern social cognition research, particularly when responses are measured using self-Â�report or behavioral assessments involving semantic judgments (Breckler, 1984). That is, cognitive and affective processes typically operate in concert, and the degree to which each contributes to a response is very difficult to determine. Yet, as noted, a key defining characteristic of an affective response is autonomic arousal. To the extent that word categorizations on a priming task occur with little arousal, it is difficult to interpret them as “affective.” What, then, is the role of affect in implicit bias? In light of these issues, Amodio, Harmon-Jones, and Devine (2003) sought to examine affective processes associated with implicit race bias in a way that could be dissociated from semantically driven evaluations. Recent advances in the neuroscience literature suggested that subcortical brain structures such as the amygdala were specifically involved in affective responses to threatening stimuli. Importantly, the brain regions involved in this type of affective response were different from those involved in semantic processing. Amodio and colleagues proposed that a neuroscience approach could be used to identify affective forms of implicit bias that were independent of semantically driven evaluative and stereotypic associations. To this end, they used an emotion-Â�modulated startleÂ�eyeblink assessment of amygdala activity (Lang, Bradley, & Cuthbert, 1990). When a person is startled (e.g., by a loud noise), they show a whole-body startle reflex. One component of this reflex is the defensive eyeblink. This blink response is larger when a person is in an aversive state just before being startled but smaller when in an appetitive state just before being startled, an effect mediated by amygdala inputs to the reflexive blink circuit (Davis, 1992). Thus, a magnified blink reflects an aroused aversive response (and greater amygdala activity) to a stimulus preceding the startling event, whereas an attenuated blink reflects an aroused appetitive state (and lower amygdala activity). Amodio and colleagues (2003) chose to use the startle-Â�eyeblink measure because it could assess changes in amygdala activity within a few hundred milliseconds after the presentations of an ingroup versus outgroup face. By comparison, functional magnetic resonance imaging (fMRI) methods at that time could only measure slow shifts in brain activity across long blocks of trials (e.g., Phelps et al., 2000). Furthermore, the startle-Â�eyeblink method measures amygdala activity associated specifically with an aroused affective state, given that the startle reflex is modulated via

360

IV. DOMAIN-SPECIFIC PERSPECTIVES

the central nucleus of the amygdala, which activates autonomic responses (LeDoux, 2000). By contrast, current fMRI methods cannot distinguish between the activity of amygdala subnuclei and thus cannot clearly assess a response related to aroused affect. Amodio and colleagues (2003) observed larger startle-�eyeblink amplitudes to black versus white faces, indicating a negative affective response to blacks among white participants, on average. The degree of affective bias was unrelated to self�reported racial attitudes (assessed by the Attitudes Toward blacks scale; Brigham, 1993). These findings provided evidence of a rapidly activated and implicit form of affective bias. This general pattern of biased amygdala activity toward outgroups has been conceptually replicated in several studies (e.g., Cunningham et al., 2004; Lieberman, Hariri, Jarcho, Eisenberger, & Bookheimer, 2005; Wheeler & Fiske, 2005). However, as noted, it is unclear whether fMRI assessments of amygdala activity during passive face-�viewing tasks can probe affective responses as effectively as the startle-�eyeblink procedure. Although neural assessments allow researchers to probe the psychological mechanisms of affective bias, several researchers have used peripheral physiology measures to index intergroup affect (see Guglielmi, 1999, for a review). Following the tradition of Rankin and Campbell (1955), Vanman, Paul, Ito, and Miller (1997) used electromyography (EMG) to measure subtle changes in facial muscles associated with frowning and smiling at ingroup versus outgroup faces. Although white participants who reported either high or low levels of prejudice on the MRS provided equally high ratings of perceived friendliness for white and black people in the pictures, facial EMG measures revealed more negativity toward black faces among the high-�prejudice participants (Study 3). Mendes, Blascovich, and their colleagues have measured patterns of cardiovascular responding in intergroup interactions and have observed greater threat-�related patterns of activity toward outgroup members that may be characteristic of implicit affective responses (Blascovich, Mendes, Hunter, Lickel, & Kowai-Bell, 2001; Mendes, Blascovich, Lickel, & Hunter, 2002). Finally, it is notable that research using event-�related potential (ERP) methods suggest that racial and gender categorizations may be made in as little as 200 msec following face presentation (Ito & Urland, 2003), but it is unclear whether this effect reflects an affective or semantic process. Interest in the affective component of implicit race bias has grown in recent years, and

we expect to see major advances in the future as researchers develop new methods for assessing affective responses.

EFFECTS OF IMPLICIT BIAS ON€BEHAVIOR A large accumulation of research findings attests to the existence of an implicit racial bias. But does the phenomenon of implicit race bias have any real significance for social behavior? One can argue that implicit bias is only a problem to the extent that it influences behavior and leads to discrimination. Whereas most research has focused on documenting intrapersonal forms of implicit bias and exploring the conditions under which it does or does not correspond with explicit measures, attention has increasingly turned toward understanding how such biases may be expressed in behavior (Dasgupta, 2004). In early studies of implicit bias, the focus was on behavioral expressions. For example, Devine (1989) showed that stereotypes, when implicitly activated, could color judgments of a raceÂ�unspecified target person. Fazio and colleagues (1995) went a step further by examining white subjects’ behavior toward a black female experimenter. Subjects with stronger evaluative bias on the computerized priming task showed more uncomfortable nonverbal behaviors during the interaction. However, neither implicit bias nor nonverbal discomfort was associated with explicit racial beliefs or judgments. Similar results were obtained by Dovidio and colleagues (1997, 2002), who showed that a subliminally primed measure of implicit evaluative bias predicted more anxious and less friendly nonverbal behaviors during an interracial interaction, but that these responses were unrelated to explicit racial attitudes. Other studies have shown that implicit evaluative bias predicts greater personal distance from an outgroup member (Amodio & Devine, 2006; McConnell & Liebold, 2001) and that greater implicit stereotyping is associated with a reluctance to engage with an outgroup member in an interaction (Sekaquaptewa, Espinoza, Thompson, Vargas, & von Hippel, 2003).

Expressions of Bias: Hostility or Anxiety? Implicit race bias is often thought of as the nonconscious analogue of overt antipathy, and, therefore, one might expect implicit bias to be ex-



19. Implicit Intergroup Bias

pressed in hostile acts toward outgroup members. However, studies of implicit bias effects on behavior have not shown evidence for the antipathy hypothesis. Rather, implicit evaluative bias tends to be expressed as anxiety and discomfort (Dovidio et al., 1997, 2002; Fazio et al., 1995; Trawalter & Shapiro, Chapter 20, this volume). More recent work suggests that this discomfort is often interpreted as unfriendliness by one’s interaction partner, which may then perpetuate into the reciprocation of hostility (Pearson et al., 2008; West, Shelton, & Trail, 2009). Other research suggests that when individuals with high levels of implicit evaluative bias become aware that they possess a negative outgroup bias, they tend to exert stronger regulatory efforts to counteract any implicit biases, acting with greater care and increased friendliness (Monteith, Voils, & Ashburn-Nardo, 2001; Shelton, Richeson, Salvatore, & Trawalter, 2005). Hence, the way in which implicit evaluations and stereotypes are expressed in behavior is often complex and, therefore, very difficult to study. When considering the expression of implicit bias as discomfort versus antipathy, it is useful to consider that negative implicit associations with racial outgroups could reflect several different types of reactions (see Olson & Fazio, 2004). For example, an outgroup face may be a source of anxiety to a research participant rather than a target of antipathy. This anxiety could stem from perceptions of threat from outgroup members or from the concern of appearing racist on the task (as in Frantz, Cuddy, Burnett, Ray, & Hart, 2004). Outgroup faces may even automatically trigger egalitarian responses, such as sympathy, yet still produce a negative bias because of the oppression and maltreatment that is associated with low-Â�status groups (Uhlmann, Brescoll, & Paluck, 2006). Indeed, most research subjects are university students who tend to hold progressive egalitarian values. For these subjects, then, implicit bias stemming from any source (threat, anxiety, or sympathy) should correspond to uncomfortable feelings during the interaction. In this case, measures of implicit bias would predict discomfort, inhibition, and avoidance behavior rather than hostility. More research is needed to determine the situations in which implicit bias may be expressed as discomfort versus hostility.

Expressions of Implicit Stereotyping versus Implicit Evaluative Bias Whereas past research has dissociated the effects of implicit and explicit forms of race bias on dif-

361

ferent types of behaviors, Amodio and Devine (2006) examined the differential effects of evaluative versus stereotyping forms of implicit bias on behavior. On the basis of neuroscience models of learning and memory, they proposed that implicit evaluative bias was largely driven by affective systems, which are expressed through basic-level behavioral channels such as nonverbal behaviors and anxiety-Â�related responses. By contrast, they proposed that implicit stereotypes are driven by semantic memory systems, which are expressed primarily in higher level judgments and goals, such as trait impressions and plans for interacting with an outgroup member. In their studies, white subjects completed measures assessing implicit evaluative associations (pleasant/unpleasant associations unrelated to stereotype content) or stereotypic associations (in which evaluative content was controlled). Indeed, these measures of implicit evaluative bias and implicit stereotyping were independent. More importantly, they were uniquely predictive of these different classes of behavior toward a black student. For example, more negative implicit evaluation scores predicted further seating distance from a black study partner, whereas implicit stereotyping predicted subjects’ expectations that their black partner would succeed on measures of academic ability (vs. nonacademic abilities). Amodio and Devine suggested that a consideration of the distinct affective and semantic systems underlying different forms of implicit bias would permit a more refined model of how implicit biases may be expressed in behavior. Understanding how implicit biases are expressed in behavior is arguably the most important question in implicit race bias research today. Although this topic has received disproportionately little attention in the past (in part because of the challenges associated studying real intergroup social behavior), researchers are increasingly focusing on this issue. In the end, theories of how racial biases are represented inside the head matter only to the extent that they influence behavior (Amodio & Devine, 2005). Therefore, a better understanding of how implicit bias is expressed in social behavior will be critical for validating the theoretical models of implicit intergroup bias that are dominant in the extant literature.

MODERATORS OF IMPLICIT BIAS A major goal of intergroup bias researchers is to develop methods for reducing prejudice. The discovery of implicit forms of racial bias raised a new

362

IV. DOMAIN-SPECIFIC PERSPECTIVES

and formidable challenge to this goal: The automaticity of implicit bias seemed to imply that its application was inevitable. Indeed, some theorists opined provocatively that resistance to implicit racial biases was futile, that such biases were a necessary consequence of the mind’s reliance on categorical processing to deal with the overwhelming complexities of the social world (Bargh, 1999). However, other researchers pointed to humans’ profound capacity for self-Â�regulation (Devine & Monteith, 1999), and emerging research on the malleability of implicit task responses suggested that implicit race bias could indeed be moderated by a range of personal and situational factors (e.g., Dasgupta & Greenwald, 2001; Lowery, Hardin, & Sinclair, 2001; Rudman, Ashmore, & Gary, 2001; for a review, see Blair, 2002). These initial findings of implicit bias malleability served as a call to arms for intergroup bias researchers interested in reducing expressions of prejudice and stereotyping. Here we provide a brief review of the theory and methods pertaining to changes in implicit bias. The literature on implicit bias malleability is complex, with several different methodological approaches and theoretical explanations. At the level of measurement, changes in implicit bias are (virtually) always indicated by a change in behavioral responses on an implicit bias task. Thus, at a descriptive level of analysis, the evidence for change is always seen in the expression of a behavior. Theoretically, a change in behavior may be due to several different processes. For this reason, our discussion of change in implicit bias considers research on a range of underlying processes. In our discussion, we note how particular demonstrations of implicit bias change may be interpreted as evidence for a variety of mechanisms, even though an author’s preferred interpretation may favor one specific mechanism. In this way, we illustrate the complexity of psychological processes that may underlie a change in observable task behavior.

Changes in Representations The holy grail of implicit race bias research is to change the underlying associations that form the basis of implicit bias. Change in performance on implicit bias tasks is sometimes interpreted as a change in the underlying representation of racial associations. However, this interpretation is difficult, if not impossible, to test directly using behavioral or physiological measures; therefore, such explanations remain hypothetical. For example, Olson and Fazio (2006) had subjects view pairings of black faces with positive images and white faces

with negative images. After this training, subjects were quicker to identify negative words primed by white faces, which effectively reduced the effect of race on task performance. Did this task change subjects’ representations of white people? Or did it train them to expect a negative image whenever a white face was primed? In another line of research, Kawakami, Phills, Steele, and Dovidio (2007) trained a subset of subjects to move a joystick in an “approach” direction when they saw a black face. Subjects in this condition exhibited less bias in their later performance on a behavioral measure of implicit bias compared with those who did not engage in approach training. What explains the change? Did approach training change the underlying representation? Did it train subjects to adopt an approach motivation when they saw a black face? Did it create a cue to engage greater control when a black face was encountered? Or did it create a situational cue that black people are approachable and thus safe? As discussed by Kawakami and colleagues, the exact mechanism underlying the change in performance is difficult to specify. An elegant set of studies by Rydell and McConnell (2006; McConnell, Rydell, Strain, & Mackie, 2008) demonstrated a dissociation between the acquisition and change of implicit versus explicit attitudes. On the basis of dual-Â�process models positing that implicit systems change slowly whereas explicit systems change quickly (Sloman, 1996; Smith & DeCoster, 2000), they predicted that implicit biases would change after repeated trials, whereas explicit biases would change after a single instance of new counterattitudinal information. Indeed, this is what was observed across several studies. This research elucidated the distinct processing dynamics of implicit versus explicit systems. However, the mechanism underlying the observed change in implicit responses remains difficult to determine. Did it involve a change in representation? A change in accessibility? Implicit goal activation (e.g., Bargh, Gollwitzer, Lee-Chai, Barndollar, & Troetschel, 2001)? Although the effects observed in these studies may be interpreted as changes in underlying representations, it is difficult to rule out other explanations when behavioral assessments of implicit bias are used.

Goal Effects The goal to engage in a positive interaction can have a major influence on the expression of implicit bias (Lowery et al., 2001; Richeson & Shelton, 2003; Shelton et al., 2005). Exposure to positive



19. Implicit Intergroup Bias

363

exemplars of a stigmatized outgroup can also motivate a respondent to view members of that group in a more positive light, thereby reducing the expression of bias (Dasgupta & Greenwald, 2001; Govan & Williams, 2004). Exposure to egalitarian messages may activate prosocial goals in the context of an intergroup interaction (Sinclair, Lowery, Hardin, & Colangelo, 2005). The goal to perceive a person according to their race versus their gender has also been shown to moderate whether race- or gender-based stereotypes are applied to trait judgments and behaviors (Macrae et al., 1995; Pratto & Bargh, 1991). Perspective taking also constitutes a goal process, whereby a perceiver is motivated to empathize with a stigmatized social group member. Following this logic, Galinsky and Moskowitz (2000) showed that perspective taking can also reduce the expression of implicit bias. Goal strategies may be used explicitly to focus an individual on situational cues or critical aspects of the task at hand, which serves to reduce the influence of extraneous factors, such as race, on one’s behavior. For example, Mendoza, Gollwitzer, and Amodio (in press) used implementation intentions—specific if–then plans that link a situational cue to a specific action—to enhance subjects’ accuracy when performing an implicit stereotyping task. By giving subjects a strategy that increased performance accuracy and filtered out the influence of race, the implementation intentions effectively reduced the expression of implicit race bias. Similarly, Stewart and Payne (2008) gave subjects if–then plans to think counterstereotypical thoughts, which interrupted the influence of implicit racial biases on task performance. Hence, strategies that promote goal-Â�directed action may shield an individual from the influence of race and limit the effect of implicit racial biases on task performance.

individual perceives race-�related stimuli, by changing the activated representations of a racial outgroup, by activating an alternative response goal, or by cuing a more controlled mode of response. Most likely, the effects are driven by a combination of these processes.

Situational Effects

Although similar patterns of implicit race bias are usually observed across members of a culture, some research has identified consistent individual differences. These include internal and external motivations to respond without prejudice (Amodio et al., 2003; Devine, Plant, Amodio, HarmonJones, & Vance, 2002) and chronic egalitarianism (Moskowitz, Gollwitzer, Wasel, & Schaal, 1999; Moskowitz, Salomon, & Taylor, 2000). Yet, again, it remains unclear exactly why some individuals show less implicit race bias on behavioral and physiological measures than others. Do they lack biased associations in their mental representations (Devine et al., 2002)? Are they more resistant to forming biased associations in the first place (Liv-

Elements of a situation can activate thoughts, emotions, or goals that moderate perceptions of and responses to outgroup members. Several studies have shown that viewing a black man in the context of a dark alley elicits more biased responses than a church context (Barden, Maddux, Petty, & Brewer, 2004; Wittenbrink et al., 2001). Interacting with a positive exemplar of a stigmatized outgroup in a safe setting (e.g., a classroom) has also been shown to lead to reduced expressions of negative racial evaluations (Lowery et al., 2001). However, it remains unclear whether situational moderators alter expressions of bias by changing the way an

Controlled Processing Performance on implicit tasks is driven by a combination of automatic and controlled processes (Amodio, 2008; Payne, 2001, 2005; Sherman et al., 2008). Indeed, simply following task instructions to categorize a word or complete a word fragment requires a high degree of control. Furthermore, research using ERPs to assess control-�related brain activity has shown that controlled processing can be triggered implicitly when racial concepts are activated in an unfolding response (Amodio et al., 2004; Amodio, Devine, & Harmon-Jones, 2008). Thus, control need not be deliberative; therefore, it is difficult to determine when changes in performance on an implicit task are due to spontaneously engaged control or some other hypothesized process, such as a change in underlying representations (Amodio et al., 2008; Payne, 2005). When racial issues are made salient in an initial task, subjects may become more vigilant to cues that indicate the need for more careful and controlled responding (Amodio, Harmon-Jones, & Devine, 2007; Monteith, 1993; Monteith, Ashburn-Nardo, Voils, & Czopp, 2002). That is, highly controlled performance on an implicit task increases task accuracy, which may thus preclude biases from emerging in behavior. Sensitivity to cues for control may be long lasting, and thus they may constitute a form of sustained implicit bias change.

Individual Differences

364

IV. DOMAIN-SPECIFIC PERSPECTIVES

ingston & Drwecki, 2007)? Are they more sensitive to cues for responding without bias and thus more adept at control (Amodio et al., 2008; Monteith et al., 2002; Moskowitz et al., 1999)? Again, our understanding of the mechanisms underlying these effects is limited by our methodological reliance on behavioral expressions and often ambiguous physiological measures.

Evaluating Studies of Implicit Bias Malleability As should be evident from our discussion, it is exceedingly difficult to make strong inferences about the cause of an observed change in behavioral performance on an implicit measure of race bias. That is, the necessary reliance on behavior is a major limiting factor, without a clear solution. As a result of this limitation, inferences about the mechanisms underlying changes in implicit task responses are often ambiguous. However, physiological or neuroimaging measures may be used in conjunction with behavioral assessments of implicit bias to provide some insight into the possible mechanisms. Neuroimaging methods, such as ERPs and fMRI, offer clues about the involvement of neural systems associated with general forms of controlled processing, attention, and affect. However, processing distinctions that are central to sociocognitive theories, such as between representations, accessibility, and associative conceptual links, relate to complex patterns of brain activity that cannot be directly inferred using neuroimaging measures (at least not at the present time). Given the limitations in assessing changes in implicit bias described here, it may be useful to remain open to alternative mechanisms and to focus interpretations on expressions of bias rather than on presumed underlying changes that may ultimately be untestable.

THEORETICAL ACCOUNTS OF€IMPLICIT RACIAL BIAS Implicit processes are like the dark matter of social cognition. We have strong reason to believe they exist, given that so much of our behavior is unexplained by explicit beliefs and intentions. However, because implicit processes are defined by the absence of awareness, they excel at eluding concrete description (Fazio & Olson, 2003). Without a concrete description of an implicit process, it is difficult to build a cogent explanatory model. It is notable that several theoretical accounts have

been proposed to explain the operation of particular tasks designed to assess implicit bias (Brendl, Markman, & Messner, 2001; Conrey et al., 2005; Gawronski, LeBel, & Peters, 2007; Greenwald et al., 2002; Karpinski & Hilton, 2001). By contrast, few theoretical models have been articulated to delineate the specific psychological mechanisms that constitute an implicit process, beyond the basic notion that it reflects an association stored in memory (Gawronski & Bodenhausen, 2006; Greenwald & Banaji, 1995; Wilson et al., 2000). Importantly, for the present purposes, there are no models of implicit bias that pertain uniquely to racial bias. That is, most researchers assume that implicit racial bias is a specific case of a general implicit process; therefore, general models of implicit social cognition are applied. In this section, we present two general theoretical approaches for implicit social cognition that have been influential to models of implicit race bias.

Representational Approaches Research on implicit race bias originated from cognitive theories of mental representation. As such, these theories reflect the dominant model of implicit bias. Representational models address the question of how information about social groups is stored and activated in the mind and how it contributes to the mental processes of person perception and attribution. Inspired by computer-based models of the mind, representational models assume that information is stored in a network of concepts, as in associational models, or a network of smaller informational units that underlie the representation of concepts, as in connectionist models (e.g., Sherman, 1996; Smith & Branscombe, 1987; Smith & DeCoster, 2000). For example, implicit stereotypes may be represented in an associational network of attributes related to the concept of “African American” (Figure 19.1). Different connections may have different weights, which determine the degree to which the activation of one concept activates others (Bodenhausen & Macrae, 1998; Fazio, 1990; Gawronski & Bodenhausen, 2006; Smith & DeCoster, 2000). Associative models of implicit bias assume that components of the network may represent an evaluation (e.g., good, bad), a trait attribute (e.g., lazy, intelligent), or, according to some models, a generalized affective disposition such as a positive or negative feeling (Gawronski & Bodenhausen, 2006). Accordingly, implicit racial attitudes are represented by the relative strength of association between a racial group and positive versus nega-



19. Implicit Intergroup Bias

365

FIGURE 19.1.╇ Example of a network representation of implicit racial associations. Stereotype associations are depicted by weighted associative links to the concept of African Americans. Evaluative (and sometimes affective) associations are represented as the weighted valence of each link in a network. Alternatively, some theorists represent evaluation in terms of links to general concepts of “positive” and “negative.”

tive concepts in the informational network. Stereotypes, by comparison, refer to the specific set of trait attributes that are linked to a particular social group (Park & Judd, 2005). Some models differ on the specific point of whether implicit racial attitudes per se are represented in a semantic network (e.g., Fazio et al., 1995). Others posit that the network represents implicit affective and semantic associations, but that the evaluation, or attitude, is propositional in nature and more likely to operate in explicit processes (Gawronski & Bodenhausen, 2006; Gawronski, Peters, Brochu, & Strack, 2008). Representational models of implicit bias have been extremely influential in the field of social cognition and have generated a large amount of research. A major advantage to representational models of implicit social cognition is that they are amenable to formal theoretical modeling. They are also intuitively appealing. Indeed, representational models are built in accordance with the way we store information in other systems, such as computers or libraries. However, it is important to note that a representational model is hypothetical and abstracted inductively through experimentation; thus, it does not necessarily reflect the way that information is actually represented or how the mind actually operates. The advantages of representational models are balanced by some important limitations. These include a general disconnect with the noncognitive systems (e.g., emotion, attention), inconsistencies with functional neuroanatomy, and a lack of con-

nection to actual behavior. For example, several influential dual-�process models posit that implicit associations are learned through a slow, associative process in memory (Smith & Decoster, 2000). However, affective associations learned through classic conditioning occur rapidly, often after a single exposure to an association (LeDoux, 2000). Therefore, traditional representational models may provide a good account for how semantic associations with social groups (i.e., stereotypes or evaluative associations) are learned and stored, but they do not provide an adequate account of affective forms of bias. Furthermore, representational models do not address how basic emotional processes, such as autonomically aroused states like anxiety, fear, anger, or compassion, influence the activation and expression of implicit biases. Some theorists have attempted to address this issue by proposing cognitive representations of affect, which are then assumed to interact in a network with cognitive representations of bias (Gawronski & Bodenhausen, 2006; Storbeck & Clore, 2008). This approach typically focuses on how affect shapes cognitive representations. However, the approach of treating emotions as cognitive structures may not fully capture the nature of a true emotional state or the process through which it influences behavioral expressions of racial bias. Another critical limitation of representational models is that few, if any, specify a connection between mental processes and behavior, and thus they are silent regarding the mechanism through which implicit racial bias is expressed in behavior.

366

IV. DOMAIN-SPECIFIC PERSPECTIVES

Similarly, Strack and Deutsch (2004) proposed a model through which cognitive and motivational systems influence “behavioral schemata” (i.e., a representation of behavior), but the mechanisms through which schemata translate into actual behavioral responses remain unclear. For these reasons, representational models are limited in their ability to account for emotional aspects of implicit intergroup processes and their behavioral expression. We should note, however, that these limitations refer to broad and long-Â�standing questions about the cognition–Â�affect interface with which the field has grappled. Although these are general issues, we see them as critical to the understanding of implicit racial bias effects.

Memory Systems Model of€Implicit€Bias Although representational models have dominated research on implicit social cognition, alternative approaches have recently emerged from research integrating models of learning and memory from the human and nonhuman neuroscience literatures. Amodio’s memory systems model (MSM) of implicit bias applies an integrative social neuroscience approach to address questions of how implicit racial biases are learned, stored, and expressed in behavior (Amodio, 2008; Amodio & Devine, 2006; Amodio et al., 2003; see also Carlston’s, 1994, associated systems theory). Past theory and research have demonstrated multiple forms of implicit learning and memory, each associated with

distinct neural substrates (Figure 19.2; Poldrack & Packard, 2003; Squire & Zola, 1996). This model departs from traditional representational models of implicit processing derived from dual-Â�process accounts of automaticity and control, which assume that implicit processes reflect a single system of associations characterized by a uniform processing mode. The MSM posits that different implicit systems learn according to different parameters, and that they influence emotions, perceptions, cognition, and behavior via different neural and neurochemical circuits. A large body of behavioral, neuroimaging, and brain lesion research now supports the MSM view (Poldrack & Foerde, 2008). In an effort to better understand the mechanisms of implicit bias and their expression in behavior, Amodio and colleagues have applied the MSM approach to the study of intergroup bias (Amodio, 2008; Amodio et al., 2003; Amodio & Devine, 2006). They noted that affective forms of implicit bias correspond to affective forms of learning and memory, such as classic fear conditioning, which are supported by the amygdala and its associated subcortical circuitry. By contrast, implicit stereotyping reflects semantic associations, which involve conceptual forms of learning and memory, linked to regions of the neocortex such as the left prefrontal cortex (e.g., Brodman areas 45/47) and the medial temporal lobe (Thompson-Â�Schill, D’Esposito, Aguirre, & Farah, 1997). Although most research to date has focused on comparing affective and semantic systems underlying implicit evaluative bias and stereotyping, other aspects

Long-term memory

Nondeclarative (implicit) Declarative (explicit) Facts

Procedural Priming (skills and habits)

Emotional responses

Events

Meidal temporal lobe (e.g., hippocampus)

Simple Classical Conditioning

Striatum

Amygdala Neocortex (PFC, temporal cortex)

Nonassociative learning

Skeletal musculature Cerebellum

Reflex pathways

FIGURE 19.2.╇ Diagram of dissociable memory systems and their putative neural substrates, illustrating qualitatively different forms of implicit learning and memory processes. (PFC, prefrontal cortex)



19. Implicit Intergroup Bias

of implicit bias likely involve additional systems, such as those associated with habit learning and reward. The MSM is useful because it generates hypotheses for how different forms of implicit bias should influence judgments and behavior. For example, if implicit affective bias reflects a system that governs the activation of autonomic arousal and triggers avoidance behaviors in response to threat, then measures of implicit evaluations should predict basic inhibition and avoidance behavior. If, by contrast, implicit stereotyping reflects the operation of semantic memory systems, which have stronger connections to neural regions involved in judgment formation and goal representation, then implicit stereotypes should influence impressions of outgroup members and goal-Â�driven behaviors. This distinction has been supported by studies of behavior (Amodio & Devine, 2006; Amodio & Hamilton, 2009) and neural activity (Potanina, Pfeifer, & Amodio, 2009). It is notable that, according to the MSM, an implicit evaluation may reflect a combination of affective and semantic associations. In line with classic models of attitudes, an evaluation may be driven by a combination of affective and cognitive (i.e., semantic) processes (Eagly & Chaiken, 1998). Behavior-based measures of implicit bias, such as the IAT, are unable to parse the specific contributions of affect and cognition. Nevertheless, Amodio and Devine’s (2006) findings suggest that, barring abnormal brain function (Phelps, Cannistraci, & Cunningham, 2003), measures of implicit evaluative bias may reflect affective processes. The MSM also generates specific hypotheses for how affective and semantic forms of implicit bias may be learned and unlearned. For example, classically conditioned associations are learned rapidly, often after a single experience. Once learned, they are tenacious and may never be fully extinguished (Bouton, 1994). By contrast, semantic associations are learned slowly, after repeated and highly probable pairings between two stimuli (Reber & Squire, 1994). Semantic associations are presumably unlearned in a similarly slow fashion, after repeated nonpairings. It is notable that these predictions are different than those suggested by representational models, which assume that implicit associations are learned and unlearned slowly (Rydell, McConnell, Strain, Claypool, & Hugenberg, 2007; Smith & DeCoster, 2000). Amodio (2008) has suggested that past social cognitive models correspond well to the implicit semantic memory system but do not account for affective forms of implicit bias. Thus, the MSM is not inconsistent with representational

367

models per se but suggests that representational models pertain to a subset of the range of implicit processes relevant to race bias. A major advantage of the MSM approach is that it posits a model of implicit bias that is integrated with perceptual, emotional, motivational, and cognitive systems, and it delineates pathways from different memory systems to behavior. This model will become increasingly useful as researchers turn more attention to understanding how implicit biases are expressed in social behavior. Although our discussion has focused on the implications of the MSM approach for issues of racial bias, the MSM describes general, basic processes that apply to all attitude objects, social and nonsocial alike. It will be interesting for future work to consider the MSM’s predictions for behavior toward groups that are perceived with varying degrees of affect (cf. the stereotype content model; Fiske, Cuddy, Glick, & Xu, 2002). More broadly, we expect that integrative approaches such as the MSM will become more common as the field of psychology becomes increasingly interdisciplinary.

REMAINING QUESTIONS AND€CONTROVERSIES Although an enormous amount of research has been conducted on implicit bias, many important questions remain. In this section, we touch on two such issues. The first concerns the meaning of responses on an implicit measure: How should responses on implicit tasks be interpreted? The second concerns the broader controversy of whether bias on an implicit task should be considered a mark of true prejudice.

Issues in Implicit Measurement Measurements of implicit bias have a mystique about them. How do they work? How can they measure our hidden thoughts? This mystique has cultivated a view that implicit tasks provide a direct probe into the unconscious mind, such that responses on an implicit bias task provide a pure representation of our mental processes and contents. To be clear, implicit tasks measure behavior or, in some cases, physiological responses. The logic is that if a concept is cued (e.g., by a racial prime), then its effect on a subsequent behavior may be observed. For example, Bargh, Chen, and Burrows (1996) primed subjects with subliminal pictures of black faces and measured the extent to

368

IV. DOMAIN-SPECIFIC PERSPECTIVES

which it led to more hostile behavior toward an experimenter. Similarly, Devine (1989) primed African American concepts and measured the degree of stereotyping applied in later judgments of a story character. In both cases, the object of interest is cued (e.g., black people), and its effect on behavior is measured. In this same way, an implicit task primes the object of interest and then measures its effect on a behavioral response (e.g., speed to respond to a target). The main difference is that, in an implicit task, the behavioral outcome is contained within the task, and the measurement is repeated across several trials. In this sense, an implicit task may be thought of as a “behavioral assay,” or a circumscribed index of how the actual behavioral effect would occur in a social situation. We suggest that a useful distinction between explicit and implicit tasks is that an explicit measure assesses the reporting of a belief, or proposition, whereas an implicit measure assesses a behavioral or physiological response. Considered this way, the critical difference between implicit and explicit measures is the channel of expression through which the response is made rather than a hypothetical process. As noted previously, an implicit task does not provide a pure measure of implicit or automatic processes (Amodio et al., 2008; Payne, 2001, 2005) but rather a combination of processes that are expressed through behavioral channels. Similarly, explicit measures may also assess a combination of underlying processes, although they may be particularly sensitive to explicit beliefs. For this reason, it is useful (and practical) to interpret implicit task responses as behavioral expressions rather than as pure implicit processes.

Is Implicit Bias Really Prejudice? To be clear, prejudice and discrimination remain strong and pervasive in American society. Controversy and debate surrounding the meaning of implicit race bias measures do not question the existence of prejudice in America. Indeed, the finding that most Americans show more favoritism toward whites than blacks on measures such as the IAT cannot be dismissed or explained away: It truly reflects that at some level of processing people in America tend to have racist tendencies, and these tendencies are often expressed in behavior (Jost et al., in press). This is not controversial. What is controversial concerns a more subtle issue about how implicit racial bias relates to conscious beliefs and overt behavior. Setting aside the issue of whether research on implicit bias reveals a real

form of prejudice in American society (it does), this section addresses some of the finer points of evaluating the meaning of implicit bias. In their seminal paper on the measurement of implicit racial evaluations, Fazio and colleagues (1995) dubbed their sequential evaluative priming task the “bona fide pipeline.” This name was a reference to Jones and Sigall’s (1971) “bogus pipeline,” a fake physiological contraption that purported to assess subjects’ true racial attitudes. When connected to the bogus pipeline, Jones and Sigall’s subjects reported higher levels of prejudice than control subjects, with the belief that any attempt to conceal their true attitudes would expose them as liars. Fazio and colleagues’ sequential priming method purported to be a direct conduit to one’s true attitude, obviating the need for bogus procedures used in the past. Similarly, when the IAT was introduced, it was heralded as a measure of one’s “true” attitude (Banaji, 2001). Given that the vast majority of Americans, including nonÂ�whites and egalitarians, showed an anti-black bias on the IAT, this view was quite threatening to many people (e.g., Arkes & Tetlock, 2004). In essence, it pointed a finger at most people and accused them of bigotry. Several researchers voiced the concern that laypeople completing the IAT online on websites would be misled into believing that they were unconscious bigots (e.g., Blanton & Jaccard, 2006). The “true attitude” view contrasted with Devine’s (1989) theory that automatic tendencies reflected passive learning in a historically racist culture, but that one’s true belief could only be expressed with the aid of controlled processing (see also Amodio et al., 2003, 2008; Devine et al., 2002). Indeed, several researchers have made a specific point to avoid using the term prejudice to describe implicit processes because prejudice is a complex construct that is associated with a wide range of attitudes, beliefs, and behaviors, particularly as the term is used colloquially (see Payne & Cameron, Chapter 24, this volume). We ascribe to this principle of usage; the reader may have noticed the absence of the term implicit prejudice in the present chapter. A compromise position was proposed by Wilson and colleagues (2000), who argued that implicit and explicit measures assess different attitudes that exist in different modes of psychological processing. According to the dual-Â�attitudes approach, an individual may simultaneously possess negative implicit attitudes and positive explicit attitudes toward an outgroup. This approach acknowledges ownership of associations that exist within one’s



19. Implicit Intergroup Bias

mind, even if they were formed without one’s intention and contradict one’s explicit beliefs. Importantly, both Devine (1989) and Wilson and colleagues argue that implicit attitudes and stereotypes can be overridden with controlled processing, and thus the responsibility for the expression of implicit race bias ultimately resides with the individual. In the end, the question “Is implicit bias prejudice?” is too complex for a simple yes or no answer. The discussion of whether implicit bias constitutes prejudice corresponds to legal distinctions concerning punishment based on intent versus harm (Heider, 1958). If people are held accountable based on their intent, then implicit bias is not prejudice. If their intent is irrelevant, but rather harm (i.e., the expression of implicit bias as discrimination) is the key issue, then implicit bias may constitute prejudice. We leave this debate to the legal scholars (e.g., Lane, Kang, & Banaji, 2007). We hasten to add, however, that from a social psychological point of view the issue of “true prejudice” is not the critical question. That is, the goal of research on implicit bias is not to identify whether a person is prejudiced but to understand the mechanisms of the social mind as they relate to intergroup processes and social behavior.

CONCLUSION Implicit social cognition continues to represent the latest great frontier of social psychology. Although recent advances have already shed light on the psychological mechanisms that operate in the unconscious regions of the mind, they have likely just scratched the surface. Research on implicit race bias has made unique contributions to the study of implicit social cognition. As a domain of study, it stands as an exemplar for the interplay of implicit and explicit attitudes and beliefs in the context of social relationships, goals, and group structures. At the same time, implicit race bias research has revealed a new dimension of intergroup processes that inform broader theories of intergroup relations. In this way, the field of implicit race bias has come to represent an important link between intrapersonal and interpersonal processes in social psychological theory and research. In this chapter, we highlighted major extant findings from the field and discussed some of the current debates and controversies that drive much contemporary investigation. Continuously evolving, this field stands poised to contribute new insights into the expression of implicit processes in behavior, fur-

369

ther connecting research on social cognition with broader social psychological questions about the individual in society. References Allport, G. W. (1954). The nature of prejudice. Reading, MA: Addison-Â�Wesley. Amodio, D. M. (2008). The social neuroscience of intergroup relations. European Review of Social Psychology, 19, 1–54. Amodio, D. M., & Devine, P. G. (2005). Changing prejudice: The effects of persuasion on implicit and explicit forms of race bias. In T. C. Brock & M. C. Green (Eds.), Persuasion: Psychological insights and perspectives (2nd ed., pp.€249–280). Thousand Oaks, CA: Sage. Amodio, D. M., & Devine, P. G. (2006). Stereotyping and evaluation in implicit race bias: Evidence for independent constructs and unique effects on behavior. Journal of Personality and Social Psychology, 91, 652–661. Amodio, D. M., Devine, P. G., & Harmon-Jones, E. (2007). A dynamic model of guilt: Implications for motivation and self-Â�regulation in the context of prejudice. Psychological Science, 18, 524–530. Amodio, D. M., Devine, P. G., & Harmon-Jones, E. (2008). Individual differences in the regulation of intergroup bias: The role of conflict monitoring and neural signals for control. Journal of Personality and Social Psychology, 94, 60–74. Amodio, D. M., & Hamilton, H. (2009). Intergroup anxiety effects on implicit evaluative race bias vs. implicit stereotyping. Unpublished manuscript, New York University. Amodio, D. M., Harmon-Jones, E., & Devine, P. G. (2003). Individual differences in the activation and control of affective race bias as assessed by startle eyeblink responses and self-Â�report. Journal of Personality and Social Psychology, 84, 738–753. Amodio, D. M., Harmon-Jones, E., Devine, P. G., Curtin, J. J., Hartley, S. L., & Covert, A. E. (2004). Neural signals for the detection of unintentional race bias. Psychological Science, 15, 88–93. Arkes, H. R., & Tetlock, P. E. (2004). Attributions of implicit prejudice, or “would Jesse Jackson ‘fail’ the Implicit Association Test?” Psychological Inquiry, 15, 257–278. Banaji, M. R. (2001). Implicit attitudes can be measured. In H. L. Roediger, III, J. S. Nairne, I. Neath, & A. Surprenant (Eds.), The nature of remembering: Essays in honor of Robert G. Crowder (pp.€117–150). Washington, DC: American Psychological Association. Barden, J., Maddux, W. W., Petty, R. E., & Brewer, M. B. (2004). Contextual moderation of racial bias: The impact of social roles on controlled and automatically activated attitudes. Journal of Personality and Social Psychology, 87, 5–22. Bargh, J. A. (1994). The four horsemen of automaticity: Awareness, intention, efficiency, and control in social cognition. In R. S. Wyer, Jr. & T. K. Srull (Eds.),

370

IV. DOMAIN-SPECIFIC PERSPECTIVES

Handbook of social cognition (2nd ed., pp.€ 1–40). Hillsdale, NJ: Erlbaum. Bargh, J. A. (1999). The cognitive monster: The case against controllability of automatic stereotype effects. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€361–382). New York: Guilford Press. Bargh, J. A., Chen, M., & Burrows, L. (1996). Automaticity of social behavior: Direct effects of trait construct and stereotype activation on action. Journal of Personality and Social Psychology, 71, 230–244. Bargh, J. A., Gollwitzer, P. M., Lee-Chai, A., Barndollar, K., & Troetschel, R. (2001). The automated will: Nonconscious activation and pursuit of behavioral goals. Journal of Personality and Social Psychology, 81, 1014–1027. Bargh, J. A., & Pietromonaco, P. (1982). Automatic information processing and social perception: The influence of trait information presented outside of conscious awareness on impression formation. Journal of Personality and Social Psychology, 43, 437–449. Blair, I. V. (2001). Implicit stereotypes and prejudice. In G. Moskowitz (Ed.), Cognitive social psychology: On the tenure and future of social cognition (pp.€359–374). Mahwah, NJ: Erlbaum. Blair, I. V. (2002). The malleability of automatic stereotypes and prejudice. Personality and Social Psychology Review, 6, 242–260. Blanton, H., & Jaccard, J. (2006). Tests of multiplicative models in psychology: A case study using the unified theory of implicit attitudes, stereotypes, self-Â�esteem, and self-Â�concept. Psychological Review, 113, 155–169. Blascovich, J., Mendes, W. B., Hunter, S. B., Lickel, B., & Kowai-Bell, N. (2001). Perceiver threat in social interactions with stigmatized others. Journal of Personality and Social Psychology, 80, 253–267. Bodenhausen, G. V., & Macrae, C. N. (1998). Stereotype activation and inhibition. In R. S. Wyer, Jr. (Ed.), Stereotype activation and inhibition: Advances in social cognition (Vol. 11, pp.€ 1–52). Mahwah, NJ: Erlbaum. Bouton, M. E. (1994). Conditioning, remembering, and forgetting. Journal of Experimental Psychology: Animal Behavior Processes, 20, 219–231. Breckler, S. J. (1984). Empirical validation of affect, behavior, and cognition as distinct components of attitude. Journal of Personality and Social Psychology, 47, 1191–1205. Brendl, C. M., Markman, A. B., & Messner, C. (2001). How do indirect measures of evaluation work? Evaluating the inference of prejudice in the Implicit Association Test. Journal of Personality and Social Psychology, 81, 760–773. Brigham, J. C. (1993). College students’ racial attitudes. Journal of Applied Social Psychology, 23, 1933–1967. Carlston, D. E. (1994). Associated systems theory: A systematic approach to the cognitive representation of persons and events. In R. S. Wyer (Ed.), Associated systems theory: Advances in social cognition (Vol. 7, pp.€1–78). Hillsdale, NJ: Erlbaum. Cohen, N. J., & Squire, L. R. (1980). Preserved learning and pattern-Â�analyzing skill in amnesia: Dissociation of knowing how and knowing that. Science, 210, 207–210.

Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K., & Groom, C. J. (2005). Separating multiple processes in implicit social cognition: The quad model of implicit task performance. Journal of Personality and Social Psychology, 89, 469–487. Correll, J., Park, B., Judd, C. M., & Wittenbrink, B. (2002). The police officer’s dilemma: Using ethnicity to disambiguate potentially threatening individuals. Journal of Personality and Social Psychology, 83, 1314–1329. Correll, J., Park, B., Judd, C. M., & Wittenbrink, B. (2007). The influence of stereotypes on decisions to shoot. European Journal of Social Psychology, 37, 1077–1345. Crosby, F., Bromley, S., & Saxe, L. (1980). Recent unobtrusive studies of black and white discrimination and prejudice: A literature review. Psychological Bulletin, 87, 546–563. Cunningham, W. A., Johnson, M. K., Raye, C. L., Gatenby, J. C., Gore, J. C., & Banaji, M. R. (2004). Separable neural components in the processing of black and white faces. Psychological Science, 15, 806–813. Dasgupta, N. (2004). Implicit ingroup favoritism, outgroup favoritism, and their behavioral manifestations. Social Justice Research, 17, 143–169. Dasgupta, N., & Greenwald, A. G. (2001). On the malleability of automatic attitudes: Combating automatic prejudice with images of admired and disliked individuals. Journal of Personality and Social Psychology, 81, 801–814. Davis, M. (1992). The role of the amygdala in fear and anxiety. Annual Review of Neuroscience, 15, 353–375. Devine, P. G. (1989). Prejudice and stereotypes: Their automatic and controlled components. Journal of Personality and Social Psychology, 56, 5–18. Devine, P. G., & Monteith, M. J. (1999). Automaticity and control in stereotyping. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€339–360). New York: Guilford Press. Devine, P. G., Plant, E. A., Amodio, D. M., HarmonJones, E., & Vance, S. L. (2002). The regulation of explicit and implicit race bias: The role of motivations to respond without prejudice. Journal of Personality and Social Psychology, 82, 835–848. Dovidio, J. F., Brigham, J. C., Johnson, B. T., & Gaertner, S. L. (1996). Stereotyping, prejudice and discrimination: Another look. In C. N. Macrae, C. Stangor, & M. Hewstone (Eds.), Stereotypes and stereotyping (pp.€276–319). New York: Guilford Press. Dovidio, J. F., Evans, N., & Tyler, R. B. (1986). Racial stereotypes: The contents of their cognitive representations. Journal of Experimental Social Psychology, 22, 22–37. Dovidio, J. F., Kawakami, K., & Gaertner, S. L. (2002). Implicit and explicit prejudice and interracial interaction. Journal of Personality and Social Psychology, 82, 62–68. Dovidio, J. F., Kawakami, K., Johnson, C., Johnson, B., & Howard, A. (1997). On the nature of prejudice: Automatic and controlled processes. Journal of Experimental Social Psychology, 33, 510–540. Eagly, A. H., & Chaiken, S. (1998). Attitude structure and function. In D. T. Gilbert, S. T. Fiske, & G.



19. Implicit Intergroup Bias

Lindzey (Eds.), The handbook of social psychology (4th ed., Vol. 1, pp.€269–322). New York: McGraw-Hill. Fazio, R. H. (1990). Multiple processes by which attitudes guide behavior: The MODE Model as an integrative framework. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 23, pp.€75–107). New York: Academic Press. Fazio, R. H. (2007). Attitudes as object-Â�evaluation associations of varying strength. Social Cognition, 25, 603–637. Fazio, R. H., Chen, J., McDonel, E. C., & Sherman, S. J. (1982). Attitude accessibility, attitude-Â�behavior consistency and the strength of the object-Â�evaluation association. Journal of Experimental Social Psychology, 18, 339–357. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). An individual difference measure of motivation to control prejudiced reactions. Personality and Social Psychology Bulletin, 23, 316–326. Fazio, R. H., & Olson, M. A. (2003). Implicit measures in social cognition research: Their meaning and uses. Annual Review of Psychology, 54, 297–327. Fazio, R. H., Powell, M. C., & Herr, P. M. (1983). Toward a process model of the attitude-Â�behavior relation: Accessing one’s attitude upon mere observation of the attitude object. Journal of Personality and Social Psychology, 44, 723–735. Fiske, S. T., Cuddy, A. J. C., Glick, P., & Xu, J. (2002). A Model of (often mixed) stereotype content: Competence and warmth respectively follow from status and competition. Journal of Personality and Social Psychology, 82, 878–902. Frantz, C. M., Cuddy, A. J. C., Burnett, M., Ray, H., & Hart, A. (2004). A threat in the computer: The race Implicit Association Test as a stereotype threat experience. Personality and Social Psychology Bulletin, 30, 1611–1624. Freud, S. (1930). Civilization and its discontents. Oxford, UK: Hogarth. Gaertner, S. L., & McLaughlin, J. P. (1983). Racial stereotypes: Associations and ascriptions of positive and negative characteristics. Social Psychology Quarterly, 46, 23–30. Galinsky, A. D., & Moskowitz, G. B. (2000). PerspectiveÂ�taking: Decreasing stereotype expression, stereotype accessibility, and in-group favoritism. Journal of Personality and Social Psychology, 78, 708–724. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. Gawronski, B., LeBel, E. P., & Peters, K. R. (2007). What do implicit measures tell us?: Scrutinizing the validity of three common assumptions. Perspectives on Psychological Science, 2, 181–193. Gawronski, B., Peters, K. R., Brochu, P. M., & Strack, F. (2008). Understanding the relations between different forms of racial prejudice: A cognitive consistency perspective. Personality and Social Psychology Bulletin, 34, 648–665. Gilbert, D. T., & Hixon, J. G. (1991). The trouble of thinking: Activation and application of stereotypic beliefs. Journal of Personality and Social Psychology, 60, 509–517.

371

Govan, C. L., & Williams, K. D. (2004). Changing the affective valence of the stimulus items influences the IAT by redefining the category labels. Journal of Experimental Social Psychology, 40, 357–365. Graf, P., & Schacter, D. L. (1985). Implicit and explicit memory for new associations in normal and amnesic subjects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11, 501–518. Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition. Psychological Review, 102, 4–27. Greenwald, A. G., Banaji, M. R., Rudman, L. A., Farnham, S. D., Nosek, B. A., & Mellott, D. S. (2002). A unified theory of implicit attitudes, stereotypes, selfÂ�esteem, and self-Â�concept. Psychological Review, 109, 3–25. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Guglielmi, R. S. (1999). Psychophysiological assessment of prejudice: Past research, current status, and future directions. Personality and Social Psychology Review, 3, 123–157. Heider, F. (1958). The psychology of interpersonal relations. New York: Wiley. Ito, T. A., & Urland, G. R. (2003). Race and gender on the brain: Electrocortical measures of attention to the race and gender of multiply categorizable individuals. Journal of Personality and Social Psychology, 85, 616–626. Jacoby, L. L. (1991). A process dissociation framework: Separating automatic from intentional uses of Â�memory. Journal of Memory and Language, 30, 513–541. Jacoby, L. L., & Witherspoon, D. (1982). Remembering without awareness. Canadian Journal of Psychology, 36, 300–324. James, W. (1890). The principles of psychology. Oxford, UK: Holt. Jones, E. E., & Sigall, H. (1971). The bogus pipeline: A new paradigm for measuring affect and attitude. Psychological Bulletin, 76, 349–364. Jost, J. T., Rudman, L. A., Blair, I. V., Carney, D., Dasgupta, N., Glaser, J., et al. (in press). The existence of implicit bias is beyond reasonable doubt: A refutation of ideological and methodological objections and executive summary of ten studies that no manager should ignore. Research in Organizational Behavior. Judd, C. M., & Park, B. (1993). Definition and assessment of accuracy in social stereotypes. Psychological Review, 100, 109–128. Karlins, M., Coffman, T. L., & Walters, G. (1969). On the fading of social stereotypes: Studies in three generations of college students. Journal of Personality and Social Psychology, 13, 1–16. Karpinski, A., & Hilton, J. K. (2001). Attitudes and the Implicit Association Test. Journal of Personality and Social Psychology, 81, 774–788. Kawakami, K., Dion, K. L., & Dovidio, J. F. (1998). Racial prejudice and stereotype activation. Personality and Social Psychology Bulletin, 24, 407–416. Kawakami, K., Dovidio, J. F., Moll, J., Hermsen, S., & Russin, A. (2000). Just say no (to stereotyping): Effects of training on the negation of stereotypic asso-

372

IV. DOMAIN-SPECIFIC PERSPECTIVES

ciations on stereotype activation. Journal of Personality and Social Psychology, 78, 871–888. Kawakami, K., Phills, C., Steele, J. R., & Dovidio, J. F. (2007). (Close) distance makes the heart grow fonder: Improving implicit racial attitudes and interracial interactions through approach behaviors. Journal of Personality and Social Psychology, 92, 957–971. Klauer, K. C., & Musch, J. (2003). Affective priming: Findings and theories. In J. Musch & K. C. Klauer (Eds.), The psychology of evaluation: Affective processes in cognition and emotion (pp.€7–49). Mahwah, NJ: Erlbaum. Lambert, A. J., Payne, B. K., Shaffer, L. M., Jacoby, L. L., Chasteen, A., & Khan, S. (2003). Stereotypes as dominant responses: On the “social facilitation” of prejudice in anticipated public contexts. Journal of Personality and Social Psychology, 84, 277–295. Lane, K. A., Kang, J., & Banaji, M. R. (2007). Implicit social cognition and law. Annual Review of Law and Social Sciences, 3, 427–451. Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1990). Emotion, attention, and the startle reflex. Psychological Review, 97, 377–395. Langer, E. J. (1975). The illusion of control. Journal of Personality and Social Psychology, 32, 311–328. Langer, E. J., & Abelson, R. P. (1972). The semantics of asking a favor: How to succeed in getting help without really dying. Journal of Personality and Social Psychology, 24, 26–32. LaPierre, R. T. (1934). Attitudes vs. actions. Social Forces, 13, 230–237. LeDoux, J. E. (2000). Emotion circuits in the brain. Annual Review of Neuroscience, 23, 155–184. Lieberman, M. D., Hariri, A., Jarcho, J. M., Eisenberger, N. I., & Bookheimer, S. Y. (2005). An fMRI investigation of race-Â�related amygdala activity in AfricanÂ�American and Caucasian-Â�American individuals. Nature Neuroscience, 8, 720–722. Livingston, R. W., & Drwecki, B. B. (2007). Why are some individuals not racially biased?: Susceptibility to affective conditioning predicts nonprejudice toward blacks. Psychological Science, 18, 816–823. Lowery, B. S., Hardin, C. D., & Sinclair, S. (2001). Social influence effects on automatic racial prejudice. Journal of Personality and Social Psychology, 81, 842– 855. Macrae, C. N., Bodenhausen, G. V., & Milne, A. B. (1995). The dissection of selection in person perception: Inhibitory processes in social stereotyping. Journal of Personality and Social Psychology, 69, 397–407. Macrae, C. N., Stangor, C., & Milne, A. B. (1994). Activating social stereotypes: A functional analysis. Journal of Experimental Social Psychology, 30, 370–389. McConahay, J. B. (1986). Modern racism, ambivalence, and the Modern Racism Scale. In J. F. Dovidio & S. L. Gaertner (Eds.), Prejudice, discrimination, and racism (pp.€91–126). New York: Academic Press. McConnell, A. R., & Leibold, J. M. (2001). Relations among the Implicit Association Test, discriminatory behavior, and explicit measures of racial attitudes. Journal of Experimental Social Psychology, 37, 435–442. McConnell, A. R., Rydell, R. J., Strain, L. M., & Mackie, D. M. (2008). Forming implicit and explicit at-

titudes toward individuals: Social group association cues. Journal of Personality and Social Psychology, 94, 792–807. Mendes, W. B., Blascovich, J., Lickel, B., & Hunter, S. (2002). Challenge and threat during interactions with white and black men. Personality and Social Psychology Bulletin, 28, 939–952. Mendoza, S. A., Gollwitzer, P. M., & Amodio, D. M. (in press). Reducing the expression of implicit stereotypes: Reflexive control through implementation intentions. Personality and Social Psychology Bulletin. Meyer, D. E., & Schvaneveldt, R. W. (1971). Facilitation in recognizing pairs of words: Evidence of a dependence between retrieval operations. Journal of Experimental Psychology, 90, 227–234. Meyer, D. E., & Schvaneveldt, R. W. (1976). Meaning, memory, structure, and mental processes. Science, 192, 27–33. Monteith, M. J. (1993). Self-Â�regulation of prejudiced responses: Implications for progress in prejudice reduction efforts. Journal of Personality and Social Psychology, 65, 469–485. Monteith, M. J., Ashburn-Nardo, L., Voils, C. I., & Czopp, A. M. (2002). Putting the brakes on prejudice: On the development and operation of cues for control. Journal of Personality and Social Psychology, 83, 1029–1050. Monteith, M. J., Voils, C. I., & Ashburn-Nardo, L. (2001). Taking a look underground: Detecting, interpreting and reacting to implicit racial biases. Social Cognition, 19, 395–417. Moskowitz, G. B., Gollwitzer, P. M., Wasel, W., & Schaal, B. (1999). Preconscious control of stereotype activation through chronic egalitarian goals. Journal of Personality and Social Psychology, 77, 167–184. Moskowitz, G. B., Salomon, A. R., & Taylor, C. M. (2000). Implicit control of stereotype activation through the preconscious operation of egalitarian goals. Social Cognition, 18, 151–177. Neely, J. H. (1977). Semantic priming and retrieval from lexical memory: Roles of inhibitionless spreading activation and limited-Â�capacity attention. Journal of Experimental Psychology, 106, 226–254. Nosek, B. A., Smyth, F. L., Hansen, J. J., Devos, T., Lindner, N. M., Ranganath, K. A., et al. (2007). Pervasiveness and correlates of implicit attitudes and stereotypes. European Review of Social Psychology, 18, 36–88. Olson, M. A., & Fazio, R. H. (2004). Reducing the influence of extrapersonal associations on the Implicit Association Test: Personalizing the IAT. Journal of Personality and Social Psychology, 86, 653–667. Olson, M. A., & Fazio, R. H. (2006). Reducing automatically activated racial prejudice through implicit evaluative conditioning. Personality and Social Psychology Bulletin, 32, 421–433. Park, B., & Judd, C. M. (2005). Rethinking the link between categorization and prejudice within the social cognition perspective. Personality and Social Psychology Review, 9, 108–130. Payne, B. K. (2001). Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon. Journal of Personality and Social Psychology, 81, 181–192.



19. Implicit Intergroup Bias

Payne, B. K. (2005). Conceptualizing control in social cognition: How executive functioning modulates the expression of automatic stereotyping. Journal of Personality and Social Psychology, 89, 488–503. Payne, B. K. (2008). What mistakes disclose: A process dissociation approach to automatic and controlled processes in social psychology. Social and Personality Psychology Compass, 2, 1–20. Payne, B. K., Cheng, C. M., Govorun, O., & Stewart, B. D. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. Journal of Personality and Social Psychology, 89, 277–293. Payne, B. K., Lambert, A. J., & Jacoby, L. L. (2002). Best laid plans: Effects of goals on accessibility bias and cognitive control in race-based misperceptions of weapons. Journal of Experimental Social Psychology, 38, 384–396. Pearson, A. R., West, T. V., Dovidio, J. F., Powers, S. R., Buck, R., & Henning, R. (2008). The fragility of intergroup relations: Divergent effects of temporal delay in audio-Â�visual feedback in intergroup and intragroup interaction. Psychological Science, 19, 1272–1279. Perdue, C. W., Dovidio, J. F., Gurtman, M. B., & Tyler, R. B. (1990). Us and them: Social categorization and the process of intergroup bias. Journal of Personality and Social Psychology, 59, 475–486. Perdue, C. W., & Gurtman, M. B. (1990). Evidence for the automaticity of ageism. Journal of Experimental Social Psychology, 26, 199–216. Phelps, E. A., Cannistraci, C. J., & Cunningham, W. A. (2003). Intact performance on an indirect measure of race bias following amygdala damage. Neuropsychologia, 41, 203–208. Phelps, E. A., O’Connor, K. J., Cunningham, W. A., Funayama, E. S., Gatenby, J. C., Gore, J. C., et al. (2000). Performance on indirect measures of race evaluation predicts amygdala activation. Journal of Cognitive Neuroscience, 12, 729–738. Poldrack, R. A., & Foerde, K. (2008). Category learning and the memory systems debate. Neuroscience and Biobehavioral Reviews, 32, 197–205. Poldrack, R. A., & Packard, M. G. (2003). Competition between memory systems: Converging evidence from animal and human studies. Neuropsychologia, 41, 245–251. Posner, M. I., & Snyder, C. R. R. (1975). Attention and cognitive control. In R. L. Solso (Ed.), Information processing and cognition: The Loyola Symposium. Hillsdale, NJ: Erlbaum. Potanina, P. V., Pfeifer, J. H., & Amodio, D. M. (2009). Stereotyping and evaluation in intergroup bias: fMRI evidence for a multiple-Â�memory systems model. Manuscript submitted for publication. Pratto, F., & Bargh, J. A. (1991). Stereotyping based on apparently individuating information: Trait and global components of sex stereotypes under attention overload. Journal of Experimental Social Psychology, 27, 26–47. Rankin, R. E., & Campbell, D. T. (1955). Galvanic skin response to Negro and white experimenters. Journal of Abnormal and Social Psychology, 51, 30–33. Reber, P. J., & Squire, L. R. (1994). Parallel brain systems for learning with and without awareness. Learning and Memory, 1, 217–229.

373

Richeson, J. A., & Shelton, J. N. (2003). When prejudice does not pay: Effects of interracial contact on executive function. Psychological Science, 14, 287–290. Rudman, L. A., Ashmore, R. D., & Gary, M. L. (2001). “Unlearning” automatic biases: The malleability of implicit prejudice and stereotypes. Journal of Personality and Social Psychology, 81, 856–868. Rudman, L. A., Greenwald, A. G., & McGhee, D. E. (2001). Implicit self-Â�concept and evaluative implicit gender stereotypes: Self and ingroup share desirable traits. Personality and Social Psychology Bulletin, 27, 1164–1178. Rydell, R. J., & McConnell, A. R. (2006). Understanding implicit and explicit attitude change: A systems of reasoning analysis. Journal of Personality and Social Psychology, 91, 995–1008. Rydell, R. J., McConnell, A. R., Strain, L. M., Claypool, H. M., & Hugenberg, K. (2007). Implicit and explicit attitudes respond differently to increasing amounts of counterattitudinal information. European Journal of Social Psychology, 37, 867–878. Schacter, D. L. (1987). Implicit memory—Â�History and current status. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 501–518. Sekaquaptewa, D., Espinoza, P., Thompson, M., Vargas, P., & von Hippel, W. (2003). Stereotypic explanatory bias: Implicit stereotyping as a predictor of discrimination. Journal of Experimental Social Psychology, 39, 75–82. Shelton, J. N., Richeson, J. A., Salvatore, J., & Trawalter, S. (2005). Ironic effects of racial bias during interracial interactions. Psychological Science, 16, 397–402. Sherman, J. W. (1996). Development and mental representation of stereotypes. Journal of Personality and Social Psychology, 70, 1126–1141. Sherman, J. W., Gawronski, B., Gonsalkorale, K., Hugenberg, K., Allen, T. J., & Groom, C. J. (2008). The selfÂ�regulation of automatic associations and behavioral impulses. Psychological Review, 115, 314–335. Shiffrin, R., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending, and a general theory. Psychological Review, 84, 127–190. Sigall, H., & Page, R. (1971). Current stereotypes: A little fading, a little faking. Journal of Personality and Social Psychology, 18, 247–255. Sinclair, S., Lowery, B., Hardin, C., & Colangelo, A. (2005). Social tuning of automatic racial attitudes: The role of affiliative motivation. Journal of Personality and Social Psychology, 89, 583–592. Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119, 3–22. Smith, E. R., & Branscombe, N. R. (1987). ProcedurallyÂ�mediated social inferences: The case of category accessibility effects. Journal of Experimental Social Psychology, 23, 361–382. Smith, E. R., & DeCoster, J. (2000). Dual-Â�process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4, 108–131. Smith, E. R., & Miller, F. D. (1979). Attributional information processing: A response time model of causal subtraction. Journal of Personality and Social Psychology, 37, 1723–1731.

374

IV. DOMAIN-SPECIFIC PERSPECTIVES

Spencer, S. J., Fein, S., Wolfe, C. T., Fong, C., & Dunn, M. A. (1998). Automatic activation of stereotypes: The role of self-image threat. Personality and Social Psychology Bulletin, 24, 1139–1152. Squire, L. R. (1986). Mechanisms of memory. Science, 232, 1612–1619. Squire, L. R., & Zola, S. M. (1996). Structure and function of declarative and nondeclarative memory systems. Proceedings of the National Academy of Sciences USA, 93, 13515–13522. Srull, T. K., & Wyer, R. S. (1980). Category accessibility and social perception: Some implications for the study of person memory and interpersonal judgments. Journal of Personality and Social Psychology, 38, 841–856. Stewart, B. D., & Payne, B. K. (2008). Bringing automatic stereotyping under control: Implementation intentions as efficient means of thought control. Personality and Social Psychology Bulletin, 34, 1332–1345. Storbeck, J., & Clore, G. L. (2008). The affective regulation of semantic and affective priming. Emotion, 8, 208–215. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Taylor, S. E., Fiske, S. T., Etcoff, N. L., & Ruderman, A. J. (1978). Categorization and contextual bases of person memory and stereotyping. Journal of Personality and Social Psychology, 36, 778–793. Thompson-Â�Schill, S. L., D’Esposito, M., Aguirre, G. K., & Farah, M. J. (1997). Role of left inferior prefrontal

cortex in retrieval of semantic knowledge: A reevaluation. Proceedings of the National Academy of Sciences USA, 94, 14792–14797. Uhlmann, E. L., Brescoll, V. L., & Paluck, E. L. (2006). Are members of low status groups perceived as bad, or badly off?: Egalitarian negative associations and automatic prejudice. Journal of Experimental Social Psychology, 42, 491–499. Vanman, E. J., Paul, B. Y., Ito, T. A., & Miller, N. (1997). The modern face of prejudice and structural features that moderate the effect of cooperation on affect. Journal of Personality and Social Psychology, 73, 941– 959. West, T. V., Shelton, J. N., & Trail, T. E. (2009). Relational anxiety in interracial interactions. Psychological Science, 20, 289–292. Wheeler, M. E., & Fiske, S. T. (2005). Controlling racial prejudice: Social-Â�cognitive goals affect amygdala and stereotype activation. Psychological Science, 16, 56–63. Wilson, T. D., Lindsey, S., & Schooler, T. Y. (2000). A model of dual attitudes. Psychological Review, 107, 101–126. Wittenbrink, B., Judd, C. M., & Park, B. (1997). Evidence for racial prejudice at the implicit level and its relationship with questionnaire measures. Journal of Personality and Social Psychology, 72, 262–274. Wittenbrink, B., Judd, C. M., & Park, B. (2001). Spontaneous prejudice in context: Variability in automatically activated attitudes. Journal of Personality and Social Psychology, 81, 815–827.

C h a p t e r 20

Racial Bias and Stereotyping Interpersonal Processes Sophie Trawalter and Jenessa R. Shapiro

O

ver the last decades, racial bias has declined steadily. The vast majority of white Americans today want racially integrated schools and neighborhoods, they reject bans against interracial marriage, and they would vote for a qualified black political candidate (Bobo, 2001; Bonilla-Silva, 2003; Schuman, Steeh, Bobo, & Krysan, 1997). On November 4, 2008, they did: Americans, including many whites, voted for Barack Obama, making him the first black American president. As racial biases have declined, racial diversity has increased in many social domains, including workplaces, schools, and neighborhoods. In spite of these encouraging trends, interracial contact remains stressful and even negative (Crosby, Bromley, & Saxe, 1980; Shelton & Richeson, 2006b; Tropp, 2006). In the present chapter, we examine this disjunction between increasing racial tolerance and diversity on the one hand and negative interracial contact on the other. Our aim is to shed light on the interpersonal dynamics of interracial contact by moving beyond traditional approaches that have examined white individuals’ explicit racial biases and interracial contact experiences and integrating insights from contemporary research that considers whites’ and racial minorities’ explicit and implicit cognitions, behaviors, and experiences in interracial contexts. Although we focus on black–white interactions, we believe that

our conceptualization will generalize to intergroup interactions more generally. Traditional approaches to understanding intergroup behavior have examined individual differences in racial biases as a predictor of intergroup behavior (see Figure 20.1, panel 1; Dovidio, 2001; Dovidio & Gaertner, 2000; Duckitt, 1992; McConahay, 1983). Much evidence corroborates this approach. For example, research has found that, compared with white individuals who report low levels of racial bias, those who report high levels of racial bias evaluate black job candidates less favorably (Dovidio & Gaertner, 2000; Gilens, 1995; McConahay, 1983; Sears & Jessor, 1996). However, this approach leaves many questions unanswered. Primarily, it cannot explain why intergroup behavior remains negative and racial disparities persist in the face of declining racial biases. Moreover, it cannot provide an explanation for some recent findings, including the finding that whites who are more racially biased sometimes behave more positively during interracial interactions than those who are less racially biased (e.g., Shelton, Richeson, Salvatore, & Trawalter, 2005). To make sense of these and other findings, social psychologists have turned to the literature on implicit social cognition. The advent of implicit social cognition research in social psychology has revolutionized our un375



376

Intergroup Behavior

Controlled Intergroup Behavior

Explicit Racial Bias

Racial Minorities’ Controlled AnxietyManagement Behavior

Racial Minorities’ Automatic AnxietyRelated Behavior

Interpersonal Dynamics of Interracial Contact

Whites’ Controlled AnxietyManagement Behavior

Whites’ Automatic AnxietyRelated Behavior

Racial Minorities’ Cognitions

Racial Minorities’ Explicit Racial Bias

Racial Minorities’ Prejudice Concerns

Racial Minorities’ Implicit Racial Bias

FIGURE 20.1.╇ Approaches to understanding behavior during interracial contact.

Whites’ Cognitions

Whites’ Explicit Racial Bias

Whites’ Prejudice Concerns

Whites’ Implicit Racial Bias

Panel 3. Emergent framework for understanding behavior during interracial contact.

Automatic Intergroup Behavior

Implicit Racial Bias

Panel 2. Updated model for understanding behavior during interracial contact.

Racial Bias

Panel 1. Traditional approach to understanding behavior during interracial contact.



20. Bias and Interpersonal Processes

derstanding of intergroup contact. According to implicit social cognition theory, cognitions can be the result of two different processes: (1) a relatively controlled process that is slow and resource dependent or (2) a relatively automatic process that is fast, resource independent, and often uncontrollable (e.g., Smith & DeCoster, 2000; Strack & Deutsch, 2004). In the context of race-Â�related cognitions, these processes are thought to give rise to “explicit” and “implicit” racial bias, respectively.1 Consistent with this claim, hundreds of studies using implicit, unobtrusive measures such as the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) and affective priming measures (e.g., Fazio, Jackson, Dunton, & Williams, 1995) have revealed that many white Americans exhibit implicit racial biases toward black Americans and nonblack racial minorities (black Americans: e.g., Devine, 1989; Gaertner & McLaughlin, 1983; Greenwald et al., 1998; Lepore & Brown, 1997; Payne, Cheng, Govorun, & Stewart, 2005; Sekaquaptewa, Espinoza, Thompson, Varga, & von Hippel, 2003; nonblack racial minorities: e.g., Devos & Banaji, 2005; Ottaway, Hayden, & Oakes, 2001; Rudman, Greenwald, Mellott, & Schwartz, 1999; Son Hing, Li, & Zanna, 2002; Wittenbrink, Judd, & Park, 2001). These implicit racial biases often predict negative intergroup behavior regardless of explicit racial biases (e.g., Dovidio, Kawakami, & Gaertner, 2002; Dovidio, Kawakami, Johnson, Johnson, & Howard, 1997; Fazio et al., 1995; McConnell & Leibold, 2001). Accordingly, the implicit social cognition approach can offer an explanation for why intergroup behavior remains negative in spite of declining explicit racial biases: Negative intergroup behavior today may stem largely from implicit, not explicit, racial biases. Not only has implicit social cognition research provided new insights into the nature of contemporary racial biases, it has prompted a more nuanced look at intergroup behavior. Specifically, social psychologists have revised models of intergroup behavior that link racial biases to intergroup behavior (see Figure 20.1, panel 2). If racial biases arise from automatic and controlled processes, as implicit social cognition theory and research suggest, then behavior too may be the product of automatic and controlled processes. This reasoning has led to the straightforward prediction that implicit racial biases guide relatively automatic intergroup behaviors, whereas explicit biases guide relatively controlled intergroup behaviors. Indeed, research has found that negative implicit racial biases tend to predict difficult-to-Â�control behaviors such as blinking, fidgeting, and eye contact (Dovidio et al.,

377

1997; Fazio, Jackson, Dunton, & Williams, 1995; McConnell & Leibold, 2001), whereas explicit racial biases tend to predict more controllable behaviors such as verbal friendliness and explicit judgments of racial minority targets (e.g., Dovidio et al., 1997, 2002; Fazio et al., 1995). Thus, implicit social cognition research has provided an updated model for understanding intergroup behavior. Still, models of intergroup behavior of this ilk cannot account for why high-bias whites sometimes behave more positively than low-bias whites (Shelton, Richeson, Salvatore, & Trawalter, 2005; Vorauer & Turpie, 2004). These models still assume that negative racial biases result in negative intergroup behavior. In addition, although current models of intergroup behavior may apply to racial minorities just as they apply to whites, data on racial minorities’ intergroup cognitions, behaviors, and experiences are scarce. Whether racial minorities’ implicit and explicit social cognitions shape relatively automatic and relatively controlled intergroup behaviors remains an empirical question. Finally, mounting evidence suggests that other race-Â�related cognitions—Â�namely whites’ concerns about appearing prejudiced and racial minorities’ concerns about being the target of prejudice—need to be incorporated into models of intergroup behavior (Fazio, 1990; Shelton, Richeson, & Vorauer, 2006; Vorauer, 2006). In the present chapter, we review the literature on the interpersonal dynamics of interracial interactions. We focus on black–white interracial contact because of its historical prominence in the United States and, accordingly, the considerable theoretical and empirical attention it has received. Building on current models and extant data, we propose an emergent framework for understanding behavior during interracial contact (see Figure 20.1, panel 3) and intergroup contact more generally (see also Trawalter, Richeson, & Shelton, 2009). More specifically, we propose that implicit racial biases shape relatively automatic reactions to interracial contact, reactions best characterized as anxious reactions (e.g., fidgeting and blinking), whereas explicit biases shape relatively controlled responses to interracial contact, responses best characterized as efforts to manage intergroup anxiety (e.g., compensatory behavior and verbal friendliness). We incorporate theory and research on how prejudice-Â�related concerns affect both relatively automatic as well as more controlled behavior during interracial contact. As will become evident, prejudice-Â�related concerns add complexity to our understanding of intergroup dynamics, a complexity we have yet to fully

378

IV. DOMAIN-SPECIFIC PERSPECTIVES

comprehend. Finally, we examine how whites’ and racial minorities’ relatively automatic and more controlled intergroup behaviors affect their interaction partners. This general framework organizes major works on intergroup contact, using advances in implicit social cognition research to expose gaps in our understanding of intergroup dynamics. We thus end our discussion with important directions for future work.

AUTOMATIC RESPONSES TO€INTERRACIAL CONTACT: INTERGROUP ANXIETY Theoretical and empirical work finds that intergroup contact is stressful and anxiety provoking (Allport, 1954; Crocker, Major, & Steele, 1998; Shelton & Richeson, 2006b; Stephan & Stephan, 2000). However, researchers who have taken a relational approach—those who have examined intergroup contact from the perspective of both whites and racial minority individuals—find that the experience of intergroup anxiety may differ for majority and minority group members.

Whites’ Intergroup Anxiety The literature on intergroup contact has been unequivocal: Interracial contact is stressful for white individuals. But why? The answer lies, in part, in implicit social cognitions. Despite improving explicit racial attitudes, many white individuals continue to have negative implicit racial biases. That is, they continue spontaneously and perhaps unconsciously to associate racial minorities with negative concepts (e.g., death, garbage) and attributes (e.g., lazy, dangerous) (Devine, 1989; Greenwald et al., 1998; Payne, 2001). In addition, many whites are concerned about appearing prejudiced (Dovidio & Gaertner, 1998; Dunton & Fazio, 1997; Plant & Devine, 1998; Shelton & Richeson, 2006b; Vorauer, 2006; Vorauer, Hunter, Main, & Roy, 2000; Vorauer, Main, & O’Connell, 1998) and fear embarrassment, ridicule, and rejection in intergroup contexts (Shelton & Richeson, 2005; Stephan & Stephan, 2000; Vorauer, 2006). Given these concerns and implicit biases, it is not surprising that interracial contact remains stressful and anxiety provoking for many white individuals (Shelton & Richeson, 2006b; Stephan & Stephan, 2000; Vorauer, 2006). However, just as individuals are often not able or willing to report negative racial biases, they are often not able or willing to report intergroup anxiety. Therefore, research using unobtrusive measures has

been instrumental in investigating and revealing intergroup anxiety. This research has found that white individuals tend to exhibit neural activity in regions of the amygdala, a brain structure involved in fear conditioning and the processing of fear- and anxiety-Â�related stimuli, in response to black target faces (see Dovidio, Pearson, & Orr, 2008; Eberhardt, 2005, for reviews). Many white individuals also exhibit patterns of maladaptive physiological reactivity indicative of threat during interracial contact (Blascovich, Mendes, Hunter, Lickel, & Kowai-Bell, 2001; Littleford, Wright, & Sayoc-Â�Parial, 2005; Major, Townsend, Sawyer, & Mendes, 2009; Mendes, Blascovich, Lickel, & Hunter, 2002; Page-Gould, Mendoza-Â�Denton, & Tropp, 2008), and they automatically orient to black target faces as if these faces were a threat (Donders, Correll, & Wittenbrink, 2008; Trawalter & Richeson, 2008). Taken together, these various lines of research suggest that interracial contact elicits anxiety and fear in many white Americans. Consistent with the notion that intergroup anxiety stems from implicit biases and prejudiceÂ�related concerns, research has found that white individuals with negative implicit racial biases and those concerned about appearing prejudiced are most likely to experience intergroup anxiety. In a study on physiological responses to interracial contact (Mendes, Gray, Mendoza-Â�Denton, Major, & Epel, 2007), for instance, experimenters asked white participants to interact with two white or two black interviewers. Before and after these interactions, participants provided saliva samples, assayed for two hormones: cortisol, a principal stress hormone, and DHEA-S, a growth hormone that counters some of the negative stress effects of cortisol. Results revealed that participants exhibited significant cortisol increases in response to these interactions regardless of the interviewers’ race. In interracial interactions, however, participants with lower levels of implicit bias (as measured by the IAT) also exhibited increases in DHEA-S. These results suggest that whites with higher levels of implicit racial bias experience interracial interactions differently—less adaptively—than those with lower levels of implicit racial bias. Similarly, prejudice-Â�related concerns can heighten intergroup anxiety and amplify maladaptive physiological responses to interracial contact (Plant & Butz, 2006; Shelton, 2003; Trawalter, Adam, Chase-Â�Lansdale, & Richeson, 2009). A recent study (Trawalter et al., 2009) found that white participants who were concerned about appearing prejudiced exhibited significant increases in cortisol stress hormone levels during interracial, but not same-race, interactions. In contrast, white



20. Bias and Interpersonal Processes

participants who were relatively unconcerned about appearing prejudiced exhibited no cortisol increases during interracial contact; instead, these participants’ cortisol patterns looked similar to those of white participants who engaged in samerace interactions. Taken together, these data provide converging evidence that whites’ implicit racial biases and prejudice-Â�related concerns can give rise to stress and anxiety in interracial contexts. Of importance to our analysis, intergroup anxiety has been shown to have behavioral consequences; it can elicit relatively automatic negative behavior, behavior best characterized as anxious behavior. White individuals, on average, behave more anxiously during interracial than same-race interaction (e.g., Trawalter & Richeson, 2008), and this is especially true of those who harbor negative implicit racial biases and those who have concerns about appearing prejudiced. Numerous studies find that negative implicit racial biases predict excessive blinking and fidgeting, reduced eye contact, closed body posture, speech errors, and increased interpersonal distance during interracial but not same-race contact (Dovidio et al., 1997, 2002; Fazio et al., 1995; McConnell & Leibold, 2001; Sekaquaptewa et al., 2003). Likewise, research has found that concerns about appearing prejudiced can lead to behavioral rigidity, closed body posture, averted eye gaze, and increased interpersonal distance during interracial but not same-race contact (Goff, Steele, & Davies, 2008; Trawalter et al., 2009). In sum, extant data suggest that white individuals’ implicit racial biases and prejudice-Â�related concerns can lead to intergroup anxiety. Intergroup anxiety, in turn, can lead to relatively automatic, anxious behavior during interracial encounters. Given the prevalence of implicit racial biases and concerns about appearing prejudiced among white individuals, intergroup anxiety may pervade many, if not most, interracial interactions.

Racial Minorities’ Intergroup Anxiety Racial minorities can also experience anxiety during intergroup interactions. However, the antecedents of their anxiety—their racial biases and prejudice-Â�related concerns—Â�differ markedly from those of whites. Whereas white Americans’ explicit racial attitudes have become increasingly positive over the last decades, racial minorities’ racial attitudes toward whites have remained somewhat negative (Brigham, 1993; Johnson & Lecci, 2003; Livingston, 2002; Monteith & Spicer, 2000; Stephan et al., 2002). Often these racial biases are rooted in the expectation and concern that one

379

will be the target of whites’ prejudice and discrimination (Mendoza-Â�Denton, Downey, Purdie, Davis, & Pietrzak, 2002; Shelton & Richeson, 2006b). These concerns can be particularly poignant if and when outcomes depend on whites, as is often the case in U.S. society (e.g., Shapiro & Neuberg, 2008). Implicit social cognition research has added a layer of complexity (and confusion) to our understanding of minority group members’ racial biases, however. This research has found that some racial minority groups, black Americans in particular, show no implicit racial bias (in favor of ingroup members or against white outgroup members) on measures such as the IAT; and in many studies, they show a small albeit significant “reversal” in racial bias. That is, some black individuals exhibit a bias in favor of white outgroup members relative to black ingroup members (Ashburn-Nardo, Knowles, & Monteith, 2003; Livingston, 2002; Rudman, Feinberg, & Fairchild, 2002; Uhlmann, Dasgupta, Elgueta, Greenwald, & Swanson, 2002). To date, it is unclear whether these biases reflect attitudinal preferences. Moreover, it remains unclear whether these biases contribute to racial minorities’ intergroup anxiety. However, one study finds that these implicit biases do predict anxiety-Â�related (i.e., avoidant) behavior. In this study, racial minority participants who were higher on implicit prowhite/anti-black bias chose a white partner over a black partner for teamwork on an “intellectual” task (Ashburn-Nardo et al., 2003). More work is clearly needed to understand the nature and influence of these biases on intergroup dynamics. Although research has yet to fully examine racial minorities’ racial biases, especially implicit ones, it has considered the nature and impact of racial minorities’ prejudice-Â�related concerns. Specifically, intergroup research from the perspective of racial minorities has focused almost exclusively on the pernicious effects of prejudice and discrimination and on racial minorities’ perceptions of and concerns about prejudice and discrimination (e.g., Clark, Anderson, Clark, & Williams, 1999; Major & O’Brien, 2005; Steele & Aronson, 1995; Williams, Neighbors, & Jackson, 2003). More recently, this work has expanded its purview to consider how concerns about being the target of prejudice affect the interpersonal dynamics of interracial contact. For example, some research has found that concerns about being the target of prejudice and discrimination heighten intergroup anxiety. Racial minority participants who expect to be the target of racial bias experience more negative emotions, including anxiety, in anticipation of interracial contact (Plant & Butz, 2006; Tropp, 2003).

380

IV. DOMAIN-SPECIFIC PERSPECTIVES

Affective reactions such as these are evident in physiological responses to interracial contact. In a recent investigation of physiological responses to intergroup contact, Latino participants interacted with another Latino or white interaction partner three times over the course of several weeks (PageGould et al., 2008). Among Latino participants, those who were highly concerned about being the target of prejudice (compared with those who were not) exhibited higher levels of anxiety—Â�namely greater cortisol increases—in response to interracial but not same-race interactions. However, these anxious responses to interracial interactions diminished across the three study sessions as Latino participants and white interaction partners became friends. Racial minorities’ prejudice-Â�related concerns do not always lead to greater intergroup anxiety, however. One study, for instance, found that black participants reported no more anxiety and greater enjoyment during an interracial interaction with a white partner who was “likely to be prejudiced against blacks” than with a white partner who was not described as such (Shelton, 2003). Although these findings seem contradictory to those just described, they demonstrate the noxious effects of the ambiguity and uncertainty that can sometimes accompany prejudice-Â�related concerns (e.g., Hirsh & Inzlicht, 2008). In some cases, knowing that one’s white interaction partner is prejudiced may be less stressful than wondering whether he or she is. Perhaps having one’s prejudice-Â�related concerns validated offers certainty about the interaction (e.g., “My white interaction partner is prejudiced”) and provides the opportunity to develop a plan of action to deal with this knowledge (e.g., “Kill them with kindness”). Although more research is needed to understand when prejudice-Â�related concerns elicit or reduce intergroup anxiety, what is clear is that such concerns affect racial minorities’ intergroup anxiety about and during interracial contact. Whether racial minorities’ intergroup anxiety shapes their relatively automatic intergroup behavior is also unclear. Some research has found that racial minorities who expect to be the target of prejudice and discrimination fidget more during interracial interactions than do those who do not expect to be the target of prejudice (Shelton, 2003). This finding suggests that intergroup anxiety is evident in racial minorities’ relatively automatic anxious behavior, just as whites’ intergroup anxiety is evident in their behavior. However, there is also evidence that racial minorities do not experience intergroup anxiety to the same degree (or in the same types of situations)

as whites or, alternatively, that they do not reveal their intergroup anxiety as much as whites (Ickes, 1984; Trawalter & Richeson, 2008; cf. Hyers & Swim, 1998). In one illustrative study (Trawalter & Richeson, 2008), white participants were paired with a white or black interaction partner who was also a naive participant of the study. Participants and interaction partners discussed race-Â�related and race-Â�neutral topics. These discussions were videotaped and then independently coded for nonverbal anxiety. Results showed that black participants’ behavior revealed less anxiety than did their white interaction partners’ behavior. Moreover, this difference was most pronounced during race-Â�related discussions. These data suggest that, compared with their white interaction partners, black participants were less anxious during interracial interactions or masked their intergroup anxiety more successfully than did white partners. At first blush, these findings seem inconsistent with those of Shelton (2003) just described. As Trawalter and Richeson argued, however, race-Â�related discussions may not elicit prejudice-Â�related concerns for black participants. For black participants of their study who were students at a predominantly white college, interracial contact and race-Â�related discussions were likely familiar experiences. In other words, black participants in this study were “experts” of interracial contact and race-Â�related discussions. Such race-Â�related expertise may have provided black participants with some leverage during interactions with (more “novice”) white interaction partners. Hence, they were less anxious and behaved less anxiously. In sum, although researchers have only begun to investigate minority group members’ cognitions, especially implicit ones, the available data suggest that intergroup contact can be stressful for minority group members, although for different reasons and perhaps in different ways than it is for whites. Whether this anxiety shapes intergroup behavior remains an open question. The existing data are inconclusive and, therefore, more research is clearly needed.

CONTROLLED RESPONSES TO INTERRACIAL CONTACT: MANAGING INTERGROUP ANXIETY Given that intergroup contact is often anxiety provoking, white and racial minorities must often manage their intergroup anxiety. That is, they must reduce, override, or at least mask their anxiety to preserve positive mood or have a smooth



20. Bias and Interpersonal Processes

interracial interaction. To do that, they must regulate their thoughts (e.g., stereotypes), emotions (e.g., prejudice), and behaviors (e.g., discrimination) in the service of regulating their anxiety. Before we discuss the ways in which whites and racial minorities self-�regulate and manage their intergroup anxiety, we first present evidence that they self-�regulate during interracial contact and that this self-�regulation is intimately connected to intergroup anxiety.

Self-�Regulation of Intergroup Anxiety during Interracial Contact According to models of executive attention (e.g., Muraven & Baumeister, 2000), self-�regulation requires cognitive resources, namely executive attentional resources. These resources are thought to be limited (albeit renewable). Consequently, self-�regulation can deplete these resources and result in suboptimal performance on any concurrent or subsequent task that also requires these same resources. Interracial contact, therefore, should be cognitively depleting to the extent that it requires self-�regulation. Consistent with this notion, research has found that interracial contact is cognitively depleting for both white and racial minority individuals. Several studies have found that whites and racial minorities perform more poorly on a measure of cognitive executive function (i.e.,€ the€ Stroop task) following interracial compared with same-race contact, and these performance decrements seem to be the result of self-�regulation (Richeson & Shelton, 2003; Richeson et al., 2003; Richeson & Trawalter, 2005; Richeson, Trawalter, & Shelton, 2005; Trawalter & Richeson, 2006). If, indeed, interracial contact is cognitively depleting because individuals regulate their thoughts, feelings, and behaviors to manage their intergroup anxiety, then those who experience more intergroup anxiety should find interracial contact more depleting. Consistent with this logic, research has also found that white and black individuals who are higher on implicit racial bias (against their respective outgroup) exhibit greater cognitive depletion following interracial contact compared with those who are lower on implicit racial bias (Richeson & Shelton, 2003; Richeson et al., 2005). Additionally, studies have shown that experimentally manipulating prejudice-�related concerns results in differential levels of cognitive depletion following interracial but not same-race interactions (Richeson & Trawalter, 2005). For instance, in one study, white participants who were told that they might be more prejudiced than they thought they

381

were exhibited more cognitive depletion following interracial but not same-race interactions. These data suggest that white participants self-Â�regulated more during interracial contact when their intergroup anxiety was heightened. Conversely, white participants given a script to read during raceÂ�related discussions exhibited less cognitive depletion following interracial but not same-race contact. These findings suggest that white participants self-Â�regulated less during interracial contact when their intergroup anxiety was alleviated. Taken together, this program of research corroborates the claim that white individuals regulate their intergroup anxiety during interracial contact, leaving them cognitively depleted. Racial minorities’ prejudice-Â�related concerns, to the extent that they elicit intergroup anxiety, should also instigate self-Â�regulation and lead to cognitive depletion. In a provocative study (Salvatore & Shelton, 2007), a sample of black participants reviewed an evaluator’s hiring decisions. These hiring decisions were manipulated to be nonprejudiced, ambiguously prejudiced, or blatantly prejudiced. After reviewing the evaluator’s decisions, participants completed the Stroop task as a measure of cognitive depletion. Results revealed that black participants performed more poorly on the Stroop task—they were more depleted—after reviewing the ambiguously prejudiced hiring decision compared with the nonprejudiced or blatantly prejudiced hiring decision. As in Shelton’s (2003) previously described study, in which black participants reported greater enjoyment during an interracial interaction with a white partner who was “likely to be prejudiced against blacks,” this study speaks to the noxious effects of uncertainty and ambiguity. In some cases, the uncertainty that one may be the target of prejudice may be more aversive—more anxiety provoking and depleting—than the certainty that one is the target of prejudice. Although more research is needed to determine when the uncertainty that accompanies prejudice-Â�related concerns is more costly than the certainty that prejudice has transpired, what these findings demonstrate is that racial minorities’ prejudice-Â�related concerns can elicit not only intergroup anxiety but self-Â�regulation. In short, interracial contact can be cognitively depleting for both whites and racial minorities, particularly those experiencing intergroup anxiety. These findings suggest that whites and racial minorities self-Â�regulate and, more specifically, selfÂ�regulate their intergroup anxiety during interracial encounters. In the following section, we examine how whites and racial minorities manage their intergroup anxiety.

382

IV. DOMAIN-SPECIFIC PERSPECTIVES

Whites’ Intergroup Anxiety€Management The easiest and arguably the most effective way to manage intergroup anxiety is to avoid interracial contact. If interracial contact can be avoided, implicit biases and concerns about appearing prejudiced cannot lead to intergroup anxiety and taint interracial interactions. Indeed, self-Â�report data reveal that white individuals, particularly those who are racially biased or concerned about appearing prejudiced, avoid intergroup contact (Plant, 2004; Plant & Devine, 2003; Shelton & Richeson, 2005). When intergroup contact is unavoidable, however, individuals must manage their intergroup anxiety in a different way. They must reduce, override, or mask their intergroup anxiety, strategies that often require self-Â�regulation. Because a vast and growing majority of white Americans have positive explicit attitudes and concerns about appearing prejudiced, the anxiety management strategy of choice is often compensation: exceedingly positive behavior. Consistent with this claim, studies have found that white individuals’ controlled behavior during interracial interactions such as verbal friendliness is often quite positive even while their more automatic behavior is negative or, as we have argued, anxious (e.g., Dovidio et al., 1997; Fazio et al., 1995; Ickes, 1984; McConnell & Leibold, 2001; Trawalter & Richeson, 2008; Trawalter et al., 2009). Ironically then, whites who are prone to intergroup anxiety—those who are high on implicit racial bias and prejudice-Â�related concerns—are sometimes those who behave most positively during interracial contact (Shelton, 2003; Shelton, Richeson, Salvatore, & Trawalter, 2005; Vorauer & Turpie, 2004). For example, in a study examining prejudice-Â�related concerns, white participants given instructions to not be racially biased during an interaction (compared with white participants not given instructions) experienced more anxiety but were rated more favorably by their black interaction partner (Shelton, 2003). This is evidence that, under some conditions, whites behave quite positively during intergroup contact in spite—and we would argue because— of their intergroup anxiety. As Shelton suggested, we believe that these behaviors represent efforts to compensate for negative implicit racial attitudes and to manage intergroup anxiety. Thus, implicit racial biases and prejudice-Â�related concerns need not result in negative behavior during interracial interactions; to the contrary, they can result in positive intergroup behavior if white individuals manage their anxiety in compensatory ways.

Not all efforts to manage intergroup anxiety are successful, however. Managing intergroup anxiety is effortful, and individuals who do not have adequate resources may not manage their anxiety effectively. Consistent with this claim, recent work has found that a cognitive load can undermine individuals’ efforts to behave positively during intergroup interactions (Hofmann, Gschwendner, Castelli, & Schmitt, 2008). More specifically, in this work, participants asked to remember a word list while engaging in interracial contact behaved less positively the more implicitly biased they were. We presume that these participants were not able to manage their intergroup anxiety because of low resources. In contrast, participants in the control condition—those not asked to remember a word list—Â�behaved positively irrespective of their levels of implicit bias. They were able to manage their intergroup anxiety, masking their anxiety by engaging positively in the interaction. This study suggests that white individuals can behave positively during interracial contact—they can compensate for their intergroup anxiety—but only if they have the resources to do so. Without adequate resources, they may remain anxious and behave accordingly. Moreover, some white individuals may adopt misguided strategies to manage their intergroup anxiety. In a series of studies (Norton, Sommers, Apfelbaum, Pura, & Ariely, 2006), researchers found that white participants will go to great lengths to avoid mentioning race when interacting with black, but not white, partners even when doing so is relevant and appropriate. The rationale for such “color-blind” behavior is that “if one does not see race then one cannot be racist” (BonillaSilva, 2003). Thus, whites who are concerned about appearing prejudiced can (and often attempt to) manage their concerns by feigning “color blindness.” Follow-up studies (Apfelbaum, Sommers, & Norton, 2008, Studies 1 and 2) revealed that white participants who refrained from mentioning race during interracial contact behaved more anxiously (e.g., made less eye contact) than those who mentioned race, however. Not surprisingly, independent judges, black judges especially, evaluated participants who avoided mentioning race more negatively than participants who mentioned race (Apfelbaum et al., 2008, Study 3); judges likely noticed participants’ anxious behavior and used this behavior to guide their evaluations. These findings suggest that attempting to appear color blind may be an ineffective strategy for managing intergroup anxiety and making a positive impression on racial minority interaction partners, at least some of the time.



20. Bias and Interpersonal Processes

In brief, research suggests that many whites behave positively during interracial contact in order to manage their intergroup anxiety if they have cognitive resources and effective behavioral strategies. This can explain why high-bias whites sometimes behave more positively than low-bias whites (Shelton, Richeson, Salvatore, & Trawalter, 2005). We would argue that these high-bias whites are managing their intergroup anxiety and are doing so by behaving in exceedingly positive, compensatory ways. This interpretation can also make sense of why individual differences in implicit racial biases and prejudice-Â�related concerns can lead to more negative (i.e., relatively automatic, anxious) behavior and, concurrently, more positive (i.e., relatively controlled, compensatory) behavior. That is not to say that all of whites’ efforts to manage their intergroup anxiety results in positive behavior when they have sufficient resources and adequate behavioral strategies. Whites’ efforts to manage their anxiety can also result in negative behavior if they are not motivated to manage their anxiety in prosocial ways. There is plenty of anecdotal evidence of racially motivated affronts and even hate crimes. What the extant data suggest is that whites’ controlled behaviors are often positive and sometimes exceedingly so when whites have positive explicit attitudes and/or prejudiceÂ�related concerns. A shrinking but sizable minority of white Americans continue to hold negative explicit attitudes. For these individuals, interracial contact may unfold quite differently. Interracial contact may not be particularly stressful or anxiety provoking (see Vorauer & Kumhyr, 2001, for suggestive evidence). Presumably, they do not care about racial minorities’ intergroup experiences or about the impressions they make during these interactions. Free of intergroup anxiety, these individuals may behave as they feel during interracial interactions; they may behave negatively because they feel negatively toward racial minorities. Such negative behavior can be gleaned from studies by Vorauer and colleagues (Vorauer & Kumhyr, 2001; Vorauer & Turpie, 2004). In one of their studies (Vorauer & Turpie, Study 2), for example, white Canadian participants ranging in explicit racial biases interacted with a First Nations (ethnic minority) interaction partner. White participants who reported higher levels of explicit racial bias behaved more negatively during the interaction compared with participants who reported lower levels of explicit bias. However, participants who reported higher levels of explicit racial bias behaved more positively if their First Nations interaction partner had voiced aggravation about being the target of prejudice and discrimination

383

on campus than if their partner had not voiced such aggravation. In fact, these participants behaved as positively as participants who reported lower levels of explicit racial bias.2 These data suggest that, without external cause or reason, explicitly biased whites behave negatively during interracial encounters, thereby creating a hostile environment for minorities (Vorauer & Kumhyr, 2001). However, given an external cause or reason, high-bias whites can compensate for their negative racial attitudes just as low-bias whites often compensate for their intergroup anxiety during interracial interactions.

Racial Minorities’ Intergroup Anxiety€Management Because intergroup contact can be stressful for minority group members, they too must manage their intergroup anxiety. Like whites, many racial minorities can manage their intergroup anxiety by avoiding interracial contact. Indeed, research has found that racial minorities, especially those who have negative attitudes toward whites and those who are concerned about being the target of prejudice, avoid interracial contact with whites (Brigham, 1993; Johnson & Lecci, 2003; Levin, van Laar, & Sidanius, 2003; Mendoza-Â�Denton et al., 2002; Patchen, 1983; Powers & Ellison, 1995). In fact, racial minority students who are higher on explicit bias have less contact with their white friends, and when they do have contact with white friends, they feel less comfortable discussing personal (race-Â�neutral) and race-Â�sensitive issues (Shelton & Richeson, 2006a; Shelton, Richeson, & Salvatore, 2005, Study 1). However, racial minorities, unlike whites, often cannot avoid intergroup contact. By virtue of being a numerical minority in the population and many social domains, interracial contact is often unavoidable. Additionally, because whites often control important resources and outcomes, many racial minorities are motivated to forge and maintain amicable relationships with whites. In other words, whereas whites can often afford to avoid intergroup contact, minority group members often cannot. Instead, they must manage their intergroup anxiety and they often do so by positively engaging in interracial contact. Consistent with this proposition, the existing research has found that racial minorities often use compensatory and intimacyÂ�building behaviors (e.g., smiling, self-Â�disclosure, conforming to white mainstream norms) to deal with the demands of intergroup contact with whites (Frable, Blackstone, & Scherbaum, 1990; Hyers & Swim, 1998; Shapiro & Neuberg, 2008;

384

IV. DOMAIN-SPECIFIC PERSPECTIVES

Shelton, 2003; Shelton, Richeson, & Salvatore, 2005).

IMPLICATIONS FOR INTERGROUP CONTACT If and when whites and racial minorities manage their intergroup anxiety by behaving in these compensatory ways, their interaction partner may enjoy the interracial interaction in spite of their partner’s intergroup anxiety. What this means, unfortunately, is that an interracial interaction that is pleasant for one may not be pleasant for the other: It may be pleasant for the one whose partner is anxious and managing his or her anxiety (i.e., compensating) but not for the partner who is anxious and devoting resources to manage it (Shelton & Richeson, 2006b). Indeed, there is suggestive evidence that racial minorities often enjoy interracial interactions more when their white interaction partner is anxious about it. In one revealing study, black interaction partners evaluated more favorably white participants who were higher, not lower, on implicit racial bias (Shelton, Richeson, Salvatore, & Trawalter, 2005). Similarly, in another study black participants evaluated more favorably white interaction partners who were instructed to not be racially biased than those not given instructions for the interracial interactions (Shelton, 2003; see also Wetzel, Blalock, & Bolger, 2004). In both cases, white interaction partners were anxious about the interracial interactions and yet behaved in exceedingly positive ways to compensate for their implicit biases and to deal with their prejudice-Â�related concerns. Taken together, these studies suggest that whites’ efforts to manage their intergroup anxiety can be successful in creating a more positive experience for racial minority interaction partners. However, whites’ compensatory behaviors are not always successful; they do not always reduce or even mask their intergroup anxiety (e.g., Dovidio et al., 1997; Ickes, 1984; Trawalter et al., 2009). Often whites continue to feel anxious and behave anxiously. Consequently, even if their controlled behavior is positive, their relatively automatic behavior remains negative (i.e., anxious). Although anxiety-Â�related behaviors may be subtle (e.g., blinking, fidgeting), research has found that whites’ anxious behavior does not go unrecognized by racial minorities. A number of studies find that black interaction partners and observers evaluate whites who are higher on implicit racial bias more negatively than those who are lower on implicit

bias (Dovidio et al., 2002; Fazio et al., 1995; McConnell & Leibold, 2001; Richeson & Shelton, 2005; Sekaquaptewa et al., 2003). These findings imply that blacks are sensitive to whites’ relatively automatic anxious behavior during interracial encounters. In one telling study (Dovidio et al., 2002), black interaction partners evaluated white participants less positively the more nonverbal anxiety (e.g., averted eye gaze) participants exhibited. White participants, on the other hand, evaluated their own behavior more positively the more positive their verbal behavior. This study highlights the divergent experiences racial minorities and whites are likely to have during interracial encounters. Unbeknownst to the white participants who mainly attended to their positive (compensatory) behavior and assumed that their behavior made a positive impression on their racial minority interaction partners, racial minority partners primarily attended to the white participants’ relatively automatic anxious behavior and interpreted it as negative and even hostile. These data stand in stark contrast to those suggesting that whites’ intergroup anxiety fuels efforts to behave positively and effectively improve the quality of interracial contact for racial minority interaction partners. For now, what is clear is that whites’ compensatory efforts and their intergroup anxiety affect racial minorities’ intergroup impressions and experiences. In addition, whites’ anxious behavior can inadvertently affect racial minorities’ performance in interracial contexts. In a study of interracial contact and work performance, black participants were paired with a white partner who either had negative explicit and negative implicit racial bias,€ positive explicit and negative implicit racial€bias, or positive explicit and positive implicit racial bias3 (Dovidio, 2001). Not surprisingly, results revealed that the interracial dyads performed best when the white partner had positive explicit and implicit racial biases. Presumably, these white individuals’ relatively automatic and more controlled behaviors were both positive, resulting in a comfortable and safe atmosphere for their black partner and, ultimately, good performance. However, interracial dyads in which the white partner had negative explicit and implicit racial biases performed better than dyads in which the white partner had positive explicit but negative implicit racial biases. These latter dyads were more difficult to navigate for black partners, perhaps because they had to contend with mixed signals, namely white partners’ anxious and compensatory behaviors. Again, it appears that ambiguity in interracial



20. Bias and Interpersonal Processes

interactions can be difficult to manage. The certainty that one’s partner is prejudiced (i.e., having a white partner high in both explicit and implicit bias) seemed to be less challenging than the ambiguity that one’s partner may or may not be prejudiced (i.e., having a white partner high in implicit but low in explicit bias). Taken together, these data provide some evidence that subtle behaviors that likely reflect intergroup anxiety may have serious negative consequences for racial minorities (see also Word, Zanna, & Cooper, 1974). These findings dovetail nicely with the literature on stereotype threat, which finds that situational characteristics can give rise to concerns that one’s performance on a stereotype-Â�relevant task might confirm a negative stereotype. As a result, these concerns can usurp the cognitive resources needed to do well on many tasks (e.g., academic tests) and can lead to underperformance (Shapiro & Neuberg, 2007; Steele & Aronson, 1995). In a now classic series of studies, black, but not white, students underperformed on difficult SAT-like tests when the tests were labeled as diagnostic of intellectual ability or when the students were asked to report their race beforehand (Steele & Aronson, 1995). What is so powerful about this phenomenon is that it is triggered by situational cues that make salient one’s group identity and the negative stereotypes associated with it. Accordingly, intergroup contexts can elicit stereotype threat. For example, black participants are more likely to experience stereotype threat and underperform on a diagnostic verbal test in the presence of a white, but not a black, experimenter (Marx & Goff, 2005). Research also finds that stigmatized group members with “solo” status, who are the sole representative of their group in an otherwise majority-Â�dominated setting, are more likely to experience stereotype threat and exhibit stereotype threat performance decrements compared with those without solo status (e.g., Inzlicht & BenZeev, 2000; Lord & Saenz, 1985). The use of implicit social cognitive measures and theory in stereotype threat research has deepened our understanding of stereotype threat. It has revealed that intergroup contexts elicit stereotype threat and subsequent performance decrements through two mechanisms: anxiety and cognitive depletion, the two interpersonal processes inherent to interracial interactions. More specifically, this research has found that anxiety, measured implicitly but not explicitly (e.g., Bosson, Haymovitz, & Pinel, 2004; Johns, Inzlicht, & Schmader, 2008), and cognitive depletion (Beilock, 2008; Johns et al., 2008; Schmader, Johns, & Forbes,

385

2008; Schmeichel, Vohs, & Baumeister, 2003) mediate stereotype threat performance decrements. That said, the use of implicit social cognitive measures has also challenged our understanding of stereotype threat. Earlier research had suggested that stereotype threat transpires when individuals believe that some stereotype could be true of their personal abilities in a domain (i.e., when individuals explicitly endorse the stereotype about their group; Schmader, Johns, & Barquissau, 2004). However, more recent research using implicit measures of stereotypic associations has found that stereotype threat is most likely to occur when group-Â�identified individuals implicitly associate their group with stereotypic traits regardless of whether they endorse these stereotypes explicitly (Kiefer & Sekaquaptewa, 2007). These more recent findings seem incompatible with those of Schmader and colleagues (2004) but we believe that they can be reconciled. Shapiro and Neuberg (2007) articulated a multithreat framework, in which six qualitatively distinct stereotype threats arise depending on the target of the stereotype threat (who will the performance reflect on: the self or the group) and the source of the stereotype threat (who will have access to this performance: the self, outgroup others, ingroup others). In particular, negative stereotypes about one’s group may lead one to experience a threat to one’s (1) personal self-Â�concept (i.e., “What if this stereotype is true of me?”), (2) group concept (“What if this stereotype is true of my group?”), (3) own reputation in the eyes of outgroup members (“What if outgroup others see me as stereotypic?”), (4) ingroup’s reputation in the eyes of outgroup members (“What if outgroup others see my group as stereotypic?”), (5) own reputation in the eyes of ingroup members (“What if ingroup others see me as stereotypic?”), and (6) ingroup’s reputation in the eyes of ingroup members (“What if ingroup others see our group as stereotypic?”). This framework also identifies the different individual differences and situational characteristics that give rise to each of these different stereotype threats. Returning to the mixed findings regarding implicit and explicit stereotype associations and stereotype threat, we take the implicit measures of gender stereotyping used by Kiefer and Sekaquaptewa (2007) as providing evidence that participants were knowledgeable about the applicability of the stereotype to their gender, knowledge that should be necessary, in some form, for all stereotype threats to emerge; if one does not know of the stereotype or its applicability to the group, there is no reason to be threatened by it. However, this

386

IV. DOMAIN-SPECIFIC PERSPECTIVES

does not mean that participants believed the stereotypes are likely to be true of their own personal abilities, which is the construct that Schmader and colleagues assessed explicitly—and one that should be necessary for the emergence of some but not all forms of stereotype threats (e.g., selfas-Â�source stereotype threats). Clearly, a more systematic examination of the implicit and explicit social cognitions underlying stereotype threat will be useful in understanding the varied experiences of stereotype threat. In sum, interracial contact and, more specifically, whites’ intergroup behavior can have negative consequences for racial minorities. Whites’ implicit racial biases and prejudice-Â�related concerns can give rise to anxious behavior and, in turn, undermine the quality and outcomes of interracial contact. That said, whites who are concerned about being prejudiced can sometimes compensate for their intergroup anxiety and behave in exceedingly positive ways. In that case, whites’ compensatory behavior can improve the quality of interracial contact for their racial minority interaction partner. More research is now needed to delineate the conditions under which whites’ intergroup anxiety can improve rather than undermine racial minorities’ intergroup experiences and outcomes. Just as whites’ intergroup anxiety and anxiety management can sometimes improve racial minorities’ intergroup experiences, so too can racial minorities’ anxiety management improve whites’ experiences. For example, white participants of one study reported less anxiety when their black partner expected them to be racially biased compared with when their partner did not have this expectation (Shelton, 2003). Consequently, they enjoyed the interactions more. In another study (Shelton, Richeson, & Salvatore, 2005, Study 2), racial minority participants primed to expect prejudiced treatment experienced more negative affect and felt less authentic during interactions with white interaction partners compared with those not primed with prejudice-Â�related expectancies. However, these participants also self-Â�disclosed more and, consequently, were evaluated more favorably by their white interaction partners. Taken together, these data suggest that racial minorities can increase white partners’ comfort and enjoyment during interracial interactions, especially when they are concerned about being the target of prejudice and managing their intergroup anxiety by using compensatory strategies. Over time, such divergent experiences can deepen racial divides. In a longitudinal 3-week field study (Shelton, Richeson, & Salvatore, 2005, Study

1), racial minority college students who were concerned about being the target of prejudice reported experiencing more negative (anxious) emotions during interactions with their white roommate compared with those who were less concerned.4 Daily diary data also revealed that these racial minority students self-�disclosed more personal information to their white roommate compared with those who were less concerned. The authors speculated that these students managed their concerns about being the target of prejudice by using compensatory, intimacy-�building behavior toward their white roommate. Managing concerns using compensatory strategies came at a cost, however. Racial minority students who were concerned about being the target of prejudice reported feeling more inauthentic after interactions with a white roommate compared with those who were less concerned. Overall, these data suggest that racial minorities can often create a positive and warm atmosphere for white interaction partners if and when they use compensatory strategies to deal with their intergroup anxiety. However, doing so may result in feelings of inauthenticity. Again, it seems that what makes interracial contact good for one (in this case, the white interaction partner) does not make it good for the other (the racial minority interaction partner; see Shelton & Richeson, 2006b, for a discussion).

CURRENT AND FUTURE DIRECTIONS Over the last decades, social cognition theory and research have offered tremendous insights into the interpersonal dynamics of interracial contact. Social psychological research taking a social cognitive approach has found that implicit racial biases tend to predict relatively automatic reactions to interracial contact, reactions perhaps better characterized as intergroup anxiety, whereas explicit racial biases tend to predict more controlled responses to interracial contact, responses associated with efforts to manage intergroup anxiety. Additionally, research taking a relational perspective that considers both whites’ and racial minorities’ perspectives has shown that whites’ behavior affects and is affected by racial minorities’ intergroup behavior and vice versa. Whites who are anxious about interracial contact and concerned about appearing prejudiced can sometimes compensate and create a more positive environment for their racial minority partner. Likewise, racial minorities who are concerned about being the target of prejudice often



20. Bias and Interpersonal Processes

compensate to ward off prejudice and discrimination and, by so doing, improve whites’ interracial contact experiences. Still, many questions remain. Throughout the chapter, we have highlighted areas of research that require further investigation. Here, we outline more general future directions. Although research has clearly illustrated that the dynamics of interracial contact are complicated, few studies have examined the interplay between explicit and implicit racial biases and prejudiceÂ�related concerns (but see Fazio & Towles-Â�Schwen, 1999; Gawronski et al., 2008), and no studies to our knowledge have examined how this interplay affects intergroup behavior in actual interracial interactions. Moreover, few studies have taken a truly relational approach, considering how whites’ and racial minorities’ cognitions and behaviors interact to shape the dynamics of interracial contact (cf. West, Shelton, & Trail, 2009). We thus echo prior calls for more relational research and greater attention to the complex interactions between race-Â�related cognitions (Devine, Evett, & VasquezSuson, 1996; Hebl & Dovidio, 2005; Shelton, 2000; Shelton & Richeson, 2006b) because only this type of research will be able to develop interventions to increase the comfort of both whites and racial minorities during interracial contact. In addition, we urge researchers to consider more diverse samples. We primarily discussed research on black–white interactions because the bulk of research has focused on these interracial interactions. We encourage future research to consider the similarity and differences that emerge in other types of intergroup interactions (e.g., King, Shapiro, Hebl, Singletary, & Turner, 2006). Furthermore, nearly all of the studies reviewed in the current chapter were conducted with college samples. College students are not only younger but often more liberal than individuals in the larger population. Consequently, white college students may be more (and more uniformly) concerned about appearing prejudiced than noncollege samples. White college students’ prejudiceÂ�related concerns may also differ qualitatively from those of noncollege samples. Many white college students’ concerns may stem primarily from internal motivations (i.e., personal values), whereas at least some noncollege samples’ concerns are likely to stem primarily from external motivations (i.e., social pressure to behave nonprejudicially). Likewise, racial minority college students, especially those on predominantly white college campuses, likely differ from racial minority individuals in the larger population. Compared with noncollege samples, for example, these students may have

387

more interracial contact experiences, and these experiences may account for some of the results reviewed herein. For instance, it may explain why black students navigated interracial interactions with ease and skill (Trawalter & Richeson, 2008). It is possible that noncollege minority samples—Â� racial minority individuals with less interracial contact experience—would not demonstrate such ease and skill navigating interracial contact. As of yet, no studies have examined how whites’ different motivations to respond without prejudice and racial minorities’ experiences with interracial contact affect interpersonal behavior during actual interracial interactions. Along the same lines, we also urge researchers to consider more diverse interracial contexts. Nearly all of the studies reviewed in the present chapter were conducted in social psychology laboratories on college campuses. These laboratories are (and ought to be) “safe spaces.” Accordingly, the stated or presumed interaction goal of most if not all of these studies has been to have a positive interaction, to be liked. In real-life contexts, interaction goals may vary. In times of conflict, interaction goals may be opposite; these goals may include eliciting fear in others, for instance. The use of college samples in laboratory settings coupled with a focus on implicit biases and subtle behaviors has turned our attention to awkward intergroup dynamics and subtle discrimination. At the same time, it has shifted our attention away from negative explicit attitudes, malevolent intergroup dynamics, and overt discrimination in the world, outside of the laboratory, all of which still occur with some frequency. A more comprehensive and nuanced understanding of the dynamics of interracial contact, including negative contact and overt discrimination, will thus require the consideration of more diverse samples and settings as well as the examination of whites’ and racial minorities’ implicit and explicit cognitions.

FINAL COMMENTS To fulfill the promise of a multiracial society, we will need to meet the challenges of diversity, manage our intergroup anxieties in adaptive and prosocial ways and, ultimately, reduce our intergroup anxieties. Identifying the contextual factors and individual differences that give rise to these adaptive and maladaptive responses will allow us, as a field and as individuals, to improve the quality of intergroup contact and reap the benefits of diversity.

388

IV. DOMAIN-SPECIFIC PERSPECTIVES

Notes 1. We use the terms implicit and explicit to describe features of cognitions that arise from relatively automatic and more controlled cognitive processes, respectively. We recognize that these terms are contentious because of current debates regarding the automaticity and unconscious nature of implicit biases and the process specificity of implicit measures. However, rather than using these terms in quotation marks throughout the chapter, we simply make note of this theoretical and methodological complexity here. 2. This effect, we should note, was partly driven by participants low on racial bias behaving less positively after learning that their First Nations interaction partner was upset by prejudice and discrimination on campus, not only by participants high on racial bias behaving more positively. This finding is somewhat inconsistent with other findings (e.g., Shelton, 2003; Shelton, Richeson, & Salvatore, 2005; Shelton, Richeson, Salvatore, & Trawalter, 2005) that prejudice-Â�related concerns increase intergroup anxiety and positive engagement during interracial contact. We suspect that, in Vorauer and Turpie’s study, the low-bias participants’ behavioral negativity stemmed, at least in part, from increased intergroup anxiety. Indeed, some of the “intimacy-Â�building” behaviors that Vorauer and Turpie examined (e.g., eye contact) are sensitive to interpersonal anxiety. Furthermore, Vorauer and Turpie’s explanation—that low-bias participants “choked” when concerns were heightened—is consistent with our analysis that low-bias whites’ intergroup behavior is negative insofar as they are anxious about the interaction. 3. Negative explicit and positive implicit racial biases are an unlikely combination and, therefore, are typically left out of study designs such as this one (however, for an exception see Gawronski, Peters, Brochu, & Strack, 2008). 4. Interestingly, racial minority students who were concerned about being the target of prejudice experienced more positive emotions during interactions with a racial minority roommate than those who were less concerned.

References Allport, G. W. (1954). The nature of prejudice. Reading, MA: Addison-Â�Wesley. Apfelbaum, E. P., Sommers, S. R., & Norton, M. I. (2008). Seeing race and seeming racist?: Evaluating strategic colorblindness in social interaction. Journal of Personality and Social Psychology, 95, 918–932. Ashburn-Nardo, L., Knowles, M. L., & Monteith, M. J. (2003). Black Americans’ implicit racial associations and their implications for intergroup judgment. Social Cognition, 21, 61–87.

Beilock, S. L. (2008). Math performance in stressful situations. Current Directions in Psychological Science, 17, 339–343. Blascovich, J., Mendes, W. B., Hunter, S. B., Lickel, B., & Kowai-Bell, N. (2001). Perceiver threat in social interactions with stigmatized others. Journal of Personality and Social Psychology, 80, 253–267. Bobo, L. D. (2001). Racial attitudes and relations at the close of the twentieth century. In N. J. Smelser, W. J. Wilson, & F. Mitchell (Eds.), Racial trends and their consequences (Vol. 1, pp.€264–301). Washington, DC: National Academy Press. Bonilla-Silva, E. (2003). Racism without racists. Lanham, MD: Roman & Littlefield. Bosson, J. K., Haymovitz, E. L., & Pinel, E. C. (2004). When saying and doing diverge: The effects of stereotype threat on self-Â�reported versus non-Â�verbal anxiety. Journal of Experimental Social Psychology, 40, 247–255. Brigham, J. C. (1993). College students’ racial attitudes. Journal of Applied Social Psychology, 23, 1933–1967. Clark, R., Anderson, N. B., Clark, V. R., & Williams, D. R. (1999). Racism as a stressor for African Americans: A biopsychosocial model. American Psychologist, 54, 805–816. Crocker, J., Major, B., & Steele, C. (1998). Social stigma. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (Vol. 2, pp.€ 504–553). New York: McGraw-Hill. Crosby, F., Bromley, S., & Saxe, L. (1980). Recent unobtrusive studies of black and white discrimination and prejudice: A literature review. Psychological Bulletin, 87, 546–563. Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of Personality and Social Psychology, 56, 5–18. Devine, P. G., Evett, S. R., & Vasquez-Suson, K. A. (1996). Exploring the interpersonal dynamics of intergroup contact. In R. M. Sorrentino & E. T. Higgins (Eds.), Handbook of motivation and cognition: Vol. 3. The interpersonal context (pp.€423–464). New York: Guilford Press. Devos, T., & Banaji, M. R. (2005). American = white? Journal of Personality and Social Psychology, 88, 447– 466. Donders, N. C., Correll, J., & Wittenbrink, B. (2008). Danger stereotypes predict racially biased attentional allocation. Journal of Experimental Social Psychology, 44, 1328–1333. Dovidio, J. F. (2001). On the nature of contemporary prejudice: The third wave. Journal of Social Issues, 57, 829–849. Dovidio, J. F., Evans, N., & Tyler, R. B. (1986). Racial stereotypes: The contents of their cognitive representations. Journal of Experimental Social Psychology, 22, 22–37. Dovidio, J. F., & Gaertner, S. L. (1998). On the nature of contemporary prejudice: The causes, consequences, and challenges of aversive racism. Thousand Oaks, CA: Sage. Dovidio, J. F., & Gaertner, S. L. (2000). Aversive racism and selection decisions: 1989 and 1999. Psychological Science, 11, 315–319. Dovidio, J. F., Gaertner, S. E., Kawakami, K., & Hodson,



20. Bias and Interpersonal Processes

G. (2002). Why can’t we just get along?: Interpersonal biases and interracial distrust. Cultural Diversity and Ethnic Minority Psychology, 8, 88–102. Dovidio, J. F., Kawakami, K., & Gaertner, S. E. (2002). Implicit and explicit prejudice and interracial interaction. Journal of Personality and Social Psychology, 82, 62–68. Dovidio, J. F., Kawakami, K., Johnson, C., Johnson, B., & Howard, A. (1997). On the nature of prejudice: Automatic and controlled processes. Journal of Experimental Social Psychology, 33, 510–540. Dovidio, J. F., Pearson, A. R., & Orr, P. (2008). Social psychology and neuroscience: Strange bedfellows or a healthy marriage? Group Processes and Intergroup Relations, 11, 247–263. Duckitt, J. H. (1992). Patterns of prejudice: Group interests and intergroup attitudes. South African Journal of Psychology, 22, 147–156. Dunton, B. C., & Fazio, R. H. (1997). An individual difference measure of motivation to control prejudiced reactions. Personality and Social Psychology Bulletin, 23, 316–326. Eberhardt, J. L. (2005). Imaging race. American Psychologist, 60, 181–190. Fazio, R. H. (1990). Multiple processes by which attitudes guide behavior: The MODE model as an integrative framework. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 23, pp.€ 75–109). Orlando, FL: Academic Press. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobstrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Fazio, R. H., & Towles-Â�Schwen, T. (1999). The MODE model of attitude-Â�behavior processes. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€97–116). New York: Guilford Press. Frable, D. E., Blackstone, T., & Scherbaum, C. (1990). Marginal and mindful: Deviants in social interactions. Journal of Personality and Social Psychology, 59, 140–149. Gaertner, S. L., & McLaughlin, J. P. (1983). Racial stereotypes: Associations and ascriptions of positive and negative characteristics. Social Psychology Quarterly, 46, 23–30. Gawronski, B., Peters, K. R., Brochu, P. M., & Strack, F. (2008). Understanding the relations between different forms of racial prejudice: A cognitive consistency perspective. Personality and Social Psychology Bulletin, 34, 648–665. Gilens, M. (1995). Racial attitudes and opposition to welfare. Journal of Politics, 57, 994–1014. Goff, P. A., Steele, C. M., & Davies, P. G. (2008). The space between us: Stereotype threat and distance in interracial contexts. Journal of Personality and Social Psychology, 94, 91–107. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Hebl, M. R., & Dovidio, J. F. (2005). Promoting the “social” in the examination of social stigmas. Personality and Social Psychology Review, 9, 156–182.

389

Hirsh, J. B., & Inzlicht, M. (2008). The devil you know: Neuroticism predicts neural response to uncertainty. Psychological Science, 19, 962–967. Hofmann, W., Gschwendner, T., Castelli, L., & Schmitt, M. (2008). Implicit and explicit attitudes and interracial interaction: The moderating role of situationally available control resources. Group Processes and Intergroup Relations, 11, 69–87. Hyers, L. L., & Swim, J. K. (1998). A comparison of the experiences of dominant and minority group members during an intergroup encounter. Group Processes and Intergroup Relations, 1, 143–163. Ickes, W. (1984). Compositions in black and white: Â�Determinants of interaction in interracial dyads. Journal of Personality and Social Psychology, 47, 330– 341. Inzlicht, M., & Ben-Zeev, T. (2000). A threatening intellectual environment: Why females are susceptible to experiencing problem-Â�solving deficits in the presence of males. Psychological Science, 11, 365–371. Johns, M., Inzlicht, M., & Schmader, T. (2008). Stereotype threat and executive resource depletion: Examining the influence of emotion regulation. Journal of Experimental Psychology: General, 137, 691–705. Johnson, J. D., & Lecci, L. (2003). Assessing anti-white attitudes and predicting perceived racism. Personality and Social Psychology Bulletin, 29, 299–312. Kiefer, A. K., & Sekaquaptewa, D. (2007). Implicit stereotypes, gender identification, and math-Â�related outcomes. Psychological Science, 18, 13–18. King, E. B., Shapiro, J. R., Hebl, M. R., Singletary, S. L., & Turner, S. (2006). The stigma of obesity in customer service: A mechanism for remediation and bottom-line consequences of interpersonal discrimination. Journal of Applied Psychology, 91, 579–593. Lepore, L., & Brown, R. (1997). Category and stereotype activation: Is prejudice inevitable? Journal of Personality and Social Psychology, 72, 275–287. Levin, S., van Laar, C., & Sidanius, J. (2003). The effects of ingroup and outgroup friendships on ethnic attitudes in college: A longitudinal study. Group Processes and Intergroup Relations, 6, 76–92. Littleford, L. N., Wright, M. O., & Sayoc-Â�Parial, M. (2005). White students’ intergroup anxiety during same-race and interracial interactions: A multiÂ� method approach. Basic and Applied Social Psychology, 27, 85–94. Livingston, R. W. (2002). The role of perceived negativity in the moderation of African Americans’ implicit and explicit racial attitudes. Journal of Experimental Social Psychology, 38, 405–413. Lord, C. G., & Saenz, D. S. (1985). Memory deficits and memory surfeits: Differential cognitive consequences of tokenism for tokens and observers. Journal of Personality and Social Psychology, 49, 918–926. Major, B., & O’Brien, L. T. (2005). The social psychology of stigma. Annual Review of Psychology, 56, 393–421. Major, B., Townsend, S., Sawyer, P., & Mendes, W. B. (2009). Can the absence of prejudice be more threatening than its presence?: It depends on one’s worldview. Manuscript under review. Marx, D. M., & Goff, P. A. (2005). Clearing the air: The effect of experimenter race on target’s test perfor-

390

IV. DOMAIN-SPECIFIC PERSPECTIVES

mance and subjective experience. British Journal of Social Psychology, 44, 645–657. McConahay, J. B. (1983). Modern racism and modern discrimination. Personality and Social Psychology Bulletin, 9, 551–558. McConnell, A. R., & Leibold, J. M. (2001). Relations among the Implicit Association Test, discriminatory behavior, and explicit measures of racial attitudes. Journal of Experimental Social Psychology, 37, 435–442. Mendes, W. B., Blascovich, J., Lickel, B., & Hunter, S. (2002). Challenge and threat during social interactions with white and black men. Personality and Social Psychology Bulletin, 28, 939–952. Mendes, W. B., Gray, H. M., Mendoza-Â�Denton, R., Major, B., & Epel, E. S. (2007). Why egalitarianism might be good for your health: Physiological thriving during stressful intergroup encounters. Psychological Science, 18, 991–998. Mendoza-Â�Denton, R., Downey, G., Purdie, V. J., Davis, A., & Pietrzak, J. (2002). Sensitivity to status-based rejection: Implications for African American students’ college experience. Journal of Personality and Social Psychology, 83, 896–918. Monteith, M. J., & Spicer, C. V. (2000). Contents and correlates of whites. Journal of Experimental Social Psychology, 36, 125–154. Muraven, M., & Baumeister, R. F. (2000). Self-Â�regulation and depletion of limited resources: Does self-Â�control resemble a muscle? Psychological Bulletin, 126, 247–259. Norton, M. I., Sommers, S. R., Apfelbaum, E. P., Pura, N., & Ariely, D. (2006). Colorblindness and interracial interaction: Playing the political correctness game. Psychological Science, 17, 949–953. Ottaway, S. A., Hayden, D. C., & Oakes, M. A. (2001). Implicit attitudes and racism: Effects of word familiarity and frequency on the Implicit Association Test. Social Cognition, 19, 97–144. Page-Gould, E., Mendoza-Â�Denton, R., & Tropp, L. R. (2008). With a little help from my cross-group friend: Reducing anxiety in intergroup contexts through cross-group friendship. Journal of Personality and Social Psychology, 95, 1080–1094. Patchen, M. (1983). Students’ own racial attitudes and those of peers of both races, as related to interracial behaviors. Sociology and Social Research, 68, 59–77. Payne, B. K. (2001). Prejudice and perception: The role of automatic and controlled processes in misperceiving a weapon. Journal of Personality and Social Psychology, 81, 181–192. Payne, B. K., Cheng, C. M., Govorun, O., & Stewart, B. D. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. Journal of Personality and Social Psychology, 89, 277–293. Plant, E. A. (2004). Responses to interracial interactions over time. Personality and Social Psychology Bulletin, 30, 1458–1471. Plant, E. A., & Butz, D. A. (2006). The causes and consequences of an avoidance-focus for interracial interactions. Personality and Social Psychology Bulletin, 32, 833–846. Plant, E. A., & Devine, P. G. (1998). Internal and external motivation to respond without prejudice. Journal of Personality and Social Psychology, 75, 811–832.

Plant, E. A., & Devine, P. G. (2003). The antecedents and implications of interracial anxiety. Personality and Social Psychology Bulletin, 29, 790–801. Powers, D. A., & Ellison, C. G. (1995). Interracial contact and black racial attitudes: The contact hypothesis and selectivity bias. Social Forces, 74, 205–226. Richeson, J. A., Baird, A. A., Gordon, H. L., Heatherton, T. F., Wyland C. L., Trawalter, S., et al. (2003). An fMRI investigation of the impact of interracial contact on executive function. Nature Neuroscience, 6, 1323–1328. Richeson, J. A., & Shelton, J. N. (2003). When prejudice does not pay: Effects of interracial contact on executive function. Psychological Science, 14, 287–290. Richeson, J. A., & Shelton, J. N. (2005). Thin slices of racial bias. Journal of Nonverbal Behavior, 29, 75–86. Richeson, J. A., & Trawalter, S. (2005). Why do interracial interactions impair executive function?: A resource depletion account. Journal of Personality and Social Psychology, 88, 934–947. Richeson, J. A., Trawalter, S., & Shelton, J. N. (2005). African Americans’ implicit racial attitudes and the depletion of executive function after interracial interactions. Social Cognition, 23, 336–352. Rudman, L. A., Feinberg, J., & Fairchild, K. (2002). Minority members’ implicit attitudes: Automatic ingroup bias as a function of group status. Social Cognition, 4, 294–320. Rudman, L. A., Greenwald, A. G., Mellott, D. S., & Schwartz, J. L. K. (1999). Measuring the automatic components of prejudice: Flexibility and generality of the Implicit Association Test. Social Cognition, 17, 437–465. Salvatore, J., & Shelton, J. N. (2007). Cognitive costs of exposure to racial prejudice. Psychological Science, 18, 810–815. Schmader, T., Johns, M., & Barquissau, M. (2004). The costs of accepting gender differences: The role of stereotype endorsement in women’s experience in the math domain. Sex Roles, 50, 835–850. Schmader, T., Johns, M., & Forbes, C. (2008). An integrated process model of stereotype threat effects on performance. Psychological Review, 115, 336–356. Schmeichel, B. J., Vohs, K. D., & Baumeister, R. F. (2003). Intellectual performance and ego depletion: Role of the self in logical reasoning and other information processing. Journal of Personality and Social Psychology, 85, 33–46. Schuman, H., Steeh, C., Bobo, L., & Krysan, M. (1997). Racial attitudes in America: Trends and interpretations (Rev. ed.). Cambridge, MA: Harvard University Press. Sears, D. O., & Jessor, T. (1996). Whites’ racial policy attitudes: The role of white racism. Social Science Quarterly, 77, 751–759. Sekaquaptewa, D., Espinoza, P., Thompson, M., Vargas, P., & von Hippel, W. (2003). Stereotypic explanatory bias: Implicit stereotyping as a predictor of discrimination. Journal of Experimental Social Psychology, 39, 75–82. Shapiro, J. R., & Neuberg, S. L. (2007). From stereotype threat to stereotype threats: Implications of a multiÂ�threat framework for causes, moderators, mediators, consequences, and interventions. Personality and Social Psychology Review, 11, 107–130.



20. Bias and Interpersonal Processes

Shapiro, J. R., & Neuberg, S. L. (2008). When do the stigmatized stigmatize?: The ironic effects of being accountable to (perceived) majority group prejudiceÂ�expression norms. Journal of Personality and Social Psychology, 95, 877–898. Shelton, J. N. (2000). A reconceptualization of how we study issues of racial prejudice. Personality and Social Psychology Review, 4, 374–390. Shelton, J. N. (2003). Interpersonal concerns in social encounters between majority and minority group members. Group Processes and Intergroup Relations, 6, 171–185. Shelton, J. N., & Richeson, J. A. (2005). Intergroup contact and pluralistic ignorance. Journal of Personality and Social Psychology, 88, 91–107. Shelton, J. N., & Richeson, J. A. (2006a). Ethnic minorities’ racial attitudes and contact experiences with white people. Cultural Diversity and Ethnic Minority Psychology, 12, 149–164. Shelton, J. N., & Richeson, J. A. (2006b). Interracial interactions: A relational approach. Advances in Experimental Social Psychology, 38, 121–181. Shelton, J. N., Richeson, J. A., & Salvatore, J. (2005). Expecting to be the target of prejudice: Implications for interethnic interactions. Personality and Social Psychology Bulletin, 31, 1189–1202. Shelton, J. N., Richeson, J. A., Salvatore, J., & Trawalter, S. (2005). Ironic effects of racial bias during interracial interactions. Psychological Science, 16, 397–402. Shelton, J. N., Richeson, J. A., & Vorauer, J. D. (2006). Threatened identities and interethnic interactions. European Review of Social Psychology, 17, 321–358. Smith, E. R., & DeCoster, J. (2000). Dual-Â�process Â�models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4, 108–131. Son Hing, L. S., Li, W., & Zanna, M. P. (2002). Inducing hypocrisy to reduce prejudicial responses among aversive racists. Journal of Experimental Social Psychology, 38, 71–78. Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology, 69, 797–811. Stephan, W. G., Boniecki, K. A., Ybarra, O., Bettencourt, A., Ervin, K. S., Jackson, L. A., et al. (2002). The role of threats in the racial attitudes of blacks and whites. Personality and Social Psychology Bulletin, 28, 1242–1254. Stephan, W. G., & Stephan, C. W. (2000). An integrated threat theory of prejudice. In S. Oskamp (Ed.), Reducing prejudice and discrimination (pp.€ 23–46). Hillsdale, NJ: Erlbaum. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Trawalter, S., Adam, E. K., Chase-Â�Lansdale, P. L., & Richeson, J. A. (2009). Prejudice concerns get under the skin: External motivation to respond without prejudice shapes behavioral and physiological responses to interracial contact. Manuscript in preparation. Trawalter, S., & Richeson, J. A. (2006). Regulatory focus and executive function after interracial inter-

391

actions. Journal of Experimental Social Psychology, 42, 406–412. Trawalter, S., & Richeson, J. A. (2008). Let’s talk about race, baby!: When whites’ and blacks’ interracial contact experiences diverge. Journal of Experimental Social Psychology, 44, 1214–1217. Trawalter, S., Richeson, J. A., & Shelton, J. N. (2009). Predicting behavior during interracial interactions: A stress and coping approach. Personality and Social Psychology Review, 13, 243–268. Tropp, L. R. (2003). The psychological impact of prejudice: Implications for intergroup contact. Group Processes and Intergroup Relations, 6, 131–149. Tropp, L. R. (2006). Stigma and intergroup contact among members of minority and majority status groups. In S. Levin & C. van Laar (Eds.), Stigma and group inequality: Social psychological perspectives (pp.€171–191). Mahwah, NJ: Erlbaum. Uhlmann, E., Dasgupta, N., Elgueta, A., Greenwald, A. G., & Swanson, J. (2002). Subgroup prejudice based on skin color among Hispanics in the United States and Latin America. Social Cognition, 20, 198–226. Vorauer, J., & Kumhyr, S. M. (2001). Is this about you or me?: Self- versus other-Â�directed judgments and feelings in response to intergroup interaction. Personality and Social Psychology Bulletin, 27, 706–719. Vorauer, J. D. (2006). An information search model of evaluative concerns in intergroup interaction. Psychological Review, 113, 862–886. Vorauer, J. D., Hunter, A. J., Main, K. J., & Roy, S. A. (2000). Meta-Â�stereotype activation: Evidence from indirect measures for specific evaluative concerns experienced by members of dominant groups in intergroup interaction. Journal of Personality and Social Psychology, 78, 690–770. Vorauer, J. D., Main, K. J., & O’Connell, G. B. (1998). How do individuals expect to be viewed by members of lower status groups?: Content and implications of meta-Â�stereotypes. Journal of Personality and Social Psychology, 75, 917–937. Vorauer, J. D., & Turpie, C. A. (2004). Disruptive effects of vigilance on dominant group members’ treatment of outgroup members: Choking versus shining under pressure. Journal of Personality and Social Psychology, 87, 384–399. West, T. V., Shelton, J. N., & Trail, T. E. (2009). Relational anxiety in interracial interactions. Psychological Science, 20, 289–292. Wetzel, C., Blalock, C., & Bolger, B. (2004). [Interethnic interactions: Trying to avoid appearing prejudices can improve interaction quality]. Unpublished raw data. Williams, D. R., Neighbors, H. W., & Jackson, J. S. (2003). Racial/ethnic discrimination and health: Findings from community studies. American Journal of Public Health, 93, 200–208. Wittenbrink, B., Judd, C. M., & Park, B. (2001). Evaluative vs. conceptual judgments in automatic stereotyping and prejudice. Journal of Experimental Social Psychology, 37, 244–252. Word, C. O., Zanna, M. P., & Cooper, J. (1974). The nonverbal mediation of self-Â�fulfilling prophecies in interracial interaction. Journal of Experimental Social Psychology, 10, 109–120.

C h a p t e r 21

Two Faces of Self-�Esteem Implicit and Explicit Forms of Self-�Esteem Virgil Zeigler-Hill and Christian H. Jordan

F

ew variables studied by psychologists need less of an introduction than self-Â�esteem. It is widely discussed in the popular media and prominently featured in self-help books. If you simply ask people how much they agree with the statement “I have high self-Â�esteem,” their answers correlate remarkably highly (rs > .70) with their scores on the Rosenberg (1965) Self-Â�Esteem Scale, the most widely used measure of self-Â�esteem in psychological research (Robins, Hendin, & Trzesniewski, 2001). This finding raises the question: If our participants are so sure of what we mean by self-Â�esteem, do we have a similarly clear understanding? In a sense, we do. There is agreement on the basic definition of self-Â�esteem, but several controversies persist: Should self-Â�esteem be thought of primarily as a state or a trait, as affectively or cognitively based, as a global construct or as a domain-Â�specific one? We focus on one such controversy. Do people have two distinct forms of self-Â�esteem: one explicit and the other implicit? In this chapter, we examine questions concerning the existence of implicit and explicit self-Â�esteem: What are their origins? What problems are associated with their measurement? What is the association between them? Do they predict similar outcomes? What happens if discrepancies develop between these two forms of self-Â�esteem?

EXPLICIT SELF-ESTEEM What is generally agreed on in a definition of selfÂ�esteem? Put simply, self-Â�esteem is an evaluation of oneself (e.g., Rosenberg, 1965). That is, it is the evaluative aspect of self-Â�knowledge that reflects how much people like themselves. During the last 30 years, self-Â�esteem has been the subject of more than 25,000 publications, making it one of the most widely researched topics in modern psychology. This widespread interest is due, in part, to the fact that self-Â�esteem was once touted as a means to deal with many of society’s challenges (e.g., drug abuse, unemployment, academic underachievement, violence). Although self-Â�esteem is not the panacea many had once hoped it would be, it is clearly associated with a range of subjective outcomes (e.g., life satisfaction, relationship satisfaction, depression) and may also be associated with some important objective outcomes (e.g., academic achievement, relationship stability, physical health; for reviews, see Baumeister, Campbell, Krueger, & Vohs, 2003; Swann, Chang-Â�Schneider, & McClarty, 2007; Trzesniewski et al., 2006). Given the considerable interest in self-Â�esteem, it may not be surprising that several instruments have been developed to measure it (Blascovich & Tomaka, 1991; Bosson, 2006). Among the most 392



21. Implicit and Explicit Forms of Self-�Esteem

commonly used are the Rosenberg (1965) SelfÂ�Esteem Scale, Coopersmith (1967) Self-Â�Esteem Inventory, Self-Â�Attributes Questionnaire (Pelham & Swann, 1989), State Self-Â�Esteem Scale (Heatherton & Polivy, 1991), Self-Â�Liking and SelfÂ�Competence Scale (Tafarodi & Swann, 2001), and Single-Item Self-Â�Esteem Scale (Robins et al., 2001). There are important differences between these scales, but one feature shared by all is that they directly ask individuals to report how they feel about themselves. Respondents to the Rosenberg Self-Â�Esteem Scale, for example, rate their agreement with items such as “I feel that I’m a person of worth, at least on an equal plane with others” and “On the whole, I am satisfied with myself.” The directness of these measures makes great sense considering that self-Â�esteem is a subjective evaluation of the self. The most straightforward way to assess this sort of attitude is to ask people how they feel about themselves. This direct approach to measuring self-Â�esteem has dominated the literature and is referred to as explicit because participants undeniably have introspective access to their self-Â�esteem on these measures. The direct approach to measuring self-Â�esteem has both advantages and disadvantages (e.g., Blascovich & Tomaka, 1991; Bosson, 2006). One primary advantage is that it allows respondents to draw on self-Â�knowledge that may not be available to others. The fact that self-Â�esteem is an evaluation of the self makes it difficult to rely on others’ reports to determine one’s level of self-Â�esteem. Consistent with this concern, self-Â�ratings of self-Â�esteem are, at best, modestly associated with observer ratings, even when the observers know the target person well (e.g., Buhrmester, Furman, Wittenberg, & Reis, 1988; Watson, Suls, & Haig, 2002). There are several other advantages of direct measures of self-Â�esteem: They are quickly, cheaply, and easily administered, and their interpretation is relatively straightforward, at least compared with alternative strategies such as identifying behavioral indicators of self-Â�esteem. Finally, direct measures have strong psychometric properties such as internal consistency, test–Â�retest reliability, convergent validity, and predictive validity (see Bosson, 2006, for a review). Taken together, these advantages suggest that direct measures of self-Â�esteem are a practical means for learning how individuals feel about themselves. Despite these advantages, the direct approach to measuring self-Â�esteem has drawbacks, which mainly stem from two assumptions of this strategy: (1) Individuals will respond to direct measures of

393

self-Â�esteem in a way that accurately reflects their feelings of self-worth and (2) individuals have introspective access to all aspects of their self-Â�esteem (e.g., Farnham, Greenwald, & Banaji, 1999; Hetts & Pelham, 2001). The first assumption, that individuals will provide honest reports of their feelings of self-worth, is problematic because people might not divulge their true attitudes on direct measures. Indeed, direct measures of self-Â�esteem correlate with measures of impression management, or the tendency to intentionally dissemble on self-Â�report scales in order to appear more socially desirable (e.g., Raskin, Novacek, & Hogan, 1991). Although less common, people may also present a view of themselves that is more modest than how they really feel. Not surprisingly, individuals with high self-Â�esteem tend to be concerned with presenting themselves in a positive light (e.g., Baumeister, Tice, & Hutton, 1989), perhaps because they are often viewed more positively than those with low self-Â�esteem (Zeigler-Hill & Myers, 2009). The second assumption underlying direct measures of self-Â�esteem, that individuals are aware of all aspects of their self-Â�esteem, is troublesome because some aspects of self-Â�esteem may exist outside awareness. Indeed, direct measures of selfÂ�esteem correlate with measures of self-Â�deception. Self-Â�deception is conceptualized as the tendency to present a positive self-image that one believes to be true but that does not reflect one’s less conscious beliefs (Paulhus, 1984). The possibility that individuals may not have access to all of their selfÂ�attitudes is consistent with evidence that individuals lack awareness of aspects of their attitudes more generally. There is actually little direct evidence that people are unaware of the attitudes they possess, but subliminal presentation of attitude objects can evoke evaluations without participants’ awareness or conscious guidance (e.g., Bargh, Chaiken, Raymond, & Hymes, 1996; Fazio, Sanbonmatsu, Powell, & Kardes, 1986). Thus, people may have evaluative responses to stimuli even when they are not aware of those stimuli. This is not the same as suggesting that people are unaware of their evaluative responses, but it does suggest that people are not aware of all aspects of their attitudes (see Hofmann & Wilson, Chapter 11, this volume). Furthermore, work in cognitive psychology suggests that, in addition to explicit memories, people may have implicit memories, which allow past experiences to influence behavior even when people have no conscious recollection of those experiences (e.g., Jacoby & Kelley, 1992).

394

IV. DOMAIN-SPECIFIC PERSPECTIVES

IMPLICIT SELF-ESTEEM Following the developments concerning implicit memory, Greenwald and Banaji (1995) suggested that individuals may possess implicit self-Â�esteem, which they defined as the “introspectively unidentified (or inaccurately identified) effect of the selfÂ�attitude on evaluation of self-Â�associated and selfÂ�dissociated objects” (p.€11). An important concern about this definition is that it includes the implicit attitude as well as its consequences (e.g., evaluations of self-Â�associated objects such as one’s own name; Dijksterhuis, 2004). To separate the attitude from its consequences, it has been suggested that the definition of implicit self-Â�esteem be limited to “the strength of evaluative self-Â�associations, which operate in a relatively automatic fashion, outside of conscious awareness” (Karpinski & Steinberg, 2006, p.€ 103). Alternatively, to put it even more simply, implicit self-Â�esteem may be defined as an “implicit attitude toward the self” (Dijksterhuis, 2004, p.€353). A controversial issue, evident in these definitions, is that implicit self-Â�esteem is often defined as being nonconscious (for discussions of this issue, see Bosson, 2006; Jordan, Logel, Spencer, & Zanna, 2006). However, it may be more prudent to define implicit self-Â�esteem as evaluations that are cognitively associated with the self and activated in response to self-Â�relevant stimuli but that are not necessarily endorsed as valid reflections of how one feels about oneself. Explicit self-Â�esteem, in contrast, may be viewed as a propositional, selfÂ�evaluative judgment that people endorse as valid (see Gawronski & Bodenhausen, 2006). This approach to conceptualizing implicit self-Â�esteem leaves the question of whether and how it may be nonconscious open to empirical investigation. The existence of more than one form of selfÂ�esteem is consistent with the idea that the self consists of multiple subsystems that operate simultaneously (e.g., Epstein, 1994; Greenwald, 1982; Koole & Pelham, 2003). Dual-Â�processing models are often used to conceptualize implicit and explicit self-Â�esteem because these models propose two systems for processing information (e.g., Epstein & Morling, 1995; Smith & DeCoster, 2000; Wilson, Lindsey, & Schooler, 2000). The first system, often referred to as the cognitive system, is believed to be rule based and to operate through controlled processes. It is flexible but not very efficient. Explicit self-Â�esteem is thought to be a product of this system because it is based to some extent on deliberative analyses of self-Â�relevant feedback and

information. The second system, the experiential system, is associative and operates through automatic processes. It is highly efficient but relatively inflexible. Implicit self-�esteem is believed to result from the operation of this system and to be derived from the automatic and holistic processing of self�relevant experiences (Franck, De Raedt, Dereu, & Van den Abbeele, 2007; Jordan, Whitfield, & Zeigler-Hill, 2007).

MEASURING IMPLICIT SELF-ESTEEM As a result of the limitations of impression management and self-�deception inherent in direct measures of self-�esteem, more sophisticated strategies than simply asking people how they feel about themselves have been proposed for measuring feelings of self-worth (see Kernis, 2003, for a review). One approach is to use nonreactive measures of self-�esteem (see Bosson, 2006, for a review). These measures are often described as implicit, although this can create confusion with the construct they are often purported to measure: implicit self�esteem. Accordingly, we use the term nonreactive to describe these measures. A number of nonreactive measures of self�esteem have been developed. Among them are a self-�esteem version of the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998), name-�letter task (Nuttin, 1985, 1987), Implicit Self-�Evaluation Survey (Hetts, Sakuma, & Pelham, 1999), Self-�Apperception Test (Aidman, 1999), Go/No-Go Association Task (Nosek & Banaji, 2001), Extrinsic Affective Simon Task (De Houwer, 2003), signature effect (Stapel & Blanton, 2004), Single-�Category IAT (Karpinski & Steinman, 2006), Breadth-Based Adjective Rating Task (Karpinski, Steinberg, Versek, & Alloy, 2007), name-�liking task (Gebauer, Riketta, Broemer, & Maio, 2008), and various cognitive priming tasks (e.g., Hetts et al., 1999; Spalding & Hardin, 1999; Wentura, Kulfanek, & Greve, 2005). Many of these nonreactive measures of self-�esteem were developed by simply adapting measures of other implicit attitudes (e.g., IAT) but some are unique to self-�esteem (e.g., name-�letter task). So far there is no clear consensus as to which, if any, of these measures best captures the construct of implicit self-�esteem. Nonreactive measures of self-�esteem may be divided into two general classes: association based and indirect (Karpinski & Steinberg, 2006). As-



21. Implicit and Explicit Forms of Self-�Esteem

sociation-based measures directly assess evaluative self-Â�associations and, as a result, may capture a relatively pure form of implicit self-Â�esteem. An example of an association-based measure is the IAT because it concerns the relative strength of positive and negative associations one has with the self (see Teige-Â�Mocigemba, Klauer, & Sherman, Chapter 7, this volume). Indirect measures of self-Â�esteem fall somewhere between direct measures (e.g., Rosenberg Self-Â�Esteem Scale) and association-based measures (e.g., IAT) because they do not directly ask individuals to report on their self-Â�esteem nor do they directly measure evaluative associations with the self (Karpinski & Steinberg, 2006). Indirect measures might thus capture implicit self-Â�esteem, explicit self-Â�esteem, or some combination of the two forms, which, of course, makes the interpretation of these measures difficult. The name-Â�letter task is an example of an indirect measure because it compares an individual’s evaluation of self-Â�relevant stimuli (i.e., the initials in one’s own name) with the evaluations that others (whose names do not contain those initials) provide for the same stimuli. The primary strength of nonreactive measures is that they assess self-Â�esteem through reaction times or responses to ambiguous stimuli rather than simply asking respondents how much they like themselves. Although nonreactive measures vary in the extent to which respondents are able to discern the purpose of the task, these instruments tend to be far less obvious than direct measures (Fazio & Olson, 2003). An important advantage of nonreactive measures is that it is often difficult for individuals to control their scores even if they realize what is being measured. This means that nonreactive measures may be able to tap into aspects of self-Â�esteem of which individuals are unaware or, at the very least, do not want to report on direct measures. Despite their tremendous potential, existing nonreactive measures of self-Â�esteem suffer from some limitations. One striking weakness is that many possess psychometric properties that fall below the threshold of acceptability (e.g., poor reliability; see Bosson, Swann, & Pennebaker, 2000). The psychometric limits of some nonreactive measures, however, should not lead to their abandonment as a whole. Rather, some of these features, such as their low test–Â�retest reliability, may be due to the nature of the underlying construct. If implicit self-Â�esteem has a relatively fluid nature, for example, then nonreactive measures that are accurately capturing it may appear unreliable. Tem-

395

porally consistent measures are vitally important for capturing constructs that are stable, but it is too early to assume that implicit self-Â�esteem is stable, an issue we discuss in further detail later. Another issue is that many of the most commonly used nonreactive measures do not typically correlate with each other (e.g., Bosson et al., 2000). This has led to concerns about the convergent validity of these measures because different measures of the same construct ought to correlate with each other. Over the years, a number of plausible explanations for this lack of convergence have been offered. One possibility is that implicit self-Â�esteem may not be a single, unitary construct and the various nonreactive measures may capture different facets of implicit self-Â�esteem (Koole & Pelham, 2003). The multifaceted nature of implicit self-Â�esteem is supported by studies that include separate nonreactive measures of agency versus communion (Campbell, Bosson, Goheen, Lakey, & Kernis, 2007) and self-Â�liking versus selfÂ�attractiveness (Sakellaropoulo & Baldwin, 2007). A second explanation for the weak correlations between measures of implicit self-Â�esteem is that these measures rely on different cognitive processes (Koole & Pelham, 2003). There is evidence that nonreactive measures that rely on similar cognitive processes tend to have at least somewhat higher correlations with each other (e.g., Bosson et al., 2000; Farnham et al., 1999; Koole, Dijksterhuis, & van Knippenberg, 2001; Rudolph, Schröder-Abé, Schütz, Gregg, & Sedikides, 2008). For example, the name-Â�letter task has been shown to be more strongly associated with other measures that capture evaluations of self-Â�relevant stimuli (e.g., bias for birth date numbers; Koole et al., 2001) than with association-based measures such as the IAT (e.g., Bosson et al., 2000). A third explanation is that the weak associations between nonreactive measures may simply be a reflection of their poor psychometric properties (e.g., low reliability; Fazio & Olson, 2003). A final possibility is that some nonreactive measures may be capturing only implicit self-Â�esteem, whereas others may be capturing a combination of implicit and explicit self-Â�esteem (Karpinski & Steinberg, 2006). Concerns about the convergent validity of nonreactive measures of self-Â�esteem are serious, but they are not unique to these measures. This issue appears to be endemic to research on implicit constructs more generally. Measures of implicit memory, for example, have also been found to have relatively weak correlations with each other (Buchner & Wippich, 2000; Perruchet & Baveux, 1989).

396

IV. DOMAIN-SPECIFIC PERSPECTIVES

Some strategies have been suggested to compensate for the weak psychometric properties of nonreactive measures. One approach involves the creation of a composite score that consists of various nonreactive measures (e.g., Baccus, Baldwin, & Packer, 2004; Cunningham, Preacher, & Banaji, 2001) or computing average scores across repeated assessments with the same nonreactive measure (e.g., Zeigler-Hill, 2006). Early research using these strategies appears promising. For example, measures of implicit prejudice have been shown to correlate more strongly with each other when multiple nonreactive measures are used at more than one point in time and their error variance is modeled with structural equation modeling (Cunningham et al., 2001). Another means for dealing with the weak psychometric properties of nonreactive measures is to examine the scoring procedures that are used. As examples, an improved scoring algorithm was developed for the IAT that improved its psychometric properties (Greenwald, Nosek, & Banaji, 2003), and alternative scoring systems for the name-�letter task have been compared in recent studies (Albers, Rotteveel, & Dijksterhuis, 2009; LeBel & Gawronski, 2009). As with the measures intended to capture other implicit attitudes (e.g., prejudice), the development of theory and measurement for implicit self�esteem have been highly interdependent (Vargas, Sekaquaptewa, & von Hippel, 2007). An unfortunate result of this interdependence is that the distinction between nonreactive measures and the constructs they attempt to capture has often been lost (Banse & Greenwald, 2007; De Houwer, 2006). With regard to implicit self-�esteem, there has been a tendency for researchers to refer to nonreactive measures as implicit and to behave as though scores on these measures correspond to implicit self-�esteem before their validity is adequately established. The distinction between a measurement approach and the underlying construct is especially important for implicit self-�esteem because it appears that some nonreactive measures may be reflecting underlying associations with the self (i.e., measuring implicit self-�esteem), whereas others may be indirectly tapping deliberative self�evaluations that individuals are reluctant to report on direct measures (i.e., implicitly measuring explicit self-�esteem; see Fazio & Olson, 2003). That is, some nonreactive measures may simply be capturing the same underlying self-�attitude as direct measures, but without the distorting influences of impression management and self-�deception (e.g., De Houwer, 2006; Gawronski, LeBel, & Peters, 2007; Krizan, 2008; Ranganath, Smith, & Nosek,

2008). The issue is whether direct and nonreactive measures of self-�esteem ever reflect truly distinct forms of self-�evaluation. It has been suggested that for nonreactive measures to actually capture implicit self-�esteem or any other implicit attitude, individuals should be unaware that a particular attitude is being measured, should not have conscious access to the attitude, and should have no control over the measurement outcome (De Houwer, 2006). There is little evidence that any of the existing nonreactive measures meet all of these criteria (Fazio & Olson, 2003; Gawronski, Hofmann, & Wilbur, 2006). In fact, recent research casts doubt on whether nonreactive measures capture implicit self-�esteem at all, in the sense of measuring self-�esteem that exists entirely outside awareness, by showing that these assumptions are often violated (Gawronski & Bodenhausen, 2006; Gawronski et al., 2007; Krizan, 2008; Olson, Fazio, & Hermann, 2007). Of course, this perspective assumes that implicit self-�esteem is entirely nonconscious, and as we have already noted, that is a controversial assumption. We believe that these criteria do not need to be fully satisfied for nonreactive measures of self�esteem to be useful, nor for them to capture a distinct construct that might be called implicit self�esteem, although the nature of this construct may be somewhat different than has been commonly assumed. Nevertheless, questions concerning what is actually captured by nonreactive measures deserve serious consideration because of their importance for understanding the rapidly accumulating body of research on implicit self-�esteem.

EVIDENCE FOR THE€DISTINCTION OF IMPLICIT AND€EXPLICIT SELF-ESTEEM We suspect the concerns about nonreactive measures of self-�esteem outlined previously have led some researchers to abandon their use. This is a shame because we believe nonreactive measures still have a great deal to offer. It is said that the proof of the pudding is in the eating. We similarly believe that the proof of any psychological measure is in its construct validity. Is it related to other variables in theoretically significant ways? Does it reveal meaningful effects and produce interesting findings? Do nonreactive measures expand what we know about self-�evaluations and how they influence judgments and behavior? The answer is, yes, they do. In this sense, nonreactive measures of self-�esteem have proven their usefulness. Evidence



21. Implicit and Explicit Forms of Self-�Esteem

of their construct validity also gets at the question of whether nonreactive and direct measures assess distinct constructs. One of the first observations noted about nonreactive measures of self-Â�esteem is that they rarely correlate highly with direct measures. Although there is some variability across studies, in most cases, correlations between nonreactive and direct measures are slightly positive or nil (e.g., Bosson et al., 2000; Farnham et al., 1999; Jordan, Spencer, & Zanna, 2003). This is potentially good evidence of discriminant validity—that the two kinds of measures really do capture distinct forms of selfÂ�evaluation—but it might simply reflect the poor psychometric properties of nonreactive measures. Perhaps they do not correlate with direct measures because they are too unreliable to correlate with anything at all. This latter explanation seems unlikely because nonreactive measures of self-Â�esteem have been found to relate to other constructs in meaningful ways.

Convergent Findings for Implicit and€Explicit Self-�Esteem If nonreactive measures of self-�esteem really measure self-�evaluations, they might be expected to relate to the same variables as direct measures, and in many cases they do (see Dijksterhuis, 2008). Both high explicit and implicit self-�esteem (measured directly and nonreactively, respectively) help people cope with negative experiences and stressors. As is true of people with high explicit self-�esteem, those with high implicit self-�esteem are less neurotic and experience fewer negative emotions (Conner & Barrett, 2005; Robinson & Meier, 2005; Robinson & Wilkowski, 2006). People with high implicit self-�esteem showed no change in mood after receiving a negative evaluation, whereas those with lower implicit self�esteem had more negative mood (Dijksterhuis, 2004). Their physical health is also more robust against the impact of recent life events (Shimizu & Pelham, 2004). People with low implicit self�esteem also show less persistence at difficult tasks after experiencing an initial failure (Greenwald & Farnham, 2000; Jordan, Spencer, & Zanna, 2003), as do those with low explicit self-�esteem (e.g., Shrauger & Rosenberg, 1970). Similarly, negative evaluations and poor performances have been shown to often decrease explicit self-�esteem (particularly for individuals with low-trait explicit self-�esteem; e.g., Dutton & Brown, 1997; Heatherton & Polivy, 1991), and more recently these experiences have been found

397

to decrease implicit self-�esteem as well. Writing about a personal flaw causes drops in implicit self�esteem (Jones, Pelham, Mirenberg, & Hetts, 2002; for individuals with low explicit self-�esteem) as does negative feedback on intelligence tests (Dijksterhuis, 2004) and being asked to summarize a bewildering statistics lesson (McGregor, Nail, Marigold, & Kang, 2005). Although self-�threats often lead to drops in explicit self-�esteem, there are studies in which the opposite effect is found, with threatening information causing increases in self-�esteem (e.g., Baumeister, 1982; Brown & Smart, 1991; Greenberg & Pyszczynski, 1985). Parallel effects have emerged for implicit self-�esteem, with threats, such as social rejection or threats to gender identity, actually increasing implicit self-�esteem (Rudman, Dohn, & Fairchild, 2007; Smurda, Wittig, & Gokalp, 2006; for individuals with high explicit self-�esteem, see also Dodgson & Wood, 1998; Pelham, Mirenberg, & Jones, 2002). This counterintuitive reaction appears to be a motivated compensation effect aimed at protecting the self from threat because it is eliminated in people who self-�affirm before a threat occurs (Rudman et al., 2007). The precise factors that moderate whether threat causes self-�esteem to increase or decrease remain to be specified by further research (see Gawronski & Sritharan, Chapter 12, this volume), but for now it is interesting to note that both responses occur for explicit and implicit self-�esteem.

Divergent Findings for Explicit and€Implicit Self-�Esteem There is thus convergence in the roles played by implicit and explicit self-�esteem in individual psychology, despite their lack of correlation with each other. All of these findings, however, may simply indicate that nonreactive and direct measures assess the same underlying self-�evaluations. Nonreactive measures might just get at those evaluations more cleanly, without distortion by self�presentational concerns. However, many of the effects we have described for implicit self-�esteem, such as affective responses to performance feedback and persistence in the face of failure, were found even when the effects of explicit self-�esteem had been statistically controlled (e.g., Greenwald & Farnham, 2000; Jordan, Spencer, & Zanna, 2003). Although this is not definitive proof by any means, it does suggest that they may be measuring distinct self-�evaluations. At the very least, it suggests that direct and nonreactive measures capture meaningfully distinct aspects of self-�evaluations.

398

IV. DOMAIN-SPECIFIC PERSPECTIVES

Both implicit self-Â�esteem and explicit self-Â�esteem can also predict different responses. Explicit attitudes generally have been posited to influence more deliberative, controlled behaviors, whereas implicit attitudes are thought to influence relatively spontaneous, less controllable behaviors (e.g., Dovidio, Kawakami, Johnson, Johnson, & Howard, 1997; Fazio, Jackson, Dunton, & Williams, 1995). Consistent with this view, explicit self-Â�esteem has predicted self-Â�ratings of anxiety for people waiting to undergo a personal interview, whereas implicit self-Â�esteem predicted nonverbal indicators of anxiety (Spalding & Hardin, 1999). Importantly, these effects occurred in a double-Â�dissociation pattern (e.g., Asendorpf, Banse, & Mücke, 2002; see also Perugini, Richetin, & Zogmaister, Chapter 14, this volume). That is, explicit self-Â�esteem predicted self-Â�ratings of anxiety but implicit self-Â�esteem did not, whereas the latter predicted nonverbal indicators but the former did not. More studies of this kind would certainly help to further establish the unique predictive power of measures of implicit self-Â�esteem (e.g., Conner & Barrett, 2005; Krizan & Suls, 2008).

Discrepancies between Implicit and€Explicit Self-�Esteem Additional evidence that direct and nonreactive measures of self-�esteem capture distinct constructs comes from work suggesting that the degree of correspondence (or discrepancy) between the two defines unique psychological states within individuals (see also Rydell & McConnell, Chapter 16, this volume). Because measures of implicit and explicit self-�esteem are only weakly correlated, many people possess discrepancies between the two such that their implicit self-�esteem is either higher or lower than their explicit self-�esteem. The form of discrepant self-�esteem that has garnered the vast majority of theoretical and empirical attention thus far has been discrepant high self-�esteem, which refers to high levels of explicit, but low levels of implicit, self-�esteem (e.g., Bosson, Brown, ZeiglerHill, & Swann, 2003; Jordan, Spencer, Zanna, Hoshino-�Browne, & Correll, 2003; but see Jordan, Logel, Spencer, Zanna, & Whitfield, 2008). Individuals with discrepant high self-�esteem are thought to experience underlying insecurities and self-�doubts stemming from their low levels of implicit self-�esteem, which causes these individuals to question their positive attitudes toward themselves (Jordan et al., 2008). Discrepant high self-�esteem is considered to be one form of fragile high self�esteem (Kernis, 2003), which refers to feelings of

self-worth that are vulnerable to challenge, need frequent validation, and require some degree of self-Â�deception. As a consequence of the fragile nature of their feelings of self-worth, these individuals are highly defensive. In contrast, individuals with congruent high self-Â�esteem are thought to possess secure high self-Â�esteem. This form of self-Â�esteem is characterized by positive attitudes toward the self that are realistic, well anchored, and resistant to threat (see Kernis, 2003, for a review of secure and fragile self-Â�esteem). Consistent with the notion that discrepant high self-Â�esteem represents a form of fragile self-Â�esteem, implicit self-Â�esteem relates to other markers of fragile self-Â�esteem (Kernis, 2003). Fragile high self-Â�esteem is primarily indexed by contingent or unstable self-Â�esteem, that is, whether individuals’ self-Â�esteem depends on continually meeting selfÂ�imposed performance standards in important domains (contingent self-Â�esteem) or whether it tends to fluctuate day to day (unstable self-Â�esteem). People with high explicit but low implicit self-Â�esteem have been found to have more contingent and less stable self-Â�esteem than those who are high in both explicit and implicit self-Â�esteem (Gunn & Jordan, 2008; Kernis, Lakey & Heppner, 2008; ZeiglerHill, 2006), corroborating the idea that they have fragile high self-Â�esteem. Fragile high self-Â�esteem is similar to narcissism, although it is considered to be a distinct syndrome (Kernis et al., 2008). Indeed, some early views of narcissism, known collectively as the psychodynamic mask model of narcissism, suggest that narcissists have grandiose self-views that mask negative self-Â�evaluations at less conscious levels (e.g., Kernberg, 1975, 1986; Kohut, 1966, 1977; see Bosson et al., 2008, for a review). Consistent with this possibility, some studies have found that narcissists have discrepant high self-Â�esteem (high explicit but low implicit self-Â�esteem; Jordan, Spencer, Zanna, Hoshino-Â�Browne, & Correll, 2003; ZeiglerHill, 2006). These studies found that individuals with high explicit but low implicit self-Â�esteem have the highest levels of narcissism overall. These initial findings are appealing, in part, because they are consistent with classic views of narcissism. However, a meta-Â�analysis by Bosson and her colleagues (2008) suggests that support for the relation between discrepant high self-Â�esteem and narcissism emerges in some studies but not others. Indeed, Zeigler-Hill (2006) found the effect for only two of three measures of implicit self-Â�esteem. The lack of robustness of this finding may simply reflect the poor reliability of measures of implicit self-Â�esteem. However, it may also be related to the



21. Implicit and Explicit Forms of Self-�Esteem

possibility that implicit self-Â�esteem has different facets. Some evidence suggests that narcissists have rather positive views of themselves on agentic traits, even at the implicit level, but relatively poor views of themselves on communal qualities (Campbell et al., 2007; see Bosson et al., 2008, for a more detailed analysis). As noted, however, narcissism is considered to be distinct from fragile high self-Â�esteem, despite their similarities because it contains some features that are not necessarily associated with fragile high self-Â�esteem, such as feelings of entitlement, a willingness to exploit others, and inflated feelings of self-worth (Kernis, 2003). Discrepant high self-Â�esteem is related to selfÂ�aggrandizement. Among individuals with high explicit self-Â�esteem, those with relatively low implicit self-Â�esteem show more unrealistic optimism, more strongly endorse a highly flattering personality profile as self-Â�descriptive, and report that their current self is closer to their ideal self than participants with high implicit self-Â�esteem (Bosson et al., 2003). They also rationalize their decisions more and show more ingroup bias and more ethnic discrimination when their high selfÂ�esteem is threatened (Jordan, Spencer, & Zanna, 2005; Jordan, Spencer, Zanna, Hoshino-Â�Browne, & Correll, 2003). Individuals with discrepant high self-Â�esteem also harden their attitudes more in response to self-Â�threat by holding them with greater conviction and perceiving greater social support for them (McGregor & Marigold, 2003; McGregor et al., 2005). People with discrepant high self-Â�esteem also have a tendency to rationalize and distort selfÂ�threatening information more than those with consistent self-Â�esteem (Kernis et al., 2008). Discrepant high self-Â�esteem has also been linked to less forgiveness following a transgression (Eaton, Struthers, Shomrony, & Santelli, 2007) and with poor self-Â�regulation following an ego threat (Lambird & Mann, 2006). Lastly, children with discrepant high self-Â�esteem are rated by their teachers as more aggressive than those with congruent high self-Â�esteem (Sandstrom & R. Jordan, 2008). It is not just discrepant high self-Â�esteem that may be important: Discrepancies in either direction may be consequential. For example, individuals with discrepant self-Â�esteem have been found to report lower levels of mental and physical health (e.g., depressive attributional style, days of impaired health; Schröder-Abé, Rudolph, Wiesner, & Schütz, 2007). Interestingly, this pattern was found to characterize those with discrepant high self-Â�esteem (i.e., high explicit self-Â�esteem but low implicit self-Â�esteem) as well as discrepant low self-

399

Â� esteem (i.e., low explicit self-Â�esteem but high implicit self-Â�esteem). Similar patterns have also been reported for self-Â�promotion and outgroup derogation when self-Â�evaluations are primed in a manner that is inconsistent with chronic levels of explicit self-Â�esteem, whether people with high explicit selfÂ�esteem receive negative self-Â�primes or those with low explicit self-Â�esteem receive positive self-Â�primes (Kernis et al., 2005). Although discrepant low self-Â�esteem has received less attention than discrepant high selfÂ�esteem, there has been recent interest in this particular combination of implicit and explicit self-Â�esteem. For example, Spencer, Jordan, Logel, and Zanna (2005) suggest that individuals with discrepant low self-Â�esteem may be characterized as possessing a glimmer of hope. That is, those individuals may be somewhat more optimistic and less self-Â�protective than is typically found for those with low self-Â�esteem. A number of studies have provided results that are consistent with this glimmer-of-hope hypothesis (e.g., Bosson et al., 2003; Jordan, Spencer, & Zanna, 2003; Zeigler-Hill & Terry, 2007). People with discrepant implicit and explicit selfÂ�esteem may be motivated to reconcile the underlying ambivalence in their self-views (Briñol, Petty, & Wheeler, 2006). Indeed, when given new information that is framed as being self-Â�relevant, individuals with discrepant self-Â�esteem spend more time processing it and thinking about it (Briñol et al., 2006). However, other work finds that people with discrepant self-Â�esteem spend less time reviewing negative evaluations of themselves than do people with consistent self-Â�esteem (Schröder-Abé et al., 2007). These findings may be reconciled by considering that threats to self-Â�esteem make people with discrepant self-Â�esteem uncomfortable (i.e., they experience more negative arousal; Jordan et al., 2008). Thus, when self-Â�relevant information is critical or negative (as in Schröder-Abé et al., 2007), they may be motivated to suppress and avoid it. Individuals may be more motivated to pay attention to self-Â�relevant information that is more neutral (as in Briñol et al., 2006) because it offers them the chance to reconcile their discrepant self-views. There is thus considerable evidence of the validity, and unique usefulness, of nonreactive measures of self-Â�esteem. Despite the problems we identified with nonreactive measures, they enhance our understanding of how self-Â�esteem affects judgments and behavior. However, as we have noted, theory and measurement in this area are so interdependent that there is often little distinction made

400

IV. DOMAIN-SPECIFIC PERSPECTIVES

between nonreactive measures of self-�esteem and implicit self-�esteem as a construct. Implicit self�esteem was initially posited to be automatic and, accordingly, to be nonconscious, unintentional, efficient, and uncontrollable (Bargh, 1994). When measures were developed to capture implicit self�esteem, it was widely assumed that they captured automatic self-�evaluations in much the same form as they were originally envisioned. Surprisingly, relatively little work has been devoted to understanding the nature of the self-�evaluations that are captured by nonreactive measures. They may not be automatic in all of the ways that were originally supposed (these issues are discussed in further detail later).

DETERMINANTS OF€IMPLICIT SELF-ESTEEM AND CONSIDERATION OF ITS STABILITY VERSUS€MALLEABILITY Because implicit self-Â�esteem was originally conceptualized as being automatic, it was commonly assumed to be deeply ingrained and stable. For selfÂ�esteem, or any attitude, to operate without conscious guidance, it was presumed that the evaluations were habitual and overlearned (e.g., Koole et al., 2001; Wilson et al., 2000). These evaluations were expected to be resistant to change. Indeed, an influential account of the origins of implicit attitudes suggests that they often reflect the residual influence of well-Â�established attitudes after they have changed (Wilson et al., 2000; for a review of models, see Gawronski & Sritharan, Chapter 12, this volume). A new attitude is less well rehearsed, so bringing it to mind requires effort. The older, more habitual attitude, however, remains in memory and continues to influence behavior when one lacks the motivation or opportunity to bring the new, explicit attitude to mind (see also Petty, Tormala, Briñol, & Jarvis, 2006). Accordingly, it takes longer to change the underlying implicit attitude because it only changes as the new attitude becomes habitual. This view of implicit attitudes suggests that implicit self-Â�esteem is developmentally older than explicit self-Â�esteem and should be at least as stable. Consistent with this possibility, the most popular nonreactive measures of self-Â�esteem have reasonable test–Â�retest reliability. Name-Â�letter preferences are fairly stable over a period of roughly 1 month (rs = .62 and .63 for Bosson et al., 2000, and Koole et al., 2001, respectively). Birth date number pref-

erences and IAT scores were similarly stable over 1 month (rs = .53 and .69, respectively; Bosson et al., 2000). These values are not as high as those typical of direct measures of self-Â�esteem, but this may reflect the relatively poor reliability of nonreactive measures in general. Other evidence of the stability of implicit selfÂ�esteem comes from work on acculturation (Hetts et al., 1999). East Asians, who live in a relatively interdependent culture, typically have more modest self-views than North Americans, who live in an independent culture (see Heine, Lehman, Markus, & Kitayama, 1999). Interesting results emerge, however, for people who have recently emigrated from Asia to North America. They have explicit self-Â�esteem that is as high as that of native North Americans but implicit self-Â�esteem that is significantly lower (Hetts et al., 1999). This pattern may suggest that explicit self-Â�esteem adapts quickly to life in an independent culture but that implicit self-Â�esteem is slower to change. Indeed, the longer Asian immigrants lived in the United States, the higher their implicit self-Â�esteem tended to be. Thus, in this context, implicit self-Â�esteem may be more resistant to change than explicit self-Â�esteem. Finally, further evidence that implicit self-Â�esteem may be quite durable comes from the relations between university students’ implicit self-Â�esteem and their reports of how their parents interacted with them as children (DeHart, Pelham, & Tennen, 2006; see also Hetts & Pelham, 2001; Koole et al., 2001). Students who recalled their parents as highly nurturant had higher implicit self-Â�esteem, whereas those who recalled their parents as overprotective had lower implicit self-Â�esteem. Although these reports were retrospective and thus prone to bias (it is possible, for example, that implicit selfÂ�esteem affects how people remember interactions with their parents rather than early interactions with parents affecting implicit self-Â�esteem), students’ impressions of their early interactions with their parents were corroborated by independent reports from the students’ mothers (DeHart et al., 2006, Study 3). As a result, there is at least some evidence that implicit self-Â�esteem has its roots in early childhood experiences. However, there is also evidence that implicit self-Â�esteem can change quickly. As noted earlier, negative experiences affect implicit self-Â�esteem by decreasing it in some cases and increasing it in others (e.g., Dijksterhuis, 2004; Jones et al., 2002; Rudman et al., 2007). A naturalistic study of life events and implicit self-Â�esteem found that people with low explicit self-Â�esteem have significant fluctuations in their implicit self-Â�esteem when they ex-



21. Implicit and Explicit Forms of Self-�Esteem

perience negative life events (DeHart & Pelham, 2007). Finally, two independent series of studies found that positive evaluative conditioning can enhance implicit self-Â�esteem (Baccus, Baldwin, & Packer, 2004; Dijksterhuis, 2004; see also Grumm, Nestler, & von Collani, 2009). Those studies showed that consistently pairing self-Â�associated stimuli (e.g., pronouns such as I and me or the participant’s first name) with positive stimuli (e.g., pictures of smiling faces) increased participants’ levels of implicit self-Â�esteem. These findings are consistent with recent findings that implicit attitudes generally are more context dependent and malleable than originally supposed (see Blair, 2002; Gawronski & Bodenhausen, 2006; Rydell & Gawronski, 2009). Implicit attitudes are affected by perceivers’ motives and goals as well as aspects of the situation. These findings might be better understood if we consider implicit attitudes to result from patterns of activation in associative cognitive networks (see Gawronski & Bodenhausen, 2006). An implicit attitude may often reflect the strength of association between a concept (such as the self) and a positive or negative evaluation. Conditioning studies may effectively change the strength of this association, as might evaluative events that pair the self with positive or negative outcomes (e.g., doing well on an exam, experiencing social rejection). Implicit self-Â�esteem might thus change incrementally with repeated self-Â�relevant experiences. What matters most, however, is the number of events bearing on the self rather than the length of time that has elapsed. As conditioning studies demonstrate, changes in implicit attitudes can occur in a relatively short interval if enough events pair the self with positive (or, presumably, negative) stimuli. If we conceptualize implicit self-Â�esteem in this way, however, there is another way in which implicit self-Â�esteem can be malleable. Implicit selfÂ�esteem may change in different contexts to the extent that those contexts elicit different patterns of activation in the associative network. Different inputs can produce different patterns of activation; the concept basketball, for instance, may normally activate “bounce,” but if presented with the concept water may activate “float” rather than “bounce” (Gawronski & Bodenhausen, 2006; see Barsalou, 1982). With respect to self-Â�esteem, different contexts may activate different constellations of self-Â�associated stimuli and result in different evaluations of the self. Thus, when one is bewildered, has performed poorly, or has been criticized, more negative associations to the self may be activated, generating lower implicit self-Â�esteem. Such

401

effects may be short lived, however. Once the context changes, implicit self-Â�esteem may return to its usual levels. If this reasoning is correct, it is important to explore the durability of changes to implicit selfÂ�esteem. Changes to underlying association weights should produce longer lasting effects than contextual changes to patterns of activation. However, it should be noted that contextual changes can be quite stable in the sense that they will reliably occur any time the same context is encountered. That is, the same input (e.g., criticism) may reliably produce the same output (e.g., lower implicit self-Â�esteem; Gawronski & Bodenhausen, 2006; see also Mischel & Shoda, 1995). This line of reasoning suggests the possibility that other stimuli that are associated with particular aspects of the self, or self-Â�evaluations, may reliably produce changes in implicit self-Â�esteem. If one’s mother is highly critical, for example, simply being with her, or perhaps just thinking about her, may lead to drops in implicit self-Â�esteem (e.g., Baldwin, 1994).

FINAL THOUGHTS CONCERNING THE RELATION BETWEEN IMPLICIT AND€EXPLICIT SELF-ESTEEM As noted, direct measures of self-�esteem can be distorted by impression management and self�deception. These factors may suggest different reasons why implicit and explicit self-�esteem are sometimes discrepant. On the one hand, people may be aware of their implicit self-�esteem but may intentionally distort their self-views on direct measures through impression management. They may report self-views that are more positive than their implicit self-�esteem in order to make themselves appear more socially desirable, or they might present themselves more modestly than their self-�feelings warrant. On the other hand, people might lack introspective access to their implicit self-�esteem as a result of self-�deception. They might not realize that their implicit self-�esteem differs from the selfviews they report on direct measures. This latter possibility is perhaps clearest in dual-�attitude models (e.g., Epstein, 1994; Smith & DeCoster, 2000; Wilson et al., 2000), which posit that one cognitive system produces explicit self�esteem and another produces implicit self-�esteem, which may be nonconscious. There are important differences between different dual-�attitude models, but they frequently share the same assumptions: for example, that implicit and explicit attitudes

402

IV. DOMAIN-SPECIFIC PERSPECTIVES

stem from distinct mental processes (e.g., Rydell, McConnell, Mackie, & Strain, 2006), are stored in different brain regions (e.g., DeCoster, Banner, Smith, & Semin, 2006), and operate in different situations (e.g., Dovidio et al., 1997; see Petty, Briñol, & DeMarree, 2007, for a review). The lack of consistency between implicit and explicit selfÂ�esteem is easily explained by dual-Â�attitude models because these forms of self-Â�esteem result from very different processes, and people may be unaware of their implicit self-Â�esteem. The other possibility is that people are aware of their implicit self-Â�esteem, but intentionally distort it on direct measures. This possibility is perhaps most clearly articulated in the motivation and opportunity as determinants (MODE) model (Fazio & Towles-Â�Schwen, 1999), although it is also consistent with other models (e.g., Gawronski & Bodenhausen, 2006, 2007). The MODE model suggests that all attitudes are cognitive associations between attitude objects and evaluations. Nonreactive measures tap these associations directly. However, direct measures tap not only these associations but also more “downstream” influences, such as impression management (Olson & Fazio, 2008). Responses to direct and nonreactive measures will be consistent as long as individuals lack the desire to modify their responses to direct measures or the opportunity to do so. Consistent with this view, individuals demonstrate greater consistency in their responses to direct and nonreactive measures when their responses to direct measures are made quickly or under cognitive load because this limits their opportunity to alter their self-Â�esteem on direct measures (Koole et al., 2001). Even more pertinently, when participants are urged not to over- or underreport their self-Â�evaluations on direct measures, they report explicit self-Â�esteem that is more consistent with their implicit self-Â�esteem (Olson et al., 2007). These findings suggest that impression management may be more important than self-Â�deception for explaining the lack of association between scores on direct and nonreactive measures of selfÂ�esteem because people seem to be at least sometimes aware of their implicit self-Â�esteem. Much of the debate about the nature of implicit self-Â�esteem has thus focused on whether people are aware of their implicit self-Â�esteem and, accordingly, whether impression management or self-Â�deception accounts for discrepancies between implicit and explicit self-Â�esteem. There may be another possibility, however. Individuals may experience implicit attitudes in such a way that they can choose

whether to incorporate them into their explicit attitudes or not. If implicit attitudes result from associative processes (e.g., Gawronski & Bodenhausen, 2006; Grumm et al., 2009), people may be aware of their implicit attitudes but not the processes that produce them. They might thus experience their implicit attitudes as intuitions (see Hofmann & Wilson, Chapter 11, this volume). Individuals with low implicit self-�esteem may experience a palpable negative affective reaction to the self but be unaware of why they have this reaction. They might then decide to exclude this reaction from their more deliberative self-�evaluations, reasoning that it does not reflect their true self-�feelings (see Gawronski & Bodenhausen, 2006). Consistent with this possibility, people who chronically trust their intuitions, or who are induced to trust their intuitions experimentally, report self-views that are more consistent with their implicit self-�esteem (Jordan et al., 2007; see Pelham et al., 2005, for conceptually similar findings). This approach could also be used to explain previous findings that individuals report greater correspondence between their implicit and explicit self-�esteem when asked to avoid under- or overreporting self-�esteem (Olson et al., 2007). Such instructions may induce people to consider a broader range of experiences, including their intuitions, when responding to direct measures. An interesting aspect of this approach is that it does not suggest that the disconnection between implicit and explicit self-�esteem is necessarily due to self-�deception (because individuals are aware of their implicit self-�esteem) or impression management (because individuals may choose not to incorporate their implicit self-�esteem into their reports of explicit self-�esteem for reasons other than managing the impressions they make on others). Additional research is necessary to determine which, if any, of these models adequately capture the associations that exist between implicit and explicit self-�esteem.

CONCLUSION In recent years, researchers have become interested in the existence of implicit self-�esteem. This interest appears to be driven, at least in part, by the limitations of direct measures of self-�esteem and the development of nonreactive self-�esteem measures. We believe that implicit self-�esteem has tremendous potential to improve our understanding of attitudes about the self. Of course, this is still a rela-



21. Implicit and Explicit Forms of Self-�Esteem

tively new area of research, and a large number of basic questions remain to be answered concerning how implicit self-Â�esteem is similar to, and different from, explicit self-Â�esteem. We conclude this chapter by urging researchers to consider the strengths and weaknesses of both implicit and explicit forms of self-Â�esteem as well as the measures used to capture them. We believe that both implicit and explicit forms of self-esteem can contribute to our understanding of how people feel about themselves. That is, despite the limitations of direct measures of selfÂ�esteem, and the allure of nonreactive measures, we do not believe that researchers should ignore the demonstrated utility of direct measures (see Koestner & Mageau, 2006, for a similar argument). Similarly, we do not believe that nonreactive measures should be discounted on the basis of their current limitations because implicit self-Â�esteem has shown that it may provide a more complete picture of how people view themselves, either by itself or as a supplement to explicit self-Â�esteem. References Aidman, E. (1999). Measuring individual differences in implicit self-Â�concept: Initial validation of the selfÂ�apperception test. Personality and Individual Differences, 27, 211–228. Albers, L., Rotteveel, M., & Dijksterhuis, A. (2009). Towards optimizing the name letter test as a measure of implicit self-Â�esteem. Self and Identity, 8, 63–77. Asendorpf, J. B., Banse, R., & Mücke, D. (2002). Double dissociation between implicit and explicit personality self-Â�concept: The case of shy behavior. Journal of Personality and Social Psychology, 83, 380–393. Baccus, J. R., Baldwin, M. W., & Packer, D. J. (2004). Increasing implicit self-Â�esteem through classical conditioning. Psychological Science, 15, 498–502. Baldwin, M. W. (1994). Primed relational schemas as a source of self-Â�evaluative reactions. Journal of Social and Clinical Psychology, 13, 380–403. Banse, R., & Greenwald, A. G. (2007). Personality and implicit social cognition research: Past, present, and future. European Journal of Personality, 21, 371–382. Bargh, J. A. (1994). The four horseman of automaticity: Awareness, intention, efficiency, and control in social cognition. In R. S. Wyer, Jr. & T. K. Srull (Eds.), Handbook of social cognition (2nd ed., pp.€ 1–40). Hillsdale, NJ: Erlbaum. Bargh, J. A., Chaiken, S., Raymond, P., & Hymes, C. (1996). The automatic evaluation effect: Unconditional automatic attitude activation with a pronunciation task. Journal of Experimental Social Psychology, 32, 104–128. Barsalou, L. W. (1982). Context-Â�independent and context-Â�dependent information in concepts. Memory and Cognition, 10, 82–89.

403

Baumeister, R. F. (1982). Self-Â�esteem, self-Â�presentation, and future interaction: A dilemma of reputation. Journal of Personality, 50, 29–45. Baumeister, R. F., Campbell, J. D., Krueger, J. I., & Vohs, K. D. (2003). Does high self-Â�esteem cause better performance, interpersonal success, happiness, or healthier lifestyles? Psychological Science in the Public Interest, 4, 1–44. Baumeister, R. F., Tice, D. M., & Hutton, D. G. (1989). Self-Â�presentational motivations and personality differences in self-Â�esteem. Journal of Personality, 57, 547–579. Blair, I. V. (2002). The malleability of automatic stereotypes and prejudice. Personality and Social Psychology Review, 6, 242–261. Blascovich, J., & Tomaka, J. (1991). Measures of selfÂ�esteem. In J. P. Robinson, P. R. Shaver, & L. S. Wrightsman (Eds.), Measures of personality and social psychological attitudes (Vol. 1, pp.€115–160). New York: Academic Press. Bosson, J. K. (2006). Assessing self-Â�esteem via selfÂ�reports and nonreactive instruments: Issues and recommendations. In M. Kernis (Ed.), Self-Â�esteem issues and answers: A sourcebook of current perspectives (pp.€88–95). New York: Psychology Press. Bosson, J. K., Brown, R. P., Zeigler-Hill, V., & Swann, W. B., Jr. (2003). Self-Â�enhancement tendencies among people with high explicit self-Â�esteem: The moderating role of implicit self-Â�esteem. Self and Identity, 2, 169–187. Bosson, J. K., Lakey, C. E., Campbell, W. K., Zeigler-Hill, V., Jordan, C. H., & Kernis, M. H. (2008). Untangling the links between narcissism and self-Â�esteem: A theoretical and empirical review. Social and Personality Psychology Compass, 2, 1415–1439. Bosson, J. K., Swann, W. B., Jr., & Pennebaker, J. W. (2000). Stalking the perfect measure of implicit self-Â�esteem: The blind men and the elephant revisited? Journal of Personality and Social Psychology, 79, 631–643. Briñol, P., Petty, R. E., & Wheeler, S. C. (2006). Discrepancies between explicit and implicit self-Â�concepts: Consequences for information processing. Journal of Personality and Social Psychology, 91, 154–170. Brown, J. D., & Smart, S. A. (1991). The self and social conduct: Linking self-Â�representations to prosocial behavior. Journal of Personality and Social Psychology, 60, 368–375. Buchner, A., & Wippich, W. (2000). On the reliability of implicit and explicit memory measures. Cognitive Psychology, 40, 227–259. Buhrmester, D., Furman, W., Wittenberg, M. T., & Reis, H. T. (1988). Five domains of interpersonal competence in peer relationships. Journal of Personality and Social Psychology, 55, 991–1008. Campbell, W. K., Bosson, J. K., Goheen, T. W., Lakey, C. E., & Kernis, M. H. (2007). Do narcissists dislike themselves “deep down inside”? Psychological Science, 18, 227–229. Conner, T., & Barrett, L. F. (2005). Implicit self-Â�attitudes predict spontaneous affect in daily life. Emotion, 5, 476–488.

404

IV. DOMAIN-SPECIFIC PERSPECTIVES

Coopersmith, S. (1967). The antecedents of self-Â�esteem. San Francisco: Freeman. Cunningham, W. A., Preacher, K. J., & Banaji, M. R. (2001). Implicit attitude measures: Consistency, stability, and convergent validity. Psychological Science, 12, 163–170. De Houwer, J. (2003). The Extrinsic Affective Simon Task. Experimental Psychology, 50, 77–85. De Houwer, J. (2006). What are implicit measures and why are we using them? In R. W. Wiers & A. W. Stacy (Eds.), The handbook of implicit cognition and addiction (pp.€11–28). Thousand Oaks, CA: Sage. DeCoster, J., Banner, M. J., Smith, E. R., & Semin, G. R. (2006). On the inexplicability of the implicit: Differences in the information provided by implicit and explicit tests. Social Cognition, 24, 5–21. DeHart, T., & Pelham, B. W. (2007). Fluctuations in state implicit self-Â�esteem in response to daily negative events. Journal of Experimental Social Psychology, 43, 157–165. DeHart, T., Pelham, B. W., & Tennen, H. (2006). What lies beneath: Parenting style and implicit self-Â�esteem. Journal of Experimental Social Psychology, 42, 1–17. Dijksterhuis, A. (2004). I like myself but I don’t know why: Enhancing implicit self-Â�esteem by subliminal evaluative conditioning. Journal of Personality and Social Psychology, 86, 345–355. Dijksterhuis, A. (2008). Digging for the real attitude: Lessons from research on implicit and explicit selfÂ�esteem. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€229–250). New York: Psychology Press. Dodgson, P. G., & Wood, J. V. (1998). Self-Â�esteem and the cognitive accessibility of strengths and weaknesses after failure. Journal of Personality and Social Psychology, 75, 178–197. Dovidio, J. F., Kawakami, K., Johnson, C., Johnson, B., & Howard, A. (1997). On the nature of prejudice: Automatic and controlled processes. Journal of Experimental Social Psychology, 33, 510–540. Dutton, K. A., & Brown, J. D. (1997). Global self-Â�esteem and specific self-views as determinants of people’s reactions to success and failure. Journal of Personality and Social Psychology, 73, 139–148. Eaton, J., Struthers, C. W., Shomrony, A., & Santelli, A. G. (2007). When apologies fail: The moderating effect of implicit and explicit self-Â�esteem on apology and forgiveness. Self and Identity, 6, 209–222. Epstein, S. (1994). Integration of the cognitive and the psychodynamic unconscious. American Psychologist, 49, 709–724. Epstein, S., & Morling, B. (1995). Is the self motivated to do more than enhance and/or verify itself? In M. H. Kernis (Ed.), Efficacy, agency, and self-Â�esteem (pp.€9–29). New York: Plenum Press. Farnham, S. D., Greenwald, A. G., & Banaji, M. R. (1999). Implicit self-Â�esteem. In D. Abrams & M. A. Hogg (Eds.), Social identity and social cognition (pp.€230–248). Oxford, UK: Blackwell. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027.

Fazio, R. H., & Olson, M. A. (2003). Implicit measures in social cognition research: Their meaning and uses. Annual Review of Psychology, 54, 297–327. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. Fazio, R. H., & Towles-Â�Schwen, T. (1999). The MODE model of attitude-Â�behavior processes. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€ 97–116). New York: Guilford Press. Franck, E., De Raedt, R., Dereu, M., & Van den Abbeele, D. (2007). Implicit and explicit self-Â�esteem in currently depressed individuals with and without suicidal ideation. Journal of Behavior Therapy and Experimental Psychiatry, 38, 75–85. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. Gawronski, B., & Bodenhausen, G. V. (2007). Unraveling the processes underlying evaluation: Attitudes from the perspective of the APE model. Social Cognition, 25, 687–717. Gawronski, B., Hofmann, W., & Wilbur, C. J. (2006). Are “implicit” attitudes unconscious? Consciousness and Cognition, 15, 485–499. Gawronski, B., LeBel, E. P., & Peters, K. R. (2007). What do implicit measures tell us?: Scrutinizing the validity of three common assumptions. Perspectives on Psychological Science, 2, 181–193. Gebauer, J. E., Riketta, M., Broemer, P., & Maio, G. R. (2008). “How much do you like your name?”: An implicit measure of global self-Â�esteem. Journal of Experimental Social Psychology, 44, 1346–1354. Greenberg, J., & Pyszczynski, T. (1985). Compensatory self-Â�inflation: A response to the threat to self-Â�regard of public failure. Journal of Personality and Social Psychology, 49, 273–280. Greenwald, A. G. (1982). Is anyone in charge?: Personalysis vs. the principle of personal unity. In J. Suls (Ed.), Psychological perspectives on the self (Vol. 1, pp.€151–181). Hillsdale, NJ: Erlbaum. Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-Â�esteem, and stereotypes. Psychological Review, 102, 4–27. Greenwald, A. G., & Farnham, S. D. (2000). Using the Implicit Association Test to measure self-Â�esteem and self-Â�concept. Journal of Personality and Social Psychology, 79, 1022–1038. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the Implicit Association Test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85, 197–216. Grumm, M., Nestler, S., & von Collani, G. (2009). Changing explicit and implicit attitudes: The case of self-Â�esteem. Journal of Experimental Social Psychology, 45, 327–335. Gunn, G. R., & Jordan, C. H. (2008). [Convergence



21. Implicit and Explicit Forms of Self-�Esteem

between implicit, unstable, and contingent forms of self-Â�esteem]. Unpublished raw data. Heatherton, T. F., & Polivy, J. (1991). Development and validation of a scale for measuring state self-Â�esteem. Journal of Personality and Social Psychology, 60, 895– 910. Heine, S. J., Lehman, D. R., Markus, H. R., & Kitayama, S. (1999). Is there a universal need for positive selfÂ�regard? Psychological Review, 106, 766–794. Hetts, J. J., & Pelham, B. W. (2001). A case for the nonconscious self-Â�concept. In G. B. Moskowitz (Ed.), Cognitive social psychology: The Princeton symposium on the legacy and future of social cognition (pp.€ 105– 123). Mahwah, NJ: Erlbaum. Hetts, J. J., Sakuma, M., & Pelham, B. W. (1999). Two roads to positive regard: Implicit and explicit selfÂ�evaluation and culture. Journal of Experimental Social Psychology, 35, 512–559. Jacoby, L. L., & Kelley, C. M. (1992). Unconscious influences of memory: Dissociations and automaticity. In A. D. Milner & M. D. Rugg (Eds.), The neuropsychology of consciousness (pp.€ 201–233). San Diego, CA: Academic Press. Jones, J. T., Pelham, B. W., Mirenberg, M. C., & Hetts, J. J. (2002). Name letter preferences are not merely mere exposure: Implicit egotism as self-Â�regulation. Journal of Experimental Social Psychology, 38, 170–177. Jordan, C. H., Logel, C., Spencer, S. J., & Zanna, M. P. (2006). Nonconscious self-Â�esteem: Is there something you’re not telling yourself? In M. Kernis (Ed.), Self-Â�esteem issues and answers: A sourcebook of current perspectives (pp.€ 60–68). New York: Psychology Press. Jordan, C. H., Logel, C., Spencer, S. J., Zanna, M. P., & Whitfield, M. L. (2008). The heterogeneity of selfÂ�esteem: Exploring the interplay between implicit and explicit self-Â�esteem. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€251–284). New York: Psychology Press. Jordan, C. H., Spencer, S. J., & Zanna, M. P. (2003). Secure and defensive high self-Â�esteem. Journal of Personality and Social Psychology, 85, 969–978. Jordan, C. H., Spencer, S. J., & Zanna, M. P. (2005). Types of high self-Â�esteem and prejudice: How implicit self-Â�esteem relates to ethnic discrimination among high explicit self-Â�esteem individuals. Personality and Social Psychology Bulletin, 31, 693–702. Jordan, C. H., Spencer, S. J., Zanna, M. P., HoshinoÂ�Browne, E., & Correll, J. (2003). Secure and defensive high self-Â�esteem. Journal of Personality and Social Psychology, 85, 969–978. Jordan, C. H., Whitfield, M., & Zeigler-Hill, V. (2007). Intuition and the correspondence between implicit and explicit self-Â�esteem. Journal of Personality and Social Psychology, 93, 1067–1079. Karpinski, A., & Steinberg, J. A. (2006). Implicit and explicit self-Â�esteem: Theoretical and methodological refinements. In M. Kernis (Ed.), Self-Â�esteem issues and answers: A sourcebook of current perspectives (pp.€102– 109). New York: Psychology Press. Karpinski, A., Steinberg, J. A., Versek, B., & Alloy, L. B. (2007). The Breadth-Based Adjective Rating Task (BART) as an indirect measure of self-Â�esteem. Social Cognition, 25, 778–818.

405

Karpinski, A., & Steinman, R. B. (2006). The single category Implicit Association Test as a measure of implicit social cognition. Journal of Personality and Social Psychology, 91, 16–32. Kernberg, O. (1975). Borderline conditions and pathological narcissism. New York: Jason Aronson. Kernberg, O. F. (1986). Factors in the psychoanalytic treatment of narcissistic personalities. In A. P. Morrison (Ed.), Essential papers on narcissism (pp.€213–244). New York: New York University Press. Kernis, M. H. (2003). Toward a conceptualization of optimal self-Â�esteem. Psychological Inquiry, 14, 1–26. Kernis, M. H., Abend, T. A., Goldman, B. M., Shrira, I., Paradise, A. N., & Hampton, C. (2005). Self-Â�serving responses arising from discrepancies between explicit and implicit self-Â�esteem. Self and Identity, 4, 311–330. Kernis, M. H., Lakey, C. E., & Heppner, W. L. (2008). Secure versus fragile high self-Â�esteem as a predictor of verbal defensiveness: Converging findings across three different markers. Journal of Personality, 76, 477–512. Koestner, R., & Mageau, G. A. (2006). The assessment of implicit and explicit self-Â�esteem: Lessons from motive research. In M. Kernis (Ed.), Self-Â�esteem issues and answers: A sourcebook of current perspectives (pp.€96–101). New York: Psychology Press. Kohut, H. (1966). Forms and transformations of narcissism. Journal of the American Psychoanalytic Association, 14, 243–272. Kohut, H. (1977). The restoration of the self. New York: International Universities Press. Koole, S. L., Dijksterhuis, A., & van Knippenberg, A. (2001). What’s in a name: Implicit self-Â�esteem and the automatic self. Journal of Personality and Social Psychology, 80, 669–685. Koole, S. L., & Pelham, B. W. (2003). On the nature of implicit self-Â�esteem: The case of the name letter effect. In S. J. Spencer, S. Fein, M. P. Zanna, & J. M. Olson (Eds.), Motivated social perception: The Ontario symposium (Vol. 9, pp.€ 93–116). Mahwah, NJ: Erlbaum. Krizan, Z. (2008). What is implicit about implicit selfÂ�esteem? Journal of Research in Personality, 42, 1635– 1640. Krizan, Z., & Suls, J. (2008). Are implicit and explicit measures of self-Â�esteem related?: A meta-Â�analysis for the name-Â�letter test. Personality and Individual Differences, 44, 521–531. Lambird, K. H., & Mann, T. (2006). When do ego threats lead to self-Â�regulation failure?: Negative consequences of defensive high self-Â�esteem. Personality and Social Psychology Bulletin, 32, 1177–1187. LeBel, E. P., & Gawronski, B. (2009). How to find what’s in a name: Scrutinizing the optimality of five scoring algorithms for the name-Â�letter task. European Journal of Personality, 23, 85–106. McGregor, I., & Marigold, D. C. (2003). Defensive zeal and the uncertain self: What makes you so sure? Journal of Personality and Social Psychology, 85, 838–852. McGregor, I., Nail, P. R., Marigold, D. C., & Kang, S.-J. (2005). Defensive pride and consensus: Strength in imaginary numbers. Journal of Personality and Social Psychology, 89, 978–996. Mischel, W., & Shoda, Y. (1995). A cognitive-Â�affective

406

IV. DOMAIN-SPECIFIC PERSPECTIVES

system theory of personality: Reconceptualizing situations, dispositions, dynamics, and invariance in personality structure. Psychological Review, 102, 246–268. Nosek, B. A., & Banaji, M. R. (2001). The Go/No–Go Association Task. Social Cognition, 19, 625–666. Nuttin, J. M. (1985). Narcissism beyond gestalt awareness: The name letter effect. European Journal of Social Psychology, 15, 353–361. Nuttin, J. M. (1987). Affective consequences of mere ownership: The name letter effect in twelve European languages. European Journal of Social Psychology, 17, 381–402. Olson, M. A., & Fazio, R. H. (2008). Implicit and explicit measures of attitudes: The perspective of the MODE model. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€19–64). New York: Psychology Press. Olson, M. A., Fazio, R. H., & Hermann, A. D. (2007). Reporting tendencies underlie discrepancies between implicit and explicit measures of self-Â�esteem. Psychological Science, 18, 287–291. Paulhus, D. L. (1984). Two-Â�component models of socially desirable responding. Journal of Personality and Social Psychology, 46, 598–609. Pelham, B. W., Koole, S. L., Hardin, C. D., Hetts, J. J., Seah, E., & DeHart, T. (2005). Gender moderates the relation between implicit and explicit self-Â�esteem. Journal of Experimental Social Psychology, 41, 84–89. Pelham, B. W., Mirenberg, M. C., & Jones, J. T. (2002). Why Susie sells seashells by the seashore: Implicit egotism and major life decisions. Journal of Personality and Social Psychology, 82, 469–487. Pelham, B. W., & Swann, W. B., Jr. (1989). From selfÂ�conceptions to self-worth: On the sources and structure of global self-Â�esteem. Journal of Personality and Social Psychology, 57, 672–680. Perruchet, P., & Baveux, P. (1989). Correlational analyses of explicit and implicit memory performance. Memory and Cognition, 17, 77–86. Petty, R. E., Briñol, P., & DeMarree, K. G. (2007). The meta-Â�cognitive model (MCM) of attitudes: Implications for attitude measurement, change, and strength. Social Cognition, 25, 657–686. Petty, R. E., Tormala, Z. L., Briñol, P., & Jarvis, W. B. G. (2006). Implicit ambivalence from attitude change: An exploration of the PAST model. Journal of Personality and Social Psychology, 90, 21–41. Ranganath, K. A., Smith, C. T., & Nosek, B. A. (2008). Distinguishing automatic and controlled components of attitudes from direct and indirect measurement methods. Journal of Experimental Social Psychology, 44, 386–396. Raskin, R., Novacek, J., & Hogan, R. (1991). Narcissistic self-Â�esteem management. Journal of Personality and Social Psychology, 60, 911–918. Robins, R. W., Hendin, H. M., & Trzesniewski, K. H. (2001). Measuring global self-Â�esteem: Construct validation of a single item measure and the Rosenberg Self-Â�Esteem Scale. Personality and Social Psychology Bulletin, 27, 151–161. Robinson, M. D., & Meier, B. P. (2005). Rotten to the core: Neuroticism and implicit evaluations of the self. Self and Identity, 4, 361–372.

Robinson, M. D., & Wilkowski, B. M. (2006). Loving, hating, vacillating: Agreeableness, implicit selfÂ�esteem, and neurotic conflict. Journal of Personality, 74, 935–978. Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press. Rudman, L. A., Dohn, M. C., & Fairchild, K. (2007). Implicit self-Â�esteem compensation: Automatic threat defense. Journal of Personality and Social Psychology, 93, 798–813. Rudolph, A., Schröder-Abé, M., Schütz, A., Gregg, A. P., & Sedikides, C. (2008). Through a glass, less darkly?: Reassessing convergent and discriminant validity in measures of implicit self-Â�esteem. European Journal of Psychological Assessment, 24, 273–281. Rydell, R. J., & Gawronski, B. (2009). I like you, I like you not: Understanding the formation of contextÂ�dependent automatic attitudes. Cognition and Emotion, 23, 1118–1152. Rydell, R. J., McConnell, A. R., Mackie, D. M., & Strain, L. M. (2006). Of two minds: Forming and changing valence-Â�inconsistent implicit and explicit attitudes. Psychological Science, 17, 954–958. Sakellaropoulo, M., & Baldwin, M. W. (2007). The hidden sides of self-Â�esteem: Two dimensions of implicit self-Â�esteem and their relation to narcissistic reactions. Journal of Experimental Social Psychology, 43, 995–1001. Sandstrom, M. J., & Jordan, R. (2008). Defensive selfÂ�esteem and aggression in childhood. Journal of Research in Personality, 42, 506–514. Schröder-Abé, M., Rudolph, A., Wiesner, A., & Schütz, A. (2007). Self-Â�esteem discrepancies and defensive reactions to social feedback. International Journal of Psychology, 42, 174–183. Shimizu, M., & Pelham, B. W. (2004). The unconscious cost of good fortune: Implicit and explicit self-Â�esteem, positive life events, and health. Health Psychology, 23, 101–105. Shrauger, J. S., & Rosenberg, S. E. (1970). Self-Â�esteem and the effects of success and failure feedback on performance. Journal of Personality, 38, 404–417. Smith, E. R., & DeCoster, J. (2000). Dual-Â�process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychological Review, 4, 108– 131. Smurda, J. D., Wittig, M. A., & Gokalp, G. (2006). Effects of threat to a valued social identity on implicit self-Â�esteem and discrimination. Group Processes and Intergroup Relations, 9, 181–197. Spalding, L. R., & Hardin, C. D. (1999). Unconscious unease and self-Â�handicapping: Behavioral consequences of individual differences in implicit and explicit self-Â�esteem. Psychological Science, 10, 535–539. Spencer, S. J., Jordan, C. H., Logel, C. E. R., & Zanna, M. P. (2005). Nagging doubts and a glimmer of hope: The role of implicit self-Â�esteem in self-image maintenance. In A. Tesser, J. V. Wood, & D. A. Stapel (Eds.), On building, defending, and regulating the self: A psychological perspective (pp.€153–170). New York: Psychology Press. Stapel, D. A., & Blanton, H. (2004). From seeing to being: Subliminal social comparisons affect implicit



21. Implicit and Explicit Forms of Self-�Esteem

and explicit self-Â�evaluations. Journal of Personality and Social Psychology, 87, 468–481. Swann, W. B., Jr., Chang-Â�Schneider, C., & McClarty, K. L. (2007). Do people’s self-views matter?: SelfÂ�concept and self-Â�esteem in everyday life. American Psychologist, 62, 84–94. Tafarodi, R. W., & Swann, W. B., Jr. (2001). TwoÂ�dimensional self-Â�esteem: Theory and measurement. Personality and Individual Differences, 31, 653–673. Trzesniewski, K., Donnellan, B., Moffitt, T., Robins, R., Poulton, R., & Caspi, A. (2006). Low self-Â�esteem during adolescence predicts poor health, criminal behavior, and limited economic prospects during adulthood. Developmental Psychology, 42, 381–390. Vargas, P. T., Sekaquaptewa, D., & von Hippel, W. (2007). Armed only with paper and pencil: “Lowtech” measures of implicit attitudes. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp.€103–124). New York: Guilford Press. Watson, D., Suls, J., & Haig, J. (2002). Global self-Â�esteem in relation to structural models of personality and af-

407

fectivity. Journal of Personality and Social Psychology, 83, 185–197. Wentura, D., Kulfanek, M., & Greve, W. (2005). Masked affective priming by name letters: Evidence for a correspondence of explicit and implicit self-Â�esteem. Journal of Experimental Social Psychology, 41, 657–663. Wilson, T. D., Lindsey, S., & Schooler, T. Y. (2000). A model of dual attitudes. Psychological Review, 107, 101–126. Zeigler-Hill, V. (2006). Discrepancies between implicit and explicit self-Â�esteem: Implications for narcissism and self-Â�esteem instability? Journal of Personality, 74, 119–143. Zeigler-Hill, V., & Myers, E. M. (2009). Is high selfÂ�esteem a path to the White House?: The implicit theory of self-Â�esteem and the willingness to vote for presidential candidates. Personality and Individual Differences, 46, 14–19. Zeigler-Hill, V., & Terry, C. (2007). Perfectionism and explicit self-Â�esteem: The moderating role of implicit self-Â�esteem. Self and Identity, 6, 137–153.

C h a p t e r 22

The Self-�Concept New Insights from Implicit Measurement Procedures Konrad Schnabel and Jens B. Asendorpf

But it was his search for self that defined him. —Video clip for Barack Obama’s â•… 2008 presidential campaign

T

he times that “the ego (or self) became sidetracked and lost to view” (Allport, 1943, p.€451) are long gone. Over the past 30 years, the self has attracted the attention of both lay and scientific psychology (e.g., Byrne, 2002), and interest in the self has even increased with the development of new implicit measurement procedures like the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) or priming procedures (e.g., Fazio, Sanbonmatsu, Powell, & Kardes, 1986). Implicit procedures have the potential to provide access to aspects of the self that are inaccessible or only partially accessible by conscious introspection (Greenwald & Banaji, 1995) but that may have an essential impact on how one thinks, feels, and behaves. This chapter reviews theories and research that deal with the dissociation between the implicit and the explicit self-Â�concept and discusses how implicit measures may help to achieve a deeper and more complete understanding of the self. We start with a general definition of the self-Â�concept and briefly discuss basic issues in traditional selfÂ�concept theories. Second, we compare and contrast the implicit and the explicit self-Â�concept. Third, we review research on different self-Â�concept domains and the behavioral validity of the implic

it personality self-�concept. Fourth, we discuss how individual differences may moderate the predictive validity of implicit and explicit self-�concept measures. Finally, we address questions related to the stability of the implicit self-�concept.

DEFINITION OF THE SELF-CONCEPT On a very broad level, the self-Â�concept can be defined as an associative network containing associations between the concept of self and attribute concepts (Greenwald et al., 2002). However, not only attribute traits per se (e.g., outgoing) but also roles (e.g., teacher), groups (e.g., one’s family of origin, antinuclear activists), objects (e.g., preferred brands), and activities (e.g., hiking) belong to the attribute concepts that may be associated with the concept of self. Generally, the self-Â�concept consists of associations between the concept of self and other concepts. The associations of the self with valence, either direct or mediated through components of the self-Â�concept, refer to one’s self-Â�esteem (Greenwald et al., 2002; see also Zeigler-Hill & Jordan, Chapter 21, this volume). This makes evident that it is difficult to draw a clear distinction 408



22. The Self-�Concept

between evaluative and specific semantic content of the self-Â�concept (cf. Schnabel, Asendorpf, & Greenwald, 2008b), and that self-Â�esteem rather represents the affective part of the self-Â�concept than an entity independent from the self-Â�concept. In order to meet the complexity of the self, Baumeister (1998) differentiated between three basic aspects. The first, reflexive consciousness, refers to the fact that the human mind is able to turn its attention back toward its own source. People can be self-aware, they are able to observe themselves in various situations, and they can collect knowledge about their own person (see also Hofmann & Wilson, Chapter 11, this volume). The second aspect refers to the interpersonal nature of the self. The self is always part of social groups and relationships, and a crucial function of the self refers to its capability to relate to others. Social interactions are influenced by the self, and social interactions, in turn, influence how one perceives and experiences oneself. The third aspect refers to the executive function of the self and describes its ability to exert control over its actions. The self is able to make choices and decisions, execute actions, and take responsibility. Considering the executive function of the self and its implications on both self-Â�evaluation and social behavior, Greenwald and Pratkanis (1984) differentiated between four different self aspects that show some similarity with Freud’s concepts of id, ego, and superego. Their hierarchically organized system describes four self components that are aimed at different targets (ego tasks) and contribute differently to self-Â�evaluation and selfÂ�esteem. Achieving targets results in an increase of self-Â�esteem; missing targets decreases the selfÂ�esteem. The first subsystem or facet of the self was called the diffuse self. It is the most primitive selfÂ�component and does not distinguish sharply between self and others. Its main target is hedonic satisfaction, that is, achieving positive affective states. This self-Â�component can be activated under the influence of drugs or in situations that provide anonymity in groups. The simple pursuit of hedonic satisfaction frequently results in the violation of norms. The main target of the second subsystem, the public self, is to win positive approval by significant others. Its responsiveness to the positive evaluation of others may result in the use of selfÂ�presentational strategies. The development of the public self implies the ability to discriminate between self and others and to identify the aspects of one’s behavior that influence the impression on others. The public self can be activated in a state

409

of public self-Â�consciousness. Besides winning social accreditation, another important ego task of the public self is to internalize the evaluative standards of significant others. The internalization of social standards and norms may lead to the development of the third self-Â�component: the private self. Its ego task is individual achievement, that is, the pursuit of personal targets according to internal standards. The private self allows for self-Â�evaluation that is independent from the estimation of others. It is activated by a state of private self-Â�consciousness. The fourth self-Â�component, the collective self, may develop when the goals of groups that function as personal reference groups are internalized. Its ego task is collective achievement. Whereas the public self merely results in the adaptation to external standards, the collective self deals with internalizing the goals of significant others and groups. The collective self may be activated when aspects of the reference group are made salient. Even though Greenwald and Pratkanis’s (1984) approach represents rather a theoretical model than empirically based evidence, it is very useful to illustrate the complexity of the self and to organize its broad consequences on social behavior and self-Â�evaluation.

IMPLICIT AND EXPLICIT SELF-CONCEPT The dissociation between associative and propositional processes (see Deutsch & Strack, Chapter 4, this volume) is also relevant for the self-�concept. In line with dual-�process models in social cognition research, differences between the implicit and the explicit self-�concept can be explained in terms of their underlying processes as differences between an associative and a propositional way of information processing (Gawronski & Bodenhausen, 2006). Many dual-�process models (e.g., Strack & Deutsch, 2004) assign associative and propositional processes to different systems of information processing (e.g., the impulsive and reflective system). The conceptualization of different systems implies that they are instantiated in different brain networks. However, the neurophysiological identification of different brain networks for the two systems remains an issue of further research, and the differentiation between associative and propositional processes does not necessarily call for two different systems. Therefore, we refrain from using the term system and simply contrast between associative and propositional processes.

410

IV. DOMAIN-SPECIFIC PERSPECTIVES

Associative processes operate quickly and automatically and correspond to spread of activation processes across associations that are represented in an associative store (Strack & Deutsch, 2004). Associations consist of concepts (e.g., the concepts of I and extroverted) that are associatively linked (e.g., I–extroverted with “–” representing the associative link) and develop on the basis of feature similarity and spatiotemporal contiguity between the involved concepts. For instance, if a person is repeatedly gregarious and outgoing in various situations, he or she may develop an associative link between the self-Â�concept and the concept of extroversion that is represented in the associative store. The associative link triggers the fast and automatic coactivation of the two concepts whenever one or both concepts is activated. In contrast, propositional processes operate slowly but consciously and correspond to explicit reasoning processes. The reasoning processes retrieve information from the associative store and transform it from an associative to a propositional format. Differently from associations, propositions consist of concepts that are linked by a relation (e.g., “I am extroverted” with am representing the relation). The relational link between the concepts is the result of syllogistic inferences that gauge the validity of the proposition. As a consequence, the resulting propositions can be either accepted as true or rejected as false. For instance, if a person is asked to self-rate his or her extroversion, he or she may have some introspective access to the automatic associations that are activated in the associative store. At the same time, he or she may think about the adequacy of these associations and modify the resulting propositions on the basis of syllogistic rules (e.g., “I can’t be extroverted if I don’t like parties”) and self-Â�presentational concerns (e.g., “If I describe myself as extroverted, I will get the job”). Associative and propositional processes differ with respect to the format in which information is made available. We call information that is made available by associative processes associative representations and information that is made available by propositional processes propositional representations, respectively. Associative and propositional representations refer to information that is activated within a given situation. They do not necessarily need to be stored in long-term memory. Considering the self-Â�concept, we refer to the explicit self-Â�concept as propositional representations that describe the self and that are the output of reasoning processes. In contrast, we refer to the implicit self-Â�concept as associations between the self and other concepts that are the result of automatic

activation processes. On one hand, self associations develop as the consequence of individual experiences that are encoded in the associative store. On the other hand, self associations are activated situationally as the consequence of a particular set of external input stimuli. Thus, the pattern of self associations that is activated in a given situation depends on the relative fit between the particular context on one side and the preexisting structure of self associations in the associative store on the other. For instance, associations between the self and anxiety may be activated in socially anxious people who love dogs when they deliver a public speech but not when they meet a giant dog, whereas the opposite may be true for passionate speakers who are dog phobic. Greenwald and Pratkanis’s (1984) differentiation of the diffuse, the public, the private, and the collective self (see prior discussion) shows some parallels to the differentiation of the implicit and the explicit self-Â�concept. The implicit self-Â�concept may be seen as related to the diffuse self because both are strongly influenced by spontaneous processes and the satisfaction of basic needs. The explicit self-Â�concept seems to be more similar to hierarchically higher self-Â�aspects because it also deals with gaining public, private, or collective acceptance by its behavior. Nevertheless, aspects of the public, the private, and even the collective self-Â�concept may become part of the implicit self-Â�concept if individual learning experiences integrate these aspects in one’s associative selfÂ�representations. The conceptualization of the explicit and the implicit self-Â�concept as propositional versus associative representations is useful to describe and explain their dissociations that occur with respect to (1) the explicit and implicit measurement procedures that assess the self-Â�concept, (2) the predictive validity of those procedures for the prediction of behavior, and (3) the stability and malleability of the explicit and implicit self-Â�concept. Even though calling the measurement procedures “explicit” or “implicit” implies that they assess solely propositional or associative representations, there is now considerable consensus among researchers that this is not the case (see Sherman, Klauer, & Allen, Chapter 9, this volume). Nevertheless, we use the labels “explicit” and “implicit” also for the measurement procedures because this labeling is now deeply entrenched in the literature. Furthermore, we consider it adequate to adopt an operational definition of the explicit and implicit self-Â�concept. Thus, we talk about the explicit selfÂ�concept when we refer to information about the



22. The Self-�Concept

self that is assessed by explicit questionnaire measures and about the implicit self-Â�concept when we refer to information about the self that is assessed by implicit measurement procedures (cf. Asendorpf, Banse, & Mücke, 2002). Different from using the labels “explicit” and “implicit” for both the measurement procedures and the constructs that are assessed, De Houwer (2006) suggested using these labels only for the measurement outcomes. Accordingly, an implicit measure is defined as a measurement outcome that reflects the assessed construct in an automatic way. This definition highlights the functional properties (i.e., automaticity) of the measurement outcome. Automaticity itself refers to several functional features of the measurement process, such as unawareness, unintentionality, efficiency, and uncontrollability (Bargh, 1994). When so-Â�called implicit measures are examined with respect to these functional features, it becomes evident that most measures rely on a mixture of automatic and nonautomatic processes (De Houwer, 2006). In contrast to the measurement outcomes, De Houwer suggested defining the measurement procedures as “direct” or “indirect” depending on whether they directly ask the participant to describe her- or himself or whether they indirectly assess a respective trait, attitude, or cognition by examining its effects on behavior. As noted, we do not adopt this terminology because the labels “implicit” and “explicit” are now largely used for both measurement outcomes and measurement procedures. Differences between explicit and implicit procedures and between different variants of implicit procedures are discussed extensively at the theoretical and operational levels in Sections I and II of this volume. Even though most research used the IAT, assessment of the implicit self-Â�concept is possible also with other procedures like the Go/No-Go Association Task (Boldero, Rawlings, & Haslam, 2007), the Single-Block IAT (Teige-Â�Mocigemba, Klauer, & Rothermund, 2008), or the Implicit Association Procedure (Schnabel, Banse, & Asendorpf, 2006b) but seems less promising with the Extrinsic Affective Simon Task (Teige, Schnabel, Banse, & Asendorpf, 2004). Here, it is important to note that propositional and associative self-Â�representations as assessed by explicit and implicit measurement procedures, respectively, do not necessarily need to diverge. Individuals may, by default, base the information they use to inform about their self on their associative self-Â�representations (cf. Gawronski & Bodenhausen, 2006). However, higher order processes of propositional reasoning will typically lead to the rejection of some self associations that

411

are considered inadequate. The remainder of this chapter deals with the dissociations between the explicit and implicit self-�concept that result from those reasoning processes with regard to different self-�concept domains, the prediction of behavior, and the stability of the implicit and explicit self�concept.

DOMAINS OF IMPLICIT SELF-CONCEPT In this section we discuss research that deals with dissociations between the implicit and explicit self-�concept with respect to different self-�concept domains. We refer to the self in balanced identities, the personality self-�concept, and the role of the implicit self-�concept in adult attachment.

The Self-Â�Concept in Balanced Identities A powerful example that the self is meaningfully connected to social entities that go beyond the individual self is provided by research on balanced identities. Balanced-Â�identity designs were introduced within a unified theory of social cognition by Greenwald and colleagues (2002) and conceptualize knowledge about the self as a network of associations among the self, a social category, and an attribute. The association networks are conceptualized as cognitive triads and typically assessed by three IATs that measure associations between (1) the self and a social category (e.g., female vs. male, young vs. old, American vs. German), (2) the self and an attribute (e.g., warm vs. cold, smoking vs. nonsmoking, pleasant vs. unpleasant), and (3) the attribute and the social category. As these examples make evident, a sharp differentiation between attribute and social category is not always possible because many attributes represent social categories. The triads were called balanced identities because the pattern of associations within the triads was expected to follow principles of Heider’s (1958) balance theory and because the triads always contain an identity association. Identity associations were defined as an association between the self and a social category. It is evident that social categories (e.g., male vs. female) are sometimes difficult to be separated from attributes (e.g., smoking vs. nonsmoking) such that attributes may also represent a social category or group. As a consequence, a balanced-Â�identity triad may, in fact, contain more than one identity association.

412

IV. DOMAIN-SPECIFIC PERSPECTIVES

This view slightly differs from Greenwald and colleagues’ (2002) definition, which strictly separates associations between the self and social categories or groups (i.e., identity associations) from associations between the self and attribute concepts. Furthermore, Greenwald and colleagues labeled self–Â�attribute associations either as self-Â�esteem associations or as self-Â�concept associations depending on whether the attribute concept is a valence concept (e.g., pleasant vs. unpleasant) or a specific semantic concept (e.g., smoking vs. nonsmoking), respectively. In the same way, associations between the social category and the attribute concept were identified either as attitude associations or as stereotype associations depending on whether the attribute concept is a valence concept or not. These definitions make evident that balanced identities are able to build an integrative framework for the most prominent concepts in social psychology. Thus, studies about balanced identities reveal a great deal about the self because they always include two associations of the self with other concepts. Additionally, they allow exploration of how associations of the self with other concepts are related to associations between those other concepts. Most importantly, results for balancedÂ�identity studies showed evidence for implicit–Â� explicit dissociations such that certain predicted cognitive consistency patterns were evident for implicit measures but not for parallel explicit selfÂ�report measures (Greenwald et al., 2002). The predictions of consistency effects on the balanced identity triads drew their inspiration from theories of affective-Â�cognitive consistency, particularly from Heider’s (1958) balance theory, and were based on (1) the balance-Â�congruity, (2) the imbalance-Â�dissonance, and (3) the differentiation principles. The balance-Â�congruity principle postulates that the association between two unassociated or weakly associated concepts should be strengthened if the two concepts are both associated with a third concept. For instance, the association between the concepts of male and smoking should be strengthened if both concepts are associated with the self. In contrast, the imbalance-Â�dissonance principle states that the formation of new associations between two concepts should be suppressed if both concepts are associated with bipolar-Â�opposed concepts. For instance, tendencies to create an association between male and smoking should be suppressed if male is associated with positive valence and smoking is associated with negative valence. The suppression of tendencies to create an association between male and smoking may conflict with

the balance-�congruency principle that suggests an association between those concepts because of their shared association with self. As a consequence, male and smoking become so-�called pressured concepts that are pushed to develop associations to both of two bipolar-�opposed concepts, in this case positive and negative valence. The third, differentiation principle deals with a solution for pressured concepts, assuming that they tend to split into subconcepts that are each associated with a different one of the pressuring bipolar�opposed concepts. For instance, smoking may be pressured to develop a subconcept that links youth smoking with positive valence and allows for an association with male that is itself associated with positive valence. Greenwald and colleagues (2002) tested predictions that follow primarily from the first principle (i.e., balance congruity) and formulated additional but untested predictions that result from the second (i.e., imbalance dissonance) and third (differentiation) principles. Balance-�congruity hypotheses were tested by exploring interaction patterns between the three IATs that assessed the respective balanced identity triad. We illustrate balance-�congruity hypotheses with the balanced-�identity investigation of gender attitude in a female sample. For females, the association between self and the concept female should be strong (female gender identity), and the same should be true for the association between self and positive valence (positive self-�esteem). According to the balance-�congruity principle, the existence of these associations should also strengthen the association between the concept female and positive valence (positive gender attitude). More concretely, the strength of a positive attitude toward females as the criterion should be the multiplicative product of the strengths of positive self-�esteem and female gender identity as the criteria. The results supported this prediction: As the positive attitude toward females increased, so did positive self-�esteem and female gender identity (cf. Experiment 1 by Rudman & Goodwin, 2004). Further support for related hypotheses was evident in studies on age and race identities as well as studies that included nonevaluative attribute concepts and assessed balanced identities that included, for instance, gender stereotypes (cf. Aidman & Carrol, 2003; Nosek, Banaji, & Greenwald, 2002; Rudman, Greenwald, & McGhee, 2001). Interestingly, such effects of the balance�congruity principle were obtained only when tested with implicit measures but received no support when tested with explicit measuring tools. These



22. The Self-�Concept

implicit–Â�explicit dissociations may be explained by the differences between associative and propositional information processing that were discussed earlier in this chapter. More precisely, the assessment of consistency between explicit propositions in the process of propositional reasoning may lead to the rejection of the automatic associations that are implied by the balanced-Â�identity triads (Gawronski & Bodenhausen, 2007). Motivational or response factors and introspective limits that affect information processing at the propositional level may be an additional source of implicit–Â� explicit dissociations (cf. Greenwald & Banaji, 1995). Concerning the implicit self-Â�concept, it may be interesting to explore differences between balanced and unbalanced association triads with respect to their effects on (1) the stability of self associations and (2) the consistency between explicit and implicit self-Â�concept measures. According to the balance-Â�congruity principle, the associations in a balanced triad should strengthen each other and, as a consequence, also stabilize each other. In contrast, reciprocal strengthening between the associations is missing in an unbalanced triad according to the imbalance-Â�dissonance principle. For instance, if an individual has a positive selfÂ�esteem and associates her- or himself with shyness, she or he may associate shyness with either positive (balanced triad) or negative (imbalanced triad) valence. In this example, positive associations with shyness seem to be plausible for people who have a preference for being unobtrusive or cautious, whereas negative associations with shyness seem to be plausible for people who associate shyness with a lack of self-Â�security. Expecting that associations in balanced triads are more stable, test–Â�retest correlations of implicit measures should be higher for participants who show a balanced pattern of associations within a given association triad than for participants with an imbalanced triad. According to the same logic, implicit–Â�explicit correlations are expected to be higher for participants with balanced triads than for those with imbalanced triads. If the propositions that are generated to inform about the self within a questionnaire are based on one’s automatic associations (Gawronski & Bodenhausen, 2006), there should be more conflicting associations when generating the explicit self-Â�report for participants with imbalanced triads than for those with balanced triads. Those conflicting associations may be a reason for participants not to base their, for instance, shyness self-Â�report on their spontaneous

413

self–Â�shyness associations but rather on additional processes of propositional reasoning (cf. Briñol, Petty, & Wheeler, 2006). As a consequence, there should be smaller correlations between implicit and explicit shyness measures for participants with imbalanced association triads than for those with balanced association triads. These correlational differences should occur even after controlling for the reduced retest reliability that is expected for the implicit measures in the group of participants with imbalanced association triads. To our knowledge, these hypotheses concerning the stability of self associations and their implicit–Â�explicit consistency have not yet been tested, but they may represent an interesting starting point for future studies. It should be noted that in the strict sense of the spread of activation model that is used to describe associative processes (see prior discussion) cognitive imbalance is impossible at the associative level (Gawronski, Strack, & Bodenhausen, 2009). Nevertheless, implicit imbalance in the sense of conflicting associations between concepts may be reflected by implicit measures that do not exclusively rely on spread of activation processes. Interestingly, theoretical assumptions according to the Associative–Propositional Evaluation model (Gawronski & Bodenhausen, 2006, 2007) state that consistency is only a concern of propositional information processing and does not play a role in associative processes (Gawronski & Strack, 2004; Gawronski et al., 2009). This conceptualization is in conflict with the results for balanced identities (Greenwald et al., 2002) showing that effects of consistency between associations were obtained only for implicit, and not for explicit, measures. The reason for the differences between these models may stem from their different ways of defining cognitive consistency. Greenwald and colleagues (2002) defined consistency as a balanced triad of associations within the balanced-Â�identity design. Gawronski and Bodenhausen (2006, 2007) defined consistency as the result of a propositional process that assesses the consistency between propositions by the application of syllogistic rules and the assignment of truth values. According to this model, consistency between two propositions is achieved when both propositions are regarded as true and when they do not contradict each other. In contrast, two propositions are inconsistent with each other when both are regarded as true but one implies the opposite of the other. Inconsistency may be resolved by changing the truth value of one proposition or by generating another proposition that removes the inconsistency. Importantly, all of these consistency considerations take place only at

414

IV. DOMAIN-SPECIFIC PERSPECTIVES

the level of propositional reasoning and not at the level of associative information processing. Evidence for such consistency effects at the propositional but not at the associative level was shown in several studies (Gawronski & Strack, 2004). However, none of these studies involved balanced-Â�identity triads, as used by Greenwald and colleagues (2002) to assess patterns of cognitive consistency effects. Additionally, the studies by Greenwald and colleagues showed consistency effects on implicit measures for 16 different IATs and included studies with fairly large sample sizes (cf. the comment on sample sizes in correlational studies in Schnabel, Asendorpf, & Greenwald, 2008a). Further evidence for consistency effects at the implicit level may result from associative transfer effects that were evident in two studies by Gawronski, Bodenhausen, and Becker (2007, Experiments 3 and 4). In these studies, participants had to choose between two equally attractive pictures. Results showed that participants’ implicit attitude toward the chosen picture (but not toward the rejected picture) correlated with their implicit self-Â�esteem. Even though these studies did not assess associations for the complete triad and the association between the self and the chosen picture remained unassessed, they may indicate effects of cognitive consistency at the associative level. Associative consistency appeared in a way that objects that were related to the self were evaluated rather positively or negatively depending on whether the self was evaluated rather positively or negatively. Together, the diverging findings on consistency effects in the studies by Gawronski and Strack (2004) and Greenwald and colleagues (2002) seem less contradictory if the differences in the way consistency was defined are recognized. For example, Gawronski and colleagues (2009) argued that the balanced-Â�identity patterns in Greenwald and colleagues’ studies reflect the operation of spreading activation processes at the associative level, which are conceptually distinct from the notion of logical consistency in Gawronski and Strack’s research. An opportunity to combine both approaches and an interesting avenue for further studies may be to explore whether inconsistency at the associative level in the sense of Greenwald and colleagues increases efforts to achieve consistency at the propositional level in the sense of Gawronski and Bodenhausen (2006, 2007) because the spontaneously generated propositions should be more contradictory. As discussed earlier in this section, the lack of cognitive consistency at the implicit level should also reduce implicit–Â�explicit consistency.

Preliminary evidence for this assumption may be derived from studies that show that cognitive elaboration decreases the correlation between implicit and explicit measures (cf. Hofmann, Gawronski, Gschwendner, Le, & Schmitt, 2005). Given that cognitive consistency is a concern of propositional reasoning (Gawronski & Bodenhausen, 2006), the degree of cognitive elaboration at the propositional level should be higher when cognitive consistency is nonexistent already at the associative level. These patterns of cognitive inconsistency at the associative level may be able to explain a lack of reflexive consciousness in the sense of Baumeister (1998; see prior discussion) at the propositional level. Thus, the differentiation between associative and propositional processes helps to promote a deeper understanding of possibilities and limitations of reflexive consciousness.

The Personality Self-Â�Concept The part of the self-Â�concept that refers to personality-Â�describing attributes and that may be assessed by the use of personality measures is called the personality self-Â�concept (cf. Asendorpf et al., 2002). Personality-Â�describing attributes are individual, relatively stable, and nonpathological characteristics of the person (Asendorpf et al., 2002). The most comprehensive studies on the validity of implicit and explicit personality selfÂ�concept measures were completed by Egloff and colleagues (Back, Schmukle, & Egloff, 2008; Schmukle, Back, & Egloff, 2008) and included IATs for all Big Five personality dimensions (i.e., neuroticism, extroversion, openness, agreeableness, and conscientiousness). Implicit personality self-Â�concept measures were also used for the assessment of morality (Aquino & Reed, 2002) and exercise behavior (Banting, Dimmock, & Lay, 2009). Banting and colleagues (2009) used an exerciser self-Â�concept IAT, with exercise versus sedentary as attribute categories, and explored its predictive validity for the prediction of exercise behavior. Results showed that both the exerciser selfÂ�concept IAT and the respective explicit measure were independent predictors of exercise behavior. Aquino and Reed (2002) explored correlations of a moral identity IAT that assessed associations between the self-Â�concept and moral traits. They found significant correlations between the moral identity IAT and explicit judgments of the selfÂ�importance of moral traits. However, correlations were significant only when the explicit judgments referred to private aspects of the self-Â�importance of moral identity traits. In contrast, implicit–Â�explicit



22. The Self-�Concept

correlations were nonsignificant when the explicit judgments referred to rather public or selfÂ�presentational aspects of moral identity. A set of two studies by Schmukle and colleagues (2008) explored the psychometric properties of IAT measures for all Big Five dimensions. Results of confirmatory factor analyses validated the five-Â�factor structure not only for the explicit but also for the implicit Big Five personality measures (Schmukle et al., 2008). Given that all Big Five personality dimensions are confounded with positive and negative valence and that self-Â�concept IATs may be particularly affected by a valence confound (Banse & Greenwald, 2007; Schnabel et al., 2008b; Schnabel, Asendorpf, & Greenwald, 2008c), it is an important finding to show that their factorial structure cannot be reduced to a single evaluation factor. In addition, the studies by Schmukle and colleagues (2008) revealed satisfactory internal consistencies for the Big Five IATs and showed that the patterns of means and correlations between the Big Five factors were highly similar for IATs and explicit self-Â�reports. This structural equivalence between implicit and explicit measures was evident even though implicit–Â�explicit correlations were relatively small (mean r = .13) and significant only for extroversion and conscientiousness. Interestingly, the authors explained structural equivalence by similar evaluation processes in implicit and explicit measures. Thus, observed means were below zero on the IAT and below the scale midpoint on the explicit self-Â�report measures only for the more negatively evaluated dimension neuroticism and not for the other Big Five dimensions. Similarly, only neuroticism was consistently negatively correlated with the other more positively evaluated dimensions extroversion, openness, agreeableness, and conscientiousness, all of which positively correlated with each other. Thus, valence effects may explain the structural equivalence between implicit and explicit measures even though the factorial structure could not be reduced to a single evaluation factor for implicit as well as for explicit measures. Further and even more compelling evidence for the assumption that the positive–Â�negative valence polarity of the Big Five IATs does not represent a threat to their validity is provided by Back and colleagues (2008). Most importantly, this study dealt with the prediction of actual (i.e., directly observable) behavior and explored the predictive validity of all Big Five dimensions both at the level of implicit IAT and explicit self-Â�report measures (see also Steffens & Schulze-König, 2006, for a small-

415

Â� sample study of this type). Back and colleagues assessed 50 behavioral indicators for the Big Five dimensions in 13 different experimental situations, and the behavioral indicators were assigned to the Big Five dimensions on the basis of a priori considerations. Subsequently, the behavioral indicators for each Big Five dimension were aggregated in order to obtain a single behavioral validation criterion for this dimension. The results showed that explicit self-Â�reports predicted actual behavior for all Big Five dimensions, whereas only the neuroticism IAT and the extroversion IAT, but not the other Big Five IATs, were able to predict actual behavior. Nevertheless, the implicit measures showed incremental validity over and above explicit measures, and behavior predictions for the neuroticism and extroversion IAT remained significant even after controlling for explicit measures. Additionally, these effects were unaffected if they were controlled for valence. Valence influences were assessed by a self-Â�esteem IAT and an explicit self-Â�esteem measure. Results showed relatively weak correlations of the Big Five IATs with the self-Â�esteem IAT (highest r = .30) and somewhat higher correlations of the Big Five self-Â�reports with the explicit self-Â�esteem measure (highest r = .50; cf. Grumm & von Collani, 2007). Furthermore, neither the predictive validity of explicit measures nor the predictive or incremental validity of implicit measures was reduced if the influence of valence was controlled for. The fact that only neuroticism, extroversion, and to a smaller extent agreeableness were able to predict behavior at the level of implicit measures was theoretically explained on the basis of whether the Big Five dimensions refer to behavior that is influenced by automatic processes and basic motivational orientations. Back and colleagues (2008) based their theoretical assumptions on a Behavioral Process Model of Personality (BPMP) that represents a personality-Â�oriented adaptation of the Reflective–Â�Impulsive Model by Strack and Deutsch (2004). According to the BPMP, associative representations of the self develop from the repeated activation of the self together with association patterns that trigger impulsive behavior. These association patterns are characterized by perceptual features of the situation, automatic motivational tendencies, the resulting impulsive behavior, and the trait concept representing that behavior. Thus, associative representations of the personality self-Â�concept are considered to reflect past activation of automatic behavior that is condensed in an associative format. In contrast, propositional representations of the personality

416

IV. DOMAIN-SPECIFIC PERSPECTIVES

self-Â�concept are considered to reflect past activation of controlled behavior that is condensed in a propositional format. Controlled behavior is triggered by reflective processes and is evident in the way in which situations are typically perceived and categorized, in the behavioral options that are preferred, and in the deliberate realization of the preferred behavior. Based on these theoretical assumptions, selfÂ�concept IATs were expected to be valid for the prediction of behavior only if they assess self– trait associations that are based on the frequent coactivation of the concept of self and impulsive behavioral tendencies. In contrast, self-Â�concept IATs were expected to be less valid if they refer to behavior that is primarily guided by propositional reasoning and offers fewer opportunities to develop chronic associations between the self and impulsive behavioral tendencies. Considering extroversion, neuroticism, and agreeableness, Back and colleagues argued that these traits are more closely related to impulsive behavior activation than openness and agreeableness. However, this argumentation is not fully convincing. For instance, several aspects of low conscientiousness (e.g., sloppy) may be guided by impulsive information processes and, therefore, also be represented as chronic self associations. More research is needed that replicates this finding of differential predictive validity for different Big Five factors and offers a more convincing explanation for it. A similar pattern of results—Â�explicit selfÂ�concept measures were related to controlled behavior whereas implicit self-Â�concept measures were related to spontaneous behavior—was found by Asendorpf and colleagues (2002) for the personality trait of shyness. Different from Back and colleagues (2008), Asendorpf and colleagues differentiated between explicit and implicit measures not only at the theoretical level and with respect to different self-Â�representations but also at the operational level and with respect to different behavioral indicators of actual shy behavior. Within a double-Â�dissociation model, the results by Asendorpf and colleagues showed that the explicit shyness measures uniquely predicted indicators of controlled but not spontaneous shy behavior, whereas the shyness IAT uniquely predicted indicators of spontaneous but not controlled shy behavior. Double-Â�dissociation designs are an excellent way to demonstrate the specific validity of implicit measures for the prediction of spontaneous behavior while simultaneously ruling out that this effect should be attributed to a general lack of behavioral validity of the corresponding explicit measure.

Results that show the validity of double� dissociation models for implicit and explicit measures are rare (Asendorpf et al., 2002; Egloff & Schmukle, 2002; McConnell & Leibold, 2001). In contrast, more evidence was found for additive models that conceptualize implicit and explicit measures as explaining different portions of variance of a relevant criterion. This is true for studies that used personality self-�concept measures (Back et al., 2008; Schnabel, Banse, & Asendorpf, 2006a; Schnabel et al., 2006b) as well as for studies that explored other domains in social psychology (Greenwald, Poehlman, Uhlmann, & Banaji, 2009). On one hand, the relatively scarce evidence for double-�dissociation models may be explained by the difficulty of finding adequate behavioral indicators for spontaneous and controlled behavior. Not only are behavioral observations generally complex and costly, but they take even more time and effort if different behavioral indicators need to be identified. On the other hand, the differences between implicit and explicit self-�concept measures may be relative rather than absolute in nature. Associative and propositional processes are related to each other because propositions are generated on the basis of associative representations (Gawronski & Bodenhausen, 2006, 2007). Additionally, indicators of spontaneous behavior as well as measures of the implicit self-�concept are not process pure, and different implicit measures may vary in the extent to which they tap into associative or propositional processes (cf. Sections I and II of this volume). Together, the results for implicit measures of the personality self-�concept provide compelling evidence that implicit neuroticism and extroversion measures show incremental validity for the prediction of behavior over and above their corresponding explicit self-�report measures (Back et al., 2008). In this context, the incremental validity that was found for implicit shyness measures (Asendorpf et al., 2002) may be explained by the fact that shyness is positively related to neuroticism and negatively related to extroversion. Importantly, no evidence was found for the hypothesis that a valence confound represents a threat to the validity of implicit self-�concept measures (Back et al., 2008). In a similar vein, other studies have shown the validity of IAT measures for the assessment of specific semantic content as distinct from a general evaluative dimension. For instance, Perkins and Forehand (2006) disentangled influences of semantic meaning and valence in self-�concept IATs and revealed independent effects for both the positive and negative valence of personality describ-



22. The Self-�Concept

ing attributes and their specific semantic meaning. Amodio and Devine (2006) separated stereotyping and evaluation effects in implicit race biases and used both stereotyping (mental vs. physical) and evaluative (pleasant vs. unpleasant) race IATs. Their results showed discriminant behavioral validity for the stereotype and the attitude IAT. Concerning the incremental validity of implicit over and above explicit measures, the search for individual and situational factors that moderate the predictive validity of implicit and explicit measures represents another approach to explore their differential validity (Friese, Hofmann, & Schmitt, 2008). Such moderators are discussed in more detail in the Moderators of the Predictive Validity of the Implicit and Explicit Self-�Concept section. We now briefly examine how the self-�concept is related to adult attachment.

The Role of the Implicit Self-Â�Concept in Adult Attachment The role of associative and propositional processes in interpersonal relations are extensively discussed by Baldwin, Lydon, McClure, and Etchison in Chapter 23 of this volume. Here, we just point to two studies that deal with the role of the implicit self-Â�concept or, more precisely, implicit self-Â�esteem in adult attachment. Studies by Dewitte, De Houwer, and Buysse (2008) and by Zayas and Shoda (2005) explored the relationship between a selfÂ�esteem IAT and attachment variables. Whereas the self-Â�esteem IAT in the study by Zayas and Shoda was not related to explicit measures of adult attachment styles, Dewitte and colleagues found meaningful correlations between a relational selfÂ�esteem IAT and adult attachment orientation. The relational self-Â�esteem IAT aimed to assess relation-Â�specific rather than global self-Â�esteem and used relationally worthy versus relationally worthless as attribute categories and the corresponding attributes as stimuli (e.g., loved, liked vs. inferior, rejected). The results showed a significant negative correlation of the relational self-Â�esteem IAT with the Preoccupied scale of the Relationship Questionnaire (RQ; Bartholomew & Horowitz, 1991) but no significant correlations with the other three RQ scales. This is only partially consistent with Bartholomew and Horowitz’s (1991) attachment model, which deals with the correlations between self-Â�esteem and attachment styles and postulates that preoccupied and fearful attachment are characterized by a negative evaluation of the self, whereas secure and dismissing attachment are characterized by a positive evaluation of the self.

417

Given that results for the correlations of implicit self-Â�esteem with different attachment styles were somewhat mixed, more research is needed in order to elucidate these inconsistent findings. Further studies on the influences of associative processes on attachment orientation and behavior seem especially worthwhile because many aspects of attachment behavior should operate in an impulsive way. Additionally, different attachment styles can be categorized along the dimensions attachmentÂ�related anxiety and attachment-Â�related avoidance (Brennan, Clark, & Shaver, 1998), while at least for anxiety as a personality trait there is now compelling evidence for meaningful and significant implicit–Â�explicit dissociations (cf. Back et al., 2008). Generally, further studies are needed that explore the effects of different attachment styles on behavior and examine the differential validity of implicit and explicit attachment measures for the prediction of behavior.

MODERATORS OF THE€PREDICTIVE VALIDITY OF€THE IMPLICIT AND EXPLICIT SELF-CONCEPT The validity of the implicit personality self-Â�concept for the prediction of behavior has been discussed in the previous section. In this section, we refer to situational and particularly individual aspects that were shown to moderate the predictive validity of implicit and explicit self-Â�concept measures. Recently, Friese and colleagues (2008) provided a comprehensive review of moderators that influence the predictive validity of implicit and explicit measures. The authors organized these moderators along two dimensions. The first describes different determinants of moderation and distinguishes between moderators according to whether they influence the opportunity to control, the motivation to control, or the reliance on automatic or controlled processes. The second dimension describes different modes of functioning and categorizes the moderators of the first dimension according to whether they refer to an individual disposition, a situational factor, or the behavior itself. These moderators of the predictive validity of implicit and explicit measures tell a great deal about the executive function of the self that was described as an important self-Â�aspect by Baumeister (see prior discussion). Considering these moderators allows one to draw specific conclusions about the preconditions and consequences of the self’s ability to exert control over its actions.

418

IV. DOMAIN-SPECIFIC PERSPECTIVES

The first two moderators of the first dimension, opportunity and motivation to control, were derived from dual-Â�process models (e.g., Fazio, 1990). According to these models, the opportunity and motivation to control can change the relative influences of automatic and controlled processes on behavior. Because controlled processes are effortful, they will only influence behavior if the opportunity to engage in effortful information processing is given and if the person is motivated to do so. Without the opportunity or motivation for controlled information processing, automatic processes will have a larger impact. However, opportunity and motivation to control are not the only determinants that influence the relative weight of controlled and automatic information processing in guiding behavior. Therefore, Friese and colleagues included a third determinant of moderation, process reliance, which describes the extent to which individuals rely on either automatic or controlled processes without any changes in opportunity or motivation to control behavior (cf. Bless & Schwarz, 1999). Examples of process reliance are reliance on one’s intuition, on one’s inner affective reactions, or on one’s mood (Smith & DeCoster, 2000). The second dimension of Friese et al.’s classification scheme categorizes the determinants of the first dimension across different modes of functioning. This dimension distinguishes whether the moderators pertain to an individual disposition, a situational factor, or an actual behavior that may be more or less easy to control (cf. Asendorpf et al., 2002). In the following, we mainly focus on the dispositional moderators of the validity of implicit and explicit measures because they are most closely linked to the concept of self. Dispositional moderators that reduce the opportunity to control behavior and that were explored in previous studies are low working memory capacity (WMC), low-trait self-Â�control, high-trait impulsivity, and low control over nonverbal behavior. In a study by Hofmann, Gschwendner, Friese, Wiers, and Schmitt (2008, Study 1) that explored moderator effects of individual differences in WMC, a Single-Â�Category (SC) IAT (Karpinski & Steinman, 2006) was used in order to assess the attitude toward erotic pictures. The SCIAT predicted viewing time of erotic pictures in a later task only for participants with low WMC and not for participants with high WMC. The opposite pattern was evident for an explicit attitude measure. Similar results emerged in a second study that predicted candy consumption by implicit and explicit attitudes toward candies. A third study

was more closely related to self-Â�concept measures and showed that an angriness self-Â�concept IAT predicted negative social feedback to a provocateur after a mild provocation only for participants with low but not high WMC. In contrast, self-Â�reported anger control had a buffering effect on negative social feedback only for participants with high, not low, WMC. Similar results were obtained in studies that predicted alcohol or tobacco consumption by implicit and explicit measures (Grenard et al., 2008; Thush et al., 2008). Friese and Hofmann (2008) used a trait selfÂ�control scale (Tangney, Baumeister, & Boone, 2004) and a trait impulsivity scale (Eysenck, Daum, Schugens, & Diehl, 1990) to explore moderator effects on the predictive validity of implicit and explicit measures. Whereas results for the trait impulsivity scale were rather mixed, the trait selfÂ�control scale moderated the predictive validity of implicit measures in several studies. One study used an SC-IAT to assess attitudes toward potato chips and revealed that the SC-IAT predicted potato chip consumption in a later taste-and-rate task better for participants with low-trait selfÂ�control than for those with high-trait self-Â�control. Two other studies replicated this finding for an SC-IAT and an affect misattribution procedure (AMP; Payne, Cheng, Govorun, & Stewart, 2005) that predicted self-Â�reported alcohol consumption for participants with low-trait. but not high-trait, self-Â�control. Additionally, these effects remained significant when they were controlled for the influence of trait impulsivity. In two studies that explored attitudes toward gays with a gay–Â�heterosexual IAT, Dasgupta and Rivera (2006) also used self-Â�report measures to assess the control over nonverbal behavior and the motivation to be egalitarian. Whereas control over nonverbal behavior was subsumed to dispositional moderators pertaining to the opportunity to control, motivation to be egalitarian was subsumed to dispositional moderators pertaining to the motivation to control behavior (Friese et al., 2008). The results of the two studies by Dasgupta and Rivera revealed that the correlation between the gay–Â�heterosexual IAT and negative nonverbal behavior was stronger for participants with both low motivation to be egalitarian and low behavioral control than for those with low motivation to be egalitarian and high behavioral control. Among participants with high motivation to be egalitarian, implicit attitudes toward gays were uncorrelated with nonverbal behavior, and this correlation was unaltered by high or low behavioral control.



22. The Self-�Concept

In addition to the motivation to be egalitarian, need for cognition and motivation to control prejudiced reactions were conceptualized as further dispositional moderators pertaining to the motivation to control (Friese et al., 2008). However, evidence for need for cognition as a moderator of the behavioral validity of implicit measures is rather mixed, and the moderating role of motivation to control prejudiced behavior has not yet been studied with respect to observed behavior. Therefore, we do not discuss these possible moderators in further detail. Dispositional moderators pertaining to process reliance are (1) the preference for intuition and (2) chronic regulatory focus. In a study by Hofmann and Baumert (in press), implicit attitudes toward moral stimuli were assessed with the AMP and participants also completed a preference for intuition measure. The AMP predicted whether an offer to split money in an unfair way between the participant and an ostensibly second participant was rejected for participants high in preference for intuition but not for participants low in preference for intuition. Differences in chronic regulatory focus differentiate between whether individuals focus on the promotion of positive outcomes or on the prevention of negative outcomes (Higgins, 1998). Whereas a prevention focus is related to controlled information processing, a promotion focus fosters the reliance on automatic processes such as affective responses. Notably, influences of different prevention and promotion focus appear independently from different motivations to control (Pham & Avnet, 2004). Influences of chronic regulatory focus on the predictive validity of implicit measures have not yet been studied with regard to real behavior. However, these influences may be an interesting subject for further studies. Together, the two dimensions presented by Friese and colleagues (2008) offer an excellent opportunity to organize different moderators of the predictive validity of implicit measures with regard to their influences on automatic versus controlled information processing. Nevertheless, especially with respect to dispositional moderators, more research is needed that explores the independence and differential validity of these moderators. For instance, Levesque and Brown (2007) explored mindfulness as a dispositional moderator of the validity of an autonomy self-�concept IAT. Results showed that implicit autonomy orientation predicted self-�reported day-to-day levels of experienced autonomy only for participants low in dispositional mindfulness. Participants high

419

in dispositional mindfulness reported more autonomously motivated behavior independent from their implicit autonomy orientation. Importantly, this moderator effect was specific for dispositional mindfulness and did not appear for dispositional self-Â�consciousness or self-Â�reflection. Thus, the prereflexive awareness of inner states and actual behavior that is specific for dispositional mindfulness seemed to be crucial for this moderator effect. As another moderator of the behavioral validity of implicit measures, Gschwendner, Hofmann, and Schmitt (2008a) explored the correspondence between different implicit and explicit anxiety measures and anxious behavior. More precisely, they varied the content similarity (spider vs. speech anxiety) and specificity similarity (general vs. situation-Â�specific anxiety) of implicit and explicit anxiety measures and explored their predictive validity for the prediction of anxious behavior during a stressful speech. For each participant, three different types of implicit and explicit anxiety were measured: general anxiety, speech anxiety, and spider anxiety. The general anxiety IAT assessed associations between self versus other as target concepts and between anxiety versus calmness as attribute concepts. The speech IAT and the spider IAT used the identical attribute concepts but used public speech versus take-home exams and spider versus butterflies as target concepts, respectively. As explicit measures, questionnaires that assessed these different types of anxiety were used. The results showed that correlations between implicit and explicit anxiety measures were significant only when their content and specificity level were equal. Additionally, confirmatory factor analyses corroborated this finding and revealed best fit for a model that conceptualized different factors for implicit and explicit measures within each of the three types of anxiety, resulting in six latent anxiety factors. Of the two variants of this model that were tested, the model that allowed for correlations between implicit and explicit measures only within the three types of anxiety showed better fit than the model that allowed for implicit–Â�explicit correlations across different anxiety types. With respect to the effects of specificity similarity and content similarity on the predictive validity of implicit and explicit anxiety measures, Gschwendner and colleagues (2008a) regressed latent speech anxiety behavior on the six latent anxiety factors that were previously determined. Latent speech–Â�anxiety behavior was estimated by four different behavioral indicators of speech–Â�anxiety (speech dysfluency, overall impression, eye contact, and speech duration). Results showed that

420

IV. DOMAIN-SPECIFIC PERSPECTIVES

only implicit and explicit speech anxiety predicted speech anxiety behavior, whereas general anxiety and specific spider anxiety did not contribute significantly to the prediction of behavior. Notably, implicit and explicit speech anxiety measures predicted unique variance portions and contributed independently to the prediction of speech–Â�anxiety behavior. In sum, the study by Gschwendner and colleagues highlights the important role of correspondence between implicit and explicit measures and relevant behavioral criteria for the behavioral validity of these measures.

STABILITY OF THE IMPLICIT SELF-CONCEPT A necessary precondition for the assessment of individual differences with implicit and explicit measures is, apart from their internal consistency, sufficient temporal stability (test–Â�retest reliability). For IAT measures, test–Â�retest reliabilities have been observed to be considerably lower than their internal consistencies in several studies. A meta-Â�analysis by Hofmann and colleagues (2005) showed high internal consistencies for different IAT measures with an average of .79 and much lower test–Â�retest reliabilities with an average of .51. Similar results were obtained by Nosek, Greenwald, and Banaji (2006) for different IAT measures that showed a median test–Â�retest reliability of .56. For an anxiety IAT, Egloff, Schwerdtfeger, and Schmukle (2005) report test–Â�retest reliabilities that ranged from .62 (2-week interval) to .47 (1-year interval). Attempts that tried to separate enduring individual differences and systematic situation-Â�specific effects on IAT measures showed that IATs capture both reliable trait-Â�specific and situation-Â�specific variation (Schmukle & Egloff, 2005; Steffens & Buchner, 2003). Two studies by Gschwendner, Hofmann, and Schmitt (2008b) aimed to identify one situationÂ�specific (contextual constraints) and one individual-Â�specific (chronic accessibility) influence on the stability of IAT measures. Study 1 used different versions of an anxiety IAT and revealed higher test–Â�retest reliabilities for IAT versions that embedded the IAT stimuli in an anxiety-Â�relevant context (background pictures of a snake or an injection) than for IATs that used an anxiety-Â�neutral context (background picture of a park bench). Study 2 replicated this finding for an IAT that assessed racial attitudes toward Turks and Germans. Again, test–Â�retest correlations were higher for the IAT that used an attitude-Â�relevant (mosque)

rather than an attitude irrelevant (garden) background picture. Additionally, this context effect was moderated by individual differences in chronic accessibility of the relevant concept (i.e., Turks) as assessed by a lexical decision task. In the group that completed the IAT with the context-Â�relevant background picture, test–Â�retest correlations were especially high for participants with high chronic accessibility compared with participants with low chronic accessibility of the relevant concept. With respect to the moderator effect of the chronic accessibility variable, more studies are needed that replicate this double-Â�interaction effect. This is especially important because chronic accessibility as an individual-Â�specific effect failed to be a significant moderator in Study 1 by Gschwendner and colleagues (2008b). Additionally, the sample sizes in their Study 2 were quite small, and internal consistency of the chronic accessibility measure was only .45 and rather unsatisfactory. Also, further studies may explore whether variants of the original IAT procedure are less affected by the problem of relatively low test–Â�retest reliability. For instance, there are now two new IAT variants that avoid effects of different block order on IAT scores by simultaneously testing different associations within one single IAT block (Bar-Anan, Nosek, & Vianello, 2009; Teige-Â�Mocigemba et al., 2008). Effects of different block order, however, may be the reason why standard IATs show a systematic attenuation in effect size from first to subsequent administrations (Greenwald, Nosek, & Banaji, 2003). The decrease of IAT effect size may be attributed to the fact that the block order that produces the largest IAT effect (i.e., compatible block first and incompatible block second) is less effective in the IAT retest because participants then always have at least some experience with the incompatible block. Because these variations between test and retest seem to be sidestepped by new IAT variants, their test–Â�retest reliabilities may achieve levels that are more satisfactory than those of traditional IATs. Several studies explored contextual effects on the implicit self-Â�concept and also dealt with possible long-term effects on the formation of the implicit self-Â�concept. A study by Uhlmann and Swanson (2004) revealed that participants who played a violent video game for 10 minutes associated themselves more with aggressive traits in a self-Â�concept IAT but they did not rate themselves as more aggressive on explicit measures. In contrast, both implicit and explicit aggressiveness measures were significantly and independently correlated with self-Â�reported prior exposure to



22. The Self-�Concept

violent video games. Together, these results suggest short-term effects of violent media on implicit measures and long-term effects on both implicit and explicit measures and indicate that influences on the two measures may be exerted independently from each other. However, because of the crossÂ�sectional design of the study, direction of causality is not clear for the relation between self-Â�reported prior exposure to violent video games and levels of implicit and explicit aggressiveness. Thus, it may also be the case that participants with high scores in implicit and explicit aggressiveness are more interested in and tend to play more violent video games. Results of Study 1 by Devos, Viera, Diaz, and Dunn (2007) showed that female participants associated themselves less with college education if motherhood cues rather than neutral cues were used as task-Â�irrelevant background stimuli within the association task. Whereas this rather subtle manipulation produced assimilation effects in the way that participants assimilated their implicit selfÂ�concept to the background stimuli, contrast effects were evident in Study 2 after a blatant activation of the motherhood or the academic education concept. When participants had to deliberately reflect on their aspirations to become a college graduate, their implicit academic identification was weaker than when they had to reflect on their aspirations to become a mother. This contrast effect was explained by the authors’ assumption that deliberately thinking about one’s aspirations for academic achievement also activates self-Â�representations that are in conflict with these aspirations. As a consequence, participants might have felt that academic achievement is not the ultimate goal in their life and the experience of such conflicting thoughts might have undermined their implicit academic identification. Interestingly, explicit identification with college education or motherhood was not affected by whether participants had to reflect about their aspirations to become a college graduate or a mother. In a similar vein, Devos and colleagues revealed that students with children showed stronger explicit and implicit motherhood identification than students without children. Because of the cross-Â�sectional design of this study, however, it is impossible to identify whether increased motherhood identification evolved as a consequence of the situational influences or rather represents a reason why these participants became mothers. Contrast-like effects were also found in a study by McCall and Dasgupta (2007) that showed that male participants associated themselves stronger with stereotypically male attributes representing

421

power and authority if they were in a subordinate rather than a superior role-play position. This compensatory effect was explained as the males’ attempt to protect against the threat to their selfÂ�concept that was caused by the subordinate position. Interestingly, this effect of different status was not evident in the explicit self-Â�concept measure where male participants generally described themselves as agentic and authoritative independent from whether they were in a subordinate or a superior role. However, authoritative and masculine self-Â�stereotyping on the explicit measure was more pronounced when male participants interacted with a female rather than a male interaction partner. Thus, whereas a threat to the gender self-Â�concept elicited automatic compensatory responses with respect to the implicit self-Â�concept, salience of gender evoked more gender-Â�stereotypic self-Â�descriptions with respect to the explicit selfÂ�concept. In contrast, assimilation effects were evident in a study by Haines and Kray (2005). Their results revealed that female participants showed stronger self–power associations and higher explicit selfÂ�ratings of power if they were assigned to the leading group rather than the inferior group of a group game. Additionally, female participants showed stronger self–Â�masculine associations if they were assigned to the more powerful recruiter role rather than the less powerful applicant role in a simulated job interview. However, assignment of different roles did not affect explicit self associations with masculinity. Another study showed that a groupbased cognitive-Â�behavioral treatment was able to decrease implicit and explicit anxiety scores in a group of socially anxious participants (Gamer, Schmukle, Luka-Â�Krausgrill, & Egloff, 2008). In several studies, Pöhlmann and Hannover (2006) explored effects of the independent versus the interdependent self on implicit liking of close friends and family members. Whereas the interdependent self is oriented toward social relations of which the person is part, the independent self is concerned about being separate and different from others. Interestingly, the results revealed that both interdependents and independents showed implicit preference of close others in a name-Â�letter task if their respective chronic self was primed. Thus, preference of close others in the name-Â�letter task appeared only if the independent self of independents or the interdependent self of interdependents was activated by a preceding priming task. Additionally, only interdependents but not independents showed a name-Â�letter effect for the name of their mother but also only if their interdependent

422

IV. DOMAIN-SPECIFIC PERSPECTIVES

self was primed. These results demonstrate that effects of the implicit self may depend on whether the specific content of the self-Â�concept is made accessible by situational cues or not. Because of these results on contextual influences, it is evident that more research is necessary that deals with long-term effects of situational influences on implicit and explicit self-Â�representations. We need to know much more about the formation of the implicit self-Â�concept and about how the accumulation of social experiences crystallizes in implicit and explicit self-Â�representations. The cited studies show that the implicit self-Â�concept can be susceptible already to slight contextual changes that do not have any visible effects on the explicit self-Â�concept. A study by Schmukle, Liesenfeld, Back, and Egloff (2007) indicates possible longterm effects of early developmental influences on the implicit but not on the explicit self-Â�concept. The study explored correlations of individual differences in the ratio of index finger length and ring finger length (2D:4D) with the implicit and explicit gender self-Â�concept. Results showed that, at least for men, 2D:4D ratios correlated with the implicit gender self-Â�concept, such that men with smaller 2D:4D ratios had a more masculine implicit gender self-Â�concept. In contrast, the explicit gender self-Â�concept showed only weak correlations with 2D:4D ratios for both men and women. Because 2D:4D ratios are considered to be a marker of prenatal androgen and estrogen levels, these results suggest that the implicit gender self-Â�concept of men is more greatly influenced by early developmental events than the explicit gender selfÂ�concept. Similarly, future research should explore how early social and educational influences affect the implicit and explicit self-Â�concept. Possible influences on both the explicit and the implicit self-Â�concept may be moderated by whether they are self relevant or self irrelevant (cf. Pöhlmann & Hannover, 2006). Early developmental influences may play an important role because they should be crucial in establishing relevant and more irrelevant aspects of the self.

CONCLUSION This chapter illustrates the multifacetedness of the self-�concept and the research that deals with it. Whereas self-�concept research some 25 years ago was rather theoretical but integrated various developmental and motivational aspects (Greenwald & Pratkanis, 1984), it became more data driven but less theory oriented over time. The develop-

ment of reliable implicit measurement procedures such as the IATs added a new and exciting dimension to the study of the self-Â�concept that began to stimulate new theoretical models that describe and explain differences between implicit and explicit measurement procedures (e.g., Gawronski & Bodenhausen, 2006; Strack & Deutsch, 2004). This research has added new evidence that associative information processes influence human behavior in many effective ways, and this applies also to the associations within the concept of self. It is one of the great adventures of our time that new measurement methods allow us to open windows to barely consciously accessible processes that influence our behavior and that were previously largely unexplored. References Aidman, E. V., & Carrol, S. M. (2003). Implicit individual differences: Relationships between implicit self-Â�esteem, gender identity, and gender attitudes. European Journal of Personality, 17, 19–37. Allport, G. W. (1943). The ego in contemporary psychology. Psychological Review, 50, 451–478. Amodio, D. M., & Devine, P. G. (2006). Stereotyping and evaluation in implicit race bias: Evidence for independent constructs and unique effects on behavior. Journal of Personality and Social Psychology, 91, 652–661. Aquino, K., & Reed, A. (2002). The self-Â�importance of moral identity. Journal of Personality and Social Psychology, 83, 1423–1440. Asendorpf, J. B., Banse, R., & Mücke, D. (2002). Double dissociation between implicit and explicit personality self-Â�concept: The case of shy behavior. Journal of Personality and Social Psychology, 83, 380–393. Back, M. D., Schmukle, S. C., & Egloff, B. (2008). Predicting actual behavior from explicit and implicit measures of personality. Unpublished manuscript. Banse, R., & Greenwald, A. G. (2007). Commentary—Â� Personality and implicit social cognition research: Past, present, and future. European Journal of Personality, 21, 371–382. Banting, L. K., Dimmock, J. A., & Lay, B. S. (2009). The role of implicit and explicit components of exerciser self-Â�schema in the prediction of exercise behavior. Psychology of Sport and Exercise, 10, 80–86. Bar-Anan, Y., Nosek, B. A., & Vianello, M. (2009). The Sorting Paired Features Task: A measure of association strengths. Experimental Psychology, 56, 329–343. Bargh, J. A. (1994). The four horsemen of automaticity: Awareness, intention, efficiency, and control in social cognition. In R. S. Wyer, Jr. & T. K. Srull (Eds.), Handbook of social cognition: Vol. 1. Basic processes (2nd ed., pp.€1–40). Hillsdale, NJ: Erlbaum. Bartholomew, K., & Horowitz, L. M. (1991). Attachment styles among young adults: A test of a four-Â�category model. Journal of Personality and Social Psychology, 61, 226–244.



22. The Self-�Concept

Baumeister, R. F. (1998). The self. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), Handbook of social psychology (pp.€680–740). New York: McGraw-Hill. Bless, H., & Schwarz, N. (1999). Sufficient and necessary conditions in dual-mode models: The case of mood and information processing. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€423–440). New York: Guilford Press. Boldero, J. M., Rawlings, D., & Haslam, N. (2007). Convergence between GNAT-assessed implicit and explicit personality. European Journal of Personality, 21, 341–358. Brennan, K. A., Clark, C. L., & Shaver, P. R. (1998). Self-Â�report measurement of adult attachment: An integrative overview. In J. A. Simpson & W. S. Rholes (Eds.), Attachment theory and close relationships (pp.€46–76). New York: Guilford Press. Briñol, P., Petty, R. E., & Wheeler, S. C. (2006). Discrepancies between explicit and implicit self-Â�concepts: Consequences for information processing. Journal of Personality and Social Psychology, 91, 154–170. Byrne, B. M. (2002). Validating the measurement and structure of self-Â�concept: Snapshots of past, present, and future research. American Psychologist, 57, 897–909. Dasgupta, N., & Rivera, L. M. (2006). From automatic anti-gay prejudice to behavior: The moderating role of conscious beliefs about gender and behavioral control. Journal of personality and Social Psychology, 91, 268–280. De Houwer, J. (2006). What are implicit measures and why are we using them? In R. W. Wiers & A. W. Stacy (Eds.), The handbook of implicit cognition and addiction (pp.€11–28). Thousand Oaks, CA: Sage. Devos, T., Viera, E., Diaz, P., & Dunn, R. (2007). Influence of motherhood on the implicit academic selfÂ�concept of female college students: Distinct effects of subtle exposure to cues and directed thinking. European Journal of Psychology of Education, 22, 371–386. Dewitte, M., De Houwer, J., & Buysse, A. (2008). On the role of the implicit self-Â�concept in adult attachment. European Journal of Psychological Assessment, 24, 282–289. Egloff, B., & Schmukle, S. C. (2002). Predictive validity of an Implicit Association Test for assessing anxiety. Journal of Personality and Social Psychology, 83, 1441–1455. Egloff, B., Schwerdtfeger, A., & Schmukle, S. C. (2005). Temporal stability of the Implicit Association TestÂ�anxiety. Journal of Personality Assessment, 84, 82–88. Eysenck, S. B. G., Daum, I., Schugens, M. M., & Diehl, J. M. (1990). A cross cultural study of impulsiveness, venturesomeness, and empathy: Germany and England. Zeitschrift für Differentielle und Diagnostische Psychologie, 11, 209–213. Fazio, R. H. (1990). Multiple processes by which attitudes guide behavior: The MODE model as an integrative framework. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 23, pp.€ 75–109). San Diego, CA: Academic Press. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238.

423

Friese, M., & Hofmann, W. (2008). Control me or I will control you: Impulses, trait self- control, and the guidance of behavior. Unpublished manuscript. Friese, M., Hofmann, W., & Schmitt, M. (2008). When and why do implicit measures predict behavior? Empirical evidence for the moderating role of opportunity, motivation, and process reliance. European Review of Social Psychology, 19, 285–338. Gamer, J., Schmukle, S. C., Luka-Â�Krausgrill, U., & Egloff, B. (2008). Examining the dynamics of the implicit and the explicit self-Â�concept in social anxiety: Changes in the Implicit Association Test–Â�Anxiety and the Social Phobia Anxiety Inventory following treatment. Journal of Personality Assessment, 90, 476–480. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. Gawronski, B., & Bodenhausen, G. V. (2007). Unraveling the processes underlying evaluation: Attitudes from the perspective of the APE model. Social Cognition, 25, 687–717. Gawronski, B., Bodenhausen, G. V., & Becker, A. P. (2007). I like it, because I like myself: Associative self-Â�anchoring and post-Â�decisional change of implicit evaluations. Journal of Experimental Social Psychology, 43, 221–232. Gawronski, B., & Strack, F. (2004). On the propositional nature of cognitive consistency: Dissonance changes explicit, but not implicit attitudes. Journal of Experimental Social Psychology, 40, 535–542. Gawronski, B., Strack, F., & Bodenhausen, G. V. (2009). Attitudes and cognitive consistency: The role of associative and propositional processes. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights from the new implicit measures (pp.€86–117). New York: Psychology Press. Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-Â�esteem, and stereotypes. Psychological Review, 102, 4–27. Greenwald, A. G., Banaji, M. R., Rudman, L. A., Farnham, S. D., Nosek, B. A., & Mellot, D. S. (2002). A unified theory of implicit attitudes, stereotypes, selfÂ�esteem, and self-Â�concept. Psychological Review, 109, 3–25. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the Implicit Association Test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85, 197–216. Greenwald, A. G., Poehlman, T. A., Uhlmann, E., & Banaji, M. R. (2009). Understanding and using the Implicit Association Test: III. Meta-Â�analysis of predictive validity. Journal of Personality and Social Psychology, 97, 17–41. Greenwald, A. G., & Pratkanis, A. R. (1984). The self. In R. S. Wyer, Jr. & T. K. Srull (Eds.), Handbook of social cognition (pp.€129–178). Hillsdale, NJ: Erlbaum. Grenard, J. L., Ames, S. L., Wiers, R. W., Thush, C., Sussman, S., & Stacy, A. W. (2008). Working mem-

424

IV. DOMAIN-SPECIFIC PERSPECTIVES

ory moderates the predictive effects of drug-Â�related associations on substance use. Psychology of Addictive Behaviors, 22, 426–432. Grumm, M., & von Collani, G. (2007). Measuring BigFive personality dimensions with the Implicit Association Test—Â�Implicit personality traits or self-Â�esteem? Personality and Individual Differences, 43, 2205–2217. Gschwendner, T., Hofmann, W., & Schmitt, M. (2008a). Convergent and predictive validity of implicit and explicit anxiety measures as a function of specificity similarity and content similarity. European Journal of Psychological Assessment, 24, 254–262. Gschwendner, T., Hofmann, W., & Schmitt, M. (2008b). Differential stability: The effects of acute and chronic construct accessibility on the temporal stability of the Implicit Association Test. Journal of Individual Differences, 29, 70–79. Haines, E. L., & Kray, L. (2005). Self-power associations: The possession of power impacts women’s selfÂ�concepts. European Journal of Social Psychology, 35, 643–662. Heider, F. (1958). The psychology of interpersonal relations. New York: Wiley. Higgins, E. T. (1998). Promotion and prevention as a motivational principle. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 30, pp.€1–46). San Diego, CA: Academic Press. Hofmann, W., & Baumert, A. (in press). Immediate affect as a basis for moral judgment: An adaptation of the affect misattribution procedure. Cognition and Emotion. Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., & Schmitt, M. (2005). A meta-Â�analysis on the correlation between the Implicit Association Test and explicit self-Â�report measure. Personality and Social Psychology Bulletin, 31, 1369–1385. Hofmann, W., Gschwendner, T., Friese, M., Wiers, R. W., & Schmitt, M. (2008). Working memory capacity and self-Â�regulatory behavior: Towards an individual differences perspective on behavior determination by automatic versus controlled processes. Journal of Personality and Social Psychology, 95, 962–977. Karpinski, A., & Steinman, R. B. (2006). The single category Implicit Association Test as a measure of implicit social cognition. Journal of Personality and Social Psychology, 91, 16–32. Levesque, C., & Brown, K. W. (2007). Mindfulness as a moderator of the effect of implicit motivational selfÂ�concept on day-to-day behavioral motivation. Motivation and Emotion, 31, 284–299. McCall, C., & Dasgupta, N. (2007). The malleability of men’s gender self-Â�concept. Self and Identity, 6, 173–188. McConnell, A. R., & Leibold, J. M. (2001). Relations among the Implicit Association Test, discriminatory behavior, and explicit measures of racial attitudes. Journal of Experimental Social Psychology, 37, 435–442. Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). Math = male, me = female, therefore math ≠ me. Journal of Personality and Social Psychology, 83, 44–59. Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2006). The Implicit Association Test at age 7: A methodological and conceptual review. In J. A. Bargh (Ed.),

Social psychology and the unconscious: The automaticity of higher mental processes (pp.€265–292). New York: Psychology Press. Payne, B. K., Cheng, C. M., Govorun, O., & Stewart, B. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. Journal of Personality and Social Psychology, 89, 277–293. Perkins, A. W., & Forehand, M. R. (2006). Decomposing the implicit self-Â�concept: The relative influence of semantic meaning and valence on attribute selfÂ�association. Social Cognition, 24, 387–408. Pham, M. T., & Avnet, T. (2004). Ideals and oughts and the reliance on affect versus substance in persuasion. Journal of Consumer Research, 30, 503–518. Pöhlmann, C., & Hannover, B. (2006). Who shapes the self of independents and interdependents?: Explicit and implicit measures of the self’s relatedness to family, friends, and partner. European Journal of Personality, 20, 525–547. Rudman, L. A., & Goodwin, S. A. (2004). Gender differences in automatic in-group bias: Why do women like women more than men like men? Journal of Personality and Social Psychology, 87, 494–509. Rudman, L. A., Greenwald, A. G., & McGhee, D. E. (2001). Implicit self-Â�concept and evaluative implicit gender stereotypes: Self and ingroup share desirable traits. Personality and Social Psychology Bulletin, 27, 1164–1178. Schmukle, S. C., Back, M. D., & Egloff, B. (2008). Validity of the five-Â�factor model for the implicit selfÂ�concept of personality. European Journal of Psychological Assessment, 24, 263–272. Schmukle, S. C., & Egloff, B. (2005). A latent state-trait analysis of implicit and explicit personality measures. European Journal of Psychological Assessment, 21, 100–107. Schmukle, S. C., Liesenfeld, S., Back, M. D., & Egloff, B. (2007). Second to fourth digit ratios and the implicit gender self-Â�concept. Personality and Individual Differences, 43, 1267–1277. Schnabel, K., Asendorpf, J. B., & Greenwald, A. G. (2008a). Assessment of individual differences in implicit cognition: A review of IAT measures. European Journal of Psychological Assessment, 24, 210–217. Schnabel, K., Asendorpf, J. B., & Greenwald, A. G. (2008b). Understanding and using the Implicit Association Test: V. Measuring semantic aspects of trait self-Â�concepts. European Journal of Personality, 22, 695–706. Schnabel, K., Asendorpf, J. B., & Greenwald, A. G. (2008c). Using Implicit Association Tests for the assessment of implicit personality self-Â�concept. In G. J. Boyle, G. Matthews, & D. H. Saklofske (Eds.), Personality theory and assessment: Vol. 2. Personality measurement and testing (pp.€ 508–528). Thousand Oaks, CA: Sage. Schnabel, K., Banse, R., & Asendorpf, J. B. (2006a). Assessment of implicit personality self-Â�concept using the Implicit Association Test (IAT): Concurrent assessment of anxiousness and angriness. British Journal of Social Psychology, 45, 373–396. Schnabel, K., Banse, R., & Asendorpf, J. B. (2006b). Employing automatic approach and avoidance tendencies for the assessment of implicit personality self-



22. The Self-�Concept

Â� concept: The Implicit Association Procedure (IAP). Experimental Psychology, 53, 69–76. Smith, E. R., & DeCoster, J. (2000). Dual-Â�process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4, 108–131. Steffens, M. C., & Buchner, A. (2003). Implicit Association Test: Separating transsituationally stable and variable components of attitudes toward gay men. Experimental Psychology, 50, 33–48. Steffens, M. C., & Schulze-König, S. (2006). Predicting spontaneous Big Five behavior with Implicit Association Tests. European Journal of Psychological Assessment, 22, 13–20. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High self-Â�control predicts good adjustment, less pathology, better grades, and interpersonal success. Journal of Personality, 72, 271–322.

425

Teige, S., Schnabel, K., Banse, R., & Asendorpf, J. B. (2004). Assessment of multiple implicit self-Â�concept dimensions using the Extrinsic Affective Simon Task. European Journal of Personality, 18, 495–520. Teige-Â�Mocigemba, S., Klauer, K. C., & Rothermund, K. (2008). Minimizing method-Â�specific variance in the IAT: A single block IAT. European Journal of Psychological Assessment, 24, 237–245. Thush, C., Wiers, R. W., Ames, S. L., Grenard, J. L., Sussman, S., & Stacy, A. W. (2008). Interactions between implicit and explicit cognition and working memory capacity in the prediction of alcohol use in at-risk adolescents. Drug and Alcohol Dependence, 94, 116–124. Uhlmann, E. L., & Swanson, J. (2004). Exposure to violent video games increases automatic aggressiveness. Journal of Adolescence, 39, 653–660. Zayas, V., & Shoda, Y. (2005). Do automatic reactions elicited by thoughts of romantic partner, mother, and self relate to adult romantic attachment? Personality and Social Psychology Bulletin, 31, 1011–1025.

Chapter 23

Measuring Implicit Processes in€Close€Relationships Mark W. Baldwin, John E. Lydon, M. Joy McClure, and€Sara€Etchison

I

n social psychology we often study people’s cognitive processes about the self or about other people as if self and other were completely independent, isolated entities. It is only too easy to lose sight of the fact that social life is interpersonal: Our sense of self is rooted in our relationships and our assumptions about the way we will be treated by others who are important to us, and our sense of other people is saturated with expectancies about how they will react to our own behaviors and communications. Human life is lived in interaction with others, physically and symbolically, and much of social cognition involves thinking about the interpersonal space in which self and other meet. In this chapter we survey the literature on implicit processes in the context of close, significant relationships. The cognitive revolution really began to take hold in the relationships literature in the early 1990s as various researchers began to apply principles of attribution, schematic processing, prototype theory, and so on to their understanding of attraction, liking, and loving in people’s most important relationships (see, e.g., Berscheid, 1994; Fletcher & Fincham, 1991; Fletcher & Fitness, 1996). Since then, there has been a steady growth of data demonstrating the profound influence that implicit processes have on relationship dynamics.

In conducting our review, we try to avoid getting too caught up in debates among different theoretical perspectives and models such as attachment theory (Bowlby, 1969), interdependence theory (Kelley, 1979), dependency regulation (Murray & Holmes, 2008), relational schemas (Baldwin, 1992), and so forth. Rather, we focus on the psychological material these theories are designed to address, including issues of attraction, closeness, dependency, commitment, conflict, and rejection. Much of this work has been done with respect to romantic relationships in particular, although familial and friendship relationships are often covered as well. Theories of relationship cognition typically address attitudes and expectancies, with a focus on how these influence the ongoing processing of information about relationship partners and interactions. Most generally, the implicit activation of expectancies about whether a partner can be trusted to be reliable and emotionally available, rather than rejecting or unpredictable, is thought to play a powerful role in shaping the way relationship partners orient toward one another (e.g., Bowlby, 1969; Murray & Holmes, 2008). Self-�report approaches to assess such influences are good as far as they go, but it is not enough to simply ask people about their views of their relationship partner: Because cognitive structures can be 426



23. Measuring Implicit Processes in Close Relationships

automatically activated outside of awareness (and in many cases even transferred from one relationship onto a different relationship; e.g., Andersen & Berk, 1998), straightforward explicit measurement techniques are often simply not up to the task. Most theories address social cognitive variables pertaining to specific as well as general interpersonal orientations, and we include both of these levels in our review. That is, people clearly hold relationship-Â�specific attitudes, such as their feeling of connection to their current romantic partner or their expectation for how their partner will act in a certain situation. At the same time, they are influenced by their general orientations toward their interpersonal world, such as their chronic attachment orientation or their level of rejection sensitivity. Within this latter category, many of the findings we review relate to individual differences in insecurity, as observed in the form of insecure attachment, low self-Â�esteem, or heightened rejection sensitivity. Within the former category of relationship-Â�specific attitudes, we examine implicit processes relating to being highly committed to a given relationship. Of course, the relationships domain, more than some others, is focused on interpersonal dynamics and the give-and-take of interaction. As such, it has been necessary to develop implicit measures to capture, for example, the implicit activation of interpersonal strategies for achieving desired goals or of interaction expectations such as “If I act in certain way, how is my partner likely to respond?” Often the measure of choice has been an adaptation of the Implicit Association Test (IAT) or some version of a sequential priming task in which stimulus A is presented and then the person’s reaction time to stimulus B is measured. We also include a sampling of other approaches, such as paper-andÂ�pencil measures, visual attention measures, virtual reality approaches, and measures of reaction time for making interpersonal judgments of various kinds. We have tried to cast a fairly broad net to include procedures and findings relevant to the question of how to measure implicit processes in this complex domain. Note that our goal is primarily to survey approaches to the measurement of implicit processes rather than to review the entire literature demonstrating that such processes exist. That is, relationship researchers have become quite adept at using priming techniques to activate a specific relationship (e.g., with Mom) or type of relationship context (e.g., being rejected) and then observing outcome variables (e.g., emotional reaction) such that the participant does not make an explicit link

427

between the prime and the dependent measure. This kind of paradigm is rightly construed as demonstrating implicit processes (Bargh, 1989), particularly given that similar effects can be found when the prime is kept out of awareness using subliminal priming techniques (e.g., Baldwin, Carrell, & Lopez, 1990; Glassman & Andersen, 1999). However, to review this enormous literature is beyond the scope of this chapter. Instead, we focus on the more circumscribed goal of reviewing techniques for the relatively direct assessment of implicit processing of relationship information.1 We start by considering the assessment of implicit relationship attitudes in terms of characteristics such as valence and accessibility. Then we turn to expectancies and examine people’s overall outlook on interpersonal interactions as well as their implicit understanding of the contingencies of how relationship partners will respond to them. Last, we examine implicit motivation and strategies, with an eye toward goals, both interpersonal and noninterpersonal, that tend to be implicitly activated in a given relational context for a given individual and how this might vary as a function of insecurity and relationship commitment.

RELATIONSHIP ATTITUDES The core of any attitude—Â�whether about a comfortable pair of shoes or about the love of one’s life—is a good/bad evaluation or pleasant/unpleasant affective reaction, so this is where we begin. Implicit relationship attitudes can be about relationships in general, or they can reflect reactions to specific relationships. For example, implicit measures can be used to assess people’s overall view of being in a close, committed relationship. In an article reviewing IAT findings across 57 domains, Nosek (2005) reported that implicit good–bad attitudes toward married versus single were significantly correlated (r = .21) with an explicit “feeling thermometer” attitude measure about the same topic. Asking a similar question, Cross, Morris, and Gore (2002) administered a relationship IAT to the highest and lowest scorers on their explicit Relational-Â�Interdependent Self-Â�Construal (RISC) Scale. The IAT assessed associations between relationship words such as commitment, share, and together (vs. separate, independent, and uncommitted) and evaluative words such as happy, paradise, and heaven (vs. hatred, cancer, and poison). Participants who scored highly on the RISC, who explicitly consider relationships as central to their sense of self, scored higher on this IAT than those scoring

428

IV. DOMAIN-SPECIFIC PERSPECTIVES

low on the RISC, revealing high scorers’ implicit attitude that committed relationships tend to be more heavenly than poisonous. This effect was not moderated by self-Â�esteem, lending support to the conclusion that the IAT measured relationship attitudes in particular and not some more nonspecific social positivity. People’s relationship-Â�specific implicit attitudes can also be assessed. Within the adult attachment literature, Zayas and Shoda (2005) used the IAT to verify and extend our understanding of mental representations of specific attachment figures. Attachment theory (Bowlby, 1969; Hazan & Shaver, 1987) proposes that if relationship experiences, starting in childhood, lead a person to expect that close significant others are reliable and can be depended on, the person will typically adopt a relatively secure orientation toward relationships. If experiences instead suggest that significant others are unreliable, unresponsive, or rejecting, the person may become insecure: either anxiously clinging or else avoidantly downplaying the importance of relationships. Zayas and Shoda assessed individual differences in reaction times to categorizing one’s romantic partner and self with pleasant versus unpleasant words. In two additional studies, they went beyond the current partner to measure participants’ implicit attitudes about another significant attachment figure, their mothers. They also went beyond simple evaluative judgments to be more precise about the nature of the association: having the participant categorize their mother with supportive versus rejecting categories. Results consistently showed that securely attached participants had stronger positive–Â�partner and supportive–Â�mother associations than did insecure participants. However, associations between self and positive were not different between attachment groups, again demonstrating the specificity of the finding. These correlational patterns were consistent across various self-Â�report measures of attachment style and across general or relationshipÂ�specific orientations. Banse and Kowalick (2007) administered a similar romantic partner IAT (using good–bad and partner–Â�stranger categories) to a wider range of participants, including women in a domestic abuse refuge, women hospitalized for pregnancy complications, and women who reported having recently fallen in love, in addition to a control group of female students, to assess the usefulness of implicit partner attitudes as a predictor of wellbeing. Results indicated that abused women had the lowest implicit and explicit attitudes about their (ex-)partners. For women in continuing re-

lationships, partner IAT was positively correlated with secure attachment and well-being. Further hierarchical multiple regression analyses demonstrated that, after controlling for explicit partner attitudes, implicit attitudes were significantly correlated with well-being only among the hospitalized pregnancy group. The authors concluded that implicit attitudes can be a useful predictor of wellbeing, above and beyond explicit attitudes, particularly among those experiencing a high degree of life stress. Banse (1999) used a version of the affective priming paradigm (Murphy & Zajonc, 1993) to assess evaluative responses to relationship partners. Participants rated their evaluation of novel Chinese letters, which were preceded by subliminal presentations of either the participant’s own name or face image or the name or face image of the participant’s partner. Ratings of the Chinese letters were more positive when primed by a representation of the partner, suggesting a positive attitude toward the partner (although there were no significant correlations with explicit relationship satisfaction or attachment orientation). DeHart, Pelham, and Murray (2004) created a paper-and-Â�pencil measure of implicit partner attitudes by adapting the name-Â�letter measure of implicit self-Â�esteem (see Sekaquaptewa, Vargas, & von Hippel, Chapter 8, and Zeigler-Hill & Jordan, Chapter 21, this volume). Participants rated their liking for the 26 letters of the alphabet, and their implicit attitude was calculated as the degree to which the participant liked their romantic partner’s initials versus other letters, controlling for other participants’ ratings of those same letters. In a second study, participants’ implicit attitudes toward their best friends were examined in the same way. In both studies there was a correspondence between implicit attitudes and explicit ratings of relationship quality and closeness. However, this correlation was evident only for individuals with low self-Â�esteem: The authors suggest that this might indicate either that such individuals are particularly likely to respond to relationship difficulty by withdrawing (thereby reducing dependency and lowering implicit attitudes) or, conversely, that for these individuals more so than others implicit attitudes shape explicit perceptions of ongoing relationship quality.

Self–Other Integration Implicit measures can assess relationship–Â�attitude qualities beyond valence. Several theoretical models of relationship closeness emphasize the person’s



23. Measuring Implicit Processes in Close Relationships

perception of an overlap, or interrelation, of self and partner, and implicit measures can assess this merging of self and other into a collective cognitive unit. In a paper-and-Â�pencil measure, Agnew, Van Lange, Rusbult, and Langston (1998) had participants list thoughts about their romantic relationship, and the researchers coded these thoughts for the inclusion of plural pronouns (we, our, us). The number of plural thoughts was correlated with explicit measures of relationship commitment and centrality of the relationship to the self. The correlation with commitment persisted across a 6-week break between experimental sessions, and indeed a higher number of plural pronouns at the first session significantly predicted positive changes in commitment level across that time period. In a related line of research, Aron, Aron, Tudor, and Nelson (1991) argued that people in close relationships come to expand their self-Â�concept to include their partner and, therefore, experience a kind of sharing of the partner’s traits and characteristics. They had participants make yes–no judgments about whether traits were descriptive of themselves and hypothesized that judgments might be facilitated when self and other overlapped in a given trait domain. Indeed, RTs were faster for those traits that participants felt they shared with their partner. Smith, Coats, and Walling (1999) turned this paradigm around and found similar results when making speeded judgments about one’s partner: RTs were again faster for traits that were perceived as shared. The potential usefulness of these tasks as an implicit measure of relationship attitude is demonstrated by the finding that both of these RT effects showed a linear correlation with explicit feelings of closeness: The greater the facilitation on shared traits whether making judgments either about self (Aron & Fraley, 1999) or other (Smith et al., 1999), the closer the participant self-Â�reported feeling to their partner (on Aron et al.’s, 1991, measure of Including Other in the Self).

Attitude Accessibility In the broader attitudes literature, attitude accessibility has been shown to moderate the impact of attitudes, such that more highly accessible attitudes tend to have the greatest effect in guiding judgments and behavior (see, e.g., Fazio, 1995). Whitaker, Beach, Etherton, Wakefield, and Anderson (1999) used a reaction time measure of accessibility to predict the impact of attachment orientations on hopes and fears about long-term relationship outcomes. Participants quickly made

429

good–bad judgments on positive (kindness, care) and negative (reject, blame) relationship words and also completed explicit measures, including an attachment questionnaire and several measures of how they would anticipate their relationships progressing over 5 years of marriage. Results indicated that participants in the top third of accessibility of positive attachment words showed a correlation between expected marital satisfaction and attachment orientation; however, this correlation was not found among those in the bottom third of accessibility of positive attachment words. A parallel pattern was found for accessibility of negative attachment words, but here the finding was a linking of attachment orientation to expected problem-Â�solving efficacy after 5 years of marriage. These results demonstrate that the accessibility of an attitude dimension (in this case, relating to positive attachment concepts or negative attachment concepts) moderates the extent to which an attitude affects judgments. To summarize, implicit measures have been used to assess relationship attitudes, including general orientations and specific relationship evaluations, and examine both the valence and accessibility of these attitudes. As is often observed in other domains, these measures tend to correlate only weakly or moderately with explicit measures. Some research has begun to document predictive validity of implicit measures (e.g., predicting a change over time in relationship commitment; Agnew et al., 1998), while other research has raised interesting questions about when and for whom implicit attitudes may be especially influential (Banse & Kowalick, 2007; DeHart et al., 2004).

EXPECTANCIES One of the main functions of relationship cognition is to anticipate and predict interpersonal dynamics. How will my partner act toward me? If I act in a certain manner, how will my partner respond? Can I trust my partner? Am I at risk of being hurt or rejected? These kinds of expectancies, typically derived from past experience with a specific partner or across a range of relationships, can shape the perception of ongoing interactions. An expectancy can be broadly defined as a belief about a future state of affairs (Olson, Roese, & Zanna, 1996), and implicit measures can assess different types of relationship expectancies. We begin by examining people’s general default outlook on interactions and then turn to more nuanced if–then interpersonal expectancies.

430

IV. DOMAIN-SPECIFIC PERSPECTIVES

Cognitive Set A person may generally anticipate a certain kind of behavior from others. This is similar to the notion of set, which, although theoretically distinguishable from expectancies (see, e.g., Olson et al., 1996), implies a general preparation for or orientation toward a certain type of experience. For example, several theoretical models link relationship insecurity to a defensive interpersonal orientation, involving a hypervigilance for and selective processing of social threats such as rejection. A cognitive set of this kind can be assessed using all manner of tasks that measure activation of, attention to, or distraction by words or images associated with specific interpersonal outcomes. In the social anxiety literature, for example, several researchers have used the dot-probe or Visual Probe Test (VPT), in which stimuli relating to social threat (e.g., an angry face) and other stimuli that are more neutral (e.g., a nonexpressive face) are briefly displayed simultaneously on the screen, then a probe of some kind is displayed where one of the images had been, and reaction times are measured for identifying probes replacing the threatening versus neutral stimuli. Such studies have found that socially anxious individuals show distinct patterns of attention in which they systematically orient toward threatening faces (or, under certain conditions, away from them; e.g., Mogg & Bradley, 1998; see Teachman, Cody, & Clerkin, Chapter 26, this volume, for details). In studies on interpersonal insecurity within nonclinical samples, Dandeneau, Baldwin, Baccus, Sakellaropoulo, and Pruessner (2007) found that the tendency to orient attention toward frowning, rejecting faces (vs. neutral faces) in a VPT was associated with lower levels of self-�esteem and higher levels of the stress hormone cortisol in reactions to negative social evaluation. Similarly, Dandeneau and Baldwin (2004) found that individuals with low self-�esteem showed greater Stroop interference effects for words relating to rejection (e.g., ignored, unwanted, disliked) versus acceptance (e.g., welcomed, included, caring). In a related line of research, Downey, Mougios, Ayduk, London, and Shoda (2004) examined the cognitive set of individuals high on rejection sensitivity, who are hypothesized to have a disposition to expect, perceive, and react strongly to rejection. These authors hypothesized that rejection-�sensitive individuals would show heightened activation of a defensive motivational system, particularly orienting toward potential interpersonal threats in the environment. The potentiation of the threat re-

sponse was assessed by measuring people’s startle reflex, as revealed in a large eyeblink response to a loud noise. They found that highly rejectionÂ�sensitive individuals showed a heightened startle reflex, particularly when they were looking at images relating to social rejection. In a follow-up study using a version of the emotional Stroop task, Berenson and colleagues (2009) found that rejection sensitivity was associated with interference effects on rejection-Â�related words, again supporting the idea of a cognitive set toward interpersonal rejection. Other studies have found evidence that certain individuals show an avoidance of, rather than hypervigilance for, threatening information. Dewitte, Koster, De Houwer, and Buysse (2007) used a VPT with verbal stimuli, including attachment–Â�threat words (separation, rejected, ignored), and found that a combination of attachment avoidance and anxiety—an orientation identified by Bartholomew (1990) as fearful avoidance—was correlated with a tendency to orient away from attachment–Â� threat verbal stimuli. These authors (Dewitte & De Houwer, 2008; see also Cooper, Rowe, PentonVoak, & Ludwig, 2009) replicated this effect with a modified VPT using pictures of angry, happy, and neutral faces (although in this study they also found a similar pattern for happy as well as threatening faces, perhaps indicating that fearful avoidance leads to the cognitive avoidance of all highly emotional stimuli). In a related finding, Berenson and colleagues (2009) found that highly rejectionÂ�sensitive individuals oriented away from threatening faces in a VPT, a result the authors interpret as reflecting an overlearned attentional avoidance strategy for coping with perceived threat (possibly following an initial vigilance for such threat cues, as shown in their Stroop study mentioned earlier).

If–Then Expectancies Beyond general cognitive sets, people have many nuanced expectancies about the kinds of outcomes that can be anticipated in certain kinds of situations or relationships or in response to certain kinds of behavior. An interpersonal expectancy can be characterized as the anticipation of an if– then, or behavior–Â�outcome sequence: a scripted interaction pattern in which one’s own behavior is expected to lead to a particular partner response. For example, through a history of repeating the same pattern of interaction over and over, a person might develop a script along the lines of “If I make demands of my partner, then he will withdraw



23. Measuring Implicit Processes in Close Relationships

from me” (e.g., Sullaway & Christensen, 1983) or “If I need help, then Mommy will take care of me” (Bretherton, 1990). Using a range of questionnaire methods, people have been asked about their explicit if–then expectancies regarding such things as the attachmentÂ�related outcomes of seeking dependency, closeness, and trust (Baldwin, Keelan, Fehr, Enns, & Koh-Â�Rangarajoo, 1996), their partner’s likely responses to expressions of anger (Fehr, Baldwin, Collins, Patterson, & Benditt, 1999), requests for help and closeness (Downey & Feldman, 1996), the likely outcome of acting highly assertively in a negotiation setting (Ames, 2008), the kinds of partner behavior expected in response to agentic or communal behavior (Hill & Safran, 1994), and more. Consistent with findings in other domains of social cognition, interpersonal expectancies are hypothesized to guide attention to relevant information, structure interpretation of and memory for such information, and so forth, much like any cognitive structure (see Baldwin, 1992). They also lend themselves to self-Â�fulfilling prophecies, such that acting on an interpersonal expectancy can bring about the expected or, in some cases, feared outcome (e.g., Berk & Andersen, 2000; Snyder & Swann, 1978). Implicit measures have been used to assess the automatic functioning of these interpersonal expectancies. Fehr (2004) used a reaction time measure to examine the kinds of if–then interaction patterns that people associated with the concept of intimacy in same-sex friendships. She presented a series of statements such as “If I want to talk, (then) my friend will listen” and asked participants to make speeded judgements of whether each statement reflected intimacy. Consistent with a prototype model of cognitive structure, she found that participants were quickest to make judgments about if–then patterns that other participants had explicitly identified as prototypical of the concept of intimacy. When a person has an if–then expectancy of this kind, the phenomenal experience is that they “can easily imagine” it happening. For example, the thought “If I forget our anniversary” might make “then my partner will be miffed” highly accessible and fluently imagined. As such, sequential priming approaches (see Wentura & Degner, Chapter 6, this volume) are well suited to the study of these structures: Activate the “if” and activation should spread to the associated “then.” The implicit activation of the anticipated outcome can be assessed via lexical decision tasks or similar techniques.

431

For example, a core idea of attachment theory is that people form expectancies about their partner’s availability and responsiveness in times of need. Will the person be there when needed? Is the partner trustworthy, dependable, and unconditionally accepting? Baldwin, Fehr, Keedian, Seidel, and Thomson (1993) presented participants of different attachment styles with a range of sentence stems such as “If I trust my partner, then my partner will .â•›.â•›. ” and then measured their word–Â� nonword reaction times to attachment-Â�related targets such as care and hurt. Participants with an avoidant attachment style were quicker than others to identify the outcome of hurt in the context of trusting their partner: an implicit expectancy that plausibly contributes to their tendency to avoid closeness and trust in their relationships (see Goals and Strategies section). Mikulincer (1998a) used a similar methodology to examine anger expectancies, of the kind “If I’m angry at my partner then my partner will .â•›.â•›. ,” with outcomes including understand, accept, attack, and leave. He found that following the anger sentence stem, securely attached individuals showed quicker reaction times to positive outcome words, whereas insecurely attached (i.e., avoidant or anxious-Â�ambivalent) individuals showed quicker reaction times to negative outcomes, again reflecting how insecurity could be based on the implicit expectancy that the partner may not be entirely accepting and supportive in times of distress. Interpersonal expectancies involving contingencies of acceptance and rejection have been shown to be related to insecurity in the form of low self-Â�esteem. According to interpersonal models of self-Â�esteem (e.g., sociometer theory; Leary & Baumeister, 2000), the feelings of insecurity often associated with failure are due to the implicit expectancy that failing will result in a loss of acceptance from others. In a series of studies consistent with this formulation, Baldwin and Sinclair (1996; see also Baldwin, Baccus, & Fitzsimons, 2004) found that activation of words related to failure (vs. success) facilitated lexical decision reaction times to target words related to rejection (vs. acceptance) for individuals with low self-Â�esteem. This supports the notion that insecurity arises from the implicit sense that social acceptance is always provisional and contingent on being successful. Some research has involved adapting this approach to examine relationship-Â�specific expectancies. In their examination of failure–Â�rejection contingencies, Baldwin and Sinclair (1996) had some participants first visualize a person they knew who “tends to be very accepting and non-

432

IV. DOMAIN-SPECIFIC PERSPECTIVES

evaluative of you and simply accepts you for who you are,” whereas others visualized “a person who tends to be very evaluative of you and seems to accept you only if you live up to certain standards of performance.” Consistent with the notion that relationship-Â�specific implicit expectancies about contingency can be assessed, the failure–Â�rejection contingency pattern was evident in the reaction times of only the latter group, who had been primed with thoughts of a critical and demanding significant other. Commentators from physicist Niels Bohr to baseballer Yogi Berra have wryly observed that there is nothing more difficult to predict than the future, yet people persist in trying to do so. Relationship expectancies can include a general orientation or outlook as well as nuanced if–then expectancies about likely dynamics of interpersonal interactions. Such expectancies can serve as default orientations or can be activated by specific relationship contexts and partners. The accessibility and activation of these expectancies can be assessed implicitly and are linked to important outcomes like insecurity.

GOALS AND STRATEGIES Relationship researchers are keenly aware of James’s (1890) dictum “Thinking is for doing,” and much work has examined implicit processes relating to interpersonal goals, responses, and strategies. This follows from studies done 20 to 30 years ago that did not investigate implicit processes per se but certainly set the stage for recent theoretical and empirical work. For example, Levenson and Gottman (1983) found that physiological linkage between partners (i.e., having similar heart rate, skin conductance) while discussing an interpersonal conflict was associated with relationship satisfaction, independent of explicit expressions of affect during the interactions. Subsequently, they showed that the level of physiological arousal in couple members predicted changes in satisfaction 3 years later (Levenson & Gottman, 1985). A more direct social cognitive test used a time pressure manipulation to show that the ability to respond constructively to a partner transgression is significantly decreased when a participant is instructed to respond as quickly as possible (Yovetich & Rusbult, 1994). These findings clearly implicate implicit processes and may, in part, explain the wisdom of marital therapy research recommending that couples take a “5-minute time-out” in the heat of conflict: The immediate, automatic, gut-

level response in the context of high arousal and limited regulatory resources may amplify a conflict and short-Â�circuit more elaborate and constructive cognitive processing, such that conflict and insecurity become a recursive loop. In the following sections, we examine the implicit assessment of relationship-Â�related goals and the strategies for attaining those goals. We start with a consideration of goals that a person might associate with a given relationship but that are not interpersonal goals per se. Next we discuss more directly interpersonal goals such as intimacy and sexuality. Finally, we examine people’s implicit strategies for managing threats, either to the relationship or to the self in an interpersonal context. Here we give special consideration to the influence of insecurity and also relationship commitment.

Activation of Noninterpersonal Goals Associated with Relationships Specific significant others can become associated in various ways with specific goals, with the result that interpersonal cognition becomes tightly integrated with processes of self-Â�regulation. One might associate one’s father with the goal of being an analytical thinker, for example, such that thinking about one’s father automatically activates that goal (e.g., Fitzsimons & Bargh, 2003; Shah, 2003a, 2003b). This relationship-Â�associated goal activation can be assessed using implicit measures. In a study by Shah (2003a), participants who knew they were going to be working on an anagram task were subliminally primed to think of their father (e.g., with the word dad) or received no prime. They next performed a lexical decision task that included words such as anagram and analytic and finally worked on the set of anagrams. Primed participants whose fathers valued analytic reasoning and who also felt close to their father showed increased accessibility of the relevant words in the lexical decision task. Moreover, the degree of this goal activation predicted their persistence and performance on the subsequent anagrams. A subsequent study also showed that if the primed significant other clearly does not value a given goal, this goal becomes cognitively inhibited rather than activated. Because of this link between relationships and goal pursuit, people may be motivated to approach significant others who facilitate a current goal (e.g., seeking out a fun-Â�loving friend when in the mood for relaxation) while avoiding those who interfere with a current goal (e.g., avoiding a “couch potato” friend when motivated to get some exercise). Fitz-



23. Measuring Implicit Processes in Close Relationships

simons and Shah (2008) premeasured participants’ views on which of their goals were facilitated or interfered with by various significant others. They then administered an approach–Â�avoidance reaction time task, in which participants were asked to move a cursor toward or, on other trials, away from significant others’ names displayed on a computer screen. Before each trial a goal word was subliminally presented. Results showed that participants were quicker to make approach movements toward and slower to make avoidance movements away from significant others who would be expected to facilitate currently activated goals. As a final example of how relationship cognition influences self-Â�regulation, even outside of the direct context of the relationship, consider Rudman and Heppen’s (2003) work on what they call the “glass slipper effect.” These authors argue that women may be inhibited in their pursuit of personal power (including high-Â�status jobs and high income) to the extent that they implicitly subscribe to a romantic fantasy of being rescued and protected by some version of Prince Charming on a white horse. Three studies included IATs in which associations were measured between relationship partner and symbols of this kind of romantic fantasy (e.g., Prince Charming, superhero). Results indicate that women’s (but not men’s) implicit fantasy associations correlated negatively with a desire for social power, measured as expected income, educational goals, and desire to take on a leadership role. Note that explicit measures of romantic fantasy did not show the same patterns, nor did an IAT that measured pleasant–Â�unpleasant implicit attitude toward the romantic partner; it was the specific implicit romantic fantasy that correlated with lower aspirations for goals relating to power and status.

Activation of Interpersonal Goals and€Responses We now turn to goals and strategies that are more obviously interpersonal. We start by examining attachment-�related goals and then consider goals relating to sexuality.

Attachment-Â�Related Goals Recent social cognitive analyses of attachment working models place a heavy emphasis on the types of strategies that people use in trying to achieve various attachment-Â�related goals. From an individual differences perspective, people’s chronic attachment orientations can be seen as reflecting

433

the specific strategies that are most accessible for them. In an intimate context, for example, chronically secure individuals tend to favor closeness and interdependency, whereas insecure individuals tend to become clingy and security seeking (in the case of anxious individuals) or else dismissive and controlling (in the case of avoidant individuals). Dewitte and De Houwer (2008) used an IAT approach to assess implicit motivational orientations associated with chronic attachment styles. They found that chronic avoidance was correlated with a positive IAT response to stimuli representing goals related to maintaining distance (e.g., separation, independent, autonomous, and distant) rather than proximity (e.g., nearby, intimacy, hug, and close). Mikulincer (1998b) used sequential priming tasks to examine the contextual activation of attachment-Â�related goals. When primed to think of trust, for example, individuals who were anxiously attached were quicker to recognize security as a word, whereas individuals who were avoidantly attached were quicker to recognize control as a word. This increase in accessibility was specific to a condition with trust priming—that is, a context that should activate attachment-Â�related concerns—and so evidently not reflective of a general disposition. A similar logic can be applied when examining relationship-Â�specific attachment strategies. We now know that people commonly have available in memory a range of interpersonal relationships representing various attachment styles. This is why priming individuals with a specific relationship partner with whom they have a secure attachment can activate attachment security in individuals independent of individual differences (Mikulincer & Shaver, 2007; Pierce & Lydon, 1998) and why it is also possible to prime even anxious and avoidant attachment relationships and their corresponding attachment orientations (Baldwin et al., 1996; Bartz & Lydon, 2004). Priming a relationship of a certain style, therefore, should be expected to activate the attachment goals that are associated with that relationship. Indeed, Gillath and his colleagues showed that priming a secure attachment figure increased not only an explicit willingness to self-Â�disclose but also the cognitive accessibility of self-Â�disclosure intentions, as assessed with a reaction time measure (Gillath et al., 2006). Moreover, priming the name of a significant other who represented one of the three types of attachment relationship increased accessibility of interpersonal goal words associated with that type of relationship (Gillath et al., 2006). That is, priming a figure

434

IV. DOMAIN-SPECIFIC PERSPECTIVES

who makes one feel anxiously attached increases implicit activation of cling and possess, whereas in the context of an avoidant relationship there is increased activation of avoid and withdraw, but in the context of a secure relationship there is increased activation of comfort and support. Examining this kind of implicit goal activation in an ongoing interpersonal context can reveal the kinds of thoughts that plague those with insecure attachment orientations, especially when goals of closeness and intimacy do get activated. Bartz and Lydon (2006) led single participants to consider the possibility of a communal relationship with an interaction partner. Among chronically secure participants, the more this manipulation increased the accessibility of closeness (as assessed with a lexical decision task), the better they performed on a subsequent puzzle that required concentration. For secure participants, then, the activation of closeness goals was evidently salutary, nonthreatening, and functional for self-�regulation. Among chronically anxious participants, by contrast, the implicit activation of the closeness goal was disruptive: To the extent that the interpersonal context implicitly activated thoughts of closeness, this led to distraction, rumination, and poor performance on the puzzle.

Sexuality Goals Interpersonal contexts involving sexuality are particularly well suited for implicit measures, given societal constraints and intrapsychic conflicts that might inhibit or bias explicit sexual thoughts, feelings, and behaviors. Unobtrusive measures have been used to assess men’s behavioral and cognitive responses to erotic stimuli. In one study, for example, men classified as gender schematic exhibited more sexualized behavior toward a confederate after viewing a short video of soft-core pornography (McKenzie-Mohr & Zanna, 1990). Dependent measures included how much the participant moved his wheeled chair toward the interviewer, how much she rated him as sexually motivated, and how much the men remembered the content of the interview versus her physical appearance (such as a political button strategically placed just above her breast). Reaction time measures of various kinds have also been used to examine the activation of sexuality. For example, when heterosexual participants are subliminally shown sexual stimuli, such as pictures of nude, opposite-sex individuals, this facilitates lexical decision reaction times to sex-Â�related words (e.g., penis, breast; Gillath, Mikulincer, Birn-

baum, & Shaver, 2007) as well as reaction times to categorize subsequent sex-Â�related pictures as sexual (Gillath et al., 2007; Janssen, Everaerd, Spiering, & Janssen, 2000). Priming memories of romantic and sexual arousal increases attention to images of attractive opposite-sex images compared with control images and with a happy memory control group (Maner, Gailliot, Rouby, & Miller, 2007). As one might expect, results on implicit measures of this sort have been shown to correlate with self-Â�reported sexual orientation: Snowden, Wichter, and Gray (2008) devised two different implicit measures, using erotic photographs of women or men, and verbal stimuli relating to sexual attraction (e.g., arousing) or revulsion (e.g., repugnant). One measure was a sequential priming task, in which the photographs were shown subliminally before words that were to be categorized as either sexually attractive or unattractive. An IAT measure was also devised in which associations were assessed between (visible) male and female nudes and words reflecting attractiveness versus unattractiveness. The two implicit measures correlated with each other, and with self-Â�reports of sexual orientation, among 50 heterosexual and 25 homosexual men. The fact that nude photographs of appropriate partners can activate thoughts of sexuality certainly demonstrates the ready activation of sexual goals, but the finding is not overly surprising. A more interesting question perhaps, as addressed in the McKenzie-Mohr and Zanna (1990) study cited earlier, is whether and for whom sexual (and/or sexist) goals are spontaneously activated in more innocuous contexts. Rudman and Borgida (1995), for example, found that priming men with sextyped television ads increased accessibility of sexist words in a lexical decision task but not nonsexist female words such as mother or sister. Priming also increased accessibility of sexual double entendres such as cherry, easy, and strip. Research on heterosexual interactions has suggested that men are generally more likely than women to perceive a cross-sex interaction in terms of its potential for sexuality as opposed to friendliness. Lindgren, George, and Shoda (2007) used the IAT to examine gender differences in implicit sexual attitudes. In a series of IATs pairing male/ female/self categories with friendly/sexual categories, they first found that men showed a stronger implicit female–Â�sexuality link than did women (but there were no effects on the IAT with male stimuli). In addition, there was evidence that among men the female–Â�sexuality IAT score was correlated with explicit adversarial heterosexual



23. Measuring Implicit Processes in Close Relationships

beliefs. Among women, this implicit link between female and sexuality was correlated with the number of dates the participant had had over the preceding 4 weeks. In a second study, the researchers examined shifts in IAT scores after reading a vignette about a first date and visualizing themselves in that situation. After imagining being on a date, both women and men showed greater self–sex IAT associations. Interestingly, whereas women also showed increased associations between sex and opposite-sex other, men showed lowered associations of this kind even though they self-Â�reported sexual interest toward the woman in the vignette. One might imagine, at least based on cultural stereotypes, that religiosity would reduce the accessibility of sexuality goals, and it does. Among religious undergraduates, priming prayer, religion, God, and Bible decreased accessibility to premarital and to sex (Fishbach, Friedman, & Kruglanski, 2003). On the other hand, priming premarital and sex increased accessibility for prayer, religion, and Bible among religious students. One interpretation of these findings is that religion is the overarching long-term goal, and premarital sex is a momentary temptation in the pursuit of the goal. Once a situation is construed as relevant to sexuality, implicit measures can also assess the activation of other interpersonal goals that are associated with sexuality. Consider the link between sexuality and interpersonal intimacy. Birnie and Lydon (2009) examined the association between sex and intimacy by administering a prime with a scenario including either words prototypical of intimacy generally or words prototypical of sexual intimacy in particular. Explicit judgments of intimacy and sexual intimacy correlated, suggesting that the presence of one implied the presence of the other. However, on a lexical decision task, only the priming of sexual intimacy words increased accessibility of intimacy words and not the reverse. This suggests that when asked to make a judgment about sexual intimacy the primed presence of intimacy influenced explicit evaluations; however, the same prime did not influence implicit activation of sexual intimacy. Although sexuality is commonly linked to intimacy, for some individuals sexuality seems to be strongly associated with the goal of power. Relationship researchers have identified such individuals with the Likelihood to Sexually Harrass Scale (LSH). Bargh, Raymond, Pryor, and Strack (1995) used a sequential priming pronunciation task in which the participant was to say a series of words out loud. To the extent that a certain prime facilitated pronunciation of a certain target word,

435

as measured by the onset of speech, the two constructs were interpreted to have an automatic association. Results showed a bidirectional association between power and sex, among high-LSH men: For these men, the presentation of power-Â�related words (e.g., boss, authority) facilitated the pronunciation reaction times of sex-Â�related words (e.g., motel, date) and vice versa. A follow-up study (Zurbriggen, 2000) examined moderators of this sex–power association in both men and women. Among women, a measure of their affiliation/intimacy motivation was found to moderate the degree to which an implicit sex–power linkage predicted a history of using coercive behavior to obtain sex from men. Among men, their level of power motivation moderated the degree to which the sex–power linkage in memory predicted a history of coercive sexual behavior. This study suggests that higher order implicit motives may interact with contextualized if–then contingencies to influence interpersonal strategies and responses. For example, men with chronic power motivation are more likely to construe interpersonal experiences in terms of power and store such associations in memory that can be implicitly activated in similar contexts in the future.

Strategies for Managing Threat Into every life and every relationship some rain must fall, and implicit measures can reveal the automatic strategies people activate to deal with a range of threats to the self and to the relationship. In examining these, we follow the literature in focusing on how responses to threat are often colored by insecurity, as defined in various ways, and how commitment to a relationship can protect against threat.

Insecurity and Threat Murray, Holmes, and Collins (2006) argue persuasively that close relationships are inherently risky undertakings: potentially fulfilling to be sure but also fraught with possibilities of rejection, conflict, suffering, and disappointment. As such, these researchers have examined the cognitive processes involved in regulating risk in the face of interpersonal uncertainty. First, they have shown that the default response when an important relationship is threatened appears to be the automatic activation of connectedness goals. Murray, Derrick, Leder, and Holmes (2008) found that, after recalling an interpersonal rejection from a close other, participants exhibited increased accessibility for

436

IV. DOMAIN-SPECIFIC PERSPECTIVES

connectedness words in a lexical decision task. More recently, DeWall, Maner, and Rouby (2009) found that social exclusion threats increase selective attention to signs of acceptance, whether it be speed at finding a smiling face in a crowd of neutral faces, attention to smiling faces in a visual dot probe, or fixating on smiling faces as assessed with an eye-Â�tracking procedure. However, Murray and colleagues have found that for insecure individuals (with low self-Â�esteem) such threats also trigger an explicit control system to correct for self-Â�protection concerns, which can lead to withdrawal or derogation of the relationship rather than increased closeness. From a different theoretical perspective, attachment researchers have examined the interpersonal strategies and cognitive responses that become activated in the context of threat. Threat, particularly social threat, should activate the attachment system for everyone, with differing appraisal and coping responses based on one’s chronic and/ or activated attachment style. A core tenet of attachment theory is that the significant other ideally serves as an emotional “safe haven,” such that feelings of insecurity or threat should lead an individual to turn to the significant other for comfort, solace, and protection. This movement toward the significant other can involve actual physical approach or can simply involve cognitively activating thoughts of the other as a means of self-Â�soothing (Mikulincer & Shaver, 2003, 2007). In one series of studies, a sequential priming procedure was used to show the implicit association between threat and proximity seeking. Subliminal priming of failure, death, and illness were used as threat stimuli in successive studies and shown to increase accessibility of proximity words such as love and hug (Mikulincer, Birnbaum, Woddis, & Nachmias, 2000). Beyond general proximity seeking, Mikulincer, Gillath, and Shaver (2002) have further shown that social threat primes increase the accessibility of a specific representation of one’s attachment figure. In studies using lexical and Stroop methods, sequential priming of failure and separation increased accessibility of the name of a secure attachment figure but not the name of other close relationship figures, underscoring the specificity of the attachment security effect. Names can also be used in a VPT task, as shown by Dewitte and colleagues (2007). Several studies showed that anxiously attached individuals showed greater attentional bias toward the first names of attachment figures rather than acquaintances, particularly after imagining a threatening separation experience (or, in one study, a pleasant day spent with the attachment figure).

A clever measure devised by DeWitte, DeHouwer, Buysse, and Koster (2008) nicely captures the phenomenon of seeking out attachment figures when under stress. Participants were shown a stick-Â�figure drawing of a person on a computer screen, and on each trial they were instructed to control (using specified computer keys) the figure to walk either toward or away from names that were displayed. Results showed that reaction times to move the avatar toward an attachment figure’s name were quicker when thoughts of separation or failure had been experimentally primed. Although this priming effect did not interact with individual differences on attachment orientations (possibly because of inadequate statistical power), there was evidence of main effects such that chronic attachment anxiety correlated with faster approach responses to an attachment figure, and chronic avoidance correlated with a slower approach speed. The previous example provides a nice illustration of both the universal activation of attachment related themes in the face of social threat and the ways in which this response differs based on chronic attachment orientations. As might be expected, individual differences are also evident in reactions to more specific threats such as having one’s trust violated. Trust is a major component of the attachment system and as such has specific implications for each attachment style. Secure individuals are theorized to be relatively confident about the love and acceptance of others; therefore, a trust violation should immediately and implicitly activate a constructive, engaging response to resolve the apparent anomaly in the partner’s behavior. Anxiously attached individuals are ambivalent, desiring closeness but lacking confidence that they are loved and accepted; therefore, a trust violation is consistent with their fear and uncertainty and activates their latent propensity to worry about their relationship. Avoidantly attached individuals are thought to have learned that others simply cannot be counted on for love and support, so a trust violation would confirm their expectation and they would strive to distance themselves at the first hint of unfaithfulness by the partner. Indeed, a sequential priming measure revealed these implicit strategies: On trials when a trust violation of the form “I trust my partner and he/she hurts me” was primed, there were striking differences in reaction times for specific goal words. Whereas this trust violation increased accessibility of the strategy talk for securely attached individuals, it facilitated worry for the anxiously attached and escape for the avoidantly attached (Mikulincer, 1998b).



23. Measuring Implicit Processes in Close Relationships

There are various ways to deal with threats, as shown in the study just described, one of which is to find ways to not think about the threat! For example, avoidantly attached individuals are thought to be so effective in distancing and disengaging from others that they can implicitly draw on cognitive strategies to deactivate the attachment system and automatically suppress thoughts of attachment threat (Fraley & Shaver, 1997). Indeed, in an adaptation of the emotional Stroop task, avoidantly attached individuals did not show interference in their color-�naming responses to socially threatening words, when they were trying to suppress thoughts of a painful breakup, whereas low avoidants did show interference when trying to suppress. However, this was only true under conditions of low cognitive load: High cognitive load overwhelmed the ability of the avoidantly attached to suppress, leading to increased accessibility of stimuli relating to separation and negative self-�traits (Mikulincer, Dolev, & Shaver, 2004; see also Edelstein & Gillath, 2008). Other forms of insecurity can also lead people to react to a partner in ways that might be less than helpful, particularly in response to feeling threatened. In their work on rejection sensitivity, Ayduck, Downey, Testa, Yen, and Shoda (1999) found that when highly rejection-�sensitive women felt rejected, they expressed hostile reactions toward a potential laboratory partner. These researchers used a sequential-�priming pronunciation task to examine automatic cognitive reactions to rejection: Participants were asked to pronounce aloud rejection (e.g., reject, abandon), hostility (e.g., anger, hit), disgust (e.g., vomit, pollute), or neutral words presented on a computer screen. Each word was preceded by the masked presentation of a prime word from one of the other categories. The findings showed that highly rejection�sensitive women were faster to pronounce hostility target words when they were preceded by rejection primes. They did not show facilitated reactions following neutral primes, however, suggesting that hostility was not chronically accessible for them but was activated specifically in response to rejection.

Commitment and Responding to Threats to the Relationship An enduring theme in the relationships literature has been commitment. What are the cognitive processes involved in maintaining a relationship with a partner, even when the going gets tough or when other relational opportunities present themselves? The commitment literature includes examination

437

of a set of key processes, including forgiveness and accommodation, usually with the assumption that these processes function with full awareness and intent once one has decided to commit to a particular relationship. Relationship properties such as commitment and acts such as forgiving a partner’s misbehavior have been assumed to require high levels of self-Â�regulatory resources and explicit motivations and intentions to act in a manner constructive for the relationship. However, consistent with social cognitive theory, relationship motives can presumably be internalized and, therefore, become easily accessible and efficiently cued by relationship threats. Activation of personally held relationship attitudes and motives can then implicitly elicit associated relationship-Â�supportive strategies, even when the individual might be depleted or responding without much deliberate thought. The recent generation of research has, therefore, incorporated methods involving implicit measures as predictors of relationship strategies and has incorporated the understanding that cognitive processes can be explicit and controlled early in a relationship but may become implicit and automatic over time. For example, Perunovic and Holmes (2008) showed that time pressure can moderate predictors of how willing people are to tolerate partner transgressions and respond in a relationship-Â�constructive manner rather than in a relationship-Â�destructive manner, a response that has been termed accommodation. They elicited accommodation responses under high versus low time pressure and found that personality factors such as agreeableness and attachment avoidance interacted with time pressure (in opposite ways) to predict accommodation. For example, agreeableness was not significantly associated with accommodation if there was no time pressure, but under time pressure there was a very large association between agreeableness and accommodation. Building on similar reasoning, Lydon, MenziesToman, Burton, and Bell (2008) examined the contributions of explicit and implicit measures in predicting a person’s willingness to accommodate. They found that a lexical decision measure of reaction times to commitment words (word/nonword responses to commitment, devotion, etc.) predicted the willingness to accommodate, independent of explicit commitment. This effect was moderated by experimental condition, however. Whereas both explicit and implicit measures predicted accommodation in the control condition, only the implicit measure of commitment accessibility predicted accommodation after a relationship threat (an interaction with an attractive single person of

438

IV. DOMAIN-SPECIFIC PERSPECTIVES

the opposite sex). This suggests that implicit measures assess people’s commitment at an automatic level, predicting whether pro-Â�relationship responses such as accommodation may be called on even when factors such as arousal, fatigue, or speed of response might otherwise limit self-Â�regulation. A different type of reaction time measure for assessing implicit processes of commitment is based on the phenomenon of attitude accessibility (cf. Whitaker et al., 1999, described earlier). Etcheverry and Le (2005) presented participants with stems for relationship commitment statements such as “Imagining myself with my partner in the distant future is. .â•›.â•›. ” After a 3.5-second presentation of the stem, the final word appeared on the screen. On one trial they would receive the word hard and on another trial the word easy, and they were timed for their response (agree or disagree). Reaction times to commitment statements interacted with self-Â�reported commitment to predict willingness to accommodate: As accessibility increased, the strength of the association between commitment and accommodation increased. Moreover, a similar finding emerged in predicting relationship survival 7 months later, demonstrating the realworld impact of attitude accessibility. Note that all forms of commitment may not be created equal when it comes to implicit processes. Commitment can derive from various sources, including societal barriers to exiting the relationship (e.g., economic factors or a perception of social stigma from divorce). When commitment is based on such external factors, and so the person is striving to maintain the relationship even if he or she might at some level prefer to end it, this creates a situation where implicit and explicit attitudes might conflict (see Nosek, 2005, for additional discussion of the factors moderating correspondence between implicit and explicit attitudes). Scinta and Gable (2007) examined this hypothesis using an IAT (partner vs. not partner, pleasant vs. not pleasant) and a sequential priming task. In one study, a lexical task was used with partner’s first name, last name, and relationship term (e.g., boyfriend). In a second study, an image-based task was used with parafoveal priming of the partner with a picture. Barriers to exiting a relationship moderated the correlation between implicit and explicit attitudes. In particular, there was a large negative correlation between implicit and explicit attitudes among individuals with high barriers to exit. These individuals explicitly reported high satisfaction with their relationship, when their implicit attitudes were much less favorable. (Significant results were obtained using the IAT and the

image sequential priming task although not the lexical sequential priming task.) These findings demonstrate the value added by implicit measures, particularly in contexts where implicit and explicit attitudes are likely to be inconsistent. We now turn to a specific form of commitment threat. One could argue that the most significant threat to a relationship is the availability of another, appealing and available partner. When confronted with a relationship threat of this kind, what automatic cognitive processes help a committed person stay the course? One key process might be to simply stop paying attention to the attractive alternative. Maner, Rouby, and Gonzaga (2008; see also Maner, Gailliot, & Miller, 2009) recently used a reaction time measure similar to the dot probe to assess attentional adhesion to attractive members of the opposite sex. Participants in committed heterosexual relationships first took 15 minutes to write about a time when they felt strong feelings of love (love prime) or a time when they were happy (control). They then completed an attentional task in which pictures of various people were presented on a computer screen, each time followed by a probe at a different quadrant of the screen. Some trials involved pictures of attractive members of the opposite sex, and reaction times on these trials represent the difficulty in switching attention away from the attractive picture to identify the probe. Results indicated that reaction times to switch attention away from the attractive alternative were significantly faster (indicating less attentional adhesion) for love�primed compared with control participants. (No such effect was found for the other three picture types.) These results help to establish that at least some relationship maintenance behaviors are triggered automatically, working at an early stage of attention, particularly when the relationship (or a threat to it) is activated. The response of directing attention away from attractive alternatives may involve a two-step process of first recognizing the relationship threat and then reacting to it. Lydon and his colleagues (2008) found that for heterosexual women visualizing an interaction with an attractive single male increased the likelihood they would complete ambiguous word fragments (e.g., be _ _ _ e) with threat words (beware instead of become) as well as commitment words (loyal instead of local). These effects were not evident among men, suggesting that women may be more likely to implicitly construe this type of situation as a commitment threat. Consistent with this idea, women in subsequent studies reported greater intentions to engage



23. Measuring Implicit Processes in Close Relationships

in relationship-Â�supportive behaviors such as accommodating their partner, whereas men actually reported less willingness to accommodate after a pleasant interaction with an available attractive woman. A similar pattern was observed in a virtual reality (VR) exercise designed to assess implicit relationship behavior. In various VR tasks, participants had the opportunity either to avoid images of attractive alternatives or to bring them closer and approach them. Consistent with the word fragment data reported previously, priming women to think about their relationship increased their tendency to repel images of attractive men (compared with control images) in VR, whereas the same priming manipulation had no influence on men in romantic relationships. However, this response could be modified through the formation and practice of implementation intentions (Gollwitzer, 1999). Training men to form implementation intentions of the sort “If the attractive girl approaches me at the bar then I will [do X] to defend my relationship” later reduced men’s likelihood of seeking out rooms in VR that had previously been subliminally associated with an attractive woman.

DISCUSSION Relationship researchers are making good use of implicit measures. Sequential priming techniques, IATs, lexical decision and Stroop tasks, and other measures have been used to assess relationshipÂ�specific and general interpersonal attitudes. Because of the dynamic, interactive nature of relationships, creative adaptations of standard measurement approaches have been developed to address people’s expectancies and strategies. Expectancies and strategies vary tremendously from one individual to the next, of course, and much progress has been made in identifying particular implicit processes that tend to correlate with interpersonal insecurity as well as with relationship commitment. We readily acknowledge that our structuring of our review into different types of implicit processes (e.g., attitudes, expectancies, goals and strategies) is rather arbitrary. For example, an overall attitude toward one’s relationship very likely arises from one’s activated interpersonal goals combined with one’s strategies for achieving them and expectancies of success (see, e.g., Fitzsimons & Shah, 2008). Our decision to separate these simply reflects our effort to group similar measurement questions together rather than to claim a theoretical structure.

439

That said, the literature might benefit from more of this kind of structuring (see, e.g., Mikulincer & Shaver, 2003; Murray & Holmes, 2008), so that researchers working from different perspectives might be able to clarify their points of agreement and disagreement about the critical cognitive processes at play and perhaps work toward a unified framework. In so doing, the links to the broader social cognitive literature such as work on reflective and impulsive determinants of social behaviour (Strack & Deutsch, 2004) or on various dual�process models (e.g., Smith & DeCoster, 2000) will also become more readily apparent.

Why Bother with Implicit Measures in€This Domain? Because relationships research was initially slow to adopt implicit measures, it also missed the firstÂ�generation reason in social psychology for using implicit measures: to overcome concerns about social desirability and assess “authentic” attitudes about self or outgroups. Some of this owes to the fact that there was only a sprinkling of research on close relationships before the 1980s, and while the rest of the field was starting to work through the implications of social cognitive theory, relationships researchers were just finding their footing. Alternative methods used to reduce bias in relationship responses, including experience sampling methods and occasionally priming or time pressure manipulations, were developed before implicit measurement caught on. As social cognitive theory has come to highlight the validity and applicability of implicit measures in different contexts, it has become clear that it is crucially important to use implicit as well as explicit measures to study interpersonal processes. Time pressure, fatigue, distraction, and multitasking are real-world manipulations of high cognitive load, creating contexts in which automatic responses are less likely to be overridden by controlled responses. As it turns out, these are often the contexts in which we pursue interpersonal interaction. For example, interactions with one’s closest others are often relegated to the end of the day when self-Â�regulation stores are running low (Baumeister, Heatherton, & Tice 1994). To the extent that implicit measures give us a window into automatic processes, such measures reveal how relationship phenomena likely operate in vivo. Implicit measures may indicate what people do when they are not on their best behavior. This kind of second-Â�generation question, which rests on automatic versus controlled and impulsive versus

440

IV. DOMAIN-SPECIFIC PERSPECTIVES

reflective (Strack & Deutsch, 2004) distinctions, raises important issues, particularly when implicit and explicit factors are in opposition (see, e.g., recent work by Murray et al., 2008, regarding the influence of self-�esteem on implicit and explicit relationship judgments). Implicit measures are also valuable for advancing theoretical development in interpersonal relationships. For example, the macromotives model (Holmes, 1981) suggested that individuals develop a broad sense of trust, commitment, and satisfaction that operates at an implicit level, regulating relationships such that individuals do not need to consciously evaluate day-to-day microbehaviors. However, Holmes suggested that implicit satisfaction and trust can be eroded, and acute conscious considerations of these macroconstructs will trigger careful examination of discrete behaviors. Implicit measures could provide a means to test the macromotive model by assessing implicit relationship satisfaction repeatedly and examining declines in implicit satisfaction as a potential harbinger for microbehavioral scrutinizing of the relationship in ways that likely will result in subsequent declines in explicit satisfaction. The theoretical formulations of other models could be fruitfully examined with implicit measures as well. Implicit measures may also help to tease apart theoretically distinct constructs such as satisfaction, commitment, and trust, which are highly correlated in self-�reports about relationships. A good deal of the overlap in such variables is likely genuine, but theory and research suggest that sometimes generally positive feelings about a relationship may be acting as a heuristic guide to self�reports about more specific relationship qualities. To date, researchers have focused more on the independent variable side to interrupt such processing and tease apart self-�reports of satisfaction and commitment (Gagne & Lydon, 2001), but new advances in implicit measures provide a means to investigate more directly the underlying cognitions and examine, for example, the association between implicit satisfaction and commitment.

Some Future Directions for the Measurement of Implicit Processes in the Relationship Context In our review we tried to survey a fairly broad set of findings, but at the same time we limited our scope to the types of approaches that are standard in the measurement of implicit processes such as IATs, reaction time measures, and so forth. When one looks farther afield, there is an almost unlimited

range of methods one could apply to this question. For example, physiological indicators hold great promise, as demonstrated early on by Levenson and Gottman’s (1983, 1985) work on reactions to conflict, by research using thermography to assess sexual arousal, and by studies of cortisol, oxytocin, or testosterone release in interpersonal contexts. Of course, social neuroscientists are using functional magnetic resonance and other imaging techniques to reveal patterns of brain activation that correlate with certain relationship thoughts (e.g., Gillath, Bunge, Shaver, Wendelken, & Mikulincer, 2005; Zilber, Goldstein, & Mikulincer, 2007), and this approach is likely to yield powerful implicit measurement techniques in the not too distant future. Unintended emotional expressions and behavioral responses can also provide a window into implicit processes. For example, Andersen and colleagues (Andersen, Reznik, & Manzella, 1996; see also Berenson & Andersen, 2006) surreptitiously videotaped participants’ spontaneous emotional expressions while exposing them to transferenceÂ�related material. In several studies, these researchers were able to find evidence of schema-Â�triggered affect, such that participants’ facial expressions revealed more positivity when they read about alleged characteristics of a novel person that were similar to those of a positively valued significant other. Similarly, unintended mimicry can serve as an implicit measure of interpersonal attraction: Simply witnessing the behavior of another person, particularly if a goal of affiliation is activated, tends to increase the likelihood of spontaneously mimicking that person’s behavior (Lakin & Chartrand, 2003). However, this response can be tempered by commitment: Merely being in an intimate relationship decreases the degree to which a person mimics an attractive person of the opposite sex. Future research will benefit from coordinated efforts to use behavioral, physiological, reaction time, and other complementary approaches to triangulate on the implicit processes occurring in relation to interpersonal experience. People think about not just self and other but also self-with-other, and implicit measures are gradually unveiling this interpersonal foundation of social cognition. Acknowledgments Preparation of this chapter was supported by grants from the Social Science and Humanities Research Council of Canada and the Fonds de Recherche sur la Société et la Culture to Mark W. Baldwin and John E. Lydon.



23. Measuring Implicit Processes in Close Relationships

Note 1. De Houwer (2006) offers a detailed analysis of the use of the term implicit measures. He outlines several characteristics typically thought to be associated with implicit measures, such as the participant not understanding the purpose of the measure, the participant being unable to control the outcome of the measurement procedure, and the participant indeed not having conscious access to the process being measured. He argues convincingly that not all instances of “implicit measures,” in fact, conform to all possible meanings of the term. We concur with his analysis, but given that research in this literature is still in its relative infancy we have decided to adopt an inclusive approach and leave to future research the task of specifying exactly which meanings of “implicit” apply to which measurement approaches.

References Agnew, C. R., Van Lange, P. A. M., Rusbult, C. E., & Langston, C. A. (1998). Cognitive interdependence: Commitment and the mental representation of close relationships. Journal of Personality and Social Psychology, 74, 939–954. Ames, D. R. (2008). Assertiveness expectancies: How hard people push depends on the consequences they predict. Journal of Personality and Social Psychology, 95, 1541–1557. Andersen, S. M., & Berk, M. S. (1998). The socialÂ�cognitive model of transference: Experiencing past relationships in the present. Current Directions in Psychological Science, 7, 1–7. Andersen, S. M., Reznik, I., & Manzella, L. M. (1996). Eliciting transient affect, motivation, and expectancies in transference: Significant-other representations and the self in social relations. Journal of Personality and Social Psychology, 71, 1108–1129. Aron, A., Aron, E. N., Tudor, M., & Nelson, G. (1991). Close relationships as including other in the self. Journal of Personality and Social Psychology, 60, 241– 253. Aron, A., & Fraley, B. (1999). Relationship closeness as including other in the self: Cognitive underpinnings and measures. Social Cognition, 17, 140–160. Ayduk, O., Downey, G., Testa, A., Yen, Y., & Shoda, Y. (1999). Does rejection elicit hostility in rejection sensitive women? Social Cognition, 17, 245–271. Baldwin, M. W. (1992). Relational schemas and the processing of social information. Psychological Bulletin, 112, 461–484. Baldwin, M. W., Baccus, J. R., & Fitzsimons, G. M. (2004). Self-Â�esteem and the dual processing of interpersonal contingencies. Self and Identity, 3, 81–93. Baldwin, M. W., Carrell, S. E., & Lopez, D. F. (1990). Priming relationship schemas: My advisor and the Pope are watching me from the back of my mind. Journal of Experimental Social Psychology, 26, 435– 454.

441

Baldwin, M. W., Fehr, B., Keedian, E., Seidel, M., & Thomson, D. W. (1993). An exploration of the relational schemata underlying attachment styles: SelfÂ�report and lexical decision approaches. Personality and Social Psychology Bulletin, 19, 746–754. Baldwin, M. W., Keelan, J. P. R., Fehr, B., Enns, V., & Koh-Â�Rangarajoo, E. (1996). Social-Â�cognitive conceptualization of attachment working models: Availability and accessibility effects. Journal of Personality and Social Psychology, 71, 94–109. Baldwin, M. W., & Sinclair, L. (1996). Self-Â�esteem and “if .â•›.â•›. then” contingencies of interpersonal acceptance. Journal of Personality and Social Psychology, 71, 1130–1141. Banse, R. (1999). Automatic evaluation of self and significant others: Affective priming in close relationships. Journal of Social and Personal Relationships, 16, 803–821. Banse, R., & Kowalick, C. (2007). Implicit attitudes towards romantic partners predict well-being in stressful life conditions: Evidence from the antenatal maternity ward. International Journal of Psychology, 42, 149–157. Bargh, J. A. (1989). Conditional automaticity: Varieties of automatic influence in social perception and cognition. In J. S. Uleman & J. A. Bargh (Eds.), Unintended thought (pp.€3–51). New York: Guilford Press. Bargh, J. A., Raymond, P., Pryor, J. B., & Strack, F. (1995). Attractiveness of the underling: An automatic power → sex association and its consequences for sexual harassment and aggression. Journal of Personality and Social Psychology, 68, 768–781. Bartholomew, K. (1990). Avoidance of intimacy: An attachment perspective. Journal of Social and Personal Relationships, 7, 147–178. Bartz, J. A., & Lydon, J. E. (2004). Close relationships and the working self-Â�concept: Implicit and explicit effects of priming attachment on agency and communion. Personality and Social Psychology Bulletin, 30, 1389–1401. Bartz, J. A., & Lydon, J. E. (2006). Navigating the interdependence dilemma: Attachment goals and the use of communal norms with potential close others. Journal of Personality and Social Psychology, 91, 77–96. Baumeister, R. F., Heatherton, T. F., & Tice, D. M. (1994). Losing control: How and why people fail at selfÂ�regulation. San Diego, CA: Academic Press. Berenson, K. R., & Andersen, S. M. (2006). Childhood physical and emotional abuse by a parent: Transference effects in adult interpersonal relations. Personality and Social Psychology Bulletin, 32, 1509–1522. Berenson, K. R., Gyurak, A., Ayduk, O., Downey, G., Garner, M. J., Mogg, K., et al. (2009). Rejection sensitivity and disruption of attention by social threat cues. Journal of Research in Personality, 43, 1064– 1072. Berk, M. S., & Andersen, S. M. (2000). The impact of past relationships on interpersonal behavior: Behavioral confirmation in the social-Â�cognitive process of transference. Journal of Personality and Social Psychology, 79, 546–562. Berscheid, E. (1994). Interpersonal relationships. Annual Review of Psychology, 45, 79–129.

442

IV. DOMAIN-SPECIFIC PERSPECTIVES

Birnie, C., & Lydon, J. E. (2009). What is sexual intimacy?: A prototype approach to understanding sexual intimacy through its relationship to intimacy. Unpublished manuscript. Bowlby, J. (1969). Attachment and loss: Vol. 1. Attachment. New York: Basic Books. Bretherton, I. (1990). Communication patterns, internal working models, and the intergenerational transmission of attachment relationships. Infant Mental Health Journal, 11, 237–252. Cooper, R. M., Rowe, A. C., Penton-Voak, I. S., & Ludwig, C. (2009). No reliable effects of emotional facial expression, adult attachment orientation, or anxiety on the allocation of visual attention in the spatial cueing paradigm. Journal of Research in Personality, 43, 643–652. Cross, S. E., Morris, M. L., & Gore, J. S. (2002). Thinking about oneself and others: The relationalÂ�interdependent self-Â�construal and social cognition. Journal of Personality and Social Psychology, 82, 399– 418. Dandeneau, S. D., & Baldwin, M. W. (2004). The inhibition of socially rejecting information among people with high versus low self-Â�esteem: The role of attentional bias and the effects of bias reduction training. Journal of Social and Clinical Psychology, 23, 584–603. Dandeneau, S. D., Baldwin, M. W., Baccus, J. R., Sakellaropoulo, M., & Pruessner, J. C. (2007). Cutting stress off at the pass: Reducing vigilance and responsiveness to social threat by manipulating attention. Journal of Personality and Social Psychology, 93, 651–666. De Houwer, J. (2006). What are implicit measures and why are we using them? In R. W. Wiers & A. W. Stacy (Eds.), The handbook of implicit cognition and addiction (pp.€11–28). Thousand Oaks, CA: Sage. DeHart, T., Pelham, B., & Murray, S. (2004). Implicit dependency regulation: Self-Â�esteem, relationship closeness, and implicit evaluations of close others. Social Cognition, 22, 126–146. DeWall, C. N., Maner, J. K., & Rouby, D. A. (2009). Social exclusion and early-stage interpersonal perception: Selective attention to signs of acceptance. Journal of Personality and Social Psychology, 96, 729–741. Dewitte, M., & De Houwer, J. (2008). Proximity and distance goals in adult attachment. European Journal of Personality, 22, 675–694. Dewitte, M., De Houwer, J., Buysse, A., & Koster, E. H. W. (2008). Proximity seeking in adult attachment: Examining the role of automatic approach-Â�avoidance tendencies. British Journal of Social Psychology, 47, 557–573. Dewitte, M., Koster, E. H. W., De Houwer, J., & Buysse, A. (2007). Attentive processing of threat and adult attachment: A dot-probe study. Behaviour Research and Therapy, 45, 1307–1317. Downey, G., & Feldman, S. I. (1996). Implications of rejection sensitivity for intimate relationships. Journal of Personality and Social Psychology, 70, 1327–1343. Downey, G., Mougios, V., Ayduk, O., London, B. E., & Shoda, Y. (2004). Rejection sensitivity and the defensive motivational system. Psychological Science, 15, 668–673.

Edelstein, R. S., & Gillath, O. (2008). Avoiding interference: Adult attachment and emotional processing biases. Personality and Social Psychology Bulletin, 34, 171–181. Etcheverry, P., & Le, B. (2005). Thinking about commitment: Accessibility of commitment and prediction of relationship persistence, accommodation, and willingness to sacrifice. Personal Relationships, 12, 103–123. Fazio, R. H. (1995). Attitudes as object-Â�evaluation associations: Determinants, consequences, and correlates of attitude accessibility. In R. E. Petty & J. A. Krosnick (Eds.), Attitude strength: Antecedents and consequences (pp.€247–282). Hillsdale, NJ: Erlbaum. Fehr, B. (2004). A prototype model of intimacy in samesex friendships. In D. J. Mashek & A. Aron (Eds.), Handbook of closeness and intimacy (pp.€9–26). Mahwah, NJ: Erlbaum. Fehr, B., Baldwin, M., Collins, L., Patterson, S., & Benditt, R. (1999). Anger in close relationships: An interpersonal script analysis. Personality and Social Psychology Bulletin, 25, 299–312. Fishbach, A., Friedman, R. S., & Kruglanski, A. W. (2003). Leading us not unto temptation: Momentary allurements elicit overriding goal activation. Journal of Personality and Social Psychology, 84, 296–309. Fitzsimons, G. M., & Bargh, J. A. (2003). Thinking of you: Nonconscious pursuit of interpersonal goals associated with relationship partners. Journal of Personality and Social Psychology, 84, 148–164. Fitzsimons, G. M., & Shah, J. Y. (2008). How goal instrumentality shapes relationship evaluations. Journal of Personality and Social Psychology, 95, 319–337. Fletcher, G. J. O., & Fincham, F. D. (1991). Cognition in close relationships. Hillsdale, NJ: Erlbaum. Fletcher, G. J. O., & Fitness, J. (Eds.). (1996). Knowledge structures in close relationships: A social psychological approach. Mahwah, NJ: Erlbaum. Fraley, R. C., & Shaver, P. R. (1997). Adult attachment and the suppression of unwanted thoughts. Journal of Personality and Social Psychology, 73, 1080–1091. Gagne, F. M., & Lydon, J. E. (2001). Mindset and close relationships: When bias leads to (in)accurate predictions. Journal of Personality and Social Psychology, 81, 85–96. Gillath, O., Bunge, S. A., Shaver, P. R., Wendelken, C., & Mikulincer, M. (2005). Attachment-style differences in the ability to suppress negative thoughts: Exploring the neural correlates. NeuroImage, 28, 835–847. Gillath, O., Mikulincer, M., Birnbaum, G. E., & Shaver, P. R. (2007). Does subliminal exposure to sexual stimuli have the same effects on men and women? Journal of Sex Research, 44, 111–121. Gillath, O., Mikulincer, M., Fitzsimons, G. M., Shaver, P. R., Schachner, D. A., & Bargh, J. A. (2006). Automatic activation of attachment-Â�related goals. Personality and Social Psychology Bulletin, 32, 1375–1388. Glassman, N. S., & Andersen, S. M. (1999). Activating transference without consciousness: Using significant-other representations to go beyond subliminally given information. Journal of Personality and Social Psychology, 77, 1146–1162. Gollwitzer, P. M. (1999). Implementation intentions. American Psychologist, 54, 493–503.



23. Measuring Implicit Processes in Close Relationships

Hazan, C., & Shaver, P. R. (1987). Romantic love conceptualized as an attachment process. Journal of Personality and Social Psychology, 52, 511–524. Hill, C. R., & Safran, J. D. (1994). Assessing interpersonal schemas: Anticipated responses of significant others. Journal of Social and Clinical Psychology, 13, 366–379. Holmes, J. G. (1981). The exchange process in close relationships: Microbehavior and macromotives. In M. J. Lerner & S. C. Lerner (Eds.), The justice motive in social behavior (pp.€ 184–261). New York: Plenum Press. James, W. (1890). The principles of psychology. New York: Holt. Janssen, E., Everaerd, W., Spiering, M., & Janssen, J. (2000). Automatic processes and the appraisal of sexual stimuli: Toward an information processing model of sexual arousal. Journal of Sex Research, 37, 8–23. Kelley, H. H. (1979). Personal relationships: Their structures and processes. Hillsdale, NJ: Erlbaum. Lakin, J. L., & Chartrand, T. L. (2003). Using nonconscious behavioral mimicry to create affiliation and rapport. Psychological Science, 14, 334–339. Leary, M. R., & Baumeister, R. F. (2000). The nature and function of self-Â�esteem: Sociometer theory. Advances in Experimental Social Psychology, 32, 2–51. Levenson, R. W., & Gottman, J. M. (1983). Marital interaction: Physiological linkage and affective exchange. Journal of Personality and Social Psychology, 45, 587–597. Levenson, R. W., & Gottman, J. M. (1985). Physiological and affective predictors of change in relationship satisfaction. Journal of Personality and Social Psychology, 49, 85–94. Lindgren, K. P., George, W. H., & Shoda, Y. (2007). Sexual intent perceptions: The role of perceiver experience and the real-Â�person reduction. Journal of Applied Social Psychology, 37, 346–369. Lydon, J. E., Menzies-Toman, D., Burton, K., & Bell, C. (2008). If–then contingencies and the differential effects of the availability of an attractive alternative on relationship maintenance for men and women. Journal of Personality and Social Psychology, 95, 50–65. Maner, J. K., Gailliot, M. T., & Miller, S. L. (2009). The implicit cognition of relationship maintenance: Inattention to attractive alternatives. Journal of Experimental Social Psychology, 45, 174–179. Maner, J. K., Gailliot, M. T., Rouby, D. A., & Miller, S. L. (2007). Can’t take my eyes off you: Attentional adhesion to mates and rivals. Journal of Personality and Social Psychology, 93, 389–401. Maner, J. K., Rouby, D. A., & Gonzaga, G. C. (2008). Automatic inattention to attractive alternatives: The evolved psychology of relationship maintenance. Evolution and Human Behavior, 29, 343–349. McKenzie-Mohr, D., & Zanna, M. P. (1990). Treating women as sexual objects: Look to the (gender schematic) male who has viewed pornography. Personality and Social Psychology Bulletin, 16, 296–308. Mikulincer, M. (1998a). Adult attachment style and individual differences in functional versus dysfunctional experiences of anger. Journal of Personality and Social Psychology, 74, 513–524.

443

Mikulincer, M. (1998b). Attachment working models and the sense of trust: An exploration of interaction goals and affect regulation. Journal of Personality and Social Psychology, 74, 1209–1224. Mikulincer, M., Birnbaum, G., Woddis, D., & Nachmias, O. (2000). Stress and accessibility of proximityÂ�related thoughts: Exploring the normative and intraindividual components of attachment theory. Journal of Personality and Social Psychology, 78, 509– 523. Mikulincer, M., Dolev, T., & Shaver, P. R. (2004). Attachment-Â�related strategies during thought suppression: Ironic rebounds and vulnerable selfÂ�representations. Journal of Personality and Social Psychology, 87, 940–956. Mikulincer, M., Gillath, O., & Shaver, P. R. (2002). Activation of the attachment system in adulthood: Threat-Â�related primes increase the accessibility of mental representations of attachment figures. Journal of Personality and Social Psychology, 83, 881–895. Mikulincer, M., & Shaver, P. R. (2003). The attachment behavioral system in adulthood: Activation, psychodynamics, and interpersonal processes. In M. Zanna (Ed.), Advances in experimental social psychology (Vol. 35, pp.€52–153). New York: Academic Press. Mikulincer, M., & Shaver, P. R. (2007). Boosting attachment security to promote mental health, prosocial values, and inter-group tolerance. Psychological Inquiry, 18, 139–156. Mogg, K., & Bradley, B. P. (1998). A cognitiveÂ�motivational analysis of anxiety. Behaviour Research and Therapy, 36, 809–848. Murphy, S. T., & Zajonc, R. B. (1993). Affect, cognition, and awareness: Affective priming with optimal and suboptimal stimulus exposures. Journal of Personality and Social Psychology, 64, 723–739. Murray, S. L., Derrick, J. L., Leder, S., & Holmes, J. G. (2008). Balancing connectedness and self-Â�protection goals in close relationships: A levels-of-Â�processing perspective on risk regulation. Journal of Personality and Social Psychology, 94, 429–459. Murray, S. L., & Holmes, J. G. (2008). The commitmentÂ�insurance system: Self-Â�esteem and the regulation of connection in close relationships. Advances in Experimental Social Psychology, 40, 1–60. Murray, S. L., Holmes, J. G., & Collins, N. L. (2006). Optimizing assurance: The risk regulation system in relationships. Psychological Bulletin, 132, 641–666. Nosek, B. A. (2005). Moderators of the relationship between implicit and explicit evaluation. Journal of Experimental Psychology General, 134, 565–584. Olson, J. M., Roese, N. J., & Zanna, M. P. (1996). Expectancies. In E. T. Higgins & A. W. Kruglanski (Eds.), Social psychology: Handbook of basic principles (pp.€211–238). New York: Guilford Press. Perunovic, M., & Holmes, J. G. (2008). Automatic accommodation: The role of personality. Pain Medicine, 15, 57–70. Pierce, T., & Lydon, J. E. (1998). Priming relational schemas: Effects of contextually activated and chronically accessible interpersonal expectations on responses to a stressful event. Journal of Personality and Social Psychology, 75, 1441–1448. Rudman, L. A., & Borgida, E. (1995). The afterglow of

444

IV. DOMAIN-SPECIFIC PERSPECTIVES

construct accessibility: The behavioral consequences of priming men to view women as sexual objects. Journal of Experimental Social Psychology, 31, 493–517. Rudman, L. A., & Heppen, J. B. (2003). Implicit romantic fantasies and women’s interest in personal power: A glass slipper effect? Personality and Social Psychology Bulletin, 29, 1357–1370. Scinta, A., & Gable, S. L. (2007). Automatic and selfÂ�reported attitudes in romantic relationships. Personality and Social Psychology Bulletin, 33, 1008–1022. Shah, J. (2003a). Automatic for the people: How representations of significant others implicitly affect goal pursuit. Journal of Personality and Social Psychology, 84, 661–681. Shah, J. (2003b). The motivational looking glass: How significant others implicitly affect goal appraisals. Journal of Personality and Social Psychology, 85, 424–439. Smith, E. R., Coats, S., & Walling, D. (1999). Overlapping mental representations of self, in-group, and partner: Further response time evidence and a connectionist model. Personality and Social Psychology Bulletin, 25, 873–882. Smith, E. R., & DeCoster, J. (2000). Dual-Â�process Â�models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4, 108–131. Snowden, R. J., Wichter, J., & Gray, N. S. (2008). Implicit and explicit measurements of sexual preference in gay and heterosexual men: A comparison of prim-

ing techniques and the Implicit Association Task. Archives of Sexual Behavior, 37, 558–565. Snyder, M., & Swann, W. B. (1978). Hypothesis-Â�testing processes in social interaction. Journal of Personality and Social Psychology, 36, 1202–1212. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Sullaway, M., & Christensen, A. (1983). Assessment of dysfunctional interaction patterns in couples. Journal of Marriage and Family, 45, 653–660. Whitaker, D. J., Beach, S. R. H., Etherton, J., Wakefield, R., & Anderson, P. L. (1999). Attachment and expectations about future relationships: Moderation by accessibility. Personal Relationships, 6, 41–56. Yovetich, N. A., & Rusbult, C. E. (1994). Accommodative behavior in close relationships: Exploring transformation of motivation. Journal of Experimental Social Psychology, 30, 138–164. Zayas, V., & Shoda, Y. (2005). Do automatic reactions elicited by thoughts of romantic partner, mother, and self relate to adult romantic attachment? Personality and Social Psychology Bulletin, 31, 1011–1025. Zilber, A., Goldstein, A., & Mikulincer, M. (2007). Adult attachment orientations and the processing of emotional pictures: ERP correlates. Personality and Individual Differences, 7, 1898–1907. Zurbriggen, E. L. (2000). Social motives and cognitive power-sex associations: Predictors of aggressive sexual behavior. Journal of Personality and Social Psychology, 78, 559–581.

C h a p t e r 24

Divided Minds, Divided Morals How Implicit Social Cognition Underpins and€Undermines Our Sense of Social Justice B. Keith Payne and C. Daryl Cameron

Virtue is a state of war, and to live in it we have always to combat with ourselves. —Jean-Jacques Rousseau

O

f all the reasons people study implicit social cognition, a concern with its implications for social justice probably ranks near the top. The message of implicit social cognition is that the thoughts people introspect and report about do not tell the whole story of why they believe the things they believe and why they do the things they do. Instead, many studies have found that implicit reactions are prone to prejudice and stereotyping, even when explicit responses are fair-Â�minded. Implicit reactions are often hierarchical, dividing the world into “us” and “them,” even when explicit responses are inclusive. Implicit reactions are impulsive, even when explicit responses are calculated. Implicit cognition can be, to paraphrase Hobbes, nasty, brutish, and short-Â�sighted. If implicit cognition is a driving force behind our personal choices and public actions, then it offers a new way to understand many puzzles in modern social life. Why do people perpetuate inequalities when they apparently mean to be fair? Why do people on the other side of culture war debates seem so certainly wrong, yet so wrongly certain? In this chapter, we survey empirical research in implicit social cognition with a view toward what it means for questions of social justice and moral con-



cerns. We begin by examining empirical studies of implicit prejudice, which have clear implications for social inequality and social justice. We then consider a broader set of phenomena, in which implicit social cognition shapes the very judgments of what is just and moral in the first place. We end by considering the ways that implicit social cognition can shed light on processes leading to both just and unjust outcomes and the role of empirical research in addressing these complex and value-laden questions.

IMPLICIT PREJUDICE AND€SOCIAL JUSTICE Research on implicit prejudice has advanced quickly. This fast pace has sparked lively debate over methods and interpretations of the research. We focus here on interpretations and conclusions that can be drawn from this research as opposed to methodological issues. Methodological questions are addressed at length in other chapters in this volume (De Houwer & Moors, Chapter 10; Teige-�Mocigemba, Klauer, & Sherman, Chapter 7; Sekaquaptewa, Vargas, & von Hippel, Chapter 8; and Wentura & Degner, Chapter 6). 445

446

IV. DOMAIN-SPECIFIC PERSPECTIVES

Subtle Behavioral Effects of€Implicit€Bias Early studies demonstrated that implicit measures were associated with certain specific aspects of behavior in cross-race interactions (Dovidio, Kawakami, & Gaertner, 2002; Fazio, Jackson, Dunton, & Williams, 1995; McConnell & Leibold, 2001). For instance, implicit tests completed by white subjects were shown to predict their nonverbal microbehaviors toward a black interaction partner. These microbehaviors included, for example, eye contact, seating distance, and speech errors. Explicit measures of race attitudes did not predict these behaviors but instead predicted the favorability of explicit ratings about the interaction partner. Consider a scenario based on this research. John is white and James is black, and the two interact with the best of intentions. It turns out that John is implicitly biased. His bias reveals itself through subtle microbehaviors such as poor eye contact, more blinking, speech errors, and less nonverbal friendliness. John is not fully aware of these behaviors, but James picks up on them and judges John to be less friendly and trustworthy because of them. If John has a stereotype that blacks are aggressive, it might subtly influence his behavior toward James, who might respond with his own hostile reaction (Chen & Bargh, 1997). Because John is unaware of the ways in which implicit stereotypes influence nonverbal behavior, he will perceive the hostile behavior from James as coming from nowhere, and this will further solidify the behavioral stereotype about blacks being aggressive (Word, Zanna, & Cooper, 1974). This kind of self-�fulfilling prophecy suggests that microbehaviors toward outgroup members can cumulatively build a hostile intergroup environment. Because of poor introspection over attitudes and behavior, people will feel as if they have acted objectively and free from bias, and any challenge to that perception will only increase antagonism (Kennedy & Pronin, 2008). Aside from building distrust and hostility, this environment may harm the performance of those who are biased against. Salvatore and Shelton (2007) had both black and white participants read resumes for fictional job applicants (some of whom were black), followed by a decision and justification by a human resources officer. There were three kinds of decisions: nondiscriminatory, ambiguous (officer chose a lower-qualified ingroup member over a higher-qualified outgroup member but did not specify reasons), and blatant

(same choice but race was given as the reason). On a Stroop task, which involves using controlled cognitive resources to inhibit the effect of word color on naming color words, white participants performed the worst when exposed to blatant prejudice, whereas black participants performed the worst when exposed to ambiguous prejudice. This finding suggests that hostile environments clouded by implicit bias predict poorer cognitive performance among minority members. This finding is reminiscent of the large literature on stereotype threat. In such studies, black students often perform worse than white students on a test when it is said to be diagnostic of intelligence and when racial group membership is made salient (Steele, 1997). This research suggests that, to the extent that an environment is made hostile through minority members’ awareness of bias around them, environment might, in turn, confirm the very stereotypes that feed the biases. Kang and Banaji (2006) have argued that studies like these throw doubt on a truly objective criterion for merit in the workplace. In light of this, a number of studies have investigated how people make decisions based on “objective” criteria like merit, only to mask their subjective biases. Norton, Vandello, and Darley (2004) investigated the degree to which people would justify hiring a man over a woman for a stereotypically male job (construction foreman). Among a pack of resumes, two stood out as the best but on different dimensions: One candidate had more education than experience, the other more experience than education. These two resumes were randomly assigned to either male or female candidates. Participants selected male candidates the majority of the time. When asked to justify their decisions, participants in all but the female-Â�highlyeducated condition appealed to the education qualification. In this last condition, participants appealed to the experience qualification instead. Subsequent studies suggested that this public casuistry might extend into private consciousness as well because casuistry-based decisions were shown to impair memory of the very criteria used to make the decision. Together, these studies make some striking claims about the ways that subtle automatic biases can pose a challenge for social justice. First, implicit bias predicts microlevel behaviors that leak out during interracial interactions. These behaviors tend to be perceived by observers, potentially feeding a cycle of behavioral confirmation and intergroup conflict. Second, an environment perceived as biased leads to performance deficits



24. Social Justice Implications

in terms of reduced cognitive performance and stereotype threat. Finally, when actions and decisions are driven by subtle biases, they may be masked using objective criteria, flexibly deployed to rationalize the decision. The foregoing research has been used by some psychologists and legal scholars to push for large-scale institutional and legal reform (Kang, 2005; Kang & Banaji, 2006; Krieger & Fiske, 2006; Lane, Kang, & Banaji, 2007). However, these conclusions have not gone unchallenged. We next consider some critiques of the implicit bias literature in light of whether it really supports conclusions that challenge social justice. Then we discuss recent research that has been stimulated, in part by this critique, to examine behaviors with greater external validity and direct societal implications.

Should We Call It Prejudice? Some scholars have argued that as researchers have probed for more and more subtle forms of bias, they have set too low a threshold for what counts as prejudice. Arkes and Tetlock (2004; see also Mitchell & Tetlock, 2007) raised a number of criticisms of implicit bias research on both methodological and conceptual grounds. Some of these criticisms were directed specifically at the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) and may not apply to the broader literature on implicit bias, which has used multiple methods. We focus here on those criticisms that directly concern whether demonstrations of implicit bias should be interpreted to reveal prejudice and whether those demonstrations are relevant for social justice. One criticism is that implicit biases in which minorities are more easily associated with negative than positive concepts could result not only from disliking the minority group but also from other affective reactions that would not count as prejudice. For example, Arkes and Tetlock (2004) point out that an implicit test might detect bias against blacks if the respondent simply feels nervous about interacting with an outgroup member or feels bad about historical injustice toward black Americans. Indeed, Uhlmann, Brescoll, and Paluck (2006) found that both dislike for a group and feelings of sympathy for the group’s experience of oppression resulted in significant implicit bias on an IAT. If apparent implicit bias can result from both dislike and sympathy, then there is indeed some ambiguity that needs to be resolved. Yet at first blush this critique only makes sense given a theory of emotions that allows for distinct emotions at the automatic level. Some theories suggest that

447

automatic affect is undifferentiated, being qualitatively parsed into discrete emotions only at later stages of processing (Baumeister, Vohs, DeWall, & Zhang, 2007; Russell, 2003). Should such theories be accurate, Arkes and Tetlock’s emotions-based criticism loses a great deal of force. From this perspective, automatic negativity is simply negativity, with no differences that can be identified as “anxiety” versus “hostility.” Presumably, the only basis for judging whether such negativity counts as prejudice would be whether it drives discriminatory treatment, which has already been demonstrated in the studies described previously. Other recent research, however, suggests that discrete automatic emotions can indeed be distinguished. Arbuckle and Payne (2008), for instance, used the affect misattribution procedure (Payne, Cheng, Govorun, & Stewart, 2005) to measure discrete emotional reactions. Participants saw white American or Middle Eastern faces as primes, followed on each trial by a Chinese pictograph. In one phase of the task, participants guessed whether each pictograph meant “fear.” In another phase, they guessed whether each pictograph meant “anger.” The Middle Eastern primes elicited greater fear and anger responses than white American primes. Participants also expressed their opinions about how to deal with American–Â�Iranian tensions over the possibility of Iran developing nuclear weapons. Implicit anger predicted support for offensive military action against Iran, whereas implicit fear predicted nonmilitary defensive action. The critique that multiple emotions may underpin implicit biases is not necessarily a weakness, if researchers realize that multiple emotions may have distinct effects and make an effort to elucidate these effects. In addition to the point about emotions, Arkes and Tetlock (2004) suggest that implicit race biases present no serious ethical challenge. They argue that these biases are not personally endorsed attitudes but rather culturally borne stereotypes that would be activated for any rational person familiar with cultural norms and statistical base rates. How could researchers then attribute the morally loaded term prejudice to a reaction that was not personally endorsed? First, some authors have suggested that whether or not an attitude is personally endorsed is irrelevant if it leads to unjust behavior (Nosek & Hansen, 2008). Moreover, the conceptual boundary of what reflects one’s true self is contestable: Some suggest that what counts as a person’s true self involves only those attitudes that are reflectively endorsed (Frankfurt, 1969), whereas others suggest

448

IV. DOMAIN-SPECIFIC PERSPECTIVES

that the most revealing attitudes are the ones that occur spontaneously and unintentionally (Smith, 2005; see Gawronski, Peters, & LeBel, 2008, for further discussion of this point). Second, even if cultural stereotypes are rational in the sense of being statistically defensible (e.g., using base rates in making social judgments), they might be immoral (Banaji, Nosek, & Greenwald, 2004). Moral claims involve not only whether a belief is statistically grounded but also whether people are harmed by it. The risk of harm for the holder of a stereotyped belief is very different from the risk of harm for the target of that belief. For example, although a police officer who racially profiles motorists might justify differential treatment using base rates, the innocent suspect who is pulled over has his or her own morally relevant considerations, often discussed in terms of rights. The motorist rightfully expects to be treated as an individual based on his or her conduct rather than group stereotypes. The moral relevance from the perspective of the target of prejudice is highlighted by empirical research over the last decade, which in our view has clear and startling implications for social justice. Most conceptions of justice argue that all people are entitled to certain rights derived from their shared humanity. History attests to the link between dehumanization and moral license: Those who are dehumanized are pushed beyond the scope of rights that would preclude the most egregious atrocities (Bandura, 1999). Studies demonstrating links between implicit social cognition and dehumanization provide striking examples of social cognition’s moral relevance. One aspect that distinguishes humans from nonhuman animals is the experience of secondary emotions: those social and self-Â�conscious emotions that tend to be distinctive to human experience. Although many animals may feel fear and rage, for example, it is often presumed that only humans feel guilt or embarrassment. Using an adaptation of the IAT, Leyens and colleagues (2000) found that participants responded faster when ingroup names and secondary emotions were paired than when outgroup names and secondary emotions were paired. Leyens and colleagues interpreted their findings as evidence that people associate outgroup members with less human qualities than ingroup members. Even more directly, Goff, Eberhardt, Williams, and Jackson (2008) recently investigated the implicit association between blacks and apes. To test for the implicit presence of this subjugating metaphor, they primed participants with either black or

white faces and asked them to identify perceptually degraded images of animals as fast as possible. Priming black faces facilitated the recognition of apes, whereas white faces slowed down this recognition. In another study, participants primed with apes on a dot-probe task showed increased attention to black faces as opposed to white faces. Moreover, this effect did not generalize to Asian faces but was specific to black faces. Using an IAT to measure the association between blacks and apes, Goff and colleagues (2008) had participants categorize human names as black or white and animals as apes or big cats. Participants were faster to respond when blacks and apes were paired, and this effect was independent of bias on a traditional evaluative race IAT. Participants also tended to deny explicit knowledge of the black–ape metaphor. Connecting this to real-world moral behavior, participants were more likely to say that police brutality against black (vs. white) suspects was justified after being primed with apes (vs. big cats). Finally, Goff and colleagues performed a content analysis of the media surrounding death penalty–Â�eligible criminal cases mentioned in the Philadelphia Inquirer between 1979 and 1999. Black defendants were described using more ape-like metaphors and imagery, and this trend was associated with higher rates of capital punishment for blacks. Many of the points raised by Arkes and Tetlock (2004) are thus being addressed and incorporated into research on implicit race bias. The critique that various emotional reactions besides antipathy can produce biased scores on implicit measures suggests a valuable caveat in interpreting some implicit bias research. The critique has also been valuable in stimulating new research that distinguishes more carefully between the specific emotional underpinnings of bias. This research shows that multiple emotional responses can have meaningful impacts. Moreover, it shows that some associations revealed by implicit tests go well beyond the simple good–bad evaluations targeted by the critique. It is hard to claim that associations between black Americans and apes are too mild to be considered prejudice. Associations between subtle biases and discriminatory behavior provide evidence that these biases have implications beyond the laboratory. In the next section, we consider more studies that make clear and direct connections to meaningful overt behavior. These studies highlight further the practical importance of implicit social cognition for social justice, by documenting how implicit bias relates to ethically unambiguous outcomes.



24. Social Justice Implications

Less-Than-Â�Subtle Behavioral Effects of€Implicit Bias Implicit Race Bias and Criminal Justice Studies of implicit bias in the laboratory often ask respondents to make snap judgments, as in the speeded responses on implicit attitudes tests. In some cases, these are far removed from the kinds of behaviors these tests are intended to model or predict. However, in other cases, it is the snap judgment itself that is the meaningful behavior. Consider the case of Amadou Diallo, the African immigrant whose wallet was mistaken for a gun by New York police officers. This mistake led to Diallo being shot to death, and subsequent questions revolved around the degree to which any bias on the police officers’ part influenced their perceptions and decisions to shoot. Moral outrage in response to this incident derived from a salient principle of justice: that an individual should be judged on the basis of his conduct, not his social category. In the wake of this incident, a number of studies examined the psychological underpinnings of race biases that could lead to such a mistake. In one study, subjects were asked to identify an object as either a gun or a harmless hand tool (Payne, 2001). Just before seeing each object, participants saw either a black or white face. When making snap judgments, subjects were more likely to make errors in saying that a harmless object was a gun after being primed with a black face. This weapon bias did not depend on intentional bias; in fact, it occurred in spite of intentions to the contrary. Another study assigned subjects one of three explicit goals: Ignore the faces, avoid using race to make judgments, or intentionally use race to make judgments (Payne, Lambert, & Jacoby, 2002). Results showed weapons bias in all conditions but greater bias in the latter two. There was no difference between the avoid-race and use-race conditions despite the difference in intentions. In a similar vein, Correll, Park, Judd, and Wittenbrink (2002) have used a video game simulation in which white and black male individuals (targets) were superimposed on a digital background with either guns or harmless objects in their hands. Participants were instructed to identify whether the target was armed, and hit buttons marked “shoot” or “don’t shoot” as quickly as possible. They found that when the target was not armed, participants mistakenly shot him more often if he was black than white. Moreover, this bias was found even among black participants. Studies examining individual differences in these biases have found that

449

both implicit and explicit measures of racial prejudice (Payne, 2001, 2005) and measures of perceptions of stereotypes in the culture (Correll et al., 2002) were associated with greater weapon bias. Does mere exposure to American cultural associations breed this kind of bias? These studies suggest that one such association is between blacks and criminality. Eberhardt, Goff, Purdie, and Davies (2004) have shown that this conceptual connection works in both directions: Priming black faces makes people think of crime, whereas priming crime makes people think of black individuals. Although many people would disavow the first link, they might be less familiar with the second and be less vigilant against its influence. Highlighting the practical significance of these results, Correll and colleagues (2007) recently used the same methodology among samples of both police officers and civilians. When under no time constraint, police officers were faster, more accurate, and more balanced in their shooting decisions than civilian populations. When the response time window was reduced to force snap decisions, both civilians and police officers were slower to shoot an unarmed white man and faster to shoot an armed black man. Yet police officers were unique in their ability to keep this automatic bias in response time from influencing their decisions to shoot, which remained less biased than for the civilian sample. There were three correlates of the latency effect among police officers: the size of the community patrolled, its crime rate, and its proportion of black civilians. Simply as a function of working within a racially diverse community, police officers might become more biased in their associations with criminality, even if not in their decisions to shoot. An encouraging finding is that practice with shooting simulations and officer firearms training decreased bias in shooting decisions (Correll et al., 2007; Plant & Peruche, 2005; Plant, Peruche, & Butz, 2005). This suggests that as new officers gain experience and training they begin to respond less like civilians and more like experts. Implicit biases may nonetheless influence the decisions of even trained professionals in the criminal justice system. Using a randomly drawn sample of inmate records, Blair, Judd, and Chapleau (2004) found that even though blacks and whites received equivalent sentences given equivalent criminal histories, those inmates with more Afrocentric features (regardless of actual race) received longer sentences. Subsequent studies showed that this kind of bias, in which targets with more Afrocentric features are assigned more stereotypical

450

IV. DOMAIN-SPECIFIC PERSPECTIVES

and negative traits, operates automatically. The bias occurs even under cognitive load, suggesting that it is efficient (Blair, Judd, & Fallman, 2004). When subjects were instructed not to use race in social judgment, their implicit race bias but not Afrocentric feature bias decreased, suggesting that this bias is uncontrollable. Finally, even when given a prior task to rate faces based on Afrocentric features, subjects still could not control against the bias. This finding has stark implications. Because people tend to find the Afrocentric feature bias surprising and counterintuitive, they may be less successful at correcting for its effects than when correcting for more general implicit race bias. The finding that Afrocentric features were associated with longer prison sentences is also striking because these studies examined actual prison sentences, not sentences suggested by research subjects in a mock trial exercise. This suggests that the defendants’ features not only influenced research subjects but also the judges who determined their sentences. The ecological relevance of these findings is highlighted even more dramatically in recent research on disparities in capital punishment. Eberhardt, Davies, Purdie-Â�Vaughns, and Johnson (2006) found that defendants with more Afrocentric features were more likely to be sentenced to death, particularly when the victim of the crime was white. These studies provide converging evidence that the influence of implicit bias can be detected in treatment of individuals in the criminal justice system. Criminal justice is an important arena for examining race bias because it has a long history of discriminatory treatment and the consequences for both inmates and society at large can be severe. However, findings of implicit bias are by no means limited to the criminal justice system. Several studies have found that implicit measures of bias are associated with discrimination in employment decisions.

Implicit Bias and Employment Discrimination A key motivation for developing implicit measures of bias was the observation that self-�reported prejudice has steadily declined for decades, whereas evidence of discrimination persists (Schuhman, Steeh, Bobo, & Kyrsan, 1997; Sniderman & Carmines, 1997). The best evidence for persistent discrimination comes from field studies in which testers of different races are matched on relevant features and sent to do things like rent apartments, buy cars, or apply for jobs (for a review, see Blank, Dabady, & Citro, 2004). For example, Bertrand

and Mullainathan (2004) sent resumes to more than 1,300 help-Â�wanted advertisements in Boston and Chicago. They manipulated whether the applicants were highly qualified or not and whether the names on the resumes implied that the applicant was black or white. They then measured the rate of callbacks, finding that white applicants received more calls than black applicants. Specifically, white applicants were called back in 10.08% of cases, whereas black applicants were called in only 6.79%, with the difference of 3.29% representing an increase in odds of 50% for white over black applications. Moreover, qualifications mattered for white applicants but not black applicants. Such findings, paired with very low rates of explicitly endorsed hiring discrimination, suggest that discrimination may be driven by implicit biases. However, these studies cannot directly test the link between implicit bias and hiring discrimination, a question that has been pursued in laboratory studies. Ziegert and Hanges (2005) simulated hiring decisions among college participants. Cast in the role of rating job candidates, participants were put into either a climate for bias or a neutral climate. Climate was manipulated by the presence or absence of a memo from the “president” of the organization encouraging racial discrimination to preserve organizational stability. Implicit bias as measured by the IAT predicted discriminatory ratings of black job candidates but only when a climate for bias had been established. The social justice implications of implicit cognition are not limited to race. Other studies have taken different approaches to study the link between implicit gender bias and employment decisions. Rudman and Glick (2001) had participants evaluate female job applicants and complete a gender-Â�stereotype IAT measuring implicit associations pairing men and women with either communality or agency. Rather than directly influencing employability ratings, implicit stereotypes of women as communal and men as agentic reduced perceived social skills of agentic women who were applying for “feminized” jobs. This bias, in turn, mediated hiring discrimination for feminized jobs. In short, implicit biases appear to subtly influence employment decisions. Demonstrations of implicit bias in criminal justice and employment domains have been joined lately by studies suggesting that implicit bias may play a role in health care disparities as well.

Implicit Bias and Healthcare The field of medicine strives to be fair and sympathetic to all its patients. Yet it seems possible that



24. Social Justice Implications

implicit social cognition will motivate frustration with “difficult” patients. One such stigmatized group is chronic drug users. In one recent study of health care, von Hippel, Brener, and von Hippel (2008) asked drug and alcohol treatment nurses to report their explicit attitudes toward injecting drug users, their job-Â�related stress, and intent to change jobs. Nurses also completed a Single-Â�Category IAT measuring implicit negative attitudes toward drug users. Implicit bias, but not explicit prejudice, mediated the relationship between job stress and intentions to change jobs. One other study has documented effects of implicit bias on health care decisions. Green and colleagues (2007) had emergency and internal medicine residents read a vignette describing a black or white patient’s symptoms, which included chest pains and a test result suggestive of a heart attack. Residents judged the likelihood of coronary artery disease, whether they would treat it with thrombolysis (a method using drugs to dissolve blood clots), and if so how strongly they felt. Residents also completed three IATs: an evaluative race IAT, an IAT measuring general stereotypes about blacks being uncooperative, and an IAT measuring specific stereotypes about blacks being medically uncooperative. The first finding of interest was that all the IAT measures revealed implicit bias against black patients among physicians, and these implicit biases were stronger than self-Â�reported explicit biases toward black patients. Second, and not accounting for implicit bias, physicians were more likely to diagnose black patients than white patients with coronary artery disease yet were equally likely to give thrombolysis as a treatment, suggesting a disparity in treatment choice. Most important, implicit bias (as either the race IAT or a composite of the three IATs) predicted the decision to give thrombolysis, even after accounting for explicit prejudice, demographic variables, and belief in treatment efficacy. Most interesting was the interaction of implicit bias and patient race. Doctors who were low in implicit bias gave thrombolysis more often to black than to white patients; yet those who were high in implicit bias gave it to black and white patients about equally. Although black patients were diagnosed more often with heart disease, doctors high in implicit bias did not recommend potentially life-Â�saving interventions that would track this demographic pattern. Still, the specific pattern of findings in this study also raises new questions. As noted, doctors high in implicit race bias treated white and black patients nearly equivalently. In contrast, doctors lowest in implicit bias were more likely to prescribe throm-

451

bolytic treatment to black patients than whites. So even though implicit bias accounted for treatment decision above and beyond other variables, the meaning of the correlation between IAT scores and treatment decisions remains ambiguous. More research is needed to understand whether the kinds of bias detected in this study contribute to explaining well-�documented disparities in health care between black and white patients.

Summary of Implicit Race Bias Using many different ways of assessing implicit bias and many ways of assessing behavior, these studies have demonstrated implicit bias that was associated with behaviors in the important domains of criminal justice, employment, and health care. Taken together, the evidence is much stronger now than even a few years ago. Implicit bias predicts not only subtle and ambiguous behaviors but also meaningful behaviors with important consequences. As the field of implicit bias continues to quickly advance, its relevance for social justice is, in our view, only becoming clearer. Implicit cognition drives perceptions of black individuals as both more criminal and less human. Implicit biases create scenarios in which individuals may systematically discriminate against minorities without or counter to intent. Nonetheless, ordinary conceptions of justice require that a person must intend an act to be held responsible for it. Yet victims of such discrimination have a justified expectation to be treated based on their conduct as an individual rather than by their group membership. This dilemma sets the stage for disagreements about which behaviors are and are not morally acceptable. That being said, prejudice is not the only way that implicit social cognition influences social justice. As we describe in the next sections, judgments about who deserves moral treatment and even about what acts are moral and just themselves are products of implicit social cognition.

IMPLICIT COGNITION, CULTURAL VALUES, AND MORAL JUDGMENT In the previous section, we used intergroup conflict to show how implicit social cognition can engender behaviors that many would deem unethical. In the next section, we review evidence that implicit processes can influence the values people adopt and the moral judgments they make. Implicit cognition not only leads to potentially unethical

452

IV. DOMAIN-SPECIFIC PERSPECTIVES

behavior but also influences what we deem to be unethical.

Nationalist Ideologies It has long been argued that there are two kinds of morality: one for our fellow group members and one for everyone else (Cohen, Montoya, & Insko, 2006; Le Bon, 1896). According to anthropologist Margaret Mead, most preindustrial societies do not recognize members of other tribal groups as fully human, a sentiment echoed in the findings on race and dehumanization described previously. Mead claimed that “most primitive tribes feel that if you run across one of these subhumans from a rival group in the forest, the most appropriate thing to do is bludgeon him to death” (as cited in Bloom, 1997, p.€74). People have greater moral regard for members of their own groups, even in modern industrialized societies (Cohen et al., 2006). However, people belong to many overlapping groups, and who is considered part of the ingroup can vary from one situation to another. There is evidence that in some cases people’s implicit judgments of who counts as an ingroup member may differ from their explicitly considered judgments. For example, who counts as an American? Devos and Banaji (2005) found that, when asked to explicitly define American identity, American participants rated emotional attachment to the nation and civic values as most important and did not refer to ethnicity. Yet on an implicit test Asians were less easily associated with American than whites and blacks and blacks slightly less so than whites. Another study used faces of famous Asian Americans (e.g., Connie Chung) and white Europeans (e.g., Hugh Grant). Despite explicitly recognizing that the Asians were American and the whites were European, participants still showed the white/American bias on an IAT measure. In a follow-up study, most American participants were ironically more likely to associate American with white than with Native American on the IAT (Devos, Nosek, & Banaji, 2007). This research suggests that the very concept of what it means to be an American is laced with (and might encourage) implicit ethnocentrism. This ethnocentrism has implications for foreign policy judgments. Uhlmann, Pizarro, Tannenbaum, and Ditto (2009) had participants evaluate the moral justifiability of “collateral damage” in the Iraq war and manipulated whether the innocent victims killed were Iraqis or Americans. They primed participants with either patriotic words or multicultural words in a sentence-Â�unscrambling task. The question was whether making patrio-

tism (as opposed to multiculturalism) accessible increased tolerance for Iraqi casualties. As expected, participants primed with patriotic words were more accepting of unintended casualties when they were Iraqi but not when they were American. Independent of this priming effect, conservatives were more tolerant of collateral damage, especially when the victims were Iraqi. These findings suggest that, independent of preexisting ideologies, simply activating patriotic concepts may not only increase devotion to one’s own country but also increase ethnocentric leanings. Nationalistic symbols have been shown to influence implicitly a range of outcomes. Ferguson and Hassin (2007; see also Ferguson, Carter, & Hassin, 2009) subliminally exposed American participants to images of the American flag. This led participants to rate the materialistic attributes of potential jobs as more important than other attributes. In another study, participants primed with an American flag in the experiment room completed word fragments more often with aggression- and war-Â�related words compared with when no flag was present. Finally, subliminally priming the American flag led to more hostile behavior in response to a mild computer-based provocation (although self-Â�reported affect levels suggested no awareness of this). Across these studies, effects emerged only for those who were high in exposure to national news, a prominent source of cultural associations. This illuminating set of studies reveals how something as simple as the American flag can encourage a range of feelings, perceptions, and behaviors that are not semantically related to flags in any obvious way. The link may instead be the larger set of values and ideology associated with the flag’s symbolic meaning. Most striking of all, political orientation had no moderating effect in any of these studies. This suggests that patriotic concepts and symbols can activate aggressive nationalistic tendencies even among individuals who do not normally endorse these values. In some cases, people act on activated values even when they contradict their own self-Â�interest, as shown in studies of system justification. System justification theory claims that individuals at all levels in social hierarchies tend to be motivated to justify and defend the status quo (Blasi & Jost, 2006). This trend is often manifested historically when a society’s current wrongdoings are rationalized away by its citizens (only to be recognized as wrong in retrospect once the status quo has changed; see Hanson & Hanson, 2006). There is a related and curious tendency for members of low-Â�status groups to justify the very systems that prevent their advancement. Consider outgroup fa-



24. Social Justice Implications

voritism, or the tendency to prefer another group to one’s own. In one study, students at high-Â�status (Stanford) and low-Â�status (San Jose State) colleges were administered IATs measuring attitudes toward each school, college stereotyping, and selfÂ�esteem (Jost, Pelham, & Carvallo, 2002). Although both sets of students displayed ingroup favoritism on average, a higher proportion of students at the low-Â�status college showed outgroup favoritism. Implicit stereotyping of Stanford as academic and San Jose State as athletic was also positively correlated with outgroup favoritism among San Jose students. Finally, San Jose students with the lowest implicit self-Â�esteem showed the greatest outgroup favoritism. This pattern of stereotyping and outgroup favoritism among lower status groups serves to support and sustain status differences. System justification can also be seen in the “paradox of the free market”: the faith in the legitimacy of the free market system among the poor despite the growing inequality between the rich and poor. Support for fair market ideology predicted minimization of the ethical importance of the Enron scandal and its potential ties to the Bush–Â�Cheney administration. The correlation was significant even when controlling for political conservatism. Jost, Blount, Pfeffer, and Hunyady (2003) primed business students with information about companies that had small or large profits or losses. When fictional company names were used, students judged profitable companies to be more ethical and losing companies to be less ethical. When actual company names were used, any large deviation from the status quo (either as profit or loss) was seen as less ethical than smaller deviations. Finally, in a study among Hungarians, Jost and colleagues induced a threat either to the current economic status quo (capitalism) or the recent status quo (communism). Those under any kind of system threat increased their support for the free market system. These studies suggest that system justification motivates the tendency to see current economic systems as fair and just, overlooking ethical failures on the part of those systems. Status differences are maintained also by complementary stereotypes, in which the negative consequences of membership in a low-Â�status group are perceived as offset by positive aspects (Kay et al., 2007). These stereotypes, such as poor but happy and rich but dishonest, have been found to persist on both the explicit and implicit levels (Kay et al., 2007). Kay and Jost (2003) primed participants with descriptions of individuals who fit complementary stereotypes (poor but happy and rich but unhappy; poor but honest and rich but dishonest) and measured explicit levels of system justification.

453

Compared with when noncomplementary stereotypes were presented (poor but unhappy, rich but happy), participants thought the general social system was more just. Moreover, on a lexical decision task, the participants who had been primed with noncomplementary stereotypes were faster at identifying justice-Â�related compared with neutral words, suggesting the activation of a justice motive. When complementary stereotypes are used in the context of economic inequality, people’s sense of injustice dissipates. The fact that these patterns are found among both liberals and conservatives, and among low-Â�status as well as high-Â�status groups, suggests that system justification can lead to a defense of the ethical as whatever is in line with the status quo, even if this contradicts other explicit values and interests. Where, then, do these values come from? The finding that exposure to news media moderated the effects in the Ferguson and colleagues (2009) studies suggests news media as one important way that these cultural values are transmitted. Research also suggests that implicitly activated values can be influenced by the present and the past. Uhlmann, Poehlman, and Bargh (2008) have suggested that Americans are “implicit puritans.” That is, their implicit associations are rooted in a web of values based on individual merit and traditional gender roles. In one study, participants primed with words related to spiritual salvation on a sentenceÂ�unscrambling task were more likely to persist in a subsequent anagram task. This effect only held for American, as opposed to Canadian, Italian, and German, participants. This finding suggests that for Americans there is an implicit cognitive association between God and hard work. In another study, Asian American students primed with work concepts in a sentenceÂ�unscrambling task endorsed traditionalist sex values but only when their American (vs. Asian) identities had been made salient. In a related study, participants read about a person who either supported or rejected a traditional American value and were then given ambiguous information about her sexual practices. On a memory test, Americans primed with a person who rejected American work values falsely remembered that person as violating traditional sex values. It should be emphasized that all of these priming effects emerged even among liberals and the nonreligious, suggesting a lingering influence of cultural associations even if not explicitly endorsed. Another vestige of implicit puritanism is moral absolutism, or the tendency to see the world as a Manichaean division of good and evil. As we already saw in the section on intergroup bias, the

454

IV. DOMAIN-SPECIFIC PERSPECTIVES

tendency to take one’s own perspective as the objective truth, either factually or morally, leads to rifts between social groups (Pronin, Gilovich, & Ross, 2004). Values are those ends that we hold most dear, yet the research just described suggests that we perceive and act based on values that we might not consider our own. In the next section, we review research that questions whether people have a clear idea of exactly which moral values ground their judgments.

Implicit Cognition and the Feeling of€Moral Clarity An emerging consensus claims that people’s moral judgments are driven by automatic, intuitive processes. Haidt’s (2001) social intuitionist model was the first to seriously incorporate the lessons of implicit social cognition. Haidt suggests that the immediate response to a moral transgression is a moral intuition, which is “the sudden appearance in consciousness, or at the fringes of consciousness, of an evaluative feeling about the character or actions of a person, without any conscious awareness of having gone through steps of search, weighing evidence, or inferring a conclusion” (Haidt & Bjorklund, 2008, p.€ 188). Moral intuition, which can but need not involve emotion, is the immediate precursor of moral judgment, which is “the conscious experience of blame/praise, including belief in the rightness or wrongness of an act” (Haidt & Bjorklund, 2008, p.€188). According to this model, moral reasoning enters primarily after the moral judgment has been made, finding post hoc reasons to support the original intuition. Although deliberate reasoning can play a causal role in reaching moral judgments, it has a rather subordinate role in producing and refining moral judgment. The strongest support for this model comes from research on moral dumbfounding (Haidt & Bjorklund, 2008). In this paradigm, participants are asked to evaluate moral transgressions such as incest, cleaning a toilet with the national flag, and eating one’s own dog. Participants are then asked to justify their moral responses, but the experimenter systematically shifts the scenario to counteract all of the possible practical justifications (e.g., incestuous siblings used birth control; it was only a one-time occurrence; neither was psychologically harmed by the experience). Participants typically revert to the position that, although that they cannot explain why they feel the way they do, the act is nevertheless simply wrong. This dissociation between moral judgment and justification has been replicated and expanded on

by Hauser’s research into innate moral grammar. Hauser’s model emerged in response to an emotion-based reading of Haidt’s theory: If emotion drives moral judgment, what drives the emotional response? Hauser (2006) has suggested that all humans have a store of operative knowledge about morality, a set of unconscious principles that lead to automatic moral judgments in the face of transgressions. Many of these principles are inaccessible to conscious awareness and operate over appraisals of intent and cause to create judgments of rightness and wrongness. On this view, emotions and conscious reasoning are the by-Â�products of this automatic appraisal process. To test these claims, Cushman, Young, and Hauser (2006) explored the conscious accessibility of three moral principles: the action principle (harmful actions are worse than harmful omissions), the intention principle (harm as a means to an end is worse than harm as foreseen, but unintended, side effect), and the contact principle (using physical contact to harm is worse than causing harm without contact). Participants were asked to make moral decisions in response to scenarios that manipulated these three principles and then to justify those decisions. Judges then coded how well the justifications could account for the causal effects of the three principles on moral judgments. Although moral judgments aligned with the three principles, justifications were more varied. Most participants gave sufficient justification for judgments based on the action principle, suggesting they had conscious awareness of it. More than half of participants were able to justify their decisions based on the contact principle. Finally, less than one-third of participants sufficiently justified their judgments based on the intention principle. The breakdown in justification for the intention principle has been replicated in a larger, cross-Â�cultural sample as well (Hauser, Cushman, Young, Jin, & Mikhail, 2007). These studies suggest that people’s justifications for a particular moral judgment sometimes track the actual causal impact of the underlying principles, but they do not necessarily do so. Together, the studies by Haidt, Hauser, and Cushman show that when people attempt to explain the basis for their moral judgments, they sometimes can give a coherent explanation, but just as often they seem to confabulate reasons that bear little relationship to the underlying causes of their judgments. Some have criticized these models for their neglect of controlled cognition. For instance, Greene (2008) has argued that at least two processes are involved in moral judgment: automatic emotional



24. Social Justice Implications

responses and controlled cognitive deliberation. To test this, he subjected participants to a cognitive load manipulation while they read a series of dilemmas that pitted strong emotion against utilitarian analysis (e.g., Would you smother your baby in order to prevent you and your entire family from being caught and killed by a death squad?). Cognitive load made participants take longer to give a utilitarian response, but not the emotional response, to these moral dilemmas, suggesting interference with utilitarian reasoning but not emotion-based judgment. Yet the exact roles of automatic and controlled processes in moral judgment are still a matter of debate. For example, Epley and Caruso (2004) speculated that because moral judgments are based in egocentric and automatic evaluations of the environment as good or bad, these judgments will tend to be self-Â�serving. Subsequent moral disagreements between groups will latch onto post hoc justifications, which may have no bearing on the real issues at hand. On the other hand, Valdesolo and DeSteno (2008) recently investigated the processes underlying moral hypocrisy, the tendency to say that a moral violation is more permissible when committed by oneself than by others. In contrast to the perspective that automatic moral judgments are always self-Â�serving, they found that placing participants under cognitive load erased tendencies toward moral hypocrisy. This suggests that attention-Â�demanding processes of motivated reasoning may underlie this selectivity. Although research on implicit and explicit aspects of moral judgment is still new, initial evidence suggests that both automatic intuitive reactions and deliberate rationalizations may both contribute to moral judgments that seem from an egocentric perspective to be obviously correct but that seem to others to be biased or arbitrary. The fact that implicit cognition drives at least some aspects of moral judgment raises the question of whether people should trust their moral intuitions. Gigerenzer (2008) argues that “moral heuristics,” including the moral intuitions in Haidt’s model, can be made more conscious and avoided if necessary. Yet he argues that this won’t often be necessary because the heuristics adaptively simplify the moral sphere. Sunstein (2005), by contrast, has emphasized how these heuristics can be taken out of context, reified as moral principles, and misapplied to improper situations. For example, many people have an intuitive disgust reaction toward marginalized social groups that translates into moral disapproval. If consciously recognized, many people would disavow this emotional heuristic as a

455

justification for moral disapproval (Schnall, Haidt, Clore, & Jordan, 2008). The key point for our purpose is that people’s intuitive moral reactions may sometimes differ starkly from their explicitly endorsed moral principles. One recent pair of studies attests to this idea. Inbar, Pizarro, Knobe, and Bloom (2009) investigated intuitive moral disapproval of homosexuals. In their first study, participants read about a film director who encouraged either gay or straight kissing. Explicitly, most people indicated that nothing was morally wrong with the kissing. Yet participants viewed the director’s action as more intentional when he encouraged gay kissing than when he encouraged straight kissing, and this effect was strongest for those high in disgust sensitivity. Based on Knobe’s (2006) finding that people are more likely to say an action is intentional if they conceive of it as morally wrong, this indirect approach suggests that people might be making an implicit moral wrongness judgment that contradicts their explicitly stated values. In a second study using an IAT that paired gay/straight with pleasant/unpleasant, people were faster to associate gay with unpleasant, especially when they were high in disgust sensitivity. In short, many people show traces of implicit moral disapproval of homosexuals, against their explicitly stated values. How much, then, should people trust their moral intuitions? Singer (2005), Greene (2008), and others argue that we should not trust our moral intuitions until we reflectively validate them against some external criterion. But external criteria are hard to come by, especially concerning moral judgments (Pizarro & Uhlmann, 2005). To the extent that people are naive realists who simply “know” that they are right, this validation process may be difficult or impossible (Pronin et al., 2004). One can think of plenty of post hoc reasons to support those intuitions, reasons that may subjectively feel as valid as any other. There are striking similarities between this research on moral judgment and research on implicit prejudice and other social attitudes. In both cases, implicit processes sometimes produce judgments that are difficult to justify and explain. Implicitly measured moral sentiments tend to show less tolerance and more hierarchy. Implicitly measured sentiments also tend to reflect historically prominent attitudes and values that may no longer be explicitly endorsed. Theories of moral intuitions have recognized that automatic and controlled processes interact to produce moral judgments, but the specific roles for these processes have rarely been specified. It could be the case that implicit

456

IV. DOMAIN-SPECIFIC PERSPECTIVES

moral cognition works the same way as implicit racial cognition, in which case lessons about the self-Â�regulation of prejudiced impulses might prove fruitful in understanding the role of controlled deliberation in moral judgment (Fine, 2006; Kennett & Fine, 2009). Research in moral cognition might be profitably expanded by integrating these insights with dual-Â�process theories that have been developed in other domains of social cognition. A recently developed set of quantitative process models offers promise in this regard. Multinomial process models allow researchers to test empirically hypotheses about the relative dominance of automatic and controlled influences on judgments (Bishara & Payne, 2009; Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005; Payne, 2001, 2005; Payne & Bishara, in press; Sherman et al., 2008). For example, in automaticity-Â�dominating models, whenever an automatic response is activated, it alone determines responses. Controlled efforts (e.g., responding based on deliberate reasoning) would come into play only in the absence of an automatic moral reaction. In contrast, in controlÂ�dominating models, whenever deliberate reasoning is engaged, it alone determines responses. However, when deliberate reasoning fails, automatic moral intuitions drive the response. These models differ in which process—Â�automatic moral intuitions versus deliberate moral reasoning—Â�dominates responding when they conflict. Such models could allow empirical tests of the relative strength of automatic and controlled aspects of moral judgment (e.g., Conway & Gawronski, 2009). Answering the question of which process dominates the other and under what conditions is relevant for determining how prominent a role moral reasoning plays in moral judgment. According to some theories such as Kohlberg’s (1971) or Turiel’s (1983), moral reasoning might be expected to be the dominant process. But according to other theories such as Haidt’s (2001) or Hauser’s (2006), automatic intuitions can be expected to play the dominant role. The more prominent the role of automatic intuitions, the more reason there is to expect that moral intuitions will commonly conflict with traditional rationalist principles and reflectively endorsed values.

EMPIRICAL RESEARCH AND NORMATIVE IMPLICATIONS: THE€ROLE OF IS AND OUGHT In the preceding pages, we have reviewed research demonstrating the interplay between implicit so-

cial cognition and matters of social justice and morality. We showed how implicit race bias sometimes leads people to act in ways that run counter to their explicitly considered values of tolerance. We showed how implicit thought processes can exclude certain groups from one’s own moral circle and operate to maintain rigid nationalistic, ethnocentric, and hierarchy-based boundaries. Finally, we described research showing that implicit cognition shapes what people consider moral and just in the first place. In each of these cases, the empirical research identifies ordinary situations that create tensions between typical conceptions of moral judgment and actual behaviors driven by implicit processes. What are we to make of these tensions? These tensions suggest that people often have less clarity over their mental lives than they would have themselves (and others) believe. On the one hand, this has clear epistemic implications for knowledge of oneself and others. Yet whether this fact has moral implications is decidedly less clear. Does wrongful certainty lead to certain wrongs? It has been said many times that you can’t get an ought from an is. As David Hume argued, empirical facts about how the world is do not determine how it ought to be. Considering implicit social cognition research, none of the findings themselves can dictate whether any aspect of behavior is morally right or wrong. Yet even though the empirical findings do not themselves establish any moral standards, the findings have direct relevance for what societies and individuals believe is morally acceptable. Knowing that implicit cognition can alter who we judge to have moral status, or what we judge to be a moral issue at all, can substantively inform moral dialogue. Knowing how implicit cognition can cause our ethicality to corrode can also help us engage better moral self-Â�regulation in pursuit of our ideals. Normative moral standards are value judgments made by communities (Gibbard, 1990). The role of empirical facts is to ground those judgments so that communities are engaged in informed (rather than uninformed) conversations. Implicit social cognition research is particularly well suited to establish facts about the role of morally relevant mental states, especially intent, conscious awareness, and controllability. Lay moral intuitions and formal legal codes distinguish between intentional versus unintentional acts, between conscious versus unconscious acts, and between controllable versus uncontrollable acts. If racial discrimination, for example, were found to be always intentional, conscious, and controllable, then presumably it would always be considered



24. Social Justice Implications

wrong. If it were found to sometimes be unconscious or unintentional, then the possibility arises that someone may discriminate without being considered morally responsible. Wherever moral or legal judgments hinge on questions of intent, consciousness, and controllability, implicit social cognition research becomes relevant. These considerations affect policy choices as well. Kang and Banaji (2006), for instance, have argued that the continued existence of implicit race bias provides a new and different rationale for affirmative action policies. Rather than viewing affirmative action as a remedy for historical injustice, research showing continued subtle forms of discrimination could be used to justify affirmative action as a remedy for present and ongoing discrimination. In this case, implicit social cognition research has been used to argue for policies aimed at reducing discrimination. Yet there is no necessary connection between these findings and the values or goals for which they are used. It is easy to imagine someone discriminating and then using the findings of implicit bias to argue that he or she is not responsible. Here again, the empirical research does not itself dictate the proper response, but it provides the scaffolds on which arguments are built. To date, most of the discussion about the moral and legal implications of implicit social cognition has taken place in the pages of academic books and journals (Bargh, 1999; Fiske, 2005; Kelly & Roedder, 2008). However, as behavioral science findings become more widely known, the popular understanding of these findings is likely to have broader impact. Similar trends can be seen for forensic science. Popular television programs such as the CBS series CSI: Crime Scene Investigation have had a noticeable impact on the kinds of evidence that juries expect. Jury members familiar with the forensic techniques used in the show have begun demanding higher and higher levels of forensic evidence such as DNA analysis before rendering a guilty verdict (Thomas, 2006). Although implicit social cognition research has not reached the prominence of this television series, it has gained relatively high notoriety for a behavioral science. Thanks in part to the popularity of the Project Implicit website, millions of individuals have completed some form of implicit test. Implicit biases have been discussed many times in the pages of the New York Times, the Wall Street Journal, Time, and Newsweek. As information about implicit social cognition becomes popular knowledge, we can expect to see effects on the public’s judgments and decisions.

457

We conclude by describing a study that takes a look at what kind of an impact such knowledge might have. Cameron, Payne, and Knobe (2008) asked subjects to read about cases of racial discrimination. The same discriminatory behaviors happened in all conditions, but the awareness and controllability of the acts were manipulated. In a control condition, no information about the actor’s mental states was provided. In a second condition, the actor was described as being consciously aware of his dislike for African Americans, but he rejected that feeling and made an effort to treat people equally. Still, he ended up discriminating unintentionally because prejudice influenced his actions in ways he could not fully control. This description reflected theories of implicit bias that emphasize automaticity rather than unconsciousness. Finally, in a third condition, subjects read about an actor who had a completely unconscious dislike for African Americans. He discriminated in the same way as in the other conditions because he had no awareness of it. This condition reflected theories of implicit bias that emphasize unconscious attitudes. Participants made judgments of how morally blameworthy the discrimination was. When the discrimination was conscious but uncontrollable, it was judged to be only slightly less blameworthy than when no information about mental states was given. However, when the discrimination was described as stemming from unconscious bias, it was judged much less blameworthy. Subjects gave a great deal of weight to the conscious awareness of the bias leading to discrimination, much more than they gave to its controllability. In sum, when implicit biases are represented as being unconscious, the discrimination that results from them is blamed significantly less. Ironically, the strategy of raising consciousness of one’s biases (Banaji, Bazerman, & Chugh, 2003) may expose a person to more blame than if the biases remain outside of awareness. This study takes a first step toward understanding the likely impact of implicit social cognition research by showing that the particular theory used to explain the findings can have a strong impact on lay judgments. The stakes are high because the image of the human mind developed in this research is likely to influence social, legal, and political decisions. The research reviewed here highlights the importance of making careful distinctions between specific aspects of automatic thinking, such as distinguishing awareness, intention, and controllability. People do appear to make these distinctions in their lay moral judg-

458

IV. DOMAIN-SPECIFIC PERSPECTIVES

ments. In our view, the key lesson for researchers is that implicit social cognition research makes a difference in collective conversations about social justice. The stronger the empirical base, the better informed is the conversation. Acknowledgments This material is based on work supported by the National Science Foundation under Grant No. 0615478. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

References Arbuckle, N., & Payne, B. K. (2008). Implicit intergroup emotions. Unpublished manuscript. Arkes, H., & Tetlock, P. E. (2004). Attributions of implicit prejudice, or “would Jesse Jackson ‘fail’ the Implicit Association Test?” Psychological Inquiry, 15, 257–278. Banaji, M. R., Bazerman, M. H., & Chugh, D. (2003). How (un)ethical are you? Harvard Business Review, 81, 56–64. Banaji, M. R., Nosek, B. A., & Greenwald, A. G. (2004). No place for nostalgia in science: A response to Arkes & Tetlock. Psychological Inquiry, 15, 279–289. Bandura, A. (1999). Moral disengagement in the perpetration of inhumanities. Personality and Social Psychology Review, 3, 193–209. Bargh, J. A. (1999). The cognitive monster: The case against the controllability of automatic stereotype effects. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€361–382). New York: Guilford Press. Baumeister, R. F., Vohs, K. D., DeWall, C. N., & Zhang, L. (2007). How emotion shapes behavior: Feedback, anticipation, and reflection, rather than direct causation. Personality and Social Psychology Review, 11, 167–203. Bertrand, M., & Mullainathan, S. (2004). Are Emily and Greg more employable than Lakisha and Jamal?: A field experiment on labor market discrimination. American Economic Review, 94, 991–1013. Bishara, A. J., & Payne, B. K. (2009). Multinomial process tree models of control and automaticity in weapon misidentification. Journal of Experimental Social Psychology, 45, 524–534. Blair, I. V., Judd, C. M., & Chapleau, K. M. (2004). The influence of Afrocentric facial features in criminal sentencing. Psychological Science, 15, 674–679. Blair, I. V., Judd, C. M., & Fallman, J. L. (2004). The automaticity of race and Afrocentric facial features in social judgments. Journal of Personality and Social Psychology, 87, 763–778. Blank, L. M., Dabady, M., & Citro, C. F. (2004). Measuring racial discrimination. Washington, DC: National Academy Press.

Blasi, G., & Jost, J. T. (2006). System justification theory and research: Implications for law, legal advocacy, and social justice. California Law Review, 94, 1119–1168. Bloom, H. (1997). The Lucifer principle: A scientific expedition into the forces of history. New York: Atlantic Monthly Press. Cameron, C. D., Payne, B. K., & Knobe, J. (2008). Do theories of implicit race bias influence moral judgments? Unpublished manuscript. Chen, M., & Bargh, J. A. (1997). Nonconscious behavioral confirmation processes: The self-Â�fulfilling consequences of automatic stereotype activation. Journal of Experimental Social Psychology, 33, 541–560. Cohen, T., Montoya, R., & Insko, C. (2006). Group morality and intergroup relations: Cross-Â�cultural and experimental evidence. Personality and Social Psychology Bulletin, 32, 1559–1572. Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K., & Groom, C. (2005). Separating multiple processes in implicit social cognition: The quadmodel of implicit task performance. Journal of Personality and Social Psychology, 89, 469–487. Conway, P. J., & Gawronski, B. (2009, February). Deontological vs. utilitarian inclinations in moral decision making: A process dissociation approach. Poster presented at the 10th Annual Meeting of the Society for Personality and Social Psychology, Tampa, FL. Correll, J., Park, B., Judd, C. M., & Wittenbrink, B. (2002). The police officer’s dilemma: Using ethnicity to disambiguate potentially threatening individuals. Journal of Personality and Social Psychology, 83, 1314–1329. Correll, J., Park, B., Judd, C. M., Wittenbrink, B., Sadler, M. S., & Keesee, T. (2007). Across the thin blue line: Police officers and racial bias in the decision to shoot. Journal of Personality and Social Psychology, 92, 1006–1023. Cushman, F. A., Young, L., & Hauser, M. D. (2006). The role of reasoning and intuition in moral judgments: Testing three principles of harm. Psychological Science, 17, 1082–1089. Devos, T., & Banaji, M. R. (2005). American = white? Journal of Personality and Social Psychology, 88, 447– 466. Devos, T., Nosek, B. A., & Banaji, M. R. (2007). Aliens in their own land?: Implicit and explicit ascriptions of national identity to Native Americans and white Americans. Unpublished manuscript. Dovidio, J. F., Kawakami, K., & Gaertner, S. L. (2002). Implicit and explicit prejudice and interracial interaction. Journal of Personality and Social Psychology, 82, 62–68. Eberhardt, J. L., Davies, P. G., Purdie-Â�Vaughns, V. J., & Johnson, S. L. (2006). Looking deathworthy: Perceived stereotypicality of black defendants predicts capital-Â�sentencing outcomes. Psychological Science, 17, 383–386. Eberhardt, J. L., Goff, P. A., Purdie, V. J., & Davies, P. G. (2004). Seeing black: Race, crime, and visual processing. Journal of Personality and Social Psychology, 87, 876–893. Epley, N., & Caruso, E. (2004). Egocentric ethics. Social Justice Research, 17, 171–187. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams,



24. Social Justice Implications

C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Ferguson, M. J., Carter, T. J., & Hassin, R. R. (2009). On the automaticity of American nationalist ideology: The case of the USA. In J. T. Jost, A. C. Kay, & H. Thorisdottir (Eds.), Social and psychological bases of ideology and system justification (pp.€ 53–84). New York: Oxford University Press. Ferguson, M. J., & Hassin, R. R. (2007). On the automatic association between America and aggression for news watchers. Personality and Social Psychology Bulletin, 33, 1632–1647. Fine, C. (2006). Is the emotional dog wagging its rational tail, or chasing it? Philosophical Explorations, 9, 83–98. Fiske, S. T. (2005). What’s in a category?: Responsibility, intent, and the avoidability of bias against outgroups. In A. G. Miller (Ed.), The social psychology of good and evil (pp.€127–140). New York: Guilford Press. Frankfurt, H. G. (1969). Alternate possibilities and moral responsibility. Journal of Philosophy, 66, 829– 839. Gawronski, B., Peters, K. R., & LeBel, E. P. (2008). What makes mental associations personal or extraÂ�personal?: Conceptual issues in the methodological debate about implicit attitude measures. Social and Personality Psychology Compass, 2, 1002–1023. Gibbard, A. (1990). Wise choices, apt feelings. Cambridge, MA: Harvard University Press. Gigerenzer, G. (2008). Moral intuition = fast and frugal heuristics? In W. Sinnott-Â�Armstrong (Ed.), Moral psychology: Volume 2. The cognitive science of morality: Intuition and diversity (pp.€ 1–26). Cambridge, MA: MIT Press. Goff, P. A., Eberhardt, J. L., Williams, M. J., & Jackson, M. C. (2008). Not yet human: Implicit knowledge, historical dehumanization, and contemporary consequences. Journal of Personality and Social Psychology, 94, 292–306. Green, A. R., Carney, D. R., Pallin, D. J., Ngo, L. H., Raymond, K. L., Iezzoni, L. I., et al. (2007). Implicit bias among physicians and its prediction of thrombolytic decisions for black and white patients. Journal of General Internal Medicine, 22, 1231–1238. Greene, J. (2008). The secret joke of Kant’s soul. In W. Sinnott-Â�Armstrong (Ed.), Moral psychology: Volume 3. The neuroscience of morality: Emotion, disease, and development (pp.€35–79). Cambridge, MA: MIT Press. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Haidt, J. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral judgment. Psychological Review, 108, 814–834. Haidt, J., & Bjorklund, F. (2008). Social intuitionists answer six questions about moral psychology. In W. Sinnott-Â�Armstrong (Ed.), Moral psychology: Volume 2. The cognitive science of morality: Intuition and diversity (pp.€181–218). Cambridge, MA: MIT Press. Hanson, J., & Hanson, K. (2006). The blame frame:

459

Justifying (racial) injustice in America. Harvard Civil Rights–Civil Liberties Law Review, 41, 414–480. Hauser, M. (2006). Moral minds. New York: HarperCollins. Hauser, M., Cushman, F., Young, L., Jin, R. K., & Mikhail, J. (2007). A dissociation between moral judgments and justifications. Mind and Language, 22, 1–21. Inbar, Y., Pizarro, D. A., Knobe, J., & Bloom, P. (2009). Disgust sensitivity predicts intuitive disapproval of gays. Emotion, 9, 435–439. Jost, J. T., Blount, S., Pfeffer, J., & Hunyady, G. (2003). Fair market ideology: Its cognitive-Â�motivational underpinnings. Research in Organizational Behavior, 25, 53–91. Jost, J. T., Pelham, B. W., & Carvallo, M. R. (2002). Non-Â�conscious forms of system justification: Implicit and behavioral preferences for higher status groups. Journal of Experimental Social Psychology, 38, 586– 602. Kang, J. (2005). Trojan horses of race. Harvard Law Review, 118, 1491–1593. Kang, J., & Banaji, M. R. (2006). Fair measures: A behavioral realist revision of “affirmative action.” California Law Review, 94, 1063–1118. Kay, A. C., & Jost, J. T. (2003). Complementary justice: Effects of “poor but happy” and “poor but honest” stereotype exemplars on system justification and implicit activation of the justice motive. Journal of Personality and Social Psychology, 85, 823–837. Kay, A. C., Jost, J. T., Mandisodza, A. N., Sherman, S. J., Petrocelli, J. V., & Johnson, A. L. (2007). Panglossian ideology in the service of system justification: How complementary stereotypes help us to rationalize inequality. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 39, pp.€305–358). San Diego, CA: Elsevier. Kelly, D., & Roedder, E. (2008). Racial cognition and the ethics of implicit bias. Philosophy Compass, 3, 522–540. Kennedy, K. A., & Pronin, E. (2008). When disagreement gets ugly: Perceptions of bias and the escalation of conflict. Personality and Social Psychology Bulletin, 34, 833–848. Kennett, J., & Fine, C. (2009). Will the real moral judgment please stand up?: The implications of social intuitionist models of cognition for meta-Â�ethics and moral psychology. Ethical Theory and Moral Practice, 12, 77–96. Knobe, J. (2006). The concept of intentional action: A case study in the uses of folk psychology. Philosophical Studies, 130, 203–231. Kohlberg, L. (1971). From is to ought: How to commit the naturalistic fallacy and get away with it in the story of moral development. In T. Mischel (Ed.), Cognitive development and epistemology (pp.€151–235). New York: Academic Press. Krieger, L. H., & Fiske, S. T. (2006). Behavioral realism in employment discrimination law: Implicit bias and disparate treatment. California Law Review, 94, 997–1062. Lane, K. A., Kang, J., & Banaji, M. R. (2007). Implicit social cognition and law. Annual Review of Law and Social Science, 3, 427–451.

460

IV. DOMAIN-SPECIFIC PERSPECTIVES

Le Bon, G. (1896). The crowd: A study of the popular mind. New York: Macmillan. Leyens, J., Paladino, P. M., Rodriguez-Â�Torres, R., Vaes, J., Demoulin, S., Rodriguez-Perez, A., et al. (2000). The emotional side of prejudice: The attribution of secondary emotions to ingroups and outgroups. Personality and Social Psychology Review, 4, 186–197. McConnell, A. R., & Leibold, J. M. (2001). Relations among the Implicit Association Test, discriminatory behavior, and explicit measure of racial attitudes. Journal of Experimental Social Psychology, 37, 435–442. Mitchell, G., & Tetlock, P. E. (2007). Antidiscrimination law and the perils of mindreading. Ohio State Law Journal, 67, 1023–1122. Norton, M. I., Vandello, J. A., & Darley, J. (2004). Casuistry and social category bias. Journal of Personality and Social Psychology, 87, 817–831. Nosek, B. A., & Hansen, J. J. (2008). The associations in our heads belong to us: Searching for attitudes and knowledge in implicit evaluation. Cognition and Emotion, 22, 553–594. Payne, B. K. (2001). Prejudice and perception: The role of automatic and controlled perceptions in misperceiving a weapon. Journal of Personality and Social Psychology, 81, 181–192. Payne, B. K. (2005). Conceptualizing control in social cognition: How executive control modulates the expression of automatic stereotyping. Journal of Personality and Social Psychology, 89, 488–503. Payne, B. K., & Bishara, A. (in press). An integrative review of process dissociation and related models in social cognition. European Review of Social Psychology. Payne, B. K., Cheng, C. M., Govorun, O., & Stewart, B. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. Journal of Personality and Social Psychology, 89, 277–293. Payne, B. K., Lambert, A. J., & Jacoby, L. L. (2002). Best laid plans: Effects of goals on accessibility bias and cognitive control in race-based misperceptions of weapons. Journal of Experimental Social Psychology, 38, 384–396. Pizarro, D. A., & Uhlmann, E. (2005). Do normative standards advance our understanding of moral judgment? Behavioral and Brain Sciences, 28, 558–559. Plant, E. A., & Peruche, B. M. (2005). The consequences of race for police officers’ responses to criminal suspects. Psychological Science, 16, 180–183. Plant, E. A., Peruche, B. M., & Butz, D. A. (2005). Eliminating automatic racial bias: Making race nonÂ�diagnostic for responses to criminal suspects. Journal of Experimental Social Psychology, 41, 141–156. Pronin, E., Gilovich, T., & Ross, L. (2004). Objectivity in the eye of the beholder: Divergent perceptions of bias in self versus others. Psychological Review, 111, 781–799. Rudman, L. A., & Glick, P. (2001). Prescriptive gender stereotypes and backlash toward agentic women. Journal of Social Issues, 57, 743–762. Russell, J. A. (2003). Core affect and the psychological

construction of emotion. Psychological Review, 110, 145–172. Salvatore, J., & Shelton, N. (2007). Cognitive costs of exposure to racial prejudice. Psychological Science, 18, 810–815. Schnall, S., Haidt, J., Clore, G., & Jordan, A. (2008). Disgust as embodied moral judgment. Personality and Social Psychology Bulletin, 34, 1096–1109. Schuhman, H., Steeh, C., Bobo, L., & Kyrsan, L. (1997). Racial attitudes in America: Trends and interpretations. Cambridge, MA: Harvard University Press. Sherman, J. W., Gawronski, B., Gonsalkorale, K., Hugenberg, K., Allen, T. J., & Groom, C. J. (2008). The selfÂ�regulation of automatic associations and behavioral impulses. Psychological Review, 115, 314–335. Singer, P. (2005). Ethics and intuitions. Journal of Ethics, 9, 331–352. Smith, A. (2005). Responsibility for attitudes: Activity and passivity in mental life. Ethics, 115, 236–271. Sniderman, P. M., & Carmines, E. G. (1997). Reaching beyond race. Political Science and Politics, 30, 466–471. Steele, C. M. (1997). A threat in the air: How stereotypes shape intellectual identity and performance. American Psychologist, 52, 613–629. Sunstein, C. (2005). Moral heuristics. Behavioral and Brain Sciences, 28, 531–573. Thomas, A. P. (2006). The CSI effect: Fact or fiction. 115 Yale Law Journal Pocket Part 70. Retrieved September 8, 2009, from www.thepocketpart.org/2006/02/ thomas.html. Turiel, E. (1983). The development of social knowledge: Morality and convention. Cambridge, UK: Cambridge University Press. Uhlmann, E. L., Brescoll, V. L., & Paluck, E. L. (2006). Are members of low status groups perceived as bad, or badly off?: Egalitarian negative associations and automatic prejudice. Journal of Experimental Social Psychology, 42, 491–499. Uhlmann, E. L., Pizarro, D. A., Tannenbaum, D., & Ditto, P. H. (2009). The motivated use of moral principles. Judgment and Decision Making, 4, 479–491. Uhlmann, E. L., Poehlman, T. A., & Bargh, J. A. (2008). American moral exceptionalism. In J. T. Jost, A. C. Kay, & H. Thorisdottir (Eds.), Social and psychological bases of ideology and system justification (pp.€ 27–52). New York: Oxford University Press. Valdesolo, P., & DeSteno, D. (2008). The duality of virtue: Deconstructing the moral hypocrite. Journal of Experimental Social Psychology, 44, 1334–1338. von Hippel, W., Brener, L., & von Hippel, C. (2008). Implicit prejudice toward injecting drug users predicts intentions to change jobs among drug and alcohol nurses. Psychological Science, 19, 7–11. Word, C., Zanna, M. P., & Cooper, J. (1974). The nonverbal mediation of self-Â�fulfilling prophecies in interracial interaction. Journal of Experimental Social Psychology, 10, 109–120. Ziegert, J. C., & Hanges, P. J. (2005). Employment discrimination: The role of implicit attitudes, motivation, and a climate for racial bias. Journal of Applied Psychology, 90, 553–562.

PA R T V

APPLIED PERSPECTIVES

C h a p t e r 25

Implicit Cognition in Health Psychology Why Common Sense Goes Out the Window Reinout W. Wiers, Katrijn Houben, Anne Roefs, Peter de Jong, Wilhelm Hofmann, and Alan W. Stacy

I

n the 1990s Tara MacDonald and colleagues performed a beautiful series of experiments that nicely illustrate a number of the central concepts in this chapter. The goal of these studies was to investigate systematically why seemingly sensible people (students, admittedly only males)1 often reported to have unprotected sex after drinking alcohol while knowing the dangers in the era of HIV and other sexually transmitted diseases. Interestingly, many of the same students, when asked in a survey, indicated that it would be very foolish to have sex without a condom. This discrepancy illustrates a central issue in health psychology: why many people in specific situations act against their own health-Â�related interests or, as MacDonald and colleagues put it, “why common sense goes out of the window” (MacDonald, Zanna, & Fong, 1996). In this chapter, we argue that dual-Â�process models may provide interesting answers to this important question. Let us consider the experiments first. MacDonald and colleagues selected students who did not have a steady relationship and who regularly used condoms. Participants received alcohol or not2 and were shown a video. In this video, two students, Mike and the attractive Rebecca, go out on a date and then go back to her apartment. There they begin to kiss on the couch and continue to make out until Mike awkwardly discloses that he did not bring any condoms. Rebecca states that



she also does not have condoms but is taking birth control pills. They discuss whether it is possible to obtain a condom, decide that this is not feasible, and discuss their sexual history. Mike states that he is “clean” and Rebecca states that she does not “sleep around.” At the end of the video, Mike asks Rebecca, “What do you want to do?” Rebecca responds, “I don’t know. What do you want to do?” The video then ends with a freeze frame, and participants complete the dependent measures while viewing the freeze frame. Across seven studies, it was found that male participants were more willing to engage in unsafe sex when they were in the alcohol condition and sexually aroused by the video (MacDonald, Fong, Zanna, & Martineau, 2000; MacDonald, MacDonald, Zanna, & Fong, 2000; MacDonald et al., 1996).3 This example illustrates some of the central concepts available to help explain a wide range of perplexing findings in health psychology. First, there is an internal conflict between the “cold,” or rational, attitudes and beliefs about the health behavior (it is foolish not to use condoms) on the one hand and the actual intentions and behavioral inclinations in a tempting, or “hot,” situation on the other. We argue that similar internal conflicts are central in many areas of health psychology, often (but not always) with associative, “implicit” processes triggering approach reactions, while “reflective” processes “know better” and suggest re463

464

V. APPLIED PERSPECTIVES

frain from approach. Importantly, participants can differ regarding both their impulsive or associative processes and the contents and strength of their reflective or controlled processes, and both can be assessed (Hofmann, Friese, & Wiers, 2008). Another key feature illustrated by this research is that “cold” measures (e.g., a survey of attitudes and intentions regarding unsafe sex) may be suboptimal to predict actual health behavior in “hot” situations. People (especially the “usual subjects”: undergraduate students) often know rather well what is good or bad for their health; the problem is that they still engage in a number of unhealthy and risky behaviors, such as binge drinking, smoking, binge eating, unhealthy eating, unsafe sex, and so on. Recent research has confirmed the relevance of this discrepancy for health psychology: In a “cold” state, people underestimate the influence of “hot cognitions” under influence of visceral states, such as hunger, thirst, sexual arousal, and craving, a phenomenon called the “cold-to-hot empathy gap” (Nordgren, van der Pligt, & van Harreveld, 2007, 2008; Sayette, Loewenstein, Griffin, & Black, 2008). The underestimation of “hot” inclinations is likely to play a role in the underestimation of the risk of becoming addicted and of risky situations in quitters (Sayette et al., 2008) as well as in judgments of impulsive (“irrational”) behaviors of oneself and of others. This is likely to contribute to the negative stigma of impulsive problem behaviors such as addiction and obesity. In a “cold” state, it appears far more difficult to feel empathy with impulsive drives compared with a judgment made in a “hot” state (Nordgren et al., 2007). Note that we are not arguing that it is useless to assess “cold” attitudes or intentions but rather that they are insufficient alone to predict health behaviors in “hot” situations. We need indices of the associative processes that are triggered in a “hot” situation. One way to conceive the large number of recently developed implicit or indirect measures is as mini-Â�experiments aimed at triggering and assessing spontaneous associative processes in reaction to relevant stimuli, similar to the video used by MacDonald and colleagues (De Houwer, 2006). The difference is that indirect measures usually provide simpler stimuli (pictures or words presented one at the time rather than a tempting video) to which participants have to react fast, by providing either a first association without reflection or a speeded response (see Sekaquaptewa, Vargas, & von Hippel, Chapter 8, Teige-Â�Mocigemba, Klauer, & Sherman, Chapter 7, and Wentura & Degner, Chapter 6, this volume). In the studies of MacDonald and colleagues, participants indicate

their subjective arousal and intention to have unprotected sex in this situation on a questionnaire. Hence, the example should not be read as indicating that implicit measures always assess “hot” processes and explicit measures “cold” processes. Rather, implicit measures aim to elicit the same processes as are operating in the actual “hot” situation, but, of course, that claim should be validated (De Houwer, 2006; De Houwer, Teige-Â�Mocigemba, Spruyt, & Moors, 2009). In other words, the whole MacDonald experiment can be taken as an analogy of what implicit measures try to do, irrespective of the fact that a simple attitude scale is used to assess the “hot” cognitions. Note further that it is incorrect to equate implicit measures to reaction time measures, as witnessed by memory association and affect misattribution measures (Payne, Cheng, Govorun, & Stewart, 2005; Payne, Govorun, & Arbuckle, 2007; Payne, McClernon, & Dobbins, 2007; Stacy, Leigh, & Weingardt, 1997; Stacy, Newcomb, & Ames, 2000). Finally, this condom-use example illustrates a common theme in health psychology: Many unhealthy behaviors occur in combination; for example, unsafe sex occurs frequently after alcohol use.

A SHORT REVIEW OF HEALTH PSYCHOLOGY APPROACHES TO€PREDICT HEALTH BEHAVIOR In our view, the idea to explicitly incorporate the notion of impulsive processes in health behavior is a relatively new one. Initially, the field of health psychology has received much stimulation and is still somewhat dominated by the application of reasoned action approaches (Ajzen, 1991; Fishbein & Ajzen, 1975) to health-Â�related decisions and behaviors such as with protection motivation theory (Rogers, 1983) or the health belief model (Janz & Becker, 1984). One common element of these models, in a nutshell, is the assumption that health behavior is the result of cognitive appraisal processes of the (1) expectancy and value of potential health threats and (2) possible coping responses. From these appraisal processes, a behavioral intention to avoid a health threat and to engage in healthy behavior may be formed. Importantly, these appraisal processes and the resulting goal-Â�directed behavior are typically seen as reasoned, conscious, and intentional acts that require a person’s willpower in order to be effective. Therefore, these models largely speak to reflective processes by which health behavior is regulated. They typically do not, however, integrate the no-



25. Implicit Cognition in Health Psychology

tion of impulsive influences on behavior (in other words treating impulsive influences on behavior as error variance). This neglect may be one reason why the predictive validity of reasoned action models is typically far from perfect (Conner & Sparks, 2002; Stacy, Bentler, & Flay, 1994). A second general approach has been to get to know better the situational and dispositional risk factors that play a role in determining health�related behavior outcomes. On the one hand, social psychology-�oriented health research has identified the situational risk factors under which people are particularly prone to engage in unhealthy behavior, such as when they are depleted of self-�regulatory resources (Vohs & Heatherton, 2000), cognitively occupied (Ward & Mann, 2000), drunk (Cooper, 2002; Steele & Josephs, 1990), or emotionally distressed (Baucom & Aiken, 1981). Personality research, on the other hand, has helped to identify stable traits that are associated with risky health behaviors, such as high impulsivity (Waldeck & Miller, 1997), high negative emotionality (Caspi et al., 1997), low conscientiousness (Bogg & Roberts, 2004), and low self-�control (Tangney, Baumeister, & Boone, 2004). However, note that these findings typically do not address the processes by which successful or unsuccessful health behavior is brought about. For instance, if we find that people who consumed alcohol are more likely to engage in risky sex than sober people, we typically infer that they must have been driven more strongly by their sexual impulses (and less by reflective considerations), but the mere group difference does not tell us much about the underlying processes that drive behavior in such circumstances. The idea that health behavior is not solely the result of reasoned processes and planning has led to extensions of reasoned action approaches in various directions and degrees with the aim of tapping more strongly into irrational, impulsive influences of health behavior. For instance, it has been suggested to assess behavioral willingness, that is, whether one might be willing to perform a risky behavior despite the fact that one originally intends not to engage in it (Gibbons, Gerrard, Blanton, & Russell, 1998; Gibbons, Gerrard, & Lane, 2003). Other researchers have proposed to extend reasoned approaches on an affective dimension by using more affect-based (as opposed to cognitively based) attitude measures (Kiviniemi, Voss-Humke, & Seifert, 2007) or assessing the anticipated emotional consequences (i.e., regret) of indulgence in health behaviors that are immediately gratifying but problematic in the long run (Richard, De Vries, & van der Pligt, 1998). Moreover, it has

465

been suggested to incorporate the notion of habit (i.e., routine past behaviors) in order to account for additional variance not accounted for by markers of intentional goal pursuit (Aarts, Verplanken, & van Knippenberg, 1998; Verplanken, Aarts, van Knippenberg, & Moonen, 1998). Note that all these approaches tend to rely on self-�report methodology. Hence, incremental predictive validity may be limited by the known problems of introspective access or by self-�presentational concerns associated with this method.

The Present Framework Relatively few attempts have been made thus far to apply the insights and the measurement technology that emanated from social cognitive dual-Â�system or dual-Â�process models (Evans, 2008; Smith & DeCoster, 2000; Strack & Deutsch, 2004) to models of health behavior (Hofmann, Friese, & Wiers, 2008; Marsh, Johnson, & Scott-Â�Sheldon, 2001; Tiffany, 1990; Wiers, Bartholow, et al., 2007). This chapter is part of this ongoing endeavor to bridge this gap. Specifically, we argue that an integrative approach should consider at least four crucial (sets of) variables that together predict individual differences in health behaviors (see Figure 25.1): 1. Individual differences in associative (or impulsive) processes in a given health domain. 2. Individual differences in reflective processes, including executive control (EC) capacity as well as motivation to control (fueled by beliefs and attitudes). 3. Global individual differences in personality across domains (e.g., impulsivity). 4. The health behavior–Â�relevant situation. The core of the model is a dual-Â�process model that distinguishes between associative/impulsive processes and reflective processes (1, 2). However, we want to emphasize that these processes interact not only with each other to determine health behaviors but also with situational variables (4), which may bias information processing more toward either associative or reflective processing. Furthermore, there are individual differences across specific health behaviors that can be conceptualized as personality variables (3) but from the present framework also as relatively stable individual differences in associative and controlled processes (e.g., tendency toward approach or avoidance, tendency to react on impulse or on further reflection).

466

V. APPLIED PERSPECTIVES

How do these variables interact? At the heart of dual-Â�process models applied to health psychology is conflict, between associative (or “impulsive,” often affective) processes in response to health-Â�relevant stimuli (depicted in the bottom part of the figure) and controlled (or reflective) processes depicted in the top of the Figure 25.1. In line with Deutsch and Strack (2006), we believe that the internal conflict between associative and reflective processes is central in both addictive behaviors and in many other health behaviors (Hofmann, Friese, & Wiers, 2008) as well as in many areas of psychopathology (Teachman, Cody, & Clerkin, Chapter 26, this volume; Wiers, Teachman, & De Houwer, 2007). In many cases, the associative process will activate approach tendencies, while reflective processes will suggest that there may be good reasons to refrain from approach (with many examples in addictive behaviors, obesity, risky sex, etc.). However, in some cases of health psychology, this pattern may be reversed, for example, when proactive health action is needed (e.g., exercise, health checkups, wearing seat belts), automatically activated associations (e.g., tired: watch TV) may interfere with health behaviors. In some health behaviors, including coping with chronic pain, automatically activated avoidance may be the primary problem (see later discussion). In some cases, a conflict between incompatible associations (e.g., approach and avoidance) may also play a role rather than a conflict between associations steering in one direction and reflection in the opposite direction (de Liver, van der Pligt, & Wigboldus, 2007; McEvoy, Stritzke, French, Lang, & Ketterman, 2004; Wiers, Houben, Smulders, Conrod, & Jones, 2006). Regarding the reflective pathway, traditional models of health psychology are largely applicable and they predict variance in a variety of health behaviors. When participants (often students!) are motivated and able to do so, they can estimate pros and cons of behavioral options in view of their goals in life (e.g., not become fatally ill). From a dual-Â�process perspective, reflective processes are seen as evolutionarily relatively recent processes that serve to shield goal-Â�directed behavior from interfering associative processes (Baddeley, 2007; Evans, 2008; Hofmann, Friese, & Wiers, 2008; Strack & Deutsch, 2004). The reflective system uses higher order mental operations, which provides flexibility and control over decisions and actions. These operations include executive functions such as making reasoned judgments and evaluations, putting together strategic action plans for goal pursuit, and inhibiting or overriding prepotent responses (e.g., impulses or habits). They

are achieved through relatively slow, controlled processes based on symbolic representations and operations (Evans, 2008; Smith & DeCoster, 2000; Strack & Deutsch, 2004). Importantly, for present purposes, people differ in their EC capacity, which leads to the prediction that individuals with relatively low EC capacity are more prone to act on strong impulses, a hypothesis recently confirmed in a series of studies (Grenard et al., 2008; Hofmann, Friese, & Roefs, 2009; Hofmann, Gschwendner, Friese, Wiers, & Schmitt, 2008; K. Houben & Wiers, 2009; Thush et al., 2008; Wiers, Beckers, Houben, & Hofmann, 2009). How should constructs from the impulsive and reflective systems be measured? Reflective processes are traditionally assessed with measures of verbal self-�report (questionnaires), assessing beliefs, expectancies, attitudes, self-�efficacy, and related constructs. The recent surge of implicit or indirect measures was fueled by the idea that these measures could tap into associative or impulsive processes directly without interference and censorship of the reflective system. It is now clear that current indirect measures are not process-pure indicators of associative processes only, and estimates can be made of the contributions of associative and controlled processes (Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005; Sherman et al., 2008). Similarly, questionnaire measures are not process-pure measures of reflective processes, as evidenced by influences on test scores by seemingly trivial variations in item orders (Schwarz, 1999). The third set of variables concern personality factors. Personality traits such as impulsivity or behavioral undercontrol have been related to addictive behaviors for a long time (e.g., Sher, 1991; Verdejo-�Garcia, Lawrence, & Clark, 2008) as well as to other health behaviors, including obesity (Nederkoorn, Smulders, Havermans, Roefs, & Jansen, 2006) and sexual risk taking (Hayaki, Anderson, & Stein, 2006). In addiction research, it has been difficult to tear apart to what extent these traits were predisposing factors or the result of addictive behaviors. We propose that both are true: Some traits can increase the vulnerability for many problematic health behaviors (including impulsivity, sensation seeking), but people can also become more impulsive as a result of substance use during adolescence, which may increase risks for other problematic health behaviors (see later discussion). Individual differences in personality may be related to genetic factors, environmental influences, or interplay between these factors (Caspi & Moffitt, 2006). Genetic differences can moderate



467 Attentional bias

Perception; Automatic Evaluation

Rational decision making: pros vs. cons of expected outcomes

Goals in life; Beliefs (about pros and cons of health behavior)

Approach bias

Motivational orientation (Approach–Avoidance)

Behavioral decision and intentions

+

Behavioral schemas (I)

Conflict

Behavioral schemas (R)

Associative/ Impulsive System

Controlled/ Reflective System

Impulsive health behavior (e.g., overeat; drug use; painful sex)

Reasoned health behavior (e.g., refrain from use; exercise despite pain)

sensitization (indulgence/avoidance)

Executive Control (working memory capacity; performance monitoring; inhibitory control)

indulgence-related decrements

FIGURE 25.1.╇ A general dual-process model of health behaviors. At the core of the model is that health behaviors are predicted by the combined input of associative or impulsive processes and controlled and reflective processes, where the relative contribution of these two classes of processes is determined by personality and situational factors. Note that the two dashed feedback loops indicate that, as a result of the (health) behavior, the cognitive processes are influenced, which will affect future behavioral tendencies.

habit

Situation/ State (e.g., mood, drunk)

Personality traits

Long-term changes in personality (e.g., binge drinking in adolescence)

-

468

V. APPLIED PERSPECTIVES

automatic appetitive reactions, as was recently demonstrated (Filbey et al., 2008; Wiers, Rinck, Dictus, & van den Wildenberg, 2009), and can also moderate individual differences in the ability to self-Â�regulate impulses (M. D. Robinson, 2007). Individual differences in self-Â�control ability can at least partly be related to environmental factors such as upbringing and education, as early interventions in self-Â�control (Blair & Diamond, 2008; Diamond, Barnett, Thomas, & Munro, 2007) and self-Â�regulation of emotions demonstrate (Izard et al., 2008). Religion, likewise, strongly influences the way people deal with temptations; religiosity has been shown to be a protective factor for many health behavior problems, including addictions (Baumeister & Exline, 1999; Wills, Gibbons, Gerrard, Murry, & Brody, 2003). The fourth variable is the health Â�behavior– relevant situation. The role of tempting situations has long been recognized in addiction research (e.g., Marlatt & Gordon, 1985) and in other health behaviors, including obesity (Irving & NeumarkÂ�Sztainer, 2002). A major difficulty in many health psychological problems is the high prevalence of tempting situations in modern society. Moreover, regarding relapse, recent research on conditioned appetitive motivation has stressed the importance of contextual cues, both in addiction and eating behaviors (Thewissen, van den Hout, Havermans, & Jansen, 2005; Van Gucht, Vansteenwegen, Beckers, & Van den Bergh, 2008). Note that acute intoxication as well as other temporary effects on the balance between associative and reflective processes (e.g., fatigue or ego depletion resulting from a previous task, which temporarily depleted control resources; Baumeister, 2003) is also conceived of in our framework as a situational factor. Similarly, enhanced internal motivational states (e.g., hunger, craving after deprivation; Nordgren et al., 2007) are also conceived of here as situational factors (in the latter case often triggered by a physical environment, interacting with the motivational system). Finally, regarding the general framework, we emphasize the relevance of the feedback loops (see Figure 25.1). Health-Â�related behaviors can influence subsequent appraisals of a situation. This can happen in a variety of ways: It can bias (1) subsequent associative processing (lower feedback loop) and/or (2) subsequent reflective processing (upper feedback loop). Considering the lower feedback loop, as a result of many drinking occasions, a heavy drinker’s attention is more readily captured by stimuli previously associated with alcohol (attentional bias; Cox, Fadardi, & Pothos, 2006; Field

& Cox, 2008), and approach tendencies can be automatically triggered (Field, Kiernan, Eastwood, & Child, 2008; Wiers, Rinck, et al., 2009). Through repeated experience, situational cues may also directly trigger overlearned behavioral schemas (e.g., habitual actions) without necessarily activating affective processes (Everitt & Robbins, 2005; Tiffany, 1990). Regarding the upper feedback loop, there is increasing evidence that alcohol and drug use negatively affect reflective processes, especially during adolescence: The still-Â�developing ability to selfÂ�regulate impulsive action tendencies is negatively affected, and motivation to perform alternative behaviors is impeded (Volkow, Fowler, & Wang, 2004; Wiers, Bartholow, et al., 2007). Both feedback loops may be especially strong for addictive behaviors, but their effects may affect subsequent addictive behaviors as well as other health-Â�related behaviors (e.g., through reduced EC). The reason for the stronger feedback loops as a consequence of addictive behaviors is that pharmacological properties of drugs may enhance the normal effects of repeated experience, which is thought to subsequently enhance appetitive biases (Franken, 2003; Robinson & Berridge, 2003; Volkow et al., 2004; Wiers, Bartholow, et al., 2007) as well as habit formation (Everitt & Robbins, 2005). The latter process may be particularly relevant in smoking, given the important role of nicotinic receptors in habit formation (Davis & Gould, 2008). Taken together, the general picture is that addictive behaviors involve many of the same processes as other health behaviors, with differences being related to pharmacological moderation of the feedback loops involved. Because of these enhanced feedback loops and the acute effects of alcohol and many other drugs of abuse on health behaviors (as illustrated in the opening example), substance use and misuse may play a more central role in health psychology than often appreciated: not only as “just another health behavior” but also as a moderator of cognitive processes involved in other health behaviors. For example, automatic sweet attitudes better predict sweet consumption after alcohol than when participants are sober (Hofmann & Friese, 2008). In the remainder of this chapter, we review what is known about the “four ingredients” (associative processes, reflective processes, personality, risky situations) and their interplay in a number of domains: substance use and misuse, overeating and dieting, sexual risk taking, and coping with pain and stress. In line with the emphasis of this Handbook on implicit social cognition, the emphasis in each section is on associative processes, but we also briefly consider the other factors (reflective



25. Implicit Cognition in Health Psychology

processes, personality, and situation). In the final section, we discuss implications for interventions and avenues for further research.

SUBSTANCE USE AND MISUSE Associative Processes In recent years, many studies have been conducted using either varieties of memory paradigms or reaction time paradigms to assess associative processes. We first briefly introduce the memory measures and findings with these measures in relation to substance use and misuse. We then turn to more commonly known RT measures. A number of studies of health behavior have used measures and paradigms that assess associations and implicit processes without reliance on response latency methods. In these paradigms, implicit processes are revealed in the absence of requests for deliberate or conscious recollection of the target association or content, consistent with traditional definitions of implicit memory (Graf & Schacter, 1985). Assessment methods used in these studies have focused primarily on either production responses or memory tests that have consistently been linked to implicit processes in basic research on memory (see Stacy, Ames, & Grenard, 2006). Some of the memory measures used in health psychology are identical to those that have detected implicit processes in more basic research, in which manipulations of test instructions and study populations (e.g., amnesic vs. nonamnesic patients) have demonstrated implicit processes. Memory paradigms that manipulate study and test trials have often been structured to reveal the operation of implicit or automatic processes. Examples of such paradigms include illusory memory (e.g., Roediger, Watson, McDermott, & Gallo, 2001), process dissociation (Jacoby, Debner, & Hay, 2001), extralist cued recall (Nelson, McEvoy, & Pointer, 2003), and a variety of “priming” designs (e.g., Zeelenberg, Pecher, Shiffrin, & Raaijmakers, 2003) that manipulate exposure to the target materials and type of test instructions (e.g., indirect or direct). A few of these paradigms have been used in health areas, particularly in research on alcohol use and misuse. For example, Reich and colleagues used the Deese-Â�Roediger-McDermott (Deese, 1959; Roediger et al., 2001) illusory memory paradigm to determine whether effects of alcohol are remembered as cohesive structures and whether an alcohol context automatically activates these structures (Reich, Goldman, & Noll, 2004). Their results revealed that heavier, but not lighter,

469

drinkers showed higher levels of false recognition to alcohol expectancy adjectives in response to an alcohol setting (naturalistic bar) compared with a neutral condition. This is consistent with the operation of an automatic template model of alcohol expectancies and with the implicit activation of alcohol concepts that were never explicitly presented. Fillmore, Vogel-Â�Sprott, and Gavrilescu (1999) used the process dissociation procedure to show that acute alcohol use administered experimentally impaired control processes but did not affect automatic processes. Krank and colleagues adapted the “famous name” memory paradigm (Jacoby, Woloshyn, & Kelley, 1989) to show that attempted negation of myths about the benefits of alcohol paradoxically increased the self-Â�generation of these myths as expected outcomes of the behavior (Krank, Ames, Grenard, Schoenfeld, & Stacy, 2008). Findings were consistent with unconscious, and counterproductive, effects of negation, predicted by dual-Â�process models (cf. Deutsch, Gawronski, & Strack, 2006; Deutsch & Strack, 2006). Stacy (1994) used an extralisted cued-Â�recall procedure (Nelson et al., 2003; Nelson, McKinney, Gee, & Janczura, 1998) to show that previous experience with alcohol predicted the effectiveness of extralist cues (not presented at study) in a surprise recall test of ambiguous alcohol words presented in the earlier exposure trial. Results were consistent with findings that previous experience with a behavior influences the strength of its associations in memory (Stacy et al., 1997) and with implicit activation of associations with nonpresented cues during exposure trials (Nelson et al., 1998). Word production procedures in health-Â�related research on implicit processes have most commonly used word association tests with indirect test instructions, providing an indirect assessment of associations in memory between different health behaviors and other concepts. Word association tests are valuable for this purpose because evidence across diverse paradigms from basic cognitive research shows that these tests are capable of detecting implicit conceptual memory (Seger, Rabin, Desmond, & Gabrieli, 1999), and associations uncovered in these tests strongly predict the activation of cognitions across a wide range of experimental procedures (for a review, see Stacy, Ames, & Grenard, 2006). Indeed, it is a challenge to find another test of association that predicts such a wide range of responses. Indicators of associations compiled from these tests also form common factors with sufficient internal consistency and good predictive utility in research on health

470

V. APPLIED PERSPECTIVES

behavior (e.g., Ames, Grenard, Thush, Sussman, & Wiers, 2007; Stacy, 1997; Stacy, Ames, Ullman, Zogg, & Leigh, 2006). Word association tests in this literature have typically used either free-�association instructions, in which the participants are requested to write the first word that comes to mind in response to a cue word, phrase, or picture (e.g., feeling good: ╅╅╅╅╅╅ ), or verb-�generation instructions, in which respondents react with the first action word or behavior that comes to mind in response to the cue. Either procedure has the capability of including cues that are neutral as well as those linked to target behaviors, making within�subject analysis possible. The procedures also allow for an assessment of relative cognition because the participant is free to answer with any response that comes to mind. This provides an index of relative spontaneous memory for a potentially enormous set size of alternatives not captured by any present test of response latency or other test format (Ames et al., 2007; Thush et al., 2007). It is important to emphasize that the nature of test instructions in word association is critical. Indirect-�association instructions, asking respondents to list the first word that comes to mind, lead to dramatically different results than asking participants to recollect (Stacy, Ames, & Grenard, 2006). The basic findings across a number of studies using indirect test instructions and word association is that associative responses to cues, using indirect tests that do not mention the target behavior, are consistently predictive of alcohol and drug use (for reviews, see Ames, Franken, & Coronges, 2006; Stacy, Ames, & Grenard, 2006). Importantly, some studies have revealed predictive effects in prospective studies in which previous habits and a variety of different confounders have been controlled for (Kelly, Masterman, & Marlatt, 2005; Stacy, 1997). A recent meta-�analysis of 89 effect sizes obtained from studies sampling nearly 20,000 participants found that associations assessed with word association procedures had the strongest effects compared with all other measures of implicit processes in addictive behaviors (Rooke, Hine, & Thorsteinsson, 2008). Other measures of associative processes have been developed that rely not on response latencies but on the affective rating of a neutral stimulus (e.g., a Chinese character) preceded by an affective prime. This strategy is the affect misattribution procedure (Payne et al., 2005), which has been successfully applied to predict alcohol use and smoking (Payne, Govorun, et al., 2007; Payne, McClernon, et al., 2007). In addition to these measures derived from basic memory research, many of the more commonly

known RT measures in social cognition research have been used to assess automatic associations with alcohol and drugs. Studies using varieties of the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998), perhaps surprisingly, have consistently demonstrated strong negative alcohol associations in heavy drinkers (De Houwer, Custers, & De Clercq, 2006; Houben & Wiers, 2006a, 2006b, 2008; Wiers, van de Luitgaarden, van den Wildenberg, & Smulders, 2005; Wiers, van Woerden, Smulders, & de Jong, 2002) as well as with smoking in smokers (Huijding, de Jong, Wiers, & Verkooijen, 2005; Sherman, Presson, Chassin, Rose, & Koch, 2003; Swanson, Rudman, & Greenwald, 2001). IAT scores of implicit alcohol attitudes also predicted drinking behavior above the variance explained by explicit measures using the same words (Houben & Wiers, 2006a, 2007a, 2007b; Wiers et al., 2002; for a meta-Â�analysis, see Rooke et al., 2008). Note that the direction of this effect is that less strong negative implicit attitudes (or relatively strong positive attitudes) for alcohol predicted drinking behavior. Implicit attitudes have also been studied for some other substances. Research has demonstrated neutral or ambivalent implicit associations with cannabis in cannabis users, while nonusers showed negative implicit associations (Field, Mogg, & Bradley, 2004). Cocaine users have relatively strong implicit associations between cocaine and positive affect (Wiers, Houben, & de Kraker, 2007). When positive and negative associations were assessed separately, findings indicated that both alcohol (Houben & Wiers, 2006a, 2008; Jajodia & Earleywine, 2003; McCarthy & Thompsen, 2006) and smoking (McCarthy & Thompsen, 2006) are, in fact, automatically associated with both negative and positive affect. Importantly, positive associations predicted unique variance in drinking and smoking behavior above that explained by explicit measures, whereas negative associations were unrelated to drinking and smoking behavior (Houben & Wiers, 2006a, 2008; Jajodia & Earleywine, 2003; McCarthy & Thompsen, 2006). This suggests that positive associations may be more personally relevant, while negative associations may primarily reflect “cultural wisdom” (Houben & Wiers, 2007b). In line with this idea, studies using personalized versions of the IAT, which prevent the activation of extrapersonal associations (Olson & Fazio, 2004), have also demonstrated positive implicit associations with both alcohol (Houben & Wiers, 2007b) and smoking (De Houwer et al., 2006). Moreover, in smokers, positive implicit associations with smoking have also been



25. Implicit Cognition in Health Psychology

demonstrated when focusing on the sensory aspects of smoking (Huijding & de Jong, 2006; Sherman et al., 2003) rather than the negative health�related aspects (Sherman et al., 2003). In line with these findings, studies using the (bipolar) Extrinsic Affective Simon Task (EAST; De Houwer, 2003) have demonstrated ambivalent implicit associations with alcohol (de Jong, Wiers, van de Braak, & Huijding, 2007) and in one study even positive implicit alcohol associations (De Houwer & De Bruycker, 2007) related to drinking behavior. Besides associations between addictive substances and positive or negative affect (or implicit attitudes), other associations have been studied, including with arousal, the second major dimension in emotion research (Lang, 1995), and between addictive substances and approach tendencies. First, studies that have used the IAT to examine implicit associations between alcohol and arousal have consistently demonstrated strong implicit arousal associations with alcohol (De Houwer, Crombez, Koster, & De Beul, 2004; Houben & Wiers, 2006a; Wiers et al., 2002, 2005). Moreover, these implicit arousal associations predict drinking behavior above explicit measures and are also strongly related to implicit positive associations with alcohol (Houben & Wiers, 2006b; Wiers et al., 2002, 2005). Although no studies have yet examined implicit associations between smoking and arousal, research has demonstrated implicit associations between cannabis and arousal that predict unique variance in cannabis use (Ames et al., 2007). Cocaine users also show stronger implicit associations between cocaine and both arousal and sedation than nonusers (Wiers, Houben, et al., 2007). Second, research has shown that alcohol is also implicitly associated with approach orientation, and that these associations are related to drinking behavior (Palfai & Ostafin, 2003) and show incremental validity with respect to drinking behavior (Ostafin & Palfai, 2006). Similarly, smokers show implicit associations between smoking and approach while nonsmokers do not (De Houwer et al., 2006). In summary, many studies using memory association measures and RT measures have demonstrated that substance cues trigger different clusters of associations: appetitive associations related to positive, arousing effects and approach motivation, and aversive associations either related to the negative stigma of alcohol and drug use in many cultures or to negative personal experiences. Subtle accents in the environment may determine whether the appetitive or aversive associations are triggered more strongly (Sherman et

471

al., 2003). There are also many studies that show that negative associations are relatively strong but not strongly related to behavior. One reason may be that the time interval between substance use and negative consequences is usually much longer than for positive and arousing consequences. It is noteworthy that results of both open-ended memory association measures and reaction time (RT) tests of associations are related to substance use and that they both uniquely predict substance use (Ames et al., 2007; Thush et al., 2007). Both types of measures show a similar pattern of prediction of substance use, which is moderated by individual differences in executive functions (Grenard et al., 2008; Houben & Wiers, 2009; Thush et al., 2008). Both types of measures have their own merits: The open-ended measures can assess unique patterns of associations in each individual, and RT measures assess predetermined associations, which allows for specific comparisons (e.g., Do arousal associations predict unique variance over and above positive associations?). In sum, research in this area has demonstrated that appetitive associations are positively related to use (moderated by executive functions), and that, in addition to appetitive associations, there are strong negative associations, but there is little evidence that these are predictive of substance use.

Reflective Processes There is a long history of positive correlations between alcohol use and a variety of cognitive motivational constructs, including positive attitudes (O’Callaghan, Chant, Callan, & Baglioni, 1997), motives (Cooper, Frone, Russell, & Mudar, 1995), and expectancies (Goldman, Del Boca, & Darkes, 1999; Jones, Corbin, & Fromme, 2001). It should be noted that predictive power of these constructs is far stronger when both attitudes or expectancies and alcohol or drug use are assessed crossÂ�sectionally compared with prospective prediction, especially after controlling for previous use (Jones et al., 2001; Sher, Wood, Wood, & Raskin, 1996). Nevertheless, long-term prediction has been found (Stacy, Newcomb, & Bentler, 1991). Negative expectancies, attitudes, and motives to abstain predict some variance in social drinkers but appear to become more important in problem drinkers, related to motivation to change behavior (Jones et al., 2001; Jones & McMahon, 1998). Second, there is some evidence that a relatively poor development of EC functions may play a role in the etiology of alcohol and drug use, a factor closely related to the personality construct of im-

472

V. APPLIED PERSPECTIVES

pulsivity or behavioral undercontrol (Nigg et al., 2006; Peterson, Finn, & Pihl, 1992; Peterson & Pihl, 1990; Wiers, Gunning, & Sergeant, 1998). Recent studies have shed new light on these two aspects of reflective processes: In a series of studies, it was found that measures of implicit appetitive associations (when assessed both with memory association and with RT measures) are better predictors of alcohol and drug use in participants with a relatively low score on a global index of executive functions than in participants with a high score (Grenard et al., 2008; Houben & Wiers, 2009; Thush et al., 2008). The opposite was true in participants with relatively high scores on executive functions: In these participants explicit expectancies were the best predictor of prospective alcohol use (Thush et al., 2008). Similar moderating effects of executive functions have been found for other impulsive behaviors (Hofmann, Gschwendner, et al., 2008; Wiers, Beckers, et al., 2009). In summary, explicit cognitions, including positive attitudes, expectancies, and motives, are strongly related to current drinking and moderately related to prospective drinking. Recent evidence suggests that they may be most important in participants with relatively well-�developed executive functions, while relatively automatic appetitive tendencies are a prime predictor in individuals with relatively poor executive functions.

Personality Behavioral disinhibition is a major personality risk factor for the development of alcohol and drug use problems (de Wit, 2009; Sher, Grekin, & Williams, 2005; Sher, Walitzer, Wood, & Brent, 1991; Verdejo-�Garcia et al., 2008). However, other personality risk factors have also been identified, including personality profiles related to internalizing problems (anxiety and depression), and recent research suggests that these different personality risk factors can be successfully targeted in adolescence (Conrod, Castellanos, & Mackie, 2008; Conrod, Stewart, Comeau, & Maclean, 2006). Note that individual differences in EC, which were shown to moderate appetitive impulses (Grenard et al., 2008; Houben & Wiers, 2009; Thush et al., 2008), are also related to personality (with relatively poor EC being related to behavioral disinhibition; see prior discussion).

Risk Situations As noted early in this chapter, it is hard to underestimate situational factors in alcohol and drug use

in the Western world, where especially alcohol use is part of many social situations (as was smoking until recently). Situational influences are also related to developmental period: College campuses have been recognized as a risk situation for alcohol abuse, which peaks during late teen and early 20s (Sher et al., 2005). During these years, drinking usually takes place in the peer group, and the speed of drinking of the peer groups appears to be the strongest predictor of alcohol consumption, with stronger effects than expectancies or personality (Bot, Engels, & Knibbe, 2005; van Schoor, Bot, & Engels, 2008). In some cultures, excessive drinking takes place in these unsupervised peer groups, for example, in recent studies on youth at campsites, consumption of more than 20 alcohol units per day has been reported (van de Luitgaarden, Wiers, Knibbe, & Candel, 2007). It is interesting from this perspective that reduced availability of alcohol has been related to lower levels of alcohol use and problems at the population level (Room, Babor, & Rehm, 2005). In addition to the large number of contextual factors that promote alcohol and drug use, there are internal situational factors that can promote alcohol and drug use as well, including stress (Sher, Bartholow, Peuser, Erickson, & Wood, 2007), fatigue or ego depletion (Baumeister, 2003), “hot” visceral states or craving (Nordgren et al., 2008; Sayette et al., 2008), and acute alcohol or drug use (Field, Schoenmakers, & Wiers, 2008; Schoenmakers, Wiers, & Field, 2008). From the present model, all of these factors can be viewed as temporarily favoring associative or impulsive processes over reflective or controlled processes.

Implicit Cognition and Protection against HIV/AIDS Concepts and tasks related to implicit cognition have only rarely been used in research on protection against HIV or AIDS. The dearth of studies on implicit cognition and protection against HIV or AIDS is surprising, given the critical problem of this infection and the palpable implications of implicit or automatic processes to sexual behavior. Given the large situational overlap with substance use (in unsafe sex, alcohol and drug use often play a role) and for reasons of space, we discuss this topic relatively briefly. Among the relatively few studies in this domain, most of the studies on implicit cognition and protection against HIV or AIDS have investigated the correlation between implicit cognition and condom use.



25. Implicit Cognition in Health Psychology

Several different assessment methods have been used in this research, focusing primarily on either memory strategies like word production tests or the IAT. For word production, studies on implicit processes and condom use also have used indirect assessment procedures, as described previously, in which the target behavior is not mentioned in the assessment. Stacy, Ames, and colleagues have used several different word production tests in this topic area, using indirect instructions in which the target behavior is not mentioned. Using ambiguous words as cues (homographs), they found that free-word association responses containing sexual contents predicted unprotected sex in a high-risk sample who should be using condoms because of risky sexual behavior (Stacy et al., 2000). Associative responses did not predict unprotected sex in a low-risk sample and did not predict sex with multiple partners in either sample. A second study (Stacy, Ames, Ullman, et al., 2006) used three different, indirect word production tests to evaluate elicitation of sexual content: letter-�completion and two different (behavior and event) phrase�completion tasks. In a high-risk sample of adult drug users, these diverse measures formed a common factor that correlated with having sex with multiple partners in both genders and with (less) condom use in males. For the IAT, the basic goal has been to measure implicit attitudes toward condom use, not other associations that can be measured with this procedure (e.g., sexual arousal or approach motivation; see prior discussion). Czopp, Monteith, Zimmerman, and Lynam (2004) found that implicit attitudes measured with the IAT predicted intentions to use condoms with main but not casual partners. Marsh and colleagues (2001) used an attitudinal IAT, a self-�identity IAT, and evaluative priming (Fazio, Sanbonmatsu, Powell, & Kardes, 1986) in a study of condom use (Marsh et al., 2001). Both IATs were positively related to condom use with casual, but not main, partners, whereas the priming measure was not correlated with condom use. Although these two studies are somewhat contradictory in correlations involving condom use with main versus casual partners, the studies had several procedural differences, such as use of a different form of the IAT. Together, the studies show at least that the IAT can be predictive of condom use and is promising for future research. Taken together, the studies using indirect measures of cognition to study risky or protective sexual behavior are promising and suggest that investigation by a wide range of alternative measurement paradigms to assess implicit processes

473

may be successful. Such an effort may lead to substantial new insights on risky sexual behavior and provide explanations that depart from traditional models of focus in health research. Regarding the other general variables in our framework, these are largely overlapping with those in the previous section: impulsivity and risk taking (Justus, Finn, & Steinmetz, 2000; Lejuez, Simmons, Aklin, Daughters, & Dvir, 2004). Work by Cooper and colleagues has suggested that, in addition to enhancement motives in extroverted and impulsive people, there is also a subgroup of neurotic individuals who engage in risky sex as a means of dealing with negative emotions, similar to the enhancement and coping motives in alcohol use (Cooper, 2002; Cooper, Agocha, & Sheldon, 2000). Furthermore, as for addictive behaviors, working memory capacity has been found to moderate the impact of sexual impulses on sexual behavior (Hofmann, Gschwendner, et al., 2008). Finally, as noted for alcohol-�related stimuli, it can hardly be underestimated how abundant sexual stimuli are in modern society (Friedman, 1992). Hence, the scarce evidence points to similar risk factors and processes for sexual risk behavior as for substance misuse, but more research in this area is needed.

OVEREATING AND DIETING What do you think of the famous Belgian chocolate? You probably find it delicious, irresistible even, but you also likely see drawbacks to overeating chocolate, such as weight gain. Which of these associations comes to mind spontaneously: the pleasurable aspects of consumption or the consequences of consumption? Which association determines how much you eat? How do these associations differ among obese people, chronic dieters, and healthy-�weight people?

Individual Differences in Associative Processes Obesity Obesity is ultimately caused by an energy imbalance: eating too much or expending too little energy for the amount of food eaten (Ravussin & Bogardus, 2000). High-fat foods are the main problem because they are high in energetic density and palatability, thereby easily promoting overconsumption (Schrauwen & Westerterp, 2000). A straightforward hypothesis relating implicit cognition to obesity is that obese people are charac-

474

V. APPLIED PERSPECTIVES

terized by spontaneous positive associations with high-fat foods, contributing to their overconsumption. However, as will become apparent, there is not much empirical evidence to support this hypothesis. On an IAT assessing associations with high-fat versus low-fat foods in obese and healthy weight adults, exactly the opposite was found. All participants had more positive associations with low-fat foods than with high-fat foods, an effect that was even especially pronounced in the obese group (Roefs & Jansen, 2002). This finding was corroborated in a sample of obese children using a personalized version of the IAT. It was found that both obese and lean children had a relative preference for healthy over unhealthy foods (Craeynest, Crombez, Haerens, & De Bourdeaudhuij, 2007). One could argue that these effects are caused by the prominent labels in the IAT (e.g., high fat vs. low fat; cf. De Houwer, 2001). However, similar effects were found using the affective priming paradigm (APP; Roefs, Stapert, et al., 2005) and the EAST (Craeynest et al., 2005). Moreover, using a semantic priming paradigm, it was found that both obese and healthy-�weight controls automatically associated palatable high-fat foods with restraint rather than with disinhibition (Werrij et al., 2008). Therefore, the lack of differentiation between obese people and healthy-�weight controls on associations with high- versus low-fat foods appears to be a stable finding, obtained using a variety of methodologies.

Chronic Dieting Chronic dieting is also hypothesized to be related to high-�calorie foods, which are often forbidden in diets, being extra desirable (Gendall & Joyce, 2001; Stice, 2002). Here the evidence is somewhat mixed. On an APP and EAST measure, participants showed an automatic preference for palatable over unpalatable foods independent of their dieting status and the fat content of the stimuli (Roefs, Herman, MacLeod, Smulders, & Jansen, 2005). However, when stimuli referring to the caloric density (e.g., fried, steamed) were used in place of food stimuli, chronic dieters exhibited more positive associations with high-�calorie content compared with nondieters (Hoefling & Strack, 2008). In addition, using a different type of paradigm (rapid serial visual presentation), chronic dieters, but not nondieters, were found to activate hedonic thoughts when presented with sentences including palatable foods compared with sentences with neutral food (Papies, Stroebe, & Aarts, 2007).

On measures of approach and avoidance, again there is mixed evidence. On the one hand, chronic dieters exhibited a faster approach to both highand low-�calorie foods in comparison to nondieting controls (de Jong & Veenstra, 2007); however, on the other hand, other research demonstrated an ambivalent response. Chronic dieters displayed an equally strong approach and avoidance response to high-fat foods (Fishbach & Shah, 2006). Nondieters in this study exhibited approach behavior toward the high-fat foods. Thus, no evidence was found for approach behavior specifically toward high-fat foods.

Do Automatic Associations with Food Predict Eating Behavior? Hofmann and colleagues conducted a series of experiments in which they showed that both momentary manipulations of self-�control resources and trait cognitive resources moderate the automatic association to behavior link. More specifically, they showed that automatic candy associations were predictive of the amount of candy consumed only when self-�control resources were temporarily depleted by an emotion suppression task (Hofmann, Rauch, & Gawronski, 2007) or by alcohol consumption (Hofmann & Friese, 2008). In addition, it was found that measures of executive attention, inhibitory control, and affect regulation acted as moderators. Automatic candy associations were predictive of candy consumption only in participants low in these three factors of impulse control (Hofmann et al., 2009). These effects were independent of an additional moderating effect of body mass index (BMI),4 indicating that automatic candy associations were significantly positively related to consummatory behavior in high-, but not low-, BMI individuals (Hofmann et al., 2009).

Reflective Processes In the studies by Hofmann and colleagues just discussed, measures of dietary restraint were negatively related to candy consumption in the control conditions. Thus, when self-�control resources were available, participants could stick to their restraint standards, but not when they have less cognitive resources available. This fits well with the typical pattern of results in counterregulation experiments (Herman & Mack, 1975), in which intake is precisely measured in the laboratory in a so-�called taste test. Results of these experiments show that restrained eaters can regulate their intake under



25. Implicit Cognition in Health Psychology

normal circumstances. They then eat less than unrestrained eaters. However, when a disinhibitor is present, such as the consumption of a preload (a small portion of food, similar to an appetizer; Herman & Mack, 1975), the induction of an emotional state (Ruderman, 1986), or merely smelling a preload (Jansen & van den Hout, 1991), restrained eaters counterregulate and consume more than the unrestrained eaters.

Personality Considering that inhibitory control was found to be a moderator of the automatic association to behavior link, it is highly relevant to assess the degree of inhibitory control and impulsivity in obese people. Indeed, it was found that both obese adults (Nederkoorn, Smulders, et al., 2006) and obese children (Guerrieri, Nederkoorn, & Jansen, 2008; Nederkoorn, Braet, Van Eijs, Tanghe, & Jansen, 2006) are more impulsive than healthy-�weight controls. This heightened degree of impulsivity in obese participants may cause them to act on positive automatic associations with high-fat food more often.

The Health Behavior–Â�Relevant Situation Current Western society contains copious reminders for the enjoyment of high-fat, palatable foods, but there is also a conflicting incentive to be thin. Situations differ in the extent to which enjoying high-fat palatable food is emphasized and to which being thin and dieting are emphasized. Roefs and colleagues investigated how these situations would affect automatic associations with high-fat foods using the APP in obese and healthy-Â�weight people (Roefs et al., 2006). The results showed that when eating enjoyment was emphasized (restaurant condition), participants showed an automatic relative preference for palatable, high-fat foods, whereas when health was emphasized, participants preferred low-fat foods. Weight status (obese vs. healthy weight) did not impact results at all. Consequently, situations seem to influence automatic food associations more strongly than does weight status. The lack of consistent findings of strong positive associations with high-fat foods in obese people and chronic dieters raises the question of whether automatic associations with highfat foods can explain overeating and obesity at all. Considering that the situation (i.e., the extent to which eating enjoyment vs. health is apparent) does influence automatic associations, it may be

475

that obese people are more often in a situation in which eating enjoyment is primed. In addition, obese people may focus their attention differently than healthy-�weight individuals, and their attention may be captured more strongly by these cues. Finally, it may also be that obese people are more likely to act on such automatic positive associations with high-fat foods. There is some evidence for this latter possibility, in that automatic candy associations more strongly affected candy consumption in participants with an above-�average BMI compared with those with a below-�average BMI (Hofmann et al., 2009).

Pain and Stress In contrast to substance misuse and obesity, the critical problems in coping with (chronic) low back pain seem related to dysfunctional avoidance rather than approach. Current models emphasize the critical role of specific fear of (re)injury in the maintenance of symptoms and associated disability (Asmundson, Norton, & Vlaeyen, 2004). Following these models, exaggerated fear of pain, movement, or injury will lead to enhanced attentional focus on pain-�related sensations and an enhanced tendency to interpret these sensations in a threatening fashion (Vancleef, Peters, & de Jong, 2008). In turn, this will give rise to avoiding many daily activities. In the longer term, such a strategy can lead to increased disability and the development of a vicious cycle between attention to pain-�related sensations, fear, pain, and activity avoidance.

Individual Differences in Associative Processes Despite our advances in understanding the role of fear and avoidance beliefs, a number of important issues remain unsolved. For example, why do some patients persist in avoiding physical activities while knowing that immobility is harmful? Along the same lines, it remains puzzling why some clinicians, in contrast to what they know to be the correct advice and in contrast to what they explicitly proclaim, implicitly and subtly induce (or reinforce) activity-Â�avoiding attitudes in their communication with pain patients. In an attempt to clarify these types of “illogical” behaviors, several authors referred to the possible role of more automatically activated (implicit) attitudes toward pain/movements that may be incongruent with the more deliberate (explicit) appraisals and beliefs (Gheldof, de Jong, Vinck, & Houben, 2004).

476

V. APPLIED PERSPECTIVES

It appears that immobility and recuperative quiescence are primitive and “natural” responses to harm and pain (Keay, Li, & Bandler, 2000; Morgan & Carrive, 2001). Accordingly, the tendency to avoid movement in response to pain or injury may be viewed as a rather instinctive and deeply ingrained response stereotype that may well be further strengthened by immediate pain relief and by social models. To the extent that pain-Â�related physical sensations indeed automatically elicit this type of alarming associations and response tendencies in memory, this may help explain why people may refrain from physical activities even when they actually know that physical activities result in positive health outcome. A recent affective priming study provided evidence that indeed people are generally characterized by a negative automatic attitude toward backÂ�stressing activities (Goubert, Crombez, Hermans, & Vanderstraeten, 2003). The influence of this type of memory associations on people’s behavior is assumed to be especially relevant under time constraints (e.g., physicians) or under acute pain or emotions (e.g., in patients). Thus, especially under these conditions, automatic pain–Â�threat associations may give rise to dysfunctional avoidance behaviors and pain-Â�enhancing information processing (in patients) and to inadequate advice (by physicians). Although recent work showed enhanced self–pain associations in chronic pain patients (Grumm, Erbe, von Collani, & Nestler, 2008), there is no convincing evidence to indicate that the automatic associations between pain or movement and threat are relatively strong in individuals suffering from chronic low back pain. The major difference between patients and healthy volunteers seems, therefore, to reside in the (in)ability to correct or override the automatic negative movement stereotype (de Jong, van den Hout, Rietbroek, & Huijding, 2003). This ability may not only vary as a function of relatively stable personality characteristics such as working memory capacity but also of variable motivational (e.g., absence vs. presence acute pain) and contextual conditions (e.g., absence vs. presence time pressure). The primary importance of automatic associations versus reflective processes and cognitive control may also vary across the various types of pain symptoms. For example, recent research in the domain of tension headaches found evidence for individual differences in associative processes (Armstrong, Wittrock, & Robinson, 2006). Although participants generally displayed automatic associations

between headache and negative evaluations (regardless of headache complaints), these associations were particularly strong among headache patients. Obviously, it remains to be seen whether these automatic associations have a causal influence. As a first step, it would be interesting to test the prognostic value of these associations for future headaches and to see whether these associations reduce in strength following treatment (cf. Grumm et al., 2008). Pain is also critically involved in dyspareunia (pain during sexual intercourse). In the vast majority of women suffering from dyspareunia, pain is located at the entrance of the vagina and, therefore, directly interferes with sexual intercourse. The observation that women with dyspareunia report more negative affect, less positive affect, and less subjective arousal than controls to exposure to sexual stimuli suggests that, at least at the explicit level, negative fear-Â�related beliefs regarding sexual intercourse are present in symptomatic women (Payne, Binik, Amsel, & Khalife, 2005). In line with information-Â�processing models of sexual arousal, it has been proposed that the prospect of penile–Â�vaginal intercourse may also automatically activate fear-Â�related associations that are likely to interfere with the development of sexual arousal (Laan & Janssen, 2007). In turn, this would further aggravate the dyspareunia-Â�related complaints. In apparent support of this view, women with dyspareunia showed delayed responding toward pain words in the context of a color-Â�naming interference task (Payne et al., 2005) as well as toward sexual pictures (irrespective of required response) in the context of an affective Simon task (Brauer, de Jong, Kuile, Huijding, & Laan, 2009). This enhanced interference effect has been explained in terms of enhanced vigilance and selective attentional bias (Payne et al., 2005). However, the affective Simon effects indicated that, regardless of their persistent intrusive painful intercourse experiences, symptomatic women displayed relatively positive rather than negative automatic associations with sexual stimuli (Brauer et al., 2009). At the self-Â�report level, however, affective associations were far less positive and more negative for women with dyspareunia than for controls. This may indicate the relevance of conscious appraisal and deliberate rather than automatic processes in the maintenance of dyspareunia. The apparent robustness of positive automatic associations in dyspareunia is consistent with emotion theories, stating that species’ survival requires that emotionally significant stimuli are



25. Implicit Cognition in Health Psychology

processed by automatic mechanisms, which immediately activate physiological responses (Lang, Bradley, & Cuthbert, 1990). Accordingly, defensive responses are activated when confronted with stimuli that threaten survival (e.g., harm, pain), whereas approach responses are primed by stimuli that promote survival, such as sexual stimuli. In agreement with such view, it has been suggested that there must be a strong link between sexual stimuli and genital responses, and that this link is likely to be highly prepared (in a biological sense) and automatic (in a cognitive sense) (Laan & Janssen, 2007). In line with this, the available evidence suggests that exposure to sexual stimuli automatically elicits a genital response in women with and without dyspareunia, but that in women with dyspareunia the sexual stimulus is deliberately appraised as relatively negative, thereby impeding genital arousal (i.e., lubrication). In turn, this may hamper genital intercourse and promote the generation of pain, resulting in a vicious cycle.

477

drug users predicted intentions to change jobs in these drug and alcohol treatment nurses. Together, these studies also show how stigma and prejudice can survive on a deeply engrained latent level and may inadvertently influence treatment efficacy despite the therapist’s best intentions. Elaborating on this, subsequent work in the context of chronic pain showed not only that health care providers may be characterized by dysfunctional automatic movement–Â�threat/injury associations but also that these automatic associations may have differential predictive validity for their treatment recommendation (Houben, Gijsen, Peterson, de Jong, & Vlaeyen, 2005). The impact of implicit attitudes can be expected to be especially prominent under time pressure, a condition that is typical for health care situations. Whether attitudinal complexities (automatic vs. deliberated) in clinicians interact with those of their patients and have an influence on the process and outcome of treatment is a virtually unexplored but fascinating area that clearly merits further attention.

Influence of Automatic Associations and Explicit Cognitions of Health Care Providers

Global Individual Differences in€Personality across Domains

Increasing evidence indicates that physicians’ recommendations concerning advisable levels of activity may influence clinical outcomes in terms of decreasing disability (Burton, Waddell, Tillotson, & Summerton, 1999) and encouraging fearÂ�avoidance (Linton, Vlaeyen, & Ostelo, 2002). Accordingly, clinicians’ judgments regarding the harmfulness of physical activities and recommendations for return to work or to normal activities were shown to relate to their treatment orientation (biomedical vs. biopsychosocial) (Houben, Ostelo, et al., 2005). Importantly, these recommendations may be based not only on their deliberate convictions but also on automatically activated associations that may well diverge from the explicit convictions. This possibility was demonstrated by Teachman and colleagues in their studies on weight stigma showing that even health care professionals specialized in treating obesity display an implicit antifat bias (Teachman & Brownell, 2001). In a similar vein, nurses treating drug users showed discrepant negative automatic associations with intravenous drug users (von Hippel, Brener, & von Hippel, 2008), which may interfere with providing proper care. Further emphasizing the relevance of differentiating between more deliberate (explicit) and more automatic (implicit) attitudes, specifically implicit attitudes toward intravenous

Anxiety sensitivity (AS) is one of the most prominent personality traits that has been considered as a risk factor for the maladjustment to (chronic) pain because it is characterized by dysfunctional beliefs regarding bodily sensations. Indeed, there is evidence indicating that AS is associated with fear of pain, pain-Â�specific avoidance, and negative pain responses after experimental pain induction (e.g., Keogh & Birkby, 1999). More recently, it has been proposed that the habitual tendency to excessively worry about future injury (injury or illness sensitivity [IS]) may also be involved in chronic pain. In support of this hypothesis, IS was found to be even a better predictor for fear of pain than AS and to be the single best predictor of imminent fear of experimentally induced pain (Vancleef, Peters, Roelofs, & Asmundson, 2006). In a subsequent study, it was tested whether IS may also facilitate the automatic evaluation of pain stimuli as threatening. Using an EAST to test individuals’ automatic threat appraisal of pain-Â�related words, it was shown that higher IS levels were indeed associated with more pronounced automatic threat appraisals of health-Â�threatening stimuli (Vancleef, Peters, Gilissen, & de Jong, 2007). Together, the available evidence indicates that IS may contribute to the “chronification” of pain symptoms by influencing both associative and more reflective processes.

478

V. APPLIED PERSPECTIVES

The Health Â�Behavior–Â�Relevant Situations The types of memory associations that are activated may vary as a function of temporary influences such as motivational cues. Hence, patients may react very differently when triggered by differential motivational states. For instance, experimental manipulation of patients’ anticipation of future pain (e.g., being faced, or not, with a certain object that has to be lifted immediately) may result in motivational changes influencing the type of associations that are elicited by (the prospect of) particular physical activity (Vlaeyen et al., 1999). In addition, patients’ pain behavior might be ruled by associations varying across situational contexts, in that being alone or being surrounded by colleagues, family, or a therapist most probably will result in different pain or disability outcomes. A recent study on implicit associations in tension-type headaches (TTH) provided preliminary empirical support for the importance of relevant context cues in this domain (Armstrong et al., 2006). This IAT study showed that the enhanced automatic associations between headache and negative evaluations in TTH patients were especially pronounced when headache stimuli were immediately preceded by a negative attribute stimulus. This finding suggests that the activation of negative associations differentially triggers the headache category among TTH patients. The activated negative–Â�headache associations may, in turn, bias the interpretation of somatic experience in a manner that is consistent with the activated associations, thereby leading to pain experiences among individuals prone to TTH but not among controls. Similar context effects may be at work related to other pain symptoms. To the extent that this type of (context dependent) automatic memory associations indeed predisposes one to develop a pain symptom such as headache, altering these associations may have important clinical value in reducing pain (e.g., headache) suffering. Germane to this, it has been shown that an attentional training procedure that involves repeatedly ignoring social threat and searching for acceptance (“Find the happy face in the crowd” task) resulted in stress-Â�reducing effects at both the subjective and physiological levels and improved work performance (Dandeneau, Baldwin, Baccus, Sakellaropoulo, & Pruessner, 2007). Thus, an intervention specifically designed to reduce automatic selective attention to threat appeared helpful to “cut stress off at the pass” (Dandeneau et al., 2007, p.€664). In a similar vein, it might be helpful to modify the

early stages of memory activation in the face of negative events or potential threats because reducing the strength of associations between negative events and headache (or other pain symptoms) may prevent the start of a cascade that eventually results in headache or other pain symptoms.

IMPLICIT COGNITION AND€HEALTH PSYCHOLOGY: GENERAL ISSUES In this chapter, we reviewed a selection of the literature in which methods from implicit cognition research were applied to a variety of health-�related behaviors, while realizing that we did not cover the full range of health behaviors or applications of implicit cognition. Nevertheless, we believe an interesting broader picture emerges that may also apply to a wider range of health behaviors than covered here. In this final section, we sketch the broader picture, suggest further lines of research, and briefly discuss implications for interventions.

The Broader Picture Dual-Â�process models distinguish between relatively automatic associative responses to a situation and reflective or “reasoned” processes, which until recently dominated theorizing in health psychology. Regarding the associative processes, two motivational orientations are distinguished: approach and avoidance (Strack & Deutsch, 2004). As noted in the previous section, specific situations are likely to automatically trigger these motivational tendencies: Appetitive stimuli trigger approach, whereas the natural response to pain and threatening stimuli is avoidance. From a dual-Â�process perspective, people can differ either in their associative responses or in their ability to control the associative processes. A straightforward idea has been that people with problematic health behaviors may have especially strong associations; for example, obese people may have stronger automatic reactions to food, drug users to drugs, and chronic pain patients to painful stimuli. Generally, this pattern of results has only received fairly consistent support in the area of substance use and misuse, where many studies found stronger appetitive associations in heavy versus light users. In research on eating, pain, and stress, this pattern of results has not been broadly supported. This difference may be related to the lower feedback loop in Figure 25.1, which is moderated by pharmacological properties of drugs.



25. Implicit Cognition in Health Psychology

The second obvious explanation for differences in health-Â�related behaviors concerns the role of executive control and related concepts (e.g., impulsivity). Both in research on substance misuse and in eating disorders involving excessive eating, impulsivity is associated with the problem, and there are indications that this may, in fact, be a causal factor (Guerrieri et al., 2007; Verdejo-Â�Garcia et al., 2008). Moreover, recent research has consistently confirmed predictions from dual-Â�process models that the impact of associative processes on overeating and addictive behaviors is moderated by executive functions (Grenard et al., 2008; Hofmann et al., 2009; Hofmann, Gschwendner, et al., 2008; Houben & Wiers, 2009; Thush et al., 2008), and acute depletion of executive control capacity appears to have similar effects (Hofmann & Friese, 2008; Hofmann et al., 2007). Individual difference variables reflecting a general tendency to rely on associative or reflective processes when making decisions (habit strength and need for cognition) appear to have similar moderating qualities (Conner, Perugini, O’Gorman, Ayres, & Prestwich, 2007). It is possible that relatively weak EC functions also play a role in avoidance-Â�related health problems (e.g., pain, stress), as has been suggested in anxiety research (Eysenck, Derakshan, Santos, & Calvo, 2007; Price & Mohlman, 2007). From that perspective, relatively weak EC could be a general risk factor for the development of psychopathology and health-Â�related problems, and individual differences in associative processes (either related to personality or to learning history and situational factors) would then predict which specific problems are most likely to develop. For example, individuals with high anxiety sensitivity may be more likely to develop pain-Â�related problems than cocaine abuse, although use of alcohol or drugs to alleviate negative affect may also be a risky outcome in this group. In view of this general importance of EC and the emerging evidence of a negative impact of substance use and misuse during adolescence (when the frontal cortices are still in development), substance use in this period may contribute to such a general risk for later psychopathology and health problems.

Issues for Future Research A review across different health-�related behaviors shows that there are similarities as well as differences in methods used, which may partly be coincidental (e.g., measures derived from basic memory research were first introduced in addiction research in health psychology). There are also some inter-

479

esting developments emerging in specific domains that may be of interest for other domains. First, there is evidence that in some cases different aspects of the situation may trigger different motivational tendencies: When the appetitive qualities are evident, appetitive responses are triggered, but subtle changes in the context may trigger avoidance responses (Roefs et al., 2006; Sherman et al., 2003). For many health behaviors, people may have both approach- and avoidance-Â�related associations, and the environment (including both the external and the internal environments in terms of EC depletion and visceral state) will determine which associations are activated more strongly. Second, a related issue concerns measurement: There is some emerging evidence that priming relevant concepts, either cognitively (Perugini, O’Gorman, & Prestwich, 2007) or physically (e.g., administering a prime dose of alcohol in heavy drinkers) (Schoenmakers et al., 2008), may optimize assessment of the associative processes involved. In case of alcohol priming, there are alternative explanations, including a better “context match” for memory processes or differential effects of alcohol on the associative versus control components in the measure used (cf. Sherman et al., 2008). Optimizing measurement of associative processes by relevant priming in different ways is clearly an important topic for further research. Related, general vulnerability factors may only become apparent in specific situations. Studies on classically conditioned contextual stimuli may shed important light on the influence of context on the psychological processes involved (Thewissen et al., 2005; Van Gucht et al., 2008). Third, studies on interactions between the processes we identified may advance our understanding. We discussed studies on interactions between EC and associative processes, but other interactions may be relevant as well, for example between personality and specific tempting situations (e.g., Guerrieri et al., 2008). Fourth, an interesting and as yet understudied topic is the transmission of associations, which was discussed between therapist and client (p.€ 477) but may also be relevant in the transmission of relevant associations from parent to child (Chassin, Presson, Rose, Sherman, & Prost, 2002). Fifth, associative processes may involve more than just approach and avoidance tendencies, as the literature on automatic goal processing suggests (e.g., Aarts, Custers, & Holland, 2007). There are some first attempts to link this concept to health psychology, for example, in research assessing automatic activation of alternative concerns and goals in life next to drinking in

480

V. APPLIED PERSPECTIVES

relation to alcohol use (Palfai, 2006; Sheeran et al., 2005) and dependence (Cox, Hogan, Kristian, & Race, 2002) and to eating behaviors (Palfai & Macdonald, 2007; Papies, Stroebe, & Aarts, 2008). Activating and stimulating alternative goals may be a relevant strategy for interventions, which brings us to the final topic.

Implications for Interventions A thorough discussion of implications of this review and model for health interventions is beyond the scope of this chapter. However, we briefly mention some issues. From the perspective of our model, it is important for a wide range of health behaviors to prevent adolescent substance misuse because of the likely negative effects on the development of EC processes. Regarding the efficacy of primary prevention, there is not much support for efficacy in the field of alcohol and addiction, and in some cases ironic effects have been noted (i.e., making youngsters curious for the substances they should not use) (Foxcroft, Ireland, Lister-Sharp, Lowe, & Breen, 2003). Age and price policies appear to do better (Room et al., 2005), which is consistent with the present framework, especially for adolescents, who may be less willing and able to reason rationally about health issues (Casey, Jones, & Hare, 2008). Still, as noted earlier in this chapter, the modern world is full of appetitive temptations, and this is unlikely to change. Regarding targeted prevention and intervention strategies, from the dual-�process model, one can try to change either associative processes or control processes (Wiers et al., 2008). With respect to associative processes, one can attempt to reduce an attentional bias or appetitive associative reactions (see Wiers et al., 2008). Alternatively, one can try to automate associations to alternative behaviors, incompatible with the unhealthy behaviors, as is successfully done in implementation intentions (Sheeran, 2002). With respect to the reflective system, most existing health-�promoting interventions are aimed at changing beliefs and motivations to choose healthy instead of unhealthy behavioral options. Most likely, these interventions primarily change explicit cognitions while leaving associations mostly intact (Wiers et al., 2005) or even, paradoxically, increasing their strength (Deutsch et al., 2006; Krank et al., 2008). Regarding the reflective system, it is noteworthy that not only beliefs relating to the health behavior, but also the ability to control impulses, can be targeted, as recent research on training of working memory in impulsive children has demonstrated (Klingberg et

al., 2005). Given the central role of impulse moderation for psychopathology and health behaviors, such training may be beneficial in high-risk children (Diamond et al., 2007). Finally, as noted earlier in this chapter, from dual-Â�process models, it does not follow that intervention strategies that target explicit cognitions are useless (there is a wealth of data showing that they are not); rather, additional strategies may be necessary to change associative processes relevant for health behaviors. In ending, we hope that this review will stimulate further research into implicit cognitive processes in health behaviors and ways to moderate their impact on health behaviors. Notes 1. Only males were included as participants for the practical reason that it is easier to administer alcohol (no risk of unknown pregnancy). Note that alcohol appears to have similar effects on females at least with respect to attractiveness ratings of the opposite sex (Jones, Jones, Thomas, & Piper, 2003). 2. In fact, there were two control conditions: Participants received either a placebo alcoholic drink or no drink at all, which did not affect results over the studies; therefore, conditions were collapsed (MacDonald, MacDonald, et al., 2000). 3. Note that MacDonald and colleagues explained their results with the alcohol-Â�myopia theory of Steele and Josephs (1990), which they contrasted with the popular notion that alcohol has a main disinhibiting effect. According to alcohol-Â�myopia theory, alcohol intoxication makes the most salient cues more important in determining the reaction and the more distal cues (e.g., long-term consequences) less important. As they demonstrated, when the most salient cues trigger aggression, alcohol intoxication leads to aggression, but when the most salient cues trigger helpfulness, that is what alcohol leads to. In line with this idea, MacDonald, MacDonald, and colleagues (2000) found that when they made the unsafe sex cues more salient, alcohol use led to less intention to engage in unsafe sex. The alcohol-Â�myopia account is consistent with the current dual-Â�process model, given that attentional control is one of the executive functions in the reflective system, when we assume that the salient cues trigger the corresponding associative processes. 4. BMI is an index comparing a person’s weight and height. BMI is defined as the individual’s body weight divided by the square of his or her height (kg/m2). A BMI between 18.5 and 25 is considered normal, lower values indicate underweight, higher values overweight, and a BMI above 30 is used to define obesity.



25. Implicit Cognition in Health Psychology

References Aarts, H., Custers, R., & Holland, R. W. (2007). The nonconscious cessation of goal pursuit: When goals and negative affect are coactivated. Journal of Personality and Social Psychology, 92, 165–178. Aarts, H., Verplanken, B., & van Knippenberg, A. (1998). Predicting behavior from actions in the past: Repeated decision making or a matter of habit? Journal of Applied Social Psychology, 28, 1356–1375. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. Ames, S. L., Franken, I. H. A., & Coronges, K. (2006). Implicit cognition and drugs of abuse. In R. W. Wiers & A. W. Stacy (Eds.), Handbook of implicit cognition and addiction (pp.€ 363–378). Thousand Oaks, CA: Sage. Ames, S. L., Grenard, J. L., Thush, C., Sussman, S., & Wiers, R. W. (2007). Comparison of indirect assessments of association as predictors of marijuana use among at-risk adolescents. Experimental and Clinical Psychopharmacology, 15, 204–218. Armstrong, J. F., Wittrock, D. A., & Robinson, M. D. (2006). Implicit associations in tension-type headaches: A cognitive analysis based on stress reactivity processes. Headache, 46, 1281–1290. Asmundson, G. J. G., Norton, P. J., & Vlaeyen, J. W. S. (2004). Fear-Â�avoidance models of chronic pain: An overview. In G. J. G. Asmundson, J. W. S. Vlaeyen, & G. Crombez (Eds.), Understanding pain related fear (pp.€3–24). Oxford, UK: Oxford University Press. Baddeley, A. D. (2007). Working memory, thought and action. Oxford, UK: Oxford University Press. Baucom, D. H., & Aiken, P. A. (1981). Effects of depressed mood on eating among obese and nonobese dieting and nondieting persons. Journal of Personality and Social Psychology, 41, 577–585. Baumeister, R. F. (2003). Ego depletion and selfÂ�regulation failure: A resource model of self-Â�control. Alcoholism: Clinical and Experimental Research, 27, 281–284. Baumeister, R. F., & Exline, J. J. (1999). Virtue, personality, and social relations: Self-Â�control as the moral muscle. Journal of Personality, 67, 1165–1194. Blair, C., & Diamond, A. (2008). Biological processes in prevention and intervention: The promotion of selfÂ�regulation as a means of preventing school failure. Development and Psychopathology, 20, 899–911. Bogg, T., & Roberts, B. W. (2004). Conscientiousness and health-Â�related behaviors: A meta-Â�analysis of the leading behavioral contributors to mortality. Psychological Bulletin, 130, 887–919. Bot, S. M., Engels, R. C., & Knibbe, R. A. (2005). The effects of alcohol expectancies on drinking behaviour in peer groups: Observations in a naturalistic setting. Addiction, 100, 1270–1279. Brauer, M., de Jong, P. J., Kuile, M. M., Huijding, J., & Laan, E. (2009). Automatic and deliberative affective associations with sexual stimuli in women with superficial dyspareunia. Archives of Sexual Behavior, 38, 486–497. Burton, A. K., Waddell, G., Tillotson, K. M., & Sum-

481

merton, N. (1999). Information and advice to patients with back pain can have a positive effect: A randomized controlled trial of a novel educational booklet in primary care. Spine, 24, 2484–2491. Casey, B. J., Jones, R. M., & Hare, T. A. (2008). The adolescent brain. Annals of the New York Academy of Sciences, 1124, 111–126. Caspi, A., Begg, D., Dickson, N., Harrington, H., Langley, J., Moffitt, T. E., et al. (1997). Personality differences predict health-risk behaviors in young adulthood: Evidence from a longitudinal study. Journal of Personality and Social Psychology, 73, 1052–1063. Caspi, A., & Moffitt, T. E. (2006). Gene-Â�environment interactions in psychiatry: Joining forces with neuroscience. Nature Reviews Neuroscience, 7, 583–590. Chassin, L., Presson, C., Rose, J., Sherman, S. J., & Prost, J. (2002). Parental smoking cessation and adolescent smoking. Journal of Pediatric Psychology, 27, 485–496. Conner, M. T., Perugini, M., O’Gorman, R., Ayres, K., & Prestwich, A. (2007). Relations between implicit and explicit measures of attitudes and measures of behavior: Evidence of moderation by individual difference variables. Personality and Social Psychology Bulletin, 33, 1727–1740. Conner, M. T., & Sparks, P. (2002). Ambivalence and attitudes. European Review of Social Psychology, 12, 37–70. Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K., & Groom, C. J. (2005). Separating multiple processes in implicit social cognition: The quad model of implicit task performance. Journal of Personality and Social Psychology, 89, 469–487. Conrod, P. J., Castellanos, N., & Mackie, C. (2008). Personality-Â�targeted interventions delay the growth of adolescent drinking and binge drinking. Journal of Child Psychology and Psychiatry, 49, 181–190. Conrod, P. J., Stewart, S. H., Comeau, N., & Maclean, A. M. (2006). Efficacy of cognitive-Â�behavioral interventions targeting personality risk factors for youth alcohol misuse. Journal of Child Psychology and Psychiatry, 35, 550–563. Cooper, M. L. (2002). Alcohol use and risky sexual behavior among college students and youth: Evaluating the evidence. Journal of Studies on Alcohol, 14, 101–117. Cooper, M. L., Agocha, V. B., & Sheldon, M. S. (2000). A motivational perspective on risky behaviors: The role of personality and affect regulatory processes. Journal of Personality, 68, 1059–1088. Cooper, M. L., Frone, M. R., Russell, M., & Mudar, P. (1995). Drinking to regulate positive and negative emotions: A motivational model of alcohol use. Journal of Personality and Social Psychology, 69, 990–1005. Cox, W. M., Fadardi, J. S., & Pothos, E. M. (2006). The addiction-Â�Stroop test: Theoretical considerations and procedural recommendations. Psychological Bulletin, 132, 443–476. Cox, W. M., Hogan, L. M., Kristian, M. R., & Race, J. H. (2002). Alcohol attentional bias as a predictor of alcohol abusers’ treatment outcome. Drug and Alcohol Dependence, 68, 237–243.

482

V. APPLIED PERSPECTIVES

Craeynest, M., Crombez, G., De Houwer, J., Deforche, B., Tanghe, A., & De Bourdeaudhuij, I. (2005). Explicit and implicit attitudes towards food and physical activity in childhood obesity. Behaviour Research and Therapy, 43, 1111–1120. Craeynest, M., Crombez, G., Haerens, L., & De Bourdeaudhuij, I. (2007). Do overweight youngsters like food more than lean peers?: Assessing their implicit attitudes with a personalized Implicit Association Task. Food Quality and Preference, 18, 1077–1084. Czopp, A. M., Monteith, M. J., Zimmerman, R. S., & Lynam, D. R. (2004). Implicit attitudes as potential protection from risky sex: Predicting condom use with the IAT. Basic and Applied Social Psychology, 26, 227–236. Dandeneau, S. D., Baldwin, M. W., Baccus, J. R., Sakellaropoulo, M., & Pruessner, J. C. (2007). Cutting stress off at the pass: Reducing vigilance and responsiveness to social threat by manipulating attention. Journal of Personality and Social Psychology, 93, 651–666. Davis, J. A., & Gould, T. J. (2008). Associative learning, the hippocampus, and nicotine addiction. Current Drug Abuse Reviews, 1, 9–19. De Houwer, J. (2001). A structural and process analysis of the Implicit Association Test. Journal of Experimental Social Psychology, 37, 443–451. De Houwer, J. (2003). The Extrinsic Affective Simon Task. Experimental Psychology, 50, 77–85. De Houwer, J. (2006). What are implicit measures and why are we using them? In R. W. Wiers & A. W. Stacy (Eds.), The handbook of implicit cognition and addiction (pp.€11–28). Thousand Oaks, CA: Sage. De Houwer, J., Crombez, G., Koster, E. H. W., & De Beul, N. (2004). Implicit alcohol-Â�related cognitions in a clinical sample of heavy drinkers. Journal of Behavior Therapy and Experimental Psychiatry, 35, 275–286. De Houwer, J., Custers, R., & De Clercq, A. (2006). Do smokers have a negative implicit attitude towards smoking? Cognition and Emotion, 20, 1274–1284. De Houwer, J., & De Bruycker, E. (2007). The identification-EAST as a valid measure of implicit attitudes toward alcohol-Â�related stimuli. Journal of Behavior Therapy and Experimental Psychiatry, 38, 133–143. De Houwer, J., Teige-Â�Mocigemba, S., Spruyt, A., & Moors, A. (2009). Implicit measures: A normative analysis and review. Psychological Bulletin, 135, 347–368. de Jong, P. J., van den Hout, M., Rietbroek, H., & Huijding, J. (2003). Dissociations between implicit and explicit attitudes toward phobic stimuli. Cognition and Emotion, 17, 521–545. de Jong, P. J., & Veenstra, E. (2007, June). Automatic approach-Â�avoidance tendencies and dysfunctional eating patterns. Paper presented at the Fifth World Congress of Behavioural and Cognitive Therapies, Barcelona. de Jong, P. J., Wiers, R. W., van de Braak, M., & Huijding, J. (2007). Using the Extrinsic Affective Simon Test as a measure of implicit attitudes toward alcohol: Relationship with drinking behavior and alcohol problems. Addictive Behaviors, 32, 881–887. de Liver, Y., van der Pligt, J., & Wigboldus, D. (2007). Positive and negative associations underlying ambiv-

alent attitudes. Journal of Experimental Social Psychology, 43, 319–326. de Wit, H. (2009). Impulsivity as a determinant and consequence of drug use: A review of underlying processes. Addiction Biology, 14, 22–31. Deese, J. (1959). On the prediction of occurrence of particular verbal intrusions in immediate recall. Journal of Experimental Psychology, 58, 17–22. Deutsch, R., Gawronski, B., & Strack, F. (2006). At the boundaries of automaticity: Negation as reflective operation. Journal of Personality and Social Psychology, 91, 385–405. Deutsch, R., & Strack, F. (2006). Reflective and impulsive determinants of addictive behaviors. In R. W. Wiers & A. W. Stacy (Eds.), Handbook of implicit cognition and addiction (pp.€45–57). Thousand Oaks, CA: Sage. Diamond, A., Barnett, W. S., Thomas, J., & Munro, S. (2007). Preschool program improves cognitive control. Science, 318, 1387–1388. Evans, J. S. B. T. (2008). Dual-Â�processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, 255–278. Everitt, B. J., & Robbins, T. W. (2005). Neural systems of reinforcement for drug addiction: From actions to habits to compulsion. Nature Neuroscience, 8, 1481– 1489. Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7, 336–353. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. Field, M., & Cox, W. M. (2008). Attentional bias in addictive behaviors: A review of its development, causes, and consequences. Drug and Alcohol Dependence, 97, 1–20. Field, M., Kiernan, A., Eastwood, B., & Child, R. (2008). Rapid approach responses to alcohol cues in heavy drinkers. Journal of Behavior Therapy and Experimental Psychiatry, 39, 209–218. Field, M., Mogg, K., & Bradley, B. P. (2004). Cognitive bias and drug craving in recreational cannabis users. Drug and Alcohol Dependence, 74, 105–111. Field, M., Schoenmakers, T., & Wiers, R. W. (2008). Cognitive processes in alcohol binges: A review and research agenda. Current Drug Abuse Reviews, 1, 263–279. Filbey, F. M., Ray, L., Smolen, A., Claus, E. D., Audette, A., & Hutchison, K. E. (2008). Differential neural response to alcohol priming and alcohol taste cues is associated with DRD4, VNTR and OPRM1 genotypes. Alcoholism: Clinical and Experimental Research, 32, 1113–1123. Fillmore, M. T., Vogel-Â�Sprott, M., & Gavrilescu, D. (1999). Alcohol effects on intentional behavior: Dissociating controlled and automatic influences. Experimental and Clinical Psychopharmacology, 7, 372–378. Fishbach, A., & Shah, J. Y. (2006). Self-Â�control in action: Implicit dispositions toward goals and away from temptations. Journal of Personality and Social Psychology, 90, 820–832. Fishbein, M., & Ajzen, I. (Eds.). (1975). Belief, attitude,



25. Implicit Cognition in Health Psychology

intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Â�Wesley. Foxcroft, D. R., Ireland, D., Lister-Sharp, D. J., Lowe, G., & Breen, R. (2003). Longer-term primary prevention for alcohol misuse in young people: A systematic review. Addiction, 98, 397–411. Franken, I. H. (2003). Drug craving and addiction: Integrating psychological and neuropsychopharmacological approaches. Progress in Neuro-Â�Psychopharmacology and Biological Psychiatry, 27, 563–579. Friedman, H. L. (1992). Changing patterns of adolescent sexual behavior: Consequences for health and development. Journal of Adolescent Health, 13, 345–350. Gendall, K. A., & Joyce, P. R. (2001). Characteristics of food cravers who binge eat. In M. M. Hetherington (Ed.), Food cravings and addiction (pp.€567–583). London: Leatherhead. Gheldof, E. L. M., de Jong, P. J., Vinck, J., & Houben, R. (2004). Attitudes towards physical activity. The role of implicit versus explicit associations. In G. J. Asmundson, J. W. Vlaeyen, & G. Crombez (Eds.), Understanding pain related fear (pp.€117–137). Oxford, UK: Oxford University Press. Gibbons, F. X., Gerrard, M., Blanton, H., & Russell, D. W. (1998). Reasoned action and social reaction: Willingness and intention as independent predictors of health risk. Journal of Personality and Social Psychology, 74, 1164–1181. Gibbons, F. X., Gerrard, M., & Lane, D. J. (2003). A social reaction model of adolescent health risk. In J. M. Suls & K. A. Wallston (Eds.), Social psychological foundations of health and illness (pp.€ 107–136). Oxford, UK: Blackwell. Goldman, M. S., Del Boca, F. K., & Darkes, J. (1999). Alcohol expectancy theory: The application of cognitive neuroscience. In K. E. Leonard & H. T. Blane (Eds.), Psychological theories of drinking and alcoholism (2nd ed., pp.€203–246). New York: Guilford Press. Goubert, L., Crombez, G., Hermans, D., & Vanderstraeten, G. (2003). Implicit attitude towards pictures of back-Â�stressing activities in pain-free subjects and patients with low back pain: An affective priming study. European Journal of Pain, 7, 33–42. Graf, P., & Schacter, D. L. (1985). Implicit and explicit memory for new associations in normal and amnesic subjects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11, 501–518. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Grenard, J. L., Ames, S. L., Wiers, R. W., Thush, C., Sussman, S., & Stacy, A. W. (2008). Working memory capacity moderates the predictive effects of drugÂ�related associations on substance use. Psychology of Addictive Behaviors, 22, 426–432. Grumm, M., Erbe, K., von Collani, G., & Nestler, S. (2008). Automatic processing of pain: The change of implicit pain associations after psychotherapy. Behaviour Research and Therapy, 46, 701–714. Guerrieri, R., Nederkoorn, C., & Jansen, A. (2008). The interaction between impulsivity and a varied food environment: Its influence on food intake and overweight. International Journal of Obesity, 32, 708–714.

483

Guerrieri, R., Nederkoorn, C., Stankiewicz, K., Alberts, H., Geschwind, N., Martijn, C., et al. (2007). The influence of trait and induced state impulsivity on food intake in normal-Â�weight healthy women. Appetite, 49, 66–73. Hayaki, J., Anderson, B., & Stein, M. (2006). Sexual risk behaviors among substance users: Relationship to impulsivity. Psychology of Addictive Behaviors, 20, 328–332. Herman, C. P., & Mack, D. (1975). Restrained and unrestrained eating. Journal of Personality, 43, 647–660. Hoefling, A., & Strack, F. (2008). The tempting effect of forbidden foods. High calorie content evokes conflicting implicit and explicit evaluations in restrained eaters. Appetite, 51, 681–689. Hofmann, W., & Friese, M. (2008). Impulses got the better of me: Alcohol moderates the influence of implicit attitudes toward food cues on eating behavior. Journal of Abnormal Psychology, 117, 420–427. Hofmann, W., Friese, M., & Roefs, A. (2009). Three ways to resist temptation: The independent contributions of executive attention, inhibitory control, and affect regulation to the impulse control of eating behavior. Journal of Experimental Social Psychology, 45, 431–435. Hofmann, W., Friese, M., & Wiers, R. W. (2008). Impulsive versus reflective influences on health behavior: A theoretical framework and empirical review. Health Psychology Review, 2, 111–137. Hofmann, W., Gschwendner, T., Friese, M., Wiers, R. W., & Schmitt, M. (2008). Working memory capacity and self-Â�regulatory behavior: Toward an individual differences perspective on behavior determination by automatic versus controlled processes. Journal of Personality and Social Psychology, 95, 962–977. Hofmann, W., Rauch, W., & Gawronski, B. (2007). And deplete us not into temptation: Automatic attitudes, dietary restraint, and self-Â�regulatory resources as determinants of eating behavior. Journal of Experimental Social Psychology, 43, 497–504. Houben, K., & Wiers, R. W. (2006a). Assessing implicit alcohol associations with the Implicit Association Test: Fact or artifact? Addictive Behaviors, 31, 1346– 1362. Houben, K., & Wiers, R. W. (2006b). A test of the salience asymmetry interpretation of the alcohol-IAT. Experimental Psychology, 53, 292–300. Houben, K., & Wiers, R. W. (2007a). Are drinkers implicitly positive about drinking alcohol?: Personalizing the alcohol-IAT to reduce negative extrapersonal contamination. Alcohol and Alcoholism, 42, 301–307. Houben, K., & Wiers, R. W. (2007b). Personalizing the alcohol-IAT with individualized stimuli: Relationship with drinking behavior and drinking-Â�related problems Addictive Behaviors, 32, 2852–2864. Houben, K., & Wiers, R. W. (2008). Implicitly positive about alcohol?: Implicit positive associations predict drinking behavior. Addictive Behaviors, 33, 979–986. Houben, K., & Wiers, R. W. (2009). Response inhibition moderates the relationship between implicit associations and drinking behavior. Alcoholism: Clinical and Experimental Research, 33, 626–633. Houben, R. M., Gijsen, A., Peterson, J., de Jong, P. J., & Vlaeyen, J. W. (2005). Do health care providers’

484

V. APPLIED PERSPECTIVES

attitudes towards back pain predict their treatment recommendations?: Differential predictive validity of implicit and explicit attitude measures. Pain, 114, 491–498. Houben, R. M., Ostelo, R. W., Vlaeyen, J. W., Wolters, P. M., Peters, M., & Stomp-van den Berg, S. G. (2005). Health care providers’ orientations towards common low back pain predict perceived harmfulness of physical activities and recommendations regarding return to normal activity. European Journal of Pain, 9, 173–183. Huijding, J., & de Jong, P. J. (2006). Automatic associations with the sensory aspects of smoking: Positive in habitual smokers but negative in non-Â�smokers. Addictive Behaviors, 31, 182–186. Huijding, J., de Jong, P. J., Wiers, R. W., & Verkooijen, K. (2005). Implicit and explicit attitudes towards smoking in a smoking and a non-Â�smoking setting. Addictive Behaviors, 30, 949–961. Irving, L. M., & Neumark-Â�Sztainer, D. (2002). Integrating the prevention of eating disorders and obesity: Feasible or futile? Preventive Medicine, 34, 299–309. Izard, C. E., King, K. A., Trentacosta, C. J., Morgan, J. K., Laurenceau, J. P., Krauthamer-Ewing, E. S., et al. (2008). Accelerating the development of emotion competence in Head Start children: Effects on adaptive and maladaptive behavior. Development and Psychopathology, 20, 369–397. Jacoby, L. L., Debner, J. A., & Hay, J. F. (2001). Proactive interference, accessibility bias, and process dissociations: Valid subjective reports of memory. Journal Experimental Psychology: Learning, Memory, and Cognition, 27, 686–700. Jacoby, L. L., Woloshyn, V., & Kelley, C. (1989). Becoming famous without being recognized: Unconscious influences of memory produced by dividing attention. Journal of Experimental Psychology: General, 118, 115–125. Jajodia, A., & Earleywine, M. (2003). Measuring alcohol expectancies with the Implicit Association Test. Psychology of Addictive Behaviors, 17, 126–133. Jansen, A., & van den Hout, M. (1991). On being led into temptation: “Counterregulation” of dieters after smelling a “preload.” Addictive Behaviors, 16, 247–253. Janz, N. K., & Becker, M. H. (1984). The health belief model: A decade later. Health Education Quarterly, 11, 1–47. Jones, B. T., Corbin, W., & Fromme, K. (2001). A review of expectancy theory and alcohol consumption. Addiction, 91, 57–72. Jones, B. T., Jones, B. C., Thomas, A. P., & Piper, J. (2003). Alcohol consumption increases attractiveness ratings of opposite-sex faces: A possible third route to risky sex. Addiction, 98(8), 1069–1075. Jones, B. T., & McMahon, J. (1998). Alcohol motivations as outcome expectancies. In N. Heather & W. R. Miller (Eds.), Treating addictive behaviors (pp.€75– 91). New York: Plenum Press. Justus, A. N., Finn, P. R., & Steinmetz, J. E. (2000). The influence of traits of disinhibition on the association between alcohol use and risky sexual behavior. Alcoholism: Clinical and Experimental Research, 24, 1028–1035.

Keay, K. A., Li, Q. F., & Bandler, R. (2000). Muscle pain activates a direct projection from ventrolateral periaqueductal gray to rostral ventrolateral medulla in rats. Neuroscience Letters, 290, 157–160. Kelly, A. B., Masterman, P. W., & Marlatt, G. A. (2005). Alcohol related associative strength and drinking behaviours: Concurrent and prospective relationships. Drug and Alcohol Review, 24, 489–498. Keogh, E., & Birkby, J. (1999). The effect of anxiety sensitivity and gender on the experience of pain. Cognition and Emotion, 13, 813–829. Kiviniemi, M. T., Voss-Humke, A. M., & Seifert, A. L. (2007). How do I feel about the behavior?: The interplay of affective associations with behaviors and cognitive beliefs as influences on physical activity behavior. Health Psychology, 26, 152–158. Klingberg, T., Fernell, E., Olesen, P. J., Johnson, M., Gustafsson, P., Dahlström, K., et al. (2005). Computerized training of working memory in children with ADHD—A randomized, controlled trial. Journal of American Academy for Child and Adolescent Psychiatry, 44, 177–186. Krank, M. D., Ames, S. L., Grenard, J. L., Schoenfeld, T., & Stacy, A. W. (2008). Paradoxical effects of unconscious influences of alcohol information on alcohol outcome expectancies. Manuscript submitted for publication. Laan, E., & Janssen, E. (2007). How do men and women feel?: Determinants of subjective experience of sexual arousal. In E. Janssen (Ed.), The psychophysiology of sex (pp.€278–290). Bloomington: Indiana University Press. Lang, P. J. (1995). The emotion probe. Studies of motivation and attention. American Psychologist, 50, 372–385. Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1990). Emotion, attention, and the startle reflex. Psychological Review, 97, 377–395. Lejuez, C. W., Simmons, B. L., Aklin, W. M., Daughters, S. B., & Dvir, S. (2004). Risk-Â�taking propensity and risky sexual behavior of individuals in residential substance use treatment. Addictive Behaviors, 29, 1643–1647. Linton, S. J., Vlaeyen, J., & Ostelo, R. (2002). The back pain beliefs of health care providers: Are we fearÂ�avoidant? Journal of Occupational Rehabilitation, 12, 223–232. MacDonald, T. K., Fong, G. T., Zanna, M. P., & Martineau, A. M. (2000). Alcohol myopia and condom use: Can alcohol intoxication be associated with more prudent behavior? Journal of Personality and Social Psychology, 78, 605–619. MacDonald, T. K., MacDonald, G., Zanna, M. P., & Fong, G. T. (2000). Alcohol, sexual arousal, and intentions to use condoms in young men: Applying alcohol myopia theory to risky sexual behavior. Health Psychology, 19, 290–298. MacDonald, T. K., Zanna, M. P., & Fong, G. T. (1996). Why common sense goes out of the window: Effects of alcohol on intentions to use condoms. Personality and Social Psychology Bulletin, 22, 763–775. Marlatt, G. A., & Gordon, J. R. (1985). Relapse prevention: Maintenance strategies in the treatment of addictive behaviors. New York: Guilford Press.



25. Implicit Cognition in Health Psychology

Marsh, K. L., Johnson, B. T., & Scott-Â�Sheldon, L. A. (2001). Heart versus reason in condom use: Implicit versus explicit attitudinal predictors of sexual behavior. Zeitschrift für Experimentelle Psychologie, 48, 161–175. McCarthy, D. M., & Thompsen, D. M. (2006). Implicit and explicit measures of alcohol and smoking cognitions. Psychology of Addictive Behaviors, 20, 436–444. McEvoy, P. M., Stritzke, W. G. K., French, D. J., Lang, A. R., & Ketterman, R. L. (2004). Comparison of three models of alcohol craving in young adults: A crossÂ�validation. Addiction, 99, 482–497. Morgan, M. M., & Carrive, P. (2001). Activation of the ventrolateral periaqueductal gray reduces locomotion but not mean arterial pressure in awake, freely moving rats. Neuroscience, 102, 905–910. Nederkoorn, C., Braet, C., Van Eijs, Y., Tanghe, A., & Jansen, A. (2006). Why obese children cannot resist food: The role of impulsivity. Eating Behaviors, 7, 315–322. Nederkoorn, C., Smulders, F. T., Havermans, R. C., Roefs, A., & Jansen, A. (2006). Impulsivity in obese women. Appetite, 47, 253–256. Nelson, D. L., McEvoy, C. L., & Pointer, L. (2003). Spreading activation or spooky action at a distance? Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 42–52. Nelson, D. L., McKinney, V. M., Gee, N. R., & Janczura, G. A. (1998). Interpreting the influence of implicitly activated memories on recall and recognition. Psychological Review, 105, 299–324. Nigg, J. T., Wong, M. M., Martel, M. M., Jester, J. M., Puttler, L. I., Glass, J. M., et al. (2006). Poor response inhibition as a predictor of problem drinking and illicit drug use in adolescents at risk for alcoholism and other substance use disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 45, 468–475. Nordgren, L. F., van der Pligt, J., & van Harreveld, F. (2007). Evaluating Eve: Visceral states influence the evaluation of impulsive behavior. Journal of Personality and Social Psychology, 93, 75–84. Nordgren, L. F., van der Pligt, J., & van Harreveld, F. (2008). The instability of health cognitions: Visceral states influence self-Â�efficacy and related health beliefs. Health Psychology, 27, 722–727. O’Callaghan, F. V., Chant, D. C., Callan, V. J., & Â�Baglioni, A. (1997). Models of alcohol use by young adults: An examination of various attitude-Â�behavior theories Journal of Studies on Alcohol, 58, 502–507. Olson, M. A., & Fazio, R. H. (2004). Reducing the influence of extrapersonal associations on the Implicit Association Test: Personalizing the IAT. Journal of Personality and Social Psychology, 86, 653–667. Ostafin, B., & Palfai, T. P. (2006). Compelled to consume: The Implicit Association Test and automatic alcohol motivation. Psychology of Addictive Behaviors, 20, 322–327. Palfai, T. P. (2006). College student alcohol use in context: The utility of goal constructs. Psychology of Addictive Behaviors, 20, 143–144. Palfai, T. P., & Macdonald, A. (2007). Effects of temptations on the affective salience of weight control goals. Behaviour Research and Therapy, 45, 449–458.

485

Palfai, T. P., & Ostafin, B. D. (2003). Alcohol-Â�related motivational tendencies in hazardous drinkers: Assessing implicit response tendencies using the modified-IAT. Behaviour Research and Therapy, 41, 1149–1162. Papies, E. K., Stroebe, W., & Aarts, H. (2007). Pleasure in the mind: Restrained eating and spontaneous hedonic thoughts about food. Journal of Experimental Social Psychology, 43, 810–817. Papies, E. K., Stroebe, W., & Aarts, H. (2008). Healthy cognition: Processes of self-Â�regulatory success in restrained eating. Personality and Social Psychology Bulletin, 34, 1290–1300. Payne, B. K., Cheng, C. M., Govorun, O., & Stewart, B. D. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. Journal of Personality and Social Psychology, 89, 277–293. Payne, B. K., Govorun, O., & Arbuckle, N. L. (2007). Automatic attitudes and alcohol: Does implicit liking predict drinking? Cognition and Emotion, 22, 238–271. Payne, B. K., McClernon, F. J., & Dobbins, I. G. (2007). Automatic affective responses to smoking cues. Experimental and Clinical Psychopharmacology, 15, 400–409. Payne, K. A., Binik, Y. M., Amsel, R., & Khalife, S. (2005). When sex hurts, anxiety and fear orient attention towards pain. European Journal of Pain, 9, 427–436. Perugini, M., O’Gorman, R., & Prestwich, A. (2007). An ontological test of the IAT: Self-Â�activation can increase predictive validity. Experimental Psychology, 54, 134–147. Peterson, J. B., Finn, P. R., & Pihl, R. O. (1992). Cognitive dysfunction and the inherited predisposition to alcoholism. Journal of Studies on Alcohol, 53, 154–160. Peterson, J. B., & Pihl, R. O. (1990). Information processing, neuropsychological function, and the inherited predisposition to alcoholism. Neuropsychological Review, 1, 343–369. Price, R. B., & Mohlman, J. (2007). Inhibitory control and symptom severity in late life generalized anxiety disorder. Behaviour Research and Therapy, 45, 2628–2639. Ravussin, E., & Bogardus, C. (2000). Energy balance and weight regulation: Genetics versus environment. British Journal of Nutrition, 83(Suppl. 1), S17–S20. Reich, R. R., Goldman, M. S., & Noll, J. A. (2004). Using the false memory paradigm to test two key elements of alcohol expectancy theory. Experimental and Clinical Psychopharmacology, 12, 102–110. Richard, R., De Vries, N. K., & van der Pligt, J. (1998). Anticipated regret and precautionary sexual behavior. Journal of Applied Social Psychology, 28, 1411–1428. Robinson, M. D. (2007). Gassing, braking, and selfÂ�regulation: Error self-Â�regulation, well-being, and goal-Â�related processes. Journal of Experimental Social Psychology, 43, 1–16. Robinson, T. E., & Berridge, K. C. (2003). Addiction. Annual Review of Psychology, 54, 25–53. Roediger, H. L., Watson, J. M., McDermott, K. B., & Gallo, D. A. (2001). Factors that determine false recall: A multiple regression analysis. Psychonomic Bulletin and Review, 8, 385–407.

486

V. APPLIED PERSPECTIVES

Roefs, A., Herman, C. P., MacLeod, C. M., Smulders, F. T., & Jansen, A. (2005). At first sight: How do restrained eaters evaluate high-fat palatable foods? Appetite, 44, 103–114. Roefs, A., & Jansen, A. (2002). Implicit and explicit attitudes toward high-fat foods in obesity. Journal of Abnormal Psychology, 111, 517–521. Roefs, A., Quaedackers, L., Werrij, M. Q., Wolters, G., Havermans, R., Nederkoorn, C., et al. (2006). The environment influences whether high-fat foods are associated with palatable or with unhealthy. Behaviour Research and Therapy, 44, 715–736. Roefs, A., Stapert, D., Isabella, L. A., Wolters, G., Wojciechowski, F., & Jansen, A. (2005). Early associations with food in anorexia nervosa patients and obese people assessed in the affective priming paradigm. Eating Behaviors, 6, 151–163. Rogers, R. W. (1983). Cognitive and physiological processes in fear appraisal and attitude change: A revised theory of protection motivation. In J. Cacioppo & R. Petty (Eds.), Social psychophysiology: A sourcebook (pp.€153–176). New York: Guilford Press. Rooke, S. E., Hine, D. W., & Thorsteinsson, E. B. (2008). Implicit cognition and substance use: A meta-Â�analysis. Addictive Behaviors, 33, 1314–1328. Room, R., Babor, T., & Rehm, J. (2005). Alcohol and public health. The Lancet, 365, 519 – 530. Ruderman, A. J. (1986). Dietary restraint: A theoretical and empirical review. Psychological Bulletin, 99, 247–262. Sayette, M. A., Loewenstein, G., Griffin, K. M., & Black, J. J. (2008). Exploring the cold-to-hot empathy gap in smokers. Psychological Science, 19, 926–932. Schoenmakers, T., Wiers, R. W., & Field, M. (2008). Effects of a low dose of alcohol on cognitive biases and craving in heavy drinkers. Psychopharmacology, 197, 169–178. Schrauwen, P., & Westerterp, K. R. (2000). The role of high-fat diets and physical activity in the regulation of body weight. British Journal of Nutrition, 84, 417–427. Schwarz, N. (1999). Self-Â�reports: How the questions shape the answers. American Psychologist, 54, 93– 105. Seger, C. A., Rabin, L. A., Desmond, J. E., & Gabrieli, J. D. E. (1999). Verb generation priming involves conceptual implicit memory. Brain and Cognition, 41, 150–177. Sheeran, P. (2002). Intention-Â�behavior relations: A conceptual and empirical review. European Review of Social Psychology, 12, 1–36. Sheeran, P., Aarts, H., Custers, R., Rivis, A., Webb, T. L., & Cooke, R. (2005). The goal-Â�dependent automaticity of drinking habits. British Journal of Social Psychology, 44, 47–63. Sher, K. J. (1991). Children of alcoholics: A critical appraisal of theory and research. Chicago: University of Chicago Press. Sher, K. J., Bartholow, B. D., Peuser, K., Erickson, D. J., & Wood, M. D. (2007). Stress-Â�response-Â�dampening effects of alcohol: Attention as a mediator and moderator. Journal of Abnormal Psychology, 116, 362–377. Sher, K. J., Grekin, E. R., & Williams, N. A. (2005). The

development of alcohol use disorders. Annual Review of Clinical Psychology, 1, 493–523. Sher, K. J., Walitzer, K. S., Wood, P. K., & Brent, E. E. (1991). Characteristics of children of alcoholics: Putative risk factors, substance use and abuse, and psychopathology. Journal of Abnormal Psychology, 100, 427–448. Sher, K. J., Wood, M. D., Wood, P. K., & Raskin, G. (1996). Alcohol outcome expectancies and alcohol use: A latent variable cross-Â�lagged panel study. Journal of Abnormal Psychology, 105, 561–574. Sherman, J. W., Gawronski, B., Gonsalkorale, K., Hugenberg, K., Allen, T. J., & Groom, C. J. (2008). The selfÂ�regulation of automatic associations and behavioral impulses. Psychological Review, 115, 314–335. Sherman, S. J., Presson, C. C., Chassin, L., Rose, J. S., & Koch, K. (2003). Implicit and explicit attitudes toward cigarette smoking: The effects of context and motivation. Journal of Social and Clinical Psychology, 22, 13–39. Smith, E. C., & DeCoster, J. (2000). Dual-Â�process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4, 108–131. Stacy, A. W. (1994, June). Evidence of implicit memory activation of alcohol concepts: Toward parallel memory models of addiction. Paper presented at the annual meeting of the Research Society on Alcoholism, Maui, Hawaii. Stacy, A. W. (1997). Memory activation and expectancy as prospective predictors of alcohol and marijuana use. Journal of Abnormal Psychology, 106, 61–73. Stacy, A. W., Ames, S. L., & Grenard, J. (2006). Word association tests of associative memory and implicit processes: Theoretical and assessment issues. In R. W. Wiers & A. W. Stacy (Eds.), Handbook of implicit cognition and addiction (pp.€75–90). Thousand Oaks, CA: Sage. Stacy, A. W., Ames, S. L., Ullman, J. B., Zogg, J. B., & Leigh, B. C. (2006). Spontaneous cognition and HIV risk behavior. Psychology of Addictive Behaviors, 20, 196–206. Stacy, A. W., Bentler, P. M., & Flay, B. R. (1994). Attitudes and health behavior in diverse populations: Drunk driving, alcohol use, binge eating, marijuana use, and cigarette use. Health Psychology, 13, 73–85. Stacy, A. W., Leigh, B. C., & Weingardt, K. R. (1997). An individual-Â�difference perspective applied to word association. Personality and Social Psychology Bulletin, 23, 229–237. Stacy, A. W., Newcomb, M. D., & Ames, S. L. (2000). Implicit cognition and HIV risk behavior. Journal of Behavioral Medicine, 23, 475–499. Stacy, A. W., Newcomb, M. D., & Bentler, P. M. (1991). Cognitive motivation and drug use: A 9-year longitudinal study. Journal of Abnormal Psychology, 100, 502–515. Steele, C. M., & Josephs, R. A. (1990). Alcohol myopia: Its prized and dangerous effects. American Psychologist, 45, 921–933. Stice, E. (2002). Risk and maintenance factors for eating pathology: A meta-Â�analytic review. Psychological Bulletin, 128, 825–848.



25. Implicit Cognition in Health Psychology

Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Swanson, J. E., Rudman, L. A., & Greenwald, A. G. (2001). Using the Implicit Association Test to investigate attitude-Â�behaviour consistency for stigmatised behaviour. Cognition and Emotion, 15, 207–230. Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High self-Â�control predicts good adjustment, less pathology, better grades, and interpersonal success. Journal of Personality, 72, 271–324. Teachman, B. A., & Brownell, K. D. (2001). Implicit anti-fat bias among health professionals: Is anyone immune? International Journal of Obesity and Related Metabolic Disorders, 25, 1525–1531. Thewissen, R., van den Hout, M., Havermans, R. C., & Jansen, A. (2005). Context-Â�dependency of cueÂ�elicited urge to smoke. Addiction, 100, 387–396. Thush, C., Wiers, R. W., Ames, S. L., Grenard, J., Sussman, S., & Stacy, A. W. (2007). Apples and oranges?: Comparing indirect measures of alcohol-Â�related cognition predicting alcohol use in at-risk adolescents. Psychology of Addictive Behaviors, 21, 587–591. Thush, C., Wiers, R. W., Ames, S. L., Grenard, J. L., Sussman, S., & Stacy, A. W. (2008). Interactions between implicit and explicit cognition and working memory capacity in the prediction of alcohol use in at-risk adolescents. Drug and Alcohol Dependence, 94, 116–124. Tiffany, S. T. (1990). A cognitive model of drug urges and drug-use behavior: Role of automatic and nonautomatic processes. Psychological Review, 97, 147–168. van de Luitgaarden, J., Wiers, R. W., Knibbe, R. A., & Candel, M. J. (2007). Single-Â�session expectancy challenge with young heavy drinkers on holiday. Addictive Behaviors, 32, 2865–2878. Van Gucht, D., Vansteenwegen, D., Beckers, T., & Van den Bergh, O. (2008). Return of experimentally induced chocolate craving after extinction in a different context: Divergence between craving for and expecting to eat chocolate. Behaviour Research and Therapy, 46, 375–391. van Schoor, G., Bot, S. M., & Engels, R. C. (2008). Alcohol drinking in young adults: The predictive value of personality when peers come around. European Addiction Research, 14, 125–133. Vancleef, L. M., Peters, M. L., & de Jong, P. J. (2009). Interpreting ambiguous health and bodily threat: Are individual differences in pain-Â�related vulnerability constructs associated with an on-line negative interpretation bias? Journal of Behavior Therapy and Experimental Psychiatry, 40, 59–69. Vancleef, L. M., Peters, M. L., Gilissen, S. M., & de Jong, P. J. (2007). Understanding the role of injury/ illness sensitivity and anxiety sensitivity in (automatic) pain processing: An examination using the Extrinsic Affective Simon Task. Journal of Pain, 8, 563–572. Vancleef, L. M., Peters, M. L., Roelofs, J., & Asmundson, G. J. (2006). Do fundamental fears differentially contribute to pain-Â�related fear and pain catastrophizing?: An evaluation of the sensitivity index. European Journal of Pain, 10, 527–536.

487

Verdejo-Â�Garcia, A., Lawrence, A. J., & Clark, L. (2008). Impulsivity as a vulnerability marker for substanceuse disorders: Review of findings from high-risk research, problem gamblers and genetic association studies. Neuroscience and Biobehavioral Reviews, 32, 777–810. Verplanken, B., Aarts, H., van Knippenberg, A., & Moonen, A. (1998). Habit versus planned behaviour: A field experiment. British Journal of Social Psychology, 37, 111–128. Vlaeyen, J. W., Seelen, H. A., Peters, M., de Jong, P., Aretz, E., Beisiegel, E., et al. (1999). Fear of movement/(re)injury and muscular reactivity in chronic low back pain patients: An experimental investigation. Pain, 82, 297–304. Vohs, K. D., & Heatherton, T. F. (2000). Self-Â�regulatory failure: A resource-Â�depletion approach. Psychological Science, 11, 249–254. Volkow, N. D., Fowler, J. S., & Wang, G. J. (2004). The addicted human brain viewed in the light of imaging studies: Brain circuits and treatment strategies. Neuropharmacology, 47(Suppl. 1), 3–13. von Hippel, W., Brener, L., & von Hippel, C. (2008). Implicit prejudice toward injecting drug users predicts intentions to change jobs among drug and alcohol nurses. Psychological Science, 19, 7–11. Waldeck, T. L., & Miller, L. S. (1997). Gender and impulsivity differences in licit substance use. Journal of Substance Abuse, 9, 269–275. Ward, A., & Mann, T. (2000). Don’t mind if I do: Disinhibited eating under cognitive load. Journal of Personality and Social Psychology, 78, 753–763. Werrij, M. Q., Roefs, A., Janssen, I., Stapert, D., Wolters, G., Mulkens, S., et al. (2008). Early associations with palatable foods in overweight and obesity are not disinhibition related but restraint related. Journal of Behavior Therapy and Experimental Psychiatry, 40, 136–146. Wiers, R. W., Bartholow, B. D., van den Wildenberg, E., Thush, C., Engels, R. C. M. E., Sher, K. J., et al. (2007). Automatic and controlled processes and the development of addictive behaviors in adolescents: A review and a model. Pharmacology, Biochemistry and Behavior, 86, 263–283. Wiers, R. W., Beckers, L., Houben, K., & Hofmann, W. (2009). A short fuse after alcohol: Implicit power associations predict aggressiveness after alcohol consumption in young heavy drinkers with limited executive control. Pharmacology, Biochemistry and Behavior, 93, 300–305. Wiers, R. W., Gunning, W. B., & Sergeant, J. A. (1998). Is a mild deficit in executive functions in boys related to childhood ADHD or to parental multigenerational alcoholism? Journal of Abnormal Child Psychology, 26, 415–430. Wiers, R. W., Houben, K., & de Kraker, J. (2007). Implicit cocaine associations in active cocaine users and controls. Addictive Behaviors, 32, 1284–1289. Wiers, R. W., Houben, K., Smulders, F. T. Y., Conrod, P. J., & Jones, B. T. (2006). To drink or not to drink: The role of automatic and controlled cognitive processes in the etiology of alcohol-Â�related problems. In R. W. Wiers & A. W. Stacy (Eds.), Handbook of im-

488

V. APPLIED PERSPECTIVES

plicit cognition and addiction (pp.€339–361). Thousand Oaks, CA: Sage. Wiers, R. W., Rinck, M., Dictus, M., & van den Wildenberg, E. (2009). Relatively strong automatic appetitive action-Â�tendencies in male carriers of the OPRM1 G-allele. Genes, Brain and Behavior, 8, 101–106. Wiers, R. W., Schoenmakers, T., Houben, K., Thush, C., Fadardi, J. S., & Cox, W. M. (2008). Can problematic alcohol use be trained away?: New behavioural treatments aimed at changing and moderating implicit cognitive processes in alcohol abuse. In C. R. Martin (Ed.), Identification and treatment of alcohol dependency (pp.€ 185–205). Keswick, UK: M&K Publishing. Wiers, R. W., Teachman, B. A., & De Houwer, J. (2007c). Implicit cognitive processes in psychopathology: An introduction. Journal of Behavior Therapy and Experimental Psychiatry, 38, 95–104.

Wiers, R. W., van de Luitgaarden, J., van den Wildenberg, E., & Smulders, F. T. Y. (2005). Challenging implicit and explicit alcohol-Â�related cognitions in young heavy drinkers. Addiction, 100, 806–819. Wiers, R. W., van Woerden, N., Smulders, F. T. Y., & de Jong, P. J. (2002). Implicit and explicit alcohol-Â�related cognitions in heavy and light drinkers. Journal of Abnormal Psychology, 111, 648–658. Wills, T. A., Gibbons, F. X., Gerrard, M., Murry, V. M., & Brody, G. H. (2003). Family communication and religiosity related to substance use and sexual behavior in early adolescence: A test for pathways through self-Â�control and prototype perceptions. Psychology of Addictive Behaviors, 17, 312–323. Zeelenberg, R., Pecher, D., Shiffrin, R. M., & Raaijmakers, J. W. W. (2003). Semantic context effects and priming in word association. Psychonomic Bulletin and Review, 10, 653–660.

C h a p t e r 26

Clinical Applications of Implicit Social€Cognition Theories and Methods Bethany A. Teachman, Meghan W. Cody, and Elise M. Clerkin

I

t is exciting to see the implicit social cognition and psychopathology research traditions come together because the two fields have often operated independently. The historic neglect of one another’s findings has been unfortunate because in many ways the two fields are vitally linked. To understand abnormal, maladaptive ways of processing information about the self and others, it is critical to understand how this occurs in healthy populations and is influenced by situational factors. Analogously, to understand the influence of the social environment on cognitive processing, it is essential to recognize the full range of individual differences and how this intra- and interpersonal variability and the environment interact. Moreover, the influences of emotions, attitudes, and beliefs on behavior are at the core of both fields, so integrating their findings holds promise to move each field forward. We are increasingly able to ask important questions that would not be possible without the influence of one another’s methods and perspectives about how human beings make sense of their social world. In this chapter, we focus primarily on indirect measures of disorder-Â�relevant attitudes and beliefs1 that capture some aspect of implicit social cognitive processing (i.e., reflecting one of the features of automaticity outlined by Bargh, 1994: uncontrollable, unintentional, efficient, or unconscious). Clinical applications of implicit association and



priming measures are emphasized given the central role of these paradigms in current social cognition research. At the same time, because much of the work in psychopathology has used more traditional measures of information processing, such as indicators of attentional interference like the emotional Stroop (labeled e-�Stroop to differentiate it from the classic Stroop) and dot-probe tasks, we also briefly summarize evidence from these tasks. Evidence from the selected paradigms has been organized into broad disorder categories, reflecting the most common problem areas and those with the strongest relevant empirical literatures: anxiety, mood, eating, body dysmorphic, psychotic, and personality disorders (substance abuse research is covered in Wiers et al., Chapter 25, this volume). Within each disorder category, we examine evidence for group differences between clinical and nonclinical samples, predictive validity, vulnerability to psychopathology or causal links between biased processing and changes in symptoms, and relations between task performance and treatment outcome. Finally, we consider future research directions needed to better understand implicit social cognitive processes in clinical populations. Emphasis has been placed on diagnosed samples, but reports of analogue samples (groups that are high in symptoms of a given disorder but have not been formally diagnosed) are also included. This inclusion was based on recognition of the continu489

490

V. APPLIED PERSPECTIVES

ous, dimensional nature of many areas of psychopathology (see Brown & Barlow, 2005) and to provide more comprehensive coverage of the current state of the literature. Also, we focus on the adult literature but have noted a couple of interesting examples from the child literature when they are relevant to disorder vulnerability.

ANXIETY DISORDERS Anxiety disorders are characterized by excessive fear, worry, panic, or anxiety that is sufficiently severe that it impairs a person’s functioning. Based on the current version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR; American Psychiatric Association, 2000), there are seven different anxiety disorders: (1) panic disorder, characterized by recurrent panic attacks that involve the rapid onset of multiple physical symptoms (such as racing heart and shortness of breath) and fear of future attacks; (2) agoraphobia, in which a person avoids situations where anxiety or panic symptoms might occur; (3) social anxiety disorder, which involves fear of negative evaluation by others and consequent avoidance of interpersonal and/or performance situations; (4) specific phobias, exemplified by excessive fear toward a particular target such as animals, certain natural environments (e.g., heights), and closed spaces; (5) obsessive–Â�compulsive disorder (OCD), characterized by repeated unwanted intrusive thoughts or images (obsessions) or ritualized behaviors designed to reduce distress (compulsions; e.g., washing one’s hands excessively); (6) generalized anxiety disorder (GAD), which involves extreme worry and catastrophic thinking about a wide range of situations; and (7) posttraumatic stress disorder (PTSD), in which exposure to a trauma is followed by reexperiencing of the event in some way (e.g., in dreams or flashbacks), avoidance of cues associated with the trauma, and increased arousal. As evident from this list, the target of the fear varies across the different anxiety disorders, but in all cases the fear or worry is excessive relative to what others would experience in that situation. In addition, individuals with these disorders usually go to great lengths to avoid their feared situation, which adds considerably to the associated impairment in occupational and social functioning. There is a rich history of theoretical and empirical work noting the biased nature of automatic processing in anxiety disorders (e.g., Beck & Clark, 1997; Mathews & MacLeod, 2005; McNally, 1995; Mogg & Bradley, 2005; Williams, Watts, Ma-

cLeod, & Mathews, 1997). In Beck’s early writings, he pointed to the role of “automatic thoughts” in anxiety problems, intrusive thoughts whose content involved danger or harm (Beck, Emery, & Greenberg, 1985). Although these thoughts may be considered automatic insofar as they are likely somewhat involuntary, they are also the conscious output of elaborative processes. In more recent writings, Beck and colleagues outline a model that more explicitly delineates periods when the relative balance between automatic and strategic processing will vary. In particular, Beck and Clark (1997) propose a schema-based informationÂ�processing model of anxiety “that involves: (a) the initial registration of a threat stimulus (orienting mode); (b) the activation of a primal threat mode; and (c) the secondary activation of more elaborative and reflective modes of thinking” (p.€49). In this model, automatic processing is expected to be active throughout all three stages, whereas the role for strategic processing increases over time. Other researchers have focused on the presence of different features of automaticity in anxiety rather than on changes over time. In particular, in a seminal article, McNally (1995) reviewed evidence for capacity-free, unconscious, and involuntary automatic processing in anxiety, concluding that the support was strongest for involuntary processing and relatively weak for capacity-free or unconscious processing: “It is the inability of the patient to terminate fear-Â�generating processing once it starts that is the hallmark of pathological anxiety” (p.€ 752). This conclusion was based on the available paradigms at that time to assess the selective processing of potentially threatening information. With the advent of a broader range of paradigms and tighter controls within tasks (e.g., for assessing unconscious processing), we can now evaluate these claims more fully.

Group Differences and Predictive Validity Implicit Association Tasks Over the past decade, there has been a growing interest in examining whether persons with anxiety difficulties differ from nonanxious individuals in their implicit associations, related to either the self or the feared object. Although the majority of demonstrations have been conducted with the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998), related measures, such as the Go/No-Go Association Task (GNAT; Nosek & Banaji, 2001) and Extrinsic Affective Simon Task



26. Clinical Applications of Theories and Methods

(EAST; De Houwer, 2003), have also been used. (See a description of this class of measures in Teige-Â�Mocigemba, Klauer, & Sherman, Chapter 7, this volume.) It is clear at this stage that at least some anxiety problems are distinguished by automatic fear associations in memory, although more work with diagnosed samples across the range of anxiety disorders is sorely needed. A great deal of this work has occurred in the area of specific animal fears and phobias, particularly spider fear, likely because these samples are readily accessible and the fear target is clearly specified. In an early demonstration that the IAT could distinguish between persons with fears of either snakes or spiders, Teachman, Gregg, and Woody (2001) found an opposite pattern of implicit associations for the different fear groups on four IAT tasks that involved classification of pictures of snakes and spiders with descriptive words indicating valence (bad/good), fear, danger, or disgust. In addition, the differential association of the animals with being afraid (vs. unafraid) was significant even after controlling for the IAT effect from the implicit bad/good evaluation, suggesting that fear associations not only are specific to the particular fear target (i.e., snakes vs. spiders) but also capture fear group differences beyond simple liking. Since this initial demonstration, a number of other researchers have also demonstrated specific spider–fear associations using the IAT (e.g., Ellwart, Rinck, & Becker, 2006; Huijding & de Jong, 2007). Similar associations have been evident on related tasks that do not require a specific, relative comparison target category as the IAT does. For instance, Teachman (2007) found stronger implicit spider–fear associations on the GNAT among spider-Â�fearful, relative to nonfearful, individuals. On the EAST, Huijding and de Jong (2005) differentiated among high- and low-Â�spider-fear groups in their automatic responses to spider pictures, and Ellwart, Becker, and Rinck (2005) found similar group differences with spider-Â�relevant words, but only when the concept of spiders had been primed. Notably, there have been failures to replicate these spider-fear group differences (e.g., de Jong, van den Hout, Rietbroek, & Huijding, 2003), but the overall pattern of implicit fear associations is clear. Importantly, the evidence for implicit associations is not limited to spider fear. In the area of specific fears and phobias more generally, there is evidence based on the IAT for greater implicit height–fear associations among people high (vs. low) in acrophobia symptoms (Teachman, Stefanucci, Clerkin, Cody, & Proffitt, 2008) and for

491

greater disgust associations with blood and injection images for people high (vs. low) in bloodÂ�injury-injection symptoms (Saporito, Heiman, Unger, & Teachman, 2004). There is also evidence for group differences on the IAT in associating “panic” versus “calm” with the self among people high (vs. low) in anxiety sensitivity (a vulnerability marker for anxiety problems characterized by fear of anxiety symptoms; Teachman, 2005) and among individuals diagnosed with panic disorder relative to healthy control participants (Teachman, Smith-Janik, & Saporito, 2007). There is less clear evidence in the area of PTSD, but see Englehard, Huijding, van den Hout, and de Jong (2007). Finally, in the area of social phobia, both de Jong (2002) and Tanner, Stopa, and De Houwer (2006) found relatively less positive implicit self-Â�esteem among women who were high, versus low, in social anxiety symptoms. Similarly, de Jong, Pasman, Kindt, and van den Hout (2001) found a tendency for women high in social anxiety to associate social situations with more negative outcomes relative to women low in social anxiety (although Teachman & Allen, 2007, did not find a relationship between implicit and explicit measures tied to social anxiety in an unselected adolescent sample). Thus, although far more work is needed with diagnosed samples, there are now considerable data suggesting the expected group differences in disorder-Â�relevant implicit associations across numerous anxiety difficulties. The evidence for predictive validity is somewhat less extensive but also promising. There are many examples of implicit associations predicting avoidance behavior and distress when participants are asked to approach their feared object (e.g., avoidance of a spider: Ellwart et al., 2006; reactions to a balcony and ladder in height fear: Teachman, Stefanucci, et al., 2008), including support for incremental validity. For instance, implicit spider–fear associations predicted degree of approach to a live spider above and beyond the variance explained by self-Â�report measures (e.g., Teachman, 2007; Teachman & Woody, 2003; see also prediction of anxiety during a stressful speech task in an unselected sample: Egloff & Schmukle, 2002). In panic disorder, relatively lower implicit associations of the self as calm predicted a latent factor reflecting a variety of panic disorder symptoms, including panic frequency, agoraphobic avoidance, and distress during a task that elicited light-Â�headedness (a common panic symptom; Teachman et al., 2007). Interestingly, in this study, the implicit associations did not significantly correlate with other measures of information-Â�processing biases (e.g., the e-Â�Stroop

492

V. APPLIED PERSPECTIVES

task), suggesting good prediction of symptoms but some independence among different aspects of biased cognitive processing. Furthermore, it appears that implicit and explicit fear measures may at times have differential predictive validity, with some hints that implicit associations may be more strongly associated with relatively more automatic, versus strategic, measures tied to anxiety-Â�relevant markers (although this is not a consistent finding). Huijding and de Jong (2006), for example, found that implicit spider–fear associations predicted eyeblink-Â�startle response (often interpreted as a measure of avoidance motivation; Lang, Bradley, & Cuthbert, 1990) more strongly than a self-Â�report measure, which, in turn, was a better predictor of more strategic avoidance behavior.

Priming Tasks Unlike the extensive work in the social cognition field using priming tasks, there are fewer examples of semantic or affective priming paradigms being applied with clinically anxious populations, so drawing general conclusions about priming effects is somewhat premature (but see intriguing evidence that trait anxiety may moderate affective and conceptual priming effects in nonclinical samples; e.g., Berner & Maier, 2004; Fox, 1994; Li, Paller, & Zinbarg, 2008; Maier, Berner, Hau, & Pekrun, 2007). This is unfortunate because the designs often lend themselves well to demonstrating uncontrollable processing, arguably the strongest feature of automaticity in anxiety disorders (McNally, 1995). Negative priming paradigms seem particularly useful because they have the potential to reflect both uncontrollable and unintentional processing, depending on the particular task instructions and design. This class of priming paradigms has been used most frequently with OCD samples to examine general cognitive processing differences and learning deficits in this population (e.g., Enright & Beech, 1990; McNally, Wilhelm, Buhlmann, & Shin, 2001), such as difficulties with selective inhibition. However, it has not typically been used to examine reactions to disorder-Â�relevant content. An interesting exception comes from a study by Amir, Cobb, and Morrison (2008) in which they used a modified negative priming paradigm that included idiographically selected words for individuals with OCD (e.g., “contaminated” for a person with washing concerns) and yoked, nonanxious control participants. They found opposite priming effects for neutral versus threat words in the OCD sample, which they interpreted to suggest that in-

dividuals with OCD have difficulty learning contingencies when information is personally relevant to their fears. Further supporting the importance of idiographically matching stimuli so that it is personally relevant, Schneider and Schulte (2007) found that individuals with panic disorder demonstrated stronger semantic priming effects than did a nonclinical control group when naming catastrophic target words (e.g., “infarction”) immediately following aurally presented prime sentences that described the perception of anxiety symptoms. Notably, there was no group difference when looking across identical stimuli for all participants (see also Schniering & Rapee, 1997); the effect was evident only when priming effects were calculated for idiographically selected stimuli. Thus, although evidence is sparse at this stage, early findings suggest it is important to take into account the heterogeneous nature of threat stimuli when examining priming effects in anxious samples. Also, there is some evidence that small variations in stimuli exposure can influence the expression of priming results. McNally, Hornig, Otto, and Pollack (1997) found evidence for priming effects for threat relative to positive words for persons diagnosed with panic disorder (but not for a nonanxious control group), but the effect was only evident for targets presented in the bottom (vs. top) of the screen. Moreover, timing of stimuli presentation is clearly important, with some studies only seeing effects at relatively longer stimulusonset asynchronies (SOAs; e.g., 2,000 msec), suggesting that relatively more controlled processing was likely influencing results (see also Clark et al., 1988). Similarly, Dalgleish, Cameron, Power, and Bond (1995) found faster endorsement of negative self-Â�descriptive adjectives following negatively valenced primes for a GAD sample at 2,000 msec (vs. at 250 msec) SOA. Notably, for other informationÂ�processing paradigms, such as the dot-probe task (described later), results are often more robust with anxious samples at shorter SOAs, so the optimal timing of stimuli presentation remains an important but unresolved question across paradigms. We suspect that these highly specific requirements for stimuli presentation and the heterogeneity of concerns among anxious samples may partly explain some prior null findings (e.g., Kindt & Brosschot, 1998) in the priming literature. For example, Bradley, Mogg, and Williams (1995) did not find an effect of priming on a lexical decision task among a mixed clinically anxious group. Taken together, we see priming paradigms as potentially useful for examining automatic processes



26. Clinical Applications of Theories and Methods

in anxiety, but this work is still in its infancy. One possible reason the paradigms have been used less frequently concerns the often-cited problem of low reliability of priming measures, which can make them difficult to use for individual differences and repeated measures designs that are common for psychopathology and treatment studies.

Attention Bias Tasks Although measures of attentional bias, such as the e-Â�Stroop and dot-probe task, are used less frequently in the implicit social cognition field, these tasks can be effective at capturing some features of automatic processing, such as uncontrollability and lack of conscious awareness (when stimuli are presented subliminally); thus, we briefly summarize some of the key findings from these tasks. To investigate unconscious processing, the subliminal e-Â�Stroop and modified dot-probe tasks have been used across a range of anxious populations. In these paradigms, latency to complete a task, such as ink color naming or detecting the location of a probe, is compared when fear-Â�relevant versus fear-Â�irrelevant stimuli are presented very briefly and then backward-Â�masked. Findings for both the subliminal e-Â�Stroop and dot-probe paradigms have been somewhat inconsistent (regarding whether group differences are observed in interference effects following presentation of the fear-Â�relevant stimuli). There is little doubt that unconscious processing of fear stimuli can occur given that there are many positive findings on the subliminal e-Â�Stroop (e.g., panic disorder with agoraphobia: Lundh, Wikstrom, Westerlund, & Öst, 1999; GAD: Bradley, Mogg, Millar, & White, 1995; Mogg, Bradley, Williams, & Mathews, 1993; PTSD: Harvey, Bryant, & Rapee, 1996; specific phobias: van den Hout, Tenney, Huygens, & de Jong, 1997) and to a lesser extent on the subliminal version of the dot-probe task (e.g., Mogg, Bradley, & Williams, 1995). However, there are also many null findings, suggesting a number of likely moderators of these effects. For the subliminal dot-probe task, results appear to be stronger for clinical relative to subthreshold analogue samples and stronger when samples are homogenous with respect to the specific anxiety disorder being examined; however, low reliability of the task has been a serious constraint (see review by Schmukle, 2005). For the subliminal e-Â�Stroop, methodological differences across studies may explain some of the null findings, including variable approaches to masking, stimuli presentation durations, presence of a stressor during the task, and small sample sizes

493

(see also Williams, Mathews, & MacLeod, 1996, for review of the likely mechanisms underlying e-Â� Stroop interference). Also, the evidence appears to be more robust for some anxiety disorders over others (e.g., results are quite mixed in specific phobias; Wenzel & Holt, 1999), although methodological variations across studies confound a simple disorder-Â�specific interpretation. More consistent evidence for phobic group differences has been observed using paradigms that present masked stimuli and then examine how quickly fear responses can be conditioned or extinguished to fear-Â�relevant stimuli (see review by Öhman & Mineka, 2001). These conditioning/extinction tasks do not necessarily investigate attentional processes directly but do support claims that fearful responses can be activated by unconscious perceptual analysis of fear-Â�relevant stimuli. Supraliminal versions of the e-Â�Stroop and dotprobe tasks have been used extensively with anxiety disorder populations (see also related measures such as the Posner task; e.g., Amir, Elias, Klumpp, & Przeworski, 2003). These tasks seem to capture uncontrollable processing well and, in some cases, may also reflect unintentional processing of emotional information. There are a great many studies using the e-Â�Stroop paradigm, and despite a number of null results (e.g., Kampman, Keijsers, Verbraak, Näring, & Hoogduin, 2002; Moritz et al., 2004), there is a plethora of examples indicating that anxious individuals have selective interference when processing fear-Â�relevant stimuli, even though task performance does not require semantic processing of the stimuli. We list just a couple of examples from the various anxiety disorders here and encourage the interested reader to see recent helpful reviews and meta-Â�analyses by Mogg and Bradley (2005), Phaf and Kan (2007), and Ehrenreich and Gross (2002, for a review of the child anxiety literature). Supraliminal e-Â�Stroop effects have been observed in each of the anxiety disorders (e.g., specific phobia: Thorpe & Salkovskis, 1997; Wikström, Lundh, Westerlund, & Högman, 2004; social phobia: Becker, Rinck, Margraf, & Roth, 2001; Spector, Pecknold, & Libman, 2003; GAD: Becker et al., 2001; Mogg, Bradley, Millar, & White, 1995; PTSD; McNally, Kaspi, Riemann, & Zeitlin, 1990; Paunovic, Lundh, & Öst, 2002; Vythilingam et al., 2007; OCD: Foa, Ilai, McCarthy, Shoyer, & Murdock, 1993; Hartston & Swerdlow, 1999; panic disorder with and without agoraphobia: Lundh et al., 1999; McNally, Riemann & Kim, 1990). At the same time, despite the many examples of significant e-Â�Stroop effects, a number of factors

494

V. APPLIED PERSPECTIVES

seem to influence the likelihood of observing strong effects: the specific anxiety disorder examined (e.g., stronger effects for PTSD and somewhat less robust evidence for panic disorder and OCD); stimuli characteristics (e.g., interference effects are enhanced when the task utilizes personally relevant negative stimuli; see Williams et al., 1996); and comorbidity (e.g., effects appear to diminish when anxiety is comorbid for depression; see Bradley, Mogg, Millar, & White, 1995). On the basis of their meta-Â�analysis, Phaf and Kan (2007) argue that e-Â�Stroop effects are more robust for the supraversus subliminal version and when words are presented in blocked format, which they interpret as evidence that the interference effect is driven by a relatively slow (vs. fast) process whereby the anxious individual has difficulty disengaging from threat stimuli (suggesting uncontrollability). The dot-probe task has been used less extensively with diagnosed anxious samples. However, there are still numerous examples of enhanced identification of the probe following threat-Â�relevant (relative to control) cues, suggesting attention was already oriented toward the threat information. For instance, group differences have been observed in GAD (e.g., Bradley, Mogg, White, Groom, & de Bono, 1999; Mogg, Mathews, & Eysenck, 1992), panic disorder (e.g., Horenstein & Segui, 1997; Kroeze & van den Hout, 2000), social phobia (e.g., Musa, Lépine, Clark, Mansell, & Ehlers, 2003, if the socially phobic participants did not also have comorbid depression), and specific fear and phobic samples (e.g., blood injury: Mogg, Bradley, Miles, & Dixon, 2004; spiders: Mogg & Bradley, 2006, when stimuli were presented at 200 msec). Again, as with the e-Â�Stroop results, there are also a number of null results (e.g., PTSD: Elsesser, Sartory, & Tackenberg, 2004; specific phobias: Elsesser, Heuschen, Pundt, & Sartory, 2006; Wenzel & Holt, 1999). Factors that seem to influence the likelihood of observing group differences include having relatively large sample sizes to capture smaller effect sizes, and the ideal timing of stimuli presentation appears to vary across disorders. For instance, Mogg and Bradley (2006) suggest stronger results in specific phobias when stimuli are presented for shorter durations (e.g., 200 msec), but results are found more consistently in GAD at longer durations (e.g., 500 msec). This difference points to likely variation across the disorders in how rapidly attention is first oriented toward threat stimuli and the rate at which disengagement of attention occurs. Along these lines, there are intriguing signs that under some conditions a bias away from threat-Â�relevant information may be evident on the

dot-probe task (e.g., social phobia: Chen, Ehlers, Clark, & Mansell, 2002). These findings raise questions about when attentional orienting versus disengagement (which may reflect avoidance2) will occur in anxious populations. Clearly, learning more about the time course of these effects is important, but results across tasks suggest that seemingly uncontrollable processing of emotional information influences subsequent task performance, and there are hints supporting these tasks’ predictive validity (e.g., Elsesser et al., 2006, found that heart rate in reaction to fear-Â�relevant stimuli was associated with attentional biases). This conclusion is further strengthened by a class of tasks known as visual search tasks that are premised on the idea that speed of detecting a target cue embedded in a matrix of other cues will vary as a function of whether the target or distracter cues are fear relevant. These tasks again point to an attentional bias in anxious samples and provide some evidence of uncontrollable processing (particularly under conditions when the distracter items are threat relevant). The primary evidence comes from phobic groups, including spider fear and phobia (Miltner, Krieschel, Hecht, Trippe, & Weiss, 2004; Rinck, Reinecke, Ellwart, Heuer, & Becker, 2005) and women with social phobia (Rinck & Becker, 2005), although see recent mixed evidence from a sample with PTSD (Pineles, Shipherd, Welch, & Yovel, 2007). In general, findings suggest that even when task performance would be enhanced by counteracting the activation of emotional information (e.g., ignoring spider distracter cues), interference effects indicate that anxious, relative to nonanxious, participants have greater difficulty controlling this process. Further reinforcing this conclusion is emerging evidence using a recently developed task, the approach–Â�avoidance task (AAT; e.g., Chen & Bargh, 1999), which also seems to capture attentional interference. This task involves comparing time to approach versus avoid different types of stimuli (typically by pushing or pulling a joystick) when the approach/avoid instructions are based on a fear-Â�irrelevant feature (e.g., the shape of the picture stimulus) and the content of the stimuli should theoretically be ignored. Greater avoidance tendencies toward threat-Â�relevant stimuli have been observed among socially anxious (Heuer, Rinck, & Becker, 2007) and spider-Â�fearful (e.g., Rinck & Becker, 2007) individuals, where the AAT also predicted actual approach behavior to a live spider. We have only reviewed a sample of the tasks that have been used to demonstrate automatic



26. Clinical Applications of Theories and Methods

processes in anxious samples, highlighting those that have the closest ties to the implicit social cognition literature or strongest empirical base in the psychopathology literature. Notwithstanding the brevity of this review, the convergent evidence supports the utility of these paradigms to differentiate anxious from nonanxious samples. Furthermore, effects on these tasks show important relationships to a variety of symptom and behavioral measures. Limitations of the existing literature highlight the need for more studies with diagnosed samples and increased understanding of the factors that moderate the strength of the observed effects (especially given the many null findings across paradigms and sensitivity of the tasks to minor procedural variations). Also, the evidence cited thus far has mostly been cross-�sectional; we consider the smaller body of evidence addressing temporal prediction and causal relationships next.

Ties to Vulnerability and Causal Links Most of the work examining vulnerability and causal links between measures relevant to implicit social cognition and anxiety disorders has occurred with attentional bias paradigms, such as the e-�Stroop and dot probe. For instance, greater interference effects on the e-�Stroop task predicted more intense emotional responding to a common panic trigger (a carbon dioxide challenge; Nay, Thorpe, Roberson-Nay, Hecker, & Sigmon, 2004) and a more negative emotional response following a stressful medical diagnostic procedure (MacLeod & Hagan, 1992). Similarly, Egloff, Wilhelm, Neubauer, Mauss, and Gross (2002) found that performance on a dot-probe task predicted subsequent cardiovascular reactivity, a common physiological marker of fear, 1 week later during a speech stressor task in an unselected sample. Less work tied to vulnerability has been conducted with implicit association or priming paradigms, likely because these paradigms are relative newcomers in the psychopathology field. Thus, although some interesting studies are underway (e.g., to examine implicit associations as predictors of future changes in anxiety symptoms), results from this work are not yet available. One early null finding is that an IAT administered to Dutch soldiers following deployment to Iraq did not predict later development of PTSD (Engelhard et al., 2007). However, it is simply too early at this stage to know whether these measures will serve as markers of vulnerability. A positive sign in this regard was the finding that implicit panic associations were elevated among persons with high anxi-

495

ety sensitivity even when this group reported no experience (yet) with clinical panic (Teachman, 2005). This suggests that the implicit associations were tied to a vulnerability marker for anxiety disorders (i.e., anxiety sensitivity) rather than occurring as a consequence of experiencing panic. Also, manipulations designed to shift beliefs that are thought to constitute vulnerability markers for OCD (specifically, shifting interpretations that intrusive thoughts are an important window into a person’s morals and character) also shifted related implicit associations about intrusive thoughts and implicit self-Â�evaluations, especially for those individuals with preexisting obsessional beliefs that likely made them more susceptible to the manipulation (Teachman & Clerkin, 2007; Teachman, Woody, & Magee, 2006). These findings again suggest an association between implicit associations and markers for anxiety vulnerability in nonclinical samples, but prospective longitudinal investigations are clearly needed. Work examining a direct causal relationship between changes in measures relevant to implicit social cognition and consequent change in anxiety is at very early stages, and again the majority of this work has occurred with attentional bias paradigms, particularly the modified dot probe. Recent research has shown that by training participants to expect that a probe will appear more consistently following nonthreat-Â�relevant stimuli (rather than equally following threat and nonthreat stimuli), participants can learn to attend to neutral stimuli. Importantly, this training can influence emotional responding to subsequent stressors (e.g., MacLeod, Rutherford, Campbell, Ebsworthy, & Holker, 2002; Mathews & MacLeod, 2002), including the finding that attentional training helped Singaporean high school students adjust to emigration to Australia (See, MacLeod, & Bridle, 2009). Initial applications of this training program with anxious samples have also been promising. Amir, Weber, Beard, Bomyea, and Taylor (2008) found that a single session of training with a socially anxious sample resulted in lower self-Â�reported anxiety following a speech and higher ratings of speech performance by condition-blind raters (compared with socially anxious participants in an attentional control condition). Similarly, repeated attentional training over a 7-day period with a socially anxious sample led to reduced scores on the Social Interaction Anxiety Scale compared with a control group (although not all measures of social anxiety were reduced, suggesting circumscribed training effects; Li, Tan, Qian, & Liu, 2008). Analogously, an eight-Â�session attentional training program with

496

V. APPLIED PERSPECTIVES

persons with GAD resulted in lower anxiety on both self-�report and interviewer measures relative to a control group (Amir, Beard, Burns, & Bomyea, 2009). Although this work is quite novel, the accumulating evidence provides exciting support for a causal connection between less biased processing of threat information and anxiety reduction. We are aware of only one study to date that has directly tried to train implicit associations with an anxious sample. Clerkin and Teachman (2009b) used a classic conditioning paradigm to train socially anxious participants to associate their speech performance with positive evaluations from others (through repeated pairings of self-�relevant stimuli and faces indicating positive social feedback). This positive training, relative to two control conditions, led participants to speak longer during a subsequent speech challenge (although not to report less anxiety) and to display less implicit rejection associations following training. These preliminary findings suggest that implicit association training may also be causally related to reductions in anxiety, akin to attentional training, but further work is needed with diagnosed samples, more intensive training designs, and longer follow-up periods to determine the durability of training.

Relation to Treatment There is a small but growing body of evidence suggesting that certain implicit associations are altered following cognitive-�behavior therapy for anxiety disorders (e.g., fear and danger associations in spider phobia: Teachman & Woody, 2003; self with panic associations in panic disorder: Teachman, Marker, & Smith-Janik, 2008), although this has not been a consistent finding (e.g., Huijding and de Jong did not find treatment effects following exposure therapy for spider phobia beyond changes seemingly caused by practice effects using either an IAT, 2007, or an EAST, 2009). The reason for the discrepant results across the spider phobia studies is not entirely clear, although one explanation may be that the Huijding and de Jong treatment occurred within one session, whereas the Teachman and Woody treatment was spaced over three sessions. The extra time may have allowed more opportunity for consolidation of the new associative learning from treatment, resulting in a more substantive effect on the measures of implicit associations. However, the Teachman and Woody (2003) study did not include an untreated anxious control group, so it is difficult to directly evaluate this hypothesis without further data. Importantly, there is also some evidence that change

in implicit associations may actually predict treatment response (as opposed to simply co-Â�occurring with change in treatment). Teachman, Marker, and Smith-Janik (2008) found that the trajectory of change on a measure of implicit panic associations predicted future change in panic symptoms over the course of therapy for panic disorder. There are few if any evaluations of change in priming measures following treatment for anxiety problems, but there are some nice demonstrations using attentional bias paradigms. There is evidence, for example, not only that e-Â�Stroop scores change over the course of treatment (e.g., van den Hout et al., 1997), but that e-Â�Stroop effects appear to be preferentially reduced for treatment responders (social phobia: Lundh & Öst, 2001; Mattia, Heimberg, & Hope, 1993) and may even predict how well treatment gains are maintained over time (e.g., e-Â�Stroop effects predicted anxious thoughts both posttreatment and 20 months after initial testing following therapy for GAD; Mogg, Bradley, Millar, & White, 1995). Thus, although more studies are needed evaluating a broader range of anxiety disorders and using a wider variety of paradigms (especially to address the question of whether change in measures tied to implicit social cognition mediates treatment response), the available evidence suggests a plausible functional relationship between change in automatic processing and symptom reduction.

Linking Implicit Social Cognition Findings with Theoretical Models of€Anxiety This updated review provides considerable support to prior theoretical proposals and reviews that emphasized the role of uncontrollable processing in anxiety disorders (e.g., Beck & Clark, 1997; McNally, 1995). At the same time, by examining results from a variety of new paradigms and a larger evidence base, we can have more confidence (than did some prior reviewers; e.g., McNally, 1995) that unconscious processing of threat stimuli also occurs preferentially for anxious persons. This conclusion is consistent with some more recent formulations (e.g., Öhman & Mineka, 2001), although results do vary across disorders and tasks (e.g., Phaf & Kan, 2007, reach a different conclusion when examining subliminal e-Â�Stroop results only). Also, critical aspects of unconscious processing, such as anxious persons’ awareness of their biased responding, have yet to be examined (see Teachman, Joormann, & Gotlib, 2009). Analogously, although the evidence reviewed here is promising



26. Clinical Applications of Theories and Methods

with regard to the role of automatic processes in enhancing vulnerability and being causally related to anxiety, as proposed by Beck and colleagues (e.g., Beck et al., 1985; Beck & Clark, 1997), the evidence is too limited at this stage to draw firm conclusions. Furthermore, with currently available paradigms, we do not yet have the methodological elegance required to test Beck and Clark’s (1997) specific predictions about changes over time in the relative balance of automatic and strategic processing in anxious responding. These advances await the next new wave of measures to capture implicit social cognition.

MOOD DISORDERS Mood disorders are characterized by a disturbance of mood (depressed, elevated, or irritable) as the primary symptom. Major depressive episodes involve depressed mood or loss of interest or pleasure in normal activities lasting for at least 2 weeks, along with a range of cognitive (e.g., impaired concentration), motivational (low), behavioral (e.g., withdrawal), and somatic (e.g., changes in sleep and appetite) symptoms. Manic episodes are defined by abnormally elevated or irritable mood lasting for at least 1 week (hypomanic episodes are less severe) and often include racing thoughts, impulsive behaviors (e.g., shopping sprees), and reduced need for sleep. These mood episodes are the building blocks of the mood disorders, which are divided into depressive and bipolar disorders. Inspired by Beck’s cognitive model of depression, which hypothesizes that depression is characterized by a schema that biases individuals toward selective processing of negative information (e.g., regarding personal failure and loss), researchers have investigated implicit cognition in depression since the late 1970s (see Beck, Rush, Shaw, & Emery, 1979). Overall, the evidence for implicit association, priming, and attention biases in depression is much more mixed than it is for anxiety disorders, with considerable variation across tasks. In their review, Hartlage, Alloy, Vazquez, and Dykman (1993) argued that depression interferes minimally with most features of automatic processing, with the exception of automatic activation of selfÂ�referent depressive content. However, in a more recent review, Teachman and colleagues (2009) proposed that the current evidence suggests that depression is characterized by uncontrollable processing of negative information (see also Hertel, 2004). Furthermore, Beevers (2005) has proposed a dual-Â�process model, which distinguishes be-

497

tween the associative (implicit) mode of information processing (which is quick and effortless and relies on preexisting associations in memory) and the reflective (explicit) processing mode (which is slow, effortful, conscious, and rule based). According to this perspective, cognitive vulnerability to depression occurs as a result of negatively biased self-Â�referent associative processing. Depending on one’s available cognitive resources and the degree to which associative processing violates expectations and triggers the need for more effortful processing, corrective reflective processing may or may not be able to overcome the negative associative bias. A feedback loop may form between negative associative processing, the absence of reflective processing, and dysphoric mood, resulting in clinical depression.

Group Differences and Predictive Validity Implicit Association Tasks Surprisingly little research has investigated group differences in implicit associations for individuals with mood disorders, although there are a few exceptions examining implicit self-Â�esteem in depression. Some research on implicit associations has supported Beevers’s (2005) prediction of negative associative processing among groups that are vulnerable to depression. For example, Gemar, Segal, Sagrati, and Kennedy (2001) found that formerly depressed participants showed significantly stronger implicit associations on the IAT between the concepts me and negative after a negative mood induction relative to control participants. Moreover, this negative bias was similar to that shown by currently depressed participants who had not undergone a mood induction, suggesting that negative state affect can activate underlying automatic critical self-Â�evaluations in vulnerable individuals. In an interesting contrast, De Raedt, Schacht, Franck, and De Houwer (2006) found that depressed participants showed more positive implicit self-Â�esteem than control participants on the EAST and found no group differences in implicit self-Â�esteem on the IAT. The authors suggest that this may be due to normal early development of a positive implicit self-Â�concept that remains latent despite the development of explicit symptoms of depression later in life. Alternatively, the discrepant results across studies may be tied to the presence of a mood manipulation (that enhanced the expression of the implicit associations) in one study and not in the other study. With regard to predictive validity,

498

V. APPLIED PERSPECTIVES

Haeffel and colleagues (2007) found that implicit but not explicit self-�esteem predicted response to a lab stressor in unselected college students. Overall, few studies have been conducted examining implicit associations in depressed groups, and results are somewhat inconsistent. The presence of negative state affect or stressors may turn out to moderate the strength of negative implicit associations in depression, although clearly more work is needed.

Priming Tasks Priming effects have been hypothesized to reflect difficulties with inhibition of task-Â�irrelevant emotional material and have been linked with rumination, a key feature of depression (Joormann, 2006). In Beevers’s (2005) dual-Â�process model, rumination is seen as a reflective processing strategy that may enhance the activation of task-Â�irrelevant material and inadvertently amplify the effects of negative automatic processing (which may be detected through priming effects). Several studies have found that depressed individuals are more likely to show priming effects for negative stimuli than nondepressed individuals. For example, Bradley, Mogg, and Millar (1996) found that depressed participants showed larger subliminal and supraliminal priming effects following depression-Â�relevant compared with neutral words on a lexical decision task, while control participants did not show this difference. Using the same task, these researchers also found that depressed participants showed greater subliminal and supraliminal priming from depression-Â�relevant words than either a healthy control group or a group of individuals with anxiety disorders (Bradley, Mogg, & Williams, 1995). Similarly, Watkins, Vache, Verney, and Mathews (1996) found that, following conceptual encoding of valenced words, depressed participants produced more of the negative previously studied words in response to free-Â�association cues, while control participants produced more of the positive previously studied words. Researchers have also found significant affective priming effects in depressed participants using more self-Â�relevant paradigms. For example, Power, Cameron, and Dalgleish (1996) asked both depressed and nondepressed individuals to read a brief emotional prime and then evaluate as quickly as possible whether or not a target trait adjective was applicable to themselves. Depressed, but not control, participants showed faster responding and a higher rate of endorsement of negative adjectives when these adjectives were preceded by a nega-

tive emotional prime. Furthermore, Goeleven, De Raedt, Baert, and Koster (2006) used a negative priming paradigm, in which participants were asked to ignore a previously presented distracter picture of a face making a negative emotional expression, to examine group differences in affective priming. They found that depressed participants showed interference effects when making valence judgments of a target picture of a face presented later, but control participants did not. In a similar negative priming paradigm that used positive and negative adjectives as distracters and targets, Joormann (2004) found that nondysphoric and never-Â�depressed control participants showed negative affective priming (interference from a previously presented, to-be-Â�ignored negative prime on subsequent responding to a negative target). However, dysphoric and formerly depressed participants showed reduced negative priming effects; that is, responding to a negative target was quicker when it was preceded by a to-be-Â�ignored negative prime (relative to the control groups). Overall, these results suggest that depressed individuals may have more difficulty inhibiting negative information, even when it is irrelevant to the task at hand, compared with nondepressed individuals. Such findings support claims that reflective processing may be unable to correct automatic biases when negative material is highly activated or especially self-Â�relevant (e.g., Beevers, 2005). However, it should be noted that other studies do not find significant group differences in priming effects. For instance, both depressed and nondepressed participants showed priming effects for neutral but not emotional words in a study that used subliminal primes and a lexical decision task (Matthews & Southall, 1991). Furthermore, depressed and nondepressed groups have not shown differences in priming of positive and negative words in two studies of implicit memory using word completion tasks (Danion, KauffmannÂ�Muller, Grangé, & Zimmerman, 1995; Denny & Hunt, 1992). Similarly, Bisson and Sears (2007) found that individuals with depression did not show larger priming effects for negative targets compared with other semantically related ones, with or without a negative mood induction. Overall, group differences in priming effects seem more likely when stimuli are personally relevant or elaboration of stimuli is encouraged (e.g., through conceptual encoding of items). However, if extensive exposure and elaboration are necessary to observe effects, this raises questions about the extent to which the priming effects are actually reflecting an automatic process (see Teachman



26. Clinical Applications of Theories and Methods

et al., 2009). Also, when priming effects are observed, the contributing mechanisms are not yet clear. Priming effects may reflect an enduring processing style associated with depression, although other researchers (e.g., Watkins et al., 1996) have suggested that mood congruency (between the participant’s mood and valence of the stimuli) may also explain enhanced accessibility of negative material for depressed individuals.

Attention Bias Tasks An attentional bias toward negative information has occasionally been found in depressed samples, using paradigms such as the e-�Stroop task and the dot-probe task (see review in Williams et al., 1996). Gotlib and Cane (1987) conducted an early study of e-�Stroop effects in participants with clinical depression. They found that depressed participants showed increased interference from negative words relative to control participants. Similarly, Segal, Hood, Shaw, and Higgins (1988) found an e-�Stroop effect for primed self-�descriptive words in depressed, relative to control, participants. However, these researchers did not find group differences for unprimed self-�descriptive words. In another study using similar methodology but with only negatively valenced words as stimuli, they obtained the same results: Primed words produced an e-�Stroop effect in depressed individuals; unprimed words did not (Segal, Gemar, Truchon, Guirguis, & Horowitz, 1995). Notably, using masked stimuli presented at short exposure durations (14 msec), other studies have not found a bias for negative words in depressed participants (Bradley, Mogg, Millar, & White, 1995; Mogg et al., 1993). This suggests that elaborative processing of the negative content of self-�relevant information is necessary for these stimuli to interfere with other cognitive tasks. Studies using the dot-probe task in depression initially produced mostly null findings. Although two early studies showed an attentional bias toward negative and social threat words in depressed, compared with control, participants (Mathews, Ridgeway, & Williamson, 1996; Mogg, Bradley, & Williams, 1995), most other studies using unmasked stimuli produced no evidence of a bias for negative words in depression (e.g., MacLeod, Mathews, & Tata, 1986; Neshat-Doost, Moradi, Taghavi, Yule, & Dalgleish, 2000; Taghavi, Neshat-Doost, Moradi, Yule, & Dalgleish, 1999). Furthermore, in the first study to examine pictures of threatening or sad faces in the dot-probe task (which also included an eye-�tracking component), Mogg, Millar, and

499

Bradley (2000) found no depression-�specific biases in either reaction time or direction and latency of first eye movement. The use of masked stimuli presented at brief exposure times has also indicated no attentional bias at the subliminal level for depressed individuals (Mathews et al., 1996; Mogg, Bradley, & Williams, 1995). Despite these null findings, more recent studies using longer stimuli exposure times and more ecologically valid stimuli, such as pictures of emotional faces, have found interesting differences in attention between depressed individuals and other groups. Gotlib, Kasch, Traill, Joormann, Arnow, and Johnson (2004) used a dot-probe task with individuals who were diagnosed with clinical depression, social phobia, or no emotional disorder. They found that depressed participants were quicker to orient toward sad faces but slower to orient toward angry or happy faces, although they did not show between-�groups differences from participants with social phobia within emotional face categories. Interestingly, severity of depressive symptoms was also related to a tendency to direct attention away from happy faces (see also Gotlib, Krasnoperova, Yue, & Joormann, 2004). Another study examining differences in attentional bias between currently and formerly depressed participants and never-�disordered control participants found that both the currently and formerly depressed groups selectively attended toward sad faces, while the control group selectively attended toward happy faces (Joormann & Gotlib, 2007). These studies raise the possibility that depression is characterized more by a lack of attention to positive information rather than excessive attention to negative information. To test this idea, Shane and Peterson (2007) had high and low scorers on the Beck Depression Inventory complete a modified dot-probe task involving positively and negatively valenced pictures and words. They found that high scorers showed processing biases away from positive information (compared with neutral information) when stimuli were displayed for 200 to 500 msec. These participants also showed biases toward depression�relevant stimuli. In contrast, low scorers showed a strong bias away from negative information as well as unbiased processing of positive information. A few investigators have used visual search tasks to examine selective attention to negative information in depression. Studies using the facein-the-crowd task, a variant of the visual search paradigm, have not found evidence that depressed individuals are distracted by negative facial expressions, relative to control stimuli (Karparova, Kersting, & Suslow, 2005). However, similar to the stud-

500

V. APPLIED PERSPECTIVES

ies using the dot probe to investigate avoidance of positive stimuli, depressed participants were slower than control participants in responding to positive facial expressions, even though they did not differ in their speed of responding to negative facial expressions (Suslow et al., 2004). Adding to the mixed findings, Rinck and Becker (2005) used a visual search task involving depression-�relevant words and found that depressed women were more distracted by these words than were control participants or participants with social phobia. Although the lack of studies makes conclusions tentative, visual search studies point to a likely attentional bias in depression; however, the extent to which the bias is specific to negative versus positive stimuli remains somewhat unclear. Although the vast majority of studies of cognitive biases in mood disorder populations have examined depression, a few have investigated attentional bias relevant to bipolar disorder. French, Richards, and Scholfield (1996) administered an e-�Stroop task to participants high in hypomanic symptoms. They found that these hypomanic participants showed e-�Stroop interference for depression-�related but not euphoria-�related information, even when anxiety levels were taken into account. These researchers concluded that hypomanic personality traits may represent an attempt to cope with an underlying depressive cognitive vulnerability. Overall, attention tasks generally show preferential attention to mood-�congruent stimuli most consistently at long exposure intervals or when stimuli are self-�relevant (see Mathews & MacLeod, 2005; Mogg & Bradley, 2005). Mixed results also seem tied to the critical question of whether depressed individuals are characterized more by avoidance of positive information or by excessive attention to negative information.

Ties to Vulnerability and Causal Links Despite this large body of research investigating cognitive vulnerability factors involved in depression, few studies have used longitudinal or experimental designs that allow the examination of temporal prediction or causal relationships. In a notable exception, Haeffel and colleagues (2007) followed college students for 5 weeks after an initial IAT and self-�report questionnaires to measure self-�esteem and found that both implicit and explicit measures interacted with life stress to predict later depressive symptoms (although only the explicit measures were unique predictors). Similarly, Beevers and Carver (2003) measured attentional

bias using a dot-probe task with college students before and after a negative mood induction. They found that shifts in attention toward negative information following the mood induction interacted with life stress to predict increases in depressive symptoms 7 weeks later. In addition, speed of mood recovery following the induction at Time 1 also interacted with life stress to predict depressive symptoms at Time 2. Extending this research to a diagnosed sample, Johnson, Joormann, and Gotlib (2007) administered an emotional face dot-probe task to individuals experiencing a major depressive episode. Attentional biases to sad faces predicted change in symptoms of depression 9 months later. These longitudinal studies suggest a temporal relationship between implicit association and attention biases and subsequent depressive symptoms, consistent with the possibility that these biases may enhance vulnerability. Although studies of processing biases in individuals at risk for the development of a mood disorder (e.g., children of mood-Â�disordered parents) are not a direct test of longitudinal prediction of clinical symptoms, they may suggest ties to vulnerability by showing biases in at-risk individuals. Along these lines, Joormann, Talbot, and Gotlib (2007) administered an emotional face dot-probe task to daughters (ages 9–14) of depressed mothers. They found that these girls showed biased attention toward negative faces compared with girls with nondepressed mothers. Similarly, Gotlib, Traill, Montoya, Joormann, and Chang (2005) administered an e-Â�Stroop task to children of parents with bipolar disorder following a negative mood induction. They found an attentional bias toward social threat and manic-Â�irritable words in these children relative to children of never-Â�disordered parents. Finally, there is very limited published work directly addressing causal links between automatic processing biases and depression. However, promising related work by Joormann, Hertel, LeMoult, and Gotlib (2009) has shown that explicit instructions regarding word substitution strategies can enhance depressed participants’ successful forgetting of negative words, and there is evidence that training attention biases away from threat content can influence subsequent negative mood in response to a stressor (e.g., MacLeod et al., 2002).

Relation to Treatment Although we are not aware of any published research examining change in implicit associations following treatment for depression or other mood disorders, a few studies have looked at treatment



26. Clinical Applications of Theories and Methods

effects on priming and attention biases. For instance, Dannlowski and colleagues (2006) examined inpatients with major depression, half of whom had a comorbid anxiety disorder, and compared them with healthy control participants on a subliminal affective priming task. Before treatment, they found strong interference effects from negatively valenced primes in the non�comorbid depressed group but not in the healthy control group. However, after 7 weeks of inpatient psychotherapy, recovered depressed individuals no longer showed interference from negative primes compared with their previous performance. Other research has shown that attention biases may also be attenuated following successful treatment for depression. For example, Gotlib and Cane (1987) found that group differences in e-�Stroop interference from negative words in depressed patients versus nondepressed control participants disappeared after inpatient treatment. Similarly, Segal and Gemar (1997) administered an e-�Stroop task before and after cognitive-�behavioral therapy for depression, accompanied by priming with self-�descriptive or non-self-�descriptive emotional phrases. After treatment, participants who were less depressed than they had been at pretreatment showed less interference for negative targets primed by self-�descriptive negative phrases (compared with targets primed by non-self-�descriptive phrases), whereas those whose depression levels did not change over the course of treatment showed the same amount of interference from negative self�descriptive primes as before treatment.

Linking Implicit Social Cognition Findings with Theoretical Models of€Depression In general, the available evidence for biased implicit processing in mood disorders is sparse and largely restricted to major depression. The few studies of implicit association biases that have been conducted have produced mixed results. However, priming studies have more often shown evidence of a bias, especially when material is self-�relevant and when elaborative processing is encouraged. Similarly, attention bias studies are more likely to show group differences when self-�relevant stimuli are presented at long exposure intervals, but the evidence for this type of bias is also somewhat inconclusive. Overall, however, the available evidence supports a likely role for some automatic processing of negative information in depression, particularly when opportunities for overriding biases with reflective processing are limited (e.g.,

501

when material is highly activated or self-�relevant; in line with Beevers, 2005, and Hartlage et al., 1993), suggesting the processing may be somewhat uncontrollable under these circumstances (in line with Hertel, 2004, and Teachman et al., 2009).

EATING DISORDERS Eating disorders (EDs) are typified by an extreme disturbance in eating behaviors and attitudes. In the current chapter, we focus primarily on evidence from tasks capturing implicit social cognition within the context of symptoms of anorexia nervosa (AN) and bulimia nervosa (BN), which are the two most well-Â�established forms of EDs (Wilson & Pike, 2001). According to the DSMIV-TR, AN is characterized by a refusal to preserve a healthy weight for one’s height and age while at the same time experiencing extreme fears of either gaining weight or becoming fat. BN involves recurrent episodes of binge eating (i.e., eating very large volumes of food within a limited period of time), coupled with recurrent compensatory behaviors designed to prevent weight gain (e.g., selfÂ�induced vomiting, excessive exercise, fasting). In both AN and BN, self-Â�evaluation is excessively tied to perceptions of one’s body size and shape. Note that a number of the studies in this section involve participants with symptoms of disordered eating who did not qualify for an ED diagnosis (e.g., chronic dieters). Including individuals with subclinical symptoms is useful in this domain given evidence that ED symptoms may exist along a continuum (e.g., Lowe et al., 1996), and because there are high rates of individuals who do not meet full criteria for a specific ED (i.e., ED not otherwise specified [NOS]). For further information on implicit social cognition research associated with eating and weight among individuals without symptoms of disordered eating, see Wiers and colleagues (Chapter 25, this volume). According to the transdiagnostic model of disordered eating, similar mechanisms are theorized to maintain both AN and BN (Fairburn, Cooper, & Shafran, 2003). In particular, individuals suffering from both disorders are thought to “overvalue” weight and shape concerns, which consequently become central to one’s self-Â�concept (Fairburn, Cooper, Shafran, & Wilson, 2008). In addition to a preoccupation with eating and appearance, cognitive-Â�behavioral models emphasize the extent to which individuals with symptoms of AN and BN attempt to control their weight through dietary restriction or compensatory behaviors (e.g.,

502

V. APPLIED PERSPECTIVES

purging), which exacerbate bingeing (Fairburn et al., 2008). EDs are, therefore, characterized by both an extreme desire to exert complete control over one’s shape and weight as well as by the inability realistically to do so. Accordingly, although theoretical models are largely silent regarding the unique role that automatic cognitive processing biases play in disordered eating, it would be unsurprising if individuals with ED symptoms had difficulty with uncontrollable processing of shape, weight, and/or food cues given the centrality of control difficulties in eating disorders.

Group Differences and Predictive Validity Implicit Association Tasks Using an IAT, Vartanian and colleagues (Vartanian, Herman, & Polivy, 2005; Vartanian, Polivy, & Herman, 2004) conducted a series of studies among restrained eaters (defined as chronic dieters) and unrestrained eaters (nondieters) investigating implicit evaluations toward ED-relevant concepts, such as meal size, fattening foods, and body fat. Contrary to initial predictions, Vartanian and colleagues (2004) found that both restrained and unrestrained eaters had pronounced negative implicit evaluations toward fatness (relative to thinness). Vartanian and colleagues (2005) also found that, regardless of dieting status, participants were much quicker at associating large meals with words synonymous with fat (and small meals with words synonymous with thin) relative to the opposite pairing contingencies. Together, this research suggests that these particular associations do not differentiate between individuals high (vs. low) in ED-relevant symptoms like chronic dieting. At the same time, other studies indicate that certain types of implicit associations may be meaningfully related to symptoms of disordered eating, even among individuals who are not diagnosed with an ED. For example, Thomas, Judge, Brownell, and Vartanian (2006) found that two self-�report measures of ED symptomatology were positively correlated with an IAT that assessed associations between anorexia and glamour (vs. danger) among an unselected sample of undergraduate women. Furthermore, Ahern, Bennett, and Hetherington (2008) found that individuals who reported a greater drive for thinness on a self-�report questionnaire were relatively faster to associate positive (vs. negative) attributes with underweight models on an IAT. However, the sample used by

Ahern et al. was unselected, and implicit association scores were unrelated to actual body dissatisfaction. Therefore, interpretability of these findings for actual ED populations is somewhat limited (see also Ahern & Hetherington, 2006). Finally, Seibt, Häfner, and Deutsch (2007) investigated motivational reactions toward pictures of food by using a version of the affective Simon task among individuals who either had or had not eaten lunch. All participants were instructed to use a lever to pull a pictorial food stimulus toward themselves as quickly as possible (approach) or push a pictorial food stimulus away from themselves as quickly as possible (avoid). Although both groups of students were faster at “approaching” (vs. avoiding) the food stimuli, this effect was especially pronounced among individuals who had not yet eaten lunch. The results were comparable when researchers investigated this paradigm among individuals who suffered from an ED (BN or AN), suggesting that the task was sensitive to hunger state for everyone regardless of ED status. It appears that implicit associations do not reliably differentiate between individuals high and low in symptoms of disordered eating, but implicit associations may be meaningfully related to certain ED symptoms (e.g., drive for thinness). More work must be conducted with diagnosed samples before firm conclusions can be drawn, however.

Priming Tasks Researchers have found that priming eating and/ or weight-�relevant concepts impacts subsequent responding among individuals with symptoms of disordered eating (e.g., Ferraro, Wonderlich, & Johnson, 1997; McKee, Nhean, Hinson, & Mase, 2006). For example, Roefs and colleagues (2005) conducted an experiment to evaluate the hypothesis that individuals diagnosed with AN or EDNOS (compared with unrestrained eaters) would not display as strong a preference for palatable versus unpalatable foods. Primes included fattening palatable foods (e.g., chocolate), nonfattening palatable foods (e.g., strawberries), and nonfattening unpalatable foods (e.g., radishes). Targets included generally positive words and generally negative words. As expected, within the AN group, palatability of the food did not appear to impact responses toward the targets. Meanwhile, within the healthy control group, primes did influence responses toward the targets insofar as this group tended to be quicker on congruent trials (e.g., palatable positive and unpalatable negative), relative



26. Clinical Applications of Theories and Methods

to incongruent trials. This research suggests that there are differences in the ways in which food cues are processed as a function of ED status, with palatability perhaps being a less important prime for individuals suffering from AN as it is for other people (Eertmans, Baeyens, & Van den Bergh, 2001; Pliner & Mann, 2004). Research also indicates that there is a relationship between symptoms of EDs and biased processing of ED-relevant stimuli on primed lexical decision tasks (e.g., Ferraro, Andres, Stromberg, & Kristjanson, 2003). For example, Meijboom, Jansen, Kampman, and Schouten (1999) asked female participants high on symptoms of restrained eating to complete a subliminal lexical decision task to investigate the hypothesis that self-�esteem is related to concerns with body weight and shape. Specifically, after low self-�esteem was primed, Meijboom and colleagues found that there was a significant relationship between low self-�esteem and overconcern tied to weight/shape. In other words, after the priming procedure, the accessibility of subliminally presented stimuli related to weight and shape were enhanced. On the supraliminal lexical decision task, this effect was not evident. Also, Cassin, von Ronson, and Whiteford (2008) found that individuals with subclinical ED symptoms (high thin-ideal internalizers) did not exhibit cognitive biases tied to disordered eating, even after being primed with pictures of attractive models. This suggests that ED symptom severity may need to be sufficiently high in order for priming paradigms to impact subsequent cognitive processing.

Attention Bias Tasks Dobson and Dozois (2004) identified 28 empirical studies in a meta-�analysis examining e-�Stroop interference effects tied to food-�related (food e-� Stroop) and body-�related (body e-�Stroop) words, from approximately 1935 to the end of May 2001. They primarily limited their review to populations exhibiting symptoms of EDs, including dieting/ restrained eaters as well as individuals diagnosed with AN and BN. Their results indicated that individuals diagnosed with BN consistently demonstrated biases in attention on the e-�Stroop across a variety of stimuli (e.g., food e-�Stroop, body e-� Stroop, and the classic Stroop, which is not ED relevant), but that attentional biases among individuals with AN were limited to weight/body�related stimuli. Furthermore, the authors concluded that there was not sufficient evidence to

503

support the presence of an attentional bias among dieting/food-Â�restricting participants (see also Lee & Shafran, 2004, and Lokken, Marx, & Ferraro, 2006, for the importance of symptom severity in the expression of e-Â�Stroop interference effects). In contrast, results from Johansson, Ghaderi, and Andersson’s (2005) meta-Â�analysis suggest that there may be small e-Â�Stroop interference effects for body- and food-Â�related words among individuals who are not diagnosed with an ED but who are overly concerned with eating and body image. Thus, although attentional biases as assessed with the e-Â�Stroop are clearly implicated in diagnosed ED populations, the extent to which these biases are present in subclinical populations awaits further testing. In general, research also suggests that individuals suffering from disordered eating show biases on the dot-probe detection task (but see Boon, Vogelzang, & Jansen, 2000); however, there appear to be a number of critical moderators of the bias effects, including type and valence of the stimuli. For example, Shafran, Lee, Cooper, Palmer, and Fairburn (2007) conducted two studies to investigate attentional biases tied to “positive,” “negative,” and neutral eating- and shape-Â�relevant stimuli and neutral-Â�weight stimuli (compared with non-ED relevant stimuli). They compared women diagnosed with an ED, healthy female control (Studies 1 and 2), and women with high levels of anxiety (Study 1). In the first study, researchers found that patients with EDs responded more rapidly toward a probe that appeared in the same spatial location as negative eating (e.g., high-Â�calorie food eaten in an uncontrolled manner) and neutral weight (e.g., scales or people being weighed) stimuli and more slowly to a probe following positive eating (e.g., low-Â�calorie food eaten in a controlled manner) stimuli. Their second study largely replicated these results, except researchers found that individuals diagnosed with an ED also displayed an attentional bias toward shape stimuli that was negative (e.g., plumper bodies or body parts) or neutral (e.g., body parts not readily associated with weight/ shape). Notably, these biases were greater for individuals diagnosed with EDs relative to the anxious and control groups, although this was not true for the positive and neutral shape stimuli. Partially in line with these results, Rieger and colleagues (1998) found that participants diagnosed with an ED (in comparison to healthy controls) were more likely to attend away from positive words signifying a thin physique, and there was a trend for them to attend to negative words signifying a large

504

V. APPLIED PERSPECTIVES

physique. Consistent with Vitousek and Orimoto (1993), Rieger and colleagues suggested that patients with EDs preferentially process information that is congruent with underlying schemas (e.g., attending toward negative eating/shape stimuli), while being resistant to “counterschematic” information. Placanica, Faunce, and Job (2002) also investigated attentional biases using a dot-probe task among individuals who scored high (vs. low) on symptoms of disordered eating. Individuals high in ED symptoms were more likely to exhibit an attentional bias toward low-Â�calorie food words but only in a condition where they had not fasted. Authors interpreted these results to mean that, as hunger increased, individuals high in ED symptoms shifted their attention away from low-Â�calorie, and toward high-Â�calorie, foods. Finally, using a visual search task, Smeets, Roefs, van Furth, and Jansen (2008) recently found that individuals diagnosed with an ED, compared with healthy controls, displayed evidence of heightened distraction by highÂ�calorie food cues on a visual search task and enhanced orienting toward body-Â�relevant cues (the authors did not differentiate between the valence of body-Â�relevant stimuli). At this point, more work is needed to clarify when the valence, stimulus type, and directionality of attentional biases will be differentially related to ED symptoms. For example, on the dot-probe task, Shafran and colleagues (2007) suggested that attentional biases toward shape stimuli are less robust than for eating or weight stimuli, and that shape stimuli may need to be personally relevant in order for biases to manifest. Work by Placanica and colleagues (2002) also indicates that current hunger or dieting status is relevant for attentional biases toward food cues (see also Seibt et al., 2007), which may partially help to explain why some studies have found biases toward low-Â�calorie stimuli (e.g., Placanica et al., 2002) while others have found attentional biases away from low-Â�calorie stimuli (e.g., Shafran et al., 2007). Notwithstanding, it appears that attentional biases associated with food- or body-Â�relevant cues are meaningfully related to ED symptoms, particularly among diagnosed samples. Similar to anxiety disorders, there is also some evidence to suggest that attentional biases involve both initial orienting toward threat as well as subsequent cognitive avoidance (Ben-Tovim & Walker, 1991; Lee & Shafran, 2008; McManus, Waller, & Chadwick, 1996; Rieger et al., 1998; Waller & Meyer, 1997; see also Lee & Shafran, 2004, for a review of information processing biases tied to ED symptoms).

Ties to Vulnerability and Causal Links Currently, there is limited research on implicit social cognition outcomes as conferring vulnerability, or being causally related, to EDs.

Relation to Treatment The evidence regarding treatment effects in EDs is mixed and is mainly in the area of attention biases. Supporting the notion that attention biases are reduced following treatment, Cooper and Fairburn (1994) found that individuals with BN showed attenuated interference on an e-�Stroop task that included eating-, shape-, and weight-�related words following successful treatment. Also, Ball, Mitchell, Touyz, Griffiths, and Beaumont (2004) investigated changes on a modified e-�Stroop task among women diagnosed with AN. Results indicated that color-�naming interference decreased following treatment for the food-�related words but not for shape-�related words. Finally, Shafran, Lee, Cooper, Palmer, and Fairburn (2008) found that female participants diagnosed with an ED (vs. healthy control participants) were more likely to display bias on a dot-probe task toward eating, weight, and shape stimuli before treatment. Following treatment, these attentional biases were significantly weakened. On the other hand, Carter, Bulik, McIntosh, and Joyce (2000) did not find treatment effects among women diagnosed with BN on an e-�Stroop task as a function of word type (e.g., body/shape relative to control words). In particular, treatment effects were not specific to the intended target of therapy (i.e., body/shape), although color-�naming latencies for all words were reduced as a function of treatment (see also Black, Wilson, Labouvie, & Heffernan, 1997). Similarly, Long, Hinton, and Gillespie (1994) found no significant change from pre- to posttreatment in color-�naming latencies for body size or food-�related words among a small group of individuals suffering from AN whose weight had normalized following a 3-month treatment period. In fact, Lovell, Williams, and Hill (1997) found that when individuals had recovered from AN, they actually showed greater interference on shape-�related stimuli compared with a small group of individuals who had recovered from BN. However, neither group differed significantly from one another or from healthy control participants on food-�related words. Thus, it is still an open question as to whether measures of attentional bias are sensitive to treatment effects among individuals with disordered



26. Clinical Applications of Theories and Methods

505

eating and whether these effects are specific to a given stimulus content or whether they are more general.

Group Differences and Predictive Validity Implicit Association Tasks

Linking Implicit Social Cognition Findings with Theoretical Models of€Eating Disorders

Researchers have recently demonstrated that implicit associations are meaningfully related to symptoms of BDD, although effects have not been consistent across BDD-relevant domains. For instance, Buhlmann, Teachman, Naumann, Fehlinger, and Rief (2009) used an IAT to evaluate implicit self�esteem, the importance of physical appearance, and an attractiveness stereotype (the association between competence and physical attractiveness). Participants included individuals diagnosed with BDD, individuals with subclinical BDD symptoms, and healthy controls. As expected, individuals diagnosed with BDD (relative to control participants) displayed lower levels of implicit self-�esteem. Meanwhile, the group with subclinical BDD symptoms had intermediary scores on the implicit self�esteem task (see also Buhlmann, Teachman, Gerbershagen, Kikul, & Rief, 2008, for similar implicit self-�esteem findings). Individuals diagnosed with BDD (relative to the other two groups) also displayed stronger implicit attractiveness stereotypes, such that they were more likely to associate attractiveness with competence. Furthermore, both the implicit self-�esteem and implicit attractiveness stereotype task predicted distress and avoidance during an exposure to a mirror. In contrast, Buhlmann and colleagues (2008, 2009) and Clerkin and Teachman (2009a) found no significant differences between individuals with BDD symptoms (vs. controls) on an implicit measure of the importance (vs. meaninglessness) of attractiveness. However, authors pointed to methodological constraints of the IAT to help explain these null findings; namely, it was difficult to find an appropriate relative comparison category for the importance of attractiveness (see discussion in Buhlmann et al., 2008, and Clerkin & Teachman, 2009a).

This research provides some support for cognitive�behavioral models of eating disorders and suggests that under certain circumstances individuals with symptoms of AN and BN will preferentially process information tied to shape, weight, or food cues. However, clarifying the extent to which various features of automaticity are important for the expression of cognitive processing biases in AN and BN will require additional work. For example, the e-�Stroop findings suggest that individuals with eating disorder symptoms may have difficulty with the unintentional and uncontrollable processing of ED-relevant cues. However, research evaluating implicit associations, which also taps uncontrollable processing of threatening information, was much more mixed.

BODY DYSMORPHIC DISORDER Body dysmorphic disorder (BDD) is classified as a somatoform disorder. Characterized by extreme distress and preoccupation regarding elements of one’s physical appearance (e.g., nose or skin), BDD symptoms cannot be better accounted for by concerns tied to one’s overall body weight or shape. Cognitive models suggest that among individuals with BDD physical appearance is “overvalued” and plays a disproportionately large role in one’s selfÂ�concept (Buhlmann & Wilhelm, 2004). Researchers also stress that individuals with BDD may selectively attend to information relevant to their perceived defects, which, in turn, exacerbates the focus on even very minor flaws (Veale et al., 1996; Wilhelm & Neziroglu, 2002). Although theoretical models have underscored the importance of cognitive factors for maintaining BDD symptoms, little emphasis has been placed on specifying which processing biases will occur relatively automatically. However, given the similarities between BDD and anxiety disorders (BDD is often conceptualized as an OCD spectrum disorder; Phillips, 1986), it seems plausible that uncontrollable or unconscious processing of threatening information is important for the expression, and perhaps development, of BDD.

Priming Tasks To our knowledge, there is no published work evaluating priming tasks among individuals with symptoms of BDD.

Attention Bias Tasks Buhlmann, McNally, Wilhelm, and Florin (2002) found that individuals diagnosed with BDD (relative to healthy controls) displayed greater interference for both positive and negative words on an

506

V. APPLIED PERSPECTIVES

e-�Stroop task regardless of their relevance to BDD. Follow-up analyses indicated that these findings were most pronounced for positive words tied to BDD (e.g., attractive). Authors interpreted these results to mean that individuals with BDD may experience biases toward emotional cues generally, and that these effects are most pronounced for stimuli relevant to their specific concerns.

Ties to Vulnerability and Causal Links, Relation to Treatment At this point, there is no published work (to our knowledge) tying implicit social cognition research to vulnerability or treatment effects within the context of BDD.

Linking Implicit Social Cognition Findings with Theoretical Models of€BDD This review provides support for cognitive models suggesting that individuals suffering from BDD overvalue the importance or meaningfulness of being physically attractive. Additionally, research investigating implicit associations and attentional biases suggests that individuals with BDD symptoms may be characterized by uncontrollable processing of threatening information, particularly when that information is tied to their specific areas of concern.

SCHIZOPHRENIA AND OTHER PSYCHOTIC DISORDERS The primary psychotic disorders encompass a broad range of psychiatric illnesses, including schizophrenia, schizophreniform disorder, schizoaffective disorder, delusional disorder, and brief and shared psychotic disorders. In the present chapter, our focus is on implicit social cognition research primarily associated with schizophrenia, a disorder characterized by both “positive” and “negative” symptoms. Positive symptoms reflect mental phenomena that are in excess or that are a distortion of healthy functioning (e.g., delusions, which are fixed, false beliefs that are maintained in spite of indisputable evidence to the contrary, and hallucinations, which are distorted or false sensory perceptions). Negative symptoms are those that constitute a restriction of the intensity or range of healthy functioning, particularly in emotional processing (e.g., anhedonia, a difficulty experiencing pleasure).

It is important to note that much of the research regarding implicit processing within the context of psychosis has evaluated general cognitive or neuropsychological deficits (e.g., using the classic, nonemotional Stroop task as an index of executive processing; see Sitskoorn, Aleman, Ebisch, Appels, & Kahn, 2004; Szöke et al., 2008). For example, Fioravanti, Carlone, Vitale, Cinti, and Clare (2005) conducted a meta-Â�analysis in which they found that individuals with schizophrenia tended to perform more poorly than healthy controls on measures of executive functioning, such as the Stroop. There have also been a number of studies utilizing priming techniques, including lexical decision tasks, within the field of schizophrenia/ psychosis research. However, most of this work has focused on investigating cognitive deficits, such as a generalized difficulty inhibiting task-Â�irrelevant information in negative priming tasks (e.g., Moritz et al., 2001). In the current chapter, our focus is on research tied to content that is disorder relevant (e.g., differential processing of positively or negatively valenced information) as opposed to processing difficulties more generally. Although biological models constitute the dominant theoretical perspective regarding the etiology of schizophrenia, researchers are beginning to emphasize psychological and social influences as important maintaining factors, particularly for positive symptoms of schizophrenia (Tarrier, 2008). Along these lines, cognitive-Â�behavioral models of schizophrenia highlight the importance of evaluating the relationship among one’s thoughts, feelings, and behaviors when responding to psychotic symptoms. Thus, one goal of treatment might be to turn one’s attention away from auditory hallucinations or to reevaluate maladaptive thoughts tied to delusional thinking (e.g., “The voices are completely out of my control”). Cognitive models of schizophrenia also emphasize the importance of enhancing one’s self-Â�esteem or feelings of selfworth (Hall & Tarrier, 2003). To our knowledge, there is little explicit discussion regarding the extent to which processing biases relevant to social cognition are occurring automatically versus strategically, although the ability to control one’s attention is clearly important in schizophrenia.

Group Differences and Predictive Validity Implicit Association Tasks Moritz, Werner, and von Collani (2006) used an IAT to evaluate implicit self-�esteem, finding that



26. Clinical Applications of Theories and Methods

patients with schizophrenia, particularly those experiencing current persecutory delusions, displayed lower levels of implicit self-�esteem relative to healthy control participants. Interestingly, patients with schizophrenia who had persecutory delusions (vs. those who did not) exhibited higher levels of explicit self-�esteem. The authors suggest that persecutory delusions may partially function as a mechanism to counteract poor implicit self�esteem. However, with so little empirical work, it is difficult to draw firm conclusions regarding the role that implicit associations play in psychosis.

Priming Tasks Because disorganized speech (composed of tangential associations) is one of the common symptoms of schizophrenia, some researchers have hypothesized that the disorder is characterized by semantic “hyperpriming,” an exacerbation of the standard associations between words (see Maher, 1983; for reviews, see Goldberg & Weinberger, 2000; Minzenberg, Ober, & Vinogradov, 2002). However, in a meta-Â�analysis of more than 30 studies of semantic priming in schizophrenia, Pomarol-Â�Clotet, Oh, Laws, and McKenna (2008) determined that direct semantic priming does not appear to be either increased or decreased in schizophrenia as a whole. Notwithstanding, studies of patients with schizophrenia who have symptoms of disorganized speech do tend to show evidence of increased priming of meaning compared with healthy control participants and patients with schizophrenia without disorganized speech. Content of the word may be an important moderator. A study that looked at semantic priming of affectively valenced words (Rossell, 2004) found that neutral and happy word pairs showed significant semantic priming, but fearful and sad word pairs did not; however, these results were the same for participants with schizophrenia as for healthy controls. Fewer studies have investigated affective priming in schizophrenia. In general, however, research suggests that individuals with schizophrenia may exhibit stronger affective priming effects toward certain types of stimuli compared with healthy control participants (see Hempel, Tulen, Van Beveren, & Hengeveld, 2005). For instance, Höschel and Irle (2001) utilized an affective priming task in which participants were subliminally primed with faces conveying positive or negative expressions; then, following a mask, neutral facial expressions were displayed. Results indicated that the neutral faces were rated as significantly more unpleasant when following negative primes compared with

507

positive and neutral primes, and that this effect was especially pronounced for patients with schizophrenia. The authors interpreted these findings to mean that the judgments of individuals with schizophrenia may be more strongly influenced by the automatic processing of negative emotional information. There is also some evidence that the judgments of individuals with schizophrenia, relative to healthy controls, are less likely to be influenced by the automatic processing of positive facial expressions (Suslow, Roestel, & Arolt, 2003; see also Rossell, Shapleske, & David, 2000). Current affective symptoms may be another moderator of priming effects in schizophrenia. Suslow, Droste, Roestel, and Arolt (2005) found that patients diagnosed with schizophrenia who did not currently have flat affect or anhedonia responded similarly to healthy control participants on an affective priming task. In general, both groups exhibited “reverse priming” or contrast effects whereby valence-Â�incongruent face pairs facilitated processing and valence-Â�congruent face pairs impeded processing of a target. However, schizophrenic patients with flat affect and with anhedonia showed a sensitivity to negative face primes, irrespective of the prime–Â�target (in)congruency. Regardless of the valence of target faces, negative primes facilitated the evaluation of target faces within the patient group with flat affect, whereas negative primes interfered with the processing of target faces among patients with anhedonia (see also Suslow, Roestel, Droste, & Arolt, 2003). Notably, it is not entirely clear what factors moderate when congruency will facilitate versus interfere with processing target stimuli. Also, reasons for the discrepant results for persons with flat affect versus with anhedonia are not resolved, although they may reflect differences in the salience of the emotional stimuli as a function of the different symptoms, given that anhedonia often involves difficulty actually experiencing pleasure while flat affect reflects a severe reduction in emotional expression (but the emotional experience may still be intact).

Attention Bias Tasks Evidence regarding attentional biases for emotional information among individuals with psychotic symptoms has been somewhat mixed (see Garety & Freeman, 1999). This inconsistency may be partially due to the fact that schizophrenia and other psychotic disorders involve a very heterogeneous symptom profile, even when looking within positive and negative symptom clusters (Carpenter & Kirkpatrick, 2005). For example, Strauss,

508

V. APPLIED PERSPECTIVES

Allen, Duke, Ross, and Schwartz (2008) utilized two versions of an e-�Stroop task among both deficit and nondeficit syndrome patients with schizophrenia as well as healthy control participants. Patients with the deficit syndrome are typically conceptualized as being less able to experience all emotions, whereas individuals with schizophrenia without the deficit syndrome are less able to incorporate positive information. Results indicated that deficit syndrome patients (compared with the other groups) failed to exhibit an attentional bias toward positive information; instead, these patients had difficulty disengaging from negative stimuli once their attention had been captured. Strauss and colleagues suggest that positive information may fail to capture the attention of deficit syndrome patients because it is inconsistent with their current mood state. These findings are also consistent with the priming studies (Suslow, Roestel, & Arolt, 2003; Suslow, Roestel, Droste, & Arolt, 2003; Suslow et al., 2005) described previously, demonstrating that negative symptoms in schizophrenia may be associated with preferential processing of negative (but not positive) emotional cues. In contrast, research by Muroi, Kasai, Uetsuki, and Suga (2007) suggests that individuals diagnosed with schizophrenia may not have an attentional bias for emotional information. Specifically, they found no significant group differences in response times or error rates across word conditions on an e-�Stroop task, when comparing individuals diagnosed with schizophrenia with healthy control participants. Furthermore, McCulloch, Clare, Howard, and Peters (2006) evaluated responses on an e-�Stroop task among patients with late-onset psychosis, older patients with depression, and healthy controls who were matched for age. Again, results indicated no significant differences in attentional bias across the three groups. Interpreting attentional bias results within the context of psychosis is further complicated by variable results depending on the presentation duration of threatening stimuli. For example, using a modified dot-probe task, Arguedas, Green, Langdon, and Coltheart (2006) found evidence for a selective attentional bias toward threatening faces among a subclinical group of individuals prone toward delusions (vs. healthy control participants who were not prone toward delusions). Interestingly, individuals high (vs. low) in delusional proneness exhibited an attentional bias toward threat�relevant (vs. happy) faces at a brief, 200-msec SOA only when the emotional faces were shown on the left side of the visual field. For longer SOAs (500

msec and 1,250 msec), the attentional bias toward angry faces remained regardless of whether stimuli were presented in the left or right visual field. According to the authors, this suggests that selective attention toward threatening cues may be present among delusion-prone individuals at both automatic, as well as more strategic, stages of attentional processing. Using the e-�Stroop task, Bentall and Kaney (1989) also found that patients with persecutory delusions exhibited a selective attentional bias toward words signifying paranoid content (relative to control participants; see also Fear, Sharp, & Healy, 1996; Kinderman, 1994). This supports the notion that there may be an orienting or vigilance bias toward threat-�relevant stimuli among paranoid individuals. However, there is also evidence from eye movement studies to suggest that, within the context of psychotic symptoms, subsequent, more controlled visual attention may be selectively biased away from threatening cues (see Freeman, Garety, & Phillips, 2000; Green, Williams, & Davidson, 2003; Phillips & David, 1997), indicating a possible parallel with anxiety disorder models that also suggest attentional vigilance and then subsequent avoidance (e.g., Mogg et al., 2004). In sum, the evidence for attentional biases tied to implicit social cognition among individuals with psychotic symptoms is mixed. The presence of affective symptoms (e.g., difficulty expressing emotions) and presentation duration of threatening stimuli appear to moderate the relationship between attentional biases and symptoms of psychosis, although it is difficult to draw firm conclusions given the relative dearth of studies and the conflicting findings to date.

Ties to Vulnerability and Causal Links, Relation to Treatment To our knowledge there is no published implicit social cognition research evaluating vulnerability, causal links, or treatment effects among individuals with symptoms of psychosis.

Linking Implicit Social Cognition Findings with Theoretical Models of€Schizophrenia This review provides some support for cognitive�behavioral models of schizophrenia, which contend that psychological and social factors, such as self-�esteem, may contribute to the maintenance of schizophrenia (Tarrier, 2008). It is also clear that under certain circumstances individuals with psy-



26. Clinical Applications of Theories and Methods

509

chotic symptoms will preferentially process threatening social information at a relatively automatic level (e.g., Arguedas et al., 2006). However, drawing broad conclusions about the ways in which implicit social cognition theoretically informs cognitive models of schizophrenia is premature given that research is still very much in the early stages.

& Beck, 2008). Schemas relevant to PDs would be expected to manifest as maladaptive implicit associations, enhanced emotional priming effects for disorder-�relevant material, and attention biases for disorder-�relevant cues.

PERSONALITY DISORDERS

Implicit Association Tasks

A personality disorder (PD) is diagnosed when a pattern of inner experience or behavior (i.e., cognition, affect, interpersonal functioning, or impulse control) deviates from the expectations of one’s culture, is inflexible and pervasive, causes significant impairment or distress, and begins by adolescence or early adulthood. PDs are divided into clusters defined by the involvement of odd/eccentric behavior (paranoid, schizoid, and schizotypal PDs), dramatic/emotional behavior (antisocial, borderline, histrionic, and narcissistic PDs), or anxious/fearful behavior (avoidant, dependent, and obsessive–Â�compulsive PDs). The majority of implicit social cognition research in the area of PDs has been done on antisocial, borderline, and schizotypal personalities. Antisocial PD involves a pattern of disregard or violation of others’ rights, including failure to conform to social norms, unlawful behavior, lying, impulsivity, and lack of remorse. Borderline PD is a pattern of instability of interpersonal relationships, self-image, and affect. It is often characterized by impulsivity, efforts to avoid abandonment, chronic feelings of emptiness, and recurrent suicidal or self-Â�mutilating behavior. Schizotypal PD is a pattern of social and interpersonal deficits, accompanied by cognitive or perceptual distortions (e.g., ideas of reference, paranoid ideation) and eccentricities of behavior (e.g., preoccupations with paranormal phenomena inconsistent with the norms of one’s subculture). Similar to models of anxiety and mood disorders, cognitive models of PDs suggest that maladaptive schemas color an affected individual’s information-Â�processing style, resulting in selective processing of schema-Â�relevant cues (see Beck, Freeman, & Davis, 2004; Linehan, 1993; Young & Lindemann, 1992). Examples of such maladaptive schemas include entitlement/grandiosity, the belief that one is entitled to special privileges and is not bound by normal social conventions (theorized to be active in antisocial PD) and defectiveness/shame, the perception that one is defective, unlovable, and likely to be rejected (likely prominent in borderline PD; Young, Rygh, Weinberger,

Two studies have examined implicit associations in violent offenders with antisocial personality characteristics. Gray, MacCulloch, Smith, Morris, and Snowden (2003) administered the IAT to a sample of individuals who had committed murder and had been admitted to a secure therapeutic community for male offenders with PDs. Half of their sample was diagnosed with psychopathy (which shares many features with antisocial PD), and the other half was diagnosed with other PDs. They found that murderers with psychopathy showed weaker implicit associations between the concepts of violent and unpleasant (relative to peaceful and pleasant) than murderers without psychopathy. These researchers also used the same IAT task with murderers who were high or low in psychopathy and a control group of offenders who had committed crimes other than murder (Snowden, Gray, Smith, Morris, & MacCulloch, 2004). Compared with nonmurderers and murderers who scored low in psychopathy, murderers who scored high in psychopathy showed weaker unpleasant associations with violence. Interestingly, no group differences were found using explicit measures of attitudes toward violence. Other research using the IAT has examined implicit shame in women with borderline PD. Rüsch, Lieb, and colleagues (2007) compared a group of women with borderline PD with control groups of women with social phobia or no emotional disorder on measures of explicit and implicit shame. Shame was hypothesized to be strongly related to borderline PD because of the connection among shame and suicidal intent, anger, and impulsivity, all prominent features associated with borderline PD (see review in Rüsch, Lieb, et al., 2007). Women with borderline PD endorsed more symptoms of shame and guilt on explicit measures than women in either control group. Mirroring this finding, they also showed stronger implicit associations of the self with shame-Â�related (vs. anxiety-Â�related) words compared with both control groups. In a separate study, however, these researchers found that women with borderline PD

Group Differences and Predictive Validity

510

V. APPLIED PERSPECTIVES

and comorbid PTSD showed a more anxiety-prone (vs. shame-prone) self-Â�concept on the IAT in contrast to women with borderline PD alone (Rüsch, Corrigan, et al., 2007). Finally, using an unselected student sample, Campbell, Bosson, Goheen, Lakey, and Kernis (2007) found that narcissism symptoms showed a complex relationship with different aspects of implicit self-Â�esteem, depending on whether agentic versus communal views of the self were being measured at the implicit level. Thus, at this stage, it seems clear that implicit associations can help differentiate among different aspects of personality pathology, supporting schema-based models, but variable results depending on the control group and specific IAT comparison categories make any general conclusions premature.

Priming Tasks Studies of affective and semantic priming in people with antisocial PD have produced divergent results. Blair and colleagues (2006) found that although individuals high in psychopathy did not differ in semantic priming effects from those low in psychopathy, they did show weaker affective priming for both positive and negative primes (see also Brinkley, Schmitt, & Newman, 2005). Blair and colleagues hypothesize that this reflects reduced sensitivity to affect in individuals with antisocial characteristics, even though processing of meaning is intact. Notably, these studies have not investigated priming with disorder-�specific stimuli. Schizotypal personality traits have also been associated with impaired emotion processing, as measured with an affective priming task. Kerns (2005) primed college students who scored high or low on measures of magical ideation and perceptual aberration (symptoms associated with schizotypy) with positive, negative, or neutral words before a lexical decision task. Individuals high in schizotypal symptoms did not show affective priming effects in reaction time or error rate, even though those low in schizotypal symptoms did. The inflexible nature of PDs was examined in a study conducted by Bowles and Meyer (2008) with a sample of undergraduates who were assessed for avoidant personality features. Avoidant PD is a pattern of social inhibition, feelings of inadequacy, and extreme sensitivity to negative evaluation. Bowles and Meyer assigned participants to conditions in which they were primed by pictures reflecting secure or insecure interpersonal attachment or received no priming. When asked

subsequently to evaluate an ambiguous vignette describing a social interaction, participants high in avoidant traits made negative evaluations, regardless of priming condition, whereas those low in avoidant traits made negative evaluations only if primed with pictures depicting insecure attachment, suggesting more sensitivity to the prime condition. Again, the lack of priming effects for the high-�avoidant group suggests that some PDs are characterized by reduced sensitivity to affective or interpersonal cues. One counterexample to this pattern comes from Domes and colleagues (2006), who suggested that borderline PD may be marked by greater sensitivity to emotional primes. They investigated the role of inhibitory dysfunction in (unmedicated) women with borderline PD compared with age�matched women without an emotional disorder. They found that women in the borderline group showed greater interference from negative primes than women in the control group did.

Attention Bias Tasks Attention bias has been examined in borderline PD with mixed results. In the Domes and colleagues study (2006; described previously), despite greater interference from negative words in the affective priming task, the women with borderline PD did not show any bias on an e-Â�Stroop task that used negative and neutral words. In contrast, Sieswerda, Arntz, Mertens, and Vertommen (2007) found that a group of participants with borderline PD showed hypervigilance for emotional cues, as seen by interference on supraliminal and subliminal e-Â�Stroop tasks. Individuals with another PD, an alternate psychological disorder, or no history of disorder did not show this bias, suggesting that results were specific to borderline PD. Furthermore, the participants with borderline PD showed the strongest interference from disorderÂ�relevant negative cues (e.g., “powerless” or “unacceptable”). Similarly, Sieswerda, Arntz, and Kindt (2007) found that individuals with borderline PD showed attentional biases on an e-Â�Stroop task for both disorder-Â�relevant and unrelated emotional stimuli relative to control participants. In addition, individuals with schizotypal personality traits may show an attention bias toward negative emotional stimuli. Mohanty and colleagues (2008) administered measures of schizotypy, anxiety, and depression to an unselected sample, who then performed an e-Â�Stroop task. Certain symptoms of schizotypy, such as percep-



26. Clinical Applications of Theories and Methods

tual disturbances and odd beliefs, were associated with attentional disturbance specific to negative words. Anxiety and depression symptoms did not mediate this relationship. Interestingly, obsessive–Â�compulsive PD (a pattern of perfectionism and preoccupation with order and control) and histrionic PD (a pattern of excessive emotionality and attention seeking) have been hypothesized to show opposite patterns of attentional focus in the presence of potentially threatening stimuli. Individuals with obsessive–Â� compulsive PD may be more likely to focus on small details, whereas those with histrionic PD may be more likely to focus on the “big picture” (see Maynard & Meyer, 1996). Yovel, Revelle, and Mineka (2005) used a visual processing paradigm in which participants were shown a large figure made up of smaller component figures and asked to respond to targets presented at either the local (the component figures) or the global (the overall figure) level. They found that participants high in obsessive–Â�compulsive personality symptoms were more distracted than control participants by the small, to-be-Â�ignored details when they attempted to respond to the global figure, suggesting interference effects at the local level.

Ties to Vulnerability and Causal Links We are not aware of any published studies that have manipulated implicit processing biases or have used longitudinal designs to examine effects on individuals with PDs. (However, see Locascio & Snyder, 1975, for an interesting early attempt to train selective attention to threat in order to influence symptoms of paranoia in an unselected sample.)

Relation to Treatment Minimal work has examined change in attention biases following treatment for a PD, and we are aware of no published treatment research using implicit association or priming paradigms. Following an intensive 3-year treatment program for borderline PD, Sieswerda, Arntz, and Kindt (2007) found that attentional biases on an e-�Stroop task for disorder-�relevant and emotional stimuli were no longer present for participants who had been judged as recovered from their disorder. However, they were still found in participants who had not recovered. It should be noted that the sample size in this study was small and showed significant attrition.

511

Linking Implicit Social Cognition Findings with Theoretical Models of€Personality Disorders Far more research in the area of PDs is needed, but the available evidence suggests some tentative conclusions for the three most studied disorders. Antisocial PD may be marked by implicit association biases and limited sensitivity to positive and negative affective primes. Borderline PD seems to be characterized by implicit association biases and greater sensitivity to negative affective primes and perhaps by attentional biases toward emotional cues as well. Schizotypal PD may be marked by limited affective priming effects and attentional biases toward negative information. Furthermore, one study suggests that implicit PD-relevant biases may be changed following successful treatment. These findings are consistent with schema theories that propose selective processing of disorder�relevant cues, but do not yet permit fine-�grained conclusions about the features of automaticity that are biased in PDs or the role of automatic biases in relation to vulnerability and response to treatment.

CONCLUSION AND FUTURE€DIRECTIONS This review of the literature has simultaneously illustrated the tremendous progress that has been made in applying implicit social cognition principles and methods to psychopathology research and the considerable gaps in the literature. We are excited by the mounting evidence across paradigms and disorders that attest to the role that automatic biases play in the ways that individuals with psychological disorders view themselves and the social world. The findings appear most robust when examining group differences between clinical and nonclinical samples and prediction of symptoms and related behaviors, especially in the anxiety disorders. The evidence seems least certain when evaluating changes over the course of treatment, ties to psychopathology vulnerability, and causal links between biased processing and changes in symptoms. For these latter issues, despite some null findings, the greatest challenge is the paucity of studies that directly test these questions. Moreover, there is much we need to learn about variation across disorders and about the effects of minor differences in paradigms. For instance, it is obvious that timing of stimuli presentation is a criti-

512

V. APPLIED PERSPECTIVES

cal variable, both in terms of stimulus onset and duration of presentation, but it is difficult at this stage to provide general recommendations given the disparate results across studies, disorders, paradigms, and so on. Some of the challenges facing clinical researchers working in this field are likely the same as those facing all researchers trying to study implicit social cognition, regardless of the population or content focus. At the same time, we see some issues as being either unique to psychopathology research or perhaps just particularly tricky to manage. We conclude by highlighting just a few of these special issues and unresolved questions in the hopes that they can motivate future research in this area. •• The low reliability of many of the reaction time measures and frequent small effect sizes present a considerable challenge to clinical researchers given the common focus on individual differences and the need for test–Â�retest reliability to examine changes following treatment or vulnerability to disorder onset (e.g., Egloff, Schwerdtfeger, & Schmukle, 2005). Practice effects on the measures are a related concern and have not received sufficient attention in the clinical literature. One reason these challenges are particularly salient for clinical researchers is the extreme difficulty of recruiting sufficiently large clinical samples to have adequate power to detect small effects and help mitigate the impact of low reliability. •• Another difficulty in applying some of the popular implicit social cognition measures to address clinical questions is that the measures are sometimes relative in nature (e.g., the IAT), so the target category of interest needs to be compared with an alternate target category. Interpretation of results from the measure is thus constrained by this comparison. For instance, while we spoke earlier in the chapter of “implicit spider–fear associations” to simplify reporting of results, the many IATs examining spider–fear associations have used variable comparison categories, so results across studies are not directly comparable. Moreover, selecting a reasonable comparison category is difficult for some of the complex associations posited by various psychopathology researchers. Take, for instance, the cognitive model of panic disorder (Clark, 1986), which proposes that panic occurs because benign bodily sensations are interpreted in a catastrophic way (e.g., a racing heart is misinterpreted as a sign of a heart attack). Attempting to measure catastrophic associations about bodily sensations is no easy feat, and our lab has had multiple failed attempts trying to show group

differences on implicit association measures of this construct (e.g., Teachman et al., 2007). We believe this is likely because there is no obvious comparison category for bodily changes. As newer implicit association measures are developed that do not require a direct target comparison category and that also have good psychometric properties (e.g., affect misattribution procedure: Payne, Cheng, Govorun, & Stewart, 2005; brief IAT: Sriram & Greenwald, 2009), we look forward to increased flexibility with these tasks to assess some of the more nuanced clinical constructs proposed to operate at an automatic level. •• In addition to the issue of a good comparison or control category within a given task design, many of our studies are limited by the absence of other good controls. For instance, many of the studies discussed in this chapter did not use an alternate psychopathology control group (helpful for showing that the bias measure is specific to a given clinical problem vs. common to psychopathology in general or to shared personality markers such as neuroticism). Establishing specificity of the biases by showing discriminant validity has also been rare (e.g., showing that group differences exist on one disorder-Â�specific task but not on a comparably valenced, disorder-Â�nonspecific task). Similarly, insufficient attention has been paid to the role of comorbidity in our samples, yet we know that performance on many tasks is heavily influenced by this factor (e.g., Bradley, Mogg, Millar, & White, 1995). •• Just as a given person often does not exhibit symptoms of only one disorder, the implicit social cognition measures themselves are not process pure. The measures capture different aspects of automaticity (e.g., unconscious, uncontrollable) with variable degrees of effectiveness, and the summary score for a task frequently does not disentangle the various processes that may influence task performance (e.g., role of guessing vs. ability to overcome an automatic bias; see Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005; Deutsch & Gawronski, 2009). This issue is especially important in the context of treatment, where clinicians hope that therapy can influence more than just the tendency to guess a certain way and can actually help clients reduce activation of a bias or learn how to overcome maladaptive automatic responses when they do occur. •• Little is understood about the influence of state affect on some of the popular implicit social cognition measures (see Schmukle & Egloff, 2004, who found no effect of a state anxiety manipulation on an IAT in an unselected sample). When



26. Clinical Applications of Theories and Methods

working with a population that has a clinical disorder (relative to a nonclinical comparison group), researchers need to contend with group differences tied to trait negative affect, of course, but often differences in state negative affect are important as well. These may be present naturally (e.g., as a result of fluctuating symptoms), or groups may show differential responsiveness to a mood manipulation or stressor. Based on past research in the attention and memory bias literatures, it is not yet clear under what conditions state negative affect will suppress (e.g., Mathews & Sebastian, 1993) versus enhance (e.g., Chen, Lewin, & Craske, 1996) bias effects. Learning how these state–trait interactions influence various implicit social cognitive processes will be essential to understanding the real-world implications of these findings for emotion regulation, self-Â�evaluation, and social interactions. We started this chapter commenting on our enthusiasm about the signs of increased dialogue between clinical and social cognition researchers. Using methods and theories across subdisciplines is likely to benefit everyone. Along these lines, we see a number of possible lessons we can learn from the broader social cognition field. For instance, there has been much interest recently in trying to determine potential moderators of relationships between implicit and explicit measures (e.g., Nosek, 2005), yet we know little about whether these will operate in similar ways with clinical populations. Social desirability is just one example of a commonly cited moderator in social cognition research. Would we expect it to operate the same way in a sample with schizophrenia who have deficits in executive functioning or distorted views of reality? What about in a sample with major depression or social anxiety, who often show reversals of the self-Â�serving biases typical of psychologically healthy populations (e.g., Arkin, Appelman, & Burger, 1980)? Is there an equivalent clinical moderator analogous to motivation to control prejudice (see Plant & Devine, 1998)? Comparable questions surround the mechanisms to alter implicit associations compared with explicit propositions (see Gawronski & Bodenhausen, 2007). Much exciting work has occurred in recent years in the social cognition field to address these questions of moderators and mediators; how these findings can inform models of psychopathology is an important but unresolved question. On the flip side, clinical treatment studies and evaluations of predictive validity of symptoms can provide some informative lessons to social cognition researchers who want

513

to learn more about the prediction of complex behaviors outside the laboratory and the durability of change in implicit social cognitive processes. We look forward to watching this research area grow in the coming years, as clinical and social cognition researchers work together to start addressing these special challenges and unresolved questions. This is a stimulating time for researchers working across these two fields as new paradigms are coming online that can help address some of the limitations of prior methodologies, and our ability to work across geographical and linguistic divides has made it easier to collaborate, share methods and ideas, and confront the challenges of recruiting clinical samples. Notes 1. There is a small but growing literature examining indirect measures of stereotypes tied to persons with mental illness that is also relevant, which is discussed by Amodio and Mendoza (Chapter 19, this volume). 2. This idea is popular among “vigilance-Â�avoidance” models that suggest anxious individuals rapidly orient to threat stimuli but are then motivated to avoid further processing of the noxious stimuli (see Amir, Foa, & Coles, 1998).

References Ahern, A. L., Bennett, K. M., & Hetherington, M. M. (2008). Internalization of the ultra-thin ideal: Positive implicit associations with underweight fashion models are associated with drive for thinness in young women. Eating Disorders, 16, 294–307. Ahern, A. L., & Hetherington, M. M. (2006). The thin ideal and body image: An experimental study of implicit attitudes. Psychology of Addictive Behaviors, 20, 338–342. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author. Amir, N., Beard, C., Burns, M., & Bomyea, J. (2009). Attention modification program in individuals with generalized anxiety disorder. Journal of Abnormal Psychology, 118, 28–33. Amir, N., Cobb, M., & Morrison, A. M. (2008). Threat processing in obsessive–Â�compulsive disorder: Evidence from a modified negative priming task. Behaviour Research and Therapy, 46, 728–736. Amir, N., Elias, J., Klumpp, H., & Przeworski, A. (2003). Attentional bias to threat in social phobia: Facilitated processing of threat or difficulty disengaging attention from threat? Behaviour Research and Therapy, 41, 1325–1335. Amir, N., Foa, E. B., & Coles, M. E. (1998). Automatic activation and strategic avoidance of threat-Â�relevant

514

V. APPLIED PERSPECTIVES

information in social phobia. Journal of Abnormal Psychology, 107, 285–290. Amir, N., Weber, G., Beard, C., Bomyea, J., & Taylor, C. T. (2008). The effect of a single-Â�session attention modification program on response to a publicÂ�speaking challenge in socially anxious individuals. Journal of Abnormal Psychology, 117, 860–868. Arguedas, D., Green, M. J., Langdon, R., & Coltheart, M. (2006). Selective attention to threatening faces in delusion-prone individuals. Cognitive Neuropsychiatry, 11, 557–575. Arkin, R. M., Appelman, A. J., & Burger, J. M. (1980). Social anxiety, self-Â�presentation, and the self-Â�serving bias in causal attribution. Journal of Personality and Social Psychology, 38, 23–35. Ball, J. R., Mitchell, P. B., Touyz, S. W., Griffiths, R. A., & Beumont, P. J. V. (2004). Clinical utility of the modified Stroop task as a treatment outcome measure: Questions raised. Clinical Psychologist, 8, 76–80. Bargh, J. A. (1994). The four horsemen of automaticity: Awareness, intention, efficiency, and control in social cognition. In R. S. Wyer, Jr. & T. K. Srull (Eds.), Handbook of social cognition (Vol. 1, pp.€1–40). Hillsdale, NJ: Erlbaum. Beck, A. T., & Clark, D. A. (1997). An information processing model of anxiety: Automatic and strategic processes. Behaviour Research and Therapy, 35, 49–58. Beck, A. T., Emery, G., & Greenberg, R. I. (1985). Anxiety disorders and phobias: A cognitive perspective. New York: Basic Books. Beck, A. T., Freeman, A., & Davis, D. D. (2004). Cognitive therapy of personality disorders (2nd ed.). New York: Guilford Press. Beck, A. T., Rush, A. J., Shaw, B. F., & Emery, G. (1979). Cognitive therapy of depression. New York: Guilford Press. Becker, E. S., Rinck, M., Margraf, J., & Roth, W. T. (2001). The emotional Stroop effect in anxiety disorders: General emotionality or disorder specificity? Journal of Anxiety Disorders, 15, 147–159. Beevers, C. G. (2005). Cognitive vulnerability to depression: A dual process model. Clinical Psychology Review, 25, 975–1002. Beevers, C. G., & Carver, C. S. (2003). Attentional bias and mood persistence as prospective predictors of dysphoria. Cognitive Therapy and Research, 27, 619–637. Ben-Tovim, D. I., & Walker, M. K. (1991). Further evidence for the Stroop test as a quantitative measure of psychopathology in eating disorders. International Journal of Eating Disorders, 10, 609–613. Bentall, R. P., & Kaney, S. (1989). Content specific information processing and persecutory delusions: An investigation using the emotional Stroop test. British Journal of Medical Psychology, 62, 355–364. Berner, M., & Maier, M. (2004). The direction of affective priming as a function of trait anxiety in the naming of target words with regular and irregular pronunciation. Experimental Psychology, 51, 180–190. Bisson, M. A. S., & Sears, C. R. (2007). The effect of depressed mood on the interpretation of ambiguity, with and without negative mood induction. Cognition and Emotion, 21, 614–645.

Black, C. M. D., Wilson, G. T., Labouvie, E., & Heffernan, K. (1997). Selective processing of eating disorder relevant stimuli: Does the Stroop test provide an objective measure of bulimia nervosa? International Journal of Eating Disorders, 22, 329–333. Blair, K. S., Richell, R. A., Mitchell, D. G. V., Leonard, A., Morton, J., & Blair, R. J. R. (2006). They know the words, but not the music: Affective and semantic priming in individuals with psychopathy. Biological Psychology, 73, 114–123. Boon, B., Vogelzang, L., & Jansen, A. (2000). Do restrained eaters show attention toward or away from food, shape and weight stimuli? European Eating Disorders Review, 8, 51–58. Bowles, D. P., & Meyer, B. (2008). Attachment priming and avoidant personality features as predictors of social-Â�evaluation biases. Journal of Personality Disorders, 22, 72–88. Bradley, B. P., Mogg, K., & Millar, N. (1996). Implicit memory bias in clinical and non-Â�clinical depression. Behaviour Research and Therapy, 34, 865–879. Bradley, B. P., Mogg, K., Millar, N., & White, J. (1995). Selective processing of negative information: Effects of clinical anxiety, concurrent depression, and awareness. Journal of Abnormal Psychology, 104, 532–536. Bradley, B. P., Mogg, K., White, J., Groom, C., & de Bono, J. (1999). Attentional bias for emotional faces in generalised anxiety disorder. British Journal of Clinical Psychology, 38, 267–278. Bradley, B. P., Mogg, K., & Williams, R. (1995). Implicit and explicit memory for emotion-Â�congruent information in clinical depression and anxiety. Behaviour Research and Therapy, 33, 755–770. Brinkley, C. A., Schmitt, W. A., & Newman, J. P. (2005). Semantic processing in psychopathic offenders. Personality and Individual Differences, 38, 1047–1056. Brown, T. A., & Barlow, D. H. (2005). Dimensional versus categorical classification of mental disorders in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders and beyond: Comment on the special section. Journal of Abnormal Psychology, 114, 551–556. Buhlmann, U., McNally, R. J., Wilhelm, S., & Florin, I. (2002). Selective processing of emotional information in body dysmorphic disorder. Journal of Anxiety Disorders, 16, 289–298. Buhlmann, U., Teachman, B. A., Gerbershagen, A., Kikul, J., & Rief, W. (2008). Implicit and explicit selfÂ�esteem and attractiveness beliefs among individuals with body dysmorphic disorder. Cognitive Therapy and Research, 32, 213–225. Buhlmann, U., Teachman, B. A., Naumann, E., Fehlinger, T., & Rief, W. (2009). The meaning of beauty: Implicit and explicit self-Â�esteem and attractiveness beliefs in body dysmorphic disorder. Journal of Anxiety Disorders, 23, 694–702. Buhlmann, U., & Wilhelm, U. B. (2004). Cognitive factors in body dysmorphic disorder. Psychiatric Annals, 34, 922–926. Campbell, W. K., Bosson, J. K., Goheen, T. W., Lakey, C. E., & Kernis, M. H. (2007). Do narcissists dislike themselves “deep down inside”? Psychological Science, 8, 227–229. Carpenter, W. T., & Kirkpatrick, B. (2005). The hetero-



26. Clinical Applications of Theories and Methods

geneity of the long-term course of schizophrenia. In L. Davidson, C. Harding, & L. Spaniol (Eds.), Recovery from severe mental illnesses: Research evidence and implications for practice (Vol. 1, pp.€147–159). Boston: Boston University, Center for Psychiatric Rehabilitation. Carter, F. A., Bulik, C. M., McIntosh, V. V., & Joyce, P. R. (2000). Changes on the Stroop test following treatment: Relation to word type, treatment condition, and treatment outcome among women with bulimia nervosa. International Journal of Eating Disorders, 28, 349–355. Cassin, S. E., von Ranson, K. M., & Whiteford, S. (2008). Cognitive processing of body and appearance words as a function of thin-ideal internalization and schematic activation. Body Image, 5, 271–278. Chen, E., Lewin, M. R., & Craske, M. G. (1996). Effects of state anxiety on selective processing of threatening information. Cognition and Emotion, 10, 225–240. Chen, M., & Bargh, J. A. (1999). Nonconscious approach and avoidance behavioral consequences of the automatic evaluation effect. Personality and Social Psychology Bulletin, 25, 215–224. Chen, Y. P., Ehlers, A., Clark, D. M., & Mansell, W. (2002). Patients with generalized social phobia direct their attention away from faces. Behaviour Research and Therapy, 40, 677–687. Clark, D. M. (1986). A cognitive approach to panic. Behaviour Research and Therapy, 24, 461–470. Clark, D. M., Salkovskis, P. M., Gelder, M. G., Koehler, C., Martin, M., Anastasiades, P., et al. (1988). Tests of a cognitive theory of panic. In I. Hand & H. U. Wittchen (Eds.), Panic and phobias II: Treatments and variables affecting course and outcome (pp.€ 71–90). Berlin: Springer-Â�Verlag. Clerkin, E. M., & Teachman, B. A. (2009a). Automatic and strategic measures as predictors of mirror gazing among individuals with body dysmorphic disorder symptoms. Journal of Nervous and Mental Disease, 197, 589–598. Clerkin, E. M., & Teachman, B. A. (2009b). Training implicit social anxiety associations: An experimental intervention. Manuscript in preparation. Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K., & Groom, C. (2005). Separating multiple processes in implicit social cognition: The quadmodel of implicit task performance. Journal of Personality and Social Psychology, 89, 469–487. Cooper, M. J., & Fairburn, C. G. (1994). Changes in selective information processing with three psychological treatments for bulimia nervosa. British Journal of Clinical Psychology, 33, 353–356. Dalgleish, T., Cameron, C. M., Power, M. J., & Bond, A. (1995). The use of an emotional priming paradigm with clinically anxious subjects. Cognitive Therapy and Research, 19, 69–89. Danion, J., Kauffmann-Â�Muller, F., Grangé, D., & Zimmerman, M. (1995). Affective valence of words, explicit and implicit memory in clinical depression. Journal of Affective Disorders, 34, 227–234. Dannlowski, U., Kersting, A., Donges, U., Lalee-Â�Mentzel, J., Arolt, V., & Suslow, T. (2006). Masked facial affect priming is associated with therapy response in clinical depression. European Archives of Psychiatry and Clinical Neuroscience, 256, 215–221.

515

De Houwer, J. (2003). The Extrinsic Affective Simon Task. Experimental Psychology, 50, 77–85. de Jong, P. J. (2002). Implicit self-Â�esteem and social anxiety: Differential self-Â�favouring effects in high and low anxious individuals. Behaviour Research and Therapy, 40, 501–508. de Jong, P. J., Pasman, W., Kindt, M., & van den Hout, M. A. (2001). A reaction time paradigm to assess (implicit) complaint-Â�specific dysfunctional beliefs. Behaviour Research and Therapy, 39, 101–113. de Jong, P. J., van den Hout, M. A., Rietbroek, H., & Huijding, J. (2003). Dissociations between implicit and explicit attitudes towards phobic stimuli. Cognition and Emotion, 17, 521–545. De Raedt, R., Schacht, R., Franck, E., & De Houwer, J. (2006). Self-Â�esteem and depression revisited: Implicit positive self-Â�esteem in depressed patients? Behaviour Research and Therapy, 44, 1017–1028. Denny, E. B., & Hunt, R. R. (1992). Affective valence and memory in depression: Dissociation of recall and fragment completion. Journal of Abnormal Psychology, 101, 575–580. Deutsch, R., & Gawronski, B. (2009). When the method makes a difference: Antagonistic effects on “automatic evaluations” as a function of task characteristics of the measure. Journal of Experimental Social Psychology, 45, 101–114. Dobson, K. S., & Dozois, D. J. A. (2004). Attentional biases in eating disorders: A meta-Â�analytic review of Stroop performance. Clinical Psychology Review, 23, 1001–1022. Domes, G., Winter, B., Schnell, K., Vohs, K., Fast, K., & Herpertz, S. C. (2006). The influence of emotions on inhibitory functioning in borderline personality disorder. Psychological Medicine, 36, 1163–1172. Eertmans, A., Baeyens, F., & Van den Bergh, O. (2001). Food likes and their relative importance in human eating behavior: Review and preliminary suggestions for health promotion. Health Education Research, 16, 443–456. Egloff, B., & Schmukle, S. C. (2002). Predictive validity of an Implicit Association Test for assessing anxiety. Journal of Personality and Social Psychology, 83, 1441–1455. Egloff, B., Schwerdtfeger, A., & Schmukle, S. C. (2005). Temporal stability of the Implicit Association TestÂ�anxiety. Journal of Personality Assessment, 84, 82–88. Egloff, B., Wilhelm, F. H., Neubauer, D. H., Mauss, I. B., & Gross, J. J. (2002). Implicit anxiety measure predicts cardiovascular reactivity to an evaluated speaking task. Emotion, 2, 3–11. Ehrenreich, J. T., & Gross, A. M. (2002). Biased attentional behavior in childhood anxiety: A review of theory and current empirical investigation. Clinical Psychology Review, 22, 991–1008. Ellwart, T., Becker, E., & Rinck, M. (2005). Activation and measurement of threat associations in fear of spiders: An application of the Extrinsic Affective Simon Task. Journal of Behavior Therapy and Experimental Psychiatry, 36, 281–299. Ellwart, T., Rinck, M., & Becker, E. S. (2006). From fear to love: Individual differences in implicit spider associations. Emotion, 6, 18–27. Elsesser, K., Heuschen, I., Pundt, I., & Sartory, G.

516

V. APPLIED PERSPECTIVES

(2006). Attentional bias and evoked heart-rate response in specific phobia. Cognition and Emotion, 20, 1092–1107. Elsesser, K., Sartory, G., & Tackenberg, A. (2004). Attention, heart rate, and startle response during exposure to trauma-Â�relevant pictures: A comparison of recent trauma victims and patients with posttraumatic stress disorder. Journal of Abnormal Psychology, 113, 289–301. Englehard, I. M., Huijding, J., van den Hout, M. A., & de Jong, P. J. (2007). Vulnerability associations and symptoms of post-Â�traumatic stress disorder in soldiers deployed to Iraq. Behaviour Research and Therapy, 45, 2317–2325. Enright, S. J., & Beech, A. R. (1990). Obsessional states: Anxiety disorders or schizotypes?: An information processing and personality assessment. Psychological Medicine, 20, 621–627. Fairburn, C. G., Cooper, Z., & Shafran, R. (2003). Cognitive behaviour therapy for eating disorders: A “transdiagnostic” theory and treatment. Behaviour Research and Therapy, 41, 509–528. Fairburn, C. G., Cooper, Z., Shafran, R., & Wilson, G. T. (2008). Eating disorders: A transdiagnostic protocol. In D. H. Barlow (Ed.), Clinical handbook of psychological disorders: A step-by-step treatment manual (4th ed., pp.€578–614). New York: Guilford Press. Fear, C., Sharp, H., & Healy, D. (1996). Cognitive processes in delusional disorders. British Journal of Psychiatry, 168, 61–67. Ferraro, F. R., Andres, M., Stromberg, L., & Kristjanson, J. (2003). Processing fat-Â�related information in individuals at risk for developing an eating disorder. Journal of Psychology, 137, 467–475. Ferraro, F. R., Wonderlich, S., & Johnson, K. (1997). Do individuals at risk for eating disorders exhibit negative priming deficits? Journal of General Psychology, 124, 381–390. Fioravanti, M., Carlone, O., Vitale, B., Cinti, M. E., & Clare, L. (2005). A meta-Â�analysis of cognitive deficits in adults with a diagnosis of schizophrenia. Neuropsychology Review, 15, 73–95. Foa, E. B., Ilai, D., McCarthy, P., Shoyer, B., & Murdock, T. (1993). Information processing in obsessiveÂ�compulsive disorder. Cognitive Therapy and Research, 17, 173–189. Fox, E. (1994). Attentional bias in anxiety: A defective inhibition hypothesis. Cognition and Emotion, 8, 165–195. Freeman, D., Garety, P. A., & Phillips, M. L. (2000). The examination of hypervigilance for external threat in individuals with generalized anxiety disorder and individuals with persecutory delusions using visual scan paths. Quarterly Journal of Experimental Psychology, 53, 549–567. French, C. C., Richards, A., & Scholfield, E. J. C. (1996). Hypomania, anxiety, and the emotional Stroop. British Journal of Clinical Psychology, 35, 617–626. Garety, P. A., & Freeman, D. (1999). Cognitive approaches to delusions: A critical review of theories and evidence. British Journal of Clinical Psychology, 38, 113–154. Gawronski, B., & Bodenhausen, G. V. (2007). Unraveling the processes underlying evaluation: Attitudes

from the perspective of the APE model. Social Cognition, 25, 687–717. Gemar, M. C., Segal, Z. V., Sagrati, S., & Kennedy, S. J. (2001). Mood-Â�induced changes on the Implicit Association Test in recovered depressed patients. Journal of Abnormal Psychology, 110, 282–289. Goeleven, E., De Raedt, R., Baert, S., & Koster, E. H. (2006). Deficient inhibition of emotional information in depression. Journal of Affective Disorders, 93, 149–157. Goldberg, T. E., & Weinberger, T. R. (2000). Thought disorder in schizophrenia: A reappraisal of older formulations and an overview of some recent studies. Cognitive Neuropsychiatry, 5, 1–19. Gotlib, I. H., & Cane, D. B. (1987). Construct accessibility and clinical depression: A longitudinal investigation. Journal of Abnormal Psychology, 96, 199–204. Gotlib, I. H., Kasch, K. L., Traill, S., Joormann, J., Arnow, B. A., & Johnson, S. L. (2004). Coherence and specificity of information-Â�processing biases in depression and social phobia. Journal of Abnormal Psychology, 113, 386–398. Gotlib, I. H., Krasnoperova, E., Yue, D. N., & Joormann, J. (2004). Attentional biases for negative interpersonal stimuli in clinical depression. Journal of Abnormal Psychology, 113, 127–135. Gotlib, I. H., Traill, S. K., Montoya, R. L., Joormann, J., & Chang, K. (2005). Attention and memory biases in the offspring of parents with bipolar disorder: Indications from a pilot study. Journal of Child Psychology and Psychiatry, 46, 84–93. Gray, N. S., MacCulloch, M. J., Smith, J., Morris, M., & Snowden, R. J. (2003). Violence viewed by psychopathic murderers: Adapting a revealing test may expose those psychopaths who are most likely to kill. Nature, 423, 497–498. Green, M. J., Williams, J., & Davidson, A. (2003). Visual scanpaths to threat-Â�related faces in deluded schizophrenia. Psychiatry Research, 119, 271–285. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Haeffel, G. J., Abramson, L. Y., Brazy, P. C., Shah, J. Y., Teachman, B. A., & Nosek, B. A. (2007). Explicit and implicit cognition: A preliminary test of a dualÂ�process theory of cognitive vulnerability to depression. Behaviour Research and Therapy, 45, 1155–1167. Hall, P. H., & Tarrier, N. (2003). The cognitiveÂ�behavioural treatment of low self-Â�esteem in psychotic patients: A pilot study. Behaviour Research and Therapy, 41, 317–332. Hartlage, S., Alloy, L. B., Vazquez, C., & Dykman, B. (1993). Automatic and effortful processing in depression. Psychological Bulletin, 113, 247–278. Hartston, H. J., & Swerdlow, N. R. (1999). Visuospatial priming and Stroop performance in patients with obsessive compulsive disorder. Neuropsychology, 13, 447–457. Harvey, A. G., Bryant, R. A., & Rapee, R. M. (1996). Preconscious processing of threat in posttraumatic stress disorder. Cognitive Therapy and Research, 20, 613–623. Hempel, R. J., Tulen, J. H. M., Van Beveren, N. J. M., &



26. Clinical Applications of Theories and Methods

Hengeveld, M. W. (2005). Emotionele informatieverwerking bij patienten met schizofrenie: Herkenning van gelaatsuitdrukkingen [Emotional information processing in patiens with schizophrenia: Recognition of facial expressions]. Tijdschrift voor Psychiatrie, 47, 83–92. Hertel, P. T. (2004). Memory for emotional and nonemotional events in depression: A question of habit? In P. T. Hertel & D. Reisberg (Eds.), Memory and emotion (pp.€186–216). New York: Oxford University Press. Heuer, K., Rinck, M., & Becker, E. S. (2007). Avoidance of emotional facial expressions in social anxiety: The approach-Â�avoidance task. Behaviour Research and Therapy, 45, 2990–3001. Horenstein, M., & Segui, J. (1997). Chronometrics of attentional processes in anxiety disorders. Psychopathology, 30, 25–35. Höschel, K., & Irle, E. (2001). Emotional priming of facial affect identification in schizophrenia. Schizophrenia Bulletin, 27, 317–327. Huijding, J., & de Jong, P. J. (2005). A pictorial version of the Extrinsic Affective Simon Task: Sensitivity to generally affective and phobia-Â�relevant stimuli in high and low spider fearful individuals. Experimental Psychology, 52, 289–295. Huijding, J., & de Jong, P. J. (2006). Specific predictive power of automatic spider-Â�related affective associations for controllable and uncontrollable fear responses toward spiders. Behaviour Research and Therapy, 44, 161–176. Huijding, J., & de Jong, P. J. (2007). Beyond fear and disgust: The role of (automatic) contamination-Â�related associations in spider phobia. Journal of Behavior Therapy and Experimental Psychiatry, 38, 200–211. Huijding, J., & de Jong, P. J. (2009). Implicit and explicit attitudes toward spiders: Sensitivity to treatment and predictive value for generalization of treatment effects. Cognitive Therapy and Research, 33, 211–220. Johansson, L., Ghaderi, A., & Andersson, G. (2005). Stroop interference for food- and body-Â�related words: A meta-Â�analysis. Eating Behaviors, 6, 271–281. Johnson, S. L., Joormann, J., & Gotlib, I. H. (2007). Does processing of emotional stimuli predict symptomatic improvement and diagnostic recovery from major depression? Emotion, 7, 201–206. Joormann, J. (2004). Attentional bias in dysphoria: The role of inhibitory processes. Cognition and Emotion, 18, 125–147. Joormann, J. (2006). Differential effects of rumination and dysphoria on the inhibition of irrelevant emotional material: Evidence from a negative priming task. Cognitive Therapy and Research, 30, 149–160. Joormann, J., & Gotlib, I. H. (2007). Selective attention to emotional faces following recovery from depression. Journal of Abnormal Psychology, 116, 80–85. Joormann, J., Hertel, P. T., LeMoult, J., & Gotlib, I. H. (2009). Training forgetting of negative material in depression. Journal of Abnormal Psychology, 118, 34–43. Joormann, J., Talbot, L., & Gotlib, I. H. (2007). Biased processing of emotional information in girls at risk for depression. Journal of Abnormal Psychology, 116(1), 135–143.

517

Kampman, M., Keijsers, G. P. J., Verbaak, M. J. P. M., Näring, G., & Hoogdiun, C. A. L. (2002). The emotional Stroop: A comparison of panic disorder patients, obsessive–Â�compulsive patients, and normal controls, in two experiments. Journal of Anxiety Disorders, 16, 425–441. Karparova, S. P., Kersting, A., & Suslow, T. (2005). Disengagement of attention from facial emotion in unipolar depression. Psychiatry and Clinical Neurosciences, 59, 723–729. Kerns, J. G. (2005). Positive schizotypy and emotion processing. Journal of Abnormal Psychology, 114, 392–401. Kinderman, P. (1994). Attentional bias, persecutory delusions and the self concept. British Journal of Medical Psychology, 67, 53–66. Kindt, M., & Brosschot, J. F. (1998). Cognitive avoidance in phobia. Journal of Psychopathology and Behavioral Assessment, 20, 43–55. Kroeze, S., & van den Hout, M. A. (2000). Selective attention for cardiac information in panic patients. Behaviour Research and Therapy, 38, 63–72. Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1990). Emotion, attention, and the startle reflex. Psychological Review, 97, 377–398. Lee, M., & Shafran, R. (2004). Information processing biases in eating disorders. Clinical Psychology Review, 24, 215–238. Lee, M., & Shafran, R. (2008). Processing biases in eating disorders: The impact of temporal factors. International Journal of Eating Disorders, 41, 372–375. Li, S., Tan, J., Qian, M., & Liu, X. (2008). Continual training of attentional bias in social anxiety. Behavioural Research and Therapy, 46, 905–912. Li, W., Paller, K. A., & Zinbarg, R. A. (2008). Conscious intrusion of threat information via unconscious priming in anxiety. Cognition and Emotion, 22, 44–62. Linehan, M. M. (1993). Cognitive-Â�behavioral treatment of borderline personality disorder. New York: Guilford Press. Locascio, J. J., & Snyder, C. R. (1975). Selective attention to threatening stimuli and field independence as factors in the etiology of paranoid behavior. Journal of Abnormal Psychology, 84, 637–643. Lokken, K. L., Marx, H. M., & Ferraro, F. R. (2006). Severity of bulimic symptoms is the best predictor of interference on an emotional Stroop paradigm. Eating and Weight Disorders, 11, 38–44. Long, C. C., Hinton, C., & Gillespie, N. K. (1994). Selective processing of food and body size words: Application of the Stroop test with obese restrained eaters, anorexics and normals. International Journal of Eating Disorders, 15, 279–283. Lovell, D., Williams, J. M. G., & Hill, A. B. (1997). Selective processing of shape related words in women with eating disorders and those who have recovered. British Journal of Clinical Psychology, 36, 421–432. Lowe, M. R., Gleaves, D. H., Di Simone-Weiss, R. T., Furgueson, C., Gayda, C. A., Kolsky, P. A., et al. (1996). Restraint, dieting, and the continuum model of bulimia nervosa. Journal of Abnormal Psychology, 105, 508–517. Lundh, L. G., & Öst, L. G. (2001). Attentional bias, self-Â�consciousness and perfectionism in social phobia

518

V. APPLIED PERSPECTIVES

before and after cognitive-Â�behaviour therapy. Scandinavian Journal of Behaviour Therapy, 30, 4–16. Lundh, L. G., Wikstrom, J., Westerlund, J., & Öst, L. G. (1999). Preattentive bias for emotional information in panic disorder. Journal of Abnormal Psychology, 108, 222–232. MacLeod, C., & Bridle, R. (2009). The reduction of anxiety vulnerability through the modification of attentional bias: A real-world study using a home-based cognitive bias modification procedure. Journal of Abnormal Psychology, 118, 65–75. MacLeod, C., & Hagan, R. (1992). Individual differences in the selective processing of threatening information, and emotional responses to a stressful life event. Behaviour Research and Therapy, 30, 151–161. MacLeod, C., Mathews, A., & Tata, P. (1986). Attentional bias in emotional disorders. Journal of Abnormal Psychology, 95, 15–20. MacLeod, C., Rutherford, E. C., Campbell, L., Ebsworthy, G., & Holker, L. (2002). Selective attention and emotional vulnerability: Assessing the causal basis of their association through the experimental manipulation of attentional bias. Journal of Abnormal Psychology, 111, 107–123. Maher, B. A. (1983). Towards a tentative theory of schizophrenic utterance. Progress in Experimental Personality Research, 12, 1–52. Maier, M. A., Berner, M. P., Hau, R. C., & Pekrun, R. (2007). Priming the trait category “hostility”: The moderating role of trait anxiety. Cognition and Emotion, 21, 577–595. Mathews, A., & MacLeod, C. (2002). Induced processing biases have causal effects on anxiety. Cognition and Emotion, 16, 331–354. Mathews, A., & MacLeod, C. (2005). Cognitive vulnerability to emotional disorders. Annual Review of Clinical Psychology, 1, 167–195. Mathews, A., Ridgeway, V., & Williamson, D. A. (1996). Evidence for attention to threatening stimuli in depression. Behaviour Research and Therapy, 34, 695–705. Mathews, A. M., & Sebastian, S. (1993). Suppression of emotional Stroop effects by fear-Â�arousal. Cognition and Emotion, 7, 517–530. Matthews, G., & Southall, A. (1991). Depression and the processing of emotional stimuli: A study of semantic priming. Cognitive Therapy and Research, 15, 283–302. Mattia, J. I., Heimberg, R. G., & Hope, D. A. (1993). The revised Stroop color-Â�naming task in social phobics. Behaviour Research and Therapy, 31, 305–313. Maynard, R. E., & Meyer, G. E. (1996). Visual information processing with obsessive–Â�compulsive and hysteric personalities. Personality and Individual Differences, 20, 389–399. McCulloch, Y., Clare, L., Howard, R., & Peters, E. (2006). Psychological processes underlying delusional thinking in late-onset psychosis: A preliminary investigation. International Journal of Geriatric Psychiatry, 21, 768–777. McKee, S. A., Nhean, S., Hinson, R. E., & Mase, T. (2006). Smoking for weight control: Effect of priming for body image in female restrained eaters. Addictive Behaviors, 31, 2319–2323.

McManus, F., Waller, G., & Chadwick, P. (1996). Biases in the processing of different forms of threat in bulimic and comparison women. Journal of Nervous and Mental Disease, 184, 547–554. McNally, R. J. (1995). Automaticity and the anxiety disorders. Behaviour Research and Therapy, 33, 747–754. McNally, R. J., Hornig, C. D., Otto, M. W., & Pollack, M. H. (1997). Selective encoding of threat in panic disorder: Application of a dual priming paradigm. Behaviour Research and Therapy, 35, 543–549. McNally, R. J., Kaspi, S. P., Riemann, B. C., & Zeitlin, S. B. (1990). Selective processing of threat cues in posttraumatic stress disorder. Journal of Abnormal Psychology, 99, 398–402. McNally, R. J., Riemann, B. C., & Kim, E. (1990). Selective processing of threat cues in panic disorder. Behaviour Research and Therapy, 28, 407–412. McNally, R. J., Wilhelm, S., Buhlmann, U., & Shin, L. M. (2001). Cognitive inhibition in obsessiveÂ�compulsive disorder: Application of a valence based negative priming paradigm. Behavioural and Cognitive Psychotherapy, 29, 103–106. Meijboom, A., Jansen, A., Kampman, M., & Schouten, E. (1999). An experimental test of the relationship between self-Â�esteem and concern about body shape and weight in restrained eaters. International Journal of Eating Disorders, 25, 327–334. Miltner, W. H. R., Krieschel, S., Hecht, H., Trippe, R., & Weiss, T. (2004). Eye movements and behavioral responses to threatening and nonthreatening stimuli during visual search in phobic and nonphobic subjects. Emotion, 4, 323–339. Minzenberg, M. J., Ober, B. A., & Vinogradov, S. (2002). Semantic priming in schizophrenia: A review and synthesis. Journal of the International Neuropsychological Society, 8, 699–720. Mogg, K., & Bradley, B. P. (2005). Attentional bias in generalized anxiety disorder versus depressive disorder. Cognitive Therapy and Research, 29, 29–45. Mogg, K., & Bradley, B. P. (2006). Time course of attentional bias for fear-Â�relevant stimuli in spider-Â�fearful individuals. Behaviour Research and Therapy, 44, 1241–1250. Mogg, K., Bradley, B. P., Miles, F., & Dixon, R. (2004). Time course of attentional bias for threat scenes: Testing the vigilance-Â�avoidance hypothesis. Cognition and Emotion, 18, 689–700. Mogg, K., Bradley, B. P., Millar, N., & White, J. (1995). A follow-up study of cognitive bias in generalized anxiety disorder. Behaviour Research and Therapy, 33, 927–935. Mogg, K., Bradley, B. P., & Williams, R. (1995). Attentional bias in anxiety and depression: The role of awareness. British Journal of Clinical Psychology, 34, 17–36. Mogg, K., Bradley, B. P., Williams, R., & Mathews, A. (1993). Subliminal processing of emotional information in anxiety and depression. Journal of Abnormal Psychology, 102, 304–311. Mogg, K., Mathews, A., & Eysenck, M. (1992). Attentional bias to threat in clinical anxiety states. Cognition and Emotion, 6, 149–159. Mogg, K., Millar, N., & Bradley, B. P. (2000). Biases in eye movement to threatening facial expression in



26. Clinical Applications of Theories and Methods

generalized anxiety disorder and depressive disorder. Journal of Abnormal Psychology, 109, 695–704. Mohanty, A., Heller, W., Koven, N. S., Fisher, J. E., Herrington, J. D., & Miller, G. A. (2008). Specificity of emotion-Â�related effects on attentional processing in schizotypy. Schizophrenia Research, 103, 129–137. Moritz, S., Jacobsen, D., Kloss, M., Fricke, S., Rufer, M., & Hand, I. (2004). Examination of emotional Stroop interference in obsessive–Â�compulsive disorder. Behaviour Research and Therapy, 42, 671–682. Moritz, S., Ruff, C., Wilke, U., Andresen, B., Krausz, M., & Naber, D. (2001). Negative priming in schizophrenia: Effects of masking and prime presentation time. Schizophrenia Research, 48, 291–299. Moritz, S., Werner, R., & von Collani, G. (2006). The inferiority complex in paranoia readdressed: A study with the Implicit Association Test. Cognitive Neuropsychiatry, 11, 402–415. Muroi, M., Kasai, K., Uetsuki, M., & Suga, M. (2007). The function of selective attention to non-Â�emotional and emotional information in patients with schizophrenia. Japanese Journal of Cognitive Psychology, 4, 65–73. Musa, C., Lépine, J., Clark, D., Mansell, W., & Ehlers, A. (2003). Selective attention in social phobia and the moderating effect of a concurrent depressive disorder. Behaviour Research and Therapy, 41, 1043–1054. Nay, W. T., Thorpe, G. L., Robertson-Nay, R., Hecker, J. E., & Sigmon, S. T. (2004). Attentional bias to threat and emotional response to biological challenge. Journal of Anxiety Disorders, 18, 609–627. Neshat-Doost, H. T., Moradi, A. R., Taghavi, M. R., Yule, W., & Dalgleish, T. (2000). Lack of attentional bias for emotional information in clinically depressed€children and adolescents on the dot probe task. Journal of Child Psychology and Psychiatry, 41, 363–368. Nosek, B. A. (2005). Moderators of the relationship between implicit and explicit evaluation. Journal of Experimental Psychology: General, 134, 565–584. Nosek, B. A., & Banaji, M. R. (2001). The Go/No-Go Association Task. Social Cognition, 19, 625–666. Öhman, A., & Mineka, S. (2001). Fears, phobias, and preparedness: Towards an evolved module of fear and fear learning. Psychology Review, 108, 483–522. Paunovic, N., Lundh, L. G., & Öst, L. G. (2002). Attentional and memory bias for emotional information in crime victims with acute posttraumatic stress disorder. Journal of Anxiety Disorders, 16, 675–692. Payne, B. K., Cheng, C. M., Govorun, O., & Stewart, B. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. Journal of Personality and Social Psychology, 89, 277–293. Phaf, R. H., & Kan, K. (2007). The automaticity of emotional Stroop: A meta-Â�analysis. Journal of Behavior Therapy and Experimental Psychiatry, 38, 184–199. Phillips, K. A. (1986). The broken mirror: Understanding and treating body dysmorphic disorder. Oxford, UK: Oxford University Press. Phillips, M. L., & David, A. S. (1997). Visual scan paths are abnormal in deluded schizophrenics. Neuropsychologia, 35, 99–105. Pineles, S. L., Shipherd, J. C., Welch, L. P., & Yovel, I. (2007). The role of attentional biases in PTSD: Is

519

it interference or facilitation? Behavior Research and Therapy, 45, 1903–1913. Placanica, J. L., Faunce, G. J., & Job, R. F. S. (2002). The effect of fasting on attentional biases for food and body shape/weight words in high and low eating disorder inventory scorers. International Journal of Eating Disorders, 32, 79–90. Plant, E. A., & Devine, P. G. (1998). Internal and external motivation to respond without prejudice. Journal of Personality and Social Psychology, 75, 811–832. Pliner, P., & Mann, N. (2004). Influence of social norms and palatability on amount consumed and food choice. Appetite, 42, 227–237. Pomarol-Â�Clotet, E., Oh, T. M. S. S., Laws, K. R., & McKenna, P. J. (2008). Semantic priming in schizophrenia: Systematic review and meta-Â�analysis. British Journal of Psychiatry, 192, 92–97. Power, M. J., Cameron, C. M., & Dalgleish, T. (1996). Emotional priming in clinically depressed subjects. Journal of Affective Disorders, 38, 1–11. Rieger, E., Schotte, D. E., Touyz, S. W., Beumont, P. J. V., Griffiths, R., & Russel, J. (1998). Attentional biases in eating disorders: A visual probe detection procedure. International Journal of Eating Disorders, 23, 199–205. Rinck, M., & Becker, E. (2005). A comparison of attentional biases and memory biases in women with social phobia and major depression. Journal of Abnormal Psychology, 114, 62–74. Rinck, M., & Becker, E. (2007). Approach and avoidance in fear of spiders. Journal of Behavior Therapy and Experimental Psychiatry, 38, 105–120. Rinck, M., Reinecke, A., Ellwart, T., Heuer, K., & Becker, E. (2005). Speeded detection and increased distraction in fear of spiders. Journal of Abnormal Psychology, 114, 235–248. Roefs, A., Stapert, D., Isabella, L. A. S., Wolters, G., Wojciechowski, F., & Jansen, A. (2005). Early associations with food in anorexia nervosa patients and obese people assessed in the affective priming paradigm. Eating Behaviors, 6, 151–163. Rossell, S. L. (2004). Affective semantic priming in patients with schizophrenia. Psychiatry Research, 129, 221–228. Rossell, S. L., Shapleske, J., & David, A. S. (2000). Direct and indirect semantic priming with neutral and emotional words in schizophrenia: Relationship to delusions. Cognitive Neuropsychiatry, 5, 271–292. Rüsch, N., Corrigan, P. W., Bohus, M., Kühler, T., Jacob, G. A., & Lieb, K. (2007). The impact of posttraumatic stress disorder on dysfunctional implicit and explicit emotions among women with borderline personality disorder. Journal of Nervous and Mental Disease, 195, 537–539. Rüsch, N., Lieb, K., Göttler, I., Hermann, C., Schramm, E., Richter, H., et al. (2007). Shame and implicit selfÂ�concept in women with borderline personality disorder. American Journal of Psychiatry, 164, 500–508. Saporito, J. M., Heiman, N. R., Unger, S. B., & Teachman, B. A. (2004, November). Disgust cognitions: Predictors of spider and blood-Â�injection-Â�injury fears. Poster presented at the annual meeting of the Association for the Advancement of Behavior Therapy, New Orleans, LA.

520

V. APPLIED PERSPECTIVES

Schmukle, S. (2005). Unreliability of the dot probe task. European Journal of Personality, 19, 595–605. Schmukle, S. C., & Egloff, B. (2004). Does the Implicit Association Test for assessing anxiety measure trait and state variance? European Journal of Personality, 18, 483–494. Schneider, R., & Schulte, D. (2007). Panic patients reveal idiographic associations between anxiety symptoms and catastrophes in a semantic priming task. Behaviour Research and Therapy, 45, 211–223. Schniering, C. A., & Rapee, R. M. (1997). A test of the cognitive model of panic: primed lexical decision in panic disorder. Journal of Anxiety Disorders, 11, 557–571. See, J., MacLeod, C., & Bridle, R. (2009). The reduction of anxiety vulnerability through the modification of attentional bias: A real-world study using a homebased cognitive bias modification procedure. Journal of Abnormal Psychology, 118, 65–75. Segal, Z. V., & Gemar, M. (1997). Changes in cognitive organization for negative self-Â�referent material following cognitive behaviour therapy for depression: A primed Stroop study. Cognition and Emotion, 11, 501–516. Segal, Z. V., Gemar, M., Truchon, C., Guirguis, M., & Horowitz, L. M. (1995). A priming methodology for studying self-Â�representation in major depressive disorder. Journal of Abnormal Psychology, 104, 205–213. Segal, Z. V., Hood, J. E., Shaw, B. F., & Higgins, E. T. (1988). A structural analysis of the self-Â�schema construct in major depression. Cognitive Therapy and Research, 12, 471–485. Seibt, B., Häfner, M., & Deutsch, R. (2007). Prepared to eat: How immediate affective and motivational responses to food cues are influenced by food deprivation. European Journal of Social Psychology, 37, 359–379. Shafran, R., Lee, M., Cooper, Z., Palmer, R. L., & Fairburn, C. G. (2007). Attentional bias in eating disorders. International Journal of Eating Disorders, 40, 369–380. Shafran, R., Lee, M., Cooper, Z., Palmer, R. L., & Fairburn, C. G. (2008). Effect of psychological treatment on attentional bias in eating disorders. International Journal of Eating Disorders, 41, 348–354. Shane, M. S., & Peterson, J. B. (2007). An evaluation of early and late stage attentional processing of positive and negative information in dysphoria. Cognition and Emotion, 21, 789–815. Sieswerda, S., Arntz, A., & Kindt, M. (2007). Successful psychotherapy reduces hypervigilance in borderline personality disorder. Behavioural and Cognitive Psychotherapy, 35, 387–402. Sieswerda, S., Arntz, A., Mertens, I., & Vertommen, S. (2007). Hypervigilance in patients with borderline personality disorder: Specificity, automaticity, and predictors. Behaviour Research and Therapy, 45, 1011–1024. Sitskoorn, M., Aleman, A., Ebisch, S. J. H., Appels, M. C. M., & Kahn, R. (2004). Cognitive deficits in relatives of patients with schizophrenia: A meta-Â�analysis. Schizophrenia Research, 71, 285–295. Smeets, E., Roefs, A., van Furth, E., & Jansen, A. (2008). Attentional bias for body and food in eating

disorders: Increased distraction, speeded detection, or both? Behaviour Research and Therapy, 46, 229–238. Snowden, R. J., Gray, N. S., Smith, J., Morris, M., & MacCulloch, M. J. (2004). Implicit affective associations to violence in psychopathic murderers. Journal of Forensic Psychiatry and Psychology, 15, 620–641. Spector, I. P., Pecknold, J. C., & Libman, E. (2003). Selective attentional bias related to the noticeability aspect of anxiety symptoms in generalized social phobia. Journal of Anxiety Disorders, 17, 517–531. Sriram, N., & Greenwald, A. G. (2009). The brief Implicit Association Test. Experimental Psychology, 56, 283–294. Strauss, G. P., Allen, D. N., Duke, L. A., Ross, S. A., & Schwartz, J. (2008). Automatic affective processing impairments in patients with deficit syndrome schizophrenia. Schizophrenia Research, 102, 76–87. Suslow, T., Dannlowski, U., Lalee Mentzel, J., Donges, U. S., Arolt, V., & Kersting, A. (2004). Spatial processing of facial emotion in patients with unipolar depression: A longitudinal study. Journal of Affective Disorders, 83, 59–63. Suslow, T., Droste, T., Roestel, C., & Arolt, V. (2005). Automatic processing of facial emotion in schizophrenia with and without affective negative symptoms. Cognitive Neuropsychiatry, 10, 35–56. Suslow, T., Roestel, C., & Arolt, V. (2003). Affective priming in schizophrenia and without affective negative symptoms. European Archives of Psychiatry and Clinical Neuroscience, 253, 292–300. Suslow, T., Roestel, C., Droste, T., & Arolt, V. (2003). Automatic processing of verbal emotion stimuli in schizophrenia. Psychiatry Research, 120, 131–144. Szöke, A., Trandafir, A., Dupont, M., Méary, A., Schürhoff, F., & Leboyer, M. (2008). Longitudinal studies of cognition in schizophrenia: Meta-Â�analysis. British Journal of Psychiatry, 192, 248–257. Taghavi, M. R., Neshat-Doost, H. T., Moradi, A. R., Yule, W., & Dalgleish, T. (1999). Biases in visual attention in children and adolescents with clinical anxiety and mixed anxiety-Â�depression. Journal of Abnormal Child Psychology, 27, 215–223. Tanner, R. J., Stopa, L., & De Houwer, J. (2006). Implicit views of the self in social anxiety. Behaviour Research and Therapy, 44, 1397–1409. Tarrier, N. (2008). Schizophrenia and other psychotic disorders. In D. H. Barlow (Ed.), Clinical handbook of psychological disorders: A step-by-step treatment manual (4th ed., pp.€463–491). New York: Guilford Press. Teachman, B. A. (2005). Information processing and anxiety sensitivity: Cognitive vulnerability to panic reflected in interpretation and memory biases. Cognitive Therapy and Research, 29, 483–503. Teachman, B. A. (2007). Evaluating implicit spider fear associations using the Go/No-Go Association Task. Journal of Behavior Therapy and Experimental Psychiatry, 38, 157–167. Teachman, B. A., & Allen, J. P. (2007). Development of social anxiety: Social interaction predictors of implicit and explicit fear of negative evaluation. Journal of Abnormal Child Psychology, 35, 63–78. Teachman, B. A., & Clerkin, E. M. (2007). Obsessional beliefs and the implicit and explicit morality of intrusive thoughts. Cognition and Emotion, 21, 999–1024.



26. Clinical Applications of Theories and Methods

Teachman, B. A., Gregg, A. P., & Woody, S. R. (2001). Implicit associations for fear-Â�relevant stimuli among individuals with snake and spider fears. Journal of Abnormal Psychology, 110, 226–235. Teachman, B. A., Joormann, J., & Gotlib, I. H. (2009). It’s out of my control: Automaticity in anxiety and depression. Manuscript in preparation. Teachman, B. A., Marker, C. D., & Smith-Janik, S. B. (2008). Automatic associations and panic disorder: Trajectories of change over the course of treatment. Journal of Consulting and Clinical Psychology, 76, 988–1002. Teachman, B. A., Smith-Janik, S. B., & Saporito, J. (2007). Information processing biases and panic disorder: Relationships among cognitive and symptom measures. Behaviour Research and Therapy, 45, 1791–1811. Teachman, B. A., Stefanucci, J. K., Clerkin, E. M., Cody, M. W., & Proffitt, D. R. (2008). A new mode of fear expression: Perceptual bias in height fear. Emotion, 8, 296–301. Teachman, B. A., & Woody, S. (2003). Automatic processing among individuals with spider phobia: Change in implicit fear associations following treatment. Journal of Abnormal Psychology, 112, 100–109. Teachman, B. A., Woody, S. R., & Magee, J. (2006). Implicit and explicit appraisals of the importance of intrusive thoughts. Behaviour Research and Therapy, 44, 785–805. Thomas, J. J., Judge, A. M., Brownell, K. D., & Vartanian, L. R. (2006). Evaluating the effects of eating disorder memoirs on readers’ eating attitudes and behaviors. International Journal of Eating Disorders, 39, 418–425. Thorpe, S., & Salkovskis, P. (1997). Information processing in spider phobics: The Stroop colour naming task may indicate strategic but not automatic attentional bias. Behaviour Research and Therapy, 35, 131–144. van den Hout, M., Tenney, N., Huygens, K., & de Jong, P. (1997). Preconscious processing bias in specific phobia. Behaviour Research and Therapy, 35, 29–34. Vartanian, L. R., Herman, P., & Polivy, J. (2005). Implicit and explicit attitudes toward fatness and thinness: The role of the internalization of societal standards. Body Image, 2, 373–381. Vartanian, L. R., Polivy, J., & Herman, C. P. (2004). Implicit cognitions and eating disorders: Their application in research and treatment. Cognitive and Behavioral Practice, 11, 160–167. Veale, D., Gournay, K., Dryden, W., Boocok, A., Shah, F., Willson, R., et al. (1996). Body dysmorphic disorder: A cognitive behavioural model and pilot

521

randomised controlled trial. Behavior Research and Therapy, 34, 717–729. Vitousek, K., & Orimoto, L. (1993). CognitiveÂ�behavioral models of anorexia nervosa, bulimia nervosa, and obesity. In K. S. Dobson & P. C. Kendall (Eds.), Psychopathology and cognition (pp.€ 191–243). New York: Academic Press. Vythilingam, M., Blair, S., McCaffrey, D., Scaramozza, M., Jones, M., Nakic, M., et al. (2007). Biased emotional attention in post-Â�traumatic stress disorder: A help as well as a hindrance? Psychological Medicine, 37, 1445–1455. Waller, G., & Meyer, C. (1997). Cognitive avoidance of threat cues: Associations with EDI scores among a non–Â�eating disordered population. International Journal of Eating Disorders, 22, 299–308. Watkins, P. C., Vache, K., Verney, S. P., & Mathews, A. (1996). Unconscious mood-Â�congruent memory bias in depression. Journal of Abnormal Psychology, 105, 34–41. Wenzel, A., & Holt, C. (1999). Dot probe performance in two specific phobias. British Journal of Clinical Psychology, 38, 407–410. Wikström, J., Lundh, L. G., Westerlund, J., & Högman, L. (2004). Preattentive bias for snake words in snake phobia? Behaviour Research and Therapy, 42, 949–970. Wilhelm, S., & Neziroglu, F. (2002). Cognitive theory of body dysmorphic disorder. In R. Frost & G. Steketee (Eds.), Cognitive approaches to obsessions and compulsions: Theory, assessment, and treatment (pp.€ 203– 214). Oxford, UK: Elsevier Science. Williams, J. M. G., Mathews, A., & MacLeod, C. (1996). The emotional Stroop task and psychopathology. Psychological Bulletin, 120, 3–24. Williams, J. M. G., Watts, F. N., MacLeod, C., & Mathews, A. (1997). Cognitive psychology and emotional disorders (2nd ed.). Chichester, UK: Wiley. Wilson, G. T., & Pike, K. M. (2001). Eating disorders. In D. H. Barlow (Ed.), Clinical handbook of psychological disorders (3rd ed., pp.€332–375). New York: Guilford Press. Young, J. E., & Lindemann, M. D. (1992). An integrative schema-Â�focused model for personality disorders. Journal of Cognitive Psychotherapy, 6, 11–23. Young, J. E., Rygh, J. L., Weinberger, A. D., & Beck, A. T. (2008). Cognitive therapy for depression. In D. H. Barlow (Ed.), Clinical handbook of psychological disorders: A step-by-step treatment manual (4th ed., pp.€250–305). New York: Guilford Press. Yovel, I., Revelle, W., & Mineka, S. (2005). Who sees trees before forest?: The obsessive–Â�compulsive style of visual attention. Psychological Science, 16, 123–129.

C h a p t e r 27

Implicit Social Cognition in€Forensic€Settings Robert J. Snowden and Nicola S. Gray

T

he many chapters of this Handbook have introduced the importance, both from a theoretical and practical point of view, of examining cognitions using implicit measures. However, we appreciate that in such a handbook few readers will start at Chapter 1 and read systematically through. So, for those who have dipped into this chapter without such diligence, we start by highlighting two major issues that recur throughout this chapter.

IMPLICIT COGNITIONS Much of our mental life goes on without us becoming conscious of it. Thus, the control of our emotions, motivations, and direct behavior is governed by this unconscious mental life (Wilson & Dunn, 2004; Winkielman & Berridge, 2004). For example, mere exposure to an object, even exposure that is so brief that we are not conscious we have even been exposed, is enough for us to change our opinion of that object so that we like it more (Zajonc, 2001). This is not to say that our behavior is beyond our control or that we cannot become aware of some of this activity, but here we stress that there are many influences of which we may not be aware. These biases, attitudes, and motivations that we hold of which we are not aware are what are normally termed implicit cognitions (see Moors,

Spruyt, & De Houwer, Chapter 2, this volume). In our working definition, a particular cognition (e.g., a liking for thrill seeking) may exist as an implicit cognition at some times and as an explicit cognition at others. Hence, at an implicit level, this may cause such a person to drive faster than someone without such a cognition, while the person at another time may choose to go rock climbing because she knows she enjoys the thrill of such an activity. However, other implicit cognitions (e.g., a bias against Muslim people) might never become an explicit cognition. The person holding such an implicit cognition might hold a quite different explicit cognition; nevertheless, this Handbook provides many examples of how such implicit cognitions, even if contrary to our explicit cognitions, can govern our behaviors (see Perugini, Richetin, & Zogmaister, Chapter 14, this volume). In an often-cited example, the quality of social interactions (measured by facial expressions and so on) between two people of different race was found to be predicted by measures of their implicit cognitions but not by explicit measures of their attitudes (Dovidio, Kawakami, & Gaertner, 2002).

IMPLICIT MEASUREMENT While the last paragraph aimed to highlight the idea that we may not be aware of much of our mental life, it is also clear that we may be well aware 522



27. Implicit Social Cognition in Forensic Settings

of some of it but decide not to report this honestly. This may be due to perceived social pressures on such cognitions (e.g., that they are politically incorrect), that we may be ridiculed or bullied for having these cognitions, or that these cognitions might have a detrimental effect on our career, social position, and so on. Again looking at the issue of race, there are many settings where expressing a bias against a person of another race might lead to being fired from a job or not hired when applying for a job. Hence, persons with such a bias might well fully appreciate that they have this explicit cognition but choose to state a quite different position (see Amodio & Mendoza, Chapter 19, and Trawalter & Shapiro, Chapter 20, this volume). Hence, implicit measurements of such cognitions have been developed in the hope of circumventing this deliberate dissimulation of the cognitions. Thus, it is important to note the distinction between implicit cognitions (a feature of the mental content) and implicitly measured cognitions (a method by which a cognition can be measured). Given the limitations of self-Â�report, implicit measures may help us index the mental states. For example, we might begin to look at physiological measurements such as heart rate, sweating, brain waves, facial expressions, body language, and so on. For example, the pupil of the eye reacts to many situations other than just changes in light. Thus, a person’s excitement has been inferred by measures of changes in pupil size in response to appropriate stimuli (Hess, 1965). More recently, these psychophysiological measurements have been joined by assessments based on cognitive methods. Implicit methods have long played a central role in the domain of cognitive psychology. For example, semantic priming experiments have aimed to reveal the nature of how semantic concepts are organized within our minds. In such experiments, a prime is presented (such as the word bread) followed by a target word, such as butter. Decisions about the target word (e.g., Was it a real word or a made-up one?) are much faster for the word butter if primed by bread (but not if primed by elbow), showing a semantic association between these two concepts. It was not surprising, therefore, that such techniques have been adopted into the domain of social cognition. For example, the priming task was adapted by Fazio, Jackson, Dunton, and Williams (1995) to look at associations to different races by presenting pictures of faces that were either white or black and then following this prime with a word that had to be categorized as a good or a bad word. It was hypothesized that if a person has a negative view of

523

a white face, then this would prime the response to bad and make such judgments faster. Using this technique, the authors showed that white people are faster when the white face prime occurs before the good words and a black face prime occurs before the bad words. This shows that there is a bias in the affective associations to pictures of different races. There are now many such implicit cognitive tasks, which are reviewed in detail in this volume (see Wentura & Degner, Chapter 6, Teige�Mocigemba, Klauer, & Sherman, Chapter 7, and Sekaquaptewa, Vargas, & von Hippel, Chapter 8, this volume). It is, therefore, these tasks based on cognitive methods that are the focus of this review of implicit social cognitions in the domain of forensic psychology.

FORENSIC SETTINGS The term forensic simply means “relating to or dealing with the application of scientific knowledge to legal problems.” Hence, forensic psychology covers any possible setting in which psychology can be used in relation to the law, including, for example, investigation of crimes, stress levels in the police force, reliability of witnesses, the court setting, behavior of prisoners, treatment and prevention of crime, and victim support (Pozzulo, Bennell, & Forth, 2006). Implicit social cognitions are likely to have a role in just about all these settings (and many more); therefore, we could not possibly cover the application of measures of implicit cognition to all such areas. So, for example, we do not discuss the possible use of implicit measures in the selection of law enforcement officers, jury selection, possible biases in judges, and so on. We refer the reader interested in such issues to the excellent review of Lane, Kang, and Banaji (2007) and to Payne and Cameron (Chapter 24, this volume). Likewise, the application of implicit measures has begun to explore whether they can access “guilty knowledge” (information relating to a crime that would not be known to most people) and autobiographical knowledge that can include ones related to crimes (Sartori, Agosta, Zogmaister, Ferrara, & Castiello, 2008; but see Verschuere, Prati, & De Houwer, 2009). Such research is exciting and will no doubt be incorporated into forensic settings in the near future. However, because these tasks do not examine social cognitions, we have refrained from including them in this chapter. The study of implicit cognitions is still in its infancy in forensic settings; therefore, we concentrate on areas where

524

V. APPLIED PERSPECTIVES

research on implicit social cognitions has commenced. The aim of this chapter is to discuss the use of implicit measurements within the domain of forensic settings. For the main part, we do not discuss whether a particular study measured an implicit cognition or whether it implicitly measured an explicit cognition (unless this has been specifically addressed). For convenience, we term whatever is measured by the implicit test as an implicit cognition. The need for implicit measurement of cognitions is perhaps greater in this field of inquiry than in any other. In most forensic settings, there are often huge stakes riding on the outcome of some particular assessment or hearing. Offenders are often strongly motivated to disguise the true nature of their thoughts, intentions, or past behaviors because this might have damaging impacts on their hopes of release or treatment. In the court setting, large amounts of money may depend on evidence that someone has received a physical injury or the perceived truthfulness of a defendant or witness can strongly influence a case. Hence, measures that may sideline deliberate dissimulation of information may be particularly useful in this setting. Of course, this, in turn, raises questions of the veracity of any technique that purports to measure such implicit cognitions, which will be vital when decisions are being made at the individual level rather than group level. Finally, implicit cognitions occur in all of us, and findings seem to show that many of us hold implicit cognitions that we would not endorse at an explicit level. For instance, most white people appear biased against black people when tested using an implicit task while explicit tests do not show this bias. Thus, we could imagine that a person charged with an attack on a person from another racial group might be tested on implicit measures and the “bias” then being used as evidence for the attack being racially motivated. Much careful research, and even more careful dissemination of this work, is required to make sure the findings of scientists are not misused in these settings.

SEX OFFENDING The domain of sexual offending is characterized by offenders that often have quite distorted cognitions relating to sexual activities. Some offenders believe that they are entitled to have sex with a women even if she does not want to, others believe that children are sexual beings, and so on (Ward,

Hudson, Johnston, & Marshall, 1997). Furthermore, there is obvious reason for an offender to minimize, deny, or distort their responses when asked about these issues using explicit methods (Cooper, 2005). It is perhaps then not surprising that implicit measures have been widely used in the domain of the understanding of those with sexual offense histories.

Implicit Association Task In order to look at child-sex-Â�related cognitions, we devised an Implicit Association Test (IAT) that we believed might be able to index associations between sex and children. We represented this sex dimension with words that offenders would associate with sex (e.g. cock, pussy) and used words of similar length that we believed were not related to sex to represent the not-sex category (e.g., elbow, eye). The adult–child dimension was represented by words such as beard and mature and the child dimension by words such as infant and school. Our results (Gray, Brown, MacCulloch, Smith, & Snowden, 2005) showed that the control offenders (no conviction for offending against children younger than age 16) showed faster reaction times when adult and sex concepts shared the same response button, but the child-sex offender group (any conviction for sexual offending against a child) had faster reaction times when the child and sex concepts shared the response button. The group differences were highly significant (p < .001) despite the quite small numbers (N = 18 for the child-sex-Â�offender group), which reflected a large effect size (Cohen’s d = 0.83). Construction of the receiver operating characteristic—a plot of the proportion of hits (correctly predicting group membership) against false alarms (incorrectly predicting group membership) for all levels of the instrument—also showed that the child-sex IAT had a moderate ability to distinguish those with a child-sex offense from the other offenders (area under the curve [AUC] = .73; note that .5 represents chance levels and 1.0 represents perfect prediction; see Hanley & McNeil, 1982). It should be noted that this AUC is likely to be a lower limit of accuracy because we have no means of proving that our control group did not include some people with unconvicted child-sex offenses. It is clear that offenders who have sexually offended against children are not a homogenous group. The motivations to offend are likely to differ among offenders (Beech, 1998; Marziano, Ward, Beech, & Pattison, 2006). For instance, distinctions have been drawn between offend-



27. Implicit Social Cognition in Forensic Settings

ers who are pedophilic—those who are sexually aroused by stimuli of prepubertal children—and offenders who are hebephilic—those who are aroused by stimuli of adolescents. We, therefore, hypothesized that it would only be the pedophilic offenders (defined here as those whose victims are younger than age 12) who would show the association between children and sex on the IAT. We (Brown, Gray, & Snowden, 2009) managed to obtain a much larger sample of sex offenders than our previous studies, and our groups contained 54 pedophiles, 21 hebephiles (defined as those whose victims are between the ages of 12 and 16), and 49 offender controls, who had no convictions for child-sex offenses. We used our shortened twostage IAT. Our IAT results clearly showed that the pedophile group had implicit cognitive associations between sex and children, while the offender controls had implicit cognitive associations between sex and adults, replicating our previous results. However, the results from hebephiles did not show associations between children and sex; instead, their implicitly measured sexual cognitions appeared similar to the offender controls. This shows that the child-sex IAT appears to have the ability to distinguish between the different sexual cognitions that may characterize individual sex offenders and, therefore, strengthens the possible use of implicit measurements in the understanding of sexual offenders at both group and individual levels. We commenced this chapter by outlining the problems of self-Â�report measures, and that in sex offenders this may be a substantial problem because of deliberate dissimulation, so it was natural to consider whether our child-sex IAT would be as effective in a sample of sex offenders who denied or minimized their offenses or sexual attraction to children compared with those that admitted their offenses. Denial is a multifaceted term that we cannot hope to cover in such a chapter, so we again used a pragmatic definition that was commensurate with practice within the U.K. prison service. Deniers and admitters were classified according to the assessments of HM Prison Service sex offender treatment program, which take into account minimization and denial of offenses among other things. We found that both groups produced scores that were indicative of an association between sex and children, and that we could not distinguish the scores of the deniers from the nondeniers. Hence, we suggest that the child-sex IAT is able to identify distorted implicit cognitions about sex and children even in offenders who are in denial of their pedophilic tendencies.

525

Nunes, Firestone, and Baldwin (2007) tested several IATs relating to possible cognitive distortions in a sample of 27 child molesters (defined as having a victim younger than age 14) in comparison to offenders without convictions for sexual offenses (and who denied ever committing such an offense). In total, they tested six IATs. Each of three concepts (pleasant vs. unpleasant; powerful vs. weak; sexy vs. not sexy) were paired with the attributes of me vs. not me and child vs. adult. Only one of these IATs revealed a significant betweenÂ�groups difference: the IAT that examined the concepts of child versus adult against the attribute of sexy versus not sexy. They found that the child molesters were faster when the concepts of child and sexy were on the same response key, and the control offenders were faster when the concepts of adult and sexy shared the same key. Nunes and colleagues (2007) also administered an actuarial measure of future risk of sexual offending to each member of the child molester group: the Static-99 (Hanson & Thornton, 2000). They found that the child-sex IAT was correlated (r = .43) with the Static-99 risk, such that those with IAT scores indicating stronger implicit associations between sex and children have greater risk scores for sexual offending. This is a suggestive finding that the IAT score may itself have some ability to predict future offending, although direct tests of such a hypothesis are needed. Mihailides, Devilly, and Ward (2004) attempted to test models of implicit cognitions (Ward & Keenan, 1999) in sexual offenders using three IATs that related to children as sexual beings, the uncontrollability of sexuality, and a sexual entitlement bias. The first of these IATs is the closest to those so far described. The technique, however, differed from those used by our group in that, instead of having a child–adult dimension, they used a child–not-child dimension (examples of not-child words were lids, rifle, and underline). They found that their experimental group (25 incarcerated male child-sex offenders) showed faster responses to the condition in which the child words shared the same button with the sex words compared with the combination of child and not-sex words. The difference between conditions was very large (895 msec). However, this difference was also true for the controls (25 incarcerated other offenders), who showed a difference of 513 msec. The groups were distinguishable in that the difference was larger for the sex offenders than the controls. Thus, this was interpreted as a greater association of child and sex in the child-sex offender group. The other IATs did not produce any significant results.

526

V. APPLIED PERSPECTIVES

Banse, Schmidt, and Clarbour (2009) have also administered IATs to child-sex offenders as part of a battery of tests (both implicit and explicit test). Using a battery of IATs that examined associations between sex and men versus women, men versus boys, and women versus girls, they obtained an overall AUC of .77 for the detection of child-sex offenders versus controls. These results, therefore, appear consistent with those of previous studies. Van Leeuwen, van Baaren, Chakhssi, Lippman, and Dijksterhuis (2009) have looked at associations between children and sex. Their study differs from those discussed previously in that they tested nonincarcerated people. On the basis of a confidentially agreement, 20 pedophiles from a Dutch organization of self-�proclaimed pedophiles were recruited. These pedophiles indicated frequent sexual fantasies about children in general and about specific children in their surrounding. Control participants reported sexual fantasies about adult women and none about children. Using an IAT that represented the concepts of sex versus nonsex via words and adult versus child via pictures, they also found that the pedophiles associated children with sex, whereas the controls associated adults with sex. This study showed a level of discrimination (AUC = .89) that was greater than previous studies (e.g., Gray et al., 2005: AUC = .73). We suspect that this greater level of discrimination probably is not due to a better IAT but rather reflects the nature of the sample. Presumably, the self-�proclaimed pedophiles are indeed all pedophilic (defined as having a sexual preference for prepubescent individuals), whereas pedophile groups defined by their criminal convictions may include individuals who do not necessarily have a preference for prepubescent children but committed these offenses because of factors such as availability of the victim. Similar arguments can be applied to the purity of the control groups for these studies. Interestingly, Brown, Gray, and Snowden (see Brown, 2006) performed further analyses of their data. To obtain a better (or purer) pedophile group, they isolated a group consisting of abusers with multiple child victims who were strangers to the offender and a group of offenders with multiple rape offenses against adult women. On the premise that multiple offenses against one specific victim type indexed the degree of sexual preference, this approach ensured they were comparing a pedophile-only group with a nonpedophile control group. Blanchard, Klassen, Dickey, Kuban, and Blak (2001) used this reasoning in assessing the diagnostic properties of their phallometric technique. As predicted, the IAT was able to better

discriminate this purer group of offenders (AUC = .81). Hence, it appears that when the groups are well defined, the IAT is capable of high levels of discrimination accuracy.

Discussion and Conclusion This review contains six independent data sets from child-sex offenders versus nonsex offender controls in which a child-sex IAT has been tested (Banse et al., 2009; Brown et al., 2009; Gray et al., 2005; Mihailides et al., 2004; Nunes et al., 2007; van Leeuwen et al., 2009). All six show the pattern of results that child-sex offenders have a greater association between child and sex than did the other offender controls. This result was consistent across differences in methodologies (e.g., use of pictures or words, two- vs. five-stage IAT) and across the groups; therefore, the finding appears robust. The child-sex IAT appears to be able to identify those offenders who have a sexual offense against children with an AUC on the order of .73 to .89, figures that are comparable (and possibly better) with those produced by phallometric techniques (Blanchard et al., 2001).

Limitations While we have done our best to highlight the possible importance of the IAT in the domain of sexual offending, we would be remiss if we did not address its limitations. First, the IAT is a relational measure. It requires the comparison of two concepts (e.g., flowers vs. insects or child vs. adult) against an evaluative dimension (e.g., good vs. bad or sex vs. not sex). Thus, the current data tell us that child-sex offenders have greater sexual associations with children than adults. This could, in theory, be because they are attracted to both (but more so to children) or not attracted to either (but less so to adults, if this makes sense). It would be of value to obtain more data that specifically examine the absolute associations between children and sex rather than the association relative to that of sex and adult that our IAT produces. There have been attempts to “rescore” the results from IAT experiments to try and obtain these absolute associations, but the evidence shows that the reaction times from the IAT cannot be rescored to allow this (Nosek, Greenwald, & Banaji, 2005). However, there are now techniques with conceptual similarities to the IAT that aim to measure attitudes to a single concept using implicit measurement (e.g., Go/No-Go Association Task: Nosek & Banaji, 2001; Extrinsic Affective Simon Task: De Houwer,



27. Implicit Social Cognition in Forensic Settings

2003). Snowden, Gray, Brown, and Power (2007) have collected some preliminary data using the Go/No-Go Association Task that show that those with a conviction for child-sex offenses show a greater association between children and sex than other offender controls and a group of rapists. Second, while the IAT (and other implicit measurements) may be able to index an association between the concepts child and sex, it does not tell us why this has arisen. It is feasible that individuals undergoing extensive therapy for their sexual offending might well have such associations because they have to discuss their offending behavior and listen to others discuss such matters. Although this is feasible, we are encouraged that this is not the likely explanation for our results because we have also found similar results in those not undergoing treatment programs (Brown et al., 2009) and in those who are now in the community (Brown, 2006). Nevertheless, any individual might have an association between child and sex for any number of reasons (e.g., an expectant mother); hence, the finding of a particular association cannot be used to infer the guilt of a person nor establish that he or she is a pedophile or any other such construct. Many factors are likely to contribute to a particular IAT score (and indeed many other measures whether implicit or explicit), which makes the interpretation of any individual’s score difficult (Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005). Third, we do not yet have any data on the predictive validity of the IAT. The methods used so far are only predictive of past behavior. Studies are needed to establish the IAT’s ability in this domain.

Other Measures of Implicit Cognitions in Sexual Offenders Our review has focused primarily on the IAT. However, this merely reflects our own bias and research work rather than any inherent superiority of this technique. We now review some other implicit measurement techniques that can be thought to tap implicit social cognitions in sexual offenders.

Viewing Times A common measure of sexual interest in clinical populations has been the time that a person views a particular picture: the viewing time. The measure is implicit in that the participant is normally asked to do some other task (e.g., rate the attractiveness of the picture) and requires very little in terms of

527

instructions or task complexity. Indeed, Glasgow, Osborne, and Croxen (2003) have shown that a particular instrument (Affinity) based on viewing times can be used with some success in those with intellectual disabilities. The idea behind this methodology is that an interesting picture will attract attention and be looked at longer than one that is not interesting. Early studies showed that homosexual men viewed pictures of men for longer durations than pictures of women, whereas heterosexual men showed the opposite pattern (Rosenzweig, 1942; Zamansky, 1956), thus providing prima facie evidence that the technique can measure sexual interest. In terms of the assessment of sexual interest in children, the most widely used and validated instrument is the Abel Screen (Abel, 1995). A series of pictures depicting various images of adults and children are viewed, and the responses of any individual can be compared with a series of norms derived from offender populations (e.g., offenders against female children). Abel, Jordan, Hand, Holland, and Phipps (2001) claim that the test (which also includes overt reports of sexual interest and hence cannot be regarded as a pure implicit measure) can discriminate between known child-sex offenders and nonoffenders with good degrees of accuracy. However, it has been noted elsewhere that the data relating to the accuracy of the Abel Screen did not use the same stimuli as in the final clinical instrument; for instance, the development of the instrument used explicit as well as nonnaked images, but these explicit images are not used in the final instrument (Kalmus & Beech, 2005). Likewise, Fischer and Smith (1999) note problems with the Abel Screen’s reliability (particularly related to the viewing time part of the assessment) and suggest several recommendations on how some of its shortcomings might be addressed. We also note that other standardized sets of stimuli exist and have shown similar potential to discriminate between different types of sex offender (Laws, Gulayets, & Frenzel, 1995). Although there may be criticisms of some aspects of a particular viewing time test, it is clear that the technique has much to offer. For example, Gress (2005) demonstrates that the technique can produce good classification accuracy with respect to both the age and the gender of previous victims of sexual offenders (see also Harris, Rice, Quinsey, & Chaplin, 1996). Indeed, from their battery of explicit and implicit tests of sexual preference in child sex offenders, Banse and colleagues (2009) demonstrate that their viewing time measure was among the best. Although viewing time measures

528

V. APPLIED PERSPECTIVES

are of interest both clinically and as a research instrument, it is clear that if the person being assessed has knowledge of how the test works then its validity would be seriously compromised.

ent implicit cognitions relating to sexual offending is not yet known.

Rapid Serial Visual Presentation

In the classic Stroop effect, the participant’s ability to name the ink color of a word (e.g., one written in red ink) is compromised if the word itself is a color word (e.g., green) (Stroop, 1935). This response-Â�interference paradigm has been extended to examine how well other words (or pictures) might also interfere in this simple ink color–Â�naming paradigm. The hypothesis is that a stimulus that demands high attention might interfere in naming the ink color simply by attracting attention to itself (rather than suggesting an alternate response, as in the original Stroop paradigm). This paradigm has often been termed the emotional Stroop (e-Â�Stroop) (e.g., Williams & Broadbent, 1986). Smith and Waterman (2004) tested a small group of offenders on an e-Â�Stroop task in which the words could have a violent theme, a sexual theme, or a neutral theme (among other conditions not of interest here). In comparison to the neutral words, all offenders appeared to show an e-Â�Stroop effect for the sexual words (whereas undergraduate controls did not), although those with sexual offenses showed the greatest e-Â�Stroop effect. For the violent words, offenders with convictions for sexual violence (e.g., rape of adult) showed an e-Â�Stroop effect (whereas undergraduate controls did not). Of further interest is that pedophiles (convicted of indecent assault) did not show this e-Â�Stroop effect for the violent words. This pattern of results is, therefore, intriguing in showing that a combination of Stroop effects to different themes might provide a profile of what attracts the attention of any individual offender and thus offer avenues for treatment and management. However, the effect sizes produced in this study and in a very similar replication (Price & Hanson, 2007) were quite small, and the crucial comparisons did not reach statistical significance in either study. A different Stroop-like task has been used by van Leeuwen and colleagues (2009). Here the participants are asked to classify words as either sexual or not sexual (like in the IAT test described previously), but these words are presented on a background picture of either an adult or a child (again using the stimuli that were utilized in their IAT paradigm). Self-Â�reported pedophiles were slower at classifying sex words when the background was of a child and faster at classifying neutral words when the background was an adult. The control participants showed exactly the opposite pattern of re-

Another measure that is of interest is Shapiro’s (1994) attentional blink paradigm. In this task, an individual is presented with a rapid sequence of stimuli (e.g., letters on a screen) at the same location, known as a rapid serial visual presentation (RSVP). The participant is instructed to detect two targets (e.g., the two white letters among the black ones) and to report these targets after the end of the sequence. The term attentional blink refers to the finding that if the second target (T2) occurs just after the first target (T1), within the next one to four items, it is often missed compared with when it is presented later in the sequence. This finding has been explained via the hypothesis that the first target captures attention in order to process this stimulus (T1) and, therefore, is unavailable if the second target (T2) is presented within a short time period. Thus, the size (both strength and duration) of the attentional blink is thought to indicate the amount of attention received by the first target (T1). Beech and colleagues (2008) presented sequences of images to both child-sex offenders and nonoffender controls. T1 could either be an image of a child or an image of an animal. As predicted, the detection of a second target (T2) was impaired if it occurred shortly after T1. Crucially, this attentional blink was greater in the child-sex offender group, but not in the control group, when T1 was a child image. The paradigm showed good ability to discriminate between these groups (equivalent to an AUC of .77). This test may provide a particularly useful paradigm because the first target stimuli can be quite specific rather than generic (e.g., images of females vs. males or of a particular age group), and because the task’s dependent variable is the detection of the second target (which can be totally unrelated to sexual or child-like images), the participant is not alerted to the effect the first target might be having. Thus, it may be difficult to fake this task. Clearly, further work is needed to develop this potential into a useful clinical instrument. We also note that Crooks, Rostill-Â�Brookes, Beech, and Bickley (2009) were unable to reliably discriminate adolescent sex offenders from other adolescent (nonsexual) offenders using this RSVP task. Whether this reflects a failure of this task, a difference in cognitive abilities of adolescents, or differ-

Stroop Effects



27. Implicit Social Cognition in Forensic Settings

sults. The effect sizes generated by this paradigm to distinguish these two group was large (AUC = .84), although somewhat smaller than an IAT using the same stimuli and participants (AUC = .89). However, the simplicity of this test may give it advantages over the IAT.

VIOLENT OFFENDING The reasons behind aggressive behavior and violence are, without doubt, multifaceted; however, there is reason to believe that a person’s cognitions about the appropriateness of the use of violence might have strong influence on his or her actual behavior. Thus, a person who thinks that violence is permissible, or even gives the perpetrator kudos, is more likely to use violence than one who does not have such a cognition. Again, these cognitions could be implicit or explicit, and, as discussed in several section of this handbook, this might lead to their influences on behavior being manifest under different circumstances. To examine implicit cognitions related to violence, we (Gray, MacCulloch, Smith, Morris, & Snowden, 2003; Snowden, MacCulloch, Smith, Morris, & Gray, 2004) developed a violence IAT. In this task, participants categorize words (e.g., kill) or pictures as either violent or peaceful and also categorize other words (e.g., vomit) as either pleasant or unpleasant. We would expect that most people would associate violence with unpleasant and, therefore, their categorizing would be fast and accurate in the blocks where these two concepts shared a response key but slow and inaccurate when the concept of pleasant shared a response key with the concept of violence. In our pilot studies, every person tested showed this pattern of results. Our aim in developing this violence IAT was to be able to examine the implicit cognitions relating to violence in certain offender populations. Of particular interest are those who have a strong history of violent acts. However, it is clear that violent acts occur for many reasons. Most violent acts appear to have a reactive component in that the person acts in a violent manner because of some immediate trigger. This trigger can have many etiologies, ranging from an insult, to perceived betrayal to a physical attack. Factors such as intoxication are also highly significant in many incidences of violence. Although it seems likely that implicit cognitions may well have a role in such violence, it is clear that many other factors are also important, and this might obscure our abil-

529

ity to demonstrate the importance of such implicit cognitions. We, therefore, turned our attention to a group of individuals who appear to engage in instrumental violence (e.g., violence for a specific end). The concept of psychopathy is characterized by the personality traits of grandiosity, shallow affect, lack of remorse and empathy, and a general disregard for the feelings or aspirations of others (Hare, 1991). For example, Woodworth and Porter (2002) examined incidences of murder and classified each murder as to whether it was primarily reactive or instrumental. They found that the vast majority of instrumental murders were carried out by those with high psychopathy scores (Psychopathy Checklist—Â�Revised [PCL-R]; Hare, 1991). Thus, this use of instrumental violence appeared to be almost confined to the psychopathic group, and this finding has been at the core of some recent theorizing about the nature of psychopathy (Blair, Mitchell, & Blair, 2005). It should be noted, however, that psychopathic individuals do not limit themselves to only instrumental violence and, in fact, are also disproportionally prevalent in incidences of reactive violence. This instrumental use of violence may be partially due to a different view of violence in people with high psychopathy scores. To measure implicit views of violence, we developed an IAT that had participants sort words as either violence or peaceful and either pleasant or unpleasant (Gray, MacCulloch, et al., 2003; Snowden et al., 2004). We tested offenders who had either been seriously violent (defined here as having a conviction for murder) or not and rated their level of psychopathy by completing a PCL-R assessment. We found that nearly everyone had the expected association between violence and unpleasant; however, the size of this association varied between our groups. For those in the nonmurderer group, level of psychopathic traits did not substantially alter the association; however, for those with a history of murder, there was a strong effect. The group with a conviction of murder and high psychopathy scores appeared to show little association between violence and unpleasant. Thus, this group appeared almost neutral (or possibly ambivalent) at an implicit level about the notion of violence and did not have the same implicit dislike for it that was shown in the other groups. A reduced IAT for those high in psychopathic traits has also been reported for a similar IAT (Blair et al., cited in Luo et al., 2006). Although many offenders openly admit to their fondness for violence, this is not always the case, and psychopathic offenders are renowned for their ability to con and lie (Hare, 1991). We, therefore,

530

V. APPLIED PERSPECTIVES

were also interested in their explicit cognitions about violence and thus measured these using a violence “feeling thermometer.” Although there were no statistically significant effects, the group that proclaimed to hate violence the most were the psychopathic murderers (Snowden et al., 2004). Psychopathy, as defined by the PCL-R, is thought to have at least two factors. Factor 1 measures the interpersonal and affective dimensions such as grandiosity and callous indifference, whereas Factor 2 seems to reflect the impulsive, reckless behavior, including criminal behavior (Hare, 1991). Our results (Snowden et al., 2004) showed that this reduced implicit association with violence was more associated with Factor 1 than Factor 2. Thus, it is the grandiose and callous aspects of psychopathy that are associated with this implicit ambivalence about violence. Such implicit cognitions may allow these individuals to use violence without causing them any distress (because they do not see it as a bad thing). A second result that emerged from this study was unexpected. The group with a conviction for murder who were low on traits of psychopathy actually had a statistically significant greater violent–Â� unpleasant association than any other group. We speculated that these offenders had developed this strong association because of the murder itself. For most people, committing a violent act can be very distressing and in some cases causes anxiety and even posttraumatic stress disorder (Crisford, Dare, & Evangeli, 2008; Gray, Carman, et al., 2003). Clearly, this is merely speculative, and the issues of cause and effect both here and for those murderers high on psychopathic traits are still to be addressed. Presumably most people’s performance on the violence IAT, being fast when the concept of violence is paired with the concept of unpleasant, is because the brain areas that evaluate the valence of the stimuli are activated by the presentation of a violent picture. Thus, the (relative) lack of an IAT effect in psychopathic murderers may be due to these violent stimuli failing to activate these brain areas. Luo and colleagues (2006) have examined the brain areas that are activated when healthy participants perform an IAT task assessing implicit cognitions to immoral acts (many of which depicted interpersonal violence). They found that the intensity of the stimulus (how violent the slides were) was associated with blood flow changes in the amygdala and the ventromedial cortex; both areas known to be involved in processing the emotional aspects of a stimulus (Blair et al., 2005; Lang, Bradley, & Cuthbert, 1998). Furthermore,

these areas have been implicated as dysfunctional in those high on psychopathic traits (Blair et al., 2005; Lapierre, Braun, & Hodgins, 1995). Thus, we can speculate that the reduced IAT effect we have observed in psychopathic murderers is due to failure of the negative emotional aspects of the violence pictures being processed in such people.

Violent Behavior The previous section discussed violence as defined via convictions for very violent offenses; however, violence can manifest in many different forms. This issue has been recently addressed in studies examining whether specific IATs can predict specific forms of aggression (Richetin, Richardson, & Mason, in press; see also Richetin & Richardson, 2008). Two versions of the IAT were produced that aimed to assess aggression through the pairing of the concepts of harmful and me (with the opposites being harmless and not me). The two versions differed in that for one IAT the words used for the harmful/harmless dimension were verbs depicting direct aggression (e.g., strangle) and for the indirect aggression IAT the verbs depicted indirect aggression (e.g., discredit). The behavioral measure taken was the evaluation of the experimenter, who during the course of the experiment delivered an insult to some of the participants! They found that the IAT that aimed to measure indirect aggression was not predictive of this evaluation and that the direct aggression IAT was significantly predictive, but only for those participants who had been provoked by the insult. The reasons for this difference between the direct and indirect aggression IAT measures are unclear but may relate to the strength with which these words carry the concept of aggression. However, the major finding suggests that measures of aggression may be context specific, and that results might vary depending on the current feelings and emotional state of the participant. IAT-type methodologies have also been used to examine the antecedents of violent behavior. Exposure to violent media, such as violent films, cartoons, and video games, has long been thought to produce an increase in aggression and violent behavior in both the short and long term (see Anderson & Bushman, 2001). Uhlmann and Swanson (2004) used an IAT that examined associations between the self (vs. others) and aggression (vs. peaceful). Their results indicate that most people associate themselves with peace (rather than with aggression) but that this effect is less in men than in women. Of interest here is that this effect is reduced after the person had played a



27. Implicit Social Cognition in Forensic Settings

violent video game (Doom), than when they had played a nonviolent game (Mahjongg). Hence, exposure to the violent video game appears to bias people’s association of themselves in the violent direction (even though overall the association is still more peaceful than aggressive). Clearly, this type of result suggests that these measures are malleable (see Gawronski & Sritharan, Chapter 12, this volume) and could, therefore, be used as an indicator of change, including change resulting from treatment programs. Indeed, there are now reports of the use of such IATs to evaluate possible changes resulting from training or treatment programs (Gollwitzer, Banse, Eisenbach, & Naumann, 2007).

Other Implicit Attitudes That May Increase Violence Implicit tests of social cognition have their roots in the measurement of cognitions that the person may have reason to hide, such as racism (e.g., Greenwald, McGhee, & Schwartz, 1998), sexism (Leibold & McConnell, 2004; Milne & Grafman, 2001), and homophobia (Banse, Seise, & Zerbes, 2001). Clearly, many crimes are committed because of these social cognitions (Alden & Parker, 2005; Parrott, 2008; Polaschek, Calvert, & Gannon, 2009; Saucier, Hockett, & Wallenberg, 2008; Sullaway, 2004) and are often referred to as “hate crimes” (Green, McFalls, & Smith, 2001). Examination of the characteristics of these hate crimes has revealed that the injuries to the victims tend to be more severe than conventional crimes and they are often excessively brutal (Levin & McDevitt, 1993; Messner, McHugh, & Felson, 2004). Although many perpetrators of such crimes are quite explicit about the motivations behind their hate crimes, many others may not wish to express these views. Therefore, we could question whether the use of implicit measures would be of value in these situations. Unfortunately, we are not yet in a position to give any answers. Studies are required that examine these implicit social cognitions in known groups so as to establish that such tests are effective in distinguishing types of offender. Maybe then such tests could be used to measure any change in these attitudes that might be brought about by therapy, with the hope that the changes in these implicit social cognitions might also be translated into future behavior. Much work needs to be done to translate this vision into a reality. Nevertheless, the use of IATs to demonstrate to people their current social cognitions may well be illuminating and beneficial in bringing such mat-

531

ters into the open and suggesting individual areas for therapy and work.

CONCLUDING COMMENTS Over the past 5 years, the application of implicit measures into forensic settings has accelerated in accord with other research on implicit social cognitions, and with every passing month the rate of new research into this area increases. We hope this chapter has illustrated the way implicit techniques are able to measure concepts of fundamental importance in forensic settings, and that they can do so with similar, and in some cases greater, accuracy than existing methods. Given that these tests are in their infancy, we believe that the levels of accuracy will improve as the tests are further developed. Likewise, these first-Â�generation tests have often measured elements in a crude manner, such as “violence—good versus bad” (Snowden et al., 2004) and are open to alternative interpretations (Rothermund, Teige-Â�Mocigemba, Gast, & Wentura, 2009). Further refinements need to incorporate more sophisticated thinking about the concepts of interest (e.g., Richetin et al., in press) and about what associations one might want to measure (e.g., violence—me vs. other; exciting vs. fearful). There seems to be little doubt that such methods can have some role in the assessment of motivations behind crimes and, therefore, in helping to define the treatment needs of the perpetrators of crime. We believe that there is now sufficient evidence across a range of implicit measures (e.g., IAT, viewing times, Stroop interference) for these tasks to be used in forensic settings in this manner, and that these implicit measurements can complement (not replace) existing methods that rely on more explicit techniques (e.g., questionnaires). The challenge ahead is to show that these implicit measurements can index therapeutic change in these attitudes and beliefs in a manner that has been demonstrated for clinical problems such as phobias (e.g., Teachman & Woody, 2003). We also note that many other problem behaviors that are often associated with crime itself or are aggravating factors to crimes, such as drug and alcohol use, are often underpinned by implicit social cognitions that may also be amenable to treatment (Rooke, Hine, & Thorsteinsson, 2008; Wiers, van Woerden, Smulders, & de Jong, 2002; see also Teachman, Cody, & Clerkin, Chapter 26, this volume). Finally, it is hoped that one day implicit measures might be used as part of a risk assessment for future problem behaviors. As yet, we do not be-

532

V. APPLIED PERSPECTIVES

lieve that there is sufficient evidence to base a risk assessment on implicit measures, but it is hoped that future studies relating implicit measures to current risk assessment instruments (e.g., Nunes et al., 2007) and to actual future behavior might pave the way for implicit measures to be added to the forensic psychologist’s toolbox. References Abel, G. G. (1995). The Abel Assessment for Sexual Interest-2 (AASI-2). Atlanta: Abel Screening Inc. Abel, G. G., Jordan, A., Hand, C. G., Holland, L. A., & Phipps, A. (2001). Classification models of child molesters utilising the Abel Assessment for Sexual Interest. Child Abuse and Neglect, 25, 703–718. Alden, H. L., & Parker, K. F. (2005). Gender role ideology, homophobia and hate crime: Linking attitudes to macro-level anti-gay and lesbian hate crimes. Deviant Behavior, 26, 321–343. Anderson, C. A., & Bushman, B. J. (2001). Effects of violent video games on aggressive behavior, aggressive cognition, aggressive affect, physiological arousal, and prosocial behavior: A meta-Â�analytic review of the scientific literature. Psychological Science, 12, 353–359. Banse, R., Schmidt, A. F., & Clarbour, J. (2009). Indirect measures of sexual interest in child sex offenders: A multi-Â�method approach. Manuscript submitted for publication. Banse, R., Seise, J., & Zerbes, N. (2001). Implicit attitudes towards homosexuality: Reliability, validity, and controllability of the IAT. Zeitschrift fur Experimentelle Psychologie, 48, 145–160. Beech, A. R. (1998). A psychometric typology of child abusers. International Journal of Sex Offender Therapy and Comparative Criminology, 42, 319–339. Beech, A. R., Kalmus, E., Tipper, S. P., Baudouin, J.-Y., Flak, V., & Humphreys, G. W. (2008). Children induce an enhanced attentional blink in child molesters. Psychological Assessment, 20, 397–402. Blair, R. J. R., Mitchell, D., & Blair, K. (2005). The psychopath: Emotion and the brain. Oxford, UK: Blackwell. Blanchard, R., Klassen, P., Dickey, R., Kuban, M. E., & Blak, T. (2001). Sensitivity and specificity of the phallometric test for pedophilia in nonadmitting sex offenders. Psychological Assessment, 13, 118–126. Brown, A. S. (2006). Developing an Implicit Association Test for forensic use: Discriminating paedophiles from other offenders. Cardiff UK: Cardiff University. Brown, A. S., Gray, N. S., & Snowden, R. J. (2009). Implicit measurement of sexual preferences in child sex abusers: Role of victim type and denial. Sexual Abuse, 21, 166–180. Conrey, F. R., Sherman, J. W., Gawronski, B., Hugenberg, K., & Groom, C. (2005). Separating multiple processes in implicit social cognition: The quadmodel of implicit task performance. Journal of Personality and Social Psychology, 89, 469–487. Cooper, S. (2005). Understanding treating and manag-

ing sex offenders who deny their offence. Journal of Sexual Aggression, 11, 85–94. Crisford, H., Dare, H., & Evangeli, M. (2008). OffenceÂ�related posttraumatic stress disorder (PTSD) symptomatology and guilt in mentally disordered violent and sexual offenders. Journal of Forensic Psychiatry and Psychology, 19, 86–107. Crooks, V. L., Rostill-Â�Brookes, H., Beech, A. R., & Bickley, J. A. (2009). Applying rapid serial visual presentation to adolescent sexual offenders: Attentional bias as a measure of deviant sexual interest. Sexual Abuse, 21, 135–148. De Houwer, J. (2003). The Extrinsic Affective Simon Task. Experimental Psychology, 50, 77–85. Dovidio, J. F., Kawakami, K., & Gaertner, S. L. (2002). Implicit and explicit prejudice and interracial interaction. Journal of Personality and Social Psychology, 82, 62–68. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Fischer, L., & Smith, G. (1999). Statistical adequacy of the Abel Assessment for interest in paraphilias. Sexual Abuse, 11, 195–205. Glasgow, D. V., Osborne, A., & Croxen, J. (2003). An assessment tool for investigating paedophile sexual interest using viewing time: An application of single case methodology. British Journal of Learning Disabilities, 31, 96–102. Gollwitzer, M., Banse, R., Eisenbach, K., & Naumann, A. (2007). Effectiveness of the Vienna social competence training on explicit and implicit aggression. European Journal of Psychological Assessment, 23, 150–156. Gray, N. S., Brown, A. S., MacCulloch, M. J., Smith, J., & Snowden, R. J. (2005). An implicit test of the associations between children and sex in paedophiles. Journal of Abnormal Psychology, 114, 304–308. Gray, N. S., Carman, N. C., Rogers, P., MacCulloch, M. J., Hayward, P., & Snowden, R. J. (2003). PostÂ�traumatic stress disorder caused by committing a serious violent or sexual offence in mentally disordered offenders. Journal of Forensic Psychiatry and Psychology, 14, 27–43. Gray, N. S., MacCulloch, M. J., Smith, J., Morris, M., & Snowden, R. J. (2003). Violence viewed by psychopathic murderers. Nature, 423, 497–498. Green, D. P., McFalls, L. H., & Smith, J. K. (2001). Hate crime: An emergent research agenda. Annual Review of Sociology, 27, 479–504. Greenwald, A. G., McGhee, J. L., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Gress, C. L. Z. (2005). Viewing time measures and sexual interest: Another piece of the puzzle. Journal of Sexual Aggression, 11, 117–125. Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 29–36. Hanson, R. K., & Thornton, D. (2000). Improving risk assessments for sex offenders: A comparison of



27. Implicit Social Cognition in Forensic Settings

three actuarial scales. Law and Human Behavior, 24, 119–136. Hare, R. D. (1991). The Hare Psychopathy Checklist–Â� Revised. Toronto, CA: Multi-Â�Health Systems. Harris, G. T., Rice, M. E., Quinsey, V. L., & Chaplin, T. C. (1996). Viewing time as a measure of sexual interest among child molesters and normal heterosexual men. Behavior Research and Therapy, 34, 389–394. Hess, E. H. (1965). Attitude and pupil size. Scientific American, 212, 46–54. Kalmus, E., & Beech, A. R. (2005). Forensic assessment of sexual interest: A review. Aggression and Violent Behavior, 10, 193–217. Lane, K. A., Kang, J., & Banaji, M. R. (2007). Implicit social cognition and law. Annual Review of the Law and Social Science, 3, 427–451. Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1998). Emotion, motivation, and anxiety: Brain mechanisms and psychophysiology. Biological Psychiatry, 44, 1248–1263. Lapierre, D., Braun, C. M. J., & Hodgins, S. (1995). Ventral frontal deficits in psychopathy: Neuropsychological test findings. Neuropsychologia, 33, 139–151. Laws, D. R., Gulayets, M. J., & Frenzel, R. R. (1995). Assessment of sex offenders using standardized slide stimuli and procedures: A multisite study. Sexual Abuse, 7, 45–66. Leibold, J. M., & McConnell, A. R. (2004). Women, sex, hostility, power, and suspicion: Sexually aggressive men’s cognitive associations. Journal of Experimental Social Psychology, 40, 256–263. Levin, J., & McDevitt, J. (1993). Hate crime: The rising tide of bigotry and bloodshed. New York: Plenum Press. Luo, Q., Nakic, M., Wheatley, T., Richell, R., Martin, A., & Blair, R. J. R. (2006). The neural basis of implicit moral attitude—An IAT study using eventÂ�related fMRI. NeuroImage, 30, 1449–1457. Marziano, V., Ward, T., Beech, A. R., & Pattison, P. (2006). Identification of five fundamental implicit theories underlying cognitive distortions in child abusers: A preliminary study. Psychology Crime and Law, 12, 97–105. Messner, S. F., McHugh, S., & Felson, R. B. (2004). Distinctive characteristics of assaults motivated by bias. Criminology, 42, 585–618. Mihailides, S., Devilly, G. J., & Ward, T. (2004). Implicit cognitive distortions and sexual offending. Sexual Abuse, 16, 333–350. Milne, E., & Grafman, J. (2001). Ventromeial prefrontal cortex lesions in humans eliminate implicit gender stereotyping. Journal of Neuroscience, 21, 1–6. Nosek, B. A., & Banaji, M. R. (2001). The Go/No-Go Association Task. Social Cognition, 19, 625–664. Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2005). Understanding and using the Implicit Association Test: II. Method variables and construct validity. Personality and Social Psychology Bulletin, 31, 166–180. Nunes, K. L., Firestone, P., & Baldwin, M. W. (2007). Indirect assessment of cognitions of child sexual abusers with the implicit association test. Criminal Justice and Behavior, 34, 454–475. Parrott, D. J. (2008). A theoretical framework for antigay aggression: Review of established and hypothesized

533

effects within the context of the general aggression model. Clinical Psychology Review, 28, 933–951. Polaschek, D. L. L., Calvert, S. W., & Gannon, T. A. (2009). Linking violent thinking implicit theorybased research with violent offenders. Journal of Interpersonal Violence, 24, 75–96. Pozzulo, J., Bennell, C., & Forth, A. (2006). Forensic psychology. Toronto: Pearson-Â�Prentice Hall. Price, S. A., & Hanson, R. K. (2007). A modified Stroop task with sexual offenders: Replication of a study. Journal of Sexual Aggression, 13, 203–216. Richetin, J., & Richardson, D. S. (2008). Automatic processes and individual differences in aggressive behavior. Aggression and Violent Behavior, 13, 423–430. Richetin, J., Richardson, D. S., & Mason, G. (in press). Predictors of direct versus indirect aggressive behavior: Validity of explicit and implicit measures. Social Psychology. Rooke, S. E., Hine, D. W., & Thorsteinsson, E. B. (2008). Implicit cognition and substance use: A meta-Â�analysis. Addictive Behaviors, 33, 1314–1328. Rosenzweig, S. (1942). The photoscope as an objective device for evaluating sexual interest. Psychosomatic Medicine, 4, 150–157. Rothermund, K., Teige-Â�Mocigemba, S., Gast, A., & Wentura, D. (2009). Minimizing the influence of recoding in the Implicit Association Test: The Recoding-Free Implicit Association Test (IAT-RF). Quarterly Journal of Experimental Psychology, 62, 84–98. Sartori, G., Agosta, S., Zogmaister, C., Ferrara, S. D., & Castiello, U. (2008). How to accurately detect autobiographical events. Psychological Science, 19, 772–780. Saucier, D. A., Hockett, J. M., & Wallenberg, A. S. (2008). The impact of racial slurs and racism on the perceptions and punishment of violent crime. Journal of Interpersonal Violence, 23, 685–701. Shapiro, K. L. (1994). The attentional blink: The brain’s “eyeblink.” Current Directions in Psychological Science, 3, 86–89. Smith, P., & Waterman, M. (2004). Processing bias for sexual material: The emotional Stroop and sexual offenders. Sexual Abuse, 16, 163–171. Snowden, R. J., Gray, N. S., Brown, A. S., & Power, S. (2007). Measuring implicit cognitions in sexual offenders using the Go/No Go Task. Unpublished manuscript. Snowden, R. J., MacCulloch, M. J., Smith, J., Morris, M., & Gray, N. S. (2004). Implicit affective associations to violence in psychopathic murderers. Journal of Forensic Psychiatry and Psychology, 15, 620–641. Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662. Sullaway, M. (2004). Psychological perspectives on hate crime laws. Psychology Public Policy and Law, 10, 250–292. Teachman, B. A., & Woody, S. R. (2003). Automatic processing in spider phobia: Implicit fear associations over the course of treatment. Journal of Abnormal Psychology, 112, 100–109. Uhlmann, E., & Swanson, J. (2004). Exposure to violent video games increases automatic aggressiveness. Journal of Adolescence, 27, 41–52. van Leeuwen, M., van Baaren, R., Chakhssi, F., Lipp-

534

V. APPLIED PERSPECTIVES

man, M., & Dijksterhuis, A. (2009). Detecting implicit paedophilic preferences: Improving predictability. Manuscript submitted for publication. Verschuere, B., Prati, V., & De Houwer, J. (2009). Cheating the lie detector: Faking in the autobiographical Implicit Association Test. Psychological Science, 20, 410–413. Ward, T., Hudson, S. M., Johnston, L., & Marshall, W. L. (1997). Cognitive distortions in sex offenders: An integrative review. Clinical Psychology Review, 17, 479–507. Ward, T., & Keenan, T. (1999). Child molesters’ implicit theories. Journal of Interpersonal Violence, 14, 821–838. Wiers, R. W., van Woerden, N., Smulders, F. T. Y., & de Jong, P. J. (2002). Implicit and explicit alcohol-Â�related cognitions in heavy and light drinkers. Journal of Abnormal Psychology, 111, 648–658. Williams, J. M. G., & Broadbent, K. (1986). Distraction

by emotional stimuli: Use of a Stroop task with suicide attempters. British Journal of Clinical Psychology, 25, 101–110. Wilson, T. D., & Dunn, E. W. (2004). Self-Â�knowledge: Its limits, value, and potential for improvement. Annual Review of Psychology, 55, 493–518. Winkielman, P., & Berridge, K. C. (2004). Unconscious emotion. Current Directions in Psychological Science, 13, 120–123. Woodworth, M., & Porter, S. (2002). In cold blood: Characteristics of criminal homicides as a function of psychopathy. Journal of Abnormal Psychology, 111, 436–455. Zajonc, R. B. (2001). Mere exposure: A gateway to the subliminal. American Psychological Society, 10, 224– 228. Zamansky, H. S. (1956). A technique for measuring homosexual tendencies. Journal of Personality, 24, 436–448.

C h a p t e r 28

Implicit Social Cognition and Indirect Measures in Consumer Behavior Andrew Perkins and Mark Forehand

A

lthough the use of indirect measures in consumer psychology has increased dramatically in the last decade, some have questioned the incremental value of these measures given that many consumer behaviors are deliberative and, therefore, may be better predicted by direct measures (see Perugini, Richetin, & Zogmaister, Chapter 14, this volume). Indeed, a recent meta-Â�analysis of the predictive validity of various versions of the Implicit Association Test (IAT), a very popular indirect measure often used to assess implicit attitudes, implicit self-Â�esteem, and implicit self-Â�identity (Greenwald, McGhee, & Schwartz, 1998), found that IAT measures were “especially valuable for socially sensitive research topics .â•›.â•›. most apparent[ly] in studies of racial, ethnic, and gender-based intergroup discrimination” (Greenwald, Poehlman, Uhlmann, & Banaji, 2009, p.€35). Other reviews have found similar correlations between IAT and parallel self-Â�report measures that were not examined in the meta-Â�analysis (Hofmann, Gawronski, Gschwendner, Le, & Schmitt, 2005; Nosek, Greenwald, & Banaji, 2005). Unlike the socially sensitive topics described previously, the first generation of research within the consumer behavior domain observed relatively high correlations between indirect and direct measures, suggesting that indirect measures of consumption intentions sometimes explain relatively little additional variance over more traditional self-Â�report measures

(Perkins, Forehand, Greenwald, & Maison, 2008). However, indirect measures such as the affective priming task (Fazio, Jackson, Dunton, & Williams, 1995) and the Extrinsic Affective Simon Task (EAST; De Houwer, 2003; De Houwer & Eelen, 1998) are predictive of consumer-�related behavioral outcomes, such as food choice (Karpinski & Hilton, 2001). Given the aforementioned review results, a potential roadblock facing indirect measure use in consumer behavior is the paucity of instances in which one would expect a disassociation between indirectly measured attitudes and self-�reported attitudes. Such high correlations between indirect and direct measures may arise because consumers are generally willing to accurately report consumption attitudes, especially compared with the more sensitive discrimination attitudes often studied within psychology (Greenwald et al., 2009). However, the fact that indirect and direct measures (reported in the meta-�analysis) show higher correlations in the consumer domain does not necessarily imply that those indirect measures do not predict variance in a criterion measure. It is possible that indirect measures (in this case, the IAT specifically) predict residual variance in the criterion measure that is not predicted by direct measures (Perugini et al., Chapter 14, this volume). In fact, Greenwald et al. found that the predictive validity of the IAT increased as a function of increasing 535

536

V. APPLIED PERSPECTIVES

correlations to corresponding self-Â�report measures, with indirect and self-Â�report measures each predicting criterion variance that was not predicted by the other. Given these findings, we suggest a rethinking of the place of indirect measures in consumer behavior. Rather than evidence of a dearth of “hidden” attitudes, the increased correlations between indirect and self-Â�report measures are instead indicative of distinct constructs that may reinforce each other in determining behavior. Recent insights into the process of generating attitudes from memory (Gawronski & Bodenhausen, 2006; Strack & Deutsch, 2004) suggest that indirect measures should be conceptualized as a probe of the process of generating attitudes (or similar cognitions) at a stage that is generally inaccessible to conscious observation and, therefore, to direct measures. Furthermore, indirect measures appear malleable (Dasgupta & Greenwald, 2001; Forehand & Perkins, 2005) because they are sensitive to the effects of experimental manipulations. This suggests that indirect measures often reveal momentary associations between constructs and do not necessarily access stable or “true” levels of a construct. As a result, indirect measures can provide researchers with a powerful tool for observing the activation of cognitive constructs in memory, whether it be the forebearers of expressed attitude, self-Â�identity, self-Â�esteem, or group membership and how those (previously unobservable) activations affect consumer behavior. Before discussing the value of indirect measures in consumer psychology more fully, it is helpful to first define precisely what we mean by the terminology implicit and indirect. Following the lead of De Houwer (2006), the term implicit is used to describe the features of the outcome of a measurement procedure. To the extent that there is some underlying process that is generating or produces that outcome, it can be said that the term implicit refers to the functional properties of that outcome. On the other hand, the term indirect describes the characteristics of the specific measurement procedure used. Thus, we refer to measures such as the IAT, the affective priming task, and the EAST as indirect measures in contrast to direct or self-Â�report measures. In this case, indirect refers to the methodological characteristics of these measurement procedures (e.g., presentation of stimuli, task duration, or other operational procedures). The goals of this chapter are threefold. First, building from the motivation and opportunity as determinants (MODE) model (Fazio & TowlesÂ�Schwen, 1999), a theoretical framework is de-

veloped that informs the relationship between indirect and direct measures in consumer decision contexts. Second, this framework is used to analyze numerous areas of extant (and working) research focused on the correlations between direct and indirect measures in consumer contexts. Finally, we examine more recently published (and in-�progress) research that identifies more fertile grounds for indirect measure use in consumer behavior.

The MODE Model and€Its€Relation to Implicit Consumer Behavior The MODE model is a dual-Â�process model originally proposed as a means to understand persuasion, social perception, and attribution (Fazio & Towles-Â�Schwen, 1999). Although it predates many of the indirect measures commonly used today and the recent focus on implicit social cognition, the MODE model’s focus on two distinct but interrelated approaches to understanding the effects of attitude on subsequent behavior fits nicely with the implicit/explicit distinction. Specifically, the MODE model proposes that attitudes can influence behavior via two processes, one that is relatively spontaneous and one that is more deliberate. These two processes can occur separately or simultaneously. The more spontaneous process relies on the automatic or immediate activation of attitudes and other cognitions resulting from encountering the attitude object. In these cases, preexisting attitudes (or attitudes formed as a result of the individual’s construal of the attitude object in situ) directly affect behavior toward the object. Whereas such spontaneous processing is thought to occur automatically or without effort, deliberative processing requires conscious cogitation and effort to undertake. As a result, the likelihood of processing deliberatively increases as motivation and opportunity to engage in such processing increase. The MODE model implies that automatically activated attitudes reflected in indirect measures should predict behavior if either the motivation or the opportunity to engage in elaborate processing is low. Within consumer behavior, there is a large class of behaviors that routinely are interpreted as occurring in situations where there is little motivation or opportunity. These low-Â�involvement decisions occur daily in the marketplace, arguably comprising the bulk of consumer decisions (Laaksonen, 1994; Zaichkowsky, 1985). LowÂ�involvement consumer behaviors might include



28. Indirect Measures in Consumer Behavior

repeat purchases of familiar products or brands or purchases made while distracted. Conversely, selfÂ�report measures should outperform indirect measures when both motivation and opportunity to engage in elaborate processing is high. In sum, this suggests that indirect measures should be better predictors of spontaneous behaviors while direct measures should better predictors of deliberate or high-Â�involvement behaviors. In addition, there are a number of contextual and individual difference factors that moderate the predictive validity of indirect and self-Â�report measures (Friese, Hofmann, & Schmitt, 2008). For example, recent research suggests that implicit measures are better predictors of consumer behavior for individuals with intuitive processing styles, while self-Â�report measures are more predictive for those with deliberate processing styles (Conner, Perugini, O’Gorman, Ayres, & Prestwich, 2007). Specifically, Conner et al. found that the habitualness of the target behavior moderated the ability of indirect measures to predict food choice. Scarabis, Florack, and Gosejohann (2006) found that indirect measures better predicted consumer choice when individuals focused on the affective response to choice options versus trying to think about the reasons for the choice. Finally, indirectly measured attitudes better predict product choice for consumers under time pressure, whereas explicit attitudes better predict product choice with no time constraints (Friese, Wanke, & Plessner, 2006). In general, these moderators are consistent with both the MODE model and are applicable to numerous consumer behavior situations.

Where We Have Been: Using€Indirect Measures to€Assess Consumer Behavior Attitudes Early consumer behavior research on indirect measurement frequently focused on the correspondence or dissociations between direct and indirect measures. Maison, Greenwald, and Bruin (2001) conducted a number of experiments exploring domains where one might expect dissociation between directly and indirectly measured attitudes. One of these studies explored attitudes toward high- and low-�calorie products. It was hypothesized that consumers (young women) hold contradictory beliefs across these product categories, perceiving high-�calorie products as good in taste but bad for their health and low-�calorie products as bad in taste but good for their health, and that

537

these competing beliefs would differentially influence implicit and explicit attitudes toward the products. Indeed, Maison and colleagues found that, although self-Â�reported preferences favored high-Â�calorie products, indirect measures suggested that young women had more positive implicit attitudes toward low-Â�calorie products. Interestingly, these implicit attitudes were more predictive of dieting activity. Another set of experiments explored potential attitudinal dissociation resulting from consumer ethnocentrism, defined as a conscious preference for one’s own native products (e.g., products produced within your country or region) compared with foreign-Â�manufactured products (Verlegh & Steenkamp, 1999; Watson & Wright, 2000). Consumer ethnocentrism is thought to result from cognitive (e.g., belief that products produced in one’s own country are better), affective (a positive affective reaction toward native products), and ideological or normative (belief that it is appropriate to purchase products manufactured in one’s own country) thought processes. Previous consumer ethnocentrism research (conducted in developed countries) typically revealed a bias in favor of products produced in the subject’s native country. However, in less economically developed countries, this domestic preference is often not observed. This is thought to be the result of experience with poorer quality native products compared with foreign brands. Maison and colleagues (2001) predicted that this situation can lead to dissociation between implicit and explicit attitudes and could produce internal conflict between automatic preference based on emotions and rational judgment based on observation and experience. Two experiments examined these potential dissociations between preferences for foreign versus local products and their relationship to behavior. Within the cigarette product category (Study 1), selfÂ�reported attitudes suggested that individuals preferred foreign brands, whereas implicit measures suggested that the subjects preferred the native cigarette brands. This dissociation was stronger among nonsmokers than smokers, suggesting that disassociation weakens as direct experience with the evaluated stimuli increases. A second experiment replicated these findings across a number of product categories. In another example where one might expect a dissociation between indirect and direct measures, Brunel, Tietje, and Greenwald (2004) examined behavioral and attitudinal responses to spokesperson race in print advertising. Because correlations between direct and indirect measures of

538

V. APPLIED PERSPECTIVES

race-Â�related attitudes are typically low (Dasgupta, McGhee, Greenwald, & Banaji, 2000; Greenwald et al., 1998), a similar dissociation was expected when individuals viewed advertisements including either white or black sports figures endorsing a target brand. They suggested that self-Â�reported attitudes would be consciously modified as a result of self-Â�presentation bias or reluctance to report true feelings, while indirect measures would capture negative attitudes related to the race of celebrity spokespersons. Interestingly, although white respondents exhibited a significant “pro-white” preference on indirect measures, they did not reveal a significant preference on direct measures. On the other hand, black respondents indicated a preference for advertisements with black spokespersons on direct measures but no significant preference on indirect measures. Furthermore, the magnitude of implicit preference for advertisements that included white spokespersons was significantly greater for white than for black respondents, whereas the opposite was true for direct measures. Further analysis confirmed a significant interaction of ethnicity and measurement method on the revealed preference for advertisements with spokespersons of one’s own ethnicity. A related disassociation between explicit and implicit measures was observed in a study of the influence of celebrity voice-overs on consumer response to advertised brands (Forehand & Perkins, 2005). In this project, favorable attitudes toward a celebrity endorser positively influenced selfÂ�reported response to brands featured in advertising utilizing that celebrity’s voice, but only when the subject was unable to identify the celebrity behind the voice. This influence reversed if the subject could correctly identify the celebrity, was motivated to eliminate irrelevant influences, and was able to consciously adjust response. Using set/ reset theory (Martin, 1986), the authors argued that this reversal on explicit measures was due to resetting, a correction of the perceived influence of the celebrity cue because of its logical irrelevance. However, this resetting was not observed in implicit response to the advertised brands, presumably because the indirect measures were uninfluenced by conscious attempts to adjust response. This experiment also demonstrated that indirect measures such as the IAT can be used to discern the underlying processes that produce effects traditionally observed on explicit measures. Disassociation between multiple indirect measures has also been observed. In a recent demonstration of such disassociation, Spruyt, Hermans, De Houwer, Vandekerckhove, and Eelen (2006)

explored the usefulness of the picture-�picture naming task, a version of the affective priming paradigm, as an indirect attitude measure. Spruyt et al. argued that the picture-�picture task should be insensitive to extrapersonal associations, a criticism of the IAT (Karpinski & Hilton, 2001; Olson & Fazio, 2004). Basing their experimental paradigm on Karpinski and Hilton (2001), Spruyt et al. found evidence that the picture-�picture naming task predicted choice behavior, while the IAT failed to predict behavior (fruit vs. candy choice).

Where We Are Going Although these previous results incorporating indirect measurement have proven exceptionally valuable in our understanding of consumer behavior in some domains, more recent research has begun to use the indirect measures as a criterion variable that can reveal subtle changes in association in response to contextual manipulations (see Gawronski & Sritharan, Chapter 12, this volume). In the following experiments, indirect measures are used as a process measure that mediates the effect of experimental manipulations on other outcome variables or as dependent variables to directly assess the results of processes that may be inaccessible to the individual.

Self-�Identity Activation Social identity has been found to influence a wide variety of consumer behaviors and attitudes, including product choice and loyalty (Briley & Wyer, 2002; Deshpande, Hoyer, & Donthu, 1986; Stayman & Deshpande, 1989; Wooten, 1995), spokesperson response (Deshpande & Stayman, 1994), advertising response (Forehand & Deshpande, 2001; Forehand, Deshpande, & Reed, 2002; Grier & Deshpande, 2001), media usage (Saegert, Hoover, & Hilger, 1985), and information�processing tendencies (Meyers-Levy & Sternthal, 1991). Consumer social identity is especially powerful to the extent that the identity is salient in the immediate social or contextual environment (Briley & Wyer, 2002; Forehand & Deshpande, 2001; Forehand et al., 2002; Reed, 2004). In these situations, the activation of a social identity motivates the consumer to actively engage in social comparison, to express identity-�consistent beliefs, and to select products that reinforce the desired social identity. In many if not all cases, the individual is not aware that a particular self-�identity has been activated or may not be able to connect



28. Indirect Measures in Consumer Behavior

the effects of self-Â�identity activation on his or her behavior. In this case, indirect measures may be able to assess self-Â�identity activation beyond what might be explicitly reported. One research project that uses indirect measures to this end examines the effects of exposure to advertising user imagery on identity activation and the resulting effects of that activation on subsequent evaluation of products that include the identity (Forehand, Perkins, & Reed, 2008). Following an assimilation/contrast model, Forehand et al. studied two factors that determined whether consumer self-Â�concept shifted toward (assimilation) or away from (contrast) the presented user imagery: (1) the discrepancy between the objective age of the characters in the advertisement and the consumer’s own chronological age and (2) whether the consumer explicitly or implicitly compared him- or herself with the depicted users. Using the IAT as a dependent measure to assess identity activation, Forehand et al. found that consumer self–youth associations assimilated toward age-based imagery when the discrepancy between the self and advertisement imagery was moderate. More specifically, after being exposed to advertising imagery featuring users in their 30s, college-aged consumers possessed stronger self associations with old than if they were exposed to college-age user imagery. However, when college students evaluated advertising featuring extremely discrepant user imagery (senior citizens), their resulting self–youth associations contrasted with the user imagery (their self associations with youth increased relative to individuals exposed to collegeage user imagery). These effects were moderated by explicit comparison processes such that the effects of explicit comparison to the user imagery were stronger. Furthermore, these IAT-measured self–youth associations mediated responses to subsequently advertised products. Luna, Ringberg, and Peracchio (2008) also used the IAT to assess implicit self-Â�identity associations. Specifically, the IAT was used to assess the extent to which bicultural individuals switch between self identities (frames) as a result of using or encountering the language associated with a particular identity. Of interest in the current chapter is the unique use of the same IAT translated into two different languages. Specifically, Hispanic respondents completed both a Spanish and an English language version of an IAT designed to assess gender selfÂ�sufficiency. Under the assumption that EnglishÂ�speaking cultures show a stronger male–selfÂ�sufficiency association than Hispanic cultures, the authors expected (and found) a larger IAT effect

539

for masculine–self-Â�sufficient associations in memory when Hispanic females responded to the IAT following activation of their English identity compared with their Hispanic identity. Similar to that discussed previously, Luna and colleagues provide additional evidence that the IAT is a useful tool for assessing the activation of self-Â�concepts and identities following experimental manipulation.

Evaluative Conditioning and Attitude Change Gibson (2008) explored the extent to which evaluative conditioning could affect previously held attitudes toward mature brands. Gibson incorporated the IAT as a dependent variable following manipulation of well-known brand attitudes using a standard evaluative conditioning paradigm (Study 1). Specifically, subjects were instructed to rapidly respond with a button press when the target brands of interest (Coke and Pepsi) appeared on a computer screen (the brands were interspersed with distracter images and words). The brands were presented differentially across conditions: In one condition Coke (Pepsi) was always presented with a positive (negative) image, while in the other the associations were reversed (Olson & Fazio, 2001). Following the evaluative conditioning task, subjects completed explicit measures toward the brands and a traditional brand attitude IAT. Results suggested that, although explicit attitudes did not change following evaluative conditioning, implicit attitudes were affected, but only for subjects who had strong a priori preferences for either of the brands. These results were corroborated in Study 2, where product choice was predicted by the conditioned implicit brand attitude, but only for subjects who were under a manipulated cognitive load. These results were interpreted as consistent with previous theorizing about decision making under cognitive load (Fazio & Towles-�Schwen, 1999).

Automatic Attitude Formation Indirect measures are also a useful tool in studies of automatic attitude formation. At the heart of this application is the unifying framework developed by Greenwald and colleagues (2002) to explain how self–Â�object associations can lead to the formation of positive attitudes toward target objects. Building from the principles of cognitive consistency theory and balance theory (Festinger, 1957; Heider, 1958; Osgood & Tannenbaum, 1955), Greenwald pro-

540

V. APPLIED PERSPECTIVES

posed that the IAT can be used to assess triads of relationships including the self, or the self-Â�concept, an object (e.g., brand) that is associated with the self-Â�concept, and a mental representation of valence, on a continuum from negative to positive (see Schnabel & Asendorpf, Chapter 22, this volume). In this paradigm, a measured association between the self-Â�concept and valence is defined as implicit self-Â�esteem (Farnham, Greenwald, & Banaji, 1999; Greenwald & Farnham, 2000), a measured association between an object and valence is an implicit attitude or stereotype (Greenwald et al., 1998), and a measured association between a group or object and the self-Â�concept is an implicit self-Â�identity (Rudman, Greenwald, & McGhee, 2001). These cognitive triads are thought to form as a result of an intrinsic motivation to balance object associations in memory. To facilitate the balancing of these objects in memory, Greenwald and colleagues posit a balance-Â�congruity principle, where two unlinked objects in memory (e.g., valence and brand) that share “first-order” links with a third object (e.g., self-Â�concept) should develop a mutual association (Greenwald et al., 2002). Assuming an a priori link between self and valence (Farnham et al., 1999; Greenwald & Banaji, 1995), an association created between self-Â�concept and some object in the environment (e.g., by experimental manipulation or repeated association in the environment) should result in the formation of a new link between the object and that valence. Both Tietje and Brunel (2005) and Gawronski, Bodenhausen, and Becker (2007) have applied Greenwald’s theoretical development in different domains. Tietje and Brunel (2005) formalized an adaptation of Greenwald and colleagues’ (2002) unified theory into a unified implicit brand theory. Tietje and Brunel suggested, similar to Greenwald and colleagues, that the concepts included in the unified theory of social cognition could be applied to knowledge about brands. Tietje and Brunel described implicit brand identification as an association between brand information and self-Â�concept. Furthermore, Tietje and Brunel put forward a testable hypothesis about the characteristics of this brand information as it relates to the self. Specifically, Tietje and Brunel suggested that brand attitude can be modeled as an interaction between self-Â�esteem and brand–self identification. In a set of experiments related to the balanceÂ�congruity principle, Gawronski and colleagues (2007) assessed the transference of self valence to objects that had recently been self-Â�associated with the subject. The authors instructed subjects to choose between two presented images to be

received after completion of the experiment (Experiments 1–3). In a final experiment, subjects rolled a die in order to learn which image would be given to them as a reward for participating. In each experiment, Gawronski et al. found that making the choice of images (either actively or via a die roll) did create associations between the self and objects in memory and led to more positive attitudes toward the chosen image. In this case, subject choice was the mechanism by which the self–Â�target object association was created. These results are consistent with findings hypothesized by the balance-Â�congruity principle. In an extension of this previous work, Perkins, Forehand, and Greenwald (2009) conducted three experiments that manipulated the creation of nonvolitional associations between neutral (i.e., no a priori attitude) objects and the self-Â�concept and assessed the resulting changes in object evaluation. Consistent with the prior discussion, experimentally created trivial self–Â�object associations dramatically influenced automatically formed attitudes toward the object categories (product categories in Experiment 1 and brand names in Experiment 2). In a third experiment, the authors also observed that initial implicit self-Â�esteem influenced the magnitude of these effects consistent with the assumptions of the balance-Â�congruity principle put forward previously (Greenwald et al., 2002; Tietje & Brunel, 2005). Indirect measures also have potential applicability to a wide range of paradigms suggesting that objects that are linked to the self-Â�concept tend to be better liked. Nuttin (1985) suggested the name-Â�letter effect, observing that letters that appeared in an individual’s name were generally better liked, whereas with mere ownership theory (Beggan, 1992) objects given to an individual attained greater than expected positive evaluations than when that object was not owned by that individual. More recently, implicit egotism results (Jones, Pelham, Carvallo, & Mirenberg, 2004; Pelham, Carvallo, & Jones, 2005; Pelham, Mirenberg, & Jones, 2002) suggest that people, places, and things that resemble the self are better liked. For example, Jones and colleagues (2004) used subliminal conditioning to induce subjects to associate a number with their own name (and a different number with other gender-Â�matched names). Subjects then evaluated a woman more favorably if the shirt she was wearing depicted their self-Â�associated number. Taken in sum, these paradigms provide a rich domain in which implicitly measured associations might reveal effects that could be missed by traditional explicit measures.



28. Indirect Measures in Consumer Behavior

Future Directions In light of the previous discussion and our current understanding of the usefulness of indirect measures as dependent variables, there appear to be a number of domains within consumer behavior that should benefit from inclusion of indirect measures, in addition to those described previously. In the next sections, we describe four areas—Â� question–Â�behavior effects, vice behaviors, ego depletion, and unconscious choice—and attempt to convince the reader that these areas are ripe for exploration using indirect measures. In some cases, indirect measures and theoretical treatment are already starting to be applied. In others, the topic is wide open.

Question–Â�Behavior Effects Captured loosely under the rubric question–Â� behavior effect, researchers have been examining the effects of merely recording the behavioral predictions of observed individuals and then examining postmeasurement behavior (Dholakia & Morwitz, 2002; Fitzsimons & Morwitz, 1996; Morwitz & Fitzsimons, 2004; Morwitz, Johnson, & Schmittlein, 1993; Spangenberg, 1997; Spangenberg, Sprott, Grohmann, & Smith, 2003; Sprott et al., 2006). Relevant to the current discussion, Fitzsimons and Williams (2000) theorize that the effect of asking about intentions related to subsequent behavior is automatic in nature and suggest a disconnect or discrepancy between the effortful, conscious reporting of intention and the activation of the automatic, usually positive affective component of attitudes toward the target behavior. Researchers have already incorporated indirect measures into examining both normative and nonnormative behaviors. Self-Â�prophecy is a simple technique in which merely asking someone to predict future behavior, typically regarding a socially normative action, increases the likelihood of performing the behavior. In general, when a target behavior is socially desirable (e.g., recycling), the behavior change is upward; when the behavior is normatively undesirable (e.g., cheating, stereotyping), it is downward. Self-Â�prophecy effects have been shown across numerous domains and behaviors (Spangenberg, 1997; Spangenberg & Greenwald, 1999; Spangenberg & Obermiller, 1996; Sprott et al., 2007; Sprott, Smith, Spangenberg, & Freson, 2004; Sprott et al., 2006; Sprott, Spangenberg, & Fisher, 2003). In general, selfÂ�prophecy effects are explained from a cognitive dissonance perspective. A cognitive dissonance

541

explanation for self-Â�prophecy posits that when people are confronted with a prediction request, psychological discomfort arises because of people being made aware of a discrepancy between values they hold (e.g., normative beliefs about a behavior; including the belief that they are a good person and good people should perform the behavior) and how they have behaved in the past with regard to the behavior. In addition, empirical evidence suggests the importance of social norms. Sprott and colleagues (2003) demonstrate that the effect of a self-Â�prediction is greater when people hold stronger (vs. weaker) social norms regarding a particular behavior. Although this finding can be considered consistent with a dissonance-based explanation for self-Â�prophecy (i.e., greater cognitive dissonance is elicited for those with stronger social norms), it also suggests the importance of how closely one identifies with a particular social identity. Social identities are self-Â�definitions that incorporate more general and normative knowledge about a particular group that an individual belongs to or identifies with (Brewer, 1991). Social identities are thought to incorporate behavioral and attitudinal information (Brewer & Gardner, 1996), such that activating a social identity affects an individual’s self-Â�esteem (Crocker, Luhtanen, Blaine, & Brodnax, 1994). Thus, when a positive social identity is activated, it is expected that the individual’s own self-Â�esteem should be affected in a positive direction as well. As described previously, examining evidence of self-Â�identity activation is an area where the IAT might excel. Thus, an initial experiment was conducted to test the hypothesis that making a self-Â�prophecy prediction activates self-Â�recycling identity in memory. Subjects completed a set of self-Â�report measures assessing their attitudes toward various normative behaviors. Following a distraction task, subjects completed a self-Â�prophecy manipulation task (predicting whether they would recycle or making no prediction about recycling) and then immediately completed three IATs: recycling identity, recycling attitude, and self-Â�esteem. Results suggested that those who make a self-Â�prophecy prediction revealed significantly stronger self-Â�recycling activation and increased self-Â�esteem compared with a control group. However, there was no difference in recycling attitude, suggesting that attitude accessibility does not seem to be motivating self-Â�prophecy effects. Although preliminary, these results show the promise of the IAT as a dependent variable and provide evidence of psychological phenomena that traditionally have been difficult to observe (e.g., self-Â�identity activation).

542

V. APPLIED PERSPECTIVES

Vice Behaviors

Exploring Unconscious Choice

Constructs that have relied on self-Â�report measures for description such as vanity (Netemeyer, Burton, & Lichtenstein, 1995), stigmatized behaviors (Mowen & Spears, 1999; Swanson, Rudman, & Greenwald, 2001), or the exploration of “dark side” behaviors such as drug and alcohol use (Mick, 1996) may be affected by subject unwillingness to report accurately because of social desirability biases. In a recent report, Fitzsimons, Nunes, and Williams (2007) attempted to examine behaviors toward which individuals held both positive and negative attitudes. Specifically, they examined vice behaviors and suggested that merely asking questions about vice behaviors makes implicit attitudes toward those behaviors more accessible, influencing consumers by “liberating” themselves (via increases in positive activation) into increased incidence of the behavior. Across four studies, Fitzsimons and colleagues persuasively show that not only can asking questions about future vice behaviors increase the occurrence of those behaviors (Study 1), but also that these behaviors result from differences in the valence of implicit and explicit attitudes (Studies 2 and 3) and occurrence of the vice behavior can be attenuated by providing defensive tools that are primarily conscious in nature, therefore tipping the balance toward conscious correction of the behavior. The authors implement an interesting indirect measure of attitudes similar to previous work (Fazio, 1990), using response times based on the respondent’s own perception of the valence of a target word rather than the a priori categories used in the IAT. The IAT has been incorporated in research that explores vice behaviors, albeit outside the consumer behavior domain. Wiers and others have explored vice behaviors within the context of health psychology, specifically drinking behaviors (Grenard et al., 2008; Schoenmakers, Wiers, & Field, 2008; Thush et al., 2008, 2009; Wiers, 2008; Wiers, van Woerden, Smulders, & de Jong, 2002). These results and others are summarized and explored by Wiers and colleagues in Chapter 25 of this volume. Other vice-like behaviors include sexual interest and food temptations (Hofmann, Friese, Gschwendner, Wiers, & Schmitt, 2008) and smoking (Robinson, Meier, Zetocha, & McCaul, 2005; Sherman, Rose, Koch, Presson, & Chassin, 2003; Swanson et al., 2001). Clearly, there is opportunity for work across disciplines in a domain that is meaningful from both a consumer behavior and public policy perspective.

Although literature is scant, it is incorrect to surmise that there has been no exploration of automatic behaviors within the consumer behavior domain. For example, researchers have started to examine where conscious and unconscious decision-Â�making processes lead to differing optimal choices for consumers. Galdi, Arcuri, and Gawronski (2008) found that indirect measures predicted future voting patterns among undecided voters. Dijksterhuis, Bos, Nordgren, and van Baaren (2006) have explored both simple and complex consumer choices, concluding that, under some circumstances, complex decisions may benefit from a more automatic, unconscious process. Dijksterhuis and colleagues posited that the reasons for these results—Â�dubbed “deliberation without attention”—were based on the characteristics of conscious and unconscious deliberation defined in their unconscious thought theory (Dijksterhuis & Nordgren, 2006). First, conscious thought is rule based and necessarily precise, requiring conscious attention to the process. Second, conscious thought suffers from the low capacity of consciousness, making it less suitable for very complex issues (unconscious thought does not have this capacity limitation) (Dijksterhuis, 2004). Applied to a consumer context, one’s capacity to accurately and optimally assess a decision consciously works well when there are few components to the decisions (because of the accuracy of conscious, rule-based processes) but begins to break down when decision complexity rises (because of the capacity limitations of conscious thought). This was borne out across a number of studies: Subjects making choices regarding an automobile presented as either a complex or simple decision task did the same when distracted with anagrams following information presentation but fared significantly worse when allowed to consciously attempt to parse the information in the complex task (Study 1). A similar effect was replicated when the target was automobile attitude instead of choice (Study 2). Furthermore, increased thinking reduced satisfaction with complex (compared with simple) purchases (Study 3), while individuals characterized as unconscious thinkers (based on survey responses) were more satisfied than conscious thinkers after making purchases characterized as “complex” (e.g., IKEA brand household goods; Study 4). Dijksterhuis and colleagues have found similar results in other consumer settings, such as choosing an apartment or a roommate (Dijksterhuis, 2004), and have found



28. Indirect Measures in Consumer Behavior

the same general pattern of results when making an actual choice rather than a hypothetical one (Dijksterhuis & Nordgren, 2006; Dijksterhuis & van Olden, 2006). In general, these results suggest meaningful unconscious effects on consumer choices.

Self-Â�Regulation and Automatic€Attitudes Consumers often behave in an impulsive way toward their own cravings and urges, whether it be choosing a slice of pizza over a healthy snack, binge drinking, or purchasing a candy bar proffered at the checkout counter. Many times, these impulsive behaviors are contrary to previously existing goals that the individual might have (e.g., “I want to lose weight”). This internal conflict is often resolved when the stronger competitor (self-Â�control vs. impulse) wins out (Baumeister, Bratslavsky, Muraven, & Tice, 1998; Baumeister, Heatherton, & Tice, 1994). This model of ego depletion suggests that, like a muscle, engaging in self-Â�regulation reduces an individual’s ability to subsequently control behavior. Ego-Â�depletion effects have been explored in consumer behavior settings, whether it be tasting ice cream after depletion (Vohs & Heatherton, 2000), controlling alcohol consumption (Muraven, Collins, & Neinhaus, 2002), choosing between candy and healthful granola snacks or movie selection (Novemsky, Wang, Dahr, & Baumeister, 2007), or dieters’ consumption habits and increased impulsive spending (Vohs & Faber, 2007; Vohs & Heatherton, 2000). Current research suggests that these impulses are often the consequence of automatically activated evaluations (Hofmann, Rauch, & Gawronski, 2007), and this supposition is consistent with previous theorizing about approach–Â�avoidance behavior (Gawronski & Bodenhausen, 2006; Strack & Deutsch, 2004). Ego depletion was thought to moderate the self-Â�control–Â�behavior link, such that automatic associations would predict behavior when self-Â�control was low, while conscious processes (personal standards) would predict behavior when self-Â�control was high (not depleted). These experiments included a modified Single-Â�Category IAT (Karpinski & Steinman, 2006) used to measure implicit candy attitudes, while personal standards (adherence to dietary restraint) were captured using self-Â�report measures. As predicted, when self-Â�regulatory resources were low, implicitly measured candy attitudes predicted candy consumption, while dietary restraint consideration

543

predicted consumption when the subject was not ego depleted. Note that, in this case, IAT attitude measurement occurred before manipulation and was used to predict the targeted outcome behavior. We include this research here not as conceptually different from the prior “where we have been” discussion, but as evidence that the IAT can be used within these impulsive behavior domains and, more importantly as indicative of cognitive processes that underlie or explain overt behavior.

Conclusions Until recently, research examining stalwart areas of consumer behavior has focused on the conscious or controlled processes that underlie behavior of interest to marketers. In reviewing previous decades of research, one notices a strong focus on theoretical explanations of consumer behavior that assume deeply cognitive processes that directly tap consciously held beliefs (Cohen & Chakravarti, 1990; Jacoby, Johar, & Morrin, 1998; Simonson, Carmon, Dhar, Drolet, & Nowlis, 2001). Most likely, this is due to the influence of research examining decision making and attitude formation in other domains (Chaiken & Trope, 1999; Petty & Cacioppo, 1986). This focus within consumer behavior lies in contrast to current research sourced in social psychology that suggests much of human behavior is influenced by uncontrolled, unobserved processes in memory (Bargh, 2002; Gawronski & Bodenhausen, 2006; Greenwald et al., 2002; Strack & Deutsch, 2004). It has been suggested that consumer research has missed out on the recent developments within psychology over the past few years (Bargh, 2002). It is in light of this stark summary of consumer behavior findings that we argue that use of indirect measures in consumer behavior research was stunted by, among other issues, increased attention to the psychometric properties of indirect measures in general and the IAT in particular. This attention to psychometric properties often came at the expense of assessing the potential usefulness of indirect measures as dependent tools that inform a variety of implicit processes. Within psychology, this attention to the IAT’s psychometric properties was an outgrowth of an initial supposition that the IAT was an immutable, stable measure of attitude (or self-Â�esteem, stereotype definition, and so on) and the theoretical position that the IAT could and does reveal hidden biases that are inaccessible or not reportable by individuals. This was espe-

544

V. APPLIED PERSPECTIVES

cially the case in situations where social norms or beliefs were thought to be in conflict with implicit attitudes. To the extent that the IAT and self-Â�reported attitudes exhibited low correlations, this was taken as evidence of social desirability, inaccessibility, or some other bias and indirectly supported the notion that the IAT was tapping a separate pathway or source for attitude construction. However, the results and accompanying theoretical developments described in this chapter should give hope that the application of implicit social cognition theory in general and the indirect measures specifically is on the right track. We argue that indirect measures are best used (or can be most advantageous) as a measure of variables or constructs that are manipulated via experimental design both within and between subjects. In general, we expect that research that incorporates indirect measures in this way will be able to assess subtle shifts in association, thus greatly informing the underlying processes that drive consumer context effects. With the ever-Â�increasing need not only to demonstrate an effect but also to better model the process by which the effect occurs, we believe the IAT and other indirect measures will become essential components in numerous research domains. References Bargh, J. A. (2002). Losing consciousness: Automatic influences on consumer judgment, behavior, and motivation. Journal of Consumer Research, 29, 280–285. Baumeister, R. F., Bratslavsky, M., Muraven, M., & Tice, D. M. (1998). Ego depletion: Is the active self a limited resource? Journal of Personality and Social Psychology, 74, 1252–1265. Baumeister, R. F., Heatherton, T. F., & Tice, D. M. (1994). Losing control: How and why people fail at selfÂ�regulation. San Diego, CA: Academic Press. Beggan, J. K. (1992). On the social nature of nonsocial perception: The mere ownership effect. Journal of Personality and Social Psychology, 62, 229–237. Brewer, M. B. (1991). The social self: On being the same and different at the same time. Personality and Social Psychology Bulletin, 17, 475–482. Brewer, M. B., & Gardner, W. (1996). Who is this “we”?: Levels of collective identity and self representations. Journal of Personality and Social Psychology, 71, 83– 93. Briley, D. A., & Wyer, R. S., Jr. (2002). The effect of group membership salience on the avoidance of negative outcomes: Implications for social and consumer decisions. Journal of Consumer Research, 29, 400–415. Brunel, F. F., Tietje, B. C., & Greenwald, A. G. (2004). Is the Implicit Association Test a valid and valuable

measure of implicit consumer social cognition? Journal of Consumer Psychology, 14, 385–404. Chaiken, S., & Trope, Y. (Eds.). (1999). Dual-Â�process theories in social psychology. New York: Guilford Press. Cohen, J. B., & Chakravarti, D. (1990). Consumer psychology. Annual Review of Psychology, 41, 243–288. Conner, M. T., Perugini, M., O’Gorman, R., Ayres, K., & Prestwich, A. (2007). Relations between implicit and explicit measures of attitudes and measures of behavior: Evidence of moderation by individual difference variables. Personality and Social Psychology Bulletin, 33, 1727–1740. Crocker, J., Luhtanen, R., Blaine, B., & Brodnax, S. (1994). Collective self-Â�esteem and psychological well-being among white, black, and Asian college students. Personality and Social Psychology Bulletin, 20, 503–513. Dasgupta, N., & Greenwald, A. G. (2001). On the malleability of automatic attitudes: Combating automatic prejudice with images of admired and disliked individuals. Journal of Personality and Social Psychology, 81, 800–814. Dasgupta, N., McGhee, D. E., Greenwald, A. G., & Banaji, M. R. (2000). Automatic preference for white Americans: Eliminating the familiarity explanation. Journal of Experimental Social Psychology, 36, 316–328. De Houwer, J. (2003). The Extrinsic Affective Priming Task. Experimental Psychology, 50, 77–85. De Houwer, J. (2006). What are implicit measures and why are we using them? In R. W. Wiers & A. W. Stacy (Eds.), The handbook of implicit cognition and addiction (pp.€11–28). Thousand Oaks, CA: Sage. De Houwer, J., & Eelen, P. (1998). An affective variant of the Simon paradigm. Cognition and Emotion, 18, 251–264. Deshpande, R., Hoyer, W. D., & Donthu, N. (1986). The intensity of ethnic affiliation: A study of the sociology of Hispanic consumption. Journal of Consumer Research, 13, 214–220. Deshpande, R., & Stayman, D. M. (1994). A tale of two cities: Distinctiveness theory and advertising effectiveness. Journal of Marketing Research, 31, 57–64. Dholakia, U. M., & Morwitz, V. G. (2002). The scope and persistence of mere-Â�measurement effects: Evidence for a field study of customer satisfaction measurement. Journal of Consumer Research, 29, 159– 167. Dijksterhuis, A. (2004). Think different: The merits of unconscious thought in preference development and decision making. Journal of Personality and Social Psychology, 87, 586–598. Dijksterhuis, A., Bos, M. W., Nordgren, L. F., & van Baaren, R. B. (2006). On making the right choice: The deliberation-Â�without-Â�attention effect. Science, 311, 1005–1007. Dijksterhuis, A., & Nordgren, L. F. (2006). A theory of unconscious thought. Perspectives in Psychological Science, 1, 95–109. Dijksterhuis, A., & van Olden, Z. (2006). On the benefits of thinking unconsciously: Unconscious thought increases post-Â�choice satisfaction. Journal of Experimental Social Psychology, 42, 627–631. Farnham, S. D., Greenwald, A. G., & Banaji, M. R.



28. Indirect Measures in Consumer Behavior

(1999). Implicit self-Â�esteem. In D. Abrams & M. A. Hogg (Eds.), Social identity and social cognition (Vol. 17, pp.€230–248). Malden, MA: Blackwell. Fazio, R. H. (1990). A practical guide to the use of response latency in social psychological research. In C. Hendrick & M. S. Clark (Eds.), Research methods in personality and social psychology. Review of personality and social psychology (Vol. 11, pp.€74–97). Thousand Oaks, CA: Sage. Fazio, R. H., Jackson, J. R., Dunton, B. C., & Williams, C. J. (1995). Variability in automatic activation as an unobstrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69, 1013–1027. Fazio, R. H., & Towles-Â�Schwen, T. (1999). The MODE model of attitude-Â�behavior processes. In S. Chaiken & Y. Trope (Eds.), Dual-Â�process theories in social psychology (pp.€97–116). New York: Guilford Press. Festinger, L. (1957). A theory of cognitive dissonance. Palo Alto, CA: Stanford University Press. Fitzsimons, G. J., & Morwitz, V. G. (1996). The effect of measuring intent on brand-level purchase behavior. Journal of Consumer Research, 23, 1–11. Fitzsimons, G. J., Nunes, J. C., & Williams, P. (2007). License to sin: The liberating role of reporting expectations. Journal of Consumer Research, 34, 22–31. Fitzsimons, G. J., & Williams, P. (2000). Asking questions can change choice behavior: Does it do so automatically or effortfully? Journal of Experimental Psychology: Applied, 6, 195–206. Forehand, M. R., & Deshpande, R. (2001). What we see makes us who we are: Priming ethnic self-Â�awareness and advertising response. Journal of Marketing Research, 38, 336–348. Forehand, M. R., Deshpande, R., & Reed, A., II. (2002). Identity salience and the influence of differential activation of the social self-Â�schema on advertising response. Journal of Applied Psychology, 87, 1086–1099. Forehand, M. R., & Perkins, A. W. (2005). Implicit assimilation and explicit contrast: A set/reset model of response to celebrity voice-overs. Journal of Consumer Research, 32, 435–441. Forehand, M. R., Perkins, A. W., & Reed, A., II. (2008). The shaping of social identity: Assimilation/contrast responses to ad exposure. Paper presented at the annual meeting of the Association of Consumer Researchers, San Francisco. Friese, M., Hofmann, W., & Schmitt, M. (2008). When and why do implicit measures predict behaviour?: Empirical evidence for moderating role of opportunity, motivation, and process reliance. European Review of Social Psychology, 19, 285–338. Friese, M., Wanke, M., & Plessner, H. (2006). Implicit consumer preferences and their influence on product choice. Psychology and Marketing, 23, 727–740. Galdi, S., Arcuri, L., & Gawronski, B. (2008). Automatic mental associations predict future choices of undecided decision-Â�makers. Science, 321, 1100–1102. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. Gawronski, B., Bodenhausen, G. V., & Becker, A. P. (2007). I like it, because I like myself: Associative

545

self-Â�anchoring and post-Â�decisional change of implicit evaluations. Journal of Experimental Social Psychology, 43, 221–232. Gibson, B. (2008). Can evaluative conditioning change attitudes toward mature brands?: New evidence from the Implicit Association Test. Journal of Consumer Research, 35, 178–188. Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-Â�esteem, and stereotypes. Psychological Review, 102, 4–27. Greenwald, A. G., Banaji, M. R., Rudman, L. A., Farnham, S. D., Nosek, B. A., & Mellott, D. S. (2002). A unified theory of implicit attitudes, stereotypes, selfÂ�esteem, and self-Â�concept. Psychological Review, 109, 3–25. Greenwald, A. G., & Farnham, S. D. (2000). Using the Implicit Association Test to measure self-Â�esteem and self-Â�concept. Journal of Personality and Social Psychology, 79, 1022–1038. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Greenwald, A. G., Poehlman, T. A., Uhlmann, E., & Banaji, M. R. (2009). Understanding and using the Implicit Association Test: III. Meta-Â�analysis of predictive validity. Journal of Personality and Social Psychology, 97, 17–41. Grenard, J. L., Ames, S. L., Wiers, R. W., Thush, C., Sussman, S., & Stacy, A. W. (2008). Working memory capacity moderates the predictive effects of drugÂ�related associations on substance abuse. Psychology of Addictive Behaviors, 22, 426–432. Grier, S. A., & Deshpande, R. (2001). Social dimensions of consumer distinctiveness: The influence of social status on group identity and advertising persuasion. Journal of Marketing Research, 38, 216–224. Heider, F. (1958). The psychology of interpersonal relations. New York: Wiley. Hofmann, W., Friese, M., Gschwendner, T., Wiers, R. W., & Schmitt, M. (2008). Working memory capacity and self-Â�regulatory behavior: Toward an individual difference perspective on behavior determination by automatic and controlled processes. Journal of Personality and Social Psychology, 95, 962–977. Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., & Schmitt, M. (2005). A meta-Â�analysis on the correlation between the Implicit Association Test and explicit self-Â�report measures. Personality and Social Psychology Bulletin, 31, 1369–1385. Hofmann, W., Rauch, W., & Gawronski, B. (2007). And deplete us not into temptation: Automatic attitudes, dietary restraint, and self-Â�regulatory resources and determinants of eating behavior. Journal of Experimental Social Psychology, 43, 497–504. Jacoby, J., Johar, G. V., & Morrin, M. (1998). Consumer behavior: A quadrennium. Annual Review of Psychology, 49, 319–344. Jones, J. T., Pelham, B. W., Carvallo, M., & Mirenberg, M. C. (2004). How do I love thee?: Let me count the Js: Implicit egotism and interpersonal attraction. Journal of Personality and Social Psychology, 84, 665–683. Karpinski, A., & Hilton, J. L. (2001). Attitudes and the

546

V. APPLIED PERSPECTIVES

Implicit Association Test. Journal of Personality and Social Psychology, 81, 774–788. Karpinski, A., & Steinman, R. B. (2006). The single category Implicit Association Test as a measure of implicit social cognition. Journal of Personality and Social Psychology, 91, 16–32. Laaksonen, P. (1994). Consumer involvement: Concepts and research. London: Routledge. Luna, D., Ringberg, T., & Peracchio, L. A. (2008). One individual, two identities: Frame switching among biculturals. Journal of Consumer Research, 35, 279– 293. Maison, D., Greenwald, A. G., & Bruin, R. (2001). The Implicit Association Test as a measure of implicit consumer attitudes. Polish Psychological Bulletin, 32, 61–69. Martin, L. L. (1986). Set/reset: Use and disuse of concepts in impression formation. Journal of Personality and Social Psychology, 51, 493–504. Meyers-Levy, J., & Sternthal, B. (1991). Gender differences in the use of message cues and judgments. Journal of Marketing Research, 28, 84–96. Mick, D. G. (1996). Are studies of dark side variables confounded by socially desirable responding?: The case of materialism. Journal of Consumer Research, 23, 106–119. Morwitz, V. G., & Fitzsimons, G. J. (2004). The mereÂ�measurement effect: Why does measuring intentions change actual behavior? Journal of Consumer Psychology, 14, 64–74. Morwitz, V. G., Johnson, E., & Schmittlein, D. (1993). Does measuring intent change behavior? Journal of Consumer Research, 20, 46–61. Mowen, J. C., & Spears, N. (1999). Understanding compulsive buying among college students: A hierarchical approach. Journal of Consumer Psychology, 8, 407–430. Muraven, M., Collins, M., & Neinhaus, K. (2002). SelfÂ�control and alcohol restraint: An initial application of the self-Â�control strength model. Psychology of Addictive behavior, 16, 113–120. Netemeyer, R. G., Burton, S., & Lichtenstein, D. R. (1995). Trait aspects of vanity: Measurement and relevance to consumer behavior. Journal of Consumer Research, 21, 612–626. Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2005). Understanding and using the Implicit Association Test: II. Methodological variables and construct validity. Personality and Social Psychology Bulletin, 31, 166–180. Novemsky, N., Wang, J., Dahr, R., & Baumeister, R. F. (2007). The interaction of ego-Â�depletion and choice. Manuscript submitted for publication. Nuttin, J. M. (1985). Narcissism beyond gestalt and awareness: The name letter effect. European Journal of Social Psychology, 15, 353–361. Olson, M. A., & Fazio, R. H. (2001). Implicit attitude formation through classical conditioning. Psychological Science, 12, 413–417. Olson, M. A., & Fazio, R. H. (2004). Reducing the influence of extrapersonal associations on the Implicit Association Test: Personalizing the IAT. Journal of Personality and Social Psychology, 86, 653–667. Osgood, C. E., & Tannenbaum, P. H. (1955). The princi-

ple of congruity in the prediction of attitude change. Psychological Review, 62, 42–55. Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14, 106–110. Pelham, B. W., Mirenberg, M. C., & Jones, J. T. (2002). Why Susie sells seashells by the seashore: Implicit egotism and major life decisions. Journal of Personality and Social Psychology, 82, 469–487. Perkins, A. W., Forehand, M. R., & Greenwald, A. G. (2009). Implicit self-Â�referencing: The self-Â�concept as a source of implicit attitude formation. Unpublished manuscript. Perkins, A. W., Forehand, M. R., Greenwald, A. G., & Maison, D. (2008). Measuring the unconscious: Implicit social cognition in consumer behavior. In C. P. Haugtvedt, P. M. Herr, & F. R. Kardes (Eds.), Handbook of consumer psychology (pp.€461–475). New York: Erlbaum. Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology, 19, 123–205. Reed, A., II. (2004). Activating the self-Â�importance of consumer selves: Exploring identity salience effects on judgments. Journal of Consumer Research, 31, 286–295. Robinson, M. D., Meier, B. P., Zetocha, K. J., & McCaul, K. D. (2005). Smoking and the Implicit Association Test: When the contrast category determines the theoretical conclusions. Basic and Applied Social Psychology, 27, 201–212. Rudman, L. A., Greenwald, A. G., & McGhee, D. E. (2001). Implicit self-Â�concept and evaluative implicit gender stereotypes: Self and ingroup share desirable traits. Personality and Social Psychology Bulletin, 27, 1164–1178. Saegert, J., Hoover, R. J., & Hilger, M. T. (1985). Characteristics of Mexican American consumers. Journal of Consumer Research, 12, 104–109. Scarabis, M., Florack, A., & Gosejohann, S. (2006). When consumers follow their feelings: The impact of affective or cognitive focus on the basis of consumers’ choice. Psychology and Marketing, 23, 1015–1034. Schoenmakers, T., Wiers, R. W., & Field, M. (2008). Effects of a low dose of alcohol on cognitive biases and craving in heavy drinkers. Psychopharmacology, 197, 169–178. Sherman, S. J., Rose, J. S., Koch, K., Presson, C. C., & Chassin, L. (2003). Implicit and explicit attitudes toward cigarette smoking: The effects of context and motivation. Journal of Social and Clinical Psychology, 22, 13–39. Simonson, I., Carmon, Z., Dhar, R., Drolet, A., & Nowlis, S. M. (2001). Consumer research: In search of identity. Annual Review of Psychology, 52, 249–275. Spangenberg, E. R. (1997). Increasing health club attendance through self-Â�prophecy. Marketing Letters, 8, 23–31. Spangenberg, E. R., & Greenwald, A. G. (1999). Social influence by requesting self-Â�prophecy. Journal of Consumer Psychology, 8, 61–89. Spangenberg, E. R., & Obermiller, C. (1996). To cheat or not to cheat: Reducing cheating by requesting selfÂ�prophecy. Marketing Education Review, 6, 95–103.



28. Indirect Measures in Consumer Behavior

Spangenberg, E. R., Sprott, D. E., Grohmann, B., & Smith, R. J. (2003). Mass-Â�communicated prediction requests: Practical application and a cognitive dissonance explanation for self-Â�prophecy. Journal of Marketing, 67, 47–62. Sprott, D. E., Smith, R. J., Spangenberg, E. R., & Freson, T. S. (2004). Specificity of prediction requests: Evidence for the differential effects of self-Â�prophecy on commitment to a health assessment. Journal of Applied Social Psychology, 34, 1176–1190. Sprott, D. E., Spangenberg, E. R., Block, L. G., Â�Fitzsimons, G. J., Morwitz, V. G., & Williams, P. (2006). The question–Â�behavior effect: What we know and where we go from here. Social Influence, 1, 128–137. Sprott, D. E., Spangenberg, E. R., & Fisher, R. J. (2003). The importance of normative beliefs to the selfÂ�prophecy effect. Journal of Applied Psychology, 88, 423–430. Spruyt, A., Hermans, D., De Houwer, J., Vandekerckhove, J., & Eelen, P. (2006). On the predictive validity of indirect attitude measures: Prediction of consumer choice behavior on the basis of affective priming in the picture-Â�picture naming task. Journal of Experimental Social Psychology, 43, 599–610. Stayman, D. M., & Deshpande, R. (1989). Situational ethnicity and consumer behavior. Journal of Consumer Research, 16, 361–371. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Swanson, J. E., Rudman, L. A., & Greenwald, A. G. (2001). Using the Implicit Association Test to investigate attitude-Â�behaviour consistency for stigmatised behaviour. Cognition and Emotion, 15, 207–230. Thush, C., Wiers, R. W., Ames, S. L., Grenard, J. L., Sussman, S., & Stacy, A. W. (2008). Interactions between implicit and explicit cognition and working memory capacity in the prediction of alcohol use in

547

at-risk adolescents. Drug and Alcohol Dependence, 94, 116–124. Thush, C., Wiers, R. W., Moerbeek, M., Ames, S. L., Grenard, J. L., Sussman, S., et al. (2009). Influence of motivational interviewing on explicit and implicit alcohol-Â�related cognition and alcohol use in at-risk adolescents. Psychology of Addictive Behaviors, 23, 146–151. Tietje, B., & Brunel, F. (2005). Towards a unified theory of implicit consumer brand cognitions. In F. R. Kardes, P. M. Herr, & J. Natel (Eds.), Applying social cognition to consumer-Â�focused strategy. Mahwah, NJ: Erlbaum. Verlegh, P. W. J., & Steenkamp, J.-B. E. M. (1999). A review and meta-Â�analysis of country-of-Â�origin research. Journal of Economic Psychology, 20, 521–546. Vohs, K. D., & Faber, R. J. (2007). Spent resources: SelfÂ�regulatory resource availability affects impulse buying. Journal of Consumer Research, 33, 537–547. Vohs, K. D., & Heatherton, T. F. (2000). Self-Â�regulatory failure: A resource-Â�depletion approach. Psychological Science, 11, 249–254. Watson, J. J., & Wright, K. (2000). Consumer ethnocentrism and attitudes toward domestic and foreign products. European Journal of Marketing, 34, 1149–1166. Wiers, R. W. (2008). Alcohol and drug expectancies as anticipated changes in affect: Negative reinforcement is not sedation. Substance Use and Misuse, 43, 501–516. Wiers, R. W., van Woerden, N., Smulders, F. T. Y., & de Jong, P. J. (2002). Implicit and explicit alcohol-Â�related cognitions in heavy and light drinkers. Journal of Abnormal Psychology, 111, 648–658. Wooten, D. B. (1995). One-of-a-kind in a full house: Some consequences of ethnic and gender distinctiveness. Journal of Consumer Psychology, 4, 205–224. Zaichkowsky, J. L. (1985). Measuring the involvement construct. Journal of Consumer Research, 12, 341– 356.

Chapter 29

Implicit Political Cognition Brian A. Nosek, Jesse Graham, and Carlee Beth Hawkins

T

here are many activities in daily life for which it is easy to imagine the influence of thoughts and feelings that people are unwilling or unable to report. Politics is not one of them. After all, people are certainly willing to report and act on their political opinions, even when those opinions completely, dramatically, even unpleasantly, conflict with what someone else just said or did. And people are quite able to articulate political opinions or reasons for their political behavior, even ones that might strike the listener as inarticulate, illogical, or otherwise absurd. These characteristics of politics are as obvious to political theorists as they are to anyone watching TV pundits bicker. Even evidence from implicit social cognition itself suggests that politics may be its least relevant frontier.1 In investigations of implicit and explicit attitudes toward 57 (Nosek, 2005) and 98 (Nosek & Hansen, 2008) topics, political attitudes consistently elicited the strongest zero-order correlations between implicit and explicit measures, for example, Democrats versus Republicans (r = .70; from Nosek & Hansen, 2008), feminism versus traditional values (r = .66), John Kerry versus George Bush (r = .63; compared with an average r = .38 across all topics). In addition, in what might seem like the final strike against a meaningful implicit political cognition discipline, an investigation of moderators of implicit–Â�explicit relations identified four factors that predict stronger corre

spondence (Nosek, 2005), all of which suggest that political topics will show little divergence between self-Â�report and implicit reactions: (1) Topics with weaker self-Â�presentational demands elicit stronger implicit–Â�explicit relations than topics with stronger self-Â�presentational demands; (2) topics that are more elaborated elicit stronger correspondence than those that are less elaborated; (3) topics with a bipolar structure (two competing alternatives) elicit stronger correspondence than those without a direct contrast; and (4) topics for which the respondents perceive themselves as being distinct from the cultural norm elicit stronger correspondence than those for which the respondents perceive themselves as being just like the norm. If it is so obvious that politics is the one domain in which people easily and willingly articulate their mental contents, then what is a chapter on politics doing in a book on implicit social cognition? Like politics itself, the mind’s “public” activities often disguise intriguing and complex activity happening behind the scenes. For the implicit social cognition researcher, political cognition is a perfect context for pushing the boundaries of how thought and feeling outside of awareness or control might influence even the most (apparently) deliberate, intentional human activities. Focusing on political behavior is perhaps the toughest test for the claim that implicit cognition can make novel additions to predicting social behavior. 548



29. Implicit Political Cognition

In scientific epochs, implicit social cognition is a young topic. Within implicit social cognition, implicit political cognition is barely past novelty status. And yet this is not politics’ first encounter with the unconscious. A popular academic sport in the mid-20th century used psychoanalytic theories to unmask the motivations of political leaders from the fiend—Â�Erikson’s (1950) and Fromm’s (1973/1992) analyses of Hitler—to the idealist—Â� George and George’s (1956) analysis of Woodrow Wilson. Psychoanalysis even provided a means of identifying national character (e.g., Benedict, 1946, on the Japanese). As they did everywhere else, psychoanalytic applications in political psychology fell short of scientific precision and replicability, giving way to perspectives like rational choice models of decision making and behavior (see McGuire, 1993). Challenges to the rational actor model, and the rise of heuristics and biases research, laid the groundwork for applications of implicit cognition in political psychology (Green & Shapiro, 1994; Hastie & Park, 1986; Tversky & Kahneman, 1974). If political actions, both elite and ordinary, are, at least partly, a product of intuitions and heuristics, then the boundary of logical deliberation separating political cognition from the rest of ordinary mental life disappears. Adding an assertion that political actions may be influenced by processes of which the actor is not aware or cannot control is now a short step, rather than a dramatic leap, from the increasingly defunct assumption of a rational polity. We are not the first to observe that deliberateness may be more veneer than substance for political thinking and acting. Kinder (1998) bemoaned the exclusive use of self-Â�report survey methods in assessing public opinion. Taber (2003) likewise noted, “Political science, long under the spell of the Enlightenment view of rationality, has not been particularly friendly ground for the germination of either affective or automatic models of information processing, and I think this biases research in public opinion today” (p.€ 462; see also Burdein, Lodge, & Taber, 2006). Common political survey questions follow a format such as “Which of the following factors influenced your decision to support or oppose candidate X?” The respondent’s answer is usually accepted credulously as indicative of the actual influences on judgment. Exclusive reliance on such survey methods ignores, or implicitly rejects, what is now foundational knowledge in implicit social cognition: that people tell more than they can know (Nisbett & Wilson, 1977); that reasons

549

may follow judgment, not precede it (Haidt, 2001); and that people consciously experience the result of some mental operations but do not observe the operations themselves (Greenwald & Banaji, 1995; Nosek, 2007). As a consequence, such reports may be personal theories, best guesses, or post hoc rationalizations about the influences on judgment or course of action (Wilson, 2002; Wilson & Schooler, 1991). This suggests that implicit political cognition is relevant despite, or perhaps because of, the strength of conviction that convictions are driving political behavior.2 The theories and methods of implicit social cognition are very new for political applications. Even so, the early returns provide a fascinating preview for how implicit cognition may shape understanding of political life. This chapter organizes and summarizes the existing literature into five categories: (1) ideology, values, and beliefs; (2) political attitudes and opinions; (3) intergroup relations; (4) candidate preferences; and (5) voting behavior. This list will strike the political psychologist (not to mention the political scientist) as a very narrow slice of politics, ignoring mass communication, political socialization, international relations, foreign policy, conflict resolution, and political mobilization among other things (Sears, Huddy, & Jervis, 2003). We agree. It is conceivable that implicit social cognition theory and evidence will become relevant, even central, to other aspects of politics. But it has not done so yet.

Ideology, Values, and€Beliefs The study of political ideology has been a primary concern of political psychology throughout the discipline’s history (Jost & Sidanius, 2004; McGuire, 1993), and it is a research area particularly suited for elaboration by the theories and methods of implicit social cognition (Jost, Nosek, & Gosling, 2008). The study of ideology has proceeded through three phases in the last several decades, from an explosion of personality-based research following World War II to a fallow period following “end of ideology” pronouncements in the 1960s, to a recent resurgence of experimental research (Jost, 2006; McGuire, 1993). This resurgence was contemporaneous with the development of implicit social cognition methods and theories, and that is not a coincidence. Historically, political psychologists defined ideology either in a value-Â�neutral way, as “any abstract, internally coherent system of belief or meaning,” or in a negatively valued way as

550

V. APPLIED PERSPECTIVES

“propagandistic belief systems that are typically misleading and systematically distorted,” such as “a motivated, system-Â�serving [or system-Â�challenging] belief system” (Jost et al., 2008, p.€127). In either case, the unstated assumption was that such belief systems could be articulated and were known to the individual. Implicit cognition theories introduced the possibility that people could be ideological without being able to articulate their ideology. Thus, implicit social cognition expands the definition of what ideology is and how it can be gauged in members of political systems. In this section, we briefly describe the history of ideology research and review the role that implicit cognition has played in its resurgence.

History What may be considered the golden age of ideology research occurred as a response to the horrors of the Holocaust, in an attempt to understand the belief systems that could lead to such atrocity. Adorno, Frenkel-Â�Brunswik, Levinson, and Sanford’s (1950) work on the authoritarian personality initiated interest in the personality underpinnings of ideological systems. Like most work in the period, ideology was assessed via direct report with self-Â�descriptions or ratings of agreement on opinion items about authority, obedience, dominance, and intergroup attitudes. Personality scales such as Right-Wing Authoritarianism (Altemeyer, 1998; Zakrisson, 2005) and Social Dominance Orientation (Pratto, Sidanius, Stallworth, & Malle, 1994) are modern formulations of this groundbreaking research. Critics confronted the inordinate focus on the person instead of the situation and questioned whether anyone could actually be considered ideological. Hannah Arendt’s Eichmann in Jerusalem (1963/1994), which concentrated on the “banality of evil,” inspired much social psychological work on the power of the situation, and became cited as a philosophical critique of Adorno’s approach looking for answers in the personalities of Nazi guards rather than in their social contexts. On another front, political psychologists began to offer evidence from national surveys that the overwhelming majority of citizens (mostly in the United States) did not meet any of a host of criteria for what it means to have an ideology (Aron, 1957/1968; Bell, 1960; Converse, 1964; Lipset, 1960; Shils, 1955/1968). They claimed that most people’s positions on political issues were inconsistent and often unformulated, that their answers to political questions could be shifted by changes

in question wording or format, that most citizens had “ideological exhaustion” following encounters with fascism and communism, and even that there were no meaningful differences between liberalism and conservatism anyway. A consensus emerged among political scientists and psychologists in the 1960s that ideology was a meaningless construct for all but the most sophisticated political elites, in large part because most of the people surveyed could not directly articulate their ideological stance on most political issues (see especially Converse, 1964). The reliance on explicit self-Â�report played a large role in the “end of ideology” pronouncements: If people couldn’t directly report their ideological stances, then how could they be said to have an ideology? Defenders of ideology were overwhelmed against the accumulating national survey data. For instance, Conover and Feldman (1981) argued that self-Â�reported liberal/conservative political identifications are important symbolic simplifications of political conflicts; yet relying on consciously endorsed judgments, they too conceded that such self-Â�identification did not always predict issue positions. Kinder (1998) argued that although individual citizens may be ideologically unsophisticated, the public in aggregate is not and fluctuates rationally and sensibly between left and right extremes. This conceptualization introduced the possibility that ideological thought might not be articulated easily by measuring political opinions or personality through self-Â�report.

Ideology from an Implicit Social Cognition Perspective We suggest that articulation of the belief system is not a necessary condition for one to have an ideology or ideological motives. Instead, we conceive ideology as an interpretational system composed of social assumptions, heuristics, rules, beliefs, and information-�organizing strategies that imposes order and comprehensibility on social life. Without ideology, life is frequently too complex, ambiguous, and difficult to understand or explain. Ideology provides an interpretational frame through which social experiences can be understood. In our view, ideology is the conceptual analogue to top-down perceptual processes that resolve ambiguity, fill in missing information, and impose perceptual order to resolve conflicting sensory experiences. This occurs automatically and without awareness of the processes shaping the ultimate perception. Just as perceptual illusions can emerge illustrating the disjoint between reality and the experience of real-



29. Implicit Political Cognition

ity, so can conceptual illusions emerge illustrating the disjoint between different ideological interpretations of the same social events. In line with our alternative conception of ideology, social psychological work on schemas began to supplement political science work on belief systems, in part because the schema concept could incorporate unconscious biases and heuristics that belief systems (based on directly reported beliefs and opinions) could not (Larson, 1994). For example, Skitka and Tetlock (1993) found that liberals and conservatives behaviorally showed consistent and predictable motives in allocating resources (liberals tending to help everyone possible and avoid making value trade-offs between money and human lives, conservatives tending to help only those deemed deserving and to punish violators of social norms), even though they did not articulate these motives as ideological positions. In sum, our conceptualization of ideology requires identification of factors that differentiate social judgment and behavioral tendencies but without the attendant assumptions that (1) such factors are known to or reported by the individual or that (2) their application to social judgment is deliberate or intentional.

Resistance to Change and Acceptance of Inequality Jost, Glaser, Kruglanski, and Sulloway (2003) concluded that ideological differences between liberals and conservatives could be characterized by two factors: acceptance of inequality and resistance to change. In some cases, liberals and conservatives explicitly articulate such beliefs, either when asked in the abstract or inferred on the basis of relevant social judgments such as attitudes toward affirmative action. In other cases, indirect assessments provide support for these factors without requiring introspective access to the underlying factor. Carney, Jost, Gosling, and Potter (2008) used measures of nonverbal behaviors in interview situations as well as behavioral trace observations of work and living spaces, finding that liberals were more open to experience than conservatives, a personality indicator related to resistance to change. Using Implicit Association Tests (IATs; Greenwald, McGhee & Schwartz, 1998; see overview in Nosek, Greenwald, & Banaji, 2007), Jost and colleagues (2008) found that conservatives (relative to liberals) had stronger implicit preferences for tradition over progress, stability over flexibility, traditional values over feminism, conformity over rebellion, and order over chaos, all evaluative con-

551

trasts that follow from the “resistance to change” factor. Simultaneously, supporting the hypothesis that conservatives are more accepting of inequality than liberals, conservatives implicitly favored higher status groups (heterosexual people, lightÂ�skinned people, white people, white children, non-Arabs, non-Jews, thin people, young people, abled people) over their lower status counterparts (gay people, dark-Â�skinned people, black people, black children, Arab Muslims, Jews, fat people, old people, disabled people; see also Jost, Banaji, & Nosek, 2004; Nosek, Banaji, & Jost, 2009; Nosek, Smyth, et al., 2007). Notably, ideological variations are stronger in self-Â�reported attitudes than in implicit evaluations even though the average magnitude of preference for higher status groups appears stronger on implicit than explicit measures (Nosek et al., 2009). For example, compared with liberals, conservatives show stronger preferences for straight people over gay people, both explicitly and implicitly. However, that political difference is much more pronounced explicitly, suggesting that liberals are more likely to override their implicit evaluations in favor of a more egalitarian response (see also Skitka, Mullen, Griffin, Hutchinson, & Chamberlin, 2002, on liberals’ motivated correction of victim-Â�blaming attributions explaining social problems). It is interesting to note that most of the studies showing ideological differences in nonconscious aspects of resistance to change or acceptance of inequality measure political orientation by selfÂ�report (i.e., a liberal–Â�conservative Likert scale), suggesting that ideological labels may be reportable even when facets underlying those ideologies are not. How might that occur? In defense of his inability to articulate the threshold of when material becomes obscene, Justice Potter Stewart famously noted “I know it when I see it” (Jacobellis v. Ohio, 1964). Just as Justice Stewart might be able to watch a steamy foreign film and be able to say whether or not it is obscene without knowing why, liberals and conservatives might know that they are liberal or conservative without being able to articulate the bases for that self-Â�identification. Even if they can articulate some of the psychological factors bolstering their political orientation, these and other factors may operate automatically, outside of awareness.

Ideological Change A substantial literature demonstrates that implicit social cognition is malleable and sensitive to the context of evaluation (Blair, 2002; Das-

552

V. APPLIED PERSPECTIVES

gupta & Greenwald, 2001; Mitchell, Nosek, & Banaji, 2003). With a few exceptions (Teachman & Woody, 2003), most of the existing research illustrates short-term malleability effects rather than long-term change and does so in the context of a laboratory situation rather than everyday life. Initial studies on the malleability of political attitudes suggest that real-world events can influence politically relevant implicit evaluations in both the short and long term. Studies using self-�reported attitudes and political opinions have demonstrated a widespread shift to the right in the months following the 9/11 terrorist attacks in the United States, including greater reported nationalism, outgroup derogation, and support for then-�President George W. Bush (Bonanno & Jost, 2006; Li & Brewer, 2004). This shift also appears to have occurred implicitly, with stronger implicit preferences for George Bush compared with Al Gore in the weeks following the attacks on New York and Washington, DC, compared with the weeks before (Nosek, 2004). Note, however, that the sample was a cross�sectional sample of online volunteers, not a longitudinal study, making it difficult to infer change unambiguously. Landau, Solomon, and Greenberg (2004) used a mortality salience priming manipulation and a subliminal exposure paradigm to show that support for George W. Bush increased after nonconsciously priming the concept of death, suggesting a possible implicit mechanism for the ideological shift following political events such as terror attacks (see also Nail, McGregor, Drinkwater, Steele, & Thompson, 2009). Ferguson, Carter, and Hassin (2009) suggest that American nationalist ideology can be automatically activated by cues as subtle as nationalist words in a scrambled sentence paradigm or the unobtrusive presence of an American flag (Ferguson & Hassin, 2007), activating concepts such as power, aggression, and materialism. Thus, a preponderance of nationalist images, such as the ubiquitous flag lapels and patriotic bumper stickers following 9/11, could activate and maintain the rise of nationalist ideology without awareness (see also Billig, 1995).

Values and Morality Psychological work on values and morality, although distinct from the literature on ideology, has had a similar move from strictly survey methodology to implicit methods. The most influential values researchers were Milton Rokeach and Shalom Schwartz, who developed increasingly sophis-

ticated theories of value based on factor analyses of long self-Â�report value surveys (Rokeach, 1973; Schwartz, 1992; see also Feldman, 2003, for review). Similarly, psychological treatment of morality and moral values was dominated by the Kohlbergian school from the 1960s until the 1990s, with research methods based on explicit answers to dilemmas and questions about justice and fairness (Kohlberg, 1969; Kohlberg & Turiel, 1971). More recently, however, moral psychology has begun to investigate the automatic, intuitive basis of moral judgments, from emotional reactions such as disgust (Haidt, 2001) to automatic causalÂ�intentionality assessments (Hauser, 2006). For instance, Wheatley and Haidt (2005) hypnotically associated disgust with certain words (an association of which the participants had no conscious recollection) and experimentally showed that it made moral judgments of even innocuous actions more severe. Research using explicit measures has shown that moral values and judgments vary systematically across the ideological spectrum (Feather, 1979; Graham, Haidt, & Nosek, 2009), and it appears that there are ideological differences in implicitly measured moral values. For instance, McAdams and colleagues (2008) coded the personal narratives of liberal and conservative Christians (thought to reveal unconscious motives and belief structures; McAdams, 2001) and found differences in moral content predicted by moral foundations theory (Haidt & Graham, 2007). Liberal narratives expressed more concerns about compassion and fairness, while conservative narratives expressed more concerns about ingroup loyalty, respect for authority and traditions, and physical and spiritual purity. Using an IAT to measure implicit political identity (strength of associations between liberal–Â�conservative and self–other), Graham and colleagues (2009) found that it predicted moral judgments beyond that accounted for by explicit political identity, suggesting ideological variance in moral values not captured by self-Â�reported ideology (even though implicit and explicit ideology correlated strongly, r ~ .6). Also, Lindner and Nosek (2009) found that implicit political identity predicted participants’ desire to protect controversial speech. Implicit liberals were more likely than implicit conservatives to protect controversial speech acts, especially controversial left-wing rather than right-wing speech. Finally, Henry and Reyna (2007) suggested that political attitudes and positions could be influenced by values, specifically via perceptions of value violations



29. Implicit Political Cognition

by groups like welfare recipients (lazy, cheaters) or gay people (hedonistic, deviant). Such perceptions of value violations may occur spontaneously, without deliberate reflection or even awareness on the part of the perceiver. Huckfeldt, Levine, Morgan, and Sprague (1999) found that ideological orientations had greater political utility (predicting issue positions, candidate evaluations, and persuasiveness) among people for whom partisan and ideological identifiers were more accessible (i.e., participants who were faster at self-�identifying their political orientation). This suggests that implicit measures such as response latencies could be useful not just in revealing ideological differences not seen previously but in identifying those for whom ideological distinctions are more or less predictive of political opinions and behavior. In the next section, we turn to political opinions and attitudes to examine the unique contributions of implicit methods in this area.

PUBLIC OPINION AND POLICY ATTITUDES Accessibility Implicit social cognition could be considered the progeny of research on attitude accessibility. Attitude accessibility is the likelihood that an attitude will be activated from memory upon exposure to the attitude object (Fazio, 1986, 1989). A popular means of assessing accessibility is measuring the speed with which attitude objects are evaluated. Rapid evaluation is taken as an indicator that the attitude is more accessible in memory and, perhaps, is evoked automatically. Respondents do not control how quickly information is recalled from memory, even when they initiate information search deliberately. As such, research on attitude accessibility directly preceded and influenced the emergent research literature on the automatic components of social evaluation. Research on accessibility of political attitudes suggests that more accessible policy attitudes are more likely to be seen as more important to the individual and that those accessible attitudes will be more influential in driving policy judgment (Lavine, Borgida, Sullivan, & Thomsen, 1996). Furthermore, political actors high in attitude involvement may pursue selective information processing that makes a particular policy evaluation more accessible and likely to drive subsequent judgment and decision making (Lavine, Borgida, & Sullivan, 2000). Finally, demonstrating the rel-

553

evance of accessibility in political behavior, Fazio and Williams (1986) found that candidate attitudes predicted voting behavior in the 1984 election more strongly among participants with more accessible attitudes compared with those with less accessible attitudes. In sum, to the extent that particular attitudes are accessed more quickly than others, they appear to have greater influence on political thinking and behavior.

Automaticity in Political Attitudes and€Opinions Following the work on accessibility, a key initial finding in implicit social cognition was that evaluations can be activated automatically after exposure to an attitude object (Fazio, Sanbonmatsu, Powell, & Kardes, 1986). Later theory and evidence suggested that automatic evaluation occurs for all concepts to varying degrees (Bargh, Chaiken, Govender, & Pratto, 1992), even novel concepts (Duckworth, Bargh, Garcia, & Chaiken, 2002). Morris, Squires, Taber, and Lodge (2003) used event-�related potentials to show that political concepts are evaluated automatically. Furthermore, there is evidence that goals and attitudes toward goals can be activated automatically, including ideological goals like egalitarianism (Ferguson, 2007), and that these may be influential in political judgment (Lieberman, Schreiber, & Ochsner, 2003). Lodge and Taber (2005) used sequential priming methods to demonstrate that attitudes toward political leaders, groups, and issues could be elicited automatically, and that these effects were strongest among political sophisticates. Finally, priming mindsets that are related to ideology can shift political positions. For example, priming a mindset of hard work leads people to support more conservative political policies than does priming a mindset of good fortune (Bryan, Dweck, Ross, Kay, & Mislavsky, 2009). As such, political judgment will be influenced by automatic processes to the extent that an individual is effective or ineffective at self-�regulating those implicit evaluations.

Party over Policy Early in this chapter, we suggested that ideology may operate as a framework through which information is organized and interpreted, and that this may occur implicitly. Cohen (2003), for example, presented U.S. participants with two policy alternatives for welfare—one generous, one strict—Â�

554

V. APPLIED PERSPECTIVES

offered by members of different parties—one by Republicans, the other by Democrats. Cohen observed that party identification significantly predicted participants’ support for the welfare plans such that the respondents tended to prefer the plan offered by their party, regardless of policy content. Smith, Ranganath, and Nosek (2008) replicated this effect with an implicit measure of liking for the two proposals. Implicit evaluations toward the proposals shifted in line with the participant’s political affiliation, and this implicit evaluation completely mediated the effect of the manipulation on explicit judgments of the proposals. Furthermore, participants largely denied that the political affiliation of the policy proposers influenced their judgment. This lack of awareness, paired with mediation of the ideology–Â�explicit judgment link by implicit evaluations, suggests that the influence of party affiliation on liking for the policy was automatic. Evaluation of the policy details could not be disentangled from the knowledge of who proposed it.

Affect–Â�Cognition and Dual-Â�Process Models Perhaps the most obvious link for implicit social cognition in politics follows the discovery that political judgment is not just a cool, deliberate process. Politicians and political operatives have long understood that hitting an emotional chord is as good as or better than a reasoned argument for gaining policy or candidate support. However, during the heyday of rational choice models, political theories seemed rather naive to this possibility. Modern conceptualizations recognize that affect and emotion play a substantial role in political judgment (e.g., Glaser & Salovey, 1998; Marcus, Sullivan, Theiss-Morse, & Stevens, 2005). Although primarily rooted in a theoretical distinction between affect and cognition, most dual-Â�process treatments of political judgment (Knutson, Wood, & Spampinato, 2006; Way & Masters, 1996; Westen, Blagov, & Harenski, 2006) bear more than a passing resemblance to the wide variety of dual-Â�process theories that are popular in implicit social cognition (Chaiken & Trope, 1999; Gawronski & Bodenhausen, 2006; Strack & Deutsch, 2004), with affect roughly corresponding to implicit or associative processes and cognition roughly corresponding to explicit or propositional processes. As with other domains, the evidence suggests that political judgment is influenced both by automatic processes outside of awareness or

control and deliberate processes that reflect the person’s intentions and explicit goals.

GROUPS, NATIONS, AND€INTERGROUP RELATIONS The study of intergroup relations and the social cognitive processes involved (e.g., social categorization, stereotyping, ingroup bias, outgroup prejudice, and discriminatory behavior) has been the primary point of application for the methods and theories of implicit social cognition (see Brewer, 2007, and Trawalter & Shapiro, Chapter 20, this volume, for reviews). Implicit social cognition first developed to help explain how individuals may hold egalitarian explicit attitudes and beliefs but nonetheless demonstrate intergroup biases in behavior. Conflicts between national and ethnic groups have also been a central locus of study for political psychology, with a wealth of theories (e.g., authoritarian personality, realistic conflict, social identity, social dominance, system justification) arising to account for the reasons and processes behind intergroup hostilities (Jost & Sidanius, 2004). In this section, we review the points of overlap for these two research programs, in which implicit measures and methods have been used by psychologists to understand politically relevant intergroup processes. Until recently, international relations and foreign policy decision making were usually treated as deliberate processes that are measurable by self�reported opinions and reflections on the decision�making context. However, the subtle contributions of social cognitive factors are being recognized here as well. For example, McGraw and Dolan (2007) suggest that political decision makers are likely to personify (anthropomorphize) nations and states because, as abstract entities, they are too difficult to understand without some form of embodiment. In their research, treating political states as people facilitated automatic, online processing and produced stronger attitudes about the state, relative to conceptualizing the state more abstractly as a parliamentary institution. The social cognitive strategy of personification may be a simplifying heuristic that assists decision-maker reasoning, even if it has attendant costs in misrepresenting the nature of the concept (i.e., a state is not a person). Another important social cognitive process that contributes to intergroup relations is ingroup favoritism. Preferences for the ingroup over outgroups are pervasive in both explicit and implicit



29. Implicit Political Cognition

evaluations (Brewer, 2007; Nosek, Smyth, et al., 2007; Nosek et al., 2009; Tajfel & Turner, 1986). However, there are important exceptions to this general trend. Socially devalued groups tend to be disliked compared with high-Â�status groups implicitly, even among members of the lower status group (Jost et al., 2004; Nosek et al., 2009). For example, African Americans show little to no implicit ingroup preference for blacks compared with whites on a race IAT, even though they self-Â�report such a preference (Jost et al., 2004; Nosek, Smyth, et al., 2007); overweight people show an implicit preference for thin people on average (Schwartz, Vartanian, Nosek, & Brownell, 2006); a high proportion of gay people implicitly prefer straight people (Jost et al., 2004); and old people show an implicit preference for young people that is as strong as young people’s implicit preference (Jost et al., 2004; Nosek, Smyth, et al., 2007). Livingston (2002) showed that African American participants’ implicit ingroup preference (measured by both a race IAT and an affective priming task) was weaker among those who perceived more pervasive cultural prejudice by whites against blacks than those that perceived less cultural prejudice by whites. The opposite relation was observed with explicit ingroup preferences. This suggests that even when lower status group members explicitly react against perceived prejudice from the high-Â�status outgroup, implicitly they may internalize it. System justification theory (Jost & Banaji, 1994; Jost et al., 2004) anticipates the general motivation to prefer ingroups as well as the frequent implicit preference for an outgroup among lower status group members. The theory posits that, in addition to self-Â�justifying and group-Â�justifying needs, people have an automatic (and often nonconscious) need to justify the status quo social system, even if that system oppresses their group. Thus, for a low-Â�status or disadvantaged group member, the theory predicts implicit outgroup favoritism. Such motivations and implicit outgroup preferences provide a cognitive impediment to political behaviors that would upend the existing system in order to redress potential grievances of disadvantaged groups. The implicit preference for high-Â�status versus low-Â�status social groups observed across a wide variety of social groups (Nosek, Smyth, et al., 2007) is stronger for those on the right ideologically versus those on the left (Jost et al., 2008; Nosek et al., 2009). The implicit outgroup favoritism explained by system justification theory also interacts with situational variables to predict behavior. For instance, black participants who showed an implicit preference for

555

whites were more likely to prefer a white partner versus a black partner for a task, but only if the task was in a domain stereotypically assigned a white advantage (e.g., intellect; Ashburn-Nardo & Johnson, 2008). Belief in a just-world theory, a theoretical antecedent of system justification, posits that people have an implicit desire to maintain belief in the inherent justice of the world and current social structure (Lerner, 1980). Using a modified Stroop task, Hafer (2000) found that participants were slower on justice words (relative to neutral words) after exposure to a story about an innocent victim, suggesting threat to their just-world beliefs. More recently, this effect has been shown to occur when the victim is an ingroup member but not when the victim is an outgroup member (Aguiar, Vala, Correia, & Pereira, 2008), suggesting that ingroup status moderates the degree to which another’s suffering impacts implicit concerns about the justice of the world. Van den Bos and Maas (2009) further demonstrate that innocent-Â�victim derogation can occur even in the absence of explicitly held beliefs in a just world. Participants primed with an “experiential” mindset blamed an innocent victim of robbery or assault regardless of their just-world beliefs, whereas only participants who believed in a just world blamed the victim when they were primed with a “rational” mindset. The authors argued that the experiential mindset encourages formation of associative relations between the victim and the negative event that are not deliberated processed and evaluated for logical consistency. Implicit attitudes toward social groups can predict opinions on political issues and voting intentions. Craemer (2008) developed an implicit measure of closeness to African Americans based on reaction times to trait ratings for self, African Americans, and whites, and found that this measure predicted support for pro-black policies even when controlling for explicit closeness feelings, ideological preferences, and racial group membership. Racial attitudes can also be activated nonconsciously and affect policy decisions. In a representative survey of white Americans, Hurwitz and Peffley (2005) gauged support for spending money on prisons “to lock up violent criminals.” half the time inserting “inner-city” between “violent” and “criminals” The authors presumed that the influence of the racialized connotations of this prime occurred outside conscious awareness, even though subjects in the prime condition were obviously conscious of the phrase itself. They found that the insertion of this racialized code word

556

V. APPLIED PERSPECTIVES

made whites with negative attitudes toward African Americans more likely to support prison funding; racial attitudes predicted policy support only in the “inner-city” condition. Intergroup contact, specifically exposure to admired outgroup members and disliked ingroup members, has been shown to decrease implicit bias for the ingroup and against the outgroup (Dasgupta & Greenwald, 2001; Joy-Gaba & Nosek, in press). Variations of this intervention have been applied to the changing of behavioral intentions in the context of policy positions. For example, Dasgupta and Rivera (2008) found that a short intervention (exposure to admired gay and lesbian exemplars) weakened implicit preferences for straight over gay people and voting intentions to define marriage as a strictly heterosexual institution. However, the effects on voting intentions were not mediated by implicit attitudes, suggesting separate mechanisms behind changes in implicit attitudes and explicit behavioral intentions. In the next two sections, we focus more specifically on the connections and disconnections among implicit attitudes, candidate evaluations, and actual voting behaviors.

CANDIDATE AND PARTY PREFERENCES AND PERCEPTIONS Popular models of candidate perceptions suggest that citizens accumulate information about candidates and store it in memory. When later evaluating a candidate, according to this view, citizens retrieve the stored information and make a summary evaluation (Kelley & Mirer, 1974). Hastie and Park (1986) offered an alternative “online” theory of judgment and decision making that has been applied to impression formation of political candidates. Applied to politics (Lodge, McGraw, & Stroh, 1989), the online model of information processing suggests that citizens spontaneously evaluate information upon encountering it and update a “running tally” of the individual being evaluated. That is, evaluations of candidates are not made at a single instant based on nonvalenced stored information; evaluations are constantly being made as the information is stored, constantly updating the overall evaluation of a candidate or party. Although the online theory is not couched in the language of automaticity, the conceptualization and use of terms such as spontaneous, cognitively efficacious, and bounded rationalists (Lodge et al., 1989) imply automatic processes at work (Bargh,

1994). In this way, the online model of information processing can be seen as a building block for the study of implicit political cognition for candidate and party preferences. During political elections, the goal of many citizens is to acquire information about a candidate for the purposes of evaluating him or her. Lodge and colleagues (1989) argued that the online information-Â�processing model is most likely to explain candidate evaluations when these impression formation goals are present. Thus, a tenet of the online model, contra-Â�rational memory models, holds that information recall is not necessary to make an evaluation. This is supported by Lodge et al.’s findings that when people are encouraged to process information with the goal of forming an impression of a political candidate, memory for information about the candidate was unrelated to evaluations of the candidate. However, the final candidate evaluation was related to evaluations made while information was being processed. The “running tally” stores the evaluation of the information, even though the information itself is largely forgotten (Lodge et al., 1989). Similarly, Lodge, Steenbergen, and Brau (1995) demonstrated that message content in a mock election affected later candidate evaluations despite very little recall of the actual details of the message. These findings call traditional memory models into question because citizens are able to report only little knowledge about campaign information. Rather, candidate preferences are informed by stored evaluations, suggesting a more associative process at work. It may not even be necessary to have an explicit impression formation goal for the online evaluation of candidates to occur. When exposed to information about political candidates, German undergraduates readily formed both explicit and implicit evaluations of the candidate. Participants were told the study was about grammar in political statements and were given no indication that they would later be evaluating the candidates, yet they evaluated them all the same (Plessner, Betsch, Schallies, & Schwieren, 2008).

Priming Priming is a common measure and manipulation for detecting the role of implicit processes in judgment. Weinberger and Westen (2008) demonstrated that candidate evaluations can be influenced through priming. They were inspired by a controversial television advertisement aired by



29. Implicit Political Cognition

George Bush’s campaign in the 2000 U.S. presidential election that briefly highlighted the RATS in Democrats (Berke, 2000). Presenting the word RATS subliminally immediately before a photo of a fictitious politician led to lower evaluations of the politician compared with control primes. They also applied this methodology to a real-world setting in the 2003 California recall election of Governor Gray Davis: Presenting a photo of Bill Clinton before a photo of Gray Davis led to differential partisan evaluations of Governor Davis. Republicans, who were presumably unfavorable toward Clinton, evaluated Davis more negatively when primed with Clinton, while Democrats and Independents, who were presumably favorable toward Clinton, evaluated Davis less negatively when primed with Clinton (Weinberger & Westen, 2008). Krosnick and Kinder (2004) investigated the priming effects of an actual political event: the Iran-Â�Contra controversy during the Reagan administration. Using correlational data from the American National Election Studies, they separated the data into a precontroversy group and a postcontroversy group. Although the assignment was not random, the two groups were similar on several political and demographic factors. The authors hypothesized that the Iran-Â�Contra controversy and subsequent news coverage would be salient in citizens’ minds and would prime relevant national security concerns and influence judgments about President Reagan. They found that political novices, more than political experts, were affected by exposure to the event. Postcontroversy novices expressed much lower presidential approval ratings compared with precontroversy novices, and their approval ratings appeared to be more strongly influenced by their related national security judgments (Krosnick & Kinder, 2004). When paired with Weinberger and Westen’s (2008) empirical priming evidence, these findings suggest that candidate evaluations can be differentially affected by primed social information, even outside of conscious awareness.

Other Implicit Processes in Candidate/Party Perceptions Physical appearance, a seemingly meaningless political dimension, has been found to influence candidate evaluation. Todorov, Mandisodza, Goren, and Hall (2005) demonstrated that perceived competence of candidates’ photos predicted U.S. congressional winners in the 2000, 2002, and

557

2004 elections. Participants were given photos of two candidates’ faces and quickly chose (in a little more than a second) which one was more competent. These rapid judgments of competence predicted the winner of the Senate election more than two-Â�thirds of the time (Todorov et al., 2005). Physical similarity between candidate and perceiver faces also affects evaluation. Bailenson, Garland, Iyengar, and Yee (2006) digitally morphed a fictitious politician’s face to be similar to participants’ faces, and few participants noticed this manipulation. Male participants evaluated the male politician more positively when viewing the morphed version of his photo (Bailenson et al., 2006). This suggests that people are sensitive to self-Â�similarity in faces and, at least for males, they tend to prefer candidates who look more like themselves. The valence of political campaign messages has been shown to differentially affect implicit and explicit evaluations. Carraro, Gawronski, and Castelli (in press) presented participants with negative and positive campaign statements allegedly spoken by political candidates. Compared with positive campaign messages, negative messages produced more negative explicit evaluations of the source candidate, the one who delivered the message, but did not affect explicit evaluations of the targeted opponent. However, negative campaign messages produced negative implicit evaluations for both the source candidate and the opponent. The authors suggested that explicit evaluations are a product of propositional processing, so when confronted with a negative campaign message about an opponent candidate participants were able to discount the negativity. However, because implicit evaluations are a product of associative processing, the negativity of the campaign message influenced the implicit evaluation of the opponent candidate (Carraro et al., in press). Such associations, although resisted explicitly at first, may eventually come to influence explicit judgment as memory for the source of the negative association fades (Ranganath & Nosek, 2008). Automaticity has also been implicated in the use of partisan schemas for forming impressions of candidates. Lodge and Hamill (1986) examined the effect of partisan schemas on the processing and recall of information about a candidate. They found that individuals who were politically sophisticated (i.e., had high political knowledge and interest) were better able to recognize political statements they had previously read about a fictional candidate. However, political sophisticates were

558

V. APPLIED PERSPECTIVES

not as good at remembering statements that were inconsistent with the candidate’s partisan view. Lodge and Hamill concluded that such people have a memory structure that allows them to automatically encode and retrieve information more easily when it is consistent along partisan lines. Although these findings suggest that implicit social cognition processes can have an effect on politically relevant evaluations and attitudes, for many this is still only an “intrapsychic” phenomenon and not necessarily an indication that implicit social cognition is relevant for political behavior.

VOTING AND OTHER POLITICAL€BEHAVIOR Perhaps the most compelling way for implicit social cognition to establish its relevance to the study of politics is to enhance researchers’ ability to predict political behavior. Although there are many varieties of political behavior, the one that demands the greatest attention from academics, pollsters, politicians, and the public is voting. Voting is a wonderful behavior for academic researchers because it can be simulated effectively in the laboratory and its real-world manifestations are startlingly convenient: The situational circumstances are relatively controlled, everyone follows the procedural rules, a large percentage of the general public does it, and the data are all collected on the same day. (To the public, the relatively recent option of absentee voting might be welcome and popular, but for the researchers it is a disruption to a perfectly good system of measurement.) Long before social cognition emerged as a field of study, some researchers recognized the powerful difference between persuading the “head” versus the “gut.” Hartmann (1936) cleverly manipulated campaign message content across different wards in a Pennsylvania city. Two campaign pamphlets were created in support of the Socialist Party for the 1935 local election. One pamphlet used a rational appeal and featured an “intelligence test” that framed Socialist answers as the more intelligent choices. A second pamphlet used an emotional appeal and featured a sentimental letter written by “Your Sons and Daughters” pleading for a socialist future. Compared with the previous election, votes for the Socialists increased more with the emotional appeals than with the rational appeals and the no-Â�appeals control. This presaged the modern understanding that political behavior

is subject to both affective and reasoned processes, a close cousin to dual-�process models of implicit and explicit cognition (Chaiken & Trope, 1999; Gawronski & Bodenhausen, 2006).

Accessibility and Voting Although reaction time-based implicit measures did not gain significant popularity until the 1990s, Fazio and Williams (1986) used response latency methodology to predict voting in the 1984 presidential election. The summer before the election, community members reported their attitudes toward the presidential candidates (Ronald Reagan and Walter Mondale) by pressing a key to indicate their agreement to tape-Â�recorded statements. The response latency from the completion of the statement until the button press was used as a measure of attitude accessibility. As discussed in the earlier section, the accessibility of participants’ attitudes toward the candidates, measured months before Election Day, moderated the relationship between their attitudes and their Election Day vote. Participants with highly accessible candidate attitudes showed higher attitude–Â�behavior correlations compared with participants with less accessible attitudes. Bassili (1995) modified Fazio and Williams’s paradigm for use with computerÂ�assisted telephone surveys. He found that the response latency of voting intentions moderated the relationship between voting intention and voting behavior in the 1990 Ontario provincial election, in that those with faster latencies showed a stronger relationship between voting intention and actual vote. This was some of the first evidence that social cognitive factors that may operate automatically could help predict elections and perhaps close the occasional gap between expressed voting intentions and actual voting behavior.

Voting Behavior Political parties, media outlets, and other groups spend millions of dollars to predict the outcome of elections. Accurate knowledge of candidate and policy preferences is invaluable information for government officials, campaign managers, and lawmakers. Although the technology and creativity of survey tools used to predict election outcomes have advanced tremendously in past years, self-�report methodology is limited by the interest and ability of respondents to report the factors that will predict their ultimate behavior. For those who do express explicit preferences, can implicit measures offer any additional predictive value?



29. Implicit Political Cognition

In the 2001 Italian general election, Arcuri, Castelli, Galdi, Zogmaister, and Amadori (2008) demonstrated that implicit candidate preferences measured with the IAT predicted the voting behavior of decided voters. Friese, Bluemke, and Wänke (2007) measured implicit political party preferences with Single-Target IATs in the 2002 German Parliamentary election. Implicit attitudes toward the five parties (measured 3 months before the election) predicted voting behavior over and above explicit party preference. Undoubtedly, selfÂ�reported preferences are predictive of voting behavior and election outcomes, but implicit preferences appear to provide added predictive value (but see Karpinski, Steinman, & Hilton, 2005, for an example in which implicit measures did not predict voting after controlling for explicit evaluations). This is quite stunning considering that voting behavior appears to be a “very explicit” behavior in that voters have plenty of time to deliberate, often have highly elaborated opinions, and have plenty of time to plan and implement their intention.

Undecideds The most obvious challenge to predicting voting behavior is when voters are unable to express an intention because they have not yet decided. Prediction errors in election outcomes are influenced by the challenge of predicting whether and how undecided citizens will vote (Visser, Krosnick, Marquette, & Curtin, 2000). Although undecided voters may be explicitly uncommitted, they may have implicit preferences that should bias subsequent information search as they attempt to resolve the indecision or provide a nudge toward one position over the other when making a decision. Arcuri and colleagues (2008) identified a relationship between implicit candidate attitudes and voting behavior for undecided voters. A month before a 2005 local election in northern Italy, undecided voters completed an IAT measuring their implicit attitudes toward the candidates and after the election reported their actual vote. The previously undecided voters who later chose a candidate had implicit preferences for their candidates a month before the election despite being unable (or unwilling) to report an explicit favoring for either candidate. Implicit and explicit attitudes may differentially predict policy preferences. Galdi, Arcuri, and Gawronski (2008) measured implicit and explicit attitudes toward the expansion of a U.S. military base in Italy, and directly asked participants whether they would choose (in the future) to

559

support the policy. For decided participants, selfÂ�reported attitudes predicted future choice better than implicit attitudes. However, undecided participants’ implicit attitudes predicted their future policy choice better than their self-Â�reported attitudes. Additionally, initial implicit attitudes predicted later self-Â�reported attitudes for undecided participants but not for decided participants. This suggests that some undecided voters had implicitly “decided” before they became aware of their decision.

Contextual Cues Despite such evidence of voting decisions made weeks before the election, there is also evidence that contextual cues of voting locations can have a nonconscious effect on voting behavior. Berger, Meredith, and Wheeler (2008) showed that the kind of venue Americans are assigned to vote in (e.g., church, school) influence voting decisions: Namely, those voting at a school were more likely to support funding for schools, even controlling for political ideology and other demographic variables. A follow-up experiment suggested that these contextual effects were due to nonconscious priming from the environment around the voting booth. This suggests that, even among “decideds,” the social and situational context may have an unanticipated influence on what actually occurs in the voting booth.

CONCLUSION In its first decade of substantial activity, implicit political cognition research has established that its methods and measures can add predictive utility to understanding political thought and behavior. Because assumptions of deliberation and intent are so enmeshed with politics, the maturation of models that explain how automatic and controlled processes interact to shape behavior will have an especially strong impact on theories relevant to politics, building on the existing interest in affective influences on political behavior. The hope that the political mind could rise above the ordinary mind (Marcus, 2003) may finally have given way to the gloomy reality that political thinking is just like all other thinking: Political actors are influenced by information that they do not recognize or might even resist were they aware of or able to control its influence. This, of course, is not to say that explicit thinking is irrelevant. Deliberate, intentional overriding of au-

560

V. APPLIED PERSPECTIVES

tomatic responses is an achievement—a hallmark of the human ability to self-Â�regulate—and one that characterizes the ongoing interplay between the mind’s automatic and controlled processes. Politics, as a system of social governance, aims to provide self-Â�regulatory constraints through structural mechanisms outside of individual minds. Passing through its infancy into a mature contributor to political psychology, implicit social cognition research may assist in demonstrating how political systems shape and are shaped by the minds of the polity. Acknowledgments The research described in this chapter was supported by a grant from the National Science Foundation (No. REC-0634041) to Brian Nosek. We thank Selin Kesebir for comments on an earlier draft.

Notes 1. There are a variety of uses of the terms implicit and explicit for describing procedures, measures, and constructs. Here we take a theory-Â�uncommitted conceptualization of a measure as implicit “if it does not require awareness of the relations of the attribute to the response” (Nosek & Greenwald, 2009, pp.€374), and that the resulting effects are mental contents that respondents might be unwilling or unable to report because they actively disagree with their validity, do not want others to know about them, or are not aware that they possess them. These definitions avoid particular commitments to the processes that underlie the implicit–Â�explicit distinction and allow a more inclusive review of what evidence can be considered relevant for implicit political cognition. Other chapters of this Handbook address the challenging questions of parsing the components of what makes something implicit. 2. Note that this is not to imply that using implicit cognition, or even heuristics, is necessarily irrational. Rather, the research on heuristics and implicit cognition suggests that the potential rationality of an event is less in its process (a deliberate, logical evaluation of the claims and evidence) and more in its outcome (an ultimate judgment that is defensible regardless of how it was generated).

References Adorno, T. W., Frenkel-�Brunswik, E., Levinson, D. J., & Sanford, R. N. (1950). The authoritarian personality. New York: Harper.

Aguiar, P., Vala, J., Correia, I., & Pereira, C. (2008). Justice in our world and in that of others: Belief in a just world and reactions to victims. Social Justice Research, 21, 50–68. Altemeyer, B. (1998). The other “authoritarian personality.” Advances in Experimental Social Psychology, 30, 47–92. Arcuri, L., Castelli, L., Galdi, S., Zogmaister, C., & Amadori, A. (2008). Predicting the vote: Implicit attitudes as predictors of the future behavior of decided and undecided voters. Political Psychology, 29, 369–387. Arendt, H. (1994). Eichmann in Jerusalem: A report on the banality of evil. New York: Penguin Books. (Original work published 1963) Aron, R. (1968). The end of the ideological age? In C. I. Waxman (Ed.), The end of ideology debate (pp.€27– 48). New York: Simon & Schuster. (Original work published 1957) Ashburn-Nardo, L., & Johnson, N. J. (2008). Implicit outgroup favoritism and intergroup judgment: The moderating role of stereotypic context. Social Justice Research, 21, 490–508. Bailenson, J. N., Garland, P., Iyengar, S., & Yee, N. (2006). Transformed facial similarity as a political cue: A preliminary investigation. Political Psychology, 27, 373–385. Bargh, J. A. (1994). The four horsemen of automaticity: Awareness, intention, efficiency, and control in social cognition. In R. S. Wyer, Jr. & T. K. Srull (Eds.), Handbook of social cognition: Vol. 1. Basic processes (2nd ed., pp.€1–40). Hillsdale, NJ: Erlbaum. Bargh, J. A., Chaiken, S., Govender, R., & Pratto, F. (1992). The generality of the automatic attitude activation effect. Journal of Personality and Social Psychology, 62, 893–912. Bassili, J. N. (1995). Response latency and the accessibility of voting intentions: What contributes to accessibility and how it affects vote choice. Personality and Social Psychology Bulletin, 21, 686–695. Bell, D. (1960). The end of ideology. Glencoe, IL: Free Press. Benedict, R. (1946). The chrysanthemum and the sword. Boston: Houghton Mifflin. Berger, J., Meredith, M., & Wheeler, S. C. (2008). Contextual priming: Where people vote affects how they vote. Proceedings of the National Academy of Sciences USA, 105, 8846–8849. Berke, R. L. (2000, September 12). The 2000 campaign: The ad campaign: Democrats see, and smell, rats in G.O.P. ad. New York Times, p.€1. Billig, M. (1995). Banal nationalism. London: Sage. Blair, I. V. (2002). The malleability of automatic stereotypes and prejudice. Personality and Social Psychology Review, 6, 242–261. Bonanno, G. A., & Jost, J. T. (2006). Conservative shift among high-Â�exposure survivors of the September 11th terrorist attacks. Basic and Applied Social Psychology, 28, 311–323. Brewer, M. B. (2007). The social psychology of intergroup relations: Social categorization, ingroup bias, and outgroup prejudice. In A. W. Kruglanski & E. T. Higgins (Eds.), Social psychology: Handbook of basic principles (2nd ed., pp.€695–715). New York: Guilford Press.



29. Implicit Political Cognition

Bryan, C. J., Dweck, C. S., Ross, L., Kay, A. C., & Mislavsky, N. O. (2009). Political mindset: Effects of schema priming on liberal-Â�conservative political positions. Journal of Experimental Social Psychology, 45, 890–895. Burdein, I., Lodge, M., & Taber, C. (2006). Experiments on the automaticity of political beliefs and attitudes. Political Psychology, 27, 359–371. Carney, D. R., Jost, J. T., Gosling, S. D., & Potter, J. (2008). The secret lives of liberals and conservatives: Personality profiles, interaction styles, and the things they leave behind. Political Psychology, 29, 807–840. Carraro, L., Gawronski, B., & Castelli, L. (in press). Losing on all fronts: The effects of negative versus positive person-based campaigns on implicit and explicit evaluations of political candidates. British Journal of Social Psychology. Chaiken, S., & Trope, Y. (Eds.). (1999). Dual-Â�process theories in social psychology. New York: Guilford Press. Cohen, G. L. (2003). Party over policy: The dominating impact of group influence on political beliefs. Journal of Personality and Social Psychology, 85, 808–822. Conover, P. J., & Feldman, S. (1981). The origins and meaning of liberal/conservative self-Â�identification. American Journal of Political Science, 25, 617–645. Converse, P. E. (1964). The nature of belief systems in mass publics. In D. Apter (Ed.), Ideology and discontent (pp.€206–261). New York: Free Press. Craemer, T. (2008). Nonconscious feelings of closeness toward African Americans and support for pro-black policies. Political Psychology, 29, 407–436. Dasgupta, N., & Greenwald, A. G. (2001). On the malleability of automatic attitudes: Combating automatic prejudice with images of admired and disliked individuals. Journal of Personality and Social Psychology, 81, 800–814. Dasgupta, N., & Rivera, L. M. (2008). When social context matters: The influence of long-term contact and short-term exposure to admired outgroup members on implicit attitudes and behavioral intentions. Social Cognition, 26, 112–123. Duckworth, K. L., Bargh, J. A., Garcia, M., & Chaiken, S. (2002). The automatic evaluation of novel stimuli. Psychological Science, 13, 513–519. Erikson, E. H. (1950). Childhood and society. New York: WW Norton. Fazio, R. H. (1986). How do attitudes guide behavior? In R. M. Sorrentino & E. T. Higgins (Eds.), The handbook of motivation and cognition: Vol. 1. Foundations of social behavior (pp.€204–243). New York: Guilford Press. Fazio, R. H. (1989). On the power and functionality of attitudes: The role of attitude accessibility. In A. R. Pratkanis, S. J. Breckler, & A. G. Greenwald (Eds.), Attitude structure and function (pp.€ 153–179). New York: Erlbaum. Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50, 229–238. Fazio, R. H., & Williams, C. J. (1986). Attitude accessibility as a moderator of the attitude-Â�perception and attitude-Â�behavior relations: An investigation of the 1984 presidential election. Journal of Personality and Social Psychology, 51, 505–514.

561

Feather, N. T. (1979). Value correlates of conservatism. Journal of Personality and Social Psychology, 37, 1617– 1630. Feldman, S. (2003). Values, ideology, and the structure of political attitudes. In D. O. Sears, L. Huddy, & R. Jervis (Eds.), Oxford handbook of political psychology (pp.€477–508). Oxford, UK: Oxford University Press. Ferguson, M. J. (2007). On the automatic evaluation of end-Â�states. Journal of Personality and Social Psychology, 92, 596–611. Ferguson, M. J., Carter, T. C., & Hassin, R. R. (2009). On the automaticity of nationalist ideology: The case of the USA. In J. T. Jost, A. C. Kay, & H. Thorisdottir (Eds.), Social and psychological bases of ideology and system justification (pp.€ 53–82). New York: Guilford Press. Ferguson, M. J., & Hassin, R. R. (2007). On the automatic association between America and aggression in news-Â�watchers. Personality and Social Psychology Bulletin, 33, 1632–1647. Friese, M., Bluemke, M., & Wänke, M. (2007). Predicting voting behavior with implicit attitude measures: The 2002 German parliamentary election. Experimental Psychology, 54, 247–255. Fromm, E. (1992). The anatomy of human destructiveness. New York: Macmillan. (Original work published 1973) Galdi, S., Arcuri, L., & Gawronski, B. (2008). Automatic mental associations predict future choices of€Â�undecided decision-Â�makers. Science, 321, 1100– 1102. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731. George, A. L., & George, J. L. (1956). Woodrow Wilson and Colonel House: A personality study. New York: John Day. Glaser, J., & Salovey, P. (1998). Affect in electoral politics. Personality and Social Psychology Review, 2, 156–172. Graham, J., Haidt, J., & Nosek, B. A. (2009). Liberals and conservatives rely on different sets of moral foundations. Journal of Personality and Social Psychology, 96, 1029–1046. Green, D. P., & Shapiro, I. (1994). Pathologies of rational choice: A critique of applications in political science. New Haven, CT: Yale University Press. Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-Â�esteem, and stereotypes. Psychological Review, 102, 4–27. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. Hafer, C. L. (2000). Do innocent victims threaten the belief in a just world?: Evidence from a modified Stroop task. Journal of Personality and Social Psychology, 79, 165–173. Haidt, J. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral judgment. Psychological Review, 108, 814–834. Haidt, J., & Graham, J. (2007). When morality opposes justice: Conservatives have moral intuitions that lib-

562

V. APPLIED PERSPECTIVES

erals may not recognize. Social Justice Research, 20, 98–116. Hartmann, G. W. (1936). A field experiment on the comparative effectiveness of “emotional” and “rational” political leaflets in determining election results. Journal of Abnormal and Social Psychology, 31, 99–114. Hastie, R., & Park, B. (1986). The relationship between memory and judgment depends on whether the task is memory-based or on-line. Psychological Review, 93, 258–268. Hauser, M. D. (2006). The liver and the moral organ. Social Cognitive and Affective Neuroscience, 1, 214–220. Henry, P. J., & Reyna, C. (2007). Value judgments: The impact of perceived value violations on American political attitudes. Political Psychology, 28, 273–298. Huckfeldt, R., Levine, J., Morgan, W., & Sprague, J. (1999). Accessibility and the political utility of partisan and ideological orientations. American Journal of Political Science, 43, 888–911. Hurwitz, J., & Peffley, M. (2005). Playing the race card in the post-Â�Willie Horton era: The impact of racialized code words on support for punitive crime policy. Public Opinion Quarterly, 69, 99–112. Jacobellis v. Ohio, 378 U.S. 184 (1964). Jost, J. T. (2006). The end of the end of ideology. American Psychologist, 61, 651–670. Jost, J. T., & Banaji, M. R. (1994). The role of stereotyping in system-Â�justification and the production of false consciousness. British Journal of Social Psychology, 33, 1–27. Jost, J. T., Banaji, M. R., & Nosek, B. A. (2004). A decade of system justification theory: Accumulated evidence of conscious and unconscious bolstering of the status quo. Political Psychology, 25, 881–919. Jost, J. T., Glaser, J., Kruglanski, A. W., & Sulloway, F. (2003). Political conservatism as motivated social cognition. Psychological Bulletin, 129, 339–375. Jost, J. T., Nosek, B. A., & Gosling, S. D. (2008). Ideology: Its resurgence in social, personality, and political psychology. Perspectives on Psychological Science, 3, 126–136. Jost, J. T., & Sidanius, J. (2004). Political psychology: An introduction. In J. T. Jost & J. Sidanius (Eds.), Political psychology: Key readings (pp.€1–21). New York: Psychology Press. Joy-Gaba, J. A., & Nosek, B. A. (in press). The surprisingly limited malleability of implicit racial evaluations. Social Psychology. Karpinski, A., Steinman, R. B., & Hilton, J. L. (2005). Attitude importance as a moderator of the relationship between implicit and explicit attitude measures. Personality and Social Psychology Bulletin, 31, 949– 962. Kelley, S., & Mirer, T. (1974). The simple act of voting. American Political Science Review, 61, 572–591. Kinder, D. R. (1998). Opinion and action in the realm of politics. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (4th ed., pp.€778–867). New York: Oxford University Press. Knutson, K. M., Wood, J. N., & Spampinato, M. V. (2006). Politics on the brain: An fMRI investigation. Social Neuroscience, 1, 25–40. Kohlberg, L. (1969). Stage and sequence: The cognitive-

Â� developmental approach to socialization. In D. A. Goslin (Ed.), Handbook of socialization theory and research. Chicago: Rand McNally. Kohlberg, L., & Turiel, E. (1971). Moral development and moral education. In G. Lesser (Ed.), Psychology and educational practice. Chicago: Scott Foresman. Krosnick, J. A., & Kinder, D. R. (2004). Altering the foundations of support for the president through priming. In J. T. Jost & J. Sidanius (Eds.), Political psychology: Key readings (pp.€ 150–162). New York: Psychology Press. Landau, M. J., Solomon, S., & Greenberg, J. (2004). Deliver us from evil: The effects of mortality salience and reminders of 9/11 on support for President George W. Bush. Personality and Social Psychology Bulletin, 30, 1136–1150. Larson, D. W. (1994). The role of belief systems and schemas in foreign-Â�policy decision-Â�making. Political Psychology, 15, 17–33. Lavine, H., Borgida, E., & Sullivan, J. L. (2000). On the relationship between attitude involvement and attitude accessibility: Toward a cognitive-Â�motivational model of political information processing. Political Psychology, 21, 81–106. Lavine, H., Borgida, E., Sullivan, J. L., & Thomsen, C. J. (1996). The relationship of national and personal issue salience to attitude accessibility on foreign and domestic policy issues. Political Psychology, 17, 293– 316. Lerner, M. J. (1980). The belief in a just world: A fundamental delusion. New York: Plenum Press. Li, Q., & Brewer, M. B. (2004). What does it mean to be an American?: Patriotism, nationalism, and American identity after 9/11. Political Psychology, 25, 727–739. Lieberman, M. D., Schreiber, D., & Ochsner, K. N. (2003). Is political cognition like riding a bicycle?: How cognitive neuroscience can inform research on political thinking. Political Psychology, 24, 681–704. Lindner, N. M., & Nosek, B. A. (2009). Alienable speech: Ideological variations in the application of free-Â�speech principles. Political Psychology, 30, 67– 92. Lipset, S. (1960). Political man. Garden City, NY: Doubleday. Livingston, R. W. (2002). The role of perceived negativity in the moderation of African Americans’ implicit and explicit racial attitudes. Journal of Experimental Social Psychology, 38, 405–413. Lodge, M., & Hamill, R. (1986). A partisan schema for political information processing. American Political Science Review, 80, 505–519. Lodge, M., McGraw, K. M., & Stroh, P. (1989). An impression-Â�driven model of candidate evaluation. American Political Science Review, 83, 399–419. Lodge, M., Steenbergen, M. R., & Brau, S. (1995). The responsive voter: Campaign information and the dynamics of candidate evaluation. The American Political Science Review, 89, 309–326. Lodge, M., & Taber, C. S. (2005). The automaticity of affect for political leaders, groups, and issues: An experimental test of the hot cognition hypothesis. Political Psychology, 2, 455–482. Marcus, G. E. (2003). The psychology of emotion and



29. Implicit Political Cognition

politics. In D. O. Sears, L. Huddy, & R. Jervis (Eds.), Oxford handbook of political psychology (pp.€182–221). Oxford, UK: Oxford University Press. Marcus, G. E., Sullivan, J. L., Theiss-Morse, E., & Stevens, D. (2005). The emotional foundation of political cognition: The impact of extrinsic anxiety on the formation of political tolerance judgments. Political Psychology, 26, 949–963. McAdams, D. P. (2001). The psychology of life stories. Review of General Psychology, 5, 100–122. McAdams, D. P., Albaugh, M., Farber, E., Daniels, J., Logan, R. L., Olson, B., et al. (2008). Family metaphors and moral intuitions: How conservatives and liberals narrate their lives. Journal of Personality and Social Psychology, 95, 978–990. McGraw, K. M., & Dolan, T. M. (2007). Personifying the state: Consequences for attitude formation. Political Psychology, 28, 299–327. McGuire, W. J. (1993). The poly-psy relationship: Three phases of a long affair. In S. Iyengar & W. J. McGuire (Eds.), Explorations in political psychology (pp.€9–35). Durham, NC: Duke University Press. Mitchell, J. P., Nosek, B. A., & Banaji, M. R. (2003). Contextual variations in implicit evaluation. Journal of Experimental Psychology: General, 132, 455–469. Morris, J. P., Squires, N. K., Taber, C. S., & Lodge, M. (2003). Activation of political attitudes: A psychophysiological examination of the hot cognition hypothesis. Political Psychology, 24, 727–745. Nail, P. R., McGregor, I., Drinkwater, A. E., Steele, G. M., & Thompson, A. W. (2009). Threat causes liberals to think like conservatives. Journal of Experimental Social Psychology, 45, 901–907. Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84, 231–259. Nosek, B. A. (2004, January). Moderators of the relationship between implicit and explicit attitudes. Paper presented at the annual meeting of the Society for Personality and Social Psychology, Austin, TX. Nosek, B. A. (2005). Moderators of the relationship between implicit and explicit evaluation. Journal of Experimental Psychology: General, 134, 565–584. Nosek, B. A. (2007). Implicit-Â�explicit relations. Current Directions in Psychological Science, 16, 65–69. Nosek, B. A., Banaji, M. R., & Jost, J. T. (2009). The politics of intergroup attitudes. In J. T. Jost, A. C. Kay, & H. Thorisdottir (Eds.), The social and psychological bases of ideology and system justification (pp.€480–506). Oxford, UK: Oxford University Press. Nosek, B. A., & Greenwald, A. G. (2009). (Part of) the case for a pragmatic approach to validity: Comment on De Houwer, Teige-Â�Mocigemba, Spruyt, and Moors (2009). Psychological Bulletin, 135, 373–376. Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2007). The Implicit Association Test at age 7: A methodological and conceptual review. In J. A. Bargh (Ed.), Social psychology and the unconscious: The automaticity of higher mental processes (pp.€265–292). New York: Psychology Press. Nosek, B. A., & Hansen, J. J. (2008). The associations in our heads belong to us: Searching for attitudes and knowledge in implicit evaluation. Cognition and Emotion, 22, 553–594.

563

Nosek, B. A., Smyth, F. L., Hansen, J. J., Devos, T., Lindner, N. M., Ranganath, K. A., et al. (2007). Pervasiveness and correlates of implicit attitudes and stereotypes. European Review of Social Psychology, 18, 36–88. Plessner, H., Betsch, T., Schallies, E., & Schwieren, C. (2008). Automatic online formation of implicit attitudes towards politicians as a basis for intuitive voting behavior. In H. Plessner, C. Betsch, & T. Betsch (Eds.), Intuition in judgment and decision making (pp.€107–119). New York: Erlbaum. Pratto, F., Sidanius, J., Stallworth, L. M., & Malle, B. F. (1994). Social dominance orientation: A personality variable predicting social and political attitudes. Journal of Personality and Social Psychology, 67, 741–763. Ranganath, K. A., & Nosek, B. A. (2008). Implicit attitude generalization occurs immediately, explicit attitude generalization takes time. Psychological Science, 19, 249–254. Rokeach, M. (1973). The nature of human values. New York: Free Press. Schwartz, S. H. (1992). Universals in the content and structure of values. In M. P. Zanna (Ed.), Advances in experimental social psychology: Vol. 25 (pp.€1–65). New York: Academic Press. Schwartz, M. B., Vartanian, L. R., Nosek, B. A., & Brownell, K. D. (2006). The influence of one’s own body weight on implicit and explicit anti-fat bias. Obesity, 14, 440–447. Sears, D. O., Huddy, L., & Jervis, R. (2003). Oxford handbook on political psychology. Oxford, UK: Oxford University Press. Shils, E. A. (1968). The end of ideology? In C. Waxman (Ed.), The end of ideology debate (pp.€ 49–63). New York: Simon & Schuster. (Original work published 1955) Skitka, L. J., Mullen, E., Griffin, T., Hutchinson, S., & Chamberlin, B. (2002). Dispositions, scripts, or motivated correction?: Understanding ideological differences in explanations for social problems. Journal of Personality and Social Psychology, 83, 470–487. Skitka, L. J., & Tetlock, P. E. (1993). Providing public assistance: Cognitive and motivational processes underlying liberal and conservative policy preferences. Journal of Personality and Social Psychology, 65, 1205–1223. Smith, C. T., Ranganath, K. A., & Nosek, B. A. (2008). Instant assimilation: Automatically integrating new information with existing beliefs. Unpublished manuscript. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. Taber, C. S. (2003). Information processing and public opinion. In D. O. Sears, L. Huddy, & R. Jervis (Eds.), Oxford handbook on political psychology (pp.€433–476). Oxford, UK: Oxford University Press. Tajfel, H., & Turner, J. C. (1986). The social identity theory of intergroup behavior. In S. Worchel & W. G. Austin (Eds.), The psychology of intergroup relations (pp.€7–24). Chicago: Nelson-Hall. Teachman, B. A., & Woody, S. R. (2003). Automatic processing in spider phobia: Implicit fear associations over the course of treatment. Journal of Abnormal Psychology, 112, 100–109.

564

V. APPLIED PERSPECTIVES

Todorov, A., Mandisodza, A. N., Goren, A., & Hall, C. C. (2005). Inferences of competence from faces predict election outcomes. Science, 308, 1623–1626. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131. Van den Bos, K., & Maas, M. (2009). On the psychology of the belief in a just world: Exploring experiential and rationalistic paths to victim blaming. Personality and Social Psychology Bulletin, 35, 1567–1578. Visser, P. S., Krosnick, J. A., Marquette, J., & Curtin, M. (2000). Improving election forecasting: Allocation of undecided respondents, identification of likely voters, and response order effects. In P. J. Lavrakas & M. W. Traugott (Eds.), Election polls, the news media, and democracy (pp.€224–262). New York: Chatham House. Way, B. M., & Masters, R. D. (1996). Political attitudes: Interactions of cognition and affect. Motivation and Emotion, 20, 205–236. Weinberger, J., & Westen, D. (2008). RATS, we should

have used Clinton: Subliminal priming in political campaigns. Political Psychology, 29, 631–651. Westen, D., Blagov, P. S., & Harenski, K. (2006). Neural bases of motivated reasoning: An fMRI study of emotional constraints on partisan political judgment in the 2004 U.S. presidential election. Journal of Cognitive Neuroscience, 18, 1947–1958. Wheatley, T., & Haidt, J. (2005). Hypnotic disgust makes moral judgments more severe. Psychological Science, 16, 780–784. Wilson, T. D. (2002). Strangers to ourselves: Discovering the adaptive unconscious. Cambridge, MA: Harvard University Press. Wilson, T. D., & Schooler, J. W. (1991). Thinking too much: Introspection can reduce the quality of preferences and decisions. Journal of Personality and Social Psychology, 60, 181–192. Zakrisson, I. (2005). Construction of a short version of the Right-Wing Authoritarianism (RWA) scale. Personality and Individual Differences, 39, 863–872.

Author Index

Aarts, H., 62, 68, 69, 200, 265, 282, 314, 316, 319, 320, 321, 323, 325, 326, 474, 479, 480 Abel, G. G., 527 Abele, A. E., 280 Abelson, R. P., 39, 68, 69, 342, 354 Aboud, F. E., 245, 247 Abrams, R. A., 324 Abrams, R. L., 32, 54, 99 Ach, N., 313 Achtziger, A., 68 Acker, F., 200, 289 Ackerman, J. M., 319 Adam, E. K., 378 Adjali, I., 259, 265, 270, 271, 278 Adolphs, R., 282 Adorno, T. W., 550 Agnew, C. R., 429 Agocha, V. B., 473 Agosta, S., 523 Aguiar, P., 555 Aguirre, G. K., 366 Aharon, I., 324 Ahern, A. L., 502 Aidman, E., 394 Aidman, E. V., 412 Aiken, P. A., 465 Ainslie, G., 72 Ajzen, I., 65, 67, 143, 268, 282, 340, 464 Aklin, W. M., 473 Alba, J. W., 41 Albarello, F., 144 Albarracin, D., 141, 304 Albers, L., 396 Alden, H. L., 531 Aleman, A., 506



Allen, D. N., 507 Allen, J. P., 491 Allen, T. J., 10, 120, 156, 158, 166, 168, 169, 182, 232, 286, 300, 410 Alloy, L. B., 145, 394, 497 Allport, G. W., 151, 222, 247, 354, 378, 408 Almeida, J., 42 Alpers, G. W., 70 Altemeyer, B., 550 Amadori, A., 257, 559 Ambady, N., 70, 229, 230, 280, 338 Ames, D. R., 431 Ames, S. L., 147, 176, 464, 469, 470, 471, 473 Amir, N., 492, 493, 495, 496, 513 Amodio, D. M., 1, 3, 12, 66, 67, 80, 84, 85, 88, 89, 157, 162, 163, 169, 216, 271, 353, 356, 357, 359, 360, 361, 363, 364, 366, 367, 368, 417, 513, 523 Amsel, R., 476 Amunts, K., 88 Anastasi, A., 179 Andersen, S. M., 320, 427, 431, 440 Anderson, B., 466 Anderson, C. A., 530 Anderson, J. R., 44, 47, 48, 242, 287 Anderson, N. B., 379 Anderson, N. E., 223 Anderson, N. H., 209 Anderson, P. L., 429 Andersson, G., 503 Andreoletti, C., 281 Andres, M., 503 Angleitner, A., 70, 142 Apanovich, A. M., 143 Apfelbaum, E. P., 382

565

Appelman, A. J., 513 Appels, M. C. M., 506 Appiah, K. A., 3 Aquino, K., 414 Arafat, Y., 85 Arbuckle, N., 447 Arbuckle, N. L., 271, 464 Arcuri, L., 65, 72, 144, 216, 224, 229, 257, 260, 271, 285, 542, 559 Arendt, H., 550 Arens, E., 227 Arguedas, D., 508, 509 Ariely, D., 382 Arkes, H., 447, 448 Arkes, H. R., 3, 368 Arkin, R. M., 513 Armitage, C. J., 67 Armstrong, D. M., 199 Armstrong, J. F., 476, 478 Arnow, B. A., 499 Arntz, A., 510, 511 Arolt, V., 507, 508 Aron, A., 429 Aron, E. N., 429 Aron, R., 550 Aronson, E., 343 Aronson, J., 146, 150, 379, 385 Aronson, J. A., 282 Asendorpf, J., 176 Asendorpf, J. B., 1, 63, 118, 122, 123, 131, 208, 210, 257, 259, 270, 271, 398, 408, 409, 411, 414, 415, 416, 418, 540 Asgari, S., 227, 338 Ashburn-Nardo, L., 169, 224, 268, 361, 363, 379, 555 Ashmore, R. D., 227, 296, 338, 362

566 Author Index Asmundson, G. J., 475, 477 Atkinson, A., 201 Atkinson, J. W., 312, 318 Atkinson, R. C., 42 Avivi, Y. E., 69 Avnet, T., 419 Ayduk, O., 430, 437 Ayres, K., 69, 260, 479, 537 Baars, B. J., 198, 199, 200, 201, 202, 211, 326 Babor, T., 472 Baccus, J. R., 225, 396, 401, 430, 431, 478 Bachmann, T., 108 Back, M. D., 129, 414, 415, 416, 417, 422 Baddeley, A. D., 42, 43, 46, 51, 54, 200, 201, 466 Badre, D., 85 Baert, S., 498 Baeyens, F., 69, 223, 225, 503 Baglioni, A., 471 Bagozzi, R. P., 67 Bailenson, J. N., 557 Baird, A. A., 82 Baker, S. M., 347 Balcetis, E., 313, 317 Balci, F., 278 Baldwin, M., 431 Baldwin, M. W., 1, 55, 144, 225, 395, 396, 401, 417, 426, 427, 430, 431, 433, 440, 478, 525 Ball, J. R., 504 Balota, D. A., 105, 324 Banaji, M. R., 3, 4, 5, 6, 7, 8, 31, 47, 63, 67, 68, 81, 96, 97, 101, 102, 103, 107, 111, 118, 119, 120, 121, 122, 123, 130, 132, 133, 150, 160, 161, 168, 169, 176, 182, 205, 210, 216, 218, 222, 223, 243, 246, 247, 248, 250, 251, 255, 266, 270, 271, 296, 311, 312, 335, 338, 355, 356, 364, 368, 369, 377, 393, 394, 396, 408, 412, 413, 416, 420, 446, 447, 448, 452, 457, 490, 523, 526, 535, 538, 540, 549, 551, 552, 555 Bandler, R., 476 Bandura, A., 65, 67, 311, 448 Banerjee, R., 248 Banner, M. J., 402 Banse, R., 7, 32, 63, 67, 109, 119, 121, 122, 123, 131, 176, 208, 227, 229, 232, 246, 251, 257, 264, 265, 267, 270, 271, 281, 285, 286, 396, 398, 411, 415, 416, 428, 429, 526, 527, 531 Banting, L. K., 414 Bar-Anan, Y., 133, 134, 176, 420 Barch, D. M., 83 Barden, J., 6, 168, 218, 219, 220, 221, 229, 269, 338, 346, 348, 363 Bargh, J. A., 2, 3, 20, 22, 23, 27, 28, 29, 40, 41, 44, 45, 55, 64, 66, 68, 69, 70, 81, 82, 88, 97, 145, 157, 158, 160, 168, 177, 181, 197, 198, 199, 200, 203, 230, 242, 243, 245, 271, 279, 286, 297, 311, 312, 315, 317, 318, 319, 320, 322, 323, 325, 326, 336, 354, 355, 362, 363, 367, 393, 400, 411, 427, 432, 435, 446, 453, 457, 489, 494, 543, 553, 556

Bar-Haim, Y., 245 Barlow, D. H., 490 Barndollar, K., 199, 286, 318, 362 Barnes-Holmes, D., 176 Barnes-Holmes, Y., 176 Barnett, W. S., 468 Baron, A., 247, 250 Baron, A. S., 243, 246, 250, 251 Barquissau, M., 385 Barrett, L. F., 264, 397, 398 Barrett, M., 245 Barsalou, L. W., 46, 401 Bartholomew, K., 417, 430 Bartholow, B. D., 83, 84, 168, 232, 465, 468, 472 Bartlett, F. C., 40 Bartlett, M. Y., 224, 346 Bartz, J. A., 433, 434 Bassili, J. N., 46, 64, 558 Batchelder, W. H., 112, 157, 160, 165 Baucom, D. H., 465 Baumeister, R. F., 71, 200, 232, 263, 265, 381, 385, 392, 393, 397, 409, 414, 417, 418, 431, 439, 447, 465, 468, 472, 543 Baumert, A., 68, 265, 271, 419 Baumgardner, M. H., 255 Baveux, P., 395 Bazerman, M. H., 457 Beach, L., 53 Beach, S. R. H., 429 Beard, C., 495, 496 Becerra, A., 226 Bechara, A., 67, 199, 282, 283, 284 Beck, A. T., 490, 496, 497, 509 Beck, J. M., 284 Becker, A., 223 Becker, A. P., 280, 303, 414, 540 Becker, C. A., 105 Becker, E., 176, 179, 271, 494, 500 Becker, E. S., 223, 491, 493 Becker, G. S., 67 Becker, M. H., 464 Beckers, L., 466, 472 Beckers, T., 64, 131, 232, 248, 468 Beech, A. R., 492, 524, 527, 528 Beer, J. S., 82, 86, 89, 157 Beevers, C. G., 497, 498, 500, 501 Beggan, J. K., 540 Beilock, S. L., 385 Bell, C., 437 Bell, D., 550 Bell, D. E., 342 Bellezza, F. S., 289 Bem, D. J., 208 Benditt, R., 431 Benedict, R., 549 Bennell, C., 523 Bennett, K. M., 502 Bentall, R. P., 508 Bentler, P. M., 465, 471 Ben-Tovim, D. I., 504 Ben-Zeev, T., 385 Berenson, K. R., 430, 440 Berger, C., 227 Berger, J., 559 Berger, J. A., 264

Berk, M. S., 427, 431 Berke, R. L., 557 Bermpohl, F., 210 Berner, M., 492 Berner, M. P., 492 Berntson, G. G., 141, 336, 338 Berra, Y., 432 Berridge, K. C., 62, 71, 199, 283, 323, 324, 468, 522 Berry, D. S., 149 Berscheid, E., 426 Bertrand, M., 450 Besner, D., 109, 111 Bessenoff, G. R., 97, 271 Betsch, T., 71, 289, 326, 340, 556 Bettman, J. R., 199, 200, 283, 289 Beumont, P. J. V., 504 Biciotti, E., 144 Bickley, J. A., 528 Bieri, R., 263 Bigler, R. S., 245 Billig, M. G., 224, 552 Binik, Y. M., 476 Birch, C. D., 231 Birch, D., 318 Birkby, J., 477 Birnbaum, G. E., 434, 436 Birnie, C., 435 Biro, S., 325 Bishara, A. J., 11, 160, 162, 164, 456 Bisson, M. A. S., 498 Bizer, G. Y., 342 Bjork, B. A., 46 Bjorklund, F., 454 Black, C. M. D., 504 Black, J. J., 464 Blackstone, T., 383 Blagov, P. S., 554 Blaine, B., 541 Blair, C., 468 Blair, I. V., 6, 82, 88, 120, 160, 168, 218, 219, 221, 228, 262, 266, 268, 287, 338, 339, 355, 362, 401, 449, 450, 552 Blair, K., 529 Blair, K. S., 510 Blair, R. J. R., 529, 530 Blak, T., 526 Blalock, C., 384 Blanchard, R., 526 Blank, A., 70 Blank, H., 217, 219, 223, 225, 303 Blank, L. M., 450 Blanton, H., 8, 133, 144, 176, 229, 269, 368, 394, 465 Blascovich, J., 287, 360, 378, 392, 393 Blasi, G., 452 Bleckley, M. K., 200 Bless, H., 209, 263, 264, 285, 346, 418 Block, L., 286 Block, N., 22, 204 Bloom, H., 452 Bloom, P., 278, 325, 326, 455 Blount, S., 453 Bluemke, M., 8, 123, 130, 133, 232, 260, 270, 559 Bniewosz, B., 54

Author Index Bobo, L., 375, 450 Bodenhausen, G. V., 1, 9, 10, 12, 43, 47, 49, 52, 62, 64, 65, 67, 72, 73, 88, 120, 132, 151, 168, 187, 206, 209, 212, 216, 220, 221, 223, 225, 228, 232, 233, 246, 261, 268, 269, 278, 280, 284, 285, 286, 296, 297, 299, 301, 303, 304, 305, 307, 324, 336, 338, 356, 358, 364, 365, 394, 396, 401, 402, 409, 411, 413, 414, 416, 422, 513, 536, 540, 543, 554, 558 Bodner, G. E., 99, 101 Boeker, H., 210 Bogardus, C., 473 Bogg, T., 465 Bohner, G., 7, 229, 269, 299, 346 Bohr, N., 11, 432 Boldero, J. M., 133, 411 Bolger, B., 384 Bolte, A., 263 Bomyea, J., 495, 496 Bonacci, A. M., 141 Bonanno, G. A., 552 Bond, A., 492 Bond, R. N., 45 Bongers, K. C. A., 200 Bonilla-Silva, E., 375, 382 Booker, J., 269 Bookheimer, S. Y., 82, 360 Boon, B., 503 Boone, A. L., 265, 418, 465 Borgida, E., 434, 553 Borkenau, P., 266 Borsboom, D., 123, 177, 178, 179, 180, 182 Bos, M. W., 200, 288, 289, 542 Boschen, M. J., 223 Boss, A., 285 Bosson, J. K., 144, 180, 223, 251, 266, 267, 385, 392, 393, 394, 395, 397, 398, 399, 400, 510 Bot, S. M., 472 Botvinick, M. M., 83, 85 Bouton, M. E., 367 Bovaird, J. A., 112 Bower, G. H., 29, 42, 44, 47, 52 Bowers, J., 269 Bowlby, J., 426, 428 Bowles, D. P., 510 Boyett-Anderson, J. M., 42 Bradburn, N. M., 147 Bradley, B. P., 176, 430, 470, 490, 492, 493, 494, 496, 498, 499, 500, 512 Bradley, M. M., 69, 105, 359, 477, 492, 530 Braet, C., 475 Brandtstädter, J., 102, 106 Branscombe, N. R., 45, 364 Bransford, J. D., 52 Brass, M., 284 Bratslavsky, E., 263 Bratslavsky, M., 71, 543 Brau, S., 556 Brauer, M., 476 Braun, C. M. J., 530 Braver, T. S., 83, 85 Breckler, S. J., 357, 359 Breen, R., 480 Brehm, J. W., 280, 320

Breitmeyer, B. G., 108 Brendl, C. M., 68, 69, 120, 124, 125, 131, 135, 170, 314, 315, 316, 322, 364 Brener, L., 216, 270, 451, 477 Brennan, K. A., 417 Brent, E. E., 472 Brescoll, V. L., 361, 447 Bretherton, I., 431 Brewer, M. B., 6, 9, 165, 168, 218, 229, 269, 338, 348, 363, 541, 552, 554, 555 Brewer, M. C., 87 Bridle, R., 495 Brigham, J. C., 210, 359, 360, 379, 383 Briley, D. A., 538 Brinkley, C. A., 510 Briñol, P., 1, 53, 63, 206, 219, 226, 259, 295, 298, 304, 305, 306, 307, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 399, 400, 402, 413 Britton, J. C., 82 Broadbent, D., 2 Broadbent, K., 528 Brochu, P. M., 144, 209, 221, 226, 365, 388 Brock, T. C., 337, 342 Brockbank, M., 325 Brodnax, S., 541 Brody, G. H., 468 Broemer, P., 394 Bromley, S., 354, 375 Brosschot, J. F., 492 Brown, A. S., 524, 525, 526, 527 Brown, G. D. A., 270 Brown, J. D., 397 Brown, K. W., 419 Brown, R., 67, 377 Brown, R. D., 46, 64 Brown, R. P., 144, 398 Brown, T. A., 490 Brownell, K. D., 477, 502, 555 Bruin, R., 537 Bruin, R. H., 258 Brunel, F. F., 258, 537, 540 Bruner, J. S., 40, 87, 140, 312, 313, 315 Brunstein, J. C., 320 Brunswik, E., 206 Bryan, C. J., 553 Bryant, J., 280 Bryant, R. A., 493 Buchner, A., 266, 395, 420 Buck, R., 70 Buhlmann, U., 492, 505 Buhrmester, D., 393 Bulik, C. M., 504 Bundy, R. P., 224 Bunge, S. A., 85, 440 Burdein, I., 549 Burger, J. M., 513 Burkley, M., 7, 159, 269 Burkley, M. A., 122, 210 Burnett, M., 231, 361 Burns, M., 496 Burrows, L., 199, 320, 367 Burton, A. K., 477 Burton, K., 437 Burton, P. C., 25, 84, 99 Burton, S., 542

567 Busemeyer, J. R., 71, 324 Bush, G. W., 142, 146 Bushman, B. J., 141, 530 Buss, A. H., 208 Butcher, J., 183 Butz, D. A., 158, 228, 378, 379, 449 Buysse, A., 417, 430, 436 Byrne, B. M., 408 Byrne, D., 141 Cabanac, M., 322 Cacioppo, J. T., 9, 80, 82, 87, 141, 225, 264, 271, 284, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 348, 543 Cadinu, M. R., 224 Cajdric, A., 224, 346 Callan, V. J., 471 Calvert, S. W., 531 Calvo, M. G., 479 Cameron, C. D., 1, 3, 368, 445, 457, 523 Cameron, C. M., 492, 498 Cameron, L., 243 Campbell, C., 54 Campbell, D. T., 354, 360 Campbell, J. D., 392 Campbell, L., 495 Campbell, W. K., 395, 399, 510 Candel, M. J., 472 Cane, D. B., 499, 501 Canli, T., 81 Cannistraci, C. J., 82, 367 Cantor, N., 41, 42, 311 Cantril, H., 151 Carey, G., 70 Carlisle, C., 145 Carlisle, K., 231 Carlone, O., 506 Carlsmith, J. M., 226 Carlsson, K., 185 Carlston, D. E., 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 52, 53, 54, 55, 366 Carman, N. C., 530 Carmines, E. G., 450 Carmon, Z., 543 Carney, D. R., 551 Carpenter, W. B., 197 Carpenter, W. T., 507 Carr, T. H., 111 Carraro, L., 557 Carrell, S. E., 55, 427 Carrive, P., 476 Carrol, S. M., 412 Carter, B. L., 315 Carter, C. S., 83, 84 Carter, F. A., 504 Carter, T. C., 552 Carter, T. J., 452 Caruso, E., 455 Carvallo, M., 144, 540 Carvallo, M. R., 453 Carver, C. S., 6, 62, 68, 69, 71, 306, 311, 323, 325, 500 Casey, B. J., 480 Caspi, A., 465, 466 Cassin, S. E., 503 Castellanos, N., 472

568 Author Index Castelli, L., 65, 69, 88, 217, 218, 224, 229, 247, 257, 260, 271, 286, 382, 557, 559 Castiello, U., 523 Cejka, M. A., 67, 297 Cervone, D., 11 Cesario, J., 320 Chadwick, P., 504 Chaiken, S., 3, 9, 27, 69, 72, 81, 89, 97, 140, 164, 165, 225, 229, 243, 271, 279, 284, 285, 295, 335, 336, 337, 338, 339, 344, 345, 357, 367, 393, 543, 553, 554, 558 Chajczyk, D., 22 Chakhssi, F., 526 Chakravarti, D., 543 Chamberlin, B., 551 Chan, E., 105, 107 Chang, K., 500 Chang-Schneider, C., 392 Chanowitz, B., 70 Chant, D. C., 471 Chapleau, K. M., 88, 449 Chaplin, T. C., 527 Chappel, M., 50 Charness, N., 287 Chartrand, T. L., 55, 87, 145, 199, 317, 318, 320, 325, 440 Chase-Lansdale, P. L., 378 Chasiotis, A., 147 Chassin, L., 122, 222, 230, 315, 470, 479, 542 Chater, N., 270 Chee, M. W., 86 Chein, J. M., 64, 68 Chen, E., 513 Chen, E. E., 82 Chen, J., 358 Chen, M., 70, 199, 320, 367, 446, 494 Chen, S., 165, 225, 320 Chen, Y. P., 494 Cheng, C. M., 1, 9, 63, 101, 104, 111, 176, 210, 271, 359, 377, 418, 447, 464, 512 Cheng, S. M., 233 Cherau, J. F., 46 Cheung, J. C. H., 289 Chiao, J. Y., 82 Chiao, K. W., 225, 339 Child, R., 468 Cho, Y. S., 130 Christensen, A., 431 Christie, C., 8, 133, 176, 269 Chugh, D., 457 Chun, M. M., 39, 81 Chun, W., 10 Chung, C., 452 Chung-Yan, G. A., 146 Churchill, G. A., 148 Cialdini, R. B., 210 Cifa, C., 225, 339 Cinciripini, P. M., 315 Cinti, M. E., 506 Citro, C. F., 450 Clarbour, J., 526 Clare, L., 506, 508 Clark, C. L., 417 Clark, D., 494 Clark, D. A., 490, 496, 497 Clark, D. M., 492, 494, 512 Clark, H. H., 112

Clark, L., 147, 466 Clark, R., 379 Clark, R. A., 312 Clark, V. R., 379 Claypool, H. M., 223, 296, 338, 367 Cleeremans, A., 200, 201, 202, 207, 211 Clerkin, E. M., 1, 12, 233, 248, 430, 466, 489, 491, 495, 496, 505, 531 Clinton, B., 85 Clore, G., 161, 455 Clore, G. L., 55, 206, 208, 283, 365 Coats, S., 429 Cobb, M., 492 Cody, M. W., 1, 12, 233, 248, 430, 466, 489, 491, 531 Coffman, T. L., 356 Cohen, G. L., 553, 554 Cohen, J. B., 283, 543 Cohen, J. D., 83, 85, 282 Cohen, N. J., 354 Cohen, R. G., 62 Cohen, T., 452 Cokely, E. T., 289 Colangelo, A., 230, 363 Coles, M. E., 513 Coles, M. G., 84, 87, 141 Collins, A. M., 44, 48, 96, 180, 324 Collins, L., 431 Collins, M., 543 Collins, N. L., 435 Coltheart, M., 508 Comeau, N., 472 Conner, M., 67, 69, 260, 264, 265 Conner, M. T., 465, 479, 537 Conner, T., 397, 398 Conover, P. J., 550 Conrey, F. R., 10, 46, 64, 65, 89, 112, 120, 121, 129, 157, 158, 161, 164, 166, 167, 168, 182, 232, 245, 250, 268, 269, 286, 301, 358, 364, 456, 466, 512, 527 Conrod, P. J., 466, 472 Constantinidis, C., 47 Converse, B. A., 321 Converse, P. E., 550 Conway, A. R. A., 200 Conway, P. J., 456 Cook, S. W., 141, 142, 151 Cooper, J., 306, 343, 385, 446 Cooper, L. A., 54 Cooper, M. J., 504 Cooper, M. L., 465, 471, 473 Cooper, R. M., 430 Cooper, S., 524 Cooper, Z., 501, 503, 504 Coopersmith, S., 393 Corbin, W., 471 Corneille, O., 264 Coronges, K., 147, 470 Correia, I., 555 Correll, J., 83, 84, 101, 104, 157, 160, 161, 259, 287, 357, 378, 398, 399, 449 Corrigan, P. W., 510 Corwin, J., 161 Cowan, N., 43 Cox, M. G., 53 Cox, W. M., 468, 480 Craemer, T., 555

Craeynest, M., 248, 249, 474 Craighero, L., 55 Craske, M. G., 513 Creelman, C. D., 161 Crisford, H., 530 Crites, S. L., 141, 271 Crocker, J., 378, 541 Croizet, J. C., 32, 224 Crombez, G., 33, 69, 223, 225, 249, 471, 474, 476 Crooks, V. L., 528 Crosby, F., 354, 375 Cross, S. E., 427 Crouch, Z., 129 Croxen, J., 527 Crutchfield, R., 140 Csibra, G., 325 Cuddy, A. J. C., 231, 280, 361, 367 Cunningham, W. A., 7, 30, 81, 82, 85, 86, 87, 111, 122, 160, 185, 210, 224, 266, 360, 367, 396 Curtin, M., 559 Cushman, F., 454 Cushman, F. A., 454 Custers, R., 69, 200, 316, 320, 323, 325, 470, 479 Cuthbert, B. N., 69, 105, 359, 477, 492, 530 Cyders, M. A., 69 Czellar, S., 130, 232 Czopp, A. M., 169, 257, 270, 363, 473 Czyzewska, M., 227, 279, 285 Dabady, M., 450 Dahl, H., 150 Dahr, R., 543 Dal Cin, S., 227 Dale, R., 324 Dalgleish, T., 492, 498, 499 Dallas, M., 4 Damasio, A. R., 54, 67, 199, 202, 282, 284, 326 Damasio, H., 67, 199, 284 Dandeneau, S. D., 430, 478 Dane, E., 287 Danion, J., 498 Dannlowski, U., 501 Daprati, E., 55 Dare, H., 530 Darkes, J., 471 Darley, J., 446 Darwin, C., 70 Dasgupta, N., 6, 70, 168, 217, 218, 219, 220, 221, 224, 227, 228, 230, 287, 300, 338, 346, 360, 362, 363, 379, 418, 421, 536, 538, 552, 556 Daughters, S. B., 473 Daum, I., 418 David, A. S., 507, 508 Davidson, A., 508 Davies, J., 280 Davies, P. G., 67, 379, 449, 450 Davis, A., 379 Davis, D. D., 509 Davis, J. A., 468 Davis, M., 359 Davis, S. C., 245 De Beul, N., 471

Author Index De Boeck, P., 185 de Bono, J., 494 De Bourdeaudhuij, I., 249, 474 De Bruycker, E., 124, 132, 271, 471 De Charms, R., 312, 313 De Clercq, A., 470 De Houwer, J., 5, 8, 12, 19, 20, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 40, 63, 64, 65, 68, 69, 70, 73, 84, 95, 96, 97, 100, 101, 102, 105, 107, 111, 117, 118, 121, 122, 123, 124, 125, 126, 127, 130, 131, 132, 133, 135, 156, 157, 161, 170, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 190, 198, 223, 232, 233, 242, 245, 248, 249, 260, 261, 267, 271, 279, 302, 315, 316, 335, 394, 396, 411, 417, 430, 433, 436, 441, 445, 464, 466, 470, 471, 474, 491, 497, 522, 523, 526, 535, 536, 538 de Jong, P. J., 229, 261, 271, 463, 470, 471, 474, 475, 476, 477, 491, 492, 493, 496, 531, 542 de Kraker, J., 470 de Liver, Y., 219, 304, 305, 336, 466 De Martino, B., 280 de Montes, L. G., 144 De Raedt, R., 497, 498 de Schonen, S., 245 De Vries, N. K., 465 De Vries, P., 314 de Wit, H., 472 Debner, J. A., 469 Deci, E. L., 311 DeCoster, J., 9, 45, 52, 62, 64, 72, 220, 242, 243, 279, 298, 324, 362, 364, 365, 367, 377, 394, 401, 402, 418, 439, 465, 466 Deese, J., 469 Deforche, B., 249 Degner, J., 3, 5, 8, 11, 31, 32, 54, 95, 98, 99, 101, 103, 108, 111, 112, 157, 158, 160, 170, 176, 217, 232, 245, 246, 271, 357, 358, 431, 445, 464, 523 Dehaene, S., 99, 101, 198, 199, 200, 201, 202, 203, 204, 207, 211 DeHart, T., 144, 222, 231, 346, 400, 401, 428, 429 Del Boca, F. K., 471 Delaney, H. D., 112 Delaney, S. M., 54 Dell, G., 42 Delplanque, S., 105 DeMarree, K. G., 219, 264, 298, 336, 346, 347, 402 Denes-Raj, V., 210, 265 Deneubourg, J. L., 278 Dennett, D. C., 199, 201, 203, 211, 212 Denny, E. B., 498 Derakshan, N., 479 Derksen, J., 183 Derrick, J. L., 435 Deruelle, C., 245 Deshpande, R., 538 Desmond, J. E., 85, 469 D’Esposito, M., 43, 366 DeSteno, D. A., 224, 231, 346, 455 Detrain, C., 278 Deutsch, R., 9, 10, 27, 30, 43, 62, 63, 64, 65, 66, 68, 69, 70, 71, 72, 73, 104, 113,

118, 121, 159, 181, 200, 202, 203, 204, 205, 206, 209, 220, 221, 227, 230, 232, 233, 256, 257, 258, 260, 261, 263, 270, 279, 286, 287, 297, 298, 304, 315, 324, 366, 377, 409, 410, 415, 422, 439, 440, 465, 466, 469, 478, 480, 502, 512, 536, 543, 554 Devilly, G. J., 525 Devine, P. G., 3, 11, 65, 66, 67, 82, 84, 85, 157, 158, 160, 165, 169, 199, 210, 225, 271, 339, 343, 354, 355, 356, 357, 359, 360, 361, 362, 363, 366, 367, 368, 369, 377, 378, 382, 387, 417, 513 Devos, T., 377, 421, 452 DeWall, C. N., 436, 447 Dewitte, M., 417, 430, 433, 436 Dhar, R., 314, 543 Dholakia, U. M., 541 Diallo, A., 449 Diamond, A., 468, 480 Diaz, P., 421 Dibbets, P., 250 Dickey, R., 526 Dickter, C. L., 84, 168, 232 Dictus, M., 468 Diehl, J. M., 418 Dienes, Z., 40 Dietrich, A., 198, 199, 201 Dijker, A., 262 Dijksterhuis, A., 68, 69, 143, 176, 200, 209, 223, 225, 229, 265, 288, 289, 314, 320, 321, 323, 326, 338, 394, 395, 396, 397, 400, 401, 526, 542, 543 Dik, G., 319 Dimberg, U., 70 Dimmock, J. A., 414 Dion, K. L., 102, 357 Ditto, P. H., 143, 452 Dixon, M., 212 Dixon, R., 494 Dobbins, I. G., 231, 271, 315, 464 Dobson, K. S., 503 Dodgson, P. G., 397 Dohn, M. C., 231, 397 Dolan, R. J., 81, 280 Dolan, T. M., 554 Dolev, T., 437 Domes, G., 510 Donchin, E., 84 Donders, N. C., 378 Donthu, N., 538 Doris, J. M., 3 Dotsch, R., 270, 271 Douglas, K. M., 144 Dovidio, J. F., 2, 5, 63, 70, 88, 102, 146, 160, 168, 208, 225, 226, 259, 261, 271, 281, 287, 335, 339, 354, 355, 356, 357, 359, 360, 361, 362, 375, 377, 378, 379, 382, 384, 387, 398, 402, 446, 522 Downey, G., 379, 430, 431, 437 Doyle, A. B., 247 Dozois, D. J. A., 503 Draine, S. C., 9, 27, 28, 54, 55, 65, 99, 101, 103, 108 Drinkwater, A. E., 552 Driscoll, D. M., 287 Drolet, A., 543

569 Droste, T., 507, 508 Drwecki, B. B., 223, 281, 364 Duan, C., 87 Duckitt, J. H., 375 Duckworth, K., 69 Duckworth, K. L., 243, 553 Duke, L. A., 508 Duncan, L. E., 142 Dunham, Y., 1, 6, 222, 241, 243, 246, 247, 250, 251 Dunn, D. S., 208 Dunn, E., 205, 209, 210, 222, 247 Dunn, E. W., 311, 522 Dunn, M., 146 Dunn, M. A., 67, 356 Dunn, R., 421 Dunning, D. A., 313, 317 Dunton, B. C., 1, 7, 19, 54, 63, 70, 88, 95, 140, 158, 176, 210, 217, 259, 264, 304, 315, 335, 355, 377, 378, 398, 446, 523, 535 Dupoux, E., 245 Durante, S., 246 Dutton, K. A., 397 Dvir, S., 473 Dweck, C. S., 553 Dykman, B., 497 Eagly, A. E., 140 Eagly, A. H., 89, 284, 295, 335, 339, 357, 367 Earleywine, M., 470 Eastwick, P. W., 205 Eastwood, B., 468 Eaton, J., 399 Eberhardt, J. L., 67, 378, 448, 449, 450 Ebisch, S. J. H., 506 Ebsworthy, G., 495 Edelman, G. M., 200, 201, 202, 203 Edelstein, R. S., 437 Eder, A., 124, 327, 345 Eder, A. B., 30, 32, 99, 180 Edmonds, C., 245 Edwards, J. R., 260 Edwards, W., 289 Eelen, P., 25, 26, 27, 28, 29, 30, 32, 33, 64, 65, 97, 101, 107, 123, 176, 223, 225, 267, 315, 535, 538 Eertmans, A., 503 Egloff, B., 120, 121, 129, 131, 208, 257, 259, 271, 414, 416, 420, 421, 422, 491, 495, 512 Ehlers, A., 494 Ehrenberg, K., 281 Ehrenreich, J. T., 493 Eichstaedt, J., 270 Eimer, M., 80 Eisenbach, K., 227, 531 Eisenberger, N. I., 82, 360 Eiser, J. R., 243 Eitam, B., 200, 319 Ekman, P., 70 Elfenbein, H. A., 70 Elgueta, A., 379 Elias, J., 493 Elliot, A. J., 68, 143, 323, 343 Ellison, C. G., 383 Ellwart, T., 271, 491, 494 Elsesser, K., 494 Emery, G., 490, 497

570 Author Index Engels, R. C., 472 Engle, R. W., 187, 200, 264 Englehard, I. M., 491, 495 Enright, S. J., 492 Epel, E. S., 378 Epley, N., 455 Epstein, S., 210, 261, 265, 394, 401 Erb, H., 10 Erbe, K., 476 Erceg-Hurn, D. M., 266 Erdelyi, M. H., 210 Erickson, D. J., 472 Eriksen, B. A., 96 Eriksen, C. J., 84 Eriksen, C. W., 96 Erikson, E. H., 549 Ervin, C. R., 141 Espinoza, P., 141, 145, 149, 176, 360, 377 Esses, V. M., 342 Estourget, Y., 144 Etcheverry, P., 438 Etchison, S., 1, 417, 426 Etcoff, N. L., 354 Etherton, J., 429 Evangeli, M., 530 Evans, J. S., 279 Evans, J. S. B. T., 198, 465, 466 Evans, N., 2 Everaerd, W., 434 Everaert, T., 29 Everitt, B. J., 468 Evett, S. R., 387 Exline, J. J., 468 Exner, J. E., Jr., 185 Eysenck, M., 494 Eysenck, M. W., 479 Eysenck, S. B. G., 418 Faber, R. J., 543 Fabre-Grenet, M., 245 Fadardi, J. S., 468 Fairburn, C. G., 501, 502, 503, 504 Fairchild, K., 231, 379, 397 Falces, C., 226 Falkai, P., 201 Fallman, J. L., 450 Farah, M. J., 54, 366 Farnham, S. D., 223, 224, 393, 395, 397, 540 Farthing, G. W., 204 Faunce, G. J., 504 Fazio, R. H., 1, 2, 3, 4, 5, 7, 8, 10, 11, 12, 19, 25, 26, 29, 34, 48, 51, 54, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 81, 88, 95, 96, 101, 103, 109, 110, 111, 118, 121, 122, 123, 124, 127, 130, 131, 135, 140, 151, 158, 160, 163, 165, 176, 186, 187, 206, 210, 217, 218, 219, 221, 222, 223, 225, 228, 231, 232, 233, 243, 246, 248, 256, 259, 260, 261, 262, 263, 264, 270, 271, 285, 287, 295, 296, 297, 298, 299, 301, 304, 305, 306, 307, 308, 315, 335, 336, 338, 340, 343, 355, 358, 360, 361, 362, 364, 365, 368, 377, 378, 379, 382, 384, 387, 393, 395, 396, 398, 402, 408, 418, 429, 446, 470, 473, 523, 535, 536, 538, 539, 542, 553, 558 Fear, C., 508

Feather, N. T., 67, 552 Fedorikhin, A., 263 Fehlinger, T., 505 Fehr, B., 431 Fein, S., 67, 146, 356 Feinberg, J., 379 Feinstein, A. S. H., 165 Feinstein, J. A., 264 Feldman, S., 311, 550, 552 Feldman, S. I., 431 Fellous, J. M., 281 Felson, R. B., 531 Fenigstein, A., 208 Ferguson, M. J., 1, 43, 68, 168, 199, 230, 297, 311, 312, 315, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 452, 453, 552, 553 Ferrara, S. D., 523 Ferraro, F. R., 502, 503 Ferreira, M. B., 286 Festinger, L., 209, 221, 226, 306, 539 Fiedler, K., 8, 123, 130, 148, 232 Field, A. P., 248 Field, M., 69, 176, 231, 468, 470, 472, 542 Filbey, F. M., 468 Fillmore, M. T., 469 Fincham, F. D., 426 Fine, C., 3, 456 Fink, E. L., 71 Finkel, E. J., 205 Finn, P. R., 472, 473 Fioravanti, M., 506 Firestone, P., 525 Fischer, L., 527 Fischle, M., 145 Fishbach, A., 68, 312, 314, 316, 321, 323, 326, 435, 474 Fishbein, M., 65, 67, 143, 268, 282, 340, 464 Fisher, D., 344 Fisher, R. J., 541 Fiske, S., 41, 53 Fiske, S. T., 9, 29, 41, 52, 53, 81, 82, 87, 165, 354, 360, 367, 447, 457 Fitness, J., 426 Fitzsimons, G. J., 199, 283, 541, 542 Fitzsimons, G. M., 316, 319, 322, 431, 432, 432–433, 439 Flament, C., 224 Flay, B. R., 465 Fleming, M. A., 342 Fletcher, G. J. O., 426 Florack, A., 68, 209, 262, 263, 264, 285, 537 Florin, I., 505 Foa, E. B., 493, 513 Fockenberg, D. A., 26 Fodor, J. A., 39, 199 Foerde, K., 366 Fong, C., 67, 146, 356 Fong, G., 227 Fong, G. T., 463 Forbes, C., 385 Forehand, M. R., 1, 12, 226, 233, 344, 416, 535, 536, 538, 539, 540 Foroni, F., 230, 287 Förster, J., 45, 68, 69, 286, 314, 318, 320, 322, 323, 326 Forth, A., 523 Fowler, J. S., 468

Fox, E., 492 Foxcroft, D. R., 480 Frable, D. E., 383 Fraley, B., 429 Fraley, R. C., 437 Franck, E., 497 Franck, N., 55 Franco, F. M., 144 Franken, I. H., 468 Franken, I. H. A., 147, 470 Frankfurt, H. G., 447 Franks, J. J., 52 Frantz, C. M., 231, 232, 361 Freeman, A., 509 Freeman, D., 507, 508 Freestone, D., 278 French, C. C., 500 French, D. J., 466 Frenkel-Brunswik, E., 550 Frenzel, R. R., 527 Freson, T. S., 541 Freud, S., 197, 203, 312, 354 Freund, T., 262 Friborg, O., 144 Fridlund, A. J., 70 Friedman, D., 83 Friedman, H. L., 473 Friedman, R., 45, 68, 314, 318, 323 Friedman, R. S., 68, 286, 435 Friese, F., 260 Friese, M., 62, 63, 65, 66, 67, 71, 73, 130, 133, 190, 209, 211, 216, 255, 259, 261, 262, 263, 264, 265, 270, 316, 417, 418, 419, 464, 465, 466, 468, 474, 479, 537, 542, 559 Frijda, N. H., 204, 211 Frings, C., 28, 29, 97, 99, 101, 105, 106 Frith, C. D., 80, 286, 287 Fromm, E., 549 Fromme, K., 471 Frone, M. R., 471 Fryer, J. W., 68, 323 Fujita, K., 72 Fum, D., 283 Funder, D. C., 206 Furman, W., 393 Gable, S. L., 438 Gabriel, U., 265 Gabrieli, J. D. E., 82, 85, 469 Gaertner, L., 224 Gaertner, S. E., 377 Gaertner, S. L., 2, 5, 88, 146, 208, 259, 271, 354, 356, 357, 359, 375, 377, 378, 446, 522 Gagne, F. M., 440 Gailliot, M. T., 71, 265, 434, 438 Galdi, S., 72, 216, 228, 257, 260, 285, 542, 559 Galinsky, A. D., 289, 363 Gallistel, C. R., 278 Gallo, D. A., 469 Gallois, C., 144 Gallucci, M., 268 Gamer, J., 421 Gandhi, M., 81 Gangestad, S. W., 70 Gannon, T. A., 531

Author Index Garb, H. N., 117 Garcia, M., 69, 243, 553 Garcia-Marques, L., 286 Gardner, W., 541 Gardner, W. L., 271, 312, 336 Garety, P. A., 507, 508 Garland, P., 557 Garstka, T. A., 129 Gartska, R. A., 249 Gary, M. L., 227, 296, 338, 362 Gast, A., 8, 127, 531 Gatenby, J. C., 81 Gaunt, R., 64 Gavrilescu, D., 469 Gawronski, B., 1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 27, 30, 43, 46, 49, 52, 62, 63, 64, 65, 67, 69, 72, 73, 89, 104, 112, 113, 120, 121, 122, 132, 144, 148, 151, 157, 158, 159, 168, 181, 182, 187, 198, 200, 206, 208, 209, 212, 216, 217, 218, 219, 220, 221, 223, 224, 225, 226, 227, 228, 231, 232, 233, 245, 246, 257, 260, 261, 264, 266, 268, 269, 270, 271, 280, 281, 285, 286, 287, 295, 296, 297, 299, 300, 301, 302, 303, 304, 305, 307, 312, 316, 317, 324, 326, 336, 338, 345, 356, 358, 364, 365, 387, 388, 394, 396, 397, 400, 401, 402, 409, 411, 413, 414, 416, 422, 448, 456, 466, 469, 474, 512, 513, 527, 531, 535, 536, 538, 540, 542, 543, 554, 557, 558, 559 Gebauer, J. E., 394 Gee, N. R., 469 Geldof, T., 124 Gemar, M. C., 231, 346, 497, 499, 501 Gendall, K. A., 474 George, A. L., 549 George, J. L., 549 George, W. H., 434 Gerbershagen, A., 505 Gergely, G., 325 Gerrard, M., 465, 468 Geschke, D., 7, 67, 264, 285, 286 Ghaderi, A., 503 Gheldof, E. L. M., 475 Gibbard, A., 456 Gibbons, F. X., 465, 468 Gibbons, P. A., 145 Gibson, B., 217, 218, 219, 221, 225, 227, 228, 539 Gibson, J. J., 280 Gigerenzer, G., 73, 455 Gijsen, A., 271, 477 Gilbert, D. T., 9, 64, 71, 72, 146, 200, 227, 285, 298, 357 Gilens, M., 375 Gilissen, S. M., 477 Gillath, O., 433, 434, 436, 437, 440 Gillespie, N. K., 504 Gilovich, T., 41, 454 Ginsburg, H. J., 227 Glados, P., 123 Glaser, J., 31, 107, 168, 322, 551, 554 Glaser, M. O., 29 Glaser, W. R., 29 Glasgow, D. V., 527 Glass, B., 87

Glassman, N. S., 427 Gleason, C. A., 201 Glen, I. S., 229 Glenberg, A. M., 46 Glenn, C., 230 Glick, P., 367, 450 Glockner, A., 72 Glöckner, A., 289 Glover, G. H., 85 Gniewosz, B., 99, 271 Gobbini, M. I., 80 Godden, D. R., 46, 51 Goeleven, E., 498 Goff, P. A., 67, 379, 385, 448, 449 Goheen, T. W., 395, 510 Gokalp, G., 397 Gold, J. I., 324 Goldberg, L. R., 142, 145 Goldberg, T. E., 507 Goldman, M. S., 469, 471 Goldman, R., 344 Goldman-Rakic, P. S., 47 Goldstein, A., 440 Goldstein, N. J., 319 Gollwitzer, M., 227, 228, 531 Gollwitzer, P. M., 28, 68, 87, 168, 199, 210, 286, 311, 318, 319, 320, 322, 323, 362, 363, 439 Gonsalkorale, K., 129, 133, 145, 157, 158, 166, 167, 168, 169, 231, 232, 250 Gonzaga, G. C., 438 Gonzales, P., 269 Gonzales, P. M., 8, 133, 176 González, R., 229 González-Vallejo, C., 289 Goodman, C. C., 312, 313 Goodwin, S. A., 246, 247, 412 Gordon, J. R., 468 Gore, J. C., 81 Gore, J. S., 427 Goren, A., 281, 557 Gorham, B. W., 148, 149 Goschke, T., 263, 314 Gosejohann, S., 68, 262, 537 Gosling, S. D., 549, 551 Gotlib, I. H., 81, 496, 499, 500, 501 Gotor, A., 99 Gottman, J. M., 432, 440 Goubert, L., 476 Gould, T. J., 468 Govan, C. L., 130, 168, 230, 363 Govender, R., 3, 81, 553 Govorun, O., 1, 11, 63, 104, 162, 176, 210, 232, 233, 271, 286, 359, 377, 418, 447, 464, 470, 512 Graesser, A. C., 39 Graf, P., 150, 354, 469 Grafman, J., 86, 185, 531 Graham, J., 1, 12, 233, 548, 552 Gramzow, R. H., 224 Grandjean, D., 105 Grangé, D., 498 Grant, H., 452 Gratton, G., 84 Gray, H. M., 378 Gray, N. S., 1, 12, 233, 434, 509, 522, 524, 525, 526, 527, 529, 530

571 Green, A. R., 216, 281, 451 Green, D. M., 157, 160 Green, D. P., 531, 549 Green, J., 288 Green, M. J., 508 Greenberg, J., 397, 552 Greenberg, R. I., 490 Greene, J., 454, 455 Greenspoon, J., 46 Greenwald, A. G., 1, 3, 4, 5, 6, 8, 9, 19, 27, 28, 32, 48, 54, 62, 63, 64, 67, 72, 73, 99, 101, 103, 108, 117, 118, 119, 120, 121, 123, 129, 130, 132, 134, 140, 150, 158, 160, 161, 165, 168, 169, 176, 182, 186, 189, 197, 199, 205, 217, 218, 219, 220, 221, 222, 223, 224, 228, 231, 232, 246, 247, 249, 250, 251, 255, 258, 259, 261, 268, 269, 270, 287, 300, 301, 303, 311, 312, 335, 337, 338, 342, 355, 356, 357, 358, 362, 363, 364, 377, 378, 379, 393, 394, 396, 397, 408, 409, 410, 411, 412, 413, 414, 415, 416, 420, 422, 447, 448, 470, 490, 512, 526, 531, 535, 536, 537, 538, 539, 540, 541, 542, 543, 549, 551, 552, 556, 560 Greer, S. M., 282 Gregg, A. P., 63, 65, 122, 216, 218, 219, 221, 223, 227, 228, 248, 296, 395, 491 Grekin, E. R., 472 Grenard, J., 469 Grenard, J. L., 176, 265, 418, 466, 469, 470, 471, 472, 479, 542 Gress, C. L. Z., 527 Greve, W., 99, 102, 106, 394 Grier, S. A., 538 Griffin, K. M., 464 Griffin, T., 551 Griffiths, R. A., 504 Grohmann, B., 541 Groom, C., 10, 120, 157, 182, 232, 245, 456, 494, 512, 527 Groom, C. J., 64, 89, 112, 286, 358, 466 Gross, A. M., 493 Gross, J. J., 495 Grumm, M., 216, 217, 218, 219, 221, 225, 227, 228, 229, 401, 402, 415, 476 Grunedal, S., 70 Gschwendner, T., 7, 64, 121, 122, 143, 205, 207, 208, 209, 210, 217, 221, 229, 260, 265, 268, 269, 270, 286, 296, 382, 414, 418, 419, 420, 466, 472, 473, 479, 535, 542 Guerrieri, R., 475, 479 Guglielmi, R. S., 360 Guirguis, M., 499 Gul, F., 284 Gulayets, M. J., 527 Gunn, G. R., 398 Gunning, W. B., 472 Gurtman, M. B., 354 Gutig, R., 326 Gutt, H., 246 Ha, Y.-W., 284 Haberstroh, S., 71–72 Haeffel, G. J., 498, 500 Haefner, M., 315 Haerens, L., 474

572 Author Index Hafer, C. L., 555 Häfner, M., 62, 230, 297, 502 Hagan, R., 495 Haidt, J., 454, 455, 456, 549, 552 Haig, J., 393 Haines, E. L., 421 Halberstadt, J., 288 Hall, C. C., 281, 557 Hall, P. H., 506 Ham, J., 289 Hamann, S., 82 Hamill, R., 557, 558 Hamilton, D. L., 141, 145 Hamilton, H., 367 Hamilton, L. K., 146 Hamlin, J. K., 278 Hammond, K. R., 141, 146, 150 Hampson, S. E., 145, 149 Han, H. A., 3, 131, 218, 297, 301, 302 Hand, C. G., 527 Hanges, P. J., 450 Hanley, J. A., 524 Hannover, B., 72, 421, 422 Hansen, J., 3, 11 Hansen, J. J., 131, 132, 301, 302, 447, 548 Hanson, J., 452 Hanson, K., 452 Hanson, R. K., 525, 528 Hardin, C. D., 8, 96, 97, 101, 102, 103, 131, 222, 230, 259, 271, 338, 362, 363, 394, 398 Hare, R. D., 529, 530 Hare, T. A., 480 Harenski, K., 554 Hariri, A., 82, 360 Harmon-Jones, C., 306 Harmon-Jones, E., 67, 84, 85, 157, 169, 271, 306, 359, 363 Harris, G. T., 527 Harris, L. T., 81 Harris, M. J., 249 Hart, A., 231, 361 Hart, A. J., 271 Harter, S., 141 Hartlage, S., 497 Hartmann, G. W., 558 Hartston, H. J., 493 Harvey, A. G., 493 Hasbroucq, T., 118 Hasher, L., 41, 47 Haslam, N., 133, 411 Hass, R. G., 342 Hassin, R. R., 197, 200, 311, 312, 319, 324, 325, 452, 552 Hastie, R., 42, 43, 45, 47, 549, 556 Hastorf, A. H., 151 Hau, R. C., 492 Haugtvedt, C. P., 338, 347 Hauser, M., 454, 456 Hauser, M. D., 454, 552 Havermans, R. C., 466, 468 Hawkins, C. B., 1, 12, 233, 548 Haxby, J. V., 80 Hay, J. F., 469 Hayaki, J., 466 Hayden, D. C., 377 Haye, A., 229

Hayes, J., 150 Haymovitz, E. L., 385 Haynes, J.-D., 284 Hazan, C., 428 Healy, D., 508 Heath, C., 286 Heatherton, T. F., 71, 393, 397, 439, 465, 543 Hebl, M. R., 387 Hecht, H., 494 Hecker, J. E., 495 Heffernan, K., 504 Heider, F., 40, 41, 86, 223, 325, 369, 411, 412, 539 Heier, H., 210, 265 Heil, M., 109 Heilpern, K., 224 Heiman, N. R., 491 Heimberg, R. G., 496 Heine, S. J., 400 Heinze, H.-J., 284 Hempel, R. J., 507 Hendin, H. M., 392 Hendriks, M., 320 Hengeveld, M. W., 507 Henry, P. J., 222, 552 Heppen, J. B., 219, 247, 433 Heppner, W. L., 398 Herman, C. P., 474, 475, 502 Herman, P., 502 Hermann, A. D., 396 Hermans, D., 25, 26, 27, 28, 29, 30, 31, 32, 33, 64, 69, 84, 97, 100, 101, 105, 107, 123, 223, 267, 315, 476, 538 Hermsen, B., 263 Hermsen, S., 102, 168, 226, 287, 357 Herr, P. M., 358 Hertel, P. T., 497, 500, 501 Hess, E. H., 523 Hess, M., 70 Hetherington, M. M., 502 Hetts, J. J., 144, 393, 394, 397, 400 Heuer, K., 494 Heuschen, I., 494 Hewstone, M., 222, 247 Higgins, E. T., 44, 45, 56, 68, 144, 230, 314, 318, 320, 323, 419, 499 Hilger, M. T., 538 Hill, A. B., 54, 504 Hill, C. R., 431 Hill, T., 279, 285 Hilton, J. K., 364 Hilton, J. L., 48, 123, 131, 207, 217, 219, 221, 225, 228, 268, 296, 300, 301, 535, 538, 559 Hine, D. W., 255, 470, 531 Hinson, R. E., 502 Hinton, C., 504 Hinton, G. E., 49 Hippler, H.-J., 147 Hirsh, J. B., 380 Hitch, G. J. L., 43 Hitler, A., 81 Hixon, J. G., 146, 208, 357 Hoch, S. J., 62 Hockett, J. M., 531 Hodes, R.M., 245

Hodges, S. D., 160 Hodgins, S., 530 Hodson, G., 148, 271 Hoefling, A., 474 Hofer, J., 147 Hoffman, E. Z., 80 Hoffman, H. G., 160 Hoffman, L., 112 Hofmann, W., 4, 7, 10, 22, 62, 63, 64, 65, 66, 68, 71, 73, 121, 122, 133, 143, 197, 198, 205, 207, 208, 209, 210, 212, 216, 217, 221, 229, 255, 257, 259, 260, 261, 262, 263, 264, 265, 268, 269, 270, 271, 283, 286, 296, 304, 316, 326, 382, 393, 396, 402, 409, 414, 417, 418, 419, 420, 463, 464, 465, 466, 468, 472, 473, 474, 475, 479, 535, 537, 542, 543 Hogan, L. M., 480 Hogan, R., 393 Hogarth, R. M., 287 Högman, L., 493 Holbrook, J. B., 96 Holker, L., 495 Holland, L. A., 527 Holland, P. C., 221 Holland, R. W., 8, 69, 263, 270, 320, 479 Holmes, J. G., 426, 435, 437, 439, 440 Holt, C., 493, 494 Holtz, M., 106 Hommel, B., 29 Hood, J. E., 499 Hood, K. L., 46 Hoogdiun, C. A. L., 493 Hooten, K., 288 Hoover, R. J., 538 Hope, D. A., 496 Horcajo, J., 226, 339, 340, 345, 347 Horenstein, M., 494 Horner, M. D., 42 Hornig, C. D., 492 Horowitz, L. M., 417, 499 Höschel, K., 507 Hoshino-Browne, E., 259, 398, 399 Houben, K., 257, 270, 463, 466, 470, 471, 472, 479 Houben, R., 271, 475 Houben, R. M., 477 Howard, A., 261, 281, 335, 355, 377, 398 Howard, R., 508 Hoyer, W. D., 538 Hsee, C. K., 67 Hubbard, S. M., 71 Huckfeldt, R., 553 Huddy, L., 549 Hudson, S. M., 524 Hug, F., 265 Hugenberg, K., 10, 64, 67, 72, 89, 112, 120, 157, 182, 223, 232, 245, 246, 285, 286, 296, 338, 358, 367, 456, 466, 512, 527 Hughes, G. D., 283 Huguet, P., 224 Huijding, J., 229, 261, 271, 470, 471, 476, 491, 492, 496 Hull, C. L., 68, 323 Hülsenbeck, K., 69, 281 Hume, D., 456 Hummert, M. L., 129, 249, 250

Author Index Humphries, M. S., 50 Hunt, R. R., 498 Hunter, A. J., 378 Hunter, S., 360, 378 Hunter, S. B., 360, 378 Huntsinger, J. R., 161 Hunyady, G., 453 Hurling, R., 223, 259, 265, 270, 271, 278 Hurwitz, J., 555 Hutchinson, S., 551 Hutchison, K. A., 107 Hutton, D. G., 393 Huygens, K., 493 Hyers, L. L., 380, 383 Hymes, C., 27, 97, 271, 336, 393 Ickes, W., 380, 382, 384 Ilai, D., 493 Inbar, Y., 455 Ineichen, S., 263 Ingvar, M., 185 Insko, C., 452 Insler, R. Z., 85 Inzlicht, M., 380, 385 Ireland, D., 480 Irle, E., 507 Irving, L. M., 468 Ito, T. A., 10, 80, 83, 87, 88, 197, 225, 271, 339, 360 Itti, L., 22 Iyengar, S., 557 Izard, C. E., 70 Jaccard, J., 8, 133, 176, 269, 368 Jack, A. I., 211 Jackson, J. R., 1, 19, 54, 70, 88, 95, 140, 158, 176, 210, 217, 259, 315, 335, 355, 377, 398, 446, 523, 535 Jackson, J. S., 379 Jackson, M. C., 448 Jacoby, J., 543 Jacoby, L., 40 Jacoby, L. L., 4, 10, 31, 104, 157, 158, 159, 161, 163, 165, 227, 232, 250, 271, 286, 354, 355, 356, 357, 393, 449, 469 Jahrig, J., 150 Jajodia, A., 470 James, W., 55, 68, 197, 199, 203, 354, 432 Janczura, G. A., 469 Jandolo, L., 278 Jang, K. L., 70 Janis, I. L., 288 Jansen, A., 466, 468, 474, 475, 503, 504 Janssen, E., 434, 476, 477 Janssen, J., 434 Janz, N. K., 464 Jarcho, J. M., 82, 360 Jarvis, W. B. G., 53, 219, 264, 298, 336, 400 Jax, S., 62 Jencius, S., 11 Jenkins, S. R., 147 Jervis, R., 549 Jessor, T., 375 Jiménez, L., 200, 202, 207, 211 Jin, R. K., 454 Job, R. F. S., 504 Johansson, L., 503

Johar, G. V., 543 John, O. P., 126, 142, 145 Johns, M., 385 Johnsen, I. R., 82, 185 Johnson, B., 63, 261, 281, 335, 355, 377, 398 Johnson, B. T., 262, 339, 359, 465 Johnson, C., 63, 261, 281, 335, 355, 377, 398 Johnson, D. M., 288 Johnson, E., 541 Johnson, E. J., 284 Johnson, J. D., 379, 383 Johnson, K., 502 Johnson, M. K., 81, 82, 85, 86, 87 Johnson, N. J., 555 Johnson, S. L., 450, 499, 500 Johnston, L., 524 Jolles, J., 250 Jones, B. C., 480 Jones, B. T., 466, 471, 480 Jones, E. E., 3, 212, 368 Jones, J. T., 144, 397, 400, 540 Jones, R. M., 480 Jonides, J., 43, 85, 106 Joordens, S., 199 Joormann, J., 496, 498, 499, 500 Jordan, A., 455, 527 Jordan, C. H., 1, 7, 210, 221, 228, 259, 261, 305, 392, 394, 397, 398, 399, 402, 408, 428 Jordan, R., 249, 399 Josephs, R. A., 465, 480 Jost, J., 341 Jost, J. T., 368, 452, 453, 549, 550, 551, 552, 554, 555 Joyce, P. R., 474, 504 Joy-Gaba, J. A., 556 Joynt, R. J., 197 Judd, C. M., 1, 6, 46, 54, 63, 67, 83, 88, 95, 96, 104, 157, 176, 218, 269, 271, 280, 287, 296, 356, 357, 359, 365, 377, 449, 450 Judge, A. M., 502 Justus, A. N., 473 Kahn, R., 506 Kahneman, D., 22, 68, 279, 549 Kalmus, E., 527 Kampman, M., 493, 503 Kan, K., 493, 494, 496 Kane, M. J., 187, 200 Kaney, S., 508 Kang, J., 369, 446, 447, 457, 523 Kang, S.-J., 397 Kant, I., 40 Kanwisher, N., 80 Kaplan, M. F., 295 Kaplowitz, S. A., 71 Karasawa, M., 141, 143 Kardes, F. C., 81 Kardes, F. R., 2, 26, 51, 63, 96, 206, 248, 262, 296, 315, 393, 408, 473, 553 Karlins, M., 356 Karmiloff-Smith, A., 242 Karparova, S. P., 499 Karpinski, A., 8, 48, 123, 131, 133, 141, 145, 149, 176, 207, 217, 219, 221, 225, 228,

573 268, 270, 296, 300, 301, 364, 394, 395, 418, 535, 538, 543, 559 Kasai, K., 508 Kasch, K. L., 499 Kaspi, S. P., 493 Katz, I., 342 Kauffman, S. A., 325 Kauffmann-Muller, F., 498 Kawada, C., 318 Kawada, C. L. K., 320, 325 Kawakami, K., 63, 88, 101, 102, 160, 168, 208, 225, 226, 227, 228, 259, 261, 271, 281, 287, 335, 339, 355, 356, 357, 362, 377, 398, 446, 522 Kay, A. C., 453, 553 Keay, K. A., 476 Keedian, E., 431 Keefe, D. E., 98 Keenan, T., 525 Keijsers, G. P. J., 493 Kelley, C., 40, 469 Kelley, C. M., 161, 289, 393 Kelley, H. H., 426 Kelley, S., 556 Kelly, A. B., 147, 470 Kelly, D., 457 Keltner, D., 282 Kemp-Wheeler, S. M., 54 Kennedy, K. A., 446 Kennedy, S. J., 231, 346, 497 Kennett, J., 3, 456 Keogh, E., 477 Keren, G., 73 Kernberg, O., 398 Kernis, M. H., 394, 398, 399, 510 Kerns, J. G., 510 Kerr, N. L., 70 Kersting, A., 499 Kesebir, S., 560 Ketterman, R. L., 466 Khalife, S., 476 Khan, S. R., 146 Kiefer, A. K., 385 Kiernan, A., 468 Kihlstrom, J. F., 42, 335 Kikul, J., 505 Killen, M., 245 Killeya, L. A., 339 Killion, T. H., 105 Kim, D., 222, 232 Kim, D. Y., 64, 158 Kim, E., 493 Kim, H., 282 Kim, K., 68 Kinder, D. R., 549, 550, 557 Kinderman, P., 508 Kindt, M., 491, 492, 510, 511 King, E. B., 387 King, G., 45 Kinoshita, S., 126, 130 Kinzler, K. D., 245 Kirchhof, O., 320 Kirk, E. R., 23, 323 Kirker, W. S., 41 Kirkpatrick, B., 507 Kirschbaum, M., 123 Kirscht, J. P., 282

574 Author Index Kiss, I., 289 Kitayama, S., 141, 143, 400 Kiviniemi, M. T., 465 Klassen, P., 526 Klatzky, R. L., 42 Klauer, K. C., 5, 8, 10, 11, 25, 26, 27, 28, 29, 30, 31, 32, 33, 64, 96, 98, 99, 101, 104, 107, 113, 117, 120, 125, 126, 127, 129, 130, 132, 133, 134, 150, 156, 157, 158, 160, 163, 168, 170, 176, 182, 210, 217, 232, 250, 268, 270, 281, 286, 300, 345, 358, 395, 410, 411, 445, 464, 491, 523 Klayman, J., 284 Kleiman, T., 200 Klingberg, T., 480 Klinger, M. R., 25, 32, 84, 99, 101 Klohnen, E. C., 142 Klumpp, H., 493 Knäuper, B., 147 Knibbe, R. A., 472 Knobe, J., 455, 457 Knowles, E. D., 168, 322 Knowles, M. L., 379 Knutson, B., 282, 283 Knutson, K. M., 86, 554 Koch, K., 122, 230, 315, 470, 542 Koestner, R., 312, 403 Koffka, K., 40 Kohlberg, L., 456, 552 Kohut, H., 398 Konorski, J., 53 Koo, M., 321 Koole, S. L., 26, 143, 176, 183, 209, 223, 394, 395, 400, 402 Koomen, W., 262 Koos, O., 325 Kopp, B., 83 Kordts, R., 211 Kordts-Freudinger, R., 65 Kornblum, S., 118 Koster, E. H. W., 430, 436, 471, 498 Kowai-Bell, N., 360, 378 Kowalick, C., 428, 429 Kraft, D., 208 Kramer, A. F., 129 Kramer, T., 286 Krampe, R., 287 Krank, M. D., 469, 480 Krasnoperova, E., 499 Krauth-Gruber, S., 46 Kray, L., 421 Krech, D., 140 Krieger, L. H., 447 Krieglmeyer, R., 62, 70, 211 Krieschel, S., 494 Kringelbach, M. L., 323 Kristian, M. R., 480 Kristjanson, J., 503 Krizan, Z., 396, 398 Kroeze, S., 494 Krosnick, J. A., 337, 557, 559 Krueger, J. I., 392 Kruglanski, A. W., 10, 43, 68, 72, 73, 210, 225, 226, 262, 289, 314, 323, 325, 435, 551 Krysan, M., 375 Kuban, M. E., 526 Kubota, J. T., 84

Kuhl, J., 263, 313, 314 Kuhlmeier, V., 325 Kuhn, A., 245 Kuhnen, C. M., 283 Kühnen, U., 229 Kuile, M. M., 476 Kuiper, N. A., 41 Kulfanek, M., 99, 394 Kumar, P. A., 47 Kumhyr, S. M., 383 Kumkale, G. T., 304 Kunda, Z., 67, 146 Kurthen, M., 201 Kyrsan, L., 450 Laaksonen, P., 536 Laan, E., 476, 477 LaBar, K. S., 81 Labouvie, E., 504 Lacey, S., 54 Lakey, C. E., 395, 398, 510 Lakin, J. L., 144, 440 Lam, C. Y., 315 Lambert, A. J., 10, 31, 158, 163, 168, 227, 232, 271, 357, 449 Lambert L. S., 260 Lamberth, J., 141 Lambie, J. A., 204 Lambird, K. H., 399 Lamme, V. A. F., 202, 204 Lamote, S., 223 Lamy, D., 245 Lancaster, K., 68, 211 Landau, M. J., 552 Lane, D. J., 465 Lane, K. A., 120, 146, 369, 447, 523 Lane, R. D., 81 Lang, A. R., 466 Lang, P. J., 69, 70, 105, 359, 471, 477, 492, 530 Langdon, R., 508 Langens, T. A., 142 Langer, E., 70 Langer, E. J., 354 Langer, T., 225 Langston, C. A., 429 Lapierre, D., 530 LaPierre, R. T., 354 Larrick, R. P., 286 Larsen, J. T, 87 Larsen, R. J., 105 Larson, D. W., 551 Lassiter, G. D., 289 Latham, G. P., 311 Laurenceau, J.-P., 69 Lavender, T., 29 Lavine, H., 553 Lawrence, A. J., 466 Lawrence, M. A., 246 Laws, D. R., 527 Laws, K. R., 507 Lawson, J., 248 Lay, B. S., 414 Le, B., 438 Le, H., 7, 122, 208, 221, 270, 296, 414, 535 Le Bon, G., 452 Leary, M. R., 431

LeBel, E. P., 3, 7, 30, 64, 65, 112, 113, 121, 132, 148, 159, 181, 208, 217, 218, 219, 221, 223, 225, 228, 232, 233, 271, 302, 312, 364, 396, 448 Lecci, L., 379, 383 Ledbetter, J. E., 67 Leder, S., 435 LeDoux, J. E., 81, 198, 360, 365 Lee, K. M., 86 Lee, M., 503, 504 Lee, M. R., 229 Lee-Chai, A., 199, 286, 318, 362 Lehman, D. R., 400 Leibold, J. M., 70, 259, 355, 360, 377, 379, 382, 384, 416, 446, 531 Leigh, B. C., 464, 470 Leippe, M. R., 255 Lejuez, C. W., 473 Leman, P. J., 245 Lemm, K. M., 141, 146, 150 LeMoult, J., 500 Lens, W., 186 Lenton, A., 6, 218 Lenton, A. P., 168, 287, 339 Leone, L., 261 Lépine, J., 494 Lepore, L., 67, 377 Lepper, M., 143 Lepper, M. R., 151 Lerner, J. S., 282 Lerner, M. J., 555 Leslie, A., 242 Levenson, R. W., 432, 440 Levesque, C., 419 Levin, H. S., 251 Levin, J., 531 Levin, S., 383 Levine, J., 553 Levine, J. M., 70 Levin-Sagi, M., 72 Levinson, D. J., 550 Lewicki, P., 279, 281, 284, 285 Lewin, K., 315, 318 Lewin, M. R., 513 Leyens, J., 448 Li, P., 23, 323 Li, Q., 552 Li, Q. F., 476 Li, S., 495 Li, W., 146, 377, 492 Liben, L. S., 245 Liberman, A., 284 Liberman, N., 45, 68, 72, 145, 286, 314, 318 Liberzon, I., 81, 82 Libet, B., 201 Libman, E., 493 Lichtenstein, D. R., 542 Lickel, B., 360, 378 Lieb, K., 509 Lieberman, M. D., 64, 72, 73, 82, 88, 279, 360, 553 Liesenfeld, S., 422 Lilienfeld, S. O., 117 Lin, M., 165 Lincoln, T. M., 227 Lindberg, M. J., 289 Lindemann, M. D., 509

Author Index Lindgren, K. P., 434 Lindner, N. M., 552 Lindsay, D. S., 10, 161, 163 Lindsey, S., 6, 53, 62, 122, 143, 205, 218, 260, 302, 355, 394 Linehan, M. M., 509 Linton, S., 346 Linton, S. J., 477 Linville, P. W., 41, 53 Lippman, M., 526 Lipset, S., 550 Lisle, D. J., 208 Lister-Sharp, D. J., 480 Littleford, L. N., 378 Liu, T. J., 32 Liu, X., 495 Livesley, W. J., 70 Livingston, R. W., 222, 223, 281, 363–364, 379, 555 Locascio, J. J., 511 Locke, E. A., 311 Locker, L., Jr., 112 Lockhart, L. K., 143 Lodge, M., 145, 549, 553, 556, 557, 558 Loewenstein, G., 283, 312, 464 Loewenstein, G. F., 62, 67, 69, 71 Loftus, E. F., 44, 51, 96, 180, 324 Logan, G. D., 33, 68, 98, 242 Logel, C., 394, 398 Logel, C. E. R., 399 Lokken, K. L., 503 Lombardi, W. J., 44, 45, 318 London, B. E., 430 Long, C. C., 504 Lopez, D. F., 55, 427 Lorch, R. F., 110, 111, 112 Lord, C. G., 143, 385 Lorig, T. S., 225, 339 Lovell, D., 504 Lovibond, P. F., 223 Lowe, G., 480 Lowe, M. R., 501 Lowell, E. L., 312 Lowery, B. S., 131, 222, 230, 232, 247, 338, 362, 363 Lucas, M., 96, 107 Luce, M. F., 200, 289 Luce, R. D., 157 Ludwig, C., 430 Luhtanen, R., 541 Luiga, L., 108 Luka-Krausgrill, U., 421 Lun, J., 230 Luna, D., 539 Lundh, L. G., 493, 496 Lundqvist, D., 185 Lundy, A., 147 Luo, Q., 529, 530 Lydon, J. E., 1, 417, 426, 433, 434, 435, 437, 438, 440 Lynam, D. R., 257, 473 Lynch, L., 87 Ma, J., 6 Ma, J. E., 168, 218, 287, 339 Maas, M., 555 Maass, A., 141, 144, 148, 149, 271, 347

MacCoun, R. J., 151 MacCulloch, M. J., 509, 524, 529 Macdonald, A., 480 MacDonald, G., 463, 480 MacDonald, T. K., 463, 464, 480 Machado, A., 5 Mack, D., 474, 475 Mack, R., 106 Mackie, C., 472 Mackie, D. M., 217, 219, 224, 298, 304, 343, 344, 362, 402 Maclean, A. M., 472 MacLeod, C., 187, 474, 490, 493, 495, 499, 500 Macmillan, N. A., 161 Macrae, C. N., 47, 81, 88, 224, 286, 356, 363, 364 Macy, J. T., 222 Maddux, W. W., 6, 168, 218, 229, 269, 338, 348, 363 Mageau, G. A., 403 Magee, J., 495 Magliano, J. P., 39 Mah, L., 86 Maher, B. A., 507 Maheswaran, D., 344, 345 Maier, M., 492 Maier, M. A., 492 Maier, W., 201 Main, K. J., 378 Maio, G. R., 342, 394 Maison, D., 258, 535, 537 Majone, G., 289 Major, B., 378, 379 Malle, B. F., 229, 550 Malone, P. S., 285 Mandisodza, A. N., 281, 557 Maner, J. K., 434, 436, 438 Manly, C. F., 86 Mann, L., 288 Mann, N., 503 Mann, T., 399, 465 Mannetti, L., 10 Mansell, W., 494 Manzella, L. M., 440 Mao, H., 82 Marcel, A. J., 199, 204 Marcus, G. E., 554, 559 Margraf, J., 493 Marien, H., 316 Marigold, D. C., 397, 399 Marker, C. D., 496 Markman, A. B., 68, 69, 120, 131, 170, 315, 316, 322, 364 Markus, H., 41, 209 Markus, H. R., 400 Marlatt, G. A., 147, 468, 470 Marm, L., 141 Marquette, J., 559 Marsh, K. L., 262, 267, 465, 473 Marshall, W. L., 524 Marshall-Goodell, B. S., 338 Marshuetz, C., 224 Martin, L. L., 55, 70, 346, 538 Martin, R. C., 102 Martindale, C., 53 Martineau, A. M., 463

575 Marx, D. M., 385 Marx, H. M., 503 Marziano, V., 524 Mase, T., 502 Mason, G., 264, 530 Masson, M. E. J., 96, 99, 101 Mast, M. S., 146, 150 Masterman, P. W., 147, 470 Masters, R. D., 554 Mathews, A., 490, 493, 494, 495, 498, 499, 500 Mathews, A. M., 513 Matthews, G., 498 Mattia, J. I., 496 Mattler, U., 83 Mauer, N., 266 Mauss, I. B., 495 Maxwell, S. E., 112 Maynard, R. E., 511 Mayr, U., 230, 287 Mbirkou, S., 65, 221, 227, 287 McAdams, D. P., 552 McCall, C., 230, 421 McCann, C. D., 230 McCarthy, D. M., 470 McCarthy, P., 493 McCaslin, M. J., 340, 345 McCaul, K. D., 542 McClarty, K. L., 392 McClelland, D. C., 312, 313, 318 McClelland, J. L., 49, 324 McClernon, J. F., 230–231, 271, 315, 464, 470 McClure, M. J., 1, 417, 426 McConahay, J. B., 358, 375 McConnell, A. R., 7, 9, 63, 65, 70, 168, 217, 219, 223, 224, 226, 259, 261, 295, 296, 298, 299, 300, 304, 305, 324, 338, 343, 344, 345, 355, 360, 362, 367, 377, 379, 382, 384, 398, 402, 416, 446, 531 McConnell, J., 54 McCrae, R. R., 70 McCulloch, K. C., 318, 325 McCulloch, Y., 508 McDermott, J., 80 McDermott, K. B., 469 McDevitt, J., 531 McDonald, S., 281 McDonel, E. C., 358 McElree, B. D., 161 McEvoy, C. L., 150, 469 McEvoy, P. M., 466 McFalls, L. H., 531 McFarland, D. J., 278 McFarland, S. G., 129 McGeorge, P., 243 McGhee, D. E., 1, 19, 48, 63, 117, 140, 158, 176, 199, 217, 246, 255, 342, 355, 357, 377, 394, 408, 412, 447, 470, 490, 535, 538, 540, 551 McGhee, J. L., 531 McGlothlin, H., 245 McGraw, K. M., 145, 554, 556 McGregor, I., 397, 399, 552 McGuire, W. J., 336, 347, 549 McHugh, S., 531 McIntosh, V. V., 504

576 Author Index McKee, S. A., 502 McKenna, P. J., 507 McKenzie-Mohr, D., 434 McKinney, V. M., 469 McKinstry, C., 324 McKoon, G., 42, 45, 96, 103, 108, 189 McLaughlin, J. P., 2, 5, 354, 356, 357, 377 McMahon, J., 471 McManus, F., 504 McNally, R. J., 490, 492, 493, 496, 505 McNamara, T. P., 96, 100, 101, 112 McNeil, B. J., 524 Mehl, M. R., 142 Meier, B. P., 397, 542 Meijboom, A., 503 Mellenbergh, G. J., 123, 177 Mellott, D. S., 129, 249, 377 Mendes, W. B., 360, 378 Mendoza, S. A., 1, 3, 12, 216, 353, 363, 513, 523 Mendoza-Denton, R., 378, 379, 383 Menegatti, M., 144 Menon, V., 42 Menzies-Toman, D., 437 Mercer, K. A., 105 Meredith, M., 559 Merikle, P. M., 199, 269 Mertens, I., 510 Messner, C., 8, 68, 120, 123, 131, 170, 315, 364 Messner, S. F., 531 Metcalfe, J., 69 Meulenbroek, R. G. J., 62 Meyer, B., 510 Meyer, C., 504 Meyer, D. E., 96, 289, 354 Meyer, G. E., 511 Meyers-Levy, J., 538 Mick, D. G., 542 Mierke, J., 32, 125, 126, 127, 129, 132, 210, 268 Mignault, A., 281 Mihailides, S., 525, 526 Mikhail, J., 454 Mikulincer, M., 431, 433, 434, 436, 437, 439, 440 Miles, D. R., 70 Miles, F., 494 Milgram, S., 141 Milich, R., 249 Millar, N., 493, 494, 496, 498, 499, 512 Miller, C. T., 142, 143 Miller, F. D., 354 Miller, G. A., 44, 288 Miller, J., 110 Miller, L. S., 465 Miller, N., 360 Miller, S. L., 434, 438 Milne, A., 243 Milne, A. B., 47, 88, 356 Milne, E., 531 Miltner, W. H. R., 494 Mineka, S., 493, 496, 511 Minzenberg, M. J., 507 Mirenberg, M. C., 144, 397, 540 Mirer, T., 556

Mirosevich, V. M., 266 Mischel, W., 11, 41, 69, 311, 401 Mislavsky, N. O., 553 Mitchell, C. J., 223, 230 Mitchell, D., 529 Mitchell, G., 447 Mitchell, J. P., 47, 81, 130, 168, 218, 270, 552 Mitchell, P. B., 504 Moffitt, T. E., 466 Mogg, K., 69, 176, 179, 430, 470, 490, 492, 493, 494, 496, 498, 499, 500, 508, 512 Mohanty, A., 510 Mohlman, J., 479 Moll, J., 102, 168, 226, 287, 357 Molter, B., 72 Monsell, S., 125 Montalcini, F., 144 Monteith, M. J., 146, 169, 224, 257, 268, 361, 362, 363, 364, 379, 473 Montoya, R., 452 Montoya, R. L., 500 Moors, A., 5, 12, 19, 20, 23, 24, 27, 29, 30, 34, 40, 63, 64, 65, 68, 70, 95, 96, 117, 131, 135, 156, 157, 176, 177, 178, 179, 181, 184, 188, 198, 232, 233, 242, 248, 260, 279, 315, 445, 464, 522 Moradi, A. R., 499 Morgan, C. D., 142, 143 Morgan, M. M., 476 Morgan, W., 553 Moritz, S., 493, 506 Morling, B., 394 Morrin, M., 543 Morris, C. D., 52 Morris, J. P., 553 Morris, J. S., 81 Morris, M., 509, 529 Morris, M. L., 427 Morrison, A. M., 492 Morrison, H. W., 312 Morsella, E., 55, 326 Morton, J., 245 Morton, J. B., 246 Morwitz, V. G., 541 Moscatelli, S., 144 Moscovici, S., 347 Moskowitz, G. B., 23, 87, 168, 210, 227, 314, 317, 318, 322, 323, 346, 363, 364 Mougios, V., 430 Mowen, J. C., 542 Mucchi-Faina, A., 347 Mücke, D., 63, 123, 208, 257, 398, 411 Mudar, P., 471 Mueller, A., 229 Mugny, G., 347 Mullainathan, S., 450 Mullen, E., 551 Munro, G. D., 143 Munro, S., 468 Muraven, M., 71, 232, 263, 381, 543 Murdock, T., 493 Muroi, M., 508 Murphy, S. T., 9, 101, 103, 198, 428 Murray, H. A., 142, 143 Murray, S., 428

Murray, S. L., 426, 435, 436, 439, 440 Murry, V. M., 468 Murtagh, L., 176 Musa, C., 494 Musch, C. J., 345 Musch, J., 25, 26, 28, 29, 30, 31, 32, 33, 64, 96, 98, 99, 101, 107, 358 Mussweiler, T., 284 Myers, E. M., 393 Myers, J. L., 110, 111, 112 Myung, I. J., 157, 171 Naccache, L., 198, 199, 200, 201, 202, 203, 204, 207, 211 Nachmias, O., 436 Nagengast, B., 223, 300 Nail, P. R., 397, 552 Nairne, J. S., 52 Näring, G., 493 Naumann, A., 227, 531 Naumann, E., 505 Nay, W. T., 495 N’Diaye, K., 105 Neal, D. T., 68, 69, 72 Nederkoorn, C., 466, 475 Neely, J. H., 3, 29, 96, 97, 98, 101, 118, 354 Neighbors, H. W., 379 Neinhaus, K., 543 Nelson, D. L., 150, 469 Nelson, G., 429 Nelson, L. D., 144 Neshat-Doost, H. T., 499 Nestler, S., 216, 401, 476 Netemeyer, R. G., 542 Neubauer, D. H., 495 Neuberg, S. L., 9, 87, 165, 379, 383, 385 Neumann, D. L., 223 Neumann, E., 46 Neumann, R., 69, 70, 281 Neumark-Sztainer, D., 468 Newcomb, M. D., 464, 471 Newell, B. R., 289 Newman, J. P., 510 Newsom, J. T., 210 Neziroglu, F., 505 Nhean, S., 502 Nico, D., 55 Niedenthal, P. M., 46, 55 Niederhoffer, K. G., 142 Nier, J. A., 122 Nieuwenhuis, S., 83 Nigg, J. T., 472 Nisbett, R. E., 83, 117, 204, 205, 206, 210, 212, 279, 311, 549 Noack, P., 54, 99, 271 Nock, M. K., 248, 251 Noelle-Neumann, E., 147 Noll, D. C., 85 Noll, J. A., 469 Nordgren, L. F., 200, 288, 464, 468, 472, 542, 543 Norman, D. A., 43 Norris, C. J., 82, 87 Northoff, G., 210 Norton, M. I., 382, 446 Norton, P. J., 475

Author Index Nosek, B. A., 1, 3, 7, 8, 11, 12, 47, 63, 64, 118, 119, 120, 121, 122, 130, 131, 132, 133, 143, 146, 150, 159, 161, 168, 169, 176, 182, 184, 189, 205, 207, 208, 209, 210, 216, 217, 218, 223, 224, 233, 246, 250, 255, 261, 269, 270, 271, 296, 301, 302, 355, 394, 396, 412, 420, 427, 438, 447, 448, 452, 490, 513, 526, 548, 549, 551, 552, 554, 555, 556, 557, 560 Novacek, J., 393 Novemsky, N., 543 Nowlis, S. M., 543 Nunes, J. C., 542 Nunes, K. L., 525, 526, 532 Nussbaum, R. J., 217, 230 Nussinson, R., 69 Nuttin, J. M., 143, 176, 183, 271, 394, 540 Nystrom, L. E., 282 Oakes, M. A., 160, 377 Obama, B., 375, 408 Ober, B. A., 507 Obermiller, C., 541 O’Brien, L. S., 249 O’Brien, L. T., 129, 379 O’Callaghan, F. V., 471 Ochsner, K. N., 81, 85, 553 O’Connell, G. B., 378 O’Doherty, J., 81 O’Doherty, J. P., 282 Oettingen, G., 320 Ögmen, H., 108 O’Gorman, R., 69, 257, 260, 263, 268, 479, 537 Oh, T. M. S. S., 507 Öhman, A., 185, 493, 496 Oka, T., 144 Olson, I. R., 224 Olson, J. M., 148, 429, 430 Olson, K. R., 1, 6, 222, 241, 250, 251 Olson, M. A., 3, 4, 11, 48, 63, 64, 66, 67, 109, 111, 118, 121, 122, 123, 124, 127, 130, 131, 135, 140, 151, 187, 210, 217, 218, 219, 221, 223, 225, 228, 246, 260, 261, 263, 264, 270, 271, 296, 297, 298, 301, 304, 308, 338, 340, 361, 362, 364, 395, 396, 402, 470, 538, 539 Olton, R. M., 288 Oosterhof, N. N., 280 Oppenheimer, D. M., 279, 284 Orbell, S., 265 Orimoto, L., 504 Ormerod, T. C., 288 Orr, P., 378 Ortony, A., 55 Osborne, A., 527 Osgood, C. E., 89, 142, 539 Osman, A., 118 Öst, L. G., 493, 496 Ostafin, B., 471 Ostafin, B. D., 471 Ostelo, R. W., 477 Ostendorf, F., 142 Ostrom, T. M., 140, 337 Ottaway, S. A., 377 Otten, S., 32, 99, 106, 224

Otto, M. W., 492 Ouellette, J. A., 69, 141 Pacini, R., 210, 265 Packard, M. G., 366 Packer, D. J., 82, 225, 396, 401 Padoa-Schioppa, C., 278 Page, R., 354 Page-Gould, E., 378, 380 Paladino, M.-P., 224 Palfai, T. P., 471, 480 Paller, K. A., 492 Palmer, J. C., 51 Palmer, R. L., 503, 504 Paluck, E. L., 361, 447 Pandelaere, M., 28, 64 Papies, E. K., 474, 480 Paré-Blagoev, E. J., 85 Park, B., 1, 6, 46, 54, 63, 67, 83, 88, 95, 96, 104, 157, 176, 218, 269, 271, 287, 296, 356, 357, 359, 365, 377, 449, 549, 556 Park, J. J., 229 Parker, I., 223 Parker, K. F., 531 Parrott, D. J., 531 Pascalis, O., 245 Pasman, W., 491 Pasteels, J. M., 278 Patchen, M., 383 Patterson, M. L., 141 Patterson, S., 431 Pattison, P., 524 Paul, B. Y., 360 Paulhus, D. L., 117, 393 Paunovic, N., 493 Pavelchak, M. A., 29 Payne, B. K., 1, 3, 7, 9, 10, 11, 31, 62, 63, 67, 68, 70, 71, 84, 89, 101, 104, 111, 112, 122, 157, 158, 159, 160, 161, 162, 163, 164, 176, 186, 210, 227, 230, 232, 233, 245, 250, 269, 271, 286, 315, 356, 357, 359, 363, 368, 377, 378, 418, 445, 447, 449, 456, 457, 464, 470, 476, 512, 523 Payne, J. W., 200, 289 Payne, K., 73, 233 Payne, K. A., 470, 476 Peak, H., 323, 325 Pearson, A. R., 361, 378 Pecchinenda, A., 109 Pecher, D., 469 Pecknold, J. C., 493 Peek-O’Leary, M., 126, 130 Peffley, M., 555 Pekrun, R., 492 Pelham, B. W., 141, 144, 148, 222, 231, 346, 393, 394, 395, 397, 400, 401, 402, 428, 453, 540 Penke, L., 270 Pennebaker, J. W., 142, 180, 223, 251, 266, 282, 395 Penton-Voak, I. S., 430 Peracchio, L. A., 539 Perdue, C. W., 354 Perea, M., 99 Pereira, C., 555 Perez, J. A., 347

577 Perkins, A., 1, 12, 226, 233, 344, 535 Perkins, A. W., 416, 535, 536, 538, 539, 540 Perl, D. K., 201 Perner, J., 40 Perruchet, P., 395 Peruche, B. M., 158, 228, 449 Perugini, M., 3, 6, 11, 12, 34, 62, 66, 67, 69, 121, 123, 181, 188, 216, 223, 233, 255, 257, 258, 259, 260, 261, 263, 264, 265, 267, 268, 270, 271, 278, 307, 398, 479, 522, 535, 537 Perunovic, M., 437 Pervin, L. A., 323, 325 Pesendorfer, W., 284 Pessiglione, M., 279 Peters, E., 508 Peters, K. R., 3, 7, 64, 112, 113, 121, 132, 144, 159, 181, 209, 218, 221, 226, 232, 233, 302, 304, 312, 364, 365, 388, 396, 448 Peters, M. L., 475, 477 Peterson, J., 271, 477 Peterson, J. B., 472, 499 Pettigrew, T. F., 222, 247 Petty, R. E., 1, 6, 9, 53, 63, 85, 141, 142, 143, 148, 151, 165, 168, 206, 218, 219, 220, 223, 225, 226, 228, 229, 233, 259, 264, 269, 284, 295, 296, 298, 299, 304, 305, 306, 307, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 363, 399, 400, 402, 413, 543 Peuser, K., 472 Pfeffer, J., 453 Pfeifer, J. H., 367 Phaf, R. H., 70, 493, 494, 496 Pham, M. T., 283, 419 Phan, K. L., 81, 82 Phelan, J. E., 219, 247 Phelps, E. A., 81, 82, 199, 206, 359, 367 Phillips, K. A., 505 Phillips, M. L., 508 Phills, C., 225, 339, 362 Phipps, A., 527 Pickrell, J. E., 224 Pierce, T., 433 Pierro, A., 10 Pietromonaco, P., 45, 354 Pietrzak, J., 379 Pihl, R. O., 472 Pike, K. M., 501 Pinel, E. C., 385 Pineles, S. L., 494 Pinker, S., 142 Pinter, B., 224 Piper, J., 480 Pitt, D., 39 Pitt, M., 157 Pitts, G. S., 25, 84, 99 Pizarro, D. A., 326, 452, 455 Placanica, J. L., 504 Plaks, J. E., 320 Plant, E. A., 67, 158, 161, 163, 169, 228, 363, 378, 379, 382, 449, 513 Plessner, H., 262, 326, 340, 537, 556 Plewe, I., 130

578 Author Index Pliner, P., 503 Pöder, E., 108 Poehlman, T. A., 67, 123, 251, 255, 335, 416, 453, 535 Pöhlmann, C., 421, 422 Pointer, L., 469 Polaschek, D. L. L., 531 Poldrack, R. A., 85, 366 Polivy, J., 393, 397, 502 Pollack, M. H., 492 Pollatsek, A., 112 Pomarol-Clotet, E., 507 Popper, K. R., 8 Porter, S., 529 Porter, S. C., 1, 43, 68, 199, 230, 311, 323 Posner, M. I., 2, 3, 201, 202, 354, 355 Postle, B. R., 43, 56 Potanina, P. V., 367 Pothos, E. M., 468 Potter, J., 551 Powell, M. C., 2, 26, 51, 63, 81, 96, 206, 248, 262, 296, 315, 358, 393, 408, 473, 553 Power, M. J., 492, 498 Power, S., 527 Powers, D. A., 383 Pozzulo, J., 523 Pracejus, J. W., 283 Prati, V., 232, 523 Pratkanis, A. R., 255, 409, 410, 422 Pratt, M. G., 287 Pratto, F., 3, 81, 126, 229, 363, 550, 553 Preacher, K. J., 7, 111, 122, 160, 210, 266, 396 Premack, A. J., 325 Premack, D., 325 Prentice, D. A., 30, 39, 64 Presser, S., 147 Presson, C. C., 122, 222, 230, 315, 470, 479, 542 Prestwich, A., 69, 223, 257, 260, 263, 264, 268, 479, 537 Price, R. B., 479 Price, S. A., 528 Priester, J. M., 338, 344 Priester, J. R., 85 Proctor, R. W., 130 Proffitt, D. R., 491 Pronin, E., 446, 454, 455 Proshansky, H. M., 142, 143 Prost, J., 479 Pruessner, J. C., 430, 478 Pryor, J. B., 435 Przeworski, A., 493 Puca, R. M., 142 Puckett, J., 344 Pundt, I., 494 Pura, N., 382 Purdie, V. J., 67, 379, 449 Purdie-Vaughns, V. J., 450 Pyszczynski, T., 397 Qian, M., 495 Quillian, M. R., 48 Quinn, J. G., 54 Quinn, P. C., 245 Quinsey, V. L., 527

Raaijmakers, J. G. W., 112 Raaijmakers, J. W. W., 469 Rabin, L. A., 469 Race, J. H., 480 Radvansky, G. A., 161, 250 Rakow, T., 289 Ramsey, S., 271 Randell, T., 29 Ranganath, K. A., 159, 184, 209, 224, 396, 554, 557 Rankin, R. E., 354, 360 Ranyard, R., 46 Rapee, R. M., 492, 493 Raskin, G., 471 Raskin, R., 393 Ratcliff, R., 42, 45, 96, 103, 108, 110, 112, 157, 189 Rauch, W., 257, 286, 316, 474, 543 Ravussin, E., 473 Rawlings, D., 133, 411 Ray, H., 231, 361 Raye, C. L., 82, 85, 86 Raymond, P., 27, 97, 271, 336, 393, 435 Read, D., 71 Reber, A. S., 284 Reber, P. J., 367 Reber, R., 206 Rebetez, C., 246 Reed, A., 414 Reed, A., II, 538, 539 Regier, T., 73 Rehm, J., 472 Reich, R. R., 469 Reinecke, A., 494 Reingold, E., 287 Reingold, E. M., 269 Reis, H. T., 393 Reiss, A. L., 42 Reitman, W. R., 312 Repovs, G., 42, 43, 54 Rescorla, R. A., 224, 225 Revelle, W., 511 Reyna, C., 552 Reynvoet, B., 113 Reznik, I., 440 Rhodes, G., 149 Ric, F., 46 Rice, M. E., 527 Richard, R., 465 Richards, A., 500 Richardson, D. S., 264, 530 Richeson, J. A., 82, 86, 217, 230, 338, 361, 362, 375, 377, 378, 379, 380, 381, 382, 383, 384, 386, 387, 388 Richetin, J., 3, 34, 62, 181, 216, 223, 224, 255, 257, 259, 264, 265, 268, 270, 271, 278, 307, 398, 522, 530, 531, 535 Richman, S. A., 339 Richter, T., 112 Ridderinkhof, K. R., 83 Ridgeway, V., 499 Rief, W., 227, 505 Riefer, D. M., 112, 157, 160, 165 Rieger, E., 503, 504 Riemann, B. C., 493 Riemann, R., 70 Rietbroek, H., 476, 491

Riketta, M., 394 Rilling, J. K., 282 Rinck, M., 176, 179, 223, 271, 468, 491, 493, 494, 500 Ringberg, T., 539 Rist, F., 83 Ritter, W., 83 Rivera, L. M., 70, 228, 338, 418, 556 Rizzolatti, G., 55 Robbins, T. W., 468 Roberts, B. W., 465 Robertson-Nay, R., 495 Robins, R. W., 288, 392, 393 Robinson, J. D., 315 Robinson, M. D., 30, 397, 468, 476, 542 Robinson, T. E., 62, 71, 323, 324, 468 Roccato, M., 257, 258 Roe, R. M., 324 Roedder, E., 457 Roediger, H. L., 159, 312, 469 Roefs, A., 71, 229, 265, 463, 466, 474, 475, 479, 502, 504 Roelofs, J., 477 Roese, N. J., 429 Roestel, C., 507, 508 Rogers, R., 125 Rogers, R. W., 464 Rogers, T. B., 41 Rogers, T. T., 324 Rokeach, M., 552 Rolke, B., 109 Ronis, D. L., 282 Rooke, S. A., 255 Rooke, S. E., 470, 531 Room, R., 472, 480 Rosa, E., 99 Rosano, C., 167 Rose, J., 479 Rose, J. S., 122, 230, 315, 470, 542 Rosen, P. J., 249 Rosenbaum, D. A., 62 Rosenberg, M., 392, 393 Rosenberg, S. E., 397 Rosenthal, D., 204 Rosenthal, R., 143 Rosenzweig, S., 527 Roskos-Ewoldsen, D. R., 67 Rosnow, R. L., 143 Ross, K. L., 98 Ross, L., 143, 151, 454, 553 Ross, S. A., 508 Rossell, S. L., 507 Rossnagel, C., 25, 98 Rostill-Brookes, H., 528 Roth, W. T., 493 Rothbart, M., 224 Rothermund, K., 8, 25, 30, 64, 84, 100, 102, 103, 107, 109, 112, 113, 120, 124, 126, 127, 129, 130, 132, 177, 180, 270, 271, 327, 411, 531 Rotteveel, M., 70, 396 Rouby, D. A., 434, 436, 438 Rousseau, J.-J., 445 Rowe, A. C., 430 Roy, S. A., 378 Rubini, M., 144 Rucker, D. D., 346

Author Index Ruderman, A. J., 354, 475 Rudman, L. A., 121, 219, 222, 227, 229, 231, 232, 246, 247, 296, 300, 338, 357, 362, 377, 379, 397, 400, 412, 433, 434, 450, 470, 540, 542 Rudolph, A., 122, 123, 132, 259, 395, 399 Rugg, M. D., 87 Rumelhart, D. E., 49, 53 Rusbult, C. E., 429, 432 Rüsch, N., 509, 510 Ruscher, J. B., 144 Rush, A. J., 497 Russell, D. W., 465 Russell, J. A., 199, 206, 447 Russell, M., 471 Russin, A., 102, 168, 226, 287, 357 Rutherford, E. C., 495 Rutland, A., 243, 246, 250, 251 Ryan, R. M., 311 Rydell, R. J., 7, 9, 11, 46, 63, 65, 168, 217, 218, 219, 221, 223, 224, 226, 231, 232, 261, 269, 295, 296, 298, 299, 300, 304, 305, 306, 307, 324, 338, 343, 344, 345, 362, 367, 398, 401, 402 Rygh, J. L., 509 Saegert, J., 538 Saenz, D. S., 385 Safran, J. D., 431 Sagrati, S., 231, 346, 497 Saimpont, A., 55 Sakellaropoulo, M., 144, 395, 430, 478 Sakuma, M., 144, 394 Salkovskis, P., 493 Salomon, A. R., 87, 363 Salovey, P., 554 Salvatore, J., 361, 375, 377, 381, 382, 383, 384, 386, 388, 446 Salvi, C., 271 Salvi, D., 144 Samper, A., 200, 289 Sanbonmatsu, D. M., 2, 26, 51, 63, 81, 96, 206, 248, 262, 296, 315, 393, 408, 473, 553 Sander, D., 185, 191 Sandstrom, M. J., 249, 399 Sanfey, A. G., 281, 282 Sanford, R. N., 550 Santelli, A. G., 399 Santos, R., 479 Saporito, J., 491 Saporito, J. M., 491 Sarason, B. R., 222 Sarason, I. G., 222 Sartori, G., 523 Sartory, G., 494 Sassenberg, K., 227, 228, 346 Satpute, A. B., 82, 88 Sattler, D. N., 146 Saucier, D. A., 142, 143, 531 Saucier, G., 142 Sawyer, P., 378 Saxe, L., 354, 375 Sayette, M. A., 464, 472 Sayoc-Parial, M., 378 Scarabis, M., 68, 209, 262, 263, 264, 285, 537

Scepansky, J. A., 143 Schaal, B., 87, 168, 210, 322, 363 Schacht, R., 497 Schachter, S., 55 Schacter, D. L., 4, 40, 54, 150, 199, 269, 312, 354, 355, 469 Schaller, M., 229 Schaller, S., 40 Schallies, E., 340, 556 Schank, R. C., 68, 69 Schanke, S. B., 144 Schatzberg, A. F., 42 Scheier, M. F., 68, 208, 306, 311, 323, 325 Scherbaum, C., 383 Scherer, K. R., 55, 105 Schiffrin, R. M., 64 Schimel, J., 145, 150 Schmader, T., 385, 386 Schmajuk, N. A., 221 Schmalt, H., 142 Schmeichel, B. J., 200, 385 Schmidt, A. F., 526 Schmidt, E. A., 229 Schmidt, T., 99 Schmitt, M., 7, 62, 64, 121, 122, 133, 143, 205, 207, 208, 209, 210, 216, 217, 221, 229, 255, 260, 261, 262, 265, 268, 269, 270, 286, 296, 382, 414, 417, 418, 419, 420, 466, 535, 537, 542 Schmitt, W. A., 510 Schmittlein, D., 541 Schmitz, F., 11, 120, 125, 157 Schmukle, S. C., 120, 121, 129, 131, 208, 257, 259, 271, 414, 415, 416, 420, 421, 422, 491, 493, 512 Schnabel, K., 1, 118, 120, 122, 123, 131, 133, 176, 257, 270, 271, 408, 409, 411, 414, 415, 416, 540 Schnall, S., 455 Schneider, R., 492 Schneider, W., 2, 3, 6, 64, 68, 354, 355 Schniering, C. A., 492 Schoeneich, F., 210 Schoenfeld, T., 469 Schoenmakers, T., 231, 472, 479, 542 Scholfield, E. J. C., 500 Schooler, J. W., 204, 263, 288, 549 Schooler, T. Y., 6, 53, 62, 122, 143, 205, 218, 260, 302, 355, 394 Schouten, E., 503 Schrauwen, P., 473 Schreiber, D., 553 Schreiber, T. A., 150 Schröder-Abé, M., 122, 123, 259, 395, 399 Schuette, R. A., 285 Schugens, M. M., 418 Schuh, E. S., 32 Schuhman, H., 450 Schul, Y., 73, 319 Schulte, D., 492 Schultheiss, O. C., 143 Schulz, S. M., 70 Schulze-König, S., 415 Schuman, H., 147, 375 Schumann, D. W., 339 Schütz, A., 122, 123, 259, 395, 399 Schvaneveldt, R. W., 96, 354

579 Schwartz, J., 508 Schwartz, J. L. K., 1, 19, 48, 63, 117, 140, 158, 176, 199, 217, 246, 255, 342, 355, 377, 394, 408, 447, 470, 490, 531, 535, 551 Schwartz, M. B., 555 Schwartz, S. H., 552 Schwarz, N., 6, 7, 11, 25, 63, 105, 118, 120, 147, 206, 210, 219, 228, 233, 268, 269, 283, 296, 299, 307, 315, 346, 418, 466 Schwarzer, R., 67 Schwerdtfeger, A., 120, 420, 512 Schwieren, C., 326, 556 Scinta, A., 438 Scott-Sheldon, L. A., 262, 465 Sears, C. R., 498 Sears, D. O., 375, 549 Sebastian, S., 513 Sechrist, G. B., 226, 341 Sedikides, C., 45, 122, 395 See, M., 342 Segal, Z. V., 231, 346, 497, 499, 501 Seger, C. A., 469 Segui, J., 494 Seibt, B., 62, 63, 65, 69, 70, 216, 221, 227, 230, 248, 281, 287, 296, 297, 315, 316, 322, 502, 504 Seidel, M., 431 Seidel, O., 233 Seifert, A. L., 465 Seise, J., 119, 232, 251, 531 Sekaquaptewa, D., 5, 140, 141, 142, 143, 144, 145, 149, 150, 176, 271, 360, 377, 379, 384, 385, 396, 428, 445, 464, 523 Self, E. A., 320 Selltiz, C., 141, 142, 151 Semin, G. R., 26, 46, 55, 142, 144, 145, 148, 270, 271, 402 Seo, D.-C., 222 Sergeant, J. A., 472 Sestir, M. A., 168, 232 Shadel, W. D., 11 Shadlen, M. N., 324 Shaffer, L. M., 271 Shafran, R., 501, 503, 504 Shah, A. K., 279, 284 Shah, J. Y., 312, 314, 315, 316,319, 321, 322, 323, 325, 326, 432, 433, 439, 474 Shakarchi, R. J., 144 Shallice, T., 43, 55, 201, 211 Shane, M. S., 499 Shapiro, I., 549 Shapiro, J. R., 1, 3, 216, 319, 361, 375, 379, 383, 385, 387, 523, 554 Shapiro, K. L., 528 Shapleske, J., 507 Sharot, T., 280 Sharp, H., 508 Shaver, P. R., 417, 428, 433, 434, 436, 437, 439, 440 Shaw, B. F., 497, 499 Shaw, P., 242 Sheeran, P., 68, 480 Sheldon, M. S., 473 Shelton, J. N., 86, 361, 362, 375, 377, 378, 379, 380, 381, 382, 383, 384, 386, 387, 388

580 Author Index Shelton, J. R., 102 Shelton, N., 446 Sher, K. J., 466, 471, 472 Sherman, J. W., 5, 10, 11, 12, 64, 89, 97, 112, 117, 120, 121, 124, 129, 133, 134, 145, 150, 156, 157, 158, 159, 160, 161, 162, 164, 165, 166, 167, 168, 169, 171, 172, 173, 176, 182, 217, 232, 243, 245, 250, 271, 286, 300, 302, 308, 356, 358, 363, 364, 395, 410, 445, 456, 464, 466, 479, 491, 512, 523, 527 Sherman, S. J., 122, 133, 222, 230, 286, 315, 317, 322, 358, 470, 471, 479, 542 Shiffrin, R., 2, 3, 6, 354, 355 Shiffrin, R. M., 42, 469 Shih, M., 229 Shils, E. A., 550 Shimizu, M., 397 Shimizu, Y., 101, 104 Shimojo, S., 282 Shin, L. M., 492 Shipherd, J. C., 494 Shiv, B., 263, 283 Shoda, Y., 11, 270, 401, 417, 428, 430, 434, 437 Shomrony, A., 399 Shook, N. J., 223, 243 Shoyer, B., 493 Shrauger, J. S., 397 Shumate, R., 227 Shutts, K., 250 Sidanius, J., 229, 383, 549, 550, 554 Siebler, F., 229 Sierra, B., 226 Sieswerda, S., 510, 511 Sigall, H., 3, 354, 368 Sigmon, S. T., 495 Signorella, M. L., 245 Silva, F. J., 5 Simmel, M., 40, 41, 325 Simmons, B. L., 473 Simmons, J. P., 30, 64, 144 Simonson, I., 543 Simpson, J. A., 6 Simson, R., 83 Sinclair, L., 146, 431 Sinclair, S., 131, 161, 222, 230, 247, 338, 362, 363 Singer, J. E., 55 Singer, P., 455 Singer, T., 286, 287 Singletary, S. L., 387 Sio, U. N., 288 Sirevaag, E. J., 84 Sirigatti, S., 183 Sirigu, A., 55 Sitskoorn, M., 506 Sivers, H., 81 Skitka, L. J., 551 Skorek, M., 250 Skowronski, J. J., 43, 45, 47, 48, 246, 280 Slater, A. M., 245 Slater, A. O., 245 Sloman, S. A., 52, 71, 298, 304, 324, 362 Sloore, H., 183 Small, S. L., 82 Smart, S. A., 397

Smeesters, D., 264 Smeets, E., 504 Smith, A., 448 Smith, C., 209 Smith, C. T., 159, 184, 208, 396, 554 Smith, E., 56 Smith, E. C., 465, 466 Smith, E. E., 43, 85 Smith, E. R., 9, 38, 39, 40, 41, 45, 46, 48, 51, 52, 53, 55, 62, 64, 65, 67, 72, 121, 145, 220, 224, 229, 242, 243, 268, 269, 270, 271, 279, 297, 298, 301, 305, 324, 354, 362, 364, 365, 367, 377, 394, 401, 402, 418, 429, 439 Smith, G., 527 Smith, G. T., 69 Smith, J., 509, 524, 529 Smith, J. K., 531 Smith, M. B., 140 Smith, N. K., 87 Smith, P., 528 Smith, P. K., 145, 229 Smith, R. J., 541 Smith, S. M., 46, 338, 347 Smith-Janik, S. B., 491, 496 Smolensky, P., 49, 52 Smulders, F. T. Y., 466, 474, 475, 470, 531, 542 Smurda, J. D., 397 Smyth, F. L., 122, 551, 555 Sniderman, P. M., 450 Snodgrass, J. G., 161 Snowden, R. J., 1, 12, 233, 434, 509, 522, 524, 525, 526, 527, 529, 530, 531 Snyder, C. R. R., 2, 3, 354, 355, 511 Snyder, M., 431 Sokolowski, K., 142 Solomon, S., 552 Sommers, S. R., 382 Son Hing, L. S., 141, 146, 377 Soon, C. S., 86, 284 Sorrentino, R. M., 323 Southall, A., 498 Spalding, L. R., 259, 271, 394, 398 Spampinato, M. V., 554 Spangenberg, E. R., 541 Sparks, P., 465 Spears, N., 542 Spears, R., 200 Spector, I. P., 493 Spelke, E. S., 245, 250 Spence, A., 271 Spencer, S. J., 67, 146, 259, 356, 357, 394, 397, 398, 399 Sperber, D., 241 Sperling, G., 199 Spicer, C. V., 379 Spiering, M., 434 Spivey, M. J., 324 Sprague, J., 553 Sprott, D. E., 541 Spruyt, A., 5, 19, 25, 26, 27, 28, 29, 30, 32, 34, 40, 63, 64, 96, 97, 101, 105, 117, 123, 131, 157, 176, 177, 179, 223, 233, 267, 464, 522, 538 Squire, L. R., 354, 355, 366, 367 Squires, N. K., 553

Sriram, N., 86, 134, 176, 251, 269, 512 Sritharan, R., 6, 11, 12, 158, 216, 224, 266, 268, 296, 300, 345, 397, 400, 531, 538 Srull, T. K., 47, 48, 318, 354 Stacy, A. W., 147, 176, 463, 464, 465, 469, 470, 471, 473 Stahl, C., 11, 112, 119, 120, 157, 158, 160, 170, 245 Stallworth, L. M., 229, 550 Stangor, C., 40, 87, 226, 341, 356 Stapel, D. A., 144, 229, 394 Stapert, D., 474 Stark, H., 40 Stark, H. A., 150 Stayman, D. M., 538 Steeh, C., 375, 450 Steele, C., 378 Steele, C. M., 146, 150, 379, 385, 446, 465, 480 Steele, G. M., 552 Steele, J., 225, 339 Steele, J. R., 225, 229, 246, 251, 339, 362 Steenbergen, M. R., 556 Steenkamp, J.-B. E. M., 537 Stefanucci, J. K., 491 Steffens, M. C., 123, 130, 131, 232, 251, 415, 420 Stein, M., 466 Steinberg, J. A., 145, 394 Steinman, R. B., 8, 133, 176, 207, 270, 394, 395, 418, 543, 559 Steinmetz, J. E., 473 Steinnagel, S. L., 46 Stenner, K., 145 Stephan, C. W., 378 Stephan, W. G., 378, 379 Stepper, S., 55, 70 Stern, E., 113 Sternthal, B., 538 Stevens, D., 554 Stevens, J. A., 55 Stewart, A. J., 142 Stewart, B., 1, 176, 418, 447, 512 Stewart, B. D., 11, 63, 68, 71, 104, 161, 162, 163, 210, 227, 233, 250, 271, 359, 363, 377, 464 Stewart, N., 270 Stewart, P., 551 Stewart, S. H., 472 Stice, E., 474 Stocco, A., 283 Stokes, M. B., 7, 122, 159, 210, 269 Stolz, J. A., 109, 111, 199 Stopa, L., 491 Storbeck, J., 30, 208, 211, 365 Strack, F., 9, 10, 27, 43, 55, 62, 63, 64, 65, 66, 68, 69, 70, 71, 72, 73, 118, 144, 200, 202, 203, 204, 205, 206, 209, 216, 219, 220, 221, 226, 227, 228, 256, 257, 258, 260, 261, 263, 279, 286, 287, 297, 298, 302, 303, 304, 324, 365, 366, 377, 388, 409, 410, 413, 414, 415, 422, 435, 439, 440, 465, 466, 469, 474, 478, 536, 543, 554 Strain, L. M., 217, 223, 224, 296, 298, 338, 344, 362, 367, 402 Strange, B. A., 81

Author Index Strathman, A. J., 339, 347 Strauss, G. P., 507, 508 Strayer, D. L., 129 Stritzke, W. G. K., 466 Stroebe, W., 474, 480 Stroessner, S. J., 145 Stroh, P., 556 Stromberg, L., 503 Stroop, J. R., 96, 159, 528 Struthers, C. W., 399 Suci, G. J., 89, 142 Sudheimer, K. D., 82 Sudman, S., 147 Suga, M., 508 Sullaway, M., 431, 531 Sullivan, J. L., 553, 554 Sulloway, F., 551 Suls, J., 393, 398 Summerton, N., 477 Sunstein, C., 455 Suslow, T., 499, 500, 507, 508 Sussman, S., 470 Sutton, R. M., 144 Swainson, R., 83 Swann, W. B., 180, 208, 223, 431 Swann, W. B., Jr., 144, 251, 266, 392, 393, 395, 398 Swanson, J., 230, 379, 420, 530 Swanson, J. E., 121, 470, 542 Swerdlow, N. R., 493 Swets, J. A., 157, 160 Swim, J. K., 380, 383 Szöke, A., 506 Taber, C., 549 Taber, C. S., 549, 553 Tackenberg, A., 494 Tafarodi, R. W, 393 Taghavi, M. R., 499 Tajfel, H., 224, 555 Talbot, L., 500 Tan, J., 495 Tanabe, Y., 144 Tanghe, A., 475 Tangney, J. P., 265, 418, 465 Tannenbaum, D., 452 Tannenbaum, P. H., 89, 142, 539 Tanner, R. J., 491 Tarrier, N., 506, 508 Tassinary, L. G., 338 Tata, P., 499 Taylor, C. M., 87, 363 Taylor, C. T., 495 Taylor, S. E., 41, 53, 354 Taylor, S. F., 81, 82 Teachman, B. A., 1, 12, 133, 227, 233, 248, 430, 466, 477, 489, 491, 495, 496, 497, 498, 501, 505, 512, 531, 552 Teichmen, Y., 245 Teige, S., 122, 132, 271, 411 Teige-Mocigemba, S., 5, 8, 11, 19, 26, 27, 29, 31, 32, 33, 40, 63, 96, 98, 117, 120, 125, 127, 129, 132, 150, 157, 176, 179, 217, 232, 270, 358, 395, 411, 420, 445, 464, 491, 523, 531 Tennen, H., 144, 222, 400 Tenney, N., 493

Terry, C., 399 Tesser, A., 347 Testa, A., 437 Tetlock, P. E., 3, 368, 447, 448, 551 Theiss-Morse, E., 554 Thewissen, R., 468, 479 Thistlethwaite, D. L., 142, 146 Thomas, A. P., 457, 480 Thomas, J., 468 Thomas, J. J., 502 Thomas, M., 201 Thomas, S., 223 Thompsen, D. M., 470 Thompson, A. W., 552 Thompson, E., 271 Thompson, E. P., 73, 225, 226 Thompson, M., 145, 176, 360, 377 Thompson-Schill, S. L., 366 Thomsen, C. J., 553 Thomson, D. W., 431 Thorn, T. M. J., 88 Thornhill, R., 70 Thornton, D., 525 Thorpe, G. L., 495 Thorpe, S., 493 Thorsteinsson, E. B., 255, 470, 531 Thrash, T. M., 143 Thunberg, M., 70 Thurstone, L. L., 140 Thush, C., 418, 466, 470, 471, 472, 479, 542 Tice, D. M., 71, 263, 393, 439, 543 Tiedens, L. Z., 346 Tietje, B. C., 258, 537, 540 Tiffany, S. T., 465, 468 Tillotson, K. M., 477 Toda, M., 289 Todd, A. R., 1, 82, 216, 278 Todorov, A., 280, 281, 557 Tolman, E. C., 323 Tomaka, J., 392, 393 Tomasello, M., 241 Tomelleri, S., 87, 88, 217, 247 Tononi, G., 200, 201, 202, 203 Topolinski, S., 206, 211 Tormala, Z. L., 53, 151, 206, 219, 226, 298, 335, 336, 340, 344, 400 Tota, M. E., 45 Toth, J. P., 159 Touryan, S. R., 82 Touyz, S. W., 504 Towles-Schwen, T., 5, 10, 66, 67, 222, 223, 263, 264, 387, 402, 536, 539 Townsend, E., 271 Townsend, J. T., 71, 324 Townsend, S., 378 Tracy, J. L., 288 Trafimow, D., 144 Trail, T. E., 361, 387 Traill, S. K., 499, 500 Tranel, D., 67, 199, 284 Trasselli, C., 223, 300 Trawalter, S., 1, 3, 82, 216, 361, 375, 377, 378, 379, 380, 381, 382, 383, 384, 387, 388, 523, 554 Treisman, A., 2 Triandis, H. C., 282 Trippe, R., 494

581 Trivers, R., 70 Troetschel, R., 199, 286, 318, 362 Trope, Y., 9, 64, 72, 145, 164, 279, 337, 543, 554, 558 Tropp, L. R., 222, 375, 378, 379 Trost, M. R., 210 Truchon, C., 499 Trzesniewski, K. H., 392 Tudor, M., 429 Tuerlinckx, F., 185 Tuffiash, M., 287 Tugade, M. M., 264 Tukey, J. W., 110 Tulen, J. H. M., 507 Tulving, E., 39, 40, 42, 55, 150 Turiel, E., 456, 552 Turk-Browne, N. B., 39 Turner, J. C., 555 Turner, R. N., 222, 247 Turner, S., 387 Turpie, C. A., 377, 382, 383, 388 Tversky, A., 68, 549 Tyler, R. B., 2, 354 Uetsuki, M., 508 Uhlmann, E., 67, 123, 230, 251, 255, 379, 416, 455, 530, 535 Uhlmann, E. L., 326, 335, 361, 420, 447, 452, 453 Uleman, J. S., 197, 311 Ullman, J. B., 470, 473 Unger, S. B., 491 Urland, G. R., 83, 87, 360 Vache, K., 498 Vala, J., 555 Valdesolo, P., 455 Valencia, J. F., 144 Vallacher, R. R., 55, 317 Vallone, R. P., 151 van Baaren, R. B., 145, 200, 288, 289, 526, 542 Van Bavel, J. J., 82, 185, 224 Van Beveren, N. J. M., 507 van de Braak, M., 471 van de Luitgaarden, J., 470, 472 Van den Bergh, O., 225, 468, 503 Van den Bos, K., 289, 555 Van den Bussche, E., 113 van den Hout, M. A., 491, 494 Van den Noortgate, W., 113 van den Wildenberg, E., 468, 470 Van Den Wildenberg, W., 83 van der Pligt, J., 219, 304, 336, 464, 465, 466 Van Doorn, E. A., 289 Van Eijs, Y., 475 van Furth, E., 504 Van Gucht, D., 468, 479 van Harreveld, F., 464 van Heerden, J., 123, 177 van Kamp, S., 287 van Knippenberg, A., 8, 143–144, 176, 209, 223, 263, 270, 282, 320, 395 van Laar, C., 383 Van Lange, P. A. M., 429 van Leeuwen, M., 224, 526, 528

582 Author Index van Olden, Z., 288, 543 van Rooij, I., 73 van Schoor, G., 472 van ‘t Wout, M., 281 van Veen, V., 83, 84 van Woerden, N., 470, 531, 542 Vance, S. L., 67, 169, 363 Vancleef, L. M., 475, 477 Vandekerckhove, J., 25, 101, 123, 267, 538 Vandello, J. A., 446 Vanderstraeten, G., 476 Vandromme, H., 25, 26, 29, 97 Vanman, E. J., 360 Vansteenwegen, D., 223, 468 Vargas, P., 5, 140, 142, 144, 145, 176, 271, 360, 377, 428, 445, 464, 523 Vargas, P. T., 141, 142, 143, 148, 151, 181, 271, 396 Vartanian, L. R., 502, 555 Vasquez-Suson, K. A., 387 Vasyukova, E., 287 Vaughan, H. G., 83 Vaughan, J., 62 Vazquez, C., 497 Veale, D., 505 Veenstra, E., 474 Verbaak, M. J. P. M., 493 Verdejo-Garcia, A., 466, 472, 479 Verkooijen, K., 229, 470 Verlegh, P. W. J., 537 Verney, S. P., 498 Verplanken, B., 62, 68, 144, 265, 282 Verschuere, B., 232, 523 Versek, B., 145, 394 Vertommen, S., 510 Vianello, M., 133, 176, 420 Viera, E., 421 Vinck, J., 475 Vinogradov, S., 507 Visalberghi, E., 278 Visser, P. S., 559 Vitale, B., 506 Vitousek, K., 504 Vlaeyen, J. W., 271, 475, 477, 478 Voci, A., 222, 247 Vogeley, K., 201 Vogel-Sprott, M., 469 Vogelzang, L., 503 Vohs, K. D., 71, 200, 385, 392, 447, 465, 543 Voils, C. I., 169, 224, 268, 361, 363 Volkow, N. D., 468 von Collani, G., 217, 401, 415, 476, 506 von Hippel, C., 216, 270, 451, 477 von Hippel, W., 5, 133, 140, 141, 142, 143, 144, 145, 148, 149, 157, 161, 166, 167, 168, 169, 176, 216, 231, 250, 270, 271, 360, 377, 396, 428, 445, 451, 464, 477, 523 von Ranson, K. M., 503 Vorauer, J. D., 377, 378, 382, 383, 388 Vorberg, D., 99 Voss, A., 11, 101, 104, 112, 120, 125, 157, 158, 163 Voss, J., 112, 120 Voss-Humke, A. M., 465 Vroom, V. H., 67 Vythilingam, M., 493

Waddell, G., 477 Wagar, B. M., 212 Wager, T. D., 81 Wagner, A. D., 85 Wagner, A. R., 224 Wakefield, R., 429 Waldeck, T. L., 465 Walitzer, K. S., 472 Walker, M. K., 504 Wallas, G., 288 Wallenberg, A. S., 531 Waller, G., 504 Walling, D., 429 Walters, G., 356 Walther, E., 217, 219, 221, 223, 224, 225, 228, 300, 303, 338 Waly, P., 142 Wang, C. E., 144 Wang, G. J., 468 Wang, J., 543 Wänke, M., 63, 259, 260, 262, 263, 316, 537, 559 Ward, A., 465 Ward, T., 524, 525 Warlop, L., 264 Warren, R. E., 101, 102 Warrington, E. K., 4 Wasel, W., 87, 168, 210, 322, 363 Waterman, M., 528 Waters, A. J., 231, 315 Watkins, P. C., 498, 499 Watson, B., 144 Watson, D., 393 Watson, J. B., 68 Watson, J. J., 537 Watson, J. M., 469 Watts, F. N., 490 Way, B. M., 554 Webb, T. L., 68 Weber, E. U., 67, 284 Weber, G., 495 Webster, D. M., 210 Wegener, D. T., 165, 225, 296, 336, 338, 340, 342, 344, 345, 346, 348 Wegner, D. M., 31, 55, 83, 88, 165, 201, 282, 284, 317 Weinberger, A. D., 509 Weinberger, J., 312, 556, 557 Weinberger, T. R., 507 Weingardt, K. R., 464 Weiskrantz, L., 4 Weiss, T, 494 Weisskrantz, L., 198, 199, 202 Welbourne, J., 47 Welch, L. P., 494 Welch, N., 67 Well, A. D., 112 Wells, G. L., 342, 346 Wendelken, C., 85, 440 Wentura, D., 3, 5, 8, 25, 28, 29, 32, 54, 64, 84, 95, 97, 99, 100, 101, 102, 103, 105, 106, 107, 109, 111, 112, 113, 120, 124, 126, 127, 130, 132, 176, 177, 180, 217, 224, 246, 271, 357, 358, 394, 431, 445, 464, 523, 531 Wenzel, A., 493, 494

Werner, R., 506 Wero, J. L. F., 149 Werrij, M. Q., 474 Werth, L., 62 Wertheimer, M., 53 West, T. V., 361, 387 Westen, D., 554, 556, 557 Westerlund, J., 493 Westerterp, K. R., 473 Wetter, D. W., 315 Wetzel, C., 384 Whalen, P. J., 81, 82, 199 Wheatley, T., 284, 552 Wheeler, M. E., 82, 360 Wheeler, S. C., 151, 259, 264, 305, 335, 342,€346, 399, 413, 559 Whitaker, D. J., 429, 438 Whitchurch, E. R., 230 White, J., 493, 494, 496, 499, 512 White, P., 344 White, R. W., 140 Whiteford, S., 503 Whitfield, M., 7, 210, 221, 228, 261 Whitfield, M. L., 398 Whitfield, S. L., 81 Wichter, J., 434 Wicklund, R. A., 320 Wieber, F., 228 Wiers, R. W., 1, 12, 147, 209, 211, 229, 231, 233, 257, 265, 270, 418, 463, 464, 465, 466, 468, 470, 471, 472, 479, 480, 489, 501, 531, 542 Wiesner, A., 259, 399 Wigboldus, D., 8, 176, 219, 270, 271, 304, 336, 466 Wikström, J., 493 Wilbarger, J. L., 199, 283 Wilbur, C., 4, 64, 198 Wilbur, C. J., 224, 286, 326, 396 Wilhelm, F. H., 495 Wilhelm, S., 492, 505 Wilhelm, U. B., 505 Wilkin, K., 144 Wilkowski, B. M., 397 Williams, A., 246, 250, 251 Williams, C. J., 1, 19, 54, 70, 88, 95, 140,€158, 176, 210, 217, 259, 315, 335, 355,€377, 398, 446, 523, 535, 553, 558 Williams, D., 150 Williams, D. R., 379 Williams, G. V., 47 Williams, J., 508 Williams, J. M. G., 490, 493, 494, 499, 504, 528 Williams, J. N., 105 Williams, K. D., 130, 168, 230, 363 Williams, M. J., 448 Williams, N. A., 472 Williams, P., 541, 542 Williams, R., 492, 493, 498, 499 Williams, T., 150 Williamson, D. A., 499 Wills, T. A., 468 Wilson, G. T., 501, 504 Wilson, M. D., 150

Author Index Wilson, T. D., 4, 6, 22, 53, 62, 63, 71, 72, 83, 117, 122, 143, 160, 197, 198, 200, 201, 204, 205, 206, 208, 209, 210, 211, 217, 218, 219, 222, 224, 226, 228, 231, 260, 261, 263, 279, 283, 288, 302, 303, 311, 312, 355, 356, 364, 368, 369, 393, 394, 400, 401, 402, 409, 522, 549 Wimmer, G. E., 283 Winkielman, P., 46, 71, 199, 206, 283, 323, 522 Winston, J. S., 81 Winter, D. G., 141, 142, 143, 147 Wippich, W., 266, 395 Witherspoon, D., 354, 355 Wittenberg, M. T., 393 Wittenbrink, B., 1, 6, 8, 30, 46, 54, 63, 67, 83, 88, 95, 96, 97, 99, 101, 104, 106, 118, 157, 176, 218, 219, 220, 221, 229, 269, 271, 287, 296, 315, 356, 357, 359, 363, 377, 378, 449 Wittig, M. A., 397 Wittrock, D. A., 476 Woddis, D., 436 Wojnowicz, M., 324 Wolfe, C., 146 Wolfe, C. T., 67, 356 Woloshyn, V., 469 Wonderlich, S., 502 Wong, K. Y., 289 Wood, J. M., 117 Wood, J. N., 554 Wood, J. V., 397 Wood, M. D., 471, 472 Wood, P. K., 471, 472 Wood, W., 68, 69, 72, 141 Woodworth, M., 529 Woody, S., 491, 496 Woody, S. R., 227, 491, 495, 531, 552

Woolf, K., 144 Wooten, D. B., 538 Word, C., 446 Word, C. O., 385 Woud, M. L., 223, 225 Wright, E. W., 201 Wright, K., 537 Wright, M. O., 378 Wright, R. A., 320 Wu, G., 286 Wyer, R. S., 318, 354 Wyer, R. S., Jr., 38, 40, 41, 44, 47, 48, 53, 141, 284, 538 Wynn, K., 278, 325 Xu, J., 367 Yahr, J., 245 Yaniv, I., 289 Yates, J. F., 282 Ybarra, O., 105 Yee, N., 557 Yen, Y., 437 Yeung, N., 83 Yi, D., 39 Yonelinas, A. P., 159 Young, J. E., 509 Young, L., 454 Young, P. T., 323, 325 Yovel, I., 494, 511 Yovetich, N. A., 432 Yue, D. N., 499 Yule, W., 499 Yzerbyt, V. Y., 264, 280 Zacks, R. T., 47 Zaichkowsky, J. L., 536

583 Zajonc, R. B., 9, 39, 41, 54, 55, 101, 103, 168, 198, 280, 282, 428, 522 Zakrisson, I., 550 Zald, D. H., 81, 82 Zalla, T., 185 Zamansky, H. S., 527 Zanna, M. P., 146, 227, 259, 336, 377, 385,€394, 397, 398, 399, 429, 434, 446, 463 Zayas, V., 270, 417, 428 Zebrowitz, L. A., 280, 281 Zeelenberg, R., 469 Zeigler-Hill, V., 1, 7, 144, 210, 305, 392, 393, 396, 398, 399, 408, 428 Zeitlin, S. B., 493 Zelazo, P., 250 Zemack-Rugar, Y., 199, 283 Zerbes, N., 119, 232, 251, 531 Zetocha, K. J., 542 Zhang, L., 447 Zhang, Y., 314, 321 Zhong, C. B., 289 Zhu, D. C., 82 Ziegert, J. C., 450 Zilber, A., 440 Zimmerman, M., 498 Zimmerman, R. S., 257, 473 Zinbarg, R. A., 492 Ziv, T., 245 Zogg, J. B., 470 Zogmaister, C., 3, 34, 62, 65, 181, 216, 217, 224, 229, 247, 255, 257, 258, 271, 307, 398, 522, 523, 535, 559 Zola, S. M., 366 Zukova, J., 281 Zurbriggen, E. L., 435 Zwaan, R. A., 39, 42, 45

Subject Index

Page numbers followed by f indicate figure; n, note; and t, table ABC model, 158, 170 Abel Screen, 527–528 Acceptance, 431–432 Access consciousness introspective access to mental experiences and, 206–208, 207f overview, 203–204, 203f Accessibility of representations intentional goals and, 313–314 overview, 39 political cognition and, 553, 558 relationships and, 429 unintentional goal activation and, 321 See also Mental representations Accommodation, 437–438 Action-oriented goal pursuit, 314. See also Goal pursuit Activation connectionist models and, 49–50 focal memory and, 45 introspective access to mental experiences and, 207 parallel search methods and, 44–45 of positive and negative information simultaneously, 304–305 Quad model and, 165 relationships and, 427, 437 spreading activation models and, 50 spreading inhibition and, 46–47 working memory and, 45–46 Adaptive unconscious, 204–210, 207f. See also Unconscious processing Adult development, 243–244, 249–250, 417. See also Developmental approach Affect facial expressions and, 70 predictive validity and, 263 psychotic disorders and, 507 See also Emotion



Affect misattribution procedure, 103–104, 271, 359 Affect–cognition models, 554 Affective bias, 359–360 Affective forms of representation, 55, 56. See also Mental representations Affective priming automaticity of, 26–33, 35n conscious processing and, 210 consumer behavior and, 536 overview, 24–25, 103, 176 pain and stress and, 476 predictive validity and, 271 psychotic disorders and, 507 substance use and misuse and, 470 See also Evaluative priming paradigm; Priming effects Affective response, 262–263, 282–284 Affective Simon effect, 176 Affordances, stimulus, 280–281 Aggression evolutionary processes and, 70 persuasion and, 345–346 violent offending, 529–531 Aging, 249–250 Agoraphobia, 490. See also Anxiety disorders Agreeableness, 415–416 Alcohol use, health psychology and, 463–472, 468, 480n Alcohol-myopia theory, 480n Altruism, behavior and, 70 Ambivalence, 307, 336 Amygdala implicit race bias and, 366–367, 366f intergroup bias and, 359–360 response system criterion and, 184–185 social cognition and, 81–89, 81t unconscious processing and, 198–199

584

Anorexia nervosa. See Eating disorders Anterior cingulate cortex, 81t, 83 Anticipated affect, 282 Antisocial personality disorder, 509–511 Anxiety affective reactions and, 283 intergroup anxiety, 378–384 intergroup bias and, 360–361 relationships and, 430 self-concept and, 419–420 threat and, 436 Anxiety disorders, 490–497 Appraisals, 322, 468 Approach behaviors, 316–317. See also Approach–avoidance Approach–avoidance, 225, 316–317, 494 Argument quality technique, 348n Assertiveness, 431 Assessment, 41. See also Measurement procedures Assimilation effects, 421 Associated systems theory (AST), 54, 55–56 Association, 64–65, 478 Association activation, 165, 362 Association-based measures, 395 Associative network models, 44–49 Associative processes health psychology and, 465, 466, 469–471, 478–479 implicit race bias and, 364–366, 365f mood disorders and, 497 overeating and dieting, 473–474 self-concept and, 410 substance use and misuse and, 469– 471 See also Mental associations Associative representations, 410, 415–416. See also Mental representations

Associative–propositional evaluation model attitude measures and, 297–298, 300, 301, 302–303, 304 deliberative decision making and, 285 dissonance theory and, 302–303 mental associations and, 228 overview, 43, 212n, 220–222 self-concept and, 413–414 social cognition and, 65 Atomatic processing, 2–3 Attachment developmental approach and, 247 implicit self-concept and, 417 interpersonal goals and, 433–434 overview, 426 relationships and, 428, 430, 431 threat and, 436 Attachment goals, 433–434. See also Goal pursuit Attachment theory, 428 Attention affective priming effects and, 32–33 anxiety disorders and, 493–495 deliberative decision making and, 284, 288–289 eating disorders and, 504–505 introspective access to mental experiences and, 207–208 mood disorders and, 197 relationships and, 438–439 Attention bias tasks anxiety disorders and, 493–495 body dysmorphic disorder and, 505–506 eating disorders, 503–504 mood disorders and, 499–500 personality disorders and, 510–511 psychotic disorders and, 507–508 Attention research, 2–3 Attentional blink paradigm, 528 Attitude measures consequences of discrepancies on, 305–307 mechanisms of consistency and inconsistency, 299–304 overview, 295–296, 307, 335–336 theoretical models, 296–299 Attitude theory, 295–299 Attitudes adult development and, 249–250 attention research and, 2–3 behavioral decisions and, 67–68 change and, 336–343, 337f, 343–347 consumer behavior and, 537–538, 539–540, 543 deliberative decision making and, 284–286, 285–286 developmental approach and, 244–249 Implicit Association Test (IAT) and, 130 intentional goals and, 314–316 intergroup attitudes, 244–249 introspective access to mental experiences and, 207 mathematical modeling and, 156–157 measures of, 140–151 mental representations and, 39

Subject Index metacognitive model of, 336, 336f motivation and opportunity as determinants (MODE) model and, 217–218 overview, 140–141, 335–336, 348 persuasion and, 343–347 political cognition and, 553–554 positive and negative information and, 304–305 relationships and, 427–429 social cognition and, 63, 66–67, 66f social justice and, 447–448 structure, 336, 336f unconscious processing and, 198 unintentional goal activation and, 321–322 See also Mental associations Attraction, 434–435 Automatic bias, 164, 284–286. See also Implicit bias; Implicit prejudice Automatic decision making affective reactions, 282–284 compared to deliberated decision making, 287–289 habit, 282 learned associations, 281–282 overview, 278–279, 287–289, 290 stimulus affordances, 280–281 varieties of, 279–284 See also Decision making Automatic Default model of PD, 163–164, 164f Automatic processing, 490, 500 Automatic thoughts, 339–341 Automaticity affective priming effects and, 26–33 anxiety disorders and, 495 attention research and, 2–3 attitude change and, 338, 347 consumer behavior and, 536–537, 543 implicit measures and, 20–24, 21t, 35n implicit self-esteem and, 400 implicitness criterion and, 181–182 indirect measures and, 10 overview, 355–356 political cognition and, 553, 556, 557–558 racial bias and, 377 response system criterion and, 184–185 social cognition and, 64 stereotypes and, 67 Autonomous automaticity features, 20–24, 21t Avoidance behaviors, 316–317, 479. See also Approach–avoidance Avoidant personality disorder, 509–511 Balance theory, 303–304. See also Cognitive consistency theories Balance-congruity principle, 540 Balanced identities, 411–414 Behavior adaptive unconscious and, 204–205 conflicts and, 70–72 conscious processing and, 203–204, 203f consumer behavior, 535–544 decision making and, 279

585 expectancies and, 429–432 goal pursuit and, 68, 311–312 health behavior, 463–469, 467f, 472–478 intentional goals and, 316–317 intergroup bias and, 360–361 during interracial contact, 376f irrational behavior, 70–72 mathematical modeling and, 156–157 motivation and opportunity as determinants (MODE) model and, 217–218 overeating and dieting, 473–475 overview, 62–63, 72–73, 255–256 pain and stress, 475–478 personal moderators, 264–265 personality self-concept and, 415–416 political behavior, 558–559 prediction of, 257–261, 258f, 261–266 protection against HIV/AIDS, 472–473 Quad model and, 169–170 racial bias and, 449–451 relationships and, 429–432 situational moderators, 261–264 social cognition and, 63–70, 66f social justice and, 446–447, 449–451 theory and, 72–73 types of, 261–262 unintentional goal activation and, 318–320 vice behaviors, 542 voting behavior, 558–559 See also Predictive validity Behavioral decisions, 67–68 Behavioral forms of representation, 55, 56. See also Mental representations Behavioral measures, 141, 184–185 Behavioral mediators, 67–70 Behavioral process model of personality (BPMP), 415–416 Behavioral willingness, 465 Behavior–outcome expectancies, 430–432. See also Expectancies Beliefs, political cognition and, 549–553 Bias attitude change and, 339 conscious processing and, 457 Control Default model of PD and, 163 deliberative decision making and, 284–286 developmental approach and, 247 eating disorders and, 504 learned associations and, 281–282 psychopathology and, 512 Quad model and, 168–169 Signal Detection Theory (SDT) and, 160–161 social justice and, 445–451 See also Implicit bias; Intergroup bias; Racial bias Big Five personality dimensions, 415–416 Bodily movements, 345–346 Body dysmorphic disorder, 505–506 Borderline personality disorder, 509–511 Boundaries, 325–326 Breadth-Based Adjective Rating Task (BART), 141t, 149, 394 Breadth-based adjectives, 145

586 Brief IAT (BIAT), 134, 176 Bulimia nervosa. See Eating disorders Categorization attitude measures and, 297 intergroup bias and, 357 mental associations and, 229–230 race and ethnicity and, 246–247 social neuroscience and, 87–88 Category membership, 223–224 Causality anxiety disorders and, 495–496 body dysmorphic disorder and, 506 eating disorders and, 504 mood disorders and, 500 personality disorders and, 511 psychotic disorders and, 508 violent offending and, 530–531 Change attitude and, 336–343, 337f ideological change, 551–552 implicit self-esteem and, 400–401 mental associations and, 224–228 political cognition and, 551 Child development. fears and, 248 intergroup attitudes and, 245 obesity and, 248–249 overview, 250–252 race and ethnicity and, 246–247 suicide and self-injury, 248 See also Developmental approach Choice affective reactions and, 282–283 conscious processing and, 200 deliberative decision making and, 285 overview, 279 See also Decision making Classical conditioning procedures, 338 Clinical psychology, 248 Closeness, 431 Cognitive abilities, 129–130 Cognitive capacity, 262 Cognitive consistency theories attitude measures and, 302–304 consumer behavior and, 539–540 invalidation processes and, 209–210 Cognitive control, 85–87, 286–287 Cognitive depletion, 381 Cognitive dissonance, 226, 541 Cognitive model, 197, 506 Cognitive processing behavior and, 263 deliberative decision making and, 285–286, 286–287 expectancies and, 430 goal pursuit and, 311–312 intentional goals and, 316 moral judgment and, 454–456 need for cognition, 264 predictive validity and, 263 racial bias and, 446 relationships and, 426–427, 430, 438–439 threat and, 438–439 Cognitive representations, 39. See also Mental representations

Subject Index Cognitive response approach, 337 Cognitive-behavioral models, 506 Cognitive-motivational theory, 313–314 Collaboration, 252 Collective self, 409, 410. See also Self-concept Commitment, 437–439, 440 Communication, 201–204, 203f, 343–347 Compatibility order, 132–133 Concept priming. See Semantic priming paradigm Concepts, 39 Conditioned responses, 81–82, 223 Confirmatory factor analysis, 419 Confirmatory strategies, 284–285 Conflict behavior and, 70–72 deliberative decision making and, 286–287 social neuroscience and, 83–85 Connectionist models, 49–52, 53 Conscientiousness, 465 Conscious automaticity features, 20–24, 21t Conscious processing affective priming effects and, 32 affective reactions and, 283 attention research and, 2–3 cue utilization and, 209–210 decision making and, 279, 288–289, 290 evidence for, 199–201 as a global workspace, 201–204, 203f implicit memory research, 4 intentional goals and, 314–316 memory and, 42–43 overview, 197–199, 210–211 self-concept and, 409 See also Unconscious processing Consciousness, 454–456, 457 Consistency, 393, 413–414 Constructivist perspective, 299 Consumer behavior attitude measures and, 537–538 evaluative conditioning and, 539–540 future directions in the study of, 541–543 indirect measures and, 537–538 motivation and opportunity as determinants (MODE) model and, 536–537 overview, 535–536, 543–544 self-identity activation, 538–539 Consumer ethnocentrism, 537 Contact hypothesis, 222–223 Context cues, 229 Context-specific effects developmental approach and, 247 mental associations and, 228–232 working memory and, 46 Contextualization, 231 Control, 232, 436, 465 Control Default model of PD, 158, 161–163, 162f, 171 Controlled automaticity features, 20–24, 21t Controlled processing attention research and, 2–3 deliberative decision making and, 286–287

health psychology and, 466 implicit race bias and, 363 intergroup anxiety and, 380–384 overview, 355–356 racial bias and, 377 Convergent validity, 393. See also Validity Correspondence principle, 268–269, 419 Counteracting goals, 20–24, 21t, 30–32 Counterattitudinal information, 300 Criteria to classify implicit measures, 178–190, 179t, 182f Cue utilization, 338–339 implicit race bias and, 367–368 limits to, 209–210 mental associations and, 224, 229 overview, 211 Cultural knowledge, 301–302 Cultural values, 451–456 Data analysis, priming effects and, 109–112 Decision making automaticity and, 279–284, 287–289 consumer behavior and, 536–537 deliberative decision making, 284–289 moral judgment and, 454–456 overview, 278–279, 290 See also Judgment Decisions, behavioral, 67–68 Defensive behavior, 259 Deliberate behaviors, 261–262. See also Behavior Deliberate decision making, 284–289, 290. See also Decision making Delusions, 508. See also Psychotic disorders Denial, 525 Dependency regulation, 426, 431 Dependent personality disorder, 509–511 Depletion manipulations, 286, 381 Depression. See Mood disorders Descriptive information, 223, 226 Developmental approach goal pursuit and, 325 intergroup attitudes, 244–249 older adults, 249–250 overview, 241–244, 250–252 research literature regarding, 244–250 self-concept and, 422 Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR), 490 Diagnostic behavior, 208–209, 490 Dieting behavior, 473–475, 480n Diffuse self, 409, 410. See also Self-concept Diffusion model choosing among models and, 171–172, 172f mathematical modeling and, 157 overview, 112, 170–171 Direct situational cuing, 69 Discrepant high self-esteem, 398–399. See also Self-esteem Discrimination implicit race bias and, 368–369 social justice and, 445–451, 456–457 See also Prejudices Disposition cue utilization and, 209–210 health behavior and, 465 self-concept and, 418, 418–419

self-perception theory and, 208, 209 unconscious processing and, 198 Dissociation, 325–326, 538 Dissociation model, 355, 416 Dissonance theory, 302–303, 412. See also Cognitive consistency theories Distributed associative networks, 51 Diversity, racial bias and, 375 Dot-probe task, 493–494, 499–500 Downstream consequences, 304 Drawing tasks, 245 Drug use. See Substance use Dual-attitudes model, 218–219, 401–402 Dual-process models decision making and, 279 developmental approach and, 243 goal pursuit and, 324–325 health psychology and, 466, 467f, 478–479 implicit race bias and, 362 mood disorders and, 497 overview, 9–10 political cognition and, 554 Quad model and, 165 self-concept and, 409–410 self-esteem and, 394 social cognition and, 72–73 Dual-system perspective, 257–258 Ease-of-retrieval effect, 228–229 Eating behavior, 473–475, 480n, 501–505 Eating disorders, 501–505. See also Eating behavior Ecological model, 280–281 Effect sizes, 267, 420 Efficient automaticity features, 20–24, 21t Ego, 408. See also Self-concept Ego depletion, 263, 286. See also Cognitive depletion Elaboration likelihood model (ELM), 337, 337f, 343–347 Elections, 556–558. See also Political cognition Embodiment, 338–339 Emotion affective forms of representation and, 55 facial expressions and, 70 health behavior and, 465 mental associations and, 231 pain and stress and, 476–477 persuasion and, 346–347 See also Affect; Mood Emotion regulation, 185 Emotional learning, 81–82 Emotional Stroop task anxiety disorders and, 493–494 mood disorders and, 499, 500 personality disorders and, 510–511 psychotic disorders and, 508 sex offending and, 528–529 Employment discrimination, 450. See also Discrimination Encoding, 284 Enculturation, 241 Environmental stimuli, 42 Error-related negativity (ERN), 84, 88 Ethnicity, 246–247. See also Racial attitudes

Subject Index Ethnocentrism, 452–454, 537 Evaluations, 67–68 Evaluative bias, 357–359 Evaluative conditioning, 223, 225, 539–540 Evaluative Movement Assessment (EMA), 131, 135n Evaluative priming paradigm developmental approach and, 246 overview, 96–97, 103 predictive validity and, 271 See also Affective priming; Priming effects; Sequential priming tasks Evaluative processes, 82 Event-related brain potentials (ERPs) implicit race bias and, 363 intergroup bias and, 360 social categorization and, 87–88 social neuroscience and, 83–85 Evolutionary processes, 70 Executive component of the memory system, 43 Executive control, 465 Executive functions health psychology and, 467f self-concept and, 409 social cognition and, 72–73 Expectancies, 429–432 Experimental tasks, 24–26 Explicit ambivalence, 336. See also Ambivalence Explicit attitude, 307n–308n. See also Attitude measures; Attitudes Explicit measures. See Measures, explicit Explicit mental representations associative network models and, 49 connectionist models and, 52 overview, 39–40, 56 schema theory and, 41 standard model and, 44 See also Mental representations External classification criteria, 189 External properties of a measure, 182–188, 182f Extrapersonal associations, 131–132, 301–302 Extrinsic Affective Simon Task (EAST) ABC model and, 170 anxiety disorders and, 490–492 consumer behavior and, 535–536 developmental approach and, 245, 248 health psychology and, 471 Implicit Association Test (IAT) and, 132–133 mood disorders and, 497–498 obesity and, 249 overeating and dieting, 474 overview, 8, 176 pain and stress and, 477 predictive validity and, 261, 271 self-concept and, 411 self-esteem and, 394 sex offending and, 526–527 substance use and misuse and, 471 Extroversion, 415–416 Face validity, 179–180. See also Validity Facial electromyographic activity measures, 141

587 Facial expressions amygdala functioning and, 81–82 mood disorders and, 499–500 overview, 70 psychotic disorders and, 507, 508 relationships and, 430 stimulus affordances and, 280–281 Failure–rejection contingencies, 431–432 Fan effect, 44 Fast automaticity features, 20–24, 21t Fear responses, 81–82, 248 Feedback loops, 468 Feed-forward networks, 50–51 Figure–ground asymmetry, 126 Fit, model, 171–172 Focal activated memory, 42–43, 45, 52–53. See also Short-term memory Forensic settings overview, 522–524, 531–532 sex offending, 524–529 violent offending, 529–531 Gender developmental approach and, 246 personality disorders and, 509–510 relationships and, 428 self-concept and, 421 social categorization and, 87–88 Gender identification, 300–301, 412 Generalized anxiety disorder (GAD) attention bias tasks and, 494 overview, 490 vulnerability and, 496 See also Anxiety disorders Genetic factors, 70, 506 Global workspace conscious processing and, 201–204, 203f introspective access to mental experiences and, 206–208, 207f Goal dependent automaticity features, 20–24, 21t, 27–29 Goal pursuit conscious processing and, 200–201 deliberative decision making and, 286 developmental trajectory of, 325 health psychology and, 467f implicit measures and, 35n implicit race bias and, 362–363 intentional goals and, 312–317 mechanisms of, 324–325 overview, 311–312, 312, 323–324, 327 relationships and, 432–439 social cognition and, 68 theoretical questions regarding, 323– 326 unintentional goal activation, 317–323 Goal representation, 319–320. See also Goal pursuit Goal-related automaticity features, 20–24, 21t, 35n Go/No-Go Association Task (GNAT) anxiety disorders and, 490–492 overview, 8, 133, 176 predictive validity and, 270–271 self-concept and, 411 self-esteem and, 394 sex offending and, 526–527

588 Group differences anxiety disorders and, 490–495 body dysmorphic disorder and, 505–506 eating disorders, 502–504 mood disorders and, 497–500 personality disorders, 509–511 psychotic disorders and, 506–507 Habits, 68–69 Habitualization behavior and, 265 decision making and, 282 health psychology and, 467f impulse control and, 72 overview, 69 social cognition and, 72–73 Halo effect, 204–205 Health psychology overeating and dieting, 473–475, 480n overview, 463–469, 467f, 478–480 pain and stress, 475–478 protection against HIV/AIDS, 472–473 substance use and misuse and, 469–472, 480n Healthcare, 450–451, 477 Heuristic-systematic model, 285 Hierarchical linear models, 112 Hippocampus, 82 History of implicit social cognition, 2–5 Histrionic personality disorder, 509–511 HIV/AIDS, protection against, 472–473 Homophobia, 531 Hormonal functioning, 378–379 Hostility, 360–361 How criterion, 179t, 180–181, 187 Hub effect, 44–45 Hyperpriming, 507 Hypothalamus, 82 Identification–EAST (ID-EAST), 132–133 Identity adult development and, 249–250 attitude measures and, 300–301 consumer behavior and, 538–539 nationalist ideologies and, 452–454 self-concept and, 411–414 Ideology, 549–553 If–then expectancies, 430–432. See also Expectancies Illuminating test, 243–244 Images, 54 Imbalance-dissonance principle, 412 Implementation intention, 28, 363 Implicit ambivalence, 336. See also Ambivalence Implicit Association Procedure (IAP), 131, 411 Implicit Association Test (IAT) adult development and, 250 amygdala functioning and, 82 anxiety disorders and, 490–492 attitudes and, 140–141, 301, 303, 339 body dysmorphic disorder and, 505 children and, 246 conscious processing and, 210 consumer behavior and, 535–536, 538, 540, 542

Subject Index criticism of, 123–134, 128t deliberative decision making and, 285 developmental approach and, 245, 246–247, 248, 250, 250–251 diffusion model of, 170–171 dissonance theory and, 303 eating behaviors and disorders, 249, 474, 502 fears and, 248 health psychology and, 470–471, 478 implementation of, 119 intergroup bias and, 356, 358–359 mood disorders and, 497–498 motivation and opportunity as determinants (MODE) model and, 5–6, 218 order effects and, 268 overview, 1, 5, 117–120, 118t, 134, 176, 191n paper-and-pencil IAT, 141t, 146 personality disorders, 509–510 personality self-concept and, 414–417 political cognition and, 551, 552–553 predictive validity and, 255, 259, 261–262, 263, 270 principle of correspondence and, 269 Process Dissociation (PD) and, 161 psychometric properties of, 120–123 psychopathology and, 512–513 psychotic disorders and, 506–507 relationships and, 427–428, 438, 439 scoring of, 118t, 119–120 second-generation measures and, 8–9 self-activation and, 263 self-concept and, 411, 412, 418, 419, 420–422 self-esteem and, 394–396 sex offending and, 524–527 sexuality goals and, 434–435 social cognition and, 63 social justice and, 448 Stereotypic Explanatory Bias (SEB) and, 145 stimulus–response compatibility criterion and, 186–187 substance use and misuse and, 470–471 suicide and self-injury, 248 unconscious processing and, 199 violent offending and, 529–531 Implicit attitude, 157, 307n–308n, 428. See also Attitude measures; Attitudes Implicit bias behavior and, 360–361, 449–451 Control Default model of PD and, 163 intergroup bias and, 356–360 moderators of, 361–364 overview, 367–369, 445–451 social justice and, 445–451 theoretical models, 364–367, 365f, 366f Implicit categorization, 87–88 Implicit memory, 312. See also Memory Implicit mental representations associative network models and, 49 connectionist models and, 52 overview, 39–40, 56 schema theory and, 41 standard model and, 44 See also Mental representations

Implicit self-concept consumer behavior and, 540 domains of, 411–417 moderators of, 417–420 overview, 409–411, 422 predictive validity and, 417–420 stability of, 420–422 See also Self-concept Implicit self-esteem determinants of, 400–401 distinction of explicit self-esteem and, 396–400 measuring, 394–396 overview, 394 relationship between explicit self-esteem and, 401–402 See also Self-esteem Implicit Self-Evaluation Survey, 394 Implicit stereotype, 157. See also Stereotyping Implicit–explicit relationship, 205–206 Implicitness criterion, 179t, 181–182, 188–189 Impressions, 39, 317–318, 393, 401 Impulsive system (IS) association and, 65 conflicts and, 71 deliberative decision making and, 289 health psychology and, 464–465, 466 overview, 71–72, 72–73 predictive validity and, 256–257 See also Reflective–impulsive model Inclusion, 70 Incremental validity, 257 Indirect measures consumer behavior and, 537–538 dual-process models and, 10–11 self-esteem and, 395 See also Measurement procedures Individual differences consumer behavior and, 537 health psychology and, 465, 477 implicit race bias and, 363–364 overeating and dieting, 473–474 pain and stress and, 477 Inequality, 551 Infant development, 241–243, 245. See also Developmental approach Information error test, 141t, 150 Information processing anxiety disorders and, 490 attention research and, 2–3 attitude change and, 338–339, 342 behavior and, 262–263 conscious processing and, 200 cue utilization and, 209–210 mood disorders and, 497 relationships and, 431 unintentional goal activation and, 317–318 Information retrieval, 209–210 Ingroup favoritism, 452–453 Insecurity, 431–432, 435–437 Insight, 208–209 Intentional automaticity features, 20–24, 21t, 27–29 Intentional behavior, 200–201

Intentional goals, 312–317. See also Goal pursuit Intentions, 67–68 Interdependence theory, 426 Intergroup anxiety automatic responses and, 378–380 controlled responses, 380–384 intergroup contact and, 384–386 overview, 386–387 Intergroup attitudes, 244–249 Intergroup bias behavior and, 360–361 implicit race bias, 356–360 intergroup contact and, 384–386 moderators of, 361–364 nationalist ideologies and, 452–453 overview, 353–354, 355–356, 367–369, 375–378, 376f, 386–387 research regarding, 354–355 theoretical models, 364–367, 365f, 366f See also Bias; Racial bias Intergroup relations, 554–556 Internal classification criteria overview, 188 self-assessment criterion and, 183 stimulus–response compatibility criterion and, 187 Internal consistency, 266–267. See also Reliability International Affective Picture System (IAPS), 105 Interpersonal goals, 433–435. See also Goal pursuit Interpersonal nature, 409 Interpretation, 318 Interracial contact automatic responses to, 378–380 behavior and, 376f intergroup anxiety and, 384–386, 386–387 overview, 386–387 See also Racial bias Intertemporal conflict, 71, 72. See also Conflict Interventions anxiety disorders and, 496 body dysmorphic disorder and, 506 eating disorders and, 504–505 health psychology and, 480 mental associations and, 227–228 mood disorders and, 500–501 personality disorders and, 511 Quad model and, 168–169 Intimacy, sexuality goals and, 435 Introspection, 206–208, 207f Introspective limits, 204–205 Intuition, 265, 455 Intuitive processor, 52 Invalidation processes, 209–210 Iowa Gambling Task, 283 Irrelevant feature tasks, 24–26 Judgment deliberative decision making and, 284–286 moral judgment, 451–456, 454–456 of others, 320–321 overview, 278–279

Subject Index personality self-concept and, 414–415 unintentional goal activation and, 318, 320–321 See also Decision making Knowledge intentional goals and, 313–314 schema theory and, 41 unintentional goal activation and, 321 Labeled links, 48 Lateralized readiness potential (LRP), 84 Learned associations, 281–282 Learning, 221–222 Level of representation, 48–49, 50–51. See also Associative network models; Mental representations Levels of analysis, 53 Lexical decision task, 100, 101t Life span development, 243. See also Developmental approach Likelihood to Sexually Harass Scale (LSH), 435 Linguistic bias, measures of development and application of, 148–150 overview, 141t, 142–143, 144–146 Linguistic category model, 148 Linguistic intergroup bias (LIB) development and application of, 148–149 overview, 141t, 144–145 Stereotypic Explanatory Bias (SEB) and, 145 Local effects, 51 Localist associative networks, 51 Long-term memory, 42. See also Memory Malleability, 364, 400–401 Masked priming, 99–100. See also Priming effects Masking, 108 Mathematical modeling ABC model, 170 advantages of, 160 choosing among models and, 171–172, 172f Diffusion model, 170–171 overview, 156–158 Process Dissociation (PD), 161–164, 162f, 164f Quad model, 164–170, 165f Signal Detection Theory (SDT), 160–161 See also Modeling approaches Maximum-likelihood estimations, 157 Measurement procedures attitudes and, 140–151 conscious processing and, 210 development of, 147–151 malleability and, 6–7 overview, 1, 9–11 second-generation measures, 8–9 self-report measures, 7–8 social cognition and, 63, 65 See also Implicit Association Test (IAT); Measures, explicit; Measures, implicit

589 Measures, explicit Implicit Association Test (IAT) and, 122 overview, 135n self-esteem and, 393, 394–396, 398 See also Measurement procedures Measures, implicit criteria to differentiate, 178–188, 179t, 182f difficulties with the use and interpretation of, 158–160 implicit race bias and, 367–368 models of, 160–172, 162f, 164f, 165f, 172f overview, 19–20, 33–34, 34n–35n, 135n, 176–178, 177f, 190, 522–523 predicting behavior with, 261–266 relationships and, 431, 439–440 underlying processes and, 24–26 validity and, 122–123 See also Implicit Association Test (IAT); Measurement procedures; specific measures Media influences consumer behavior and, 538–539 mental associations and, 227 violent offending and, 530–531 Mediators, behavioral. See Behavioral mediators Memory associative network models of, 44–49 associative–propositional evaluation model and, 221–222, 297–298 attention research and, 2–3 attitude measures and, 297–298 connectionist models of, 49–52 deliberative decision making and, 284 health behavior and, 478 implicit memory research, 4 implicit race bias and, 366–367, 366f mental representations and, 38–39, 56 models of, 40–53 relationships and, 431 sequential search models of, 47 sexuality goals and, 434 Signal Detection Theory (SDT), 160–161 standard model of, 42–44 substance use and misuse and, 470 Memory associations, 146–147 Memory systems model (MSM), 366–367, 366f Mental associations associative–propositional evaluation model, 220–222 changing, 224–228 context effects, 228–232 deliberative decision making and, 284–285 dual-attitudes model, 218–219 empirical evidence, 222–233 formation of, 222–224 metacognitive model, 219–220 method-related effects, 232–233 motivation and opportunity as determinants (MODE) model, 217–218 overview, 216, 233

590 Mental associations (cont.) theoretical models, 216–222 See also Associative processes; Attitudes; Mental representations Mental representations adaptive unconscious and, 205–206 associative network models of, 44–49 connectionist models of, 49–52 forms of, 53–56 goal pursuit and, 68 implicit race bias and, 362 introspective access to, 206–208, 207f models of, 40–53 overview, 38–40, 56 personality self-concept and, 415–416 sequential search models of, 47 social cognition and, 66–67 standard model of, 42–44 See also Mental associations Mere ownership, 540 Message variables, 345 Metacognitive model attitude measures and, 298, 300, 302–303 attitudes and, 336, 336f, 342–343 dissonance theory and, 302–303 overview, 219–220 See also Thoughts Method-related effects, 232–233 Minnesota Multiphasic Personality Inventory (MMPI), 183 Minorities, 379–380 Modal model, 42–44 Modeling approaches advantages of, 160 choosing among models, 171–172, 172f overview, 160–172, 162f, 164f, 165f, 172f See also Mathematical modeling Moderator effect, 420 Moderators, situational, 261–264 Mood affective forms of representation and, 55 behavior and, 263 persuasion and, 346–347 predictive validity and, 263 See also Emotion Mood disorders, 497–501 Moral clarity, social justice and, 454–456 Moral judgment, 451–456, 549–553 Mortality salience, 263–264 Motivation amount of thinking and, 341–342 attitude change and, 339 deliberative decision making and, 285–286 goal pursuit and, 312 health psychology and, 465, 467f racial attitudes and, 169 self-concept and, 418–419, 422 Motivation and opportunity as determinants (MODE) model attitude measures and, 296–297, 298, 300, 301, 302–303, 304 compared to the metacognitive model, 298 consumer behavior and, 536–537

Subject Index deliberative decision making and, 285 dissonance theory and, 302–303 irrational behavior and, 71 mental associations and, 228 overview, 5–6, 217–218 predictive validity and, 256–257, 259 self-esteem and, 402 self-report measures and, 7 social cognition and, 66 Motivation to control prejudiced reactions (MCPR), 209–210, 264 Motivational processes, 69–70, 230–231 Multinomial modeling, 112 Name-letter effect consumer behavior and, 540 implicit self-esteem and, 400 overview, 141t, 143–144, 176 self-esteem and, 394, 395 Name-letter preference assessments, 147–148 Name-liking task, 394 Naming task, 100, 101t, 102 Narcissism, 259, 398–399 Nationalist ideologies, 452–454 Need for cognition, 264 Negation, 226–227 Negative priming paradigms, 492, 498 Negativity attitude measures and, 304–305 emotion and, 346–347 health behavior and, 465 unintentional goal activation and, 320 Network representation, 364–366, 365f Neurological measures bias and, 359–360, 366–367, 366f response system criterion and, 184–185 Neuroscience, social. See Social neuroscience Neuroticism, 415–416 New Look thesis, 313 Noetic consciousness, 204. See also Access consciousness Nonreactive measures, 395–396, 398 Nonsymbolic measures, 185–186 Obesity developmental approach and, 248–249 eating disorders and, 501–505 health psychology and, 473–475, 480n Objective stimulus intensity, 34n Obsessive–compulsive disorder (OCD) overview, 490 priming effects and, 492 vulnerability and, 495 See also Anxiety disorders Obsessive–compulsive personality disorder, 509–511 Older adults, 249–250 Order effects, 267–268 Outgroup favoritism, 452–453 Overeating behavior, 473–475, 480n Pain, 475–478 Panic disorders, 490. See also Anxiety disorders

Paper-and-pencil measures development and application of, 150, 150–151 overview, 140–151, 141t, 146 predictive validity and, 271 relationships and, 428 Parallel constraint satisfaction, 49 Parallel search models, 44–47, 50. See also Associative network models Paranoid personality disorder, 509–511 Partially structured measures attitudes and, 141 development and application of, 147–148 overview, 141t, 142 Pattern completion, 50 Perception intentional goals and, 313 political cognition and, 556–558 Signal Detection Theory (SDT), 160–161 unintentional goal activation and, 321 Performance on objective tasks measures attitudes and, 141 development and application of, 150–151 overview, 141t, 142, 146–147 Personal moderators, 264–265 Personality behavior and, 70 health psychology and, 465, 466, 467f, 468, 472, 475 overeating and dieting, 475 self-concept and, 414–417 substance use and misuse and, 472 unconscious processing and, 198 unintentional goal activation and, 317–323 Personality disorders, 509–511 Personality self-concept, 414–417. See also Self-concept Persuasion amount of thinking and, 342 attitudes and, 335–336, 343–347 communication and, 343–347 mental associations and, 225–226 overview, 348 Phenomenal consciousness introspective access to mental experiences and, 206–208, 207f overview, 203–204, 203f, 211 Phobias, 490, 494. See also Anxiety disorders Physiological measures attitudes and, 141 intergroup anxiety and, 378–379 predictive validity and, 271 response system criterion and, 184–185 Policy, social justice and, 457 Political behavior Political cognition ideology and, 549–552 intergroup relations and, 554–556 overview, 548–549, 559–560 perceptions and, 556–558 political behavior, 558–559 preferences and, 556–558 values and morality and, 552–553

Positivity attitude measures and, 304–305 implicit race bias and, 362–363 unintentional goal activation and, 320 Posttraumatic stress disorder (PTSD) attention bias tasks and, 494 overview, 490 vulnerability and, 495 See also Anxiety disorders Power, sexuality goals and, 435 Preconsciousness, 206. See also Phenomenal consciousness Prediction, health behavior and, 464–465 Predictive validity additive pattern of prediction, 257–258, 258f anxiety disorders and, 490–495 body dysmorphic disorder and, 505–506 double additive pattern of prediction, 258f, 260 double-dissociation pattern of prediction, 258f, 259, 416 eating disorders, 502–504 implicit measures and, 123, 261–266 interactive pattern of prediction, 258–259, 258f methodological issues, 266–271 moderation pattern of prediction, 257, 258f, 417–420 mood disorders and, 497–500 multiplication pattern of prediction, 258–259, 258f overview, 255–256, 272 partial-dissociation pattern of prediction, 258f, 259–260 patterns of prediction and, 257–261, 258f personality disorders, 509–511 psychotic disorders and, 506–507 self-concept and, 412, 415–416, 417–420 self-esteem measures and, 393 theoretical models, 256–257 types of measures and, 270–272 See also Behavior; Validity Preferences, 70, 556–558 Prefrontal cortex cognitive control and, 85–87 implicit race bias and, 366–367, 366f social cognition and, 81t, 82 social neuroscience and, 82 Prejudices attitude change and, 338 contact hypothesis and, 222–223 deliberative decision making and, 284–286 Implicit Association Test (IAT) and, 124–125 implicit race bias and, 368–369 intergroup anxiety and, 378–379 intergroup contact and, 384–386 learned associations and, 281–282 linguistic intergroup bias (LIB) and, 144 motivation to control prejudiced reactions, 209–210, 264 Quad model and, 169 reducing, 361–364 social justice and, 445–451

Subject Index stereotype threat, 385–386 violent offending and, 531 See also Racial attitudes; Stereotyping Preschool development, 245. See also Developmental approach Prime stimuli, 105–106, 187 Priming effects analyzing data from, 109–112 association and, 64–65 attitudes and, 2–3 automaticity and, 26–33 behavior and, 264 conscious processing and, 210 design and tasks, 100–104, 101t intergroup bias and, 358 interpersonal goals and, 433–434 masked and unmasked priming, 99–100 measurement procedures and, 8–9 mood disorders and, 197 overview, 24–25, 34n–35n, 113 political cognition and, 556–557 predictive validity and, 264 procedure, 104–109 salience and, 30 social neuroscience and, 84 strategic production of, 97–99 substance use and misuse and, 470 See also Priming tasks; Sequential priming tasks Priming tasks anxiety disorders and, 492–493 body dysmorphic disorder and, 505 eating disorders, 502–503 mood disorders and, 498–499 overview, 163 personality disorders and, 510 psychotic disorders and, 507 self-concept and, 421–422 self-esteem and, 394 See also Priming effects Private self, 409, 410. See also Self-concept Procedural variations, 232–233 Process Dissociation (PD) model aging and, 250 deliberative decision making and, 286 mental associations and, 232 overview, 10, 157, 158, 161–164, 162f, 164f, 173n Process models, 124–126 Processing, schema theory and, 40–41 Processing goals, automaticity and, 20–24, 21t Processing time, 262 Projective measures, 143 Promoting goals, 20–24, 21t, 27–29 Propositional processes, 64–65, 410 Psychopathology anxiety disorders, 490–497 body dysmorphic disorder, 505–506 eating disorders, 501–505 health psychology and, 466 mood disorders, 497–501 overview, 489–490, 511–513 personality disorders, 509–511 psychotic disorders, 506–509 schizophrenia, 506–509 violent offending and, 529–531

591 Psychopathy, violent offending and, 529–531 Psychopathy Checklist—Revised (PCL-R), 529–531 Psychotic disorders, 506–509 Public self, 409, 410. See also Self-concept Purely stimulus driven automaticity features, 20–24, 21t Quad model choosing among models and, 171 mathematical modeling and, 157, 158 mental associations and, 232 overview, 10–11, 164–170, 165f Question–behavior effect, 541 Questionnaires, relationships and, 431 Racial attitudes attention research and, 3 contact hypothesis and, 222–223 developmental approach and, 245, 246–247 Extrinsic Affective Simon Task (EAST) and, 132–133 Implicit Association Test (IAT) and, 118–119, 118t, 124–125, 177 learned associations and, 281–282 Quad model and, 169 Single-Block IAT and, 127, 129 social categorization and, 87–88 social neuroscience and, 85–87 Sorting Paired Features Task (SPF) and, 133–134 stimulus–response compatibility criterion and, 187 See also Attitudes; Prejudices; Racial bias; Stereotyping Racial bias behavior and, 360–361 Control Default model of PD and, 163 implicit race bias, 356–360 intergroup anxiety and, 378–384 intergroup relations and, 384–386, 555–556 moderators of, 361–364 overview, 353–354, 355–356, 367–369, 375–378, 376f, 386–387 political cognition and, 555–556 Process Dissociation (PD) model, 158 research regarding, 354–355 social justice and, 445–451 theoretical models, 364–367, 365f, 366f violent offending and, 531 See also Intergroup bias; Prejudices; Racial attitudes Randomization, priming effects and, 109 Randomized trials, 109–112 Random-walk model, 124–125 Rapid serial visual presentation, 528 Reaction time, 167–168, 191n Reactivation, 45. see also Activation Reappraisals, 322 Reasoning, 279 Recipient variables, 345–347 Reciprocal altruism, 70 Recoding processes, 127–129, 128t Recoding-Free IAT, 8, 127, 129. See also Implicit Association Test (IAT)

592 Reconstructive memory, 51 Recurrent-network modules, 50–51 Reflective actions, 65–66, 71–72 Reflective deliberation, 284–285 Reflective experience, 204. See also Access consciousness Reflective processes health psychology and, 465, 466, 471–472, 474–475 overeating and dieting, 474–475 substance use and misuse and, 471–472 Reflective processing model, 497 Reflective system (RS) association and, 65 overview, 72–73 predictive validity and, 256–257 See also Reflective processes; Reflective– impulsive model Reflective–impulsive model association and, 65 overview, 9, 72–73 predictive validity and, 256–257 See also Impulsive system (IS); Reflective processes; Reflective system (RS) Reflexes, behavior and, 70 Reflexive consciousness, 409 Regression models, 110–111 Rehearsal, 45 Rejection, 431–432 Relational schemas, 426 Relational–Interdependent Self-Construal (RISC) Scale, 427–429 Relationship Questionnaire, 417 Relationships attitudes and, 427–429 expectancies and, 429–432 goals and strategies and, 432–439 overview, 426–427, 439–440 threat and, 435–439 Reliability developmental approach and, 251 Implicit Association Test (IAT), 120–121 overview, 266–267 predictive validity and, 266–267 priming effects and, 111 self-concept and, 420–422 self-esteem measures and, 393 See also Internal consistency; Test–retest reliability Religiosity, sexuality goals and, 435 Remote effects, 51 Representational approaches, 364–366, 365f. See also Mental representations Representational similarity, 51 Representations, mental. See Mental representations Residual activation, 45. See also Activation Response bias, 160–161, 168–169 Response content criterion, 179t, 182f, 185–186 Response priming design of, 103–104 procedure, 105 semantic priming and, 96–97 Response system criterion, 179t, 182f, 184–185

Subject Index Responses interracial contact and, 378–380 overview, 191n relationships and, 433–435 Retrospective approach, 247 Risk situations, 472–473 Roles, social. See Social roles Rorschach test, 185–186 RSVP task, sex offending and, 528 Rule-based system, 298–299, 308n Salience affective priming effects and, 29–30 behavior and, 263–264 Implicit Association Test (IAT) and, 126, 130 mental associations and, 229–230 overview, 22–23 Salient cues, 224 Schema theory, 40–41, 53–56 Schema-based information processing model, 490 Schemas, 39, 68–69 Schizoid personality disorder, 509–511 Schizophrenia, 506–509 Schizotypal personality disorder, 509–511 Screen synchronizing, 108–109 Scripts, 39, 68–69 Second-order experience, 204. See also Access consciousness Security, expectancies regarding, 431–432 Self, social cognition and, 63, 66–67, 66f Self-activation, 263 Self-aggrandizement, 399 Self-anchoring, 223 Self-Apperception Test, 394 Self-assessment criterion, 179t, 182–184, 182f. See also Self-report measures Self-Attributes Questionnaire, 393 Self-concept conscious processing and, 201, 211 consumer behavior and, 540 domains of implicit self-concept, 411–417 moderators of, 417–420 overview, 408–411, 422 personality self-concept, 414–417 predictive validity and, 417–420 stability of, 420–422 unconscious processing and, 198 Self-control behavior and, 265 goal pursuit and, 68 health psychology and, 465, 474–475 Self-esteem adult development and, 249–250 attitude measures and, 300–301 developmental approach and, 249 distinction of implicit and explicit selfesteem, 396–400 emotion and, 346–347 expectancies regarding, 431–432 explicit self-esteem, 392–393 implicit self-esteem, 394–403 mental associations and, 232 overview, 392, 402–403 predictive validity and, 259 psychotic disorders and, 506

relationship between implicit and explicit self-esteem, 401–402 relationships and, 430 self-assessment and, 183 self-concept and, 412 sexuality goals and, 436 socialization experiences and, 222 unconscious processing and, 198 Self-Esteem Inventory, 393 Self-Esteem Scale, 393 Self-identity activation, 538–539 Self-inference model adaptive unconscious and, 205–206 introspective access to mental experiences and, 206–208, 207f overview, 208–209 Self-injury, 248 Self-Liking and Self-Competence Scale, 393 Self-observation, 208–209 Self–other integration, 428–429. See also Relationships Self-perception theory, 208–209, 209–210 Self-presentation motives, 3 Self-prophecy, 541 Self-protection, 436 Self-regulation adult development and, 249–250 behavior and, 263 conflict and, 71 consumer behavior and, 543 health behavior and, 465 impulse control and, 71–72 intergroup anxiety and, 381 Quad model and, 167–168 relationships and, 439–440 Self-report measures attitudes and, 141 overview, 7–8 political cognition and, 550, 552 relationships and, 426–427 self-esteem and, 183, 393 See also Measurement procedures; Selfassessment criterion Self-security, 413 Self-validation, 341, 346 Semantic decision task, 101t, 102 Semantic priming paradigm design of, 100–104, 101t overview, 95, 176 predictive validity and, 271 procedure, 104–109 response priming and, 96–97 strategic production of, 97–99 See also Priming effects Semiactivated associative memory, 42–43, 45–46, 52–53. See also Short-term memory Sensitivity, 160–161 Sequential priming tasks analyzing data from, 109–112 conscious processing and, 210 interpersonal goals and, 433–434 overview, 95–96, 113 relationships and, 439 semantic priming vs. response priming, 96–97

threat and, 437 See also Priming tasks Sequential search model, 47 Sex offending, 524–529 Sexual behavior, 463–464, 472–473 Sexuality goals, 434–435. See also Goal pursuit; Relationships Shooter Task, 160–161 Short-term memory, 42–43, 45. See also Memory Signal Detection Theory (SDT), 157, 160–161 Signature effect, 394 Simple association pattern of prediction, 257. See also Predictive validity Simultaneous conflict, 71. See also Conflict Simultaneous processes approach, 159–160 Single association pattern of prediction, 258f Single-Block IAT overview, 8, 127, 128t, 129 predictive validity and, 270 self-concept and, 411 See also Implicit Association Test (IAT) Single-Category IAT (SC-IAT) overview, 133 self-concept and, 418 self-esteem and, 394 Single-Item Self-Esteem Scale, 393 Single-Target IAT, 176 Situated cognition perspective, 46 Situational moderators health psychology and, 465, 467f implicit race bias and, 363 predictive validity and, 261–264 Slow-learning system, 45–46 Smoking. See Substance use Social, 550–551 Social anxiety, 430, 494. See also Anxiety Social anxiety disorder, 490. See also Anxiety disorders Social categories, 411 Social cognition adaptive unconscious and, 205–206 behavior and, 62–63 conflicts and, 70–72 difficulties with the use and interpretation of implicit measures and, 160 irrational behavior, 70–72 overview, 63–70, 66f, 72–73 Quad model and, 165 racial bias and, 377, 386–387 relationships and, 439–440 social justice and, 451–456, 456–458 social neuroscience and, 80–89, 81t theory and, 72–73 Social contact, 222–223, 241 Social context, 247 Social factors, 506 Social identity, 538–539. See also Identity Social inclusion, 70 Social Interaction Anxiety Scale, 495–496 Social justice cultural values and, 451–456 moral judgment and, 451–456 overview, 445

Subject Index prejudice and, 445–451 role of “is” and “ought” in, 456–458 Social neuroscience categorization and, 87–88 cognitive control, 85–87 conflict and, 83–85 overview, 80–89, 81t Social roles, 230 Social tuning, 230 Socialization experiences, 222 Somatic marker hypothesis, 282–283 Sorting Paired Features Task (SPF), 133–134, 176 Source factors, 344–345 Spearman–Brown formula, 111. See also Internal Consistency; Reliability Spontaneous behaviors, 261–262. See also Behavior Spreading activation models. See Parallel search models Spreading inhibition, 46–47 Stability, 400–401, 420–422 Standard model, 42–44 Startle reflex, 430 Startle-eyeblink method, 359–360 State effects, 512–513 State Self-Esteem Scale, 393 State-inference measure, 212n Static-99, 525 Statistics, 266–267 Status of associative network models, 47–49 Status quo, social justice and, 453 Stereotypic Explanatory Bias (SEB), 141t, 145–146, 149–150, 176 Stereotyping behavior and, 67–68, 446 deliberative decision making and, 284–285 implicit race bias, 356–357 indirect measures and, 10 mathematical modeling and, 156–157 mental associations and, 226–227, 232 mental representations and, 39 overview, 375–378, 376f priming effects and, 31 schema theory and, 41 self-concept and, 421 sexuality goals and, 435 Signal Detection Theory (SDT) and, 160–161 social cognition and, 63, 66–67, 66f social justice and, 453 social neuroscience and, 83–84 stereotype threat, 385–386 unintentional goal activation and, 320 See also Racial attitudes Stimulus automaticity and, 23 eating disorders and, 504 evaluative conditioning and, 223 Implicit Association Test (IAT) and, 130 memory and, 42 mental representations and, 39 overview, 34n salience and, 22–23, 29–30 schema theory and, 40–41 stimulus affordances and, 280–281

593 Stimulus affordances, 280–281 Stimulus–goal comparisons, 65 Stimulus-onset asynchrony (SOA) affective priming effects and, 26–27, 28 anxiety disorders and, 492 masked and unmasked priming and, 99 psychotic disorders and, 508 semantic priming and, 98–99 timing routines and, 107–108 Stimulus–response compatibility, 124, 179t, 182f, 186–188 Strategic effects, 130–131, 232 Stress, health psychology and, 475–478 Stroop task anxiety disorders and, 493–494 automaticity and, 22 deliberative decision making and, 286 difficulties with the use and interpretation of, 159 mood disorders and, 499, 500 overview, 101t, 102 political cognition and, 555 relationships and, 430 sex offending and, 528–529 stimulus-onset asynchrony and, 98 stimulus–response compatibility criterion and, 187–188 See also Emotional Stroop Task Structural fit principle, 269 Subliminal priming, 198, 202 Substance use, 468, 469–472, 480n Suicide, developmental approach and, 248 Symbolic models, 39, 185–186 System justification theory, 452–453, 555 Systems of evaluation model (SEM), 298–299, 300, 301, 302–303, 304 Task dissociation approach, 159 Task performance implicit race bias and, 363 intergroup bias and, 356 mathematical modeling and, 156–158 Task switching, 125–126 Technological advances, 151 Temporal conflicts, 72. See also Conflict Temporo-parietal attention networks, 81t Test–retest reliability overview, 266 self-concept and, 420 self-esteem and, 393, 400 See also Reliability Thematic Apperception Test (TAT) development and application of, 147 intentional goals and, 313 overview, 141t, 142, 143 response content criterion and, 185–186 Thoughts amount of thinking, 341–343 arguments and, 341 attitude change and, 339–341 persuasion and, 344 structural features of, 340–341 See also Metacognitive model Threat, 385–386, 435–439 Timing routines, 107–108 Top-level conscious processor, 52 Training, developmental approach and, 252

594 Trait self-control, 265 Translation process, 28–29 Treatment anxiety disorders and, 496 body dysmorphic disorder and, 506 eating disorders and, 504–505 mood disorders and, 500–501 personality disorders and, 511 See also Interventions Trust, 431, 436, 440 Tuning, social. See Social tuning Unconscious processing adaptive unconscious, 204–210, 207f consumer behavior and, 542–542 evidence for, 198–199 global workspace and, 202 implicit memory research, 4 overview, 197–199, 203–204, 203f, 210–211 self-esteem and, 394 See also Consciousness Unified theory, 540 Unintentional goal activation, 317–323. See€also Goal pursuit Unmasked priming, 99–100. See also Priming effects Valence affective priming effects and, 30–31, 32–33 amygdala functioning and, 81–82 attention and, 32–33

Subject Index attitude measures and, 303–304 consciousness and, 32 eating disorders and, 504 how criterion and, 180–181 mental associations and, 224 persuasion and, 345 salience and, 29–30 self-concept and, 412 Validity Control Default model of PD, 162–163 developmental approach and, 251 Implicit Association Test (IAT), 121–123 psychopathology and, 512 self-esteem measures and, 393 Stereotypic Explanatory Bias (SEB) and, 145–146 what criterion and, 179–180 See also Incremental validity; Predictive validity Values, 549–553 Verbal forms of representation, 54, 56. See also Mental representations Vice behaviors, 542 Video games, violent offending and, 530–531 Viewing times, sex offending and, 527–528 Vignette measures, 141t Violent offending, 529–531 Visual forms of representation, 54, 55, 56. See also Mental representations Visual Probe Test (VPT), 430, 436 Visual search tasks, 499–500

Voting behavior, 558–559 Vulnerability anxiety disorders and, 495–496 body dysmorphic disorder and, 506 eating disorders and, 504 mood disorders and, 500 personality disorders and, 511 psychotic disorders and, 508 Weapon identification task, 104 Weapons Task Process Dissociation (PD) and, 161, 163–164 Signal Detection Theory (SDT) and, 160–161 What criterion, 179–180, 179t, 187 Word association tests, 469–470 Word fragment completion, 141t, 146, 150–151 Working memory modal model and, 42–43 overview, 42–43 parallel search methods and, 45–46 self-concept and, 418 See also Memory Working memory capacity, 264–265, 418 Workspace, global conscious processing and, 201–204, 203f introspective access to mental experiences and, 206–208, 207f Zones of consciousness, 203–204, 203f