Advances in Psychology Volume 72 Aging and Cognition II: Mental Processes, Self-Awareness and Interventions

AGING AND COGNITION Mental Processes, Self-Awareness and Interventions

AGING AND COGNITION Mental Processes, Self-Awareness and Interventions

ADVANCES IN PSYCHOLOGY 72 Editors:

G. E. STELMACH

P. A. VROON

NORTH-HOLLAND AMSTERDAM NEW YORK OXFORD TOKYO

AGING AND COGNITION Mental Processes, Self-Awareness and Interventions

Edited by

Eugene A. LOVELACE Psychology Di~Ysioii,Scieiice Ceritei. Alfred University, Alfr.ec1 N o t . York, U.S.A.

I990

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NORTH-HOLLA N D ASISTERDAM NEW YORK OXFORD TOKYO

NORI'H-HOI,I,AND ELSEVIER SCIENCE PUBLISHERS R.V. Sara Burgerhartstriat 2.5 P.O. Box 21 I , 1000 AE Amstertlam. The Netherland\

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L i b r a r y o f Congress Cataloging-in-Publication

Data

Aging and cognition mental processes, self-awareness, and I n t e r v e n t i o n s / e d i t e d by Eugene A. L o v e l a c e . p. c m . -- ( A d v a n c e s in p s y c h o l o g y 72) Includes bibllographlcal references and indexes. I S B N 0-444-88367-3 i. Cognition--Age factors. 2. Memory--Age f a c t c r s . 3. A g i n g -Psycnological aspects. I. L o v e l a c e , E u g e n e A.. 1939- . 11. S e r i e s A d v a n c e s in p s y c h o l o g y ( A m s t e r d a m . Ne:herlands) , 72. B F 7 2 4 . 5 5 . C 6 3 A 4 6 1990 155.67--dC20 90-48695 CIP

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1SBN:O 4-14 883673 ELSEVIER SCIENCE PUBLISHERS B.V.. 1990 All rights reserved. No part of this publication may be reproduced. stored in a retrieval system. or transmitted. in any form or by any means. electronic, mechanical. photocopying, recording or otherwise. without the prior written permission of the publisher. Elsevier Science Publishers B.VJ Physical Sciences and Engineerins Division, P.O. Box 1991. 1000 BZ Amsterdam. The Netherlands. Speciiil regulations for readers in the U.S.A. - This publication has been registered with the Copyright Clearance Center Inc. (CCC). Salem. Massachusetts. Information can be obtained from the CCC about conditions under which photocopies of parts of this publication may be made in the L1.S.A. All other copyright questions. including photocopying outside of the U.S.A.. should be referred to the copyright owner. Elsevier Science Publishers B.V.. unless otherwise specified. No responsibility is assumed by the Publisher for any iiijury and/or damage to persons or property as a matter of products liability, negligence or otherwise. or from any use or operation of any methods. products. instructions or ideas contained in the material herein. Printed in The Netherlands

To Doris W. Lovelace, my first teacher and my model for cognitive competence in late life, and the memory of Ralph A. Lovelace, Sr. who taught me to believe I can do it myself and the value of honest labor.

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Contents

Dedication

V

List of Contributors

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Preface

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Basic concepts in cognition and aging Eugene A. Lovelace

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Automaticity of encoding and episodic memory processes Donald H. Kausler

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3

Adult age differences in memory for pictures and images Anderson D. Smith and Denise C. Park

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Aging and attention: Selectivity, capacity, and arousal Dana J. Plude and Jane A. Doussard-Roosevelt

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Age-related deficits in cognitive-motor skills Noreen L. Goggin and George E. Stelmach

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Aging and metacognitions concerning memory function Eugene A. Lovelace

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I believe, therefore I can: Self-efficacy beliefs in memory aging John C. Cavanaugli and Elizabeth E. Green

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Memory interventions in aging populations Susan Kotler-Cope and Cameron J. Camp

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Current issues in cognitive training research Sheny L. Willis

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10 Aging and word retrieval: Selective age deficits in language Deborah M. Burke and Gary D. Laver

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11 The way reading and listening work: A tutorial review of discourse processing and aging Elizabeth A. L. Stine

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12 Adult age differences in traditional and practical problem solving Nancy W: Denney

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13 Interactions between personality and cognition and their implications for theories of aging Dolores P. Gold and Tannis Y.Arbuckle

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14 Intellectual abilities and age: Concepts, theories and analyses Walter R Cunningham and Adrian Tomer

379

15 Cognitive aging: A summary overview Eugene A. Lovelace

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Author index

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Subject index

449

Contributors

Tannis Y. Arbuckle Department of Psychology Concordia University Montreal, Quebec H3G 1M8, Canada

Elizabeth E. Green Department of Psychology Bowling Green State University Bowling Green, OH 43403, USA

Deborah M. Burke Department of Psychology Pomona College Claremont, CA 91711, USA

Donald € Kausler I. Department of Psychology University of Missouri Columbia, MO 65211,USA

Cameron J. Camp Department of Psychology New Orleans University New Orleans, LA 70148, USA

Susan Kotler-Cope Department of Psychology Louisiana State University Baton Rouge, Louisiana 70803, USA

John C. Cavanaugb Department of Psychology Bowling Green State University Bowling Green, OH 43403, USA

Gary D. Laver Department of Psychology The Claremont Graduate School Claremont, CA 91711, USA

Walter R Cunningham Department of Psychology University of Florida Gainesville, FL 32611, USA

Eugene A. Lovelace Department of Psychology Alfred University Alfred, NY 14802, USA

Nancy W. Denney Department of Psychology University of Wisconsin Madison, WI 53706, USA

Denise C. Park Department of Psychology University of Georgia Athens, GA 30602, USA

Jane A. Doussard-Roosevelt Department of Psychology University of Maryland College Park, MD 20742, USA

Dana J. Plude Department of Psychology University of Maryland College Park, MD 20742, USA

Noreen L. Coggin Department of Kinesiology University of North Texas Denton. TX 76203, USA

Anderson D. Smith School of Psychology

Georgia Institute of Technology Atlanta. GA 30332, USA

Dolores P. Gold Department of Psychology Concordia University Montreal, Quebec H3G 1M8, Canada

George E. Stelmach Motor Behavior Laboratory University of Wisconsin Madison, WI 53706, USA

X

Elizabeth A. L. Stine Department of Psychology Brandeis University Waltham, MA 02254, USA Adrian Tomer Department of Human Development & Family Studies Pennsylvania State University University Park, PA 16802, USA

Sherry L. Willis Department of Human Development & Family Studies Pennsylvania State University University Park, PA 16802, USA

contributors

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Cognitive aging is an area in which research efforts have been rapidly expanding. While aging was long ignored by most developmental psychologists, sudden interest began to develop about two decades ago. The first course in developmental psychology, long known among students as "kiddie psych", became "Life-span Development", an acknowledgement that developmental processes continue into old age. More courses on the psychology of aging were introduced. This sudden interest in where we are headed, rather than where we have been, coincided with dramatic changes in the demographics, i.e., the "greying" of the population. Given that a nearly universal complaint of older adults is a sense of decline in memory, and that cognition had become a dominant field in psychology, it is not at all surprising that, in the past decade, there has been a manifold increase in the number of cognitive researchers turning their attention to cognitive aging. This has occurred for several reasons. For one, if we understood the nature of the cognitive deficits seen in older adults we might be able to create programs for remediation of performance deficits. For another, to have a complete knowledge of cognitive functioning we need to be able to account for age-related declines. And lastly, the fact that age effects in cognitive tasks are seen for some variables and not for others, a "dissociation" of age effects across cognitive tasks, means that the study of adult age as a variable in cognitive research provides another procedure for informing choices among theoretical alternatives. Traditional cognitive researchers have turned to cognitive aging both to understand what the actual age-related changes are, and to see what these age effects can tell them about models of cognition. These efforts have proved beneficial both to our understanding of aging and to progress in cognitive theorizing. Today one cannot hope to provide a complete and comprehensive coverage of the field of cognitive aging in a single volume. The present book has three goals. The first is to provide a view of the current state of our research efforts, the issues being addressed, working assumptions, and empirical findings, in a broad sampling of areas of cognitive aging, e. g., attention, memory, language, problem solving, motor control, and intelligence. A second goal is to provide a consideration of some of the work concerned with continued plasticity in the behavior of older adults, the training procedures designed to enhance their performance levels. The third goal is to explore some personal factors, i.e., characteristics of the individual, in relation to levels of cognitive performance. These include metacognitive issues of the relation of self-awareness to performance, the impact of belief systems on performance, and the relationship of personality factors to cognitive function. The initial chapter of this book provides some historical context and basic concepts of cognition, as well as some of the issues in aging research, for those readers not

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already versed in these areas. The next two chapters, by Kausler and by Smith and Park, are concerned with basic memorial processes. Kausler reviews the evidence regarding differential effects of age for automatic versus effortful processing, along with a consideration of aging and memory as indexed by measures of implicit memory. Are there certain types of information, which are encoded without conscious attention or intent to encode, that are spared from the cognitive aging deficits often seen for intentional learning and memory tasks? Might this apparent lack of age effects be particularly true where the test of memory does not entail a conscious effort to retrieve a particular episode from one's past experience? Smith and Park explore memory for pictures and images. They address the questions of whether there are substantial age differences in cognitive tasks that rely on visuospatial representations and whether age differences for visuospatial tasks are greater than for verbal tasks. The chapter by Plude and Doussard-Roosevelt discusses three components of attention (arousal, divided attention, and selective attention), with particular emphasis on selective attention in visual search tasks. Are there certain processes, such as feature detection, that operate without conscious attention and independent of practice, whereas other "automatic" processes become so only by continued practice? What sorts of processing require conscious effort or attention? Can the automatic processes be carried on concurrently (in parallel) while effortful ones must be done sequentially? Are there age differences in the operation of these different sorts of processes? In their chapter Goggin and Stelmach review the substantial evidence for changes in motor control with aging. In addition to the slowing of speeded movements, are there deficits in the planning of sequences of actions? Are there age-related changes in the physical properties of limb movements when they occur? They provide a discussion of alternative theoretical notions proposed to account for changes in motor performance. The chapters on monitoring of memory (Lovelace) and on the role of beliefs such as self-efficacy (Cavanaugh & Green) focus on the relation of aspects of self concept and self awareness to performance in cognitive tasks. Several varieties of memory monitoring are discussed, along with some global judgments of memory function, and systematic questionnaire data regarding metamemory. Does the ability to monitor memory functions vary with the age of the individual? Are any such age differences in metamemory consistent across varieties of metamemory? Can age differences in metamemory account for age difference in level of memory performance? How well do individual differences in cognitive functioning predict actual performance on cognitive tasks? The relationships between several personal belief or personal style factors are discussed, e.g., self theory, implicit theories of cognition, locus of control, and selfefficacy. How are such personal beliefs involved in determining cognitive functioning? Might age differences in such beliefs produce a sizeable portion of the observed age differences in performance? If so, might training programs remove the remediable component of the performance difference?

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In the following chapter, Kotler-Cope and Camp review several conceptual distinctions regarding mnemonics. They consider what features are required for an optimal mnemonic training scheme for use with older adults. What sorts of mnemonic training are most effective, and is the answer age invariant? How can we assure that these mnemonic techniques, once learned, generalize to other tasks and situations? Are young and old adults equally likely to develop generalized applications of the techniques? They provide a review of the substantial literature, successes and failures, in programs aimed at normal, healthy older adults, and those being tested on people with memory pathology such as Alzheimer's disease. In a related chapter, Willis discusses five questions that raise important issues for research on cognitive training: Does training simply effect a remediation to prior skill levels? How broadly should any training effects be expected to generalize? What are the relative effectiveness of cognitive and non-cognitive interventions? Could the "training" effects be simply the benefit of practice? Does the training have long-term effects? The following two chapters are concerned with language and aging. Burke and Laver discuss the selective nature of certain age-related language deficits. Why is it that the aged complain of word-finding difficulties while retaining good vocabulary scores? Might this be related to the distinction between language comprehension and language production? What is occurring when one cannot retrieve a word they wish to use? Burke and Laver present original research that provides strong evidence for the reality of an age-related deficit in accessing the phonological components of lexical items. Stine provides a model of discourse processing with extensive discussion of the roles of working memory. She then considers ways in which that general model would need to be modified to account for the age-related differences in discourse processing seen in the literature. How does increasing experience, which should favor older adults, impact the model? In what ways do the age-related biologically-driven losses of sensory input or of working memory affect the functioning of the system? In the next chapter Denney considers the application of knowledge in problem solving and professional creativity. Are different patterns of age effects seen for abstract laboratory problem solving tasks than for practical everyday problems? If so, is this related to frequency or recency of utilizing such skills? What sorts of training will benefit older adults' performance on problem solving tasks? Can such training markedly reduce the age effect? How do age-related patterns of change in problem solving ability relate to patterns of professional creativity and productivity? Denney provides a review of recent literature as it bears on these questions,

Relationships between personality factors and cognitive functioning are discussed in the chapter by Gold and Arbuckle. How might personality factors impact cognitive functioning, and vice versa? Which personality factors have what type of effects? Is the structure of personality factors stable with age? Do particular personality traits have the same predictive value for cognitive functioning in young and old adults? Gold and Arbuckle propose a detailed causal model of the influence of personality traits on cognition.

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XiV

Conceptions of intelligence, and the patterns of stability and change in intelligence are dealt with in the chapter by Cunningham and Tomer. How is intelligence to be conceived, i.e., what are the components or structure of intelligence? Is that structure age-invariant, or does the meaning of intelligence depend on age of the individual? Do different measured components of intelligence show different patterns of relation to age, and if so how is this to be interpreted? In the final chapter the major conclusions and recurring themes of the present volume are summarized. For further discussion of cognitive aging the reader is directed to Charness (1989, Craik & Trehub (1982), Hess (in press), Kausler (1982), Light & Burke (1988), Poon, Rubin & Wilson (1989), Salthouse (1982, 1985), and to several chapters of the recently revised (3rd edition) Handbook of the psychology of aging (Birren & Schaie, 1990). I wish to acknowledge and thank many individuals who helped me to make this volume a reality. First, of course, the authors who contributed chapters. While there was variation in the ease with which they kept to our original time-table, they produced chapters of excellent quality. I received a semester's sabbatical leave from Alfred University during which a good bit of my time was devoted to this endeavor. Since the contract called for delivery to the publisher of a photo-ready manuscript, in a real sense the printer of this book was Mr. Bret J. Carver of the Computer Center at Alfred University. Bret spent hours helping me configure the chapters using Wordperfect 5.1, and printed the book with an HP LaserJet 11. I thank my colleagues, Gordon Atlas and Nancy Furlong, for comments on portions of my chapters, and our secretary Laurie Stewart for many little tasks that helped complete the work. And lastly, I acknowledge the important contributions of my wife, Mary Jo. Her encouragement kept me going and her patience kept her from complaining when other things were sacrificed to our completion of the book. She also served as editorial assistant, proofreading, doing word processing, and helping to create the author index.

Eugene Lovelace Alfred, New York July, 1990 References Birren, J. E., & Schaie, K. W. (Eds.) (1990). Handbook of the psychology of aging (3rd ed.). San Diego: Academic Press. Charness, N. (1985). Aging and human perjormance. New York John Wiley. Craik, F. I. M., & Trehub, S. E. (Eds.) (1982). Aging and cognitive processes. New York: Plenum. Hess, T. M. (Ed.) (in press). Aging and cognition: Knowledge organization and utilization. Amsterdam: North-Holland. Kausler, D. H. (1982). Experimental psychology and human uging. New York John Wilev. Light, L.*L., & Burke, D. M. (1988). Language, memory, and aging. New York Cambridge University Press.

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Poon, L.W.,Rubin, D. C., & Wilson, B. A. (1989). Everyday cognition in adulthood and late life. New York: Cambridge University Press. Salthouse, T. A. (1982). Adult cognition: An experimental psychology of human aging. New York Springer-Verlag. Salthouse, T. A. (1985). A theory of cognitive aging. Amsterdam: North-Holland.

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Aging and Cogdtlon: Mental Processes, SeZJAwareness and Interwntwns - Eu ene A. Louelace Editor) 8 Elsevier Science Fub%shers B.V. North-Holland). 1990

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1 Basic Concepts in Cognition and Aging Eugene A. Lovelace Alfred University

Above all else, it is our mental capacity that makes humans such extraordinary animals. We are able to store records of our experience in a very rich fashion, to retrieve and utilize that store of experience on future occasions, and so to properly recognize and interpret most events we encounter. We are able to think more abstractly, often in linguistic propositions, and to reason about future and hypothetical events, and so to solve problems in a manner which far exceeds the abilities in any other species. These special abilities to learn, remember, reason, and act appropriately in such a wide range of conditions can be collectively referred to as our cognitive skills. In addition to our unique level of cognitive abilities, humans have an unusually long life span. Most notable is the extended post-reproductive period seen in humans. Menopause, for example, typically occurs in a woman's late 4Os, and this is roughly the mid-point of that individual's adult life. Average life expectancy has increased dramatically in this century. For example, in America, the number of individuals over 65 years of age was only about 1 in 25 people early in this century, but today 1 of every 8 people is 65 or older. The "old-old, those over 75 years of age, are the fastest growing segment of our population. These changing demographics, the "greying" of the population, make the cognitive abilities of older individuals an issue of great social/cultural importance. Increased longevity without continued cognitive ability leads to great hardship, for the individual and society. We will be concerned here with these two outstanding aspects of humankind, mental skills and longevity. We seek to understand the ways in which cognitive functions may be impacted by the passing years as people move into old age, the extent of awareness of, and adaptation to, any changes in cognitive function, as well as the evidence regarding possibilities for active intervention to delay or diminish effects of cognitive aging. Life span development for most biological functions shows an increase in functioning in early life and a decrease in late life. These periods of increasing and decreasing function are commonly referred to as maturation, or "growing up", and senescence, or "growing old. It is clear that as we grow older we can expect to experience a number of physiological changes, e.g., decline in muscle strength, lung capacity, pumping capacity of the heart, elasticity of skin and blood vessels (for a recent discussion of biological and health issues in aging see Elias, Elias & Elias, 1990). What can be said of psychological functioning, particularly of our cognitive skills? In the absence of any

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clear pathology, do cognitive functions show senescent decline? Is that decline selective, affecting some abilities while others are spared? The chapters of this book provide a picture of our current state of knowledge regarding cognitive functioning in later life. For a broad range of cognitive skills the authors explore the evidence for age-related changes in functioning, and provide some ideas about remediation in some cases where there is evidence of cognitive decline. The present chapter will set the stage for those explorations by providing some historical context and a cursory overview of basic theoretical concepts and models of cognitive functioning, as well as some of the major issues in aging research. While the primary audience of this volume is those scholars working in the area of cognitive aging, the readership is assumed to extend beyond that group. With the intent of making the book accessible to that wider audience, this introductory chapter provides basic concepts in cognition and aging. Readers with a good grounding in models of cognitive processing, and who are knowledgeable about the difficulties of conducting research in which adult age is a variable of interest, may find this review superfluous. For others it will provide a conceptual framework and research methodologies which underlie the material in later chapters. The Information Processing Approach

Philosophers of science regard the work in a discipline as alternating between periods of "normal" science and periods in which there occur revolutionary changes in the basic conceptual framework and methodologies. Such a change has been referred to as a "paradigm shift" (Kuhn, 1962). The study of human cognitive processes underwent such a change during the 1960s with the emergence of the information-processingapproach. While there is no consensus on the specifics of a particular model for human cognition, for the last two decades the vast majority of scholarly research in this field has reflected a common conceptual framework of information processing. General tenets of this approach include the following notions: The individual is an active seeker of information about the world, information as received from sense experience of the world undergoes processing transformations, these informationprocessing events take time, and they can be modeled as a sequence of stages. Models of information processing typically contain both a specification of the structure of the system and of the mental processes that occur within that structure (e.g., Atkinson & Shiffrin, 1968; Murdock, 1967). In one manner or another all these models incorporate a crucial characteristic of our mental activity, namely that humans are limited capacity processors of information; we can only handle so much concurrent mental activity. This will be a recurring theme throughout the chapters of this book. The information processing framework has borrowed heavily from the terminology and concepts of the rapidly developing computer technology for the storage and processing of information. The processes by which people take in information and make an internal record of it are commonly called encoding and storage processes. Those processes by which one later makes use of that information are said to involve search and retrieval. These two sets of processes roughly correspond to the notions of learning and memory, respectively. The exact processes involved in the encoding, storage, retention, search and retrieval of information may depend on the type of

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information to be dealt with, the nature of the situation, or the uses to which the information will be put. It has been argued that the information processing approach is particularly well suited to study any changes in cognitive processing in later life (Klatzky, 1988). There is abundant evidence that the losses found are typically not global, affecting all cognitive processes equally. As Welford (1985) put it "age trends in different functions correlate to some extent, but very imperfectly. In other words, the different mechanisms within the human system age to some extent independently, and at different rates in different individuals" (p. 361). The componential analysis which characterizes an information processing approach provides a conceptual framework that is designed to identify particular processes, and so may facilitate the separation of those particular cognitive processes which show substantial age-related change from those that are essentially spared. (It must be noted that Salthouse (1985a) has argued for the theoretical stance of attempting to account for age-related differences in performance in terms of general resources rather than seeking a set of specific mechanisms.) The prevalent class of general models of human information processing has been the "multistore"models, or modified variants thereof. A brief description of characteristics common to models of this sort follows. A Basic Multistore Model

In multistore models there are postulated to be at least two different sorts of memory, short-tern memory (STM) and long-tern memory (LTM). In addition there is typically posited a set of senrory stores. Figure 1.1 depicts the structual components of a basic model. Sensory stores

Physical energy impinging on an individual's sense receptors produces activity in neural systems associated with the particular receptors. That activity outlasts the duration of the physical stimulation, persisting for a matter of seconds or fractions of a second beyond the physical stimulation. This continuing activity induced by the stimulation is referred to as information residing in a sensory store, each sense modality having its own associated sensory store. The information is rapidly decaying during this brief time, and if it does not receive further processing within this short time it decays to a point of being inaccessible. Short-term or Primary Memory

Information in the sensory stores is not directly available to conscious introspection; it may be said to be "pre-conscious". It must first be processed into the STM system before reaching conscious awareness. The STM may be thought of as memory's workbench, the place where those things one is currently attending to and working on reside. It is clear that one can only attend to, or keep in consciousness, a limited number of things at any one time. Since Miller's (1956) famous article this is

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Physical Stimulus

Sensory Store

Selective

sm

Attention

(primery)

w

search

--

Retrieval

LTM (secondary)

Figure 1.1. A basic multistore model of memory.

commonly said to be about 7 "chunks" of information, a chunk referring to a set of elements which hold together as a unit. For example, if asked to remember the letters P, N, M, and K, we will probably treat these as four items to remember, whereas if the letters had been H, 0, M, and E we would be likely to put them together as the word HOME, in which case these 4 letters will constitute a chunk and occupy only one space in the limited capacity of STM. According to such models, one consequence of this limited capacity is that the system is subject to loss of information from STM due to overload. The individual may retain information in STM by engaging in rehearsal. Rehearsal is the process by which the current contents of STM may be kept alive by the individual attending to them and actively reinstating them, i.e., by running them around a rehearsal loop. For example, one can keep a set of random digits or letters "in mind by simply covertly repeating them over and over. Long-term or Secondary Memory

This STM is clearly not what we normally mean when we refer to our memory. Instead we refer to LTM,a memory system in which things are not lost simply because we shift our attention to other events. In most multistore models LTM has been treated as a store of unlimited capacity, and sometimes one in which the memory trace, once created, is considered permanent (barring damage to the brain) (Shiffrin & Atkinson, 1969; Tulving, 1974). This does not mean that the trace will always remain the same since there is evidence that subsequent experience with very similar events may make contact with that memory trace and may change the nature of the trace (Loftus, 1979; Loftus & Loftus, 1980). It is permanent in the sense that it will not simply decay or disappear with the passage of time. This notion of a permanent memory store may seem inappropriate, since we know that in reality we do forget things that we used to know.

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But the continued existence of the memory trace does not assure us that we can successfully retrieve that information on any given occasion. Tulving and Pearlstone (1966) introduced a distinction between availability of the trace in LTM store and the accessibility of that trace when one attempts to retrieve it. This distinction is exemplified by the "tip-of-the-tongue'' experience in which something that you are attempting to recall will not come to mind at the moment, but you know that you know it, and feel as though you can almost get it out. The memory trace is said to be available but not accessible (see Lovelace, Chapter 6, and Burke & Laver, Chapter 10, this volume, for further discussion). The contents of LTM is sometimes said to be "post-conscious", i.e., the contents of LTM are events which were once in STM (and so were in conscious awareness at that time) but were "transferred to LTM. While they are in LTM one cannot be directly aware of them, but a particular cue may trigger a search of LTM and retrieval of the material into STM where it can again become part of one's conscious experience.

Creating memory traces in secondary memory. Not all information that comes into STM will create an effective permanent trace in LTM. A lev&-ofprocessing approach (Craik & Lockhart, 1972) contends that the memorability of an event depends on the nature of the processing carried on while the information resides in STM. Low levels of processing focus on the encoding of sensory features of the item, e.g., the way a tobe-remembered word looked when presented for study, or the phonemic characteristics of the word. High level processing might involve encoding semantic properties of the word, Higher levels of processing are held to induce better memory for the material. To evaluate this approach studies have been conducted in which people were given orienting tasks which induced low level or higher level processing, e.g., searching each word to see if it contains the letter E to induce a low level of processing, or deciding whether or not the presented word was the name of a living thing for a higher-level or semantic encoding. In order to make it probable that the person processed the words only in the fashion which the orienting task required, the memory measurement was typically a measure of incidental memory, i.e., people were only asked to perform the orienting task, they were not warned that they would later be asked to try to recall the words about which they had made decisions. The results of such studies are generally supportive of the levels-of-processing concept. Those tasks which appear to induce attending to higher, semantic features of words caused the words to be remembered better than when the orienting task induced attending to lower level structural features (e.g., Craik & Tulving, 1975). In putting forth this model, Craik and Lockhart suggested that the two levels of memory in the multistore model might be unnecessary, but the levels-of-processing notion is readily incorporated into the rehearsal function of S T M in the multistore model. A distinction has been made between maintenance rehearsal, a process that simply reinstates the information in STM, and elaborative rehearsal, a processing of the information so as to organize it in relation to other material in LTM and so facilitate creation of the sort of trace that will allow later retrieval of that information. One can view the levels of processing as an account of why different types of rehearsal processes, maintenance versus elaborative, have very different memory consequences. Thus maintenance rehearsal is seen as a process typically involving only some low-level

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features, e.g., the phonemic features of the item, being kept alive for current use in STM. Elaborative rehearsal, on the other hand, is viewed as a process by which the item is analyzed in terms of its higher-order, semantic features and its relations to other meaningful items in memory. As noted above, this elaboration enhances the accessibility of items that one attempts to retrieve from LTM. Accessing memory traces from secondary memory. Success in the search/retrieval process depends on a) the existence (availability) of an appropriate trace in LTM, and b) the extent to which the elements of the cue(s) directing the search manage to make contact with (map onto), or gain access to, elements of the memory trace. The above discussion of levels of processing concerns the nature of the trace that comes to exist in memory. Central to the issue of accessing memory traces is the congruence of the elements in the trace with those in the retrieval cue. One reflection of this deals with the sort of cue provided by various forms of memory tests. In general, recall tests directly provide fewer elements of the trace whereas recognition tests provide cues which reinstate many of the elements of the memory trace. Thus the accessing or retrieval requirements are held to play a much greater role in recall measures of memory than in recognition tests.

Nearly any meaningful stimulus event will trigger a large number of elements at time of encoding/storage. The exact set of elements will differ from one experience to the next, even when the nominal stimulus event is identical. This is true because some aspects of the prior context, of the physical environment such as preceding stimulus events or of the internal mental activities of the individual, will differ from one occasion to the next. Success in the retrieval of a memory trace is hindered by variation in the set of elements encoded. These notions are often referred to as the concept of encoding specijiciity (Flexser & Tulving, 1978; Tulving & Thomson, 1973) which holds that a retrieval cue will aid retrieval to the extent that it provides information that was also processed during encoding of the to-be-remembered trace. For example, suppose one is testing memory for a list of words recently studied. Even when a recognition test is used, a given word may be thought of in different ways on the two (study and test) occasions. By manipulation of the prior context one can induce such differences in encoding, e.g., SQUASH is likely to be encoded differently following CORN and PEAS than it would be following TENNIS and BADMINTON (Thomson & Tulving, 1970). For recent discussions of the interaction of encoding and retrieval processes see Jacoby (1988) and, for the notion of "transfer appropriate processing", Roediger & BIaxton (1987). To the extent that the set of elements in the encoded retrieval cue are present in many different memory traces, the search/retrieval process may fail because of intetference caused by similar memory traces. For this reason success in memory tasks may be enhanced by greater distinctiveness of the encoded representation of any to-beremembered event (e.g., Schmidt, 1985). Types of secondary memory systems. There are two major distinctions widely made concerning the form of the representation of information in LTM. One is to contrast episodic and semantic memory (Tulving, 1972). This distinction hinges on whether the memory is for a specific experience in one's life, an episode which has autobiographical reference, or for general knowledge that the person does not associate with any specific

Basic Concepts

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life experience. For example, consider the task of defining the word COLLIE. Clearly one could not do so if he or she had not had some past experience from which the meaning of the word was learned, yet few people could now provide any details about that specific experience. The use of memory to recall the meaning of a word differs from the memory involved when one is asked to remember the last time they bought a new coat. In this latter instance one must recall a specific episode, and it is very likely that a lot of contextual details can be provided as part of the memory, e.g., the number and kinds of coats considered before selecting one, who accompanied the person to the store, what time of day it was, and so forth. Recalling the meaning of a word typically involves only semantic memory, whereas remembering a particular event from one's life involves episodic memory. Episodic memories have autobiographical reference to events in the life of the individual, i.e., they are timeand place-tagged, in ways that are lacking from semantic memory. While he originally proposed these as two separate memory stores, Tulving's (1985b) more recent view is that episodic memory is subordinate to, and functionally dependent on, semantic memory. Tulving (1985a) refers to episodic memory as "remembering",which contains an awareness of that event as a veridical part of one's own past existence, as contrasted to "knowing" that which is in semantic memory. A second distinction is between declarative and procedural knowledge. This contrast is most readily understood by considering the manner in which I might demonstrate to another that I know the meaning of a red traffic light at an intersection versus that I know how to ride a bicycle. For the former I would proceed to display my knowledge by telling what the light means, i.e., describing facts in linguistic propositions. For the latter I would most likely, if a bicycle was handy, get on and demonstrate my knowledge by riding. It is very difficult to try to convey the knowledge of how to ride a bicycle in linguistic propositions. I cannot tell you how to ride, even though I know; you must learn by doing. I have procedural knowledge that is hard to convey to another. The meaning of the red traffic signal, declarative knowledge, is simple to convey in language. Note that we implicitly make this distinction in our language; we speak of demonstrating "that I know" in one case and "that I know how" in the other. The distinction has often been described as knowing that, factual content that can be declared in linguistic propositions, versus knowing how, knowledge of procedures for doing something. Remote or Tertiary Memory

The stereotype of age difference in cognitive ability often includes the idea that, while they have difficulty with memory for recent events, old people have an excellent memory for events that happened a long time ago. This memory for events well in the past has been called remote memory or tertriary memory (Botwinick, 1984). Such a selective decline in memory for recent events (Ribot's law) may characterize certain pathologies of memory, but the stereotype is generally untrue for normal, healthy older adults. Although some memories may remain surprisingly intact for many decades, in general people of all ages show steady decline in memory for material as a function of time which has elapsed since the experience (e.g., Bahrick, Bahrick & Wittlinger, 1975; Warrington & Silberstein, 1970). See Erber (1981) for a review of aging and remote memory.

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Further Modifications of the Multistore Model STM as a Subset of LTM A major issue for the multistore model presented above concerns whether STM is more appropriately conceived of as a separate memory store or as a subset of LTM containing items that are currently in a heightened state of activation (see Klatzky, 1988; Cowan, 1988). One clear problem for the original multistore models was that information from events sensorily experienced is entered into STM in a coherent, interpreted form. Such recognition and identification of the events must rely on the memory traces of prior experience which reside in LTM. Thus the path to STM would appear to be via LTM, which is not in keeping with the original conception of the sequence of stages or activities in the structure of models such as that shown in Figure 1.1 (Bower & Hilgard, 1981).

Another problem with the order of memory stores in the original model results from the growing evidence that, under some conditions, information may be entered into LTM without having ever entered the awareness of the individual (see later discussion regarding automaticity in memory). Since the contents of STM were proposed to be that which the individual currently has in consciousness,this again necessitates a direct connection of sense experience to LTM that by-passes STM. I will return to this issue of direct storage in LTM below in a discussion of controlled or attentional versus automatic processing of information. STM as Working Memory

While the basic model presented above treats STM primarily as a location for the storage of a limited amount of information, it is clear that STM is also taken to be where active, conscious processing of material occurs, e.g., where maintenance or elabortive rehearsal occurs. These two functions of storage and processing are further developed in the concept of working memory (e.g., Baddeley, 1986). Separate storage components may be distinguished on the basis of the sort of information present in the representation held in each store. For example, Baddeley (1986) has proposed that an articulatory loop contains phonological information and information regarding the articulatory control processes by which such phonemic elements are produced. This store, then, is closely tied to speech and auditory inputs. A second store, a visuospatial scratchpad, is tied to visual inputs and provides representations that code structural appearance (form) and spatial location. The active selection and processing of information is accomplished by the central executive which transforms the raw sensory inputs into meaningful representations. In the terminology of the multistore model it serves as the mediator/translator between the sensory stores, LTM, and the representations in STM. The central executive is the controller whereas the stores are viewed as subsidiary or "slave" systems. While the central executive is capable of both storage and active processing, it has a limited capacity, thus it will transfer (download) information into the passive slave stores for more efficient operation. (See Stine, Chapter 11, this volume, for a more detailed discussion of models of working memory.) The limited capacity of this central

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executive is related to issues of limitations on attentional resources to be discussed below (and, Plude & Doussard-Roosevelt, Chapter 4, this volume). The active control of the processing, the determination of what sort of processing will be undertaken, when, and in what manner, has been a continuing problem for the multistore models. The addition of another set of processes to control the information processing, so-called exeative programs, creates a sort of cognitive homunculus, since the origin and activation of these kontrol programs" remain unspecified. It is clear that these control processes include strategies, and knowledge of when to do what, that have been learned on the basis of past experience and so must reside in traces in LTM. But the fundamental issues of where the basic voluntary control processes reside, and what governs the functioning of these "volitional elements" of the system, have not been resolved.

Secondary Memory as an Associative Network Most recent models view the structure of LTM as consisting of nodes that are linked in a complex associative network (e.g., Collins & Loftus, 1975). In order for an effective trace to be created it is necessary for the item to become associatively related to other events in LTM. The strength of the associations (links) among the nodes can vary. For example, one portion of LTM is our lexical memory, a store of the words we know. In our lexical memory one word may bring others to mind, some more readily than others. If one is asked to give a word which TABLE brings to mind, "chair" is a more likely response than "meal" or ''pepper", although most of us would judge that even the last of these is weakly associated with the stimulus word. If each word is thought of as occupying a node in LTM, then one may think of the differential ease with which one can get from one word to another as depending on the strength of the link. Evidence for such structure, and for the concept of spreading activation among these nodes, is provided by the phenomenon of "priming" in lexical decisions. A person is asked to respond as quickly as they can regarding whether or not a letter string presented to them is a word. The speed with which one can decide that CHAIR is a word is faster if the preceding letter string which the individual saw was TABLE than if it was SHOE, an unrelated word (e.g., Meyer & Schvaneveldt, 1976). Reading the word TABLE resulted in the activation of that lexical node and the activation then spread to all nodes that are associatively linked to the TABLE node, the strength of activation of other nodes depending on the strength of the link to the TABLE node. Since the node for CHAIR has a strong link it is stongly activated, that activation persists for some time, and so when the stimulus word CHAIR is subsequently shown it is easier to access this activated lexical node, thus the decision is speeded up or "primed. Note that in this conceptualization the activation is spreading to many other nodes concurrently, i.e., in parallel rather than sequential processes. (The representation of a given word need not be considered a single, unitary node, but may be taken to be a constellation of nodes with each of the phonological, orthographic, and semantic features represented in separate nodes. See Burke & Laver, Chapter 10 of this volume, for a more detailed discussion.)

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Automaticity and Implicit Memory With respect to the processing of information, an important issue has centered on the concept of automaticity (e.g., Hasher & Zacks, 1979, 1984; Shiffrin & Schneider, 1977). When automatic processing occurs, events may be processed sufficiently to produce a memory trace in LTM without drawing substantially on the limited processing resources of working memory, and perhaps without coming to conscious awareness. Whereas the early multistore models typically identified awareness or consciousness with STM, more recently these concepts tend to be identified with the attentional focusing of processing resources, especially in the view that STM is simply an activated subset of items in LTM (e.g., Cowan, 1988; Klatzky, 1984). It may be that such automatic processing may yield a representation in secondary memory and in the storage of STM but not in the working (executive) component (Klapp, 1987). The possibility of storage of traces without awareness is suggested by cases where a person may be unaware of processing the information but when asked about the event may be able to consciously assess the results of that processing. For example, being able to judge the relative frequency with which various items were encountered when such frequency information was not being intentionally stored (see Kausler, Chapter 2 of this volume, for an extensive discussion). In other cases a trace appears to be formed in LTM while the individual appears unable to bring the trace to consciousness even when they attempt to do so. For example, Zajonc (1980) had people look at a long series of irregular shapes. Subsequently they were asked to look at another series that contained some of the shapes they had seen before and some they had not. They were to indicate for each shape whether they had seen it before (old/new recognition judgment) and also to make an affective rating for each shape as to how well they liked that shape. It was clear that the experience with shapes left some trace in LTM since those shapes that were familiar, i.e., had been seen before, were given higher affective ratings, were better liked. This occurred despite the individuals professing that they were not aware of which items they had seen before, and this introspection being supported by performance on the recognition task that was essentially at chance. Marcel (1983) and Balota (1983, 1986) provide further examples of occasions where individuals lack conscious access to the product of their processing. This finding is similar to one reported by Warrington and Weiskrantz (1968, 1970) with amnesics, a population of individuals known to have pathology of cognitive function. When shown a list of words and later asked to recall these words, or even recognize them in a larger set of words, the amnesics may give little or no evidence that the prior experience left any memory trace. Yet when asked to perform certain other tasks, which do not explicitly request memory for the earlier items, evidence is obtained that memory traces were in fact laid down. For example, when given the initial few letters of the word, and asked to provide a completion that makes a word, the amnesics show an increased likelihood of emitting the words that they have just seen. Others have reported similar findings with other individuals suffering brain injuries (e.g., Moscovitch, Winocur, & McLachlan, 1986; Schacter, Harbluk, & McLachlan, 1984; Schacter & Tulving, 1982). The behavior of such individuals might be taken to indicate that they "know" something now, based on their prior experience, even when they cannot "remember" that experience.

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The memory reflected in performance on tasks such as word completion has come to be termed "implicit memory", as opposed to the memory involved in testing where the individual is asked to try to remember a prior experience which is called "explicit memory" (see Kausler, Chapter 2 of this volume, for further discussion). Serial versus Parallel Processing The issue of automaticity of encoding is tied to another long-standing debate regarding whether several processes can be carried on simultaneously, in parallel, or must be carried out successively, in a serial fashion. The early multistore models assumed that cognitive processing entailed a series of discrete operations carried out sequentially. (Recall that the hardware analogy for the processor was the computer which is typically programmed to carry out sequential analyses and actions.) While it is clear that a person can do more than one thing at a time, this could result either from truly concurrent processing of several sorts of information involved in separate tasks (parallel processing), or from rapid shifting among the several processing tasks as occurs when separate terminals are time-sharing the same central processing of a computer. One task that would seem to permit an assessment of whether processing occurs in a serial or parallel fashion entails showing a person a set of items in a display and asking that they decide as quickly as they can whether a target item is contained in that set. If, when the decision times are examined as a function of the number of items in the display, it is found that those times a ) increase with the number of items in the display set (display set size), and b ) that increase shows a nearly linear slope, there is reason to believe the decisions are being made by matching the target serially (sequentially) against items in the display. Schneider and Shiffrin (1977) referred to this sequential procedure as controlled processing. If the decision times are found to be independent of the display set size, i.e., the slope of the function is essentially zero, it suggests that a sort of parallel processing is occurring in which the target is concurrently and simultaneously compared against all items in the display. This they referred to as automatic processing, and they demonstrated that extensive practice with a small set of items in which each item consistently functioned as either a target or a non-target, could produce evidence of parallel processing. Controlled processing would appear to place much greater demand on attentional resources that does automatic processing. Treisman and her colleagues (Treisman & Gelade, 1980; Treisman, 1986) have more recently offered an alternative account of when processes can be carried on automatically, in parallel (her preattentive processes), and when they require sequential processing (focused attention). Plude and Doussard-Roosevelt, Chapter 4, this volume, provide further discussion of Treisman's Feature-integration Theory. Recall that it was noted above that the activation of nodes in an associative network model of secondary memory, such as in perceptual or lexical identification, is assumed to occur in parallel. This is consistent with the theoretical framework, which takes the human brain rather than the computer as the proper hardware analog for cognitive modelling, known as parallel distributed processing models (e.g., Hinton & Anderson, 1981; McClelland & Rumelhart, 1986; Rumelhart & McClelland, 1986). While

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Salthouse (1988~)has written a provocative paper encouraging exploration of such a framework for the formalization of theories of cognitive aging, there has been relatively little work in cognitive aging to date that derives from such formal models. Bottom-up and Top-down Processing An additional processing distinction with broad applicability in cognition concerns the extent to which the contents of current thought processes derive from sensoryperceptual events versus the extent to which they derive from the reactivation of internal traces of prior experience. This contrast is commonly referred to as the difference between bottom-up (data driven) versus top-down (conceptually driven) processing. Like many dichotomies of cognitive processing that have been proposed (e.g., attentional vs. automatic), it is more appropriate to think of this as a continuum, since most cognitions derive, in varying degrees, from both sources. To appreciate the nature of this distinction one might consider how we accomplish speech perception. My ability to properly hear the word CONVENTION in another’s utterance hinges on the incoming sensory information; this bottom-up component involves discriminating the sequence of phonemes characteristic of this word that distinguish it from other similar words. I may be able to hear the word CONVENTION in the utterance, however, even if a loud noise nearby were to completely obliterate a portion of the input energy and so destroy the data-driven basis for hearing the word. In this case the running context in which each word occurs may permit me to make accurate predictions about the next word in the utterance, and so be prepared, on a conceptually-driven or top-down basis, to hear the obliterated word. For example, the speaker may be a professional colleague of mine and the context “I don’t have a finished copy of the paper, but I can give you my brief remarks that I delivered at the conven-on“, where -- is the obliterating noise.

Warren (1970; Warren & Warren, 1970) obliterated the bottom-up components by clipping a phoneme from a tape recorded message and replacing it with noise and found that the particular word one hears, given the identical sensory input, will hinge on the prior verbal context. When the missing element is as little as a single phoneme, the subjective impression is not one of having guessed at the word, or having filled in the proper missing sound, but of actually hearing the missing phoneme. The point is that we are constantly comprehending speech on the basis of both sense information and our anticipatory constructions based on past experience (see Stine, Chapter 11, this volume, for further discussion). In other domains as well, our cognitive processes, general comprehension and constructions of reality, normally have both bottom-up and top-down component. A Revised General Model

A general model of cognitive processing is presented in Figure 1.2. It shows one configuration of structures and processes that incorporates a number of the modifications of the earlier multistore model; this produces a mixture of features from multistore and network models. For simplicity, no effort is made to detail here the feature extraction processes that determine the encoding of information from sensory stores (see Plude & Doussard-Roosevelt, Chapter 4, for more detail). The figure

Secondary Store (LTMI

~

Sensory Stores

Automatic (parallel)

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Working Memory

Generic (Semantic) Memory - b r

Conceptual Phonological Orthographic

Spreading

Activation

Artic. loop isuospatial pad

1 % World Knowledge

Controlled

m (sequential)

Responding

Central Executive

Rules, Programs, and Strategies

Processing Rehearsal

4Rote I

Figme 1 2 A revised general informationprocessingmodel.

IElaborationl

Episodic Memory

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demonstrates the following characteristics of the model. Working memory is taken to be an activated subset of secondary memory. There are two parts to working memory: a) information storage components such as an articulatory loop to hold phonological/articulatory information and a visuospatial scratchpad to hold spatial and imagery information, and b) the central executive which carries out active, intentional information processing. The remainder of secondary store is divided into generic memory (roughly the equivalent of Tulving's semantic memory) and episodic memory (cf. Kausler, 1985). Generic memory is composed of representations of several kinds of information, e.g., a mental dictionary or lexicon, knowledge of general facts about the world, and sets of plans, strategies, programs or rules for what to do in a given situation or how to process a given sort of information. Some information in the sensory stores will receive automatic encoding that produces a representation in one of the storage components of working memory and directly in secondary memory. Much information is assumed to be processed concurrently, i.e., in parallel, in this automatic process. If the representation created in secondary store were to contain associated sets of contextual features that would allow the individual knowledge of a particular event, then the representation would be in episodic memory. As depicted it is in generic (semantic) secondary memory which would allow its use in implicit memory tests, but not as the subject of an active Yemembering" of the experience as part of a personal episode. Other information in sensory store is consciously attended to in controlled, sequential processing by the central executive of working memory. Any rules or strategies employed by the central executive in these controlled processes are accessed (activated) from generic LTM, and are presumed to be triggered, in part, by the initial analyses of characteristics of the information present in the sensory store. (As noted above, it is difficult to locate in such a model where the intention (volition) for the central executive resides -- the "homunculus"problem.) The central executive is assumed to be able to activate information in the lexical store, world knowledge, or strategic processes, and in doing so effectively move this information into working memory. It can initiate a search for, and retrieve information from, episodic memory. The active processing of the central executive may result in an overt response by the individual, or in any of several covert processes. These include rote rehearsal, which simply reinstates the contents of a working memory store, elaborative rehearsal which creates a personal trace in episodic memory, or processes that result in the modification of traces in the lexicon, world knowledge or rules components of generic memory. While this model is not a comprehensive depiction of all relations assumed or discussed in the following chapters, it will suffice as a first approximation to the sort of model required by the material of most chapters. It should provide a useful point of departure for the further theoretical elaborations and distinctions contained therein.

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Conceptual Issues for Aging Research The study of cognitive functioning as it relates to aging raises many issues which involve the nature of aging as a variable of interest. Some of these derive from the fact that age is a property of the individual, while we are looking for the nomothetic laws. Others reflect general concerns common to all developmental research. Still others are peculiar to the concept of aging or the notion of specifying groups of individuals on the basis of (or extent of) their being "aged. The Definition of Aging A basic issue in the definition of aging is the extent to which the concept of aging should include a consideration of those disease states which have increased incidence with increasing age. Despite the increasing likelihood of experiencing some diseases as one grows older, the fact that individuals of advanced age can be found who are free of these diseases indicates that, while they are part of the experience of growing old for many individuals, such diseases are not necessarily an intrinsic part of the aging process. Incipient disease states are very difficult to assess, however. Most of the research discussed in the present volume involves individuals who are normal, active, healthy older adults. That is, except as otherwise noted, we will not be examining the functioning of individuals with known disease states or pathologies such as senile dementia.

There are two very different conceptions of the aging process, one that aging is the cumulative effects of environmental interactions of the organism, the other that it is the consequence of genetically-programmed, time-dependent changes. In reality the aging of an individual is a joint consequence of the cumulative environmental effects, e.g., nutritional deficits, disease, gamma irradiation, normal "wear and tear", coupled with processes that are genetically programmed. While specification of the source of aging in this sense is not central to most issues of the present volume, it is related to a concern of how to conceive of an individual's age. An initial problem for the study of aging hinges on the measurement of age as a variable; how shall age be operationalized? In common parlance we are likely to talk about some particular chronological age, e.g., one is old when one reaches their 65th birthday. But is chronological age, time since one's birth, an accurate index of the variable of concern? The internal aging clock based in the genetic component appears to be ticking at different rates for different individuals. Furthermore, even two individuals whose internal clocks are running at the same rate will have had differential wear and tear as a result of their personal histories, e.g., different diseases, nutrition, etc. Both the differential rate of the genetic clock, and the variation in relevant events in the person's history, dictate that a given chronological age does not mean the same thing for different individuals. One person 69 years of age may be "dying of old age" while others of that age will have 20 or 30 years of active, healthy life ahead of them. An ideal measure of the age variable might be one which indexed where the person is in their normal life span, precluding premature death by accident or disease. Unfortunately, we have no such index. There have been attempts to specify a "functional age" for individuals by comparing how well a person performs relative to normative data on how people of various ages perform. To date attempts to use such

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measures have not gained substantial support (see Salthouse, 1987). Chronological age remains the index of age used in almost all research. It is easy to obtain, there is relatively little imprecision in measurement, and it shows a substantial correlation with the underlying variable of interest, aging. It should be noted, however, that our inability to employ a more specific index of the underlying aging variable must be expected to introduce more variability in our data and so weaken most assessments of age effects. The slippage between chronological age and the true underlying aging variable implies that as chronological age increases the variability in the underlying aging factor will increase, for individuals of the same chronological age, for any tasks which show age-related performance change. On such tasks there is often evidence of greater inter-individual variability in performance with increasing chronological age. Lack of an accurate way to group individuals in terms of the underlying aging factor may well account for this increase in performance variability. Research Design Studies of aging typically employ one of two research designs, longitudinal or crosssectional. In longitudinal research the same individuals are followed as they grow older and measures of their functioning are taken at progressively older ages. In crosssectional studies people of different ages are selected and the functioning of these individuals is compared. Neither is a completely satisfactory way to examine the extent to which functioning is age-related. In the cross-sectional study the individuals at different age levels will systematically differ in ways other than age. Since they were born at different times in the history of our culture they will grow up in somewhat different worlds, each age group sharing certain experiences with one another that are different than those of a group who are of a different age. These effects of the time of birth are often referred to as cohort effects. For example, if one had a representative sample of adults of 20,40,60, and 80 years of age, these groups would surely differ in duration of formal education; the older the individual, on average, the fewer the years of formal education the person would have received. Any differences in performance by the various age groups then might be due to age, or to differences in education (type and amount), or any number of other ways in which these age groups might differ (e.g., health, income, or activity level). Thus in cross-sectional studies one must be concerned about the confounding of age with cohort differences. In a longitudinal study, since one is tracking the same individuals over time, age will not be confounded with cohort, but it will be confounded with other variables such as practice with the tasks or tests employed, and with the time of testing. Time of testing effects refer to any systematic changes in our society over time that might creep into the data as an apparent age effect. For example, if one had begun, 40 years ago, to track one cohort of young adults and to explore their attitudes toward pre-marital sex and divorce, one would probably have seen a steady shift toward more permissive attitudes toward both as these individuals grew older. While this shift is correlated with increasing age, it cannot be causally attributed to the change in age. Presumably the researcher would have realized, in this example, that the whole society has shown such a steady shift, and so it is more likely that the changes in attitudes seen in the study sample were due to socio-cultural changes (from one time of testing to another)

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rather than due to the aging of the sample. But how any other variables that are steadily changing over time may impact the performance one is examining is frequently difficult to discern. In addition, of course, in longitudinal studies one must deal with the problem of selective attrition; typically those who refuse to participate on subsequent waves of the study are not a representative subsample. One generalization is that those who performed more poorly on cognitive tasks are more likely to drop out. If one then compares only the data for individuals who remain in for all waves of the study, the selective attrition still reduces the generality of the findings. In cognitive studies there is reason to believe that those individuals who would be most likely to show declines of function with aging are least likely to remain in the sample. Thus data from longitudinal studies must be carefully analyzed and interpreted to minimize the risk of underestimating cognitive decline. The wrinkle is, of course, that the three variables, age, cohort, and time of test, cannot be neatly separated. When there is variation in any one the remaining two must covary; a confounding is inevitable. There are approaches that include longitudinal follow-ups of cross-sectional studies. In this manner one can begin to separate the effects, but such studies are very rare. The vast majority of the studies are cross-sectional in nature. This is not because the confound with cohort is seen to be less of a problem than a confound with time of testing, but simply because the cross-sectional study is "do-able now". A cross-sectional study might be completed in a few weeks whereas longitudinal studies will take many years or decades to complete. Generality of Findings

The generality of results from research studies is often problematic, and this may be especially true when age is a variable. In a longitudinal study which is following one cohort group it is difficult to know whether the findings will generalize to other cohorts. For the more common cross-sectional approach the issue of generality of the results is tied to the problem of selection of participants at the various age levels. It might seem that ideally one would have samples of individuals of various ages that were representative of the larger population of people of that age. Of course, even if one could get such samples, this provides no protection against any confounds resulting from cohort differences. If one is simply interested in descriptions of the way in which individuals of different ages in our society today differ, then the confound of age with cohort is not a problem. If however, one wishes to learn something about changes that are intrinsic to the aging process then cohort differences must be controlled. This distinction has sometimes been characterized as looking at age differences that currently exist, for which cross-sectional studies are fine, versus looking for age changes where the differences will be taken to be directly related to the aging process. In practice, research studies typically have samples with some systematic bias, i.e., some way in which the samples are known to differ from the population of individuals of that age. For example, in many cross-sectional studies the performance of an aged sample is compared with that of a sample of college students. While the latter group is typically young, and so provide an appropriate age contrast, college students cannot be considered representative of all people their age. Certain biases are apparent, e.g., education level, vocabulary, socio-economic status. Having selected this young sample, typically for reasons of convenience, the researcher must then attempt to see that the

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aged group represents a similarly select or similarly biased sample. It is not completely clear how one can know that this has been accomplished. It is common to compare the young and old on some characteristics believed to be relevant to the task, e.g., in a task involving memory for verbal materials to measure the vocabulary of both groups, and establish that the older adults do not show lower measures (e.g., lower vocabulary scores) than the young. While it makes it more difficult to recruit participants, similarity of factors other than age may be enhanced by using only non-student volunteers from existing groups that contain a wide range of ages, e.g., church groups, business or fraternal organizations. In any case, one must keep in mind the sampled populations when deciding how broadly one can safely generalize any apparent age differences. For a further consideration of these complex issues of adult age as a variable of study see Kausler (1982, Chapters 1-S), Botwinick (1984, Chapters 20 & 21), Birren and Cunningham (1989, and Nesselroade and Labouvie (1985). Issues of Cognitive Aging Age Effects Via Non-cognitive Factors

Cognitive processes are, of course, covert. They cannot be directly observed and recorded. These processes can only be inferred from performance measures. The difficulty with such inference has long been acknowledged in the leaming/performance distinction. That is, while cognitive differences are likely to produce performance differences, the performance must be presumed to be governed by other factors as well. Among the non-cognitive determinants of performance are the broad classes of affective and motivational variables. When a study compares groups defined by subject variables, such as age, this issue is of particular concern. As Botwinick (1967) noted, the joint effects of cognitive and non-cognitive factors determining performance may lead to age-related performance differences which can either mask or enhance actual differences in cognitive function. Figure 1.3 provides a depiction of the performance difference for young versus old individuals under the nine possible combinations of cognitive factors showing age-related decline, stability, or improvement while noncognitive factors showed decline, stability, or improvement. Note from the figure that when cognitive factors are stable (unrelated to age) performance may decline or increase with aging, depending on the non-cognitive component; similarly, with either a decline or improvement in cognitive functioning, this might be offset by non-cognitive factors which showed the reverse relationship to aging. In the aging literature the relationship of these non-cognitive factors to performance on cognitive tasks has received relatively little attention to date (but for the related issue of personal trait differences see the chapters by Cavanaugh & Green, Chapter 7, and Gold & Arbuckle, Chapter 13, this volume). We know that the relationships can be complex. It is likely that for many tasks, performance will be optimal with some intermediate level of motivation or arousal (the Yerkes-Dodson principle), i.e., that the performance can be adversely affected by arousal levels that are too low or too high. Furthermore the optimal level of arousal is likely to depend on task

Basic Concepts

19 ASSU

~ y Stable codv~co,ltribibo

7 Y

High

LOW

0

Non-Cognitive Contribution

\

7 0

Y

High

Stable

\

Y

LOW

High

0

Increase

/

Y LOW

' Y

0

Y

O

Y

0

0

Figure 13. The joint effect of cognitive and non-cognitive factors in determining performance where these two factors are assumed to make equal and independent contributions to performance; only two age levels are compared so the functions are linear, Y = Young, 0 = Old. (Adapted from Figure 5 in J. Botwinick, Cognitive processes in mafzuity and old age. Springer, 1967. Reprinted by author's permission.)

complexity and the degree of prior familiarity with the task (see Plude & DoussardRoosevelt, Chapter 4 of this volume for further discussion). When the groups being compared are young and old adults it is very possible that they differ with respect to one or more of the following: knowledge of the task at the outset (introducing differential transfer of training), affect induced by participation in the task/study, degree of compliance or effort committed to the task (especially if the task might be seen as arbitrary or meaningless). While conceptually separating cognitive and non-cognitive components is important, Figure 1.3 fails to convey the complexity of the issue. As Jones (1959, cited in Salthouse, 1982) has noted, although cognitive and non-cognitive factors may be separate determinants of task performance, most probably they are often interactive rather than independent contributions. We are more highly motivated to perform on those tasks for which we have greater ability. Increasingly, researchers in this area are coming to appreciate that it may prove particularly useful to adopt a more clearly contextual and interactive approach to the study of aging and cognitive function. Rather than looking for the materials or processes that show age differences in memory tasks, understanding of the performance

20

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differences will require simultaneously considering the type of information being processed, the processing demands of the particular task, and characteristics of the individual (e.g., Jenkins, 1979; Smith, 1980b). With respect to the latter, for example, in the long run it will not suffice to compare young versus old adults on cognitive tasks, but rather it must be recognized that the magnitude of age differences depend on other subject characteristics. For example, in several studies it has been shown that the age differences in cognitive performance may be dramatically different for individuals scoring high in vocabulary or verbal skills than for those scoring low on such skills (e.g., Bowles & Poon, 1982; Cavanaugh, 1983; Rice & Meyer, 1986). Evidence of Cognitive Aging Effects Despite the difficulties inherent in assessing the cognitive effects of aging, there are a number of well-documented age effects, i.e., interactions of age with other variables. In terms of the multistore model, for the most part these involve the storage and retrieval of new information in secondary memory (LTM). Age differences in the functioning of sensory stores are slight and, as such, are "unlikely .. to significantly contribute to the observed deficits in secondary memory" (Poon, 1985, p. 430). Primary or Working Memory. Only weak age effects are seen in primary memory, at least as far as the capacity of this memory component is concerned, e.g., as indexed by measures of memory span. However, while memory span shows only small differences favoring young adults (often statistically non-significant), when an active manipulation of the information in STM is required substantial age effects emerge. For example, if the individual is required to repeat a string of random digits in the reverse order from their presentation, young do significantly better than older adults. This decrement might be attributed to a division of attention, necessary when one must both hold the items in store and operate on them so as to reverse the order, and so not be regarded as a deficit of primary memory (Craik, 1977).

If, however, one considers the active manipulation of information to be part of this memory system, i.e., one considers the functioning of working memory, then this finding would suggest that working memory declines with aging. Given recent evidence (e.g., Dobbs & Rule, 1989; Salthouse, Mitchell, Skovronek, & Babcock, 1989) this assumption of a decline in the processing (as opposed to storage) capacity of working memory is widely accepted. Since the successful retrieval of information from secondary memory hinges on type of processing engaged in during encoding and storage, the large age effects reported for many secondary tasks may be due, in part, to deficits of encoding which result from reduced processing capacity of working memory. For example, to the extent that the age differences in recall from secondary memory reflect a failure of older adults to spontaneously engage in organizational strategies and elaborative processing during encoding (Smith, 198Oa; Poon, 1985), these may reflect age differences in working memory. Attentional Resources. If these age effects are taken to reflect differences in uttentional resources available during encoding and storage (e.g., Baddeley, 1986; Craik & Byrd, 1982), then it follows that the greater the attentional demands of a task, the larger the age effects should be. This is the rationale for the proposal by Hasher and Zacks

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(1979) that age effects will be substantial in effortful processes, e.g., those requiring processing resources, but minimal for automatic processes, those making little or no demand on such resources (see Kausler, Chapter 2 of this volume for further discussion). Since high levels of practice may lead effortful processes to become automatic (Shiffrin & Schneider, 1977) one might expect age effects to diminish with extensive practice. While this prediction has received support (e.g., Plude & Hoyer, 1985) it is not always the case (Charness, 1989; Salthouse, 1990). (See Plude & Doussard-Roosevelt, Chapter 4, this volume, for further discussion.) Reduced processing resources might be expected to reduce the number of elements of experienced events that become part of the encoded representations. The finding of reduced knowledge of contextual elements present during the encoding of an event, such as the source of known factual information, (e.g., McIntyre & Craik, 1987) conforms to this expectation. (For a recent discussion of processing resources in cognitive aging see Salthouse, 1988a, 1988b.) Retrieval processes. While the separation of encoding and retrieval processes is extremely difficult (Smith, 1980a), there are several lines of evidence to suggest that some of the age effects in secondary memory tasks derive from retrieval differences. A clearly age-related difference is the speed with which cognitive processes occur. (Goggin and Stelmach, Chapter 5, this volume, provide a discussion of slowing as it impacts motor control.) Indeed, this slowing-with-aging is central to one model of agerelated cognitive differences (e.g., Birren, Woods, & Williams, 1980; Salthouse, 198.5~1, 1985b). Even when older adults are successful in retrieving information from LTM, the time needed to effect that retrieval is greater than for young adults (e.g., in semantic word recall, Bowles & Poon, 1985). In the recall of paired associates, Monge and Hultsch (1971) demonstrated dramatic reductions in the age differences when the time allowed for retrieval was increased from 2.2 to 6.6 seconds. Apparently older adults were storing a good deal of information they could only recall if given somewhat more time to complete the retrieval process. Of course a slowing of recall does not mean that the processes by which recall is achieved have necessarily changed.

Another type of evidence that age differences in retrieval processes may be a major component of cognitive aging is provided by the differential age effects seen on secondary memory tasks as a function of the sort of memory measure employed. While the data are not as completely consistent as some accounts might imply (see Salthouse, 1982 for further discussion), in general the research findings show large age differences for recall tasks, small (sometimes non-significant) age differences in recognition memory tasks, and an absence of appreciable age effects with implicit memory measures (e.g., Schonfield & Robertson, 1966; Craik, 1977; Craik & McDowd, 1987) . The relative magnitude of these age differences for the various types of memory tests is what one would expect, given the usual assumption that recall tasks involve a greater retrieval component (or require greater processing resources) since the cues for recall rarely approximate closely the stimulus elements experienced during encoding whereas high similarity of study and test elements is typical of many recognition and implicit memory tests.

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Representations in secondary memory. When one considers the type of representation involved in a secondary memory task it becomes apparent that some sorts of memory traces show much greater age difference than others. Specifically, large age effects are commonly reported for episodic memory tasks where the individual is being tested on his or her explicit memory for recently experienced (newly learned) information, whereas smaller age differences (or nearly equal performances) are often seen on secondary memory tasks involving semantic memory or procedural knowledge, e.g., Mitchell (1989). One sort of psychometric data that supports the notion of little age effect in memory for semantic information is the portion of the "classic aging pattern" (Botwinick, 1984) in which verbal subtests of intelligence tests show little relationship to age. (See Light & Burke, 1988, for an extended discussion of the relation of aging to memory systems, and Cunningham & Tomer, Chapter 14 of this volume for discussion of aging and various measures of intelligence.) Subjective loss offunction. Finally, let it be noted that one well-documented age effect concerns the beliefs of the aged that they are experiencing a decline in cognitive functioning. As Poon (1985) observed, "the feeling that one's ability to remember and to retrieve information is not as good as it used to be is an universal complaint among middle-aged and elderly persons" (p. 427). The origins and consequences of such beliefs are explored in the present volume by Cavanaugh and Green (Chapter 7) and by Lovelace (Chapter 6). Familiarity and Task Difiiculty Two factors which seem to modulate the magnitude of age effects in cognitive tasks are degree of familiarity of the person with the task, and difficulty of the task. It is clear that often these two factors are not wholly independent since familiarity with a task may have the effect of reducing task difficulty. When old adults are very familiar with a task, or have recently had extensive practice with the processing skills required by the task, age effects are minimal. This has been referred to as "decrement with compensation"; although the aging person may have some decrease in cognitive function, high levels of skilled practice at a task may permit sustained high performance levels. For example, although motor speed generally shows substantial decrement with aging, the performance of highly skilled typists does not show such age effects (Salthouse, 1984). In other cognitive domains, such as chess playing, reading, and contract bridge, expertise has similarly been shown to protect older individuals from deficits in performance levels. For recent reviews and further discussion see Charness (1989), Hoyer (1985), and Salthouse (1989). With regard to task difficulty the general finding has been that age differences are greater as the task becomes more difficult. Or put another way, "increasing the difficulty of the activity will tend to affect the performance of older adults more than young adults" (Salthouse, 1982, p. 191). This account of age-related differences in performance of cognitive tasks is variously known as the "difficulty of test hypothesis" or as the "task complexity" hypothesis (e.g, Cerella, Poon, & Williams, 1980; Welford, 1958). As Craik and McDowd observed, this difficulty account is very similar to an account in terms of processing resources, since a task that "requires a greater involvement of effortful, self-initiated processes ..will appear to be the more 'difficult' t a s k (1987, p. 475).

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Goals for the Study of Cognition and Aging There are three reasons we should be interested in research on cognition and aging. The first is largely descriptive, the focus being to provide information that helps to complete the picture of the effects of aging on cognitive activity. In this vein one explores how cognitive functioning is modulated by the aging process. The second reason reflects a desire to remediate, if possible, any cognitive declines. This leads to attempts to discover ways in which the training of particular cognitive procedures or strategies might effectively offset or ameliorate cognitive losses. A substantial literature in this area has developed in recent years. The third reason derives largely from theoretical issues. For example, the presence or absence of age effects can provide evidence for or against certain assumptions or implications of a theoretical model (e.g., Hasher & Zacks' proposal to include the absence of age effects as a criterion for automatic processing), and dissociations between the age effects across pairs of tasks can strengthen or weaken arguments regarding theoretical systems underlying performance, e.g, Mitchell's (1989) argument for distinguishing episodic and semantic memory systems. The remaining chapters of this volume review and integrate large portions of the extant literature, and some present original data. To this end they all contribute to the goal of completing our picture of age-related changes in cognitive functioning. The chapters by Kotler-Cope and Camp (Chapter 8 on Memory Interventions), and by Willis (Chapter 9 on Training and Transfer of Cognitive Skills), as well as portions of the chapters by Cavanaugh and Green (Chapter 7 on Self-Efficacy), by Denney (Chapter 12 on Problem Solving), and by Cunningham and Tomer (Chapter 14 on Intelligence) address the second goal of remediation of cognitive performance deficits. Portions of many chapters also provide evidence bearing on alternative theoretical views of cognitive processing, the third goal. For example, explorations of the evidence for age-related performance differences or dissociations are relevant to the following conceptual and theoretical issues in cognition: Effortful vs. automatic encoding (Kausler, Chapter 2; Plude & Doussard-Roosevelt, Chapter 4); predominance of generalized cognitive processes versus specificity of cognitive processing (Plude & Doussard-Roosevelt, Chapter 4; Goggin & Stelmach, Chapter 5 ) ; separation of processing of lexical versus visuospatial information (Smith & Park, Chapter 3). Other conceptual issues include: whether the monitoring of cognitive processes is a general skill or task/situation specific (Lovelace, Chapter 6; Cavanaugh & Green, Chapter 7); the separation of competence from performance (e.g., the notion of reserve capacity) (Kotler-Cope & Camp, Chapter 8); working memory and the processing resource components of a general model of discourse processing (Stine, Chapter 11); the differentiation of language comprehension from language production processes in a framework for separating levels of nodes in an associative network model of language (Burke & Laver, Chapter 10). The relation of traditional laboratory measures of problem solving to problem solving of an everyday (real world) type, and the relation of problem solving to creativity and profession productivity are addressed (Denney, Chapter 12), as well the importance of

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personality and other personal factor variables, such as self-efficacy, to cognitive functioning (Cavanaugh & Green, Chapter 7; Gold & Arbuckle, Chapter 13), and various conceptualizations of human intelligence (Cunningham & Tomer, Chapter 14). A review of the major conclusions and recurring themes is provided in the final chapter (Lovelace, Chapter 15). References Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation: Advances in research and theory (Vol2, pp. 89-195). New York: Academic Press. Baddeley, A. D. (1986). Working memory. Oxford: Oxford University Press. Bahrick, H. P., Bahrick, P. O., & Wittlinger, R. P. (1975). Fifty years of memory for names and faces: A cross-sectional approach. Journal of Experimental Psychology, 104, 54-15. Balota, D. A. (1983). Automatic semantic activation and episodic memory encoding. Journal of Verbal Learning h Verbal Behavior, 22, 88-104. Balota, D. A. (1986). Unconscious semantic processing: The pendulum keeps on swinging. Behavioral & Brain Sciences, 9, 23-24. Birren, J. E., & Cunningham, W. (1985). Research on the psychology of aging: Principles, concepts, and theory. In J. E. Birren & K. W. Schaie (Eds.), Handbook ofthe psychology of aging (2nd ed., pp. 3-34). New York: Van Nostrand Reinhold. Birren, J. E., Woods, A. M., & Williams, M. V. (1980). Behavioral slowing with age: Causes, organization, and consequences. In L. W. Poon (Ed.), Aging in the 1980s: Psychological issues (pp. 293-308). Washington, DC: American Psychological Association. Botwinick, J. (1967). Cognitive processes in maturity and old age. New York: Springer. Botwinick, J. (1984). Aging and behavior (3rd ed). New York: Springer. Bower, G. H. & Hilgard, E. R. (1981). Theories of learning (5th ed.). Englewood Cliffs, NJ: Prentice-Hall. Bowles, N. L. & Poon, L. W. (1982). An analysis of the effect of aging on recognition memory. Journal of Gerontology, 37, 212-219. Bowles, N. L. & Poon, L. W. (1985). Aging and retrieval of words in semantic memory. Journal of Gerontology, 40, 71-77. Cavanaugh, J. C. (1983). Comprehension and retention of television programs by 20and 60-year olds. Journal of Gerontology, 38, 190-196. Cerella, J., Poon, L. W., & Williams, D. M. (1980). Age and the complexity hypothesis. In L. W. Poon (Ed.), Aging in the 1980s: Psychological issues (pp. 332340). Washington, DC: American Psychological Association. Charness, N. (1989). Age and expertise: Responding to Talland's challenge. In L. W. Poon, D. C. Rubin & B. A. Wilson (Eds.), Everyday cognition in adulthood and late life (pp. 437-456). New York: Cambridge University Press. Collins, A. M. & Loftus, E. F. (1975). A spreading-activation theory of semantic memory. Psychological Review, 82, 407-428.

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Cowan, N. (1988). Evolving conceptions of memory storage, selective attention and their mutual constraints within the human information-processing system. Psychological Bulletin, 104, 163-191. Craik, F. I. M. (1977). Age differences in human memoy. In J. E. Birren & K. W. Schaie (Eds.), Handbook of the psychology of aging (pp. 384-420). New York: Van Nostrand Reinhold. Craik, F. I. M., & Byrd, M. (1982). Aging and cognitive deficits: The role of attentional resources. In F. I. M. Craik & S . Trehub (Eds.), Aging and cognitive processes (pp. 191-212). New York: Plenum Press. Craik, F. I. M., & Lockhart, R. S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior, I I , 671-684. Craik, F. I. M., & McDowd, J. M. Age differences in recall and recognition. Journal of Experimental Psychology: Learning, Memory, & Cognition, 13, 474-479. Craik, F. I. M., & Tulving, E. (1975). Depth of processing and the retention of words in episodic memory. Journal of Experimental Psychology: General, 104, 268-294. Dobbs, A. R., & Rule, B. G. (1989). Adult age differences in working memory. Psychology and Aging, 4, 500-503. Elias, M. F., Elias, J. W., & Elias, P. K. (1990). Biological and health influences on behavior. In J. E. Birren & K. W. Schaie (Eds.), Handbook of the psychology of aging (3rd ed., pp. 79-102). San Diego: Academic Press. Erber, J. T. (1981). Remote memory and age: A review. Experimental Aging Research, 7, 189-199. Flexser, A. J., & Tulving, E. (1978). Retrieval independence in recognition and recall. Psychological Review, 85, 153-171. Hasher, L. & Zacks, R. (1979). Automatic and effortful processes in memory. Journal of experimental psychology: General, 108, 356-388. Hasher, L. & Zacks, R. (1984). Automatic processing of fundamental information: The case of frequency of occurrence. American Psychologist, 39, 1372-1388. Hinton, G. E., & Anderson, J. A. (Eds.). (1981). Parallel models of associative memory. Hillsdale, NJ: Erlbaum. Hoyer, W. J. (1985). Aging and the development of expert cognition. In T. M. Shlechter and M. P. Toglia (Eds.), New directions in cognitive science (pp. 69-87). Norwood, NJ: Ablex. Jacoby, L. L. (1988). Memory observed and memory unobserved. In U. Neisser & E. Winograd (Eds.), Remembering reconsidered: Ecological and traditional approaches to the study of memory (pp. 145-177). New York: Cambridge University Press. Jenkins, J. J. (1979). Four points to remember: A tetrahedral model of memory experiments. In L. S . Cermak & F. I. M. Craik (Eds), Levels of processing in human memory (pp. 429-446). Hillsdale, NJ: Erlbaum Kausler, D. H. (1982). Experimental psychology and human aging. New York: Wiley. Kausler, D. H. (1985). Episodic memory: Memorizing performance. In N. Charness (Ed.), Aging and human performance (pp. 101-141). New York: Wiley. Klapp, S. T. (1987). SHort-term memory limits in human performance. In P. A. Hancock (Ed.), Human factors psychology (pp. 1-27). Amsterdam: North-Holland. Klatzky, R. (1984). Memory and awareness. San Francisco: Freeman.

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Klatzky, R. (1988). Theories of information processing and theories of aging. In L. L. Light & D. M. Burke (Eds.), Language, memory, and aging (pp. 1-16). New York Cambridge University Press. Kuhn, T. S. (1962). The structure of scientific revolutions. Chicago: University of Chicago Press. Light, L. L., & Burke, D. M. (1988). Patterns of language and memory in old age. In L. L. Light & D. M. Burke (Eds.), Language, memory, and aging (pp. 244-271). New York: Cambridge University Press. Loftus, E. F. (1979). The malleability of human memory. American Scientkt, 67,312320. Loftus, E. F., & Loftus, G. R. (1980). On the permanence of stored information in the human brain. American Psychologist, 35, 409-420. Marcel, A. J. (1983). Conscious and unconscious perception: Experiments on visual masking and word perception. Cognitive Psychologv, 15, 197-237. McClelland, J. L., & Rumelhart, D. E. (1986). Parallel distributed processing: Exploration in the microstructure of cognition (Vol. 2). Cambridge, MA: Bradford. McIntyre, J. S., & Craik, F. I. M. (1987). Age differences in memory for item and source information. Canadian Journal of Psyschology, 41, 175-192. Meyer, D. E., & Schvaneveldt, R. W. (1976). Meaning, memory structure, and mental processes. Science, 192, 27-33. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81-97. Mitchell, D. B. (1989). How many memory systems? Evidence from aging. Journal of Experimental Psychology: Learning, Memory, and Copition, 15, 3 1-49. Monge, R. H., & Hultsch, D. F. (1971). Paired-associate learning as a function of adult age and the length of the anticipation and inspection intervals. Journal of GerontologS 26, 157-162. Moscovitch, M., Winocur, G., & McLachlan, D. (1986). Memory as assessed by recognition and reading time in normal and memory-impaired people with Alzheimer's disease and other neurological disorders. Journal of Experimental Psychology: General, 115, 331-347. Murdock, B. B. (1967). Recent developments in short-term memory. British Journal Of P~chology,58, 421-433. Nesselroade, J. R., & Labouvie, E. W. (1985). Experimental design in research on aging. In J. E. Birren &. K. W. Schaie, (Eds.), Handbook of the psychology of aging (2nd ed., pp. 35-60). New York: Van Nostrand Reinhold. Plude, D. J. & Hoyer, W. J. (1985). Attention and performance: Identifying and localizing age deficits. In N. Charness (Ed.), Aging and human performance (pp. 47-99). New York: Wiley. Poon, L. W. (1985). Differences in human memory with aging: Nature, causes and clinical implications. In J. E. Birren & K. W. Schaie, (Eds.), Handbook of the psychology of aging (2nd ed., pp. 427-462). New York: Van Nostrand Reinhold. Rice, G. E. & Meyer, B. J. F. (1980). Prose recall: Effects of aging, verbal ability, and reading behavior. Journal of Gerontology, 41, 469-480. Roediger, H. L., & Blaxton, T. A. (1987). Retrieval modes produce dissociations in memory for surface information. In D. S. Gorfein & R. R. Hoffman (Eds.), Memory and cognitive processes: The Ebbinghaus Centennial Conference (pp. 349379). Hillsdale, NJ: Erlbaum.

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Rumelhart, D. E., & McClelland, J. L. (1986). Parallel distributed processing: Exploration in the microstructure of cognition (Vol. 1). Cambridge, MA: Bradford. Salthouse, T. A. (1982). Adult cognition: An experimental psychology of human aging. New York: Springer-Verlag. Salthouse, T, A. (1984). Effects of age and skill in typing. Journal of Gerontology, 113, 345-371. Salthouse, T. A. (1985a). A theory of cognitive aging. Amsterdam: Elsevier. Salthouse, T. A. (1985b). Speed of behavior and its implications for cognition. In J. E. Birren & K. W. Schaie (Eds.), Handbook of the psychology of aging, (2nd ed., pp. 400-426). New York: Van Nostrand Reinhold. Salthouse, T. A. (1987, March). Is functional age functional? Paper presented at American Psychological Association, Division 21 Mid-Year Symposium, Washington, D.C. Salthouse, T. A. (1988a). The role of processing resources in cognitive aging. In M. L. Howe & C. J. Brainerd (Eds.), Cognitive development in adulthood: Progress in cognitive developmental research (pp. 185-239). New York: Springer-Verlag. Salthouse, T. A. (1988b). Resource-reduction interpretations of cognitive aging. Developmental Review, 8, 238-272. Salthouse, T. A. (19%). Initiating the formalization of theories of cognitive aging. Psychology and Aging, 3, 3-16. Salthouse, T. A. (1989). Aging and skilled performance. In A. Colley & J. Beech (Eds.), The acquisition and performance of cognitive skills (pp. 247-264). New York: Wiley Salthouse, T. A. (1990). Cognitive competence and expertise in aging. In J. E. Birren & K. W. Schaie (Eds.), Handbook of the psychology of aging, (3rd ed., pp. 310-319). San Diego: Academic Press. Salthouse, T. A., Mitchell, D. R., Skovronek, E., & Babcock, R. L. (1989). Effects of adult age and working memory on reasoning and spatial abilities. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 507-5 16. Schacter, D. L., Harbluk,J. L., & McLachlan, D. R. (1984). Retrieval without recollection: An experimental analysis of source amnesia. Journal of Verbal Learning and VerbaI Behavior, 23, 593-6 11. Schacter, D. L., & Tulving, E. (1982). Memory, amnesia, and the episodic/semantic distinction. In R. L. Isaacson & N. E. Spear (Eds.), Expression of knowledge (pp. 33-65). New York: Plenum. Schmidt, S. R. (1985). Encoding and retrieval processes in the memory for conceptually distintive events. Journal of Experimental Psychology: Learning, Memory & Cognition, 1I , 565-578, Schneider, W., & Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review, 84, 1-66. Schonfield, D., & Robertson, E. A. (1966). Memory storage and aging. Canadian Journal of Pg~hology,20, 228-236. Shiffrin, R. M., & Atkinson, R. C, (1969). Storage and retrieval processes in longterm memory. Psychological Review, 76, 179-193. Shiffrin, R. M. & Schneider, W. (1977). Controlled and automatic human information processing: 11. Perceptual learning, automatic attending, and a general theory. Psychological Review, 84, 127-190.

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Smith, A. D. (1980a). Age differences in encoding, storage, and retrieval. In L. W. Poon, J. L. Fozard, L. S. Cermak, D. Arenberg, & L.W. Thompson (Eds.), New directions in memory and aging: Proceedings of the George A. Talland Memorial Conference (pp. 23-45). Hillsdale, NJ: Erlbaum. Smith, A. D. (1980b). Cognitive issues: Advances in the cognitive psychology of aging. Introduction. In L. W. Poon (Ed.), Aging in the 1980s: Psychological issues (pp. 223-225). Washington, D.C.: American Psychological Association. Thomson, D. M., & Tulving, E. (1970). Associative encoding and retrieval: Weak and strong cues. Journal of Experimental Psychology, 86,255-262. Treisman, A. M. (1986, November). Features and objects in visual processing. Scientific American, 255(5), 114B-125. Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive Psychology, 12, 97-136. Tulving, E. (1972). Episodic and semantic memory. In E. Tulving & W. Donaldson (Eds.), Organization of memory (pp. 382-404). New York: Academic Press. Tulving, E. (1974). Cue-dependent forgetting. American Scientist, 62, 74-82. Tulving, E. (1985a). Memory and consciousness. Canadian Psychology, 26, 1-12. Tulving, E. (1985b). How many memory systems are there? American Psychologist, 40, 385-398. Tulving, E., & Pearlstone, Z. (1966). Availability versus accessibility of information in memory for words. Journal of Verbal Learning & Verbal Behavwr, 5, 381-391. Tulving, E., & Thomson, D. M. (1973). Encoding specificity and retrieval processes in episodic memory. Psychological Review, 80, 352-373. Warren, R. M. (1970). Perceptual restoration of missing speech sounds. Science, 167, 392-393. Warren, R. M., & Warren, R. P. (1970, December). Auditory illusions and confusions. Scientific American, 223(6), 30-36. Warrington, E. K. & Silberstein, M. (1970). A questionnaire technique for investigating very long term memory. QuarterlyJournal of Experimental Psychologv, 22, 508-512. Warrington, E. K. & Weiskrantz, L. (1968). New methods of testing long-term retention with specific reference to amnesic patients. Nature, 21 7, 972-914. Warrington, E. K. & Weiskrantz, L. (1970). Amnesic syndrome: Consolidation or retrieval? Nature, 228, 628-630. Welford, A. T. (1958). Ageing and human skill. London: Methuen Press. Welford, A. T. (1985). Changes of performance with age: An overview. In N. Charness (Ed.), Aging and human performance (pp. 333-369). New York: Wiley. Zajonc, R. B. (1980). Feeling and thinking: Preferences need no inferences. American Psychologist, 35, 151-175.

Aging and Cognition: Mental Processes. Sel Awareness a d Interwntions - Eu erie A. huelace IEd;torl 0 Elseuier Science pllb%shersB.V. North-Holland). 1990

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Automaticity of Encoding and Episodic Memory Processes Donald H. Kausler University of Mksouri at Columbia

After decades of being on the fringe of basic memory research, research on adult age differences in memory has joined the mainstream of that research in recent years. Traditionally, studies on adult age differences served largely to identify why episodic memory is less proficient in late adulthood than in early adulthood. That is, is the decline in proficiency attributable to age-related decrements in encoding, storage, or retrieval--or in some combination of all three? This issue is, of course, of great importance in gerontology, but it has little relevance for basic memory theory/research. For the past ten or so years interest in aging research on memory phenomena has increasingly attracted the attention of basic memory researchers. Witness, for example, the frequency with which age-related studies have appeared in the prestigious basic memory research journals during the past five years (e.g., Burke, White, & Diaz, 1987; Kausler, Lichty, & Hakami, 1984; Light & Singh, 1987; Mitchell, 1989; NavehBenjamin, 1987). Surely readers of these journals are no longer surprised to find articles describing experiments in which adult age variation is a critical independent variable. A major reason for this interest in aging effects is the increasing realization of the complexity of the human memory system, and the possibility that not all components are equally age sensitive. In fact, the absence of age differences on various semantic memory tasks (e.g., lexical decision tasks; Howard, Shaw, & Heisey, 1986), combined with the presence of substantial age-related deficits on many cognitively effortful episodic memory tasks (e.g., free recall; Rissenberg & Glanzer, 1987), has strengthened considerably the position that semantic memory and episodic memory are separate, but interacting, memory systems (Tulving, 1983). These differential aging effects are an example of a dissociation of the kind in which an independent variable has very different effects on different forms of memory. In this case, the independent variable is age variation rather than an experimental variable such as one manipulating level of processing. As the number of independent variables producing such dissociations increases, our conviction that different forms of memory are involved increases. Preparation of this chapter was supported in part by Research Grant AGO8214 awarded to the author by the National Institute on Aging.

D.H.Kausler

30

Our interest, however, is not in the semantic-episodic distinction, but rather in a distinction between two different kinds of episodic encoding processes. Here too adult age variation has offered the promise of being an important independent variable for producing a dissociation of theoretical significance. The dissociation consists of a pronounced adverse effect of aging for one kind of process, and a null effect, or at least a greatly reduced effect, of aging on the other kind of process. The importance of adult age variation in this process distinction may be illustrated by the results obtained by Kausler and Puckett (1980a). Their young adult and elderly adult subjects received both a paired-associate learning task and a frequency judgment task. For the former they had four study-test trials on a 10 pair list, with total number of correct responses over the four trials serving as the dependent variable. For the latter they had two trials on each of two lists containing words varying in their frequency of occurrence (one list under incidental memory conditions, the other under intentional memory conditions). For present purposes, the dependent variable was the number of correct relative judgments out of 18 per list, averaged over all conditions. Age differences for the two tasks are shown in Figure 2.1. Note the pronounced agerelated deficit for paired-associate learning scores-a deficit relative to the mean score of young subjects of about -28% (a relative deficit that approximates that found by other investigators; e.g., -30% in Salthouse, Kausler, & Sault's, 1988, study). Expressed somewhat differently, the mean score for the elderly subjects fell more than one

16

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0 t

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2 0

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Figure 2.1. Adult age differences in paired-associate learning and memory for frequency-of-occurrence (both tasks received by the same subjects). (Adapted from data in Kausler & Puckett, 1980a. Copyright 1980 by Gerontological Society of America. Adapted by permission.)

Automatic Encoding and Episodic Memory

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standard deviation below the mean score for the young subjects. By contrast, note the slight-age-related deficit for frequency-of-occurrence memory scores--a relative deficit (hereafter referred to as a loss score) of about -3% (again one that approximates that found by other investigators; e.g., -5% in Salthouse et a1.k study). Here the mean score for elderly subjects fell about 0.2 standard deviations below the mean score for young subjects. Statistically, the main effect for age was clearly significant for the paired-associate task, but not for the frequency judgment task. To complete the picture, scores earned by Kausler and Puckett's elderly subjects on a test of fluid intelligence correlated significantly with scores on the paired-associate task but not with scores on the frequency judgment task. Clearly, something different seems to be "going on" for these two tasks, sufficiently so to produce a dissociation for two different forms of episodic memory, one that is highly age sensitive, the other possibly age insensitive. From the perspective of contemporary memory theory (e.g., Hasher & Zacks, 1979, 1984), the reason for this dissociation is not in the existence of separate systems or stores, but rather in the existence of two different kinds of episodic encoding processes. Although the encoding processes differ in their operations, they are assumed to transmit information to a common long-term episodic store. Paired-associate learning is but one kind of a highly age sensitive episodic memory task. Memory for words in a free recall list is another kind. For example, Rissenberg and Glanzer (1987) found the relative age deficit for their elderly subjects to be about -34% for concrete words and about -26% for abstract words. The episodic information conveyed by tasks of this kind is presumed to be encoded by cognitively effortful processes that diminish greatly in proficiency from early to late adulthood. By contrast, memory for frequency of occurrence is commonly presumed to be encoded by automatic memory processes that are largely, if not entirely, unaffected by aging. Hasher and Zacks (1979, 1984) proposed that the basic distinction between effortful and automatic encoding processes is in terms of the role played by a limited capacity attentional system. Effortful encoding processes (e.g., elaborative rehearsal) are those that are constrained by the system's limited capacity, automatic encoding processes are not. In their words, ". . . effortful processes require the expenditure of attention and effort and so use a portion of the limited-capacity system" (Hasher & Zacks, 1979, p. 358), whereas automatic processes 'I. . . drain minimal amounts of energy from attention capacity, allowing the organism to continue to operate even when extraordinarily high demands are made upon that capacity, as in moments of high stress or injury'' (Hasher & Zacks, 1979, p. 359). Over the past 10 years, memory researchers, and especially gerontological memory researchers, have largely replaced the concept of a limited capacity attentional system with that of a limited capacity working memory system (e.g., Salthouse, 1988). The system is postulated to have both a limited capacity store for temporarily holding information and a limited capacity "space" for processing information. Since working memory's capacity for both functions is commonly assumed to diminish with aging, the proficiency of effortful encoding processes should decrease with aging. By contrast, the proficiency of automatic encoding should be relatively unaffected by aging (see Figure 2.2). The age sensitivity/insensitivity distinction is but one of several criteria established by Hasher and Zacks (1979, 1984) for distinguishing between tasks that involve

D.H.Kausler

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Ef fortf u I Processing Input Episodic Event Episodic Event

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-Fl Elderly

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Episodic Event No Age

Difference Episodic Event

Figure 22. Postulated differential effects of decrements in working memory's capacity from early to late adulthood on tasks mediated by cognitively effortful and automatic encoding processes.

effortful encoding and those that involve automatic encoding. For example, incidental and intentional memory conditions are expected to be equally effective for tasks governed by automatic processes, but intentional learning conditions should promote better memory than incidental memory conditions for tasks governed by effortful processes. Similarly, tasks governed by automatic processes should not interfere with simultaneous performance on tasks governed by effortful processes. By contrast, performance on one task governed by effortful processes should interfere with simultaneous performance on a second task also governed by effortful processes. These various criteria "define a set of converging operations for validating the proposed distinction between processes at either end of the attentional demand continuum" (Hasher & Zacks, 1979, p. 366). Presumably, failure to satisfy any one of the criteria should disqualify any given task, such as a frequency-of-occurrence memory task, from being considered as involving automatic encoding processes. The basic null effect for adult age differences establishes an especially stringent criterion, given the widespread reporting of large age-related deficits on many memory tasks. In their seminal article, Hasher and Zacks (1979) identified three kinds of episodic memory as likely to be automatic in encoding format. Each consists of memory for noncontent attributes of episodic events. They are: frequency-of-occurrence memory, temporal memory (i.e., memory for the temporal order of episodic events), and spatial memory (i.e., memory for the location of episodic events). Thus, episodic events differ in how often they occur, when they occur, and where they occur. Each noncontent

Automatic Encoding and Episodic Memoy

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attribute was viewed as being important in maintaining continuity with our environments, and may therefore be innately programmed to be encoded automatically. Our main objectives in this chapter are to evaluate the evidence for the expected null effect of adult age variation on each of these noncontent attributes and to discuss the implications of this evidence for both understanding aging's effects on episodic memory in general and for the identification of the processes mediating "automatic" forms of memory encoding regardless of an individual's age. An additional objective is to evaluate the evidence for the age insensitivity of two other forms of episodic memory that are commonly postulated to involve automatic encoding processes and are therefore expected to be immune to age-related deficits. They share with memory for noncontent attributes the basic characteristic of encoding processes that are presumed to be unaffected by the limited capacity of working memory.

The first form is for memory of one's own actions and activities. Subjects perform a series of either discrete brief actions (e.g., placing a cup on a saucer) or continuous activities (e.g., solving anagrams for several minutes), and then receive a memory test for those actions/activities. Studies dealing with adult age differences for this form of memory have been recently reviewed extensively (Kausler & Lichty, 1988; Norris & West, in press). Consequently, our review will focus primarily on developments since the completion of these earlier reviews and on the implications of age differences in activity/activity memory for the concept of automaticity of encoding. Interestingly, the second form attracted attention initially some years ago by the discovery of a dissociation produced by an independent variable somewhat comparable to adult age variation. Organic amnesics were found by Warrington & Weiskrantz (1968,1970) to remember far fewer items of a free recall list than age matched control subjects. Such memory (usually called explicit memory in this context) requires effortful encoding processes that are markedly impaired in organic amnesics. By contrast, Warrington and Weiskrantz discovered essentially equivalent memory performances between amnesics and controls on a second form of memory (usually called implicit memory) that was tested by giving their subjects the initial letters of each word in the prior study list and asking them to generate a word beginning with those stems (a stem completion task). The proportion of generated words from the prior study list words was as large for the amnesics as for the controls, even though conscious recollection of those words's presence in the study list was much less for the amnesics than for the controls. Not surprisingly, implicit memory eventually attracted the attention of experimental aging researchers. Many normally aging individuals are diagnosed as having "Age Associated Memory Impairment" (Crook, Bartus, Ferris, Whitehouse, Cohen, & Gershon, 1986)--the extent of the impairment is simply much less than that found with either Alzheimer's Disease or organic amnesias such as Korsakoffs disease. Consequently, the dissociation between explicit and implicit memory found for amnesics/controls should also be found for normally aging individuals/controls (i.e., younger adults).

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Memory for Noncontent Attributes Frequency-of-occurrence Memory Basic research studies involving frequency judgments with symbols as items began to appear at least as early as 1963 (Erlick, 1963). Probably the first studies to test frequency judgments with verbal materials were those of Hintzman (1969) and Underwood (1969). However, it was some years later before what was apparently the first study to test for age differences in accuracy of frequency judgments appeared (Freund & Witte, 1978). With the advent of automaticity theory and the relevance of adult age differences to the validity of that theory, interest in adult age differences increased greatly, and a number of aging studies have been published in the past ten years. Part of this interest undoubtedly reflects the importance of identifying and understanding those memory processes that are age insensitive as well as those that are age sensitive (Salthouse, 1988). Another reason for this interest is in the fact that the frequency judgment task has been the prototypal one for testing the existence of and characteristics of automatic encoding processes, Failure to substantiate the absence of adult age difference in frequency memory proficiency would strike a severe blow to automaticity theory, at least of the kind proposed by Hasher and Zacks (1979). Because of the pivotal role played by frequency-of-occurrence memory in determining the existence of truly automatic processes that are unaltered by aging, our review and analysis of research on frequency judgments will be more extensive and detailed than our review and analysis of research on other noncontent attributes. The standard format in a frequency memory study calls for varying the frequency with which episodic events occur in a study list, and then requiring subjects to judge how frequently the individual events appeared in the total series of events. The events are usually words (or pictures of familiar objects when children serve as subjects), but they may also be activities or actions. A number of words (or some other kind of episodic events) are presented in a series, some only once, some perhaps three times (widely separated), some five times, and so on. Either an absolute or a relative frequency judgment test may then be given to the subjects. On an absolute judgment test subjects are shown each study list word (and usually some other words that were not included in the study list; i.e., zero frequency words), and they estimate how often it had occurred in the list. On a relative judgment test subjects are shown pairs of words that had varied in their study list frequency, and they are asked to identify for each pair the member that had occurred more frequently in the study list. In aging research, the preferred test is the relative judgment one in which frequency discriminations are made. It avoids the problem faced in an absolute test of a potential age difference in response bias (Attig 8t Hasher, 1980; Maki & Ostby, 1987). Consequently, our review will focus largely on those aging studies employing a relative judgment test. Adult age differences. Early aging studies employing a relative frequency test were conducted by Attig and Hasher (1980), Kausler and Puckett (1980a), Kausler, Wright, and Hakami (1981), and Kausler, Hakami, and Wright (1982). In the studies by Attig and Hasher and Kausler and Puckett, young adult and elderly subjects performed under both intentional and incidental memory conditions. Intentional memory subjects knew in advance that they would be tested for their memory of frequencies of

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occurrence of study list words. Incidental memory subjects knew only that they would receive a later memory test, without specification of what would be tested. In both studies, the intentional/incidental variation had a negligible effect on judgment accuracy, as expected if the encoding of frequency information is truly automatic. The overall age effect was also slight, again as expected on the basis of automaticity theory. As noted earlier, Kausler and Puckett found a loss score of about -3%, with mean judgment scores for their elderly subjects falling only about 0.2 standard deviations below the mean for their young subjects. A similar analysis of the age group scores reported by Attig and Hasher reveals a relative loss score for their elderly subjects of about -6% (standard deviations were not reported). As in Kausler and Puckett’s study, the age effect did not attain statistical significance, although a trend toward lower accuracy scores for elderly subjects @ Abstract

21%

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Adapted from Mason and Smith (1977)

In the Mason and Smith (1977) experiment discussed earlier, imagery instructions were manipulated in a free-recall task. These instructions emphasized the intraitem encoding of the individual items rather than using imagery to link the items in the list together. As can be seen in Table 3.2, young and old subjects in this experiment failed to benefit from the imagery instructions, and only a middle-aged group showed slightly

Memoy for Pictures and Images

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improved recall with the instructions. The youngest and oldest groups, however, showed the same effect. No clear conclusion can be reached with these data, but there is certainly no evidence that the older subjects failed to benefit from imagery while the young subjects did. In studies showing facilitative effects of imagery (e.g., Yesavage, Rose & Bower, 1983), and in studies showing no facilitation of imagery instruction (e.g., Mason & Smith, 1977), young and old seem to perform alike. In summary, there does not seem to be any strong evidence for older adults differing from younger adults when imagery is manipulated with verbal materials, either when the imagery value of the word lists is manipulated or when imagery instructions are used. Adding visual features to the verbal materials improves memory for the material, but does so equally for young and old subjects. Visuospatial Material Permitting Minimal Linguistic Encoding One problem with research using verbal materials, however, is the difficulty of separating the visuospatial characteristics of the words from their linguistic characteristics. For this reason, investigators have used nonverbal materials which are difficult to verbally encode. Just as abstract words are difficult to encode visuospatially, certain non-verbal, pictorial materials are difficult to encode verbally. For example, pictures of faces are notoriously difficult to represent linguistically (e.g., Winograd & Simon, 1980), and when one is asked to describe a face, visuospatial characteristics are typically reported. The same is true for abstract drawings which are rich in perceptual detail but difficult to represent propositionally except by detailing their visuospatial features. Studies using such materials may be particularly informative for understanding age differences. Unfortunately, there are relatively few studies looking at age differences in faces or abstract designs, materials in which visuospatial coding is clearly emphasized. Memory for Faces Pictures of unfamiliar faces are extremely difficult to verbalize except by describing physical features, characteristics of the face that would make up its visuospatial representation (e.g., big nose, bushy eyebrows, etc.). In fact, face recognition, unlike either word recognition or recognition of pictures of scenes, does not seem to be related at all to verbal coding skills or verbal strategies (Bartlett, Hurry,& Thorley, 1984; Goldstein, Johnson, & Chance, 1979). Furthermore, most theories of face perception and memory rely heavily on perceptual features (e.g., Davies, Ellis, & Shepherd, 1981). Smith and Winograd (1978) presented pictures of unfamiliar, different-aged faces to both young (aged 18-25 years) and old subjects (aged 50-80 years) and then tested memory for those faces by presenting a sample of the studied faces with an equal number of new faces. Large reliable age differences were found when d' scores were used to measure recognition (Young, M = 2.05; old, M = 1.24). Furthermore, age differences in face recognition were not related to the educational level of the older subjects or to the nature of the orienting task used at encoding. Subjects received either standard memory instructions, or received an orienting task which required them to rate either the friendliness of the face or the size of the nose on the face. Face

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A.D.Smith and D.C.Park

memory was improved by having subjects make attribute judgments about the face (e.g., does the face look friendly?) but not about physical features of the face (Does the face have a big nose?). The facilitation, however, was equal in both age groups. This finding has been replicated in several other experiments. Ferris, Crook, Clark, McCarthy, and Ray (1980) found large age differences in face recognition when using a continuous recognition procedure (subjects indicated "new" or "old to each face as it was presented in a continuous series of faces), and these age differences were found for all the delay intervals included in their study by varying the number of other faces intervening between the initial presentation of a face and its second presentation. They used .5, 1, 2, 4, and 40 minute delay intervals between faces. It is interesting to note that while young performed better than old subjects, there were no differences between an old-normal group and another group of older subjects that had been diagnosed as having dementia. In another experiment, Mason (1986) systematically varied the gender and age of the faces and found age differences in facial recognition in all conditions, even though the old showed a slight tendency to recognize old faces better than young faces, and the young tended to recognize young faces better than the old faces. In all the face memory experiments discussed so far, the test items were the identical photographs to those presented in the acquisition list, and only one pose of each face was used. Bartlett and Lesli (1986) looked at age differences in face recognition when different photographs of the faces were used, photographs that differed in both pose and expression. Bartlett and Lesli argued that such a procedure would be more "natural", since pose and expression vary in the everyday experience of faces. Furthermore, it allowed Bartlett and Lesli to investigate whether the large age differences seen in the above studies were due to the use of "view-specific"details over "face-specific''details. Subjects were presented a series of 48 faces, with either 12 seconds of viewing a single photograph of each face (single view), or 3 seconds for each of four different photographs of each face (multi-view). The four views of each face systematically varied both pose and expression. At test, in addition to identical face photographs from the presentation list and new faces not seen in the list, there were pictures of the presentation faces that had been changed in expression or in pose and expression. Subjects indicated whether the face at test was new, the same and identical to one presented earlier, or the same face but changed from the view seen earlier. Bartlett and Lesli (1986) replicated the finding that young subjects did better than old subjects in discriminating new faces from old faces in the single view condition (.85 for young and .77 for old in A' scores). However, in the multi-view condition, the old subjects' performance was unchanged from the single view condition (.77 in single view, .76 in multi-view) and equalled that of young adults. The young subjects, however, did worse in the multi-view condition (.85 in single view, .76 in multi-view). According to the interpretation of Bartlett and Lesli, older subjects failed to take advantage of view-specific details which would have improved performance in the single-view condition. Rather, the elderly encoded general, face-specific details (perceptual invariants) which is adaptive for everyday face recognition because it would optimize

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performance across different contexts. Such perceptual invariants are more insensitive to changes in pose or expression which would be changing in a real-world context. According to Bartlett and Lesli, young people can use view-specific cues (i.e., small details specific to the view used for the single-viewphotograph) to improve recognition, whereas older adults find them less useful. Age differences, therefore, are eliminated when view-specific details are irrelevant for correct recognition as would be the case in the multi-view condition. It is not clear, however, why this interpretation would not predict that the friendliness ratings in the Smith and Winograd study would engender more "face-specific'' encoding, and thus reduce age differences in that study. Memory for Abstract Drawings

Another type of stimulus that may be difficult to encode verbally are abstract designs which have no apparent analog to real-world objects. As mentioned earlier, Arenberg (1978) has demonstrated that older subjects make significantly more errors in reproducing geometric designs from memory than do younger subjects. Using the Benton Visual Retention Test, Arenberg found age differences with both cross-sectional and longitudinal designs. Although this test does not correlate with other tests that measure verbal ability (e.g., vocabulary), it may still be a problem that the test is composed of simple geometric objects which can at least be verbally labeled. In fact, Arenberg (1977), in another study, found that subjects benefitted substantially from a verbal description of the designs when they were being studied. While both young and old subjects showed reduced errors with the verbal descriptions, the old showed even greater improvement than the young. The young subjects' performance, however, was close to ceiling not leaving much room for improvement. There are other data on memory for abstract visual stimuli. In an early experiment, Howell (1972) found large age differences when meaningless, abstract drawings were used. Similarly, Reige and Inman (1981) used abstract design paintings by Victor Vasarely and found age to be correlated significantly with the ability to recognize the designs (r = -.46). The d' scores declined across all decades from subjects in their twenties to subjects in their seventies. Furthermore, the declines in recognition scores for the abstract designs from age 50 to age 70 were larger than age declines seen when other nonverbal materials were used which relied on other modalities (auditory bird whistles and tactile bent-wire designs). It is difficult to see how these complex designs could be verbally coded except by describing the visual features of the pictures. In a later experiment, Harker and Riege (1985) used the same abstract designs in a different experiment, comparing recognition of words with recognition of designs. The typical age differences were found in recognition of words, but unlike the earlier experiment, there was no overall age difference in the recognition of abstract designs. This result is difficult to interpret, however, because there is no comparability between the words and pictures. Moreover, each of the 30 target designs was tested twice, once in each half of the total recognition test. It appears that age differences were found with the first test of the target, but not the second, thus reducing the sensitivity of the recognition procedure. There is no evidence in these data to suggest that age differences in memory for abstract designs is greater than age differences for words when using the same task, but the basis for comparison is dubious. Taken together with the earlier study from Riege's laboratory, however, it seems the case that age

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differences are found with abstract designs, materials that are very difficult to linguistically encode or even verbally label. Visuospatial Materials Permitting Linguistic Encoding Whereas both faces and abstract designs are difficult to linguisticallyencode, pictures of real-world objects do allow distinctive linguistic descriptions. Unlike faces and abstract drawings, pictorial material can be easy to distinctively label and also easy to encode linguistically. Even with meaningful pictures representing nameable objects or scenes, however, there may be considerable variability in the degree to which linguistic and visuospatial representation can be readily developed. For example, a simple line drawing of a bicycle is easily named and can be represented linguistically, probably just as easily as the word "bicycle". If the bicycle is placed in a complex scene, however, both the linguistic representation and visuospatial representation will be richer and more elaborate, given the semantic context and the perceptual detail provided by the complex scene. In fact, a very complex, meaningful scene may be difficult to describe in a simple fashion, instead requiring a great deal of linguistic, propositional information. Memory for Line Drawings and the Picture-Superiority Effect One approach to study the hypothesis that older subjects have problems with forming visuospatial representations, as suggested by the psychometric studies described earlier, is to look at age differences in the "picture-superiority" effect. When objects are presented as pictures, they are better remembered than when just the word representing the object is presented. The advantage of pictures over words is assumed to be the visuospatial detail inherent in the picture. Both the picture and the word would be linguistically encoded, but by presenting the object as a picture, the probability of visuospatial representation is increased. If older adults have problems with visuospatial representation, the observed picture-superiority effect would be of a smaller magnitude than with young subjects. There is evidence that young and old subjects make the same lexical response to simple pictures of objects (Puglisi, Park, & Smith, 1987). Several investigators have found no differences in the typical picture-superiority effect. Winograd, Smith, and Simon (1982), for example, presented 20 items to a college-aged group and an group of older adults living in the community (average age = 71). The items were presented as words or as matched pictures of the words. On a later recall test of memory, young subjects recalled more items than did the old subjects, items presented as pictures were recalled better than words, but there was no interaction between age and the magnitude of the picture-superiority effect. Equivalent picture-superiority effects were obtained in both age groups when (1) the subjects just named the items at presentation, (2) named the items and indicated their use during presentation, and (3) when an increased number of items were presented in a second experiment. Identical picture-superiority effects have also been obtained in other experiments matching words and pictures at presentation using both recall (Keitz & Gounard, 1976) and recognition (Park & Puglisi, 1985; Park, Puglisi, & Sovacool, 1983). Only one experiment reports different results. Rissenberg and Glanzer (1986) found a greater

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picture-superiority effect in young subjects when their subjects did not name the items upon presentation. In another condition, however, identical effects were obtained when subjects named the items as in the Winograd, Smith, and Simon (1982) studies. The weight of the evidence, therefore, indicates little support for diminished visuospatial processing in older subjects as would be evidenced by a smaller picture-superiority effect in older subjects. In fact, the only study finding a reliable interaction between age and picture superiority did so when the subjects did not name the pictures, which possibly varied the degree of linguistic coding. In contrast, by increasing semantic processing through orienting tasks, as was done by Winograd, Smith, and Simon (1982), the magnitude of the picture-superiority effect was uncompromised by age. Memory for Complex Scenes

When studying the picture-superiority effect, simple line drawings are used to insure that the objects represented by the pictures can be easily named, thereby allowing identical responses to both picture and word stimuli. Thus, while the weight of the evidence suggests equivalent picture-superiority effects in different adult age groups, reliable age differences are found when one examines only memory for the pictorial items, both by recall (Keitz & Gounard, 1976; Park & Puglisi, 1985; Winograd, Smith, & Simon, 1982) and even recognition (Park, Puglisi, & Sovacool, 1983). In other words, older adults typically recall and recognize fewer simple, line-drawing pictures than do younger adults. When more complex scenes are used, however, the literature on age differences in picture memory appears more equivocal. In a series of experiments, Park and her colleagues have consistently found no age differences between young and old subjects for immediate recognition memory of complex scenes (Park, Puglisi, & Smith, 1986; Park, Puglisi, & Sovacool, 1984; Park, Royal, Dudley, & Morrell, 1988; Smith, Park, Cherry, & Berkovsky, 1990). With performance well below ceiling, recognition performance has been the same in both young and old groups for complex magazine photographs (Park, Puglisi, & Smith, 1986), cartoons (Park, Puglisi, & Sovacool, 1984), and complex line drawings of scenes (Park, Puglisi, & Smith, 1986; Smith, Park, Cherry, & Berkovsky, 1990). These experiments also involved a variety of different encoding and retrieval conditions, and the studies reported no age differences across this variety of experimental conditions. For example, in one experiment (Experiment 1 of Park, Puglisi, & Smith, 1986), three types of pictures were presented, black and white photographs, degraded, high-contrast photographs constructed by making Xerox photocopies of the photos, and line drawings of the scene presented in the photograph. Two between-group variables were included in the study, age (young or old) and type of picture (photo, Xerox, or line drawing of the same scene). Subjects were presented 60 pictures at a 5-second rate. After filling out some demographic questionnaires, subjects then responded "yes" or "no" to 180 pictures, indicating whether or not they had seen each picture in the previous list. The data from this experiment are presented in Table 3.3. With all three types of complex pictures, there was no evidence that recognition memory, as measured in hits or d' scores, was worse in older subjects than in younger subjects.

A.D.Smilh and D.C.Park

82

Table 3.3

Mean Number of Hits, False Alarms and d' Values For Three Types of Complex Pictures Hits

False Alarms

d' Values

Young Old

Young Old

Young Old

Complex Line Drawings

0.77

0.83

0.09

0.16

2.36

2.19

High-contrast Copies of Photos

0.68

0.72

0.09

0.08

1.99

2.04

Black & White Photographs

0.86

0.78

0.06

0.05

2.86

2.58

Type of Picture

From Park, Puglisi, and Smith (1986)

There are several conditions in which age differences do emerge with complex pictures, however. First, age differences are observed when the memory testing is delayed to an interval of over one week (Park, Puglisi, & Smith, 1986; Park, Royal, Dudley & Morrell, 1988). The same subjects who participated in the immediate recognition condition represented by Table 3.3 came back to the laboratory four weeks later and were tested again. After this delay, the recognition performance of both groups was worse, but the performance of the older subjects had dropped to near chance levels and the age difference in picture memory was significant with all three types of pictures. Although this study confounded item testing with retention interval, other studies which corrected this problem reported significant age differences occurring at one week. Equivalent memory performance, however, has been seen for intervals shorter than one week (e.g., 48 hours: Park, Royal, Dudley, & Morrell, 1988; Rybarczyk, Hart, & Harkins, 1987). In addition to these findings, if the conditions of retrieval are made more difficult by requiring subjects to indicate whether or not the picture has been changed from encoding at the time of test, older adults do have more difficulty in making correct decisions. Pezdek (1987) asked young and old subjects to indicate whether complex line drawings had been changed or not rather than just requiring a direct test of picture recognition. Half of the items were changed by deleting or adding background detail to the pictures. Older adults did worse than younger adults with this task which obviously involves a finer discrimination to be made than a simple recognition response. While there were no age differences in the number of "hits", correctly identifying same and different pictures, older subjects made more false alarms, incorrectly identifying changed pictures as unchanged. The contrast between the numerous studies showing no age differences with direct recognition measures and the finding of age differences with Pezdek's "same-different"task is reminiscent of Bartlett

Memory for Pictures and Inrages

83

and Lesli's (1986) study of face memory discussed earlier. Bartlett and Lesli found differential age effects between tasks that required a direct recognition response and tasks that required subjects to indicate whether the face was same or different (different poses or expressions). According to Lesli and Bartlett, older subjects do not encode "view-specific details" which would allow them to make the same-different judgments. In fact, Bartlett, Till, Gernsbacher, and Gorman (1983) performed a similar study to Pezdek's by asking subjects to differentiate target pictures from picture reversals (left-right) by making same-differentjudgments. Like Pezdek, older subjects had poorer discrimination scores than younger adults. If older subjects fail to take advantage of view-specific details, then they would be at a disadvantage when asked to indicate whether the items had been changed in experiments requiring discrimination responses. In other words, older adults may have done worse in discriminating changed from unchanged scenes because they are not as sensitive to specific contextual information provided by the detail that is changed (McIntyre & Craik, 1987). A direct test of the view that older adults are less influenced by changes in context would be provided by the encoding-specificityparadigm in which context is changed from encoding to retrieval in a systematic manner. If older adults are less sensitive to changes in context, they should show smaller "encoding-specificity'' effects. That is, they should be less affected by compatibility between encoding and retrieval conditions. Encoding specificity of pictorial stimuli was tested by Park, Puglisi, Smith, and Dudley (1987). Target pictures were paired with context pictures and some of the picture pairs were "re-paired" at retrieval, i.e., presented with a context that was the same as or different from the encoding context. Subjects were told that their task was to remember the target pictures only but that the context pictures might help them remember the targets. Furthermore, subjects performed the memory task with or without a concurrent digit-monitoring task. While divided attention caused by the digit-monitoring task reduced memory performance, and while changing the context at retrieval reduced memory performance, the reductions were equivalent in both age groups. In other words, there was no evidence for reduced encoding specificity in older adults, even when attention to the task was reduced under conditions of divided attention. This study replicated Park, Puglisi, and Sovacool (1984) who also found strong encoding-specificityeffects for pictorial contexts in both young and old subjects. A better interpretation of the Pezdek (1987) and the Bartlett and Lesli (1986) findings simply may be that the finer discrimination required of making same/different judgments is more difficult than recognition responses, and age differences emerge as the memory task is made more difficult and provides less retrieval support from the task itself. Likewise, retrieval is more difficult at long retention intervals, as age differences are found when retention intervals are delayed a week or longer. When retention is tested immediately, however, and direct recognition tests of picture memory are used, young and old show equal performance in their ability to recognize complex pictures. These results with complex pictures contrast sharply with other memory studies discussed earlier in which either simple line drawings, abstract drawings, or faces were used. With these other materials, substantial age differences were found. The findings of no difference are specific to complex, meaningful pictures and where a recognition test is used. It seems the nature of the materials

84

A.D.Smith and D.C.Park

rather than the type of retrieval test is important, however. Recognition is also used to test age differences in face memory, and reliable age differences are found. With recognition of complex scenes, however, age differences are not found. Furthermore, age differences in memory for simple line drawings are seen with both recall and recognition. Finally, the effect occurs only if immediate recognition testing occurs. As Park, Royal, Dudley, & Morrell (1988) note, age effects emerge with increased delay of testing, suggesting that forgetting of the material proceeds more rapidly in the aged. One difference between complex scenes and other pictorial materials is that the complex scenes are rich in both visuospatial detail and propositional information. Complex scenes are certainly more perceptually rich and full of visuospatial detail than simple line drawings, and it may be that the degree of visuospatial detail in complex scenes makes the representation of the elaborated memory traces easier. Complex pictures, however, also typically are richer in linguistic content than either simple line drawings or faces. While simple line drawings typically can be described with simple verbal labels, the descriptions of complex scenes are often more elaborate involving complex propositions because of the rich semantic content provided by the scene. It may be, therefore, that it is the combined elaborate content of the complex scenes, both visuospatial and propositional, which contributes to the failure to observe adult age differences in recognition memory for the scenes. Several experiments have examined the role of visual detail in determining age differences in memory for complex pictures. Park, Puglisi, and Smith (1986, Experiments 2 & 3) failed to find an interaction between age and amount of background detail. Visual detail was removed systematically from a series of complex line drawings creating three degrees of contextual detail (low, medium, and high). Recognition scores improved with increases in background detail, and the increases were the same for both young and old subjects, Even when a divided attention, digit-monitoring task was used to reduce overall performance, no significant age by detail interaction was found. The largest difference between the age groups, however, was in the low detail condition. Using cartoon drawings, background detail was varied in the Park, Puglisi, and Sovacool (1984) study (either present or absent). In this experiment, young subjects' memory performance improved slightly with the detailed cartoons (d's = 2.25 for simple, 2.69 for complex), whereas the old subjects showed the same performance for simple and complex cartoons (d's = 2.32 for simple, 2.30 for complex). This finding contrasts with the Park, Puglisi, & Smith (1986) study. Perhaps, the central figure in the cartoons was sufficiently complex itself that the added visual detail exerted a less reliable effect. Pezdek (1987) also compared memory for pictures with and without background detail in a study similar to Park, Puglisi, and Sovacool (1984). Even though the response requirement was to indicate whether the picture had been changed or not (simple to complex or complex to simple), the absolute differences between performance with the simple and complex pictures was not all that different in both age groups. The difference between the simple-same and the complex-same conditions was 3.6% in the young group and 5.4% in the old subjects. Again, like the Park, Puglisi, and Sovacool (1984) study, however, the simple versions of the pictures

Memory for Pictures and Images

85

were still fairly complex and this may be the reason for the relative insensitivity of the manipulation in these experiments. In summary, the results of the above experiments failed to provide a conclusive resolution to the role of visual detail in determining age differences in picture memory. It does seem the case, however, that age differences are more likely to be seen with simple line drawings and with abstract drawings than with complex, real-world scenes. As mentioned earlier, complex scenes differ from simple line drawings and abstract drawings, because they are rich in both visuospatial detail and propositional content. Unlike abstract drawings, they have linguistic meaning, and unlike simple line drawings of nameable objects, they have rich visual complexity. The failure to find age differences with complex, real-world scenes may be due to the encoding support provided by the availability of the combination of visuospatial and linguistic codes in the complex pictures. Craik (1986) has proposed that age differences are minimized when there is maximum encoding support at encoding and retrieval. In this case, the availability of both rich visuospatial and linguistic information found in the stimulus pictures provides just the kind of environmental support at encoding Craik suggests would minimize age differences. Furthermore, the use of a recognition task to test for memory of the pictures provides the maximum environmental support at retrieval which should contribute to reducing age effects. To investigate this view, Park, Smith, Morrell, Puglisi, & Dudley (1990) presented young and old adults with different types of pictorial cues at encoding which were presented again in a cued recall task at retrieval. The cues varied in their relationship to the target, providing different amounts of environmental support. Subjects studied target pictures of line drawings in the presence of unrelated pictures (low integration), conceptually related pictures (high semantic integration), or unrelated pictures drawn to visually interact with the target stimulus (high perceptual integration). The results of this study indicated that both groups profited from the conceptual cues relative to control performance, whereas only the elderly improved performance with the interacting cues. Moreover, the elderly's performance improved significantlymore than young adults with the two types of related cues, providing support for the environmental support hypothesis. The study suggests that environmental support does facilitate pictorial retrieval, and that a high degree of integration between the target and cue are particularly facilitative for the elderly. Another approach for examining the environmental support hypothesis using both simple and complex pictures was developed by Smith, Park, Cherry, & Berkovsky (1990). They hypothesized that complex pictures are remembered equally well by young and old due to the environmental support provided by the linguistic and visuospatial elements present in the pictures. The availability of both linguistic information and visuospatial information contribute to the ultimate semantic representation of the picture. The same simple and complex scenes used by Park, Puglisi, & Smith (1986) were made meaningless (i.e., abstract) by removing the semantic organization, and thus the linguistic information, provided by the identifiable objects in the scene. Example stimuli are presented in Figure 3.3. Each concrete-complex scene contained a central object which was maintained when background detail was removed to create the concrete-simple object. For the abstract, meaningless representations, all lines, curves, and angles in each object of each

2omplex-Abstract

\

\

,

Simple-Concrete

-."

1 Complex-concrete

I

Simple-Abstract

p.

b

Figw 3.3. picturetypes used by Smith, Park. Cheny. and Berkovsky (1990). Copyightby GemntologicalSodetyof Ameiica. 1990, reprinted with permission.

Memory for Pictures and hiages

87

concrete picture were rearranged, thereby maintaining identical visual detail in both concrete and abstract pictures. Therefore, there were four picture conditions: complex-concrete (the complex scenes themselves), simple-concrete (visual detail removed), complex-abstract (objects in scene rearranged into meaningless shapes), and simple-abstract (removal of both visual detail and meaningfulness). The complexity variable was manipulated within subjects and the meaningfulness variable was manipulated between subjects. The results of the experiment are presented in Table 3.4. As can be seen, there were no age differences when the pictures were complex real-world scenes ( d scores: Young, M = 3.72; Old, M = 3.60), replicating studies discussed earlier (e.g., Park, Puglisi, & Smith, 1986). When visual detail (complexity) was removed from the pictures (complex to simple), however, age differences emerged (Young, M = 3.45; Old, M = 2.73). While the younger subjects ability to recognize the pictures was not all that affected by the removal of visual detail, the older subjects recognized significantly fewer simple pictures than complex pictures. Likewise, when the linguistic information was removed from the pictures (concrete to abstract), age differences also emerged (Young, M = 2.81; Old, M = 2.33). Also notice that when detail was removed from the complex, abstract pictures, performance was reduced, but equally in both age groups. (Young, M diff = 23; Old, M diff = .21). Because this detail did not contriute to the semantic content of the picture, it did not differentiate between the age groups. Likewise, when the detail was removed from the Pezdek (1987) pictures, it most likely did not detract from the semantic content of the picture as the pictures remained complex, and thus, age differences were not found. Therefore, as hypothesized, the advantage of complex, real-world scenes comes from the additive effects of rich perceptual detail and rich propositional content, both contributing to the semantic interpretation of the picture. This combination of attributes seems to provide the encoding and retrieval support necessary to minimize age differences in recognition of the pictures. Smith, Park, Cherry, and Berkovsky (1990), in a single experiment, replicated results found in a variety of different studies on picture memory. Age differences were not found with complex concrete scenes (e.g., Park, Puglisi, & Smith, 1986), but were found for simple single objects (e.g., Winograd, Smith, & Simon, 1982). They were not found with complex concrete scenes, but were found with abstract drawings (e.g., Reige & Inman, 1981). Furthermore, the results suggest that the advantage of complex scenes may be because of the availability of both linguistic and visuospatial content. Again, just as with studies dealing with visuospatial coding of verbal materials discussed in the earlier section, there is no evidence from the memory studies using nonverbal materials that older adults are especially disadvantaged in visuospatial coding. In fact, the conclusion from these studies seems quite the opposite. Older adults can use visuospatial information at encoding and retrieval in the same manner as they can use verbal encoding to support memory. In fact, they perform as well as young adults on recognition of complex scenes. O n all other tasks, however, older adults perform worse than younger adults when memory of nonverbal materials is required. There seems to be little evidence, however, to suggest that these memory

A.D.Sniith and D.C.Park

88

Table 3.4

Recognition Performance as a Function of Age, Picture Complexity, and Picture Concreteness Hits

False Alarms

d' Values

Young Old

Young Old

Twe of Picture I.

Young Old CONCRETE Complex Mean

0.92

0.91

0.02

0.05

3.72

3.60

S.D.

0.07

0.07

0.05

0.09

0.49

1.08

Mean

0.91

0.87

0.03

0.11

3.45

2.73

S.D.

0.06

0.10

0.05

0.07

0.77

1.00

Mean

0.76

0.76

0.06

0.13

2.81

2.33

S.D.

0.15

0.19

0.08

0.15

1.22

1.23

Mean

0.75

0.74

0.10

0.14

2.50

2.12

S.D.

0.15

0.23

0.14

0.11

1.07

1.44

Simple

ABSTRACT Complex

Simple

From Smith, et al. (1990), copyright American Psychological Association. Reprinted with permission.

differences are due solely to specific problems with forming and using visuospatial representations. The picture-superiority discussed earlier best illustrates this point. When words and pictures of the same simple objects are compared, there is no difference in the picture-superiority effect between young and old adults. Young subjects remember more words than older adults and they remember more pictures than older adults. The advantage of pictures over words, however, assumed to be related to the increased use of visuospatial coding, is the same for both young and old alike.

Memory for Pictures and Images

89

Specific Memory for Visuospatial Attributes

The final method listed in Table 3.1 involves testing age differences in memory for characteristics of stimulus items which are clearly visuospatial, such as color or spatial position. Much of this research was motivated by the Hasher and Zacks (1979) hypothesis that the encoding of such attributes is automatic, and therefore, does not require effortful processing on the part of either young or old subjects. If these visuospatial characteristics are automatically encoded, according to Hasher and Zacks (1979), we should not find age differences when memory for the characteristics is tested. In general, as we will see, the literature suggests that there are reliable age differences in both memory for color and memory for spatial position, and that the processing of these characteristics in most instances does require at least some intentional effort on the part of subjects. Unfortunately, research on age differences in color and spatial memory does not provide a strong test of the hypothesis that there is a specific visuospatial deficit in older adults. Finding age differences in color memory or spatial memory per se, does not necessarily suggest that older adults are any more deficient in visuospatial processing as these dimensions can readily be labeled. Memory for Color

In an experiment looking at memory for color, Park and Puglisi (1985) presented two adult age groups with either words or pictures in one of four different colors. Color memory was tested both incidentally and intentionally. Subjects in the incidental condition expected to recall the pictures or words, but not the color of the items. In this experiment, older adults recalled fewer colors correctly for both the words and pictures than younger adults. Of more interest is the finding that young adults remembered the color of pictures better than words, whereas elderly subjects did not evidence this pictorial facilitation. The study, however, provides no strong evidence that the age differences were due to a specific visuospatial deficit in the older group. In fact, the evidence suggests that older adults do remember color well above chance levels on all tests even when not intentionally trying to do so. Memory for Spatial Position

There has been a great deal more research dealing with age differences in memory for spatial position. Typically, words, pictures, or objects are presented in specific spatial locations in some type of stimulus array. At the time memory is tested, subjects are asked to reproduce the spatial location of each item in the stimulus array, often by replacing the objects in the array. Experiments which have examined the interaction between aging and spatial memory are listed in Table 3.5. The table lists the to-be-remembered materials, the nature of the stimulus array, and the age effects found for each experiment. Across a variety of procedures used in the different experiments and the numerous other variables manipulated in addition to age, the cumulative results suggest a reliable age difference in the ability to remember spatial location. Out of the 23 different conditions

Table 3.5

8

Experiments Examining Age Differences in Memory for Spatial Location Experiment

Materials

Placement

Age Effects

Perlmutter, Metzger, Nezworski, Miller (1981)

Pictures of buildings

Drawn map

Young > Old

Charness (1981)

Chess pieces

Chess board

Young > Old

Waddell & Rogoff (1981)

Environmental objects

Cubicles Organized scene

Young > Old Young = Old

McCormick (1982)

Words

4 Places on card

Young = Old

Park, Puglisi, & Lutz (1982)

Pictures

2 Places on card

Young > Old

Park, Puglisi, & Sovacool(1983)

Pictures and words

4 Places on card

Young > Old

Pezdek (1983)

Objects or words

16 squares of 36 item array

Young > Old

Light & Zelinski (1983)

Pictures of buildings

Drawn map

Young > Old

Puglisi, Park, Smith, & Hill (1985)

Objects

2-D Array

Young > Old

Bruce & Herman (1986)

12 Buildings

Town grid

Young > Old

b

Sharps & Gollin (1 987)

Objects

Plain map Abstract 3-D map Distinctive model

Young > Old Young = Old Young = Old

P 3 t

b

srn

Memory for Pictures and Images

h

8

El

B 8

v

n

x2

91

A.D.Smilh and D.C.Park

92

(combination of materials and placement arrays) listed in Table 3.5, only four failed to produce age differences: (a) word placement on cards (McCormack, 1982); (b) placement of objects into an organized outdoor scene (Waddell & Rogoff, 1981); and placing objects on either (c) a distinctive map or (d) a colored, three-dimensional model (Sharps & Gollin, 1987). For each case, however, there is at least one study which does find reliable age differences with similar materials and stimulus arrays: (a) word placement on cards (Park, Puglisi, & Sovacool, 1983); (b) placement of objects into an organized outdoor scene (Zelinksi & Light, 1988); and placing objects on either (c) a distinctive map or (d) a colored, three-dimensional model (Park, Cherry, Smith, & Lafronza, 1990). Furthermore, in the three studies (four conditions) finding equivalent spatial memory performance in young and old adults, there is a problem in each study with the absolute level of performance for the age groups. As Light and Zelinski (1983) have pointed out, there are ceiling effects in the Waddell & Rogoff (1981) study, and floor effects in the McCormack (1982) study, which make the results hard to interpret. Similarly, the older group’s performance in the Sharps and Gollin (1987) study is extremely low in one of the important comparison conditions and the number of subjects used per condition was very small. These effects could have contributed to the pattern of results found in this study. Park, Cherry, Smith, and Lafronza (1990, Experiment 2) used the identical placement arrays (designed according to the specifications of Sharps and Gollin) and found reliable age differences in spatial memory. Furthermore, while the distinctive context did facilitate spatial memory, it did so in both age groups. In summary, older adults do not remember the visuospatial characteristics of presented items (e.g., color and spatial location) as well as younger adults. This does not mean, however, that older adults cannot use these visuospatial characteristics because memory performance is typically well above chance even though it is typically lower than the memory performance of younger adults. General Summary Even though early conclusions based on psychometric data suggested that older adults had greater problems with visuospatial processing than with verbal processing, this survey of the memory literature finds little evidence to support this conclusion. In fact, even the age differences seen between verbal and visuospatial tests may best be explained by the confounded emphasis on working memory or speed rather than the difference in visuospatial requirements.

The present paper reviewed studies from four experimental memory domains: (a) memory experiments in which the visuospatial characteristics of verbal materials were manipulated; (b) memory experiments in which nonverbal materials were used which were difficult to encode verbally; (c) memory experiments in which nonverbal materials were used that could easily be coded linguistically; and (d) experiments examining memory for visuospatial characteristics themselves. Evidence from all of these research domains suggests that older adults can and do use visuospatial processing to support memory. Furthermore, there is no strong evidence to suggest that older adults use visuospatial information any less than younger adults.

Memory for Pictures and Images

93

When the imagery value of words is varied, the memory advantage of the more-concrete, highly imaginable words is the same for older adults as for young adults. In addition, when imagery instructions are given to young and old subjects, there is no evidence that older adults use imagery less than younger subjects when encoding individual items in memory (intraitem encoding). While older subjects remember fewer nonverbal items resistant to linguistic coding (i.e., faces and abstract designs), they do remember them with above chance performance. The same results are found when memory for visuospatial attributes is tested (i.e., color and spatial location). When memory for nameable pictures is compared to memory for the words representing those pictures, the pictures are remembered better than words. Young and old adults show equivalent picture superiority effects. When memory for complex scenes is tested, rarely are age effects found at all. The older adult can and does use both visuospatial and linguistic elaboration to support memory encoding and retrieval. Furthermore, age differences in visuospatial coding seem no different than age differences in linguistic coding, at least as these coding operations affect memory. References

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Craik, F.I. M. (1986). A functional account of age differences in memory. In F. Klix & H. Hagendorf (Eds.), Human memory and cognitive capabilities: Mechanisms and performances. Amsterdam: Elsevier. Craik, F. I. M., & McDowd, J. M. (1987). Age differences in recall and recognition. Joumal of Experimental Psychology: Learning, Memory, & Cognition, 13,474-479. D'Agostino, P. R., ONeill, B. J., & Paivio, A. (1977). Memory for pictures and words as a function of level of processing: Depth of dual coding? Memory and Cognition, 5, 252-256 Davies, G., Ellis, H., & Shepherd, J. (Eds.). (1981). Perceiving and remembering faces. New York: Academic Press. Durso, F. T., & Johnson, M. K. (1980). The effects of orienting tasks on recognition, recall, and modality confusion of pictures and words. Journal of Verbal Learning and Verbal Behavior, 19, 426-429. Ferris, S. H., Crook, T., Clark, E., McCarthy, M., & Ray, D. (1980). Facial recognition memory deficits in normal aging and senile dementia. Journal of Gerontology, 35, 707-714. Gaylord, S. A., & Marsh, G. R. (1975). Age differences in the speed of a spatial cognitive process. Journal of Gerontology, 30, 674-678. Gazzaniga, M. S. (1977). Consistency and diversity in brain organization. Annals of the New York Academy of Sciences, 299,415-423. Goldstein, A. G., Johnson, K. S., & Chance, K. E. (1979). Does fluency of face description imply superior face recognition? Bulletin of the Psychonomic Society, 13, 15-18. Harker, J. O., & Reige, W. H. (1985). Aging and delay effects on recognition of words and designs. Journal of Gerontology, 40,601-604. Hasher, L., & Zacks, R. T. (1979). Automatic and effortful processing in memory. Journal of Experimental Psychology: General, 108, 356-388. Howell, S. (1972). Familiarity and complexity in perceptual recognition. Journal of Gerontology, 27, 364-371. Hulicka, I. M., & Grossman, J. L. (1967). Age-group comparisons for the use of mediators in paired-associate learning. Journal of Gerontologv, 22, 46-51. Hultsch, D. F. (1974). Learning to learn in adulthood. Journal of Gerontologv, 11, 197-201. Jenkins, J. J. (1979). Four points to remember: A tetrahedral model of memory experiments. In L. S. Cermak & F. I. M. Craik (Eds.), Levels ofprocessing in human memory. Hillsdale, N.J.: Lawrence Erlbaum. Keitz, S. M., & Gounard, B. R. (1976). Age differences in adults' free recall of pictorial and word stimuli. Educational Gerontology, 1, 235-241. Levy, J. (1974). Psychobiological implications of bilateral asymmetry. In S. J. Dimond & J. G. Beaumont (Eds.). Hemisphere function in the human brain. New York, John Wiley. Light, L. L., & Zelinski, E. M. (1983). Memory for spatial information in young and old adults. Developmental Psychology, 6, 901-906. Mason, S. E. (1986). Age and gender as factors in facial recognition. Experimental Aging Research, 12, 151-154. Mason, S. E., & Smith, A. D. (1977). Imagery in the aged. Experimental Aging Research, 3, 17-32.

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McCormack, P. D. (1982). Coding of spatial information by young and elderly adults. Journal of Gerontology, 37, 80-86. McIntyre, J. S., & Craik, F. I. M. (1987). Age differences for item and source information. Canadian Journal of Psychology, 41, 175-192. Meyer, B. J. F., & Rice, G. E. (1983). Learning and memory from text across the adult life span. In J. Fine & R. 0. Freedle (Eds.), Developmental studies in discome. Norwood, N.J.: Ablex. Paivio, A. (1971). Imagery and verbal processes. New York Holt, Reinhart, & Winston. Paivio, A., & Csapo, K. (1973). Picture superiority in free recall: Imagery or dual coding? Cognitive Psychology, 5, 176-206. Park, D. C., Cherry, K. E., Smith, A. D., & Lafronza, V. N. (1990). Effects of distinctive context on memory for objects and their locations in young and older adults. Psychology and Aging, 5, in press. Park, D. C., & Puglisi, J. T. (1985). Older adults' memory for the color of pictures and words. Journal of Gerontology, 40, 198-204. Park, D. C., Puglisi, J. T., & Lutz, R. (1982). Spatial memory in older adults: Effects of intentionality. Journal of Gerontology, 33, 330-335. Park, D. C., Puglisi, J. T., & Smith, A. D. (1986). Memory for pictures: Does an age-related decline exist? Psychology and Aging, I , 11-17. Park, D. C., Puglisi, J. T., & Sovacool, M. (1983). Memory for pictures, words, and spatial location in older adults: Evidence for pictorial superiority. Journal of Gerontology, 38, 582-588. Park, D. C., Puglisi, J. T., & Sovacool, M. (1984). Picture memory in older adults: Effects of contextual detail at encoding and retrieval. Journal of Gerontology, 39, 213-215. Park, D. C., Puglisi, J. T., Smith, A. D., & Dudley, W. N. (1987). Cue utilization and encoding specificity in picture recognition by older adults. Journal of Gerontology: Psychological Sciences, 42, 423-426. Park, D. C., Royal, D., Dudley, W. M., & Morrell, R. (1988). Forgetting of pictures over a long retention interval in old and young adults. Psychology and Aging, 3, 94-95. Park, D. C., Smith, A. D., Morrell, R. W., Puglisi, J. T., & Dudley, W. N. (1990). Effects of contextual integrations on picture recognition and recall in older adults. Journal of Gerontology, 4.5, P52-P57. Perlmutter, M., Metzger, R., Nezworski, T., & Miller, K. (1981). Spatial and temporal memory in 20 and 60 year olds. Journal of Gerontology, 36, 59-65. Pezdek, K. (1983). Memory for items and their spatial locations by young and elderly adults. Developmental Psychology, 19, 895-900. Pezdek, K. (1987). Memory for pictures: A life-span study of the role of visual detail. Child Development, SS,807-815. Puglisi, J. T., Park, D. C., & Smith, A. D. (1987). Picture associations among old and young adults. Experimental Aging Researclt, 2, 115-116. Puglisi, J. T., Park, D. C., Smith, A. D., & Hill, G. W. (1985). Memory for two types of spatial locations: Effects of instructions, age, and format. American Journal of P ~ c h o l o98, ~ , 101-118. Reige, W. H., & Inman, V. (1981). Age differences in nonverbal memory tasks. Journal of Gerontology, 36, 51-58.

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Rice, G. E. & Meyer, B. J. F. (1986). Prose recall: Effects of aging, verbal ability, and reading behavior. Journal of Gerontology, 41, 469-480. Rissenberg, M., & Glanzer, M. (1986). Picture superiority in free recall: The effects of normal aging and primary degenerative dementia. Journal of Gerontology, 41, 64-71. Rowe, E. J., & Schnore, M. M. (1971). Item concreteness and reported strategies in paired associate learning as a function of age. Journal of Gerontology, 26,470-475. Rybarczyk, B. D., Hart, R. P., & Harkins, S. W. (1987). Age and forgetting rate with pictorial stimuli. Psychology and Aging, 2, 404-406. Salthouse, T. A. (1982). Adult cognition: An experimental psychology of human aging. New York: Springer-Verlag. Salthouse, T. A., Mitchell, D. R., Skovronek, E., & Babcock, R. L. (1989). Effects of adult age and working memory on reasoning and spatial abilities. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 507-516. Sharps, M. J., & Gollin, E. S. (1987). Memory for object locations in young and elderly adults. Journal of Gerontology, 42, 336-341. Smith, A. D. (1980a). Advances in cognitive theories of aging. In L. W. Poon (Ed.), Aging in the 1980's: Selected contemporary issues in the psychology of aging. Washington, D.C.: American Psychological Association. Smith, A. D. (1980b). Age differences in encoding, storage, and retrieval. In L. W. Poon, J. L. Fozard, L. S. Cermak, D. Arenberg, & L. W. Thompson (Eds.), New directions in memory and aging Proceedings of the Geotge A. Talland Memorial Conference. Hillsdale, N.J.: Lawrence Erlbaum. Smith, A. D., & Fullerton, A. (1981). Age differences in episodic and semantic memory: Implications for language and cognition. In D. S. Beasley & G. A. Davis (Eds.), Aging: Communication processes and disorders. New York: Grune and Stratton. Smith, A. D., Park, D. C., Cherry, K. E., & Berkovsky, K. (1990). Age differences in memory for concrete and abstract pictures. Journal of Gerontology, 45, . Smith, A. D., & Winograd, E. (1978). Adult age differences in remembering faces. Developmental Psychology, 14, 443-444. Treat, N. J., & Reese, H. W. (1976). Age, pacing, and imagery in paired-associate learning. Developmental Psychology, 12, 119-124. Waddell, K. J., & Rogoff, B. (1981). Effect of contextual organization on spatial memory of middle-aged and older women. Developmental Pyschology, 17,878485. Winograd, E., & Simon, E. (1980). Visual memory and imagery in the aged. In L. W. Poon, J. L. Fozard, L. S. Cerrnak, D. Arenberg, & L. W. Thompson (Eds.), New directions in memory and aging: Proceedings of the George A. Talland Memorial Conference. Hillsdale, N.J.: Lawrence Erlbaum. Winograd, E., Smith, A. D., & Simon, E. (1982). Aging and the picture superiority effect in recall. Journal of Gerontology, 37, 70-75. Yesavage, J. A., Rose, T. L., & Bower, G. H. (1983). Interactive imagery and affective judgments improve face-name learning in the elderly. Journal of Gerontology, 38, 197-203. Zelinski, E. M., & Light, L. L. (1988). Young and older adults' use of context in spatial memory. Psychology & Aging, 3, 99-101.

Aging and Cognition: Mental Frocesses. Sel Awareness and Interventions - Eu ene A. huelace EJtor, 0 Elseuier Science pubgishers B.V. (North-Holland), 1990

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Aging and Attention: Selectivity, Capacity, and Arousal Dana J. Plude Jane A. Doussard-Roosevelt University of Maryland

The study of attention involves the analysis of a variety of phenomena (Parasuraman & Davies, 1984) and prohibits a single definition of the construct. Rather than opting for William James' (1890) approach in which he suggested that "we all know what attention is" we choose to consider three interdependent aspects of attention which have received varying degrees of analysis in the cognitive aging literature. The three aspects were suggested by Posner and Boies (1971) and consist of: selectivity, capacity, and arousal. The selective aspect of attention hinges on the assumption that human information processing is limited. We are unable to process all available stimuli simultaneously, thus certain stimuli must be selected at the expense of other stimuli. Although this component of attention embraces James' (1890) notion of internal (thoughts, ideas, emotions, etc.) and external (i.e., environmental) targets of attention, we restrict our consideration to the selection of signals eminating from the environment. James (1890) was quite restrictive in his consideration of this limitation when he suggested that we can attend to only one thing at a time. It is noteworthy that he distinguished this attentional limitation from the ability to perform habitual acts simultaneously, a distinction that we address later in this section. Early interest in the selective aspect of attention centered around the classical "cocktail party phenomenon" (Cherry, 1953), the ability to selectively listen to one conversation in the presence of many competing conversations. Various theories of selectivity have been put forth to account for this effect (as well as other patterns of performance tradeoffs), and amount to determining the locus of the "bottleneck in information processing (for review, see Lachman, Lachman, & Butterfield, 1979; Solso, 1988). In the domain of cognitive aging, research addressing the selective aspect of attention has consisted primarily of evaluating visual search performance (where search is conducted for a specific target among varying amounts of interfering stimuli) and contrasting this with nonsearch performance, as for example when spatial cues forewarn of the target's location (see, for example, Hoyer & Plude, 1980; Plude & Hoyer, 1985). Preparation of this chapter was supported by NIA Grant R01- AGO8060 to DJP. We appreciate the contributions of Jani Gabriel-Byme, Lisa J. Murphy, Renee Boller, and Duyen Dang to the original research reported here, and we acknowledge the thoughtful comments of Drs. John Cerella, Dan Fisk, Alan Hartley, Bill Hoyer, Gene Lovelace, and Dave Madden on various aspects of the ideas presented here. Finally, we dedicate this work in fond memory of Dr. Norm Schultz.

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The second aspect of attention refers to capacity and concerns the question of how much information can be processed at a given time. Kahneman (1973) introduced the notion of attention as capacity or a pool of resources that is available to support cognitive activity. A key assumption underlying this aspect of attention is that certain mental processes are "effortful"and as such consume some (if not all) of the available capacity. In contrast, "automatic" processes place minimal demands on attentional capacity. Certain automatic processes, e.g., feature detection (Treisman, 1986), are "pre-wired in the sense that they operate without attention independent of practice or experience, whereas other automatic processes are acquired via consistent practice (Schneider & Shiffrin, 1977; Shiffrin & Schneider, 1977). Cognitive aging research has addressed both aspects of automaticity as well as their comparison with effortful processing (e.g., Hasher & Zacks, 1979; Hoyer & Plude, 1982). In addition, research involving divided attention and dual-task conditions has informed about age differences (and similaritites) in attentional capacity (e.g., Madden, 1986; Plude & Hoyer, 1985). The arousal aspect of attention refers to the general state of alertness of the observer. Research on this aspect of attention typically involves monitoring physiological responsivity in various conditions and over sustained periods of time in order to evaluate the relationship between performance and changing states of alertness. Exemplifying research in this domain of attention is Sokolov's (1963) early work on the "orienting reflex" as manifested in the physiological responses of animals exposed to novel situations. Investigations of the arousal component of attention in human observers has involved a wide variety of physiological indicators of alertness, including cardiac, respiratory, and skin conductance measures (see Woodruff, 1982). More recent investigations have incorporated brain wave activity in assessing the arousal component of attention (e.g., Posner, 1978; Posner & Peterson, 1990). In cognitive aging research the most direct assessment of the arousal component of attention has involved measuring skin potential response latency during vigilance task performance (e.g., Giambra & Quilter, 1988; Quilter, Giambra, & Benson, 1983). In general, there is evidence of age decrement in certain aspects of sustained attention (e.g., Parasuraman, 1985).

It should be clear that these three aspects of attention are interdependent. In the present chapter, recent cognitive aging research is surveyed in each domain, with particular emphasis given to the selective aspect of attention. We justify this emphasis on the grounds that 1) age differences in this domain of attention likely contribute to age decrements in a broad spectrum of cognitive activities, including learning, memory and problem solving, and 2) the great majority of cognitive aging research on attention has focused on this component. Further, our survey is confined primarily to research involving visual information processing because 1) it has been the focus of the great majority of attention research both in mainstream cognitive psychology and in cognitive aging, 2) vision, or more specifically the loss of visual function, is of particular concern to elderly adults, ranking second only to cancer as the major health concern among aged adults (Verillo & Verillo, 1985), and 3) age effects in this domain are believed to generalize to other modalities as well. Before undertaking this survey, we address various methodological issues inherent in assessing age effects in the three components of attention.

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Methodological Issues Assessing age effects in visual attention amounts to examining patterns of performance tradeoffs in various experimental conditions. The primary dependent measures in cognitive aging research on selective attention are reaction time (RT) and error m e (ER). A primary assumption underlying their use is that mental processes require finite time and as such overt responses serve as indices of underlying stage(s) (see, for example, Garner, 1980; Pachella, 1974). One concern of such an assumption involves the nature of speed-accuracy tradeoffs in cognitive aging research: Do the elderly trade response speed for accuracy, thus undermining RT in examining cognitive processes? Three points are relevant in dismissing this concern. First, in the few studies where age-related speed-accuracy tradeoffs have been measured directly, there is little evidence of differential age effects (see Salthouse, 1979, Salthouse & Somberg, 1982; Strayer, Wickens, & Braune, 1987). Second, in a review of recent cognitive aging studies in which both RT and ER data were reported, Plude, Cerella, and Raskind (1984) found that age decrements in speed were accompanied by age decrements in accuracy in an overwhelming majority of cases (71%). In this analysis 81 studies were surveyed which yielded 201 conditions within which RT and ER data for young and old adults could be compared directly. The data were plotted in the space shown in Figure 4.1, with age differences in RT indexed along the abscissa and age differences in ER indexed along the ordinate. Within this space, age-related speed-accuracy tradeoffs are indicated by points falling in the upper right-hand quadrant (increased RT coupled with decreased ER). As can be seen, only 12% of the conditions surveyed fell into this domain while the preponderance fell into the quadrant directly below which indicates decreased speed coupled with decreased accuracy. Finally, the third argument against a cautiousness account of age deficits concerns the fact that age differences in response cautiousness are typical of procedures in which a response can be withheld, and not forced-choiceprocedures (see for example, Kausler, 1982) like those used in the large majority of cognitive aging research on selective attention. Thus, RT and ER are informative indices of performance tradeoffs in cognitive aging research. Although RT effects are emphasized in much of this review it should be clear that an unambiguous account of age effects depends on the coordinated consideration of ER effects as well. Performance tradeoffs are operationalized as nontarget interference effects on RT in visual search, and RT costs in focused and divided attention. In visual search, the slope of the linear function relating display size to RT informs about the magnitude of nontarget interference whereas the intercept of this function informs about "peripheral" aspects of performance (e.g., Atkinson, Holmgren, & Joula, 1969). In focused vs. divided attention, the proportional change in RT between various conditions informs about the nature of processing costs. In both cases, it is important that each subject serves as his/her own control, in the sense that tradeoffs are computed within the subject's own data set (i.e., comparison measures are derived from within-subject manipulations), in order to allow comparisons between young and elderly groups, as well as comparisons of pattern of tradeoffs within each group. The importance of comparing within-age group patterns of performance has to do with time as a metric in cognitive aging research (see for example, Plude & Hoyer, 1985). Assessing proportional tradeoffs acknowledges that the unit of processing time may

D.J.Plude and JADoussard-Roosevelt

100

W

50

7

40

0 W

[II

W LL LL -

n

30 20

10 0

-10

3 -20

0 -30 0 Q -40

8? -50 -200

-100

0

100

200

% REACTION TIME DIFFERENCE Figure 4.1. Age differences (Old - Young) in response latency and accuracy in 201 information processing conditions.

increase in aging (e.g., Cerella, 1985a; Salthouse, 1985), a point that is developed more fully in the concluding section of this chapter. Another point of considerable importance in cognitive aging research concerns the role played by various data-limitations that may be mistaken for resource limitations (see Hoyer & Plude, 1982; Norman & Bobrow, 1975). One such limitation that is especially relevant in assessing age effects in visual processing involves the elderly's constricted perceptual window (e.g., Cerella, 1985b; Scialfa, Nine, & Lyman, 1987). We address this limitation explicitly in the section on selectivity. Finally, it is important to recognize that the overwhelming majority of studies in cognitive aging involve cross-sectional designs which are appropriate for assessing age differences in performance but which do not inform about the nature of age changes in performance (see Kausler, 1982; Baltes, Reese, & Nesselroade, 1977). Cross-sectional studies confound age and cohort factors, and as such cannot be

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definitive in explicating the full nature of age effects. There is a clear need for longitudinal designs in order to map developmental functions (e.g., Wohlwill, 1973) for the age differences in visual search (and other aspects of selective attention) that have been identified in cross-sectional comparions. With these methodological considerations in mind, let us turn to an overview of a theoretical framework that we believe has proven most profitable in guiding cognitive aging research on attention. Feature Integration Theory Although the targets of attention may arise either from within the individual, i.e., "internal" sources, or from the environment, i.e., "external sources" (James, 1890), the present chapter addresses age effects in attending to external stimuli only. This is in keeping with the bulk of research over the past three decades both in mainstream cognitive psychology and in the more circumscribed domain of cognitive aging (Plude & Hoyer, 1985). Of central concern in our assessment is the extent to which a selected source of information can be processed without interference from non-selected sources. In the quarter century since Rabbitt's (1965) classic demonstration of an age deficit in visual search, much research has been devoted to explicating the mechanism(s) underlying the age decrement in attending to environmental stimuli. The present chapter surveys such efforts in the domains of selectivity, capacity, and (to a lesser extent) arousal, with the findings converging on an age decrement in each aspect of attention. Although significant progress has been made in delimiting the situations affecting the magnitude of age decrement, specification of its underlying cause is left unfinished. The explication of the causal mechanism has been hindered by the absence of a n explicit theoretical framework within which to couch the obtained age effects. Feature Integration Theory (FIT;Treisman, 1988; Treisman & Gelade, 1980; Treisman & Gormican, 1988) offers a useful heuristic for framing the various patterns of age effects and the present review of recent evidence supports its continued application in cognitive aging research. Within the FIT framework age decrements in visual processing may be located in either or both of two stages of processing: feature atruction and feature integration. Two attractive aspects of this theory are a) its continuity with other two-process models of visual information processing (e.g., Neisser, 1967), and b) its specification of selective attention as the "glue" that binds visual features. We acknowledge at the outset that FIT is not a panacea for all research on aging and attention, however it provides a useful framework for conceptualizing age differences and similarities in the processing of environmental information. The application of such a theory to the study of aging and visual information processing promises to inform about the locus of age deficits in visual attention. The FIT framework posits a two-stage model of visual information processing comprising an initial stage of feature detectors that "operate early, automatically, and in parallel across the visual field and a subsequent stage in which features registered during the first stage are conjoined to represent the complex components of the visual field (Treisman & Gelade, 1980, p. 98). The integration of features requires focal attention to be directed serially across the visual field. During the initial, feature extraction stage stimulus features are represented in different "feature maps," one for

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each separable stimulus dimension, such as color, form, etc. (We emphasize separable aspects of the stimulus because the theory does not address itself to integral aspects and as such they are beyond the scope of the research reviewed here [see, for example, Garner, 19741.) During the latter, feature integrution stage, features situated on different maps must be localized (in a serial process) in order to integrate the features into recognizable (identifiable) objects. According to FIT, performance limitations (operationalized as interference between sources of information) occur when visual features must be conjoined in order to meet task demands, whereas such limitations are bypassed when individual features are sufficient for performance. For example, taking the slope of the linear function relating reaction time (RT) to display size in a visual search task as an index of selective processing of the target, Treisman and Gelade (1980) have shown that the zero-slope criterion (indicating perfect selective processing of the target) is achieved when a single feature of the target is sufficient for detection. In contrast, slopes are positive and relatively large (indicating nontarget interference) when two or more target features must be conjoined for detection.

FIT raises a general question concerning aging and the selective processing of environmental information: Does aging influence either or both stages of processing? Moreover, does aging effect either the kind or quantity of features that are detected and processed? We have argued elsewhere (Hoyer & Plude, 1982) the importance of considering the role of age-related data-limitations in assessing aging and selective attention. A variety of age changes in the optical system (cornea, lens, etc.) may compromise the quality or potency of features that can be discriminated by the elderly observer (see, for review, Sekuler, Kline, & Dismukes, 1982). Thus it is critical to verify that feature values from a selected stimulus dimension, such as color, are equally discriminable (at least functionally) across age (see for example Pollack & Atkeson, 1978). In addition to qualitative constraints age-related data limitations may impose quantitative constraints by reducing the useful field of view (Cerella, 1985a; Scialfa et al., 1987). This issue is addressed more explicitly in the next section, which addresses the first of the three aspects of attention identified above, i.e. selectivity. Selectivity

In this section, we review research on visual search, spatial cuing, and attention shifting, with the intention of isolating the locus of age deficits in the selective aspect of information processing. Age decrements in visual search are well-documented (e.g., Madden, 1982, 1983, 1986; Plude & Hoyer, 1981, 1985; Rabbitt, 1965). The basic finding is that older adults exhibit less selective processing of target stimuli than do younger adults as indicated a steeper slope for the linear function relating RT to display size in visual search. Although a variety of factors have been shown to attenuate age decrements in visual search, such as consistent mapping of targets onto responses (e.g.. Madden & Nebes, 1980; Plude et al., 1983) and cuing subsets of display positions eligible for containing the target (where the number of cued positions is greater than one; Madden, 1983), as long as visual search is required, older adults exhibit greater susceptibility to nontarget interference compared with younger adults. The statement about spatial cuing is qualified because when the spatial cue obviates visual search by specifying a single relevant display position (i.e., nonsearch) then older

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adults are no more susceptible to nontarget interference than are younger adults (Plude & Hoyer, 1986; Wright & Elias, 1979). This qualification is expounded upon in the section on spatial cuing.

Visual Search In visual search, selective attention deficits are manifested as nontarget interference effects (manipulated via display size) on target detection performance. When performance (either reaction time, RT, or error rate, ER) is plotted as a function of display size, i.e., the number of nontargets in a display, the slope of the linear function informs about the selectivity of attention to the target. A zero slope is taken as evidence of perfect selectivity because nontargets do not interfere with target processing; positive slopes indicate nontarget interference with interference increasing with increasing slope. Typically, older adults exhibit larger search slopes compared with young adults (see Plude & Hoyer, 1985, for a review). The intercept of the visual search function is usually taken as indicating peripheral aspects of processing, such as item encoding time, response execution, and the like (see Egeth, Atkinson, Gilmore, & Marcus, 1973), and age effects in this parameter of search are quite robust (Plude & Hoyer, 1985). Because it informs about the selectivity of attention we focus on the slope of the visual search function in cognitive aging research. Within the FIT framework, evidence of an age decrement in feature extraction would manifest itself as a failure on the part of older adults to achieve the zero-slope criterion in a feature-search condition, indicating difficulty in capitalizing on physical features in detecting a target. On the other hand, evidence of an age decrement in feature integration would manifest itelf in larger search slopes for elderly compared with young adults in a conjunction-search condition. This latter outcome would be consistent with previous findings in the cognitive aging literature (as reviewed earlier), and would extend those findings by specifying the conditions (i.e.,feature-conjunction) under which visual search deficits arise. Furthermore, if this latter outcome were coupled with evidence that feature-extraction is uncompromised then the age deficit in visual search would be localized in the feature integration stage, calling for more specific assessment of the mechanism@)underlying the deficit. Two experiments were conducted to assess age effects in searching for features vs. conjunctions. The first study (Plude, 1986) employed a card-sorting task similar to the one used in Rabbitt's (1965) pioneering research. In this study, 20 adults in each of two age groups, young (M = 27 yrs) and elderly (M = 64 yrs), sorted four sets of card decks, with two sets corresponding to a "feature"

condition and two to a "conjunction"condition. In each set, there were four decks of cards, a practice deck and one deck each having 1,4, or 16 symbols on a card. One half of the 32 cards in each deck contained a target and the other half did not. For the practice decks, "no target" cards were blank. For the remaining decks, nontarget symbols (yellow circles and red squares) were used (with equal representation) to construct the appropriate display sizes. In the feature condition, one set of cards had "blue" as the target (with blue circles and squares as targets) and the other set had "triangle" as the target (with red and yellow triangles as targets). In the conjunction condition, one set had "red circle" as the target and the other set had "yellow square" as the target. Error rates averaged .02 within each age group and were positively

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correlated with sorting time (ST) thus ruling out speed-accuracy tradeoffs. The best-fitting linear functions (deterimined by a least-squares solution) are given in Table 4.1, where an asterisk indicates that a given slope significantly exceeds zero. Table 4.1

Intercept and Slope Parameters (in sec) of the Best-Fitting Linear Equations for the Display Size Functions in Card-Sorting (Plude, 1986). Young adults int. slope

Older adults int. slope

Red Circle

16.6

.45*

24.0

1.14*

Yellow Square

19.0

-55;

24.5

1.85;

Blue

15.5

.07

22.2

.13

Triangle

18.6

SO*

25.5

1.19*

Condition/probe Conjunction

Feature

Analyses revealed that young and elderly adults' slopes were statistically equivalent for the blue feature condition, whereas evidence of an age decrement was found for each of the other sets. Thus, within the feature condition both young and elderly adults optimized search when color was the defining target dimension whereas neither young nor elderly adults achieved zero slopes when form was the defining dimension. Furthermore, the pattern of age decrement for the triangle set was similar in magnitude (a slowing ratio of 2.4) to that obtained for both conjunction sets (2.5 for red circle and 3.4 for yellow square). These findings are at the same time informative and puzzling. They are informative for revealing evidence of parallel processing among elderly adults for the blue feature set which can be taken as evidence of an intact feature extraction stage of processing. Coupled with the age decrement in search obtained for each conjunction set, this finding implicates the feature integration stage as the locus of age deficit in visual search. However, the puzzling aspect of these findings concerns age-related performance on the triangle feature set. Two explanations seem plausible. On the one hand, it may be that color is a more salient dimension than form and target detection based on a feature value of a more salient stimulus dimension is more efficient than detection based on a less salient one. This raises the specific issue of feature-equivalence within feature integration theory (e.g., Carter, 1982; Treisman &

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Gormican, 1988) and their age-related equivalence is an empirical matter in need of further research. On the other hand, rather than arguing that color is more salient than form it is perhaps equally parsimonious to attribute the findings for the triangle feature set to a contrast problem inherent in the stimulus materials. The white background of the playing card surface provided less contrast for yellow stimulus items compared with red and blue ones. Recall that in the triangle set one half of the targets were yellow and as such they yielded a low contrast ratio with the white background. In addition, recall that yellow square served as the target in one of the conjunction sets. Analyses of practice deck data revealed that these two targets yielded longer ST compared with the other targets (although for the practice decks older adults were not more susceptible to this effect compared with younger adults). The elderly's increased susceptibility to reduced contrast and glare (e.g., Fozard, E r s t , Bell, McFarland, & Podolski, 1977), may have exacerbated the difficulty of detecting yellow targets. Of course, one difficulty with this "data-limitation" interpretation is accounting for its affect on slopes rather than on intercepts, and further research is underway to resolve the problem. The second experiment in this series (Plude & Doussard-Roosevelt, 1989) employed a computer-based methodology to collect discrete trial data and to broaden the scope of our inquiry into the locus of the age deficit in visual search. The card-sorting study (Plude, 1986) demonstrated that elderly adults are capable of capitalizing on the output of the feature extraction stage in order to bypass limitations imposed by feature integration, at least when color served as the relevant target dimension, and implicated the feature integration stage as the locus of age decrement in visual search. The present experiment sought to replicate this pattern of findings and to inform about the mechanism(s) underlying the age deficit in feature integration. Furthermore, whereas feature and conjunction conditions were blocked in Expt. 1, affording subjects opportunity to establish and maintain a "set," the present experiment randomized conditions over trials in order to assess "data-driven" selective attention effects (see, for example, Madden, 1984; Rabbitt, 1982). In this study 12 adults in each of two age groups, young (A4 = 20 yrs) and elderly (M = 71 yrs) searched for a single, fixed target that was identified by color and form. For each subject the target was selected from a pool of four symbols that served as stimuli in the experiment: Green X, Green 0, Red X, and Red 0. (The specific feature values of each dimension were selected to ensure easy discriminability.) The experiment was controlled by a microcomputer and displays were presented on the system's video display terminal. Subjects entered responses on the microcomputer keyboard and RT and accuracy were recorded online. The specific details of the experiment are reported in Plude and Doussard-Roosevelt (1989). Performance was assessed in both feature and conjunction conditions, and search load was manipulated via display sizes of 5, 15, and 25 items. Nontargets for displays in the feature condition differed on both dimensions from the target. Thus, assuming a Red X as the target, nontargets were Green 0's. Nontargets for the conjunction condition shared one each of the target's features (e.g., Green X's and Red 0's) and their appearance within and between displays (in this condition) was counterbalanced. In order to investigate the nature of processing in the feature integration stage, an "unconfounded" conjunction search condition was included in which the number of

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items sharing the target's color was held constant (at three) across display size (e.g., 3 red 0 s in target-absent displays, 2 red 0 ' s in target-present displays). This manipulation was designed to reveal a parallel-processing component of conjunction search (see Egeth, Virzi, & Garbart, 1984). In brief, the idea is that any conjunction search can be accomplished by an initial parsing (in parallel) of elements in a display followed by a serial search for the target within a parsed group. Keeping a perfect correspondence between display size and the equal representation of conjunction nontargets obscures such a process, hence the need for "unconfounding" display size and the representation of conjunction nontargets. In all, this experiment comprised three nested factors: Condition (conjunction, unconfounded, feature), display size (5, 15,25), and probe (negative, positive). Displays corresponding to these factors were randomized over six blocks of 36 trials each, with practice trials included at the outset of the experiment for familiarization.

RT and error data were summarized for each combination of experimental factors and were subjected to separate analyses. Because error rates were quite low (.02for elderly, .03 for young), were uncorrelated with RT (.03 for elderly, and -.07 for young), and their analyses mirrored the essential aspects of the RT analyses, we focus on the RT data. The best-fitting linear functions are given in Table 4.2, where an asterisk indicates that a given slope value significantly exceeds zero. Analyses revealed that both age groups attained zero-slopes in the feature condition, whereas neither age group did so in the conjunction condition. In the conjunction condition the elderly exhibited significantly larger slopes compared with young adults and moreso for negative compared with positive probes (see also Zacks, Zacks, & Hildebrandt, 1987, 1988). In the unconfounded condition the age groups were statistically indistinguishable, although young adults attained zero-slopes on negative probes whereas the elderly did not and vice versa on positive probes. Thus, consistent with the card-sorting study, the findings implicate the feature integration stage as the locus of age deficit in visual search. They contribute to the literature on cognitive aging by showing (at least) that it is not display size per se that adversely affects search performance in the elderly, but rather it is the featural relationship between targets and nontargets that mediates the impact of display size. Further, the specific nature of the age deficit appears to be quantitative rather than qualitative as indicated by various implications of the obtained results. For example, both age groups capitalized on the restricted stimulus set in the unconfounded condition, suggesting that both engage a mixed parallel-serial process in conjunction search; and, both groups exhibited serial self-terminating search in the conjunction condition in which the ratio of negative to positive slopes is nearly 2:l (but see Townsend, 1974, for alternative interpretations). The quantitative difference in conjunction slopes may take any of several different forms, but before further research is directed at selecting between candidates it is important to verify that age-related data limitations are not responsible for the differential age effect. One such limitation involves the reduced extrafoveal acuity of the elderly (e.g., Cerella, 1985b), which may place older adults at a disadvantage compared with younger adults as targets occupy more peripheral display positions. An age-increment in display size effects may result from the older adult's constricted perceptual window rather than from diminished selective attention. In order to

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Table 4.2 Intercept and Slope Parameters (in ms) of the Best-Fitting Linear Equations for the Display Size Functions in Visual Search (Plude & Doussard-Roosevelt, 1989).

Conditionlprobe

Young adults int. + slope

Older adults int. + slope

Conjunction Negative

639

+ 25.5*

Positive

586

+

13.9*

+ 49.6* 836 + 25.4.

Negative

802

+

2.2

1162 + 12.6.

Positive

644 + 3.5*

975

+

4.3

Negative

635 t 0.1

1000

-

1.3

Positive

625 - 0.8

a44

+

2.4

915

Unconfounded

Feature

* p < .01. evaluate this possibility in the present data set, correct positive probe data for the feature and conjunction conditions were re-summarized on the basis of target eccentricity. "Inner" targets fell within approximately six degrees of central fixation and "outer" targets fell on the periphery of the display (approximately ten degrees). Intercept and slope parameters of the best-fitting linear functions are given in Table 4.3, where an asterisk indicates that a given slope value significantly exceeds zero. The slopes for neither the inner nor the outer functions in the feature and unconfounded conditions differed from zero for both young and elderly adults. Thus, for both age groups targets were detected without interference from nontargets, the eccentricity of the target did not affect detection time, nor did these factors interact with one another. In contrast, slopes for both the inner and outer functions in the conjunction condition were greater than zero for both young and elderly adults. Further, there was an Age X Eccentricity interaction with the magnitude of age decrement in search for targets defined by a conjunction of features increasing with increasing eccentricity.

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Table 4.3

Intercept and Slope Parameters of the Best-Fitting Display Size Functions for Inner and Outer Targets in Visual Search (Plude & Doussard-Roosevelt, 1989). Condition/ Eccentricity

Young adults int. slope

Older adults int. slope

Inner

571

14.3*

812

20.6*

Outer

604

13.0*

883

29.5'

Inner

629

2.3

942

3.4

Outer

651

5.0

999

6.7

Inner

606

-0.8

860

0.7

Outer

622

0.4

821

5.3

Conjunction

Unconfounded

Feature

*

4

< .01.

In all, these findings argue for the role of selective attention in mediating age decrements in visual search. Eccentricity mattered neither when targets could be detected pre-attentively nor when they could be parsed (preattentively) into a small perceptual group on the basis of color. Eccentricity did matter when preattentive processing failed to isolate the target (or a subset of items containing it), thereby necessitating selective attention. In order to accomodate these patterns of age effects, the concept of an age-constrictedperceptual window must be broadened to encompass the role of feature integration in mediating age differences in the useful field of view (FOV). Perhaps age reductions in the FOV apply to conjunction situations in which nontargets give rise to "illusory conjunctions" that may be confused for the target (Treisman & Schmidt, 1982). It is also possible that conjunction situations mandate a "cognitive" adjustment that restricts the size of a perceptual sample rather than the limitation being inherent in the window itself (see Mackworth, 1976). Further still, the FOV may vary with the complexity of the task (see Downing, 1988), and proportionately moreso with advancing age. These studies raise a number of important issues, To our knowledge they constitute the first demonstration of parallel processing in elderly adults. According to feature integration theory, zero slopes are expected only when the target can be detected on

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the basis of a single feature that is extracted preattentively, and the present research demonstrates that the prediction holds true even for aged adults. Such an outcome bodes well for establishing automatic detection in the elderly. Plude and Hoyer (1981, 1985) have argued that the acquisition of automaticity in visual search presupposes the operation of low-level visual processes whose output can be exploited in order to bypass selective attention. The present research identifies the registration of color information as one such low level process. Future work must identify other stimulus dimensions that also qualify. In addition, more research is needed to specify precisely the nature of age deficit in feature integration implied by the present findings. One course for such work has been initiated in our lab, and it seeks to determine the role of spatial localization in contributing to age decrements in visual search. According to feature-integration theory, spatial localization is required in conjunction search because features must be located in their respective "maps"before being conjoined to represent a complex object. Spatial Cuing

In order to investigate this possibility, we undertook a study involving spatial location cuing (Plude & Doussard-Roosevelt, 1988). In this study 12 adults in each of two age groups, young (M = 22 yrs) and elderly (M = 70 yrs) participated. All aspects of the design were exactly the same as in the visual search experiment reviewed above (Plude & Doussard-Roosevelt, 1989) with the exception that the fixation point (an asterisk) served as a spatial cue informing (with 100% accuracy) of an impending target's position in the display. The spatial cue on negative probe trials indicated a nontarget's position in the display. The cue always preceded a display by 500 msec. If localization is compromised in aging and if it is the limiting factor in visual search, then cuing should eliminate both age and display size effects in a conjunction condition. Error rates were very low and the best-fitting linear functions (based on RT) are given in Table 4.4, where an asterisk indicates that a given slope value significantly exceeds zero. Analyses revealed that cuing eliminated age and display size effects for positive probes in each condition, but for negative probes only in the feature condition. Elderly (but not young) adults exhibited significant display size effects in the conjunction condition and in the unconfounded condition, suggesting an age-related increase in susceptibility to "illusory conjunctions" when nontargets share features that can be conjoined to form the target. The findings for positive probes are consistent with the idea that spatial localization is compromised in aging and that it mediates age decrements in visual search. However, they are also compatible with other accounts. For example, a speed-of-processing account (e.g., Cerella, 1985a) might posit that the speed of comparison is slowed with aging and that cuing reduces the comparison set to one (the cued item) thus minimizing age decrements. A perceptual-window account (e.g., Cerella, 1985b) might posit that cuing provides opportunity to redirect one's gaze at the cued position, thus minimizing the contribution of a constricted field of view. Presenting cues for 500 msec provided opportunity to refixate, so the mechanism underlying the cuing effect is unclear.

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Table 4.4 Intercept and Slope Parameters (in ms) of the Best-Fitting Linear Equations for the Display Size Functions under Spatial Cuing (Plude & Doussard-Roosevelt, 1988).

Condition/probe

Young adults int. t slope

Older adults int. t slope

Conjunction Negative

643

+

1.9

765

+

12.4'

Positive

608

t

0.4

690

+

3.0

Unconfounded Negative

628 t 1.8

774 t 5.0

Positive

573 t 1.1

713

+

1.1

Feature Negative

621

- 0.9

712 - 0.3

Positive

574

-t

1.2

662 t 0.8

= .05 < p < .lo.

The next experiment was intended to assess the perceptual window account of spatial cuing. Young (M = 20 yrs) and elderly (A4 = 68 yrs) adults participated in a spatial cuing experiment in which they were instructed to maintain central fixation while using the spatial location cue to allocate attention (but not fixation!) to a particular location in the visual display. In this experiment, display durations were also calibrated individually for each subject to prevent against subjects refixating the target position. On average, young adults viewed displays that lasted 100 ms and elderly adults viewed displays that lasted 350 ms; both durations were below the age group norms for eye movements (see Carter, Obler, Woodward, & Albert, 1983). RT and error rate data were summarized for each unique combination of factors in the experimental design. Error rates were rather large compared with our earlier experiments, surely because of the limited viewing time. Analysis of ER agreed with the RT analysis reported below, and there was no evidence of speed-accuracy tradeoffs. The best-fitting linear functions (based on RT) are given in Table 4.5.

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Table 4.5 Intercept and Slope Parameters (in ms) of the Best-Fitting Linear Equations for the Display Size Functions in the Cuing With Fivation Experiment (Plude & Doussard-Roosevelt, 1988).

Condition/probe

Young adults int. t slope

Older adults int. + slope

Conjunction Negative

733

+

3.1

927

+

Positive

692

+

1.8

761 t 6.7

Negative

751

t

0.8

913

Positive

699

+

1.3

826 t 1.6

3.7

Unconfounded

+

4.3

Feature Negative

713 t 0.3

Positive

687

+

0.9

825

- 1.3

744 t 3.0

Note: No slope is significantly different from zero. Analyses revealed that spatial cuing drastically reduced all display size effects (i.e., slope parameters did not differ significantly from zero). Another aspect of these data refutes the view that age-related data limitations are the operative factor in mediating age differences in visual search. Because of the design employed across these experiments it is possible to examine performance as a function of the target's retinal eccentricity as was done in the visual search experiment. Recall that "inner" targets fell within a six degree area centered about central fixation and "outer" targets fell along the periphery of the display, spanning approximately 10 degrees of visual angle. At each level of eccentricity within each condition, a best-fitting linear function was computed and the resultant intercept and slope parameters are provided in Table 4.6. Despite nonsignificant variations in slopes across conditions, spatial cuing eliminated retinal eccentricity effects in the conjunction condition: Although older adults were slower to respond compared with younger adults, neither eccentricity nor its interaction with age approached significance. In contrast to the conjunction condition findings reported in Table 4.3, those presented in Table 4.6 indicate no significant slopes in this

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condition. For example, whereas young adults produced a slope of 13.0 (p < .Ol)in the conjunction, outer condition in the absence of cuing (Table 4.3), they produced a slope of 2.2 (p > -10) in this same condition when spatial cues were provided (Table 4.6). Table 4.6 Intercept and Slope Parameters (in ms) of the Best-Fitting Linear Equations for Inner and Outer Targets under Cuing with Fixation (Plude & Doussard-Roosevelt, 1988).

Condition/ eccentricity

Young adults int. t slope

Older adults int. + slope

Conjunction

+

Inner

683

Outer

724 t 2.2

2.0

778 t 5.5

+

5.9

+ 812 +

3.7

+

1.4

774

Unconfounded

- 0.8

Inner

709

Outer

695 t 2.6

792

3.3

Feature

- 0.8

Inner

695

Outer

683 t 2.3

735

791 t 2.5

Note: No slope value differs significantly from zero. We conclude that these findings argue for spatial localization as the mechanism underlying the age deficit in conjunction search. The extent to which such a mechanism is compromised by diminished processing capacity or reduced speed of processing is open to further study. For now it is clear that spatial cuing alleviates age deficits in visual search. As noted in the introduction, however, this conclusion about the effectiveness of spatial cuing for eradicating age deficits appears to be specific to situations in which the cue unambiguously indicates a single target position. When spatial cues are less informative, for example when two or more of multiple positions are cued, then the differential age benefit fails to materialize. This is not to say that such cues are ineffectual for older adults, rather the magnitude of benefit associated with such cues is roughly comparable for older and younger adults (see Madden, 1983).

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This qualification on the age-related effectiveness of cuing leaves open the possibility that spatial localization is not the only limiting process underlying age decrements in visual search. Among other candidates are dividing attention between positions in a display, shifting attention between positions in a display, and the basic rate or capacity of information processing. Research concerning age-related divided-attention ability is reviewed in the next section (on attentional capacity) and it suggests that such ability is not compromised by aging. Recent research on shifting attention indicates no particular difficulty on the part of the elderly in this regard, however there is lack of consensus on the speed with which such shifts are executed. Attention Shining Using a technique pioneered by Posner and his colleagues (e.g., Posner, 1980; Posner, Nissen, & Ogden, 1978), Nissen and Corkin (1985) found no age differences in attention shifting between young and healthy older adults when spatial cues appeared either two or three seconds prior to a target event. Plude, Cerella, and Poon (1982) substantiated the lack of age differences in attention shifting with spatial cues presented in the range 0 through 1000 ms prior to a target event. In contrast, Hoyer and Familant (1987) found that older adults did not benefit from spatial cues presented under 750 ms prior to a target event. Madden (1990) also obtained evidence of an age difference in the effectiveness of spatial cues presented under 150 ms prior to a target event. The studies conducted by Nissen and Corkin (1985) and Plude et al. (1982) assessed the effects of spatial cuing in a luminance detection task in which targets appeared in a single position on either the left or right side of central fixation, whereas Hoyer and Familant (1987) and Madden (1990) assessed the effects of cuing in more complex letter identification tasks involving multiple display positions. The lack of agreement between studies may owe to the different task demands which may impose differential processing loads on elderly compared with young adults. As reviewed in the next section, task complexity often is the key determinant of age deficit, and future research should address the relationship between task complexity and attention shifting among young versus elderly adults. Capacity Cognitive aging research on attentional capacity is quite diverse. We refrain from considering the role of capacity in memory (Craik & Byrd, 1982), problem solving (Arenberg & Robertson-Tchabo, 1977), and intellectual performance (Stankov, 1988). Continuing our emphasis on the processing of environmental information, we review research involving divided attention and automatic processing in the domain of vision. We recognize that this narrow focus neglects a host of dual-task investigations of the effect of aging on dividing (or switching) attention (e.g., Lorsbach & Simpson, 1988; Madden, 1986) and that such studies may be important for fathoming age differences in attentional capacity (see Schonfield, 1982). However, given the oft-overlooked complexities endemic to dual-task methodology (Fisk, Derrick, & Schneider, 1987) and the different patterns of age effects for various combinations of tasks (e.g., Hoyer, Vaidya, & Clancy, 1989), we feel justified in our focus.

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A main goal in this section is the resolution of a discrepancy in the cognitive aging literature concerning the status of divided attention abilities. Somberg and Salthouse (1982) reported age-equivalent divided attention effects when comparing young and elderly adults, whereas other investigators (McDowd & Craik, 1988; Plude & Hoyer, 1986; Salthouse, Rogan & Prill, 1984) have reported an age decrement in divided attention. Within the FIT framework, certain tasks may emphasize feature extraction in which age deficits would not be expected and other tasks may emphasize feature integration in which age deficits would be expected. Divided Attention A divided-attention deficit is manifested as a performance fradeoff (or cost) between focused-attention vs. divided- attention conditions. Our preliminary findings suggest an age-related divided-attention deficit when feature integration is emphasized but age equivalence when feature extraction is emphasized. As noted above, the cognitive aging literature is mixed with regard to the status of age-related divided attention ability. Somberg and Salthouse (1982) used a bar-marker detection task in demonstrating age-equivalent divided-attention tradeoffs in young and elderly adults. Subjects were instructed to allocate attention differently to each of two concentric visual arrays, one consisting of crosses and the other Xs, and to detect the presence of a bar-marker eminating from the intersection of a n element in either array. The accuracy of detecting the bar-marker varied directly with attention instruction, i.e., highest accuracy for 100% attention and lowest for 0% attention allocated to the array containing the target, and it did so in exactly the same manner for both young and elderly age groups. Thus, Somberg and Salthouse argued against an age deficit in divided attention.

In contrast, Plude and Hoyer (1986) argued in favor of an age deficit in divided attention on the basis of different patterns of age effects in search versus nonsearch conditions of a letter identification task. In this task, subjects were required to indicate whether or not a specified target character appeared in a visual display containing either one or five characters. The magnitude of nontarget interference was greater among elderly (a 50% increase in R T from the one- to five-character displays) compared with young adults (a 35% increase in RT) under divided attention (search); whereas nontarget interference was comparable between young (13%) and elderly (8%) adults under focused attention (nonsearch). Importantly, this pattern of effects held even when targets were equated for foveal acuity, supporting the conclusion of an age-related divided attention deficit. This interpretation of Plude and Hoyer's (1986) findings is supported by two other studies concerning aging and divided attention. Salthouse, Rogan, and Prill (1984) investigated age-related divided attention effects in concurrent versus isolated memory span tasks which presumably impose greater demand on processing resources compared with the earlier bar-marker detection task. In this study, age-related divided attention deficits were obtained prompting Salthouse et al. (1984) to conclude that task complexity is the key determinant of age deficits. A similar conclusion was reached by McDowd and Craik (1988) in a study involving concurrent versus isolated auditory and visual tasks that varied in complexity (i.e.,

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depth of processing). Older adults exhibited larger divided attention deficits compared with younger adults, but the magnitude of deficit was comparable to that associated with the manipulation of task complexity. McDowd and Craik concluded that division of attention is but one of many means by which to increase task complexity thereby yielding age decrements. Clearly these various studies differ in many ways besides their conclusions. We have conducted research in our lab in an attempt to reconcile the contradictory findings within the FIT framework (Plude, Murphy, & Gabriel-Byrne, 1989). Target identification performance was assessed as a function of the amount of attention allocated to each of two concentric visual arrays, just as in Somberg and Salthouse's (1982) procedure. The latency and accuracy of an observer's decision response regarding the presence/absence of a pre-specified target character served as dependent measures for evaluating tradeoff effects. Attention allocation was manipulated both through instructions, i.e., divide attention equally between both arrays or focus it on one or the other, and through varying the target's likelihood of appearing in each array. Both divided attention and focused attention tradeoffs are indexed by comparing target identification performance (RT and ER) for different combinations of conditions and probes. This approach to assessing divided attention performance carries with it certain important assumptions (Wickens, 1980). For example, we assume that confining processing to a single modality (vision) and to a single task (target identification) maximizes the likelihood that a single mental resource (or pool of resources) is tapped. By varying the demand on this single resource, we are poised for assessing age-related divided attention effects. In order to facilitate the discussion of age-related tradeoff effects, it is useful to review the basic method employed in this research. The visual display consisted of two concentric arrays, inner and outer (spanning approximately three and five degrees of visual angle, respectively), each of which contained four positions arrayed about a central fixation point. Subjects were instructed to allocate attention differentially across three conditions of performance, 90/10, 50/50, and 10/90, while performing a target identification task: respond YES if the target appears in the display and respond NO if the target is absent from the display. The attention instructions were reinforced by varying the target's frequency of appearance in each array across the various conditions. Thus in the "focused attention" conditions (90/10 and 10/90) the target, in fact, appeared about 90% of the time in the expected array and 10% of the time in the unexpected array, whereas in the "divided attention" condition (50/50) the target's appearance was balanced between arrays. Notice that across conditions, negative probe trials occurred as well, i.e., the target appeared in neither array, and for the purpose of evaluating age-related processing strategies (as discussed below) a subset of trials presented the target simultaneously in both arrays. Of central interest were performance tradeoffs involved with both focused and divided attention conditions. Focused attention tradeoffs are indexed by comparing performance for an expected probe (target in attended array) with an unexpected probe (target in unattended array) and divided attention tradeoffs are indexed by comparing performance between focused and divided attention conditions.

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We report three experiments that employed this basic design but with certain critical variations intended to manipulate the degree of emphasis on feature extraction and feature integration. In the first experiment the target differed in color from the nontargets (which were homogeneous in color) and thus performance could be based on the output of the feature extraction stage. This experiment replicated the essential features of Somberg and Salthouse's (1982) experiment with the main difference being that letter identification rather than bar-marker detection is involved, however, as noted above performance could be optimized by keying on the distinguishing target feature. The second experiment differed from the first by removing the distinguishing target cue. Thus the target and nontargets were homogeneous in color. This modification was intended to place heavy demand on the feature integration stage, with the assumption being that multiple target (form) features must be integrated for accurate target identification. Further, display presentation time was set below the threshold for an eye movement thereby retaining the speeded processing demand of the first experiment. The third experiment simply removed the limitation on viewing time that characterized the earlier experiments, thus alleviating some of the demand on the feature integration stage of processing. Different groups of twelve young adults and twelve elderly adults served as subjects in each of the computer-based experiments. Practice trials were administered in order to familiarize subjects with task demands as well as for calibrating the display durations to yield 80-90% accuracy in the first two experiments (yielding display durations that averaged 125 ms for young adults and 350 ms for the elderly). A trial consisted of the following sequence: A fixation point preceeded the onset of the display by 500 msec, the display remained in view for the calibrated duration, after which only the fixation point remained until a key press was issued (with the exception of the third experiment in which the response terminated the display). R T and ER were the dependent measures, and R T tradeoffs (expressed as %) are given in Table 4.7 and discussed below. However, before reviewing the results a comment on the analytic approach is in order. Median R T is typically employed in cognitive aging research because it is less affected than is mean RT by outliers such as those that characterize older adults' R T distributions. In these experiments, however, mean R T was used because of the bias inherent in median R T versus mean R T when comparisons are based on unequal numbers of trials (see Miller, 1988). It should be noted that performance on negative probes was constant across conditions and similar between age groups in all three experiments and as such they deserve no further commentary. We defer comment on "double" probes until the evidence bearing on focused and divided attention tradeoffs has been reviewed. Table 4.7 depicts proportional tradeoff data across all three experiments. Focused attention costs are indexed by the cost of a target appearing in an unattended array versus an attended array, i.e., performance for outer vs. inner targets in the 90/10 condition and for inner vs. outer targets in the 10/90 condition. Divided attention costs involve those concerning dividing versus focusing attention, i.e., performance for inner targets in the 50/50 vs. 90/10 conditions and for outer targets in the 50/50 vs. 10/90 conditions. In addition, comparison of inner vs. outer targets in the 50/50 condition informs about performance that may be biased toward one array or the other.

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Table 4.7 Proportional Tradeoffs and Array Bias in Three Divided Attention Experiments (Plude, Murphy, & Gabriel-Byrne, 1989). Expt 1 Young Old

Expt 2 Young Old

Expt 3 Young Old

Divided Attention Costs Inner (90/1050/50) Outer (10/9050/50)

.10 .09

.03

.I5

.14

.15*

.08

.13

.02 .14*

.29' .29*

.14* .23*

.25* .62*

23'

.07

.02

.10

.01

.06

.04

.02

-.06

-.01

Focused Attention Costs Inner (90/10) Outer (10/90)

.04 .22*

Inner/Outer Bias Both (50/50)

-.09*

Taking each experiment in turn, it can be seen that in Experiment 1, which emphasized feature extraction, neither young nor elderly adults evidenced a divided attention tradeoff. Thus as expected, emphasizing feature extraction yielded equivalent divided attention performance across age. In addition to yielding age-equivalent divided attention effects, this experiment was expected to eliminate condition effects and interactions. Inspection of Table 4.7, however, reveals that both young and elderly adults suffered a focused attention cost when attending to the outer array. Further, young adults exhibited a significant bias toward the outer array. Thus we partially succeeded and partially failed in meeting the predictions of FIT. Our success lies in the nonsignficant age effect for divided attention; our failure lies in obtaining a significant tradeoff effect in both age groups. The divided attention cost for the outer array may reflect a suppression effect for foveal stimuli when attending to peripheral parts of the visual field, but in the absence of similar findings in other studies (e.g., Hartley & Kieley, 1988) we cannot argue forcefully for such an effect. In Experiment 2, which taxed the feature integration stage, young and elderly adults both experienced significant focused attention costs of roughly comparable magnitude. However, the age groups differed with regard to divided attention tradeoffs. For young adults, there were no significant divided attention tradeoffs, whereas for elderly adults a significant tradeoff occurred for the outer array. Thus the results of Experiment 2 are consistent with the hypothesis that emphasizing the feature integration stage yields an age decrement in divided attention. It is important to note that the pattern of tradeoff is directly opposed to a "perceptual window" account of age decrements. A

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constricted perceptual window should give rise to a bias for the inner array (where targets fall nearer the fovea). Such is not the case in the specific comparison relevant to the bias effect nor in computing the tradeoff cost for the outer array under divided versus focused attention. Finally, the results from the third experiment, in which speeded processing was de-emphasized by providing subject-controlled displays, indicate that the elderly can compensate the divided attention decrement provided sufficient time to do so. In this experiment, patterns of tradeoff effects were identical: Both young and elderly subjects exhibited significant focused attention tradeoffs in the 90/10 and 10/90 conditions, and neither age group exhibited significant divided attention tradeoffs. Thus putting displays under subject control eliminated the age-related divided attention deficit obtained under speeded processing in Experiment 2. Examination of ER and RT effects between Experiments 2 and 3 suggested that the mechanism underlying the compensation involved visual search. Older adults appear to have been more thorough in their search for the target compared with young adults, suggesting a greater concern with accuracy on the part of elderly compared with young adults. Whereas young adults experienced a 29% increase in RT coupled with a 40% decrease in ER, the elderly exhibited a greater than two-fold change in performance, i.e., RT increased by 74% and E R decreased by 80%, indicating a greater speed-accuracy tradeoff among elderly compared with young adults at least when displays were under subject-control. This pattern warrants further research.

In summary, we obtained evidence of an age-related divided attention deficit in Experiment 2 which placed heaviest demand on the feature integration stage of processing. We eliminated this age difference by removing the demand on speeded processing in Experiment 3 and by emphasizing feature extraction rather than feature integration in Experiment 1. Thus, the analysis of performance tradeoffs provides a mixed picture regarding the age-related status of divided attention ability. FIT is helpful to an extent in interpreting the obtained patterns of age effects but not without introducing some degree of confusion at the same time. At present it appears that an age-related divided attention deficit is specific to situations in which: 1) feature integration is involved, and 2) speeded processing is required. To this point we have emphasized age effects on divided attention performance. We have little to say about focused attention performance due to the relative comparability of young and older adults on this aspect of processing. Both age groups exhibited focused attention costs that were roughly comparable in magnitude. This outcome is consistent with the bulk of cognitive aging research (see Plude & Hoyer, 1985) and argues against the notion that older adults are deficient in the ability to ignore irrelevant information, at least when that information is spatially distinct from the focus of attention (see also Wright & Elias, 1979). The analysis of performance tradeoffs is only one of many different vehicles for analyzing divided attention ability (e.g., Kinchla, 1980). Another approach entails analyzing the ability to engage in parallel processing which may be compromised in later adulthood (e.g., Salthouse, 1988). Our recent work on visual search argues against this position (Plude & Doussard-Roosevelt, 1989) as do certain aspects of the present research.

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Consider two arrays as sources of information that compete for processing resources. Assume that in the present task a positive response is issued upon completion of the registration of some threshold amount of target evidence from either array. If resources are distributed equally across both arrays then, on average, when targets occupy both arrays (i.e., "double"probes) RT should be shorter (and less variable) than when the target occupies either array alone. In brief, we posit a classical "horse race" model of processing in the present task in which the distribution of RTs for "double" probes consists of the shortest times associated with the RT distributions of both the inner and outer arrays. On this view, evidence of parallel processing would be found in shorter mean RT (and smaller RT variance) for double probes vs. inner and outer probes in the 50/50 condition of each experiment. Figure 4.2 depicts the relevant data across the three experiments for each age group. Planned comparisons using double probes as a control for inner and outer probes combined revealed evidence of parallel processing in both age groups in both Experiments 2 and 3. Thus the latter two experiments show quite strong evidence of parallel processing among both young and elderly adults, dispelling the notion of an age decrement in this ability. The data for Experiment 1 failed to conform consistently with the predicted pattern. That this experiment failed to yield consistent evidence of divided attention lends credence to the notion that attention allocation has relatively less impact on feature extraction compared with feature integration. We recognize that this interpretation of the obtained results is not the only viable interpretation. Townsend (1974) has identified the myriad obstacles to an unambiguous account of serial vs. parallel processing. An alternative account of our results, for example, might posit a serial search that is initiated in one or the other array randomly over trials in the 50/50 condition, and which incurs costs when the target appears in the opposite array but not when it appears in both arrays. Although the present experiments are unable to decide between these alternatives, it is clear that young and elderly adults used comparable strategies across the experiments. This conclusion is also bolstered by the analyses of negative probe performance which revealed consistent decision strategies between age groups as well as between attention conditions. Coupled with the patterns of age-related performance tradeoffs discussed above, the present analysis argues against a generalized age decrement in divided attention ability. Instead it would appear that age deficits depend on the demand placed on the feature integration stage, i.e., task complexity, and future research must decide whether this owes to reductions in the speed or capacity of resources subserving this stage of processing. As testament to the role of complexity in the present research consider the mean RT data from all three experiments as plotted in Figure 4.3 which depicts elderly adult performance as a function of young adult performance. Using Cerella's (1985, 1990) convention, increasing complexity is signified by increasing RT and age-related complexity effects are revealed in the slope of the function relating performance between the two age groups. If complexity exerts comparable effects between age groups, then the slope of the function should be 1.0, because both age groups would exhibit equivalent increments in RT under increasing complexity. If complexity exacts a heavier toll on the elderly, then the slope of the function should be greater than 1.0, because older adults would register greater RT increments compared with younger adults under increasing complexity. The solid line in Figure 4.3 gives the best-fitting

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linear function describing both sets of points. The linear equation RTold = 1.72 (RTyoung) - 272 ms accounts for 90% of the variance, and indicates that elderly adults were more adversely affected by complexity than were younger adults. Analyses computed separately for divided and focused attention conditions were statistically indistinguishable from the overall analysis, supporting the argument that task complexity rather than the requirement to divide attention per se is the primary determinant of age decrement. 2200

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In conclusion, we find no evidence that divided attention ability is compromised by aging. Rather, the requirement to divide attention is one among many means by which to manipulate task complexity and it is the complexity of the task that determines the magnitude of age deficit in divided attention research, supporting the claims made by both Salthouse et al. (1984) and McDowd and Craik (1988). This conclusion provides a point of departure for considering the another procedure for manipulating complexity effects in the cognitive aging literature. Automaticity Task complexity can be directly manipulated by varying the consistency of associating stimuli and responses over the course of training. Schneider and Shiffrin (1977; Shiffrin & Schneider, 1977) demonstrated the dramatic differences in performance associated with “consistent mapping” versus ”varied mapping” procedures in hybrid

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memory search/visual search. Consistent mapping, in which the relationship between stimuli and responses is constant over training, yields an automatic detection response as evidenced in the zero slope relating performance (RT and ER) to the size of the processing load, i.e., memory set size multiplied by display set size. In contrast, varied mapping of stimuli and responses yields performance functions that are heavily dependent upon processing load, i.e., RT and ER increase with increasing processing load. Plude and Hoyer (1985) have reviewed cognitive aging research involving consistent and vaned mapping manipulations, and the typical pattern is for age deficits to be much larger in the latter compared with the former procedure. For example, we have found evidence of larger age decrements in visual search under varied mapping compared with consistent mapping conditions (Plude and Hoyer, 1981; Plude et al., 1982). These findings are consistent with the notion that minimizing task complexity (via consistent mapping) is particularly advantageous for elderly adults. Hoyer and Plude (1980, 1982) suggested that such findings implicate a diminishing processing capacity with increasing age. More recently, Clancy and Hoyer (1988) have extended this logic to domains of skilled performance in which elderly are found to maintain high levels of functioning. Such findings are consistent with a decrement-withcompensation notion of aging (see, for example, Salthouse, 1984) in which diminishing ability is compensated by expert skill. Charness (1985) has demonstrated such compensation within domains such as chess and contract bridge and Salthouse (1984) has done so in the domain of skilled typing. Although the research on automaticity and skill bodes well for preserving complex task performance in later life, recent findings by Dan Fisk and his colleagues (Fisk, McGee, & Giambra, 1988; Fisk & Rogers, 1987) point up a cautionary note. Unlike earlier research on the consistency of training which spanned only six or so sessions, Fisk's research has constituted massive amounts of training, on the order of twenty to sixty sessions(!), in order to map the full impact of training effects. Contrary to the earlier findings, Fisk has found evidence of age decrement under consistent mapping but not under varied mapping. Fisk argues that although elderly adults may benefit from consistency early on in training (perhaps via a rapid reduction in unfamiliarity), over the long haul the elderly adult is unable to acquire new automatic detection responses. Assuming a two-stage process in acquiring automaticity (see Schneider, 1985), Fisk suggests that aging does not affect the ability to "associatively learn" new stimulus-responsepairings that are consistently practiced, but aging does compromise the ability to perceptually "tag" consistently mapped targets (which is the mechanism underlying perceptual "pop-out"). These findings are likely to fuel continued debate over the ability to automatize performance in later life. Despite the cautionary note sounded by Fisk's research, there is ample evidence to suggest that older adults reap more benefits from automaticity than do younger adults. Perhaps this holds specifically for performance that has been automatized prior to later adulthood, i.e., skill that is acquired over a lifelong of performance in a particular job or avocation. The specific benefit of automaticity concerns the reduction of demand on processing resources compared with performance that is not automatic, and especially as the complexity of the task to be performed increases. As noted above, age effects typically increase with task complexity. This typical pattern applies neither to skilled performance nor performance where automaticity has been acquired.

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Arousal The idea that arousal is related to task performance has a long history in psychology. Perhaps the best known conceptualizationwas put forth by Yerkes and Dodson (1908) in which (physiological) arousal is related curvilinearly with performance such that at either extreme of arousal, low (sleep, coma, etc.) or high (frantic, panic-stricken, etc.) performance is at its lowest level and at intermediate levels (alert state) performance is maximized. Attempts to relate arousal with attentional constructs have been rather less well developed compared with the other aspects of attention reviewed above (see, for example, Eysenck, 1982). For the purpose of the present review, we restrict consideration to research that has addressed arousal with regard to sustained attention in vigilance tasks. Sustained Attention Sustained attention, the ability to maintain attention to sensory events for prolonged periods of time, is commonly measured in the context of vigilance tasks. The subject is required to monitor an information source, e.g., a video display, clockface, or some such stimulus in which repetitious events can be presented, in order to detect the occurrence of a target that typically occurs rather infrequently and unpredictably. Overall level of vigilance performance (hit rate for the target) as well as vigilance decrement, i.e., the decrease in hit rate over time, are indicators of sustained attention. In a function with performance plotted against task duration, vigilance performance would be reflected in the intercept and vigilance decrement in the intercept and slope. Borrowing from Signal Detection Theory (Green & Swets, 1966), a vigilance decrement can result from a change in sensitivity (d') or a shift in response criterion (Beta). Davies and Parasuraman (1982) reviewed 13 studies in which the effect of age on sustained attention was examined. Some studies reported age differences in sustained attention and others did not. When age differences are reported they are most frequently found in vigilance performance not vigilance decrement. Elderly adults respond less accurately and more slowly than younger adults (i.e., larger intercepts). One explanation for the lack of consistency in findings of age differences and vigilance is based on the theoretical viewpoint that multiple mechanisms underlie vigilance (Parasuraman, 1987). These various mechanisms are tapped by different variations in vigilance task procedures. Manipulation of target expectancy rates results in shifts in response criterion (Craig, 1987) whereas manipulation of target degradation results in changes in perceptual sensitivity (Parasuraman, Nestor, & Greenwood, 1989). Therefore, if age differences are present in criterion adjustment but not in sensitivity then the former but not the latter manipulation would produce age differences in vigilance. To understand age differences in sustained attention, one must employ a variety of procedures, and examine differences in a variety of mechanisms. Parasuraman (1987) addresses four separate mechanisms that may mediate age differences in sustained attention: 1) level of alertness, 2) adaptation of expectancy, 3) sustained allocation of processing capacity, and 4) development of automaticity. Parasuraman concludes that level of tonic alertness was related to vigilance performance whereas the other three measures were related to vigilance decrement.

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Further, he has found evidence for age differences in level of tonic alertness and development of automaticity, but no evidence of age differences in the allocation of capacity or the adaptation of expectancy. Let us employ Parasuraman's four mechanisms in evaluating age effects on sustained attention. Level of Arousal/Alettness. Age changes in arousal level may mediate age differences in sustained attention. Many studies that report age decrements in sustained attention use vigilance performance (rather than vigilance decrement) as the basis of comparison. Elderly adults respond less accurately and more slowly to targets compared with young adults (a finding consistent with much of the other attention literature surveyed above). In their pioneering research on aging and vigilance, Surwillo and Quilter (1964, 1965) found that the age decrement in vigilance performance was correlated with an age reduction in physiological arousal as indicated by skin potential response latency. Giambra and Quilter (1988) recently replicated this finding in a longitudinal follow-up to the earlier work. Thus when overall detection performance serves as the index of sustained attention, age decrements are obtained apparently due to an age decline in arousal. Aduptatiom and Expectancy. Parasuraman (1987) reported an investigation in which age effects on expectancy during a vigilance task were assessed. Prior to a vigilance task in which the target appeared with 5% probability, subjects were trained with either a matching (5%) probability sampling rate or a mismatching (50%) probability sampling rate. Vigilance decrement was greater under inappropriate expectancy (mismatching rate) compared with appropriate expectancy (matching rate). Although older subjects had lower detection rates overall (a decrement in vigilance performance) there was no age difference in the effect of expectancy. Thus both young and elderly adults adapted vigilance performance with respect to the trained expectancy. Allocation of Capacity. Processing capacity can be manipulated in a vigilance task either by increasing memory load or by degrading the perceptual quality of the target event. In either case, the manipulation is thought to necessitate a constant allocation of sustained attention throughout the task. This type of task can be contrasted with one in which detection becomes automatic (see Fisk & Schneider, 1981). Are the obtained age deficits due to 1) differences in the allocation of attention under controlled processing, 2) differences in automatic processing, or 3) differences in both comparisons?

Parasuraman et al., (1989) manipulated the perceptual quality of the stimuli in a target detection vigilance task. Subjects were required to detect a zero among other digits. Stimuli were presented in three conditions: low, moderate, and high degradation. The degraded stimuli taxed capacity by impairing sensitivity and yielding a vigilance decrement. Overall level of performance (hit rate) was lower in the high degradation condition, and the vigilance decrement (decrease in hit rate over time) was greater in the high degradation condition. Further, the pattern of decrease in sensitivity over time in the high degradation condition, and stability in sensitivity over time in the other two conditions, was exhibited in both age groups. Thus, as in the case of the adaptation mechanism, the allocation mechanism also appears to operate similarly in elderly and young adults,

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Development of Automaticity. Automaticity has been discussed separately under the

topic of capacity, but evidence relating automaticity to vigilance performance needs to be addressed in this section. Parasuraman (1987) reported preliminary evidence showing that young, but not elderly, adults reached a highly accurate asymptote relatively quickly in a vigilance task. Young adults performed at over 90% accuracy after ten sessions of training. In contrast, elderly adults performed at less than 75% accuracy following twenty sessions of training. Age differences in vigilance present at the first session of training did not decrease following repeated practice. These findings seem compatible with Fisk's research (summarized earlier) in showing a failure on the part of elderly adults to develop automaticity. Summary. In summary, there is no evidence of age decrement in either allocating

attention or the ability to adapt to different expectancies during vigilance task performance. Age differences are apparent in the level of physiological arousal during vigilance task performance and in the ability to develop automaticity during such performance. Either or both of these mechanisms may mediate age differences in sustained attention. Age differences are most often obtained when sustained attention is measured as overall performance (hit rate) on vigilance tasks. In such situations, age decrements appear to reflect diminished arousal. In addition, age deficits obtained when sustained attention is measured as a decrement over time appear to reflect an inability on the part of elderly adults to automatize performance. Recent psychophysiological and neuroanatomical evidence suggests that the neural substrate that supports attentional functioning diminishes with advancing age, as discussed in the next, concluding section of this chapter. Summary and Conclusions The research summarized in each section of the present chapter is consistent with the ubiquitous "complexity effect" in cognitive aging research (Birren, 1965; Cerella, 1985b; Salthouse, 1982, 1985) which refers to the proportional increase in age decrement under increasing task complexity. FIT is useful in providing a theoretical framework within which to cast the complexity effect and determine its locus in the sequence of processing. The fundamental question is: How to account for the obtained patterns of age effects? Two broad classes of explanatory constructs are available, one emphasizing specific components of information processing and the other emphasizing more generalized mechanisms. Within the class of specific constructs a distinction can be made between those that posit peripheral components of processing (e.g., encoding and response) versus those that posit central components (e.g., identification, comparison, strategies, etc.). Peripheral components seem unlikely candidates for explaining the different patterns of age effects reviewed here because although they exhibit some deterioration with age (as exemplified in the heightened intercepts of visual search functions, for example) neither feature encoding nor response execution interfered with the elderly's ability to optimize performance when feature extraction was emphasized. For example, both young and elderly adults' performance was unaffected by target eccentricity in the feature condition of our research reported in the section on selectivity; and both age groups exhibited comparable divided attention performance when targets were defined by a single feature in our research reported in the section on capacity. Together these

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findings suggest that the parallel feature extraction network is relatively unaffected by aging. The other set of specific constructs, i.e., those involving central components of processing, might be partitioned into those positing strategy differences versus those positing even more specific processes, such as identification, localization, comparison, or some other such process. The evidence against a generalized age difference in strategic processes is quite compelling. In nearly every qualitative comparison of performance elderly adults perform in ways that are consistent with young adults. Thus, for example, in conjunction search both age groups exhibit evidence of an initial parallel parsing of the display and both age groups appear to engage a serial self-terminating scanning strategy. Similarly, in focused attention, both age groups show a cost of unattended targets and both age groups appear to process both sources of information in parallel under divided attention. In certain circumstances older adults may place greater emphasis on accuracy rather than speed but this is the exception rather than the rule. The evidence bearing on more specific central processing components is mixed. On the one hand, certain specific mechanisms such as attention shifting and divided attention, appear to be intact among elderly adults, and thus are poor candidates for explaining age deficits in performance. Divided attention situations are problematical for the elderly only insofar as they are more complex compared with focused attention situations. Likewise, shifting attention in and of itself imposes no undue burden on the elderly except insofar as it involves shifts through cluttered displays under conditions requiring identification rather than simple detection. On the other hand, certain other specific processing components may be viable candidates as mediators of age deficits in performance. For example, the research involving spatial cuing is consistent with the notion of a localization deficit (see Plude & Hoyer, 1985) but it is consistent with a deficit in comparison as well because the spatial cue restricts comparison to a single item. Further research is needed to determine which, if either, of these (or other) specific mechanisms can account for age decrements in feature integration. The other class of explanatory constructs offers more generalized accounts of the complexity effect. Two of these have held particular sway over cognitive aging research in the past two decades: Generalized slowing (Birren, 1965; Cerella, 1985b; Cerella, Poon, & Williams, 1980; Salthouse, 1982; 1985) and reduced attentional capacity (Burke & Light, 1981; Craik, 1977; Craik & Byrd, 1982; Hoyer & Plude, 1980; 1982). According to the generalized slowing account, aging is characterized by diminished speed of processing which impairs each and every stage in the sequence of information processing. Sometimes a distinction is made between slowing ratios for peripheral versus central processing components (e.g., Cerella et al., 1980) but the main argument is based on the observation that some 90% of RT variance associated with age effects is accounted for by a simple linear model with a slowing factor in the range of 1.3 to 1.7 depending on the nature of task demands. As noted in our analysis of divided attention effects, the obtained slowing ratio for the complexity effect was 1.7, a value that is entirely consistent with the generalized slowing account. Despite its wide appeal, however, the general slowing model has difficulty accomodating age-equivalent performance, for example, in attention shifting (Madden, 1990; Plude et a]., 1984) and concurrent tone detection during visual search (see Madden, 1986).

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Futhermore, the generalized slowing model is based exclusivelyon performance speed and not performance accuracy. As noted earlier, a complete delineation of age effects requires coordinated consideration of both sources of information. The competing alternative posits reduced attentional capacity as the mediator of age deficits in performance. According to this perspective, aging is characterized by a diminution of processing resource (or capacity) which, in turn, compromises performance because of the proportionately greater demand made by a given task on the resources available to older compared with young adults. Within the FIT framework, this would mean that the resource(s) subserving feature integration processes are depleted with advancing age, thus yielding progressively larger age decrements as greater demand is placed on feature integration. Because they require no attentional resource feature extraction processes exhibit relatively little age decrement. One major obstacle for this class of explanatory constructs, however, is the failure thus far to articulate precisely what the attentional resource is and how best to measure it (Salthouse, 1982, 1985). Until such specification is accomplished, it may be impossible to distinguish between generalized slowing and attentional resource accounts of cognitive aging. The FIT framework has proven useful for assessing the effects of aging on attentional selectivity and capacity. By partitioning visual processing into relatively age-insensitive feature extraction components and age-dependent feature integration components, a clearer understanding of the effects of aging on visual information processing is realized. It has provided a bridge for reconciling discrepant findings concerning the status of divided attention ability in later life and may prove profitable for developing an age-related theory of complex task performance. This latter objective may be facilitated by coupling this theory with recent notions concerning the deterioration of the neural substrate upon which cognitive processes are carried out (see, for example, Cerella, 1990). Such an approach to cognitive aging would explicitly recognize the interactive nature of the three aspects of attention reviewed here, i.e., selectivity, capacity, and arousal. The arousal component has been conspicuous in its absence in the general cognitive aging literature. In reviewing recent evidence bearing on the psychophysiology of aging, Woodruff (1982) noted several patterns of age effects that have important ramifications for the cognitive neuropsychology of aging. For example, studies involving the P3 waveform of the averaged evoked potential have corroborated the central slowing that pervades behavioral research (see also, Marsh & Thompson, 1977). In addition, studies involving contingent negative variation (a term that denotes certain waveforms of relatively long duration) in conjunction with neuropsychological and neuroanatomical data, indicate selective age decline in the frontal areas of the brain which are believed to modulate attention. It is also noteworthy that studies involving brain stem auditory evoked responses indicate little age impairment of peripheral neural pathways, suggesting (as do the behavioral data) that the majority of age decrement occurs in the central cortex. Importantly, these kinds of functional measures correlate more highly with behavioral changes in aging than do structural measures, such as brain mass or brain atrophy.

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Aging and Cognition: Menial Frocesses, SelJ Awareness and Interwntions - ELI ene A. huelace (Editor] 0 Elseuier Science Pubbwrs B.V. flVorth-Hollandl. 1990

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135

Age-Related Wicifs in Cognitive-Motor Skills Noreen L. Goggin University of North Tam George E. Stelmach University of Wuconsin

This chapter will consider the psychomotor changes that occur with advancing age and will specifically focus on age-related declines in the preparation and execution of motor skills. It has been well-documented that older adults tend to become slower in their performance of motor skills. The major measures utilized to document these findings have been reaction time (RT) and movement time (MT). The first part of the review will present data that document the cognitive-motor deficits found in R T and MT measures in older adults. These data demonstrate planning deficits in both the preparation and the maintenance of preparation of movements, difficulty in programming or reprogramming a response, and control problems in the execution of movements as revealed by kinematic and kinetic analyses. The second part of the review will present hypotheses and theories which account for the slowing that occurs with advanced age. These hypotheses and theories suggest that older adults set different criteria for responding and/or adopt different strategies (e.g., emphasis on accuracy, cautionary behavior, monitoring of responses) than young adults in order to successfully perform a movement. Thus, it appears that the slowing in older adults is general in nature and cannot be linked to a specific deficit since many mechanisms show deterioration with age. Numerous data have been published in recent years describing the slowing in performance that accompanies advanced age. This observed slowing certainly has a n important effect upon daily activities of older adults. Salthouse (1985) has suggested that simple daily living tasks may be hampered. It has also been suggested that the observed slowing of older adults often prevents success in work settings (Welford, 1977). Most importantly, however, is the fact that, more likely than not, an older adult will suffer a damaging fall by the time he/she reaches 80 years of age (Stelmach & Worringham, 1985). The data that are presented in Table 5.1 provide some idea of the slowing which occurs with age in both RT and MT mechanisms. The slowing is observed in both simple and choice RT, programming and reprogramming conditions, premotor time, and MT in a variety of tasks. Thus, it is quite important to determine how age-related deficits affect the initiation and execution of motor skills. It may then become possible to determine ways of delaying or preventing the age-related declines that occur in psychomotor performance.

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Table 5.1

Reaction Time and Movement Time (MS) Deficits With Age ~~

Criterion measure

Clarkson ( 1978) Total RT Premotor lime Motor time

Jordan & Flabbttl (1977) Total RT

Condition

Young

Older

Simple choice Simple choice Simple choice Simple choice

216 24 7 129 151 96 124 130

261 307 162 194 98 112 164 170

21 24 26 28 13 17 32 31

Simple choice

531 977

582 1294

10 32

250 270 280

325 360 380

36

185 193 187

225 215 230

22 11 23

Choice Choice

860 1180

1370

59

1220

3

Simple Simple Simple

174 109 65

228 153

31 40

75

15

07

O/O

Difference

Larisk & Sfelmach (1982)

Total R T

MT

Szafran (1951) Total RT MT Weiss (1965) Total RT Premolor time Motor time

Probabilily 80% 50°/o 20010 Probability 8 0 010 50O/o 20%

30 33

Note: The column labeled % difference represents the additional time required by older adults expressed as a percent of the time for young adults. (Table is from Stelmach & Goggin (1988), "Psychomotordecline with age"in W.W.Spirduso & H.M.Eckert (Eds.) Physical activiry in aging. Champaign, IL: Human Kinetics. Copyright 1989 by the American Academy of Physical Education. Reprinted by permission.)

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Slowness of Reaction Time (RT) Mechanisms Reaction time (RT) is defined as the time interval from the presentation of a stimulus until a response is initiated. The RT interval is thought to be a reflection of the time required for cognitive or central processing. Welford (1984b) suggests that the slowing or impairment that appears with advanced age is the result of central rather than peripheral mechanisms. Therefore, it becomes necessary to manipulate measures that RT is believed to be dependent upon. These manipulations may provide clues as to which central processes are most affected by advanced age. Response Preparation One such manipulation which has received a great deal of attention is the provision of advance information to determine whether older adults do not fully prepare or prepare poorly for an upcoming response. Gottsdanker (1980a, 1980b, 1982) has performed a variety of experiments to determine how response preparation is affected by age. Two of these studies (1980a, 1982) used a simple RT task consisting of pressing a button when an auditory tone was heard to study advance preparation in adults aged 18-93. It is thought that as more information is provided in advance, RT's will decrease. Gottsdanker (1980a) manipulated response preparation and found only small differences between young and older adults in RT when preparation was easy. When preparation was difficult or impossible to attain, RT lengthened considerably among older adults (Gottsdanker, 1982). Additionally, Brinley (1965) has proposed that the disproportionate slowing and higher error rate in older adults are due to the difficulty in maintaining control of preparation, or what he termed "cognitive set". Strauss, Wagman, and Quaid (1983) utilized older women (65-88 years) as subjects and found that they were faster in simple RT if the preparatory interval was regular, or predictable. However, when the preparatory interval was regular but long (13 s), older adults were much slower in RT. Gottsdanker (1980b) has also suggested that older adults have difficulty in maintaining optimal preparation over long preparatory intervals. Gottsdanker concludes that the slowing that appears with advanced age is not attributable to general processes in the central nervous system, but rather to ineffective control processes. Stelmach, Goggin and Garcia-Colera (1987) also studied response preparation using a precueing technique. The main question they attempted to answer was whether the processes involved in the planning of a response could be responsible for the observed slowing in older adults. Subjects in three age-groups (young, middle, older) received either no information (corresponds to uncertainty level 3), partial advance information about arm, direction, or extent (uncertainty level 2), partial advance information about two of the parameters (uncertainty level l),or information about all three parameters (uncertainty level 0). It was hypothesized that older adults would specify the individual movement parameters much more slowly than young or middle-aged adults. Their results indicated that older adults were disproportionately slower than young adults and that this increase was a result of the uncertainty associated with producing a response (see Figure 5.1). The figure indicates differences between young and older adults in the slopes of the regression lines due to uncertainty level. The figure also

138

N.L.Goggin and G.EStelmach

indicates that both groups of subjects were able to use advance information since RTs were faster when full information was provided about the upcoming response. In addition, older subjects were much slower in specifying individual movement parameters of arm (78 ms vs. 23 ms for older and young subjects, respectively), direction (77 ms vs. 34 ms), and extent (60 ms vs. 18 ms). The authors concluded that the observed slowing in older adults is due to response uncertainty and response specification. Goggin, Stelmach, and Amrhein (1989) examined age differences in the preparation of movements and restructuring of movements. The authors were concerned that age differences or effects in movement preparation in previous research might be inaccurate due to long preparation intervals and long precue stimulus viewing time. Young and older adults performed movements to contact targets in which preparatory intervals were variable and ranged from 500 ms to 2 s. The results indicated that younger and older adults were differentially effected by shorter precue stimulus intervals and variable preparatory intervals. That is, older adults were less efficient at restructuring movements when the combination of precue viewing time and the preparatory interval were short. Therefore, it appears that the length of the precue stimulus display and preparatory interval are critical in age-related experiments on movement preparation and planning. Response Selection A second manipulation which can implicate central mechanisms in the observed slowing in older adults involves varying and/or examining response selection processes. Response selection is thought to reflect decision- making processes or capabilities. Simon and Pouraghabagher (1978) conducted experiments to determine the effect of aging on response encoding and response selection processes in a choice R T task. Two groups of subjects (young, age 18-25; older, age 60-87) performed a Sternberg Additive Factor task in which they pressed one key upon detecting a visual “OI and another key if they detected an “X”. The purpose of using a Sternberg Task is to indicate that the information processing system contains various stages with different dependent variables and different experimental manipulations that can be utilized for each stage. The stages examined most frequently in a Sternberg Task are stimulus encoding and response organization. Simon and Pouraghabagher’s (1978) results indicated that older adults were significantly slower than young adults and that the primary locus of slowing in older adults was in response encoding processes rather than response selection processes.

Fozard (1981) claims that in choice RT tasks, older adults may require more time to identify the stimulus signal, to decide which response goes with which signal, or possibly a combination of the two. Salthouse and Somberg (1982a) examined the slowing with age that is apparent in speeded tasks. They were interested in determining whether the slowing was of a general nature, or whether the slowing could be attributed to a specific process(es). Two groups of subjects (18-28 and 64-81) performed a task in which they were to determine whether a target number had been previously presented in a list of 1 to 4 digits. Complexity was varied as well as the clarity of the stimulus presentation. The results were analyzed using Sternberg’s

139

Aging and Cognitive-Motor Skills

500

450

400

-

RT 350 msec Y 300

250 A Young Group RT = 222.55 + 2 8 . 9 ~

Middle Group RT = 266.70 + 3 6 . 7 ~ Elderly Group RT = 327.22 + 5 7 . 7 ~

200

0

1

2

3

UNCERTAINTY LEVEL Figure 5.1. Regression analysis of RT for young, middle-aged, and older adults as a function of uncertainty level. Plot is adapted from Stelmach, Goggin, & Garcia-Colera, (1987) "Movement specificationtime with age,"Erperimenrulaging reseurch, 13, 39-46. Reprinted by permission of Beech Hill Enterprises, Inc

additive factor method. Their results indicated that older adults were slower and more detrimentally affected by all the manipulations. It was concluded that the slowness that accompanies age is not isolated in one stage, but is more general, with the entire central nervous system experiencing slowing. Strayer, Wickens, and Braune (1987) conducted an experiment to determine what stage of the information processing system is affected by advancing age. A Sternberg task was performed by two groups of subjects (ages 20-26 and 53-65) to examine RT, error rates, and P300 latency of event-related brain potentials. The slowing accompanying advanced age was due to stimulus encoding, memory set size, and speedaccuracy trade-off. It was concluded that young and older adults did not differ in

140

N.L.Go@t and G.E.Slebnach

central processing speed or capacity, but that the differences were due to slowing in perceptual-motor processes. Clark, Lanphear, and Riddick (1987) conducted an experiment to determine if the slowness of behavior in older adults could be reversed. These authors were specifically interested in improving the response selection component of information processing. A videogame playing task was chosen because of the novelty and the necessity for speeded response selection. Two groups of senior citizens participated in the experiment. One group played videogames for 7 weeks while the other group did not. All subjects were pretested on a compatible and incompatible two-choice R T task and posttested at the end of the 7 week period. Their results indicated that the older subjects who practiced videogames improved in videogame playing, but more importantly, improved their ability to select a response quickly. The authors attribute the improved performance in response selection to information processing strategies used by older adults in the videogame playing. Response Programming

Another manipulation by which central mechanisms can be elucidated in the slowing that occurs with age is the study of the programming or reprogramming of movements. Larish and Stelmach (1982) and Stelmach, Goggin, and Amrhein (1988) conducted experiments to determine if young and older adults program and reprogram movements in a similar manner. In both experiments subjects were provided advance information that was either accurate (programming situations) or inaccurate (reprogramming situations); subjects could prepare their movements in advance, but sometimes they had to change their planned movement at the time of the signal to respond. Larish and Stelmach (1982) found that older subjects were slower in both R T and MT, but did not find older adults to be more affected in the reprogramming conditions. They concluded that older adults plan, prepare, and reprogram movements similar to the way young adults do and suggested that these processes are slower, but remain intact with age. Stelmach et al. (1988) found that older adults were proportionately slower in RTs when they had to reprogram a movement. Additionally, if older subjects had to perform a short movement, they were unable to prepare this type of movement as proficiently as young subjects. It was concluded that older adults show impairment at lower levels, such as preparing a specific movement parameter, but that higher level processes, such as reprogramming a movement plan, remain intact. Response Complexity

One final manipulation thought to affect R T is the examination of response complexity, although there has not been a great deal of research in this area with older adults. Response complexity is most often described as the difficulty of the task. Griew (1959) conducted an experiment to examine stimulus choice (simple R T or choice RT), response complexity, and task continuity. At the appearance of a stimulus, young (20-26 years) and older (50-67 years) subjects moved a stylus from a home position to the appropriate target and back again. Response complexity was manipulated by varying directional changes which had to be made in the response.

Aging and Cognitive-MolorSkills

141

The results indicate that response complexity had a differential effect on the RTs of older adults. Jordan and Rabbitt (1977) have suggested that complexity only has a disproportionate effect on older adults during the early stages of practice or learning. Falduto and Baron (1986) had young and older women perform card sorting and manipulated task complexity by increasing the number of stimuli as well as making the sorting procedure dependent on a second stimulus. The results indicated that older women were slower, with greater age differences as task complexity was increased. Stelmach, Amrhein, and Goggin (1988) conducted an experiment to determine if older adults have deficits in coordinating two hands while performing a motor task. The complexity of the task was manipulated by utilizing a unimanual task, a symmetrical bimanual movement task (same movement distance), and a n asymmetrical bimanual movement task (different movement distance). They hypothesized that older adults would experience more difficulty in synchronizing the asymmetrical movements because of the added complexity. The results indicated that older adults had longer RTs and MTs than young adults in all movement conditions and that this age differential increased as task Complexity increased (see Figure 5.2). Older adults showed poor preparation for short movements (relative to long movements) in all movement conditions. Additionally, older adults were less able to coordinate the initiation of bimanual movements and were unable to compensate for this movement initiation asynchrony in order to terminate the movement in a simultaneous fashion. Kelso, Southard, & Goodman (1979) found young subjects to be able to terminate asymmetrical movements simultaneously. Stelmach, Amrhein, and Goggin (1988) showed older adults to be more affected by levels of task complexity as well as displaying control problems in coordinating two different movements. In summary, it is clear that older adults are able to utilize advance information to prepare movement, but are less efficient than young subjects in preparing. Factors such as the complexity of preparation, the length of the preparatory interval, and the time for previewing the advance information need to be considered when designing experiments with older adults. Additionally, it appears that the slowing observed in older adults cannot strictly be isolated to response selection processes, but rather a combination of factors. It also appears that deficits in response selection processes can be reversed or improved with practice on speeded tasks that have a strategy component. Finally, it is clear that young and older adults are differentially affected by manipulation of response complexity. Slowness of MT (Movement Execution) Mechanisms

Movement time (MT) is defined as the time interval from the initiation of movement until movement completion. The relationship between MT and advancing age certainly has not received as much attention as that between age and R T deficits. One possible reason for this is the difficulty of providing appropriate manipulations to study MT effects. Many studies in the literature on aging suggest that older adults are slower in response execution (Welford, 1977, 1982). However, there is a serious lack of data beyond the fact that older adults are simply slower. Figure 5.3 indicates the overall slowing that occurs with advanced age in movement execution tasks. As can be seen, there is a dramatic increase in the time taken for a complex task such as handwriting as one ages, with performance of tapping increasing slightly.

N.L.Goggin and G.E.Stelmach

142

500 -

zsir 400 0 C

3 Q)

300

c m

g

200

-

TASK AND AGE EFFECTS

Elderly

C

R

Young

0

Elderly

(--

Young

Q-----

_------

_______

-

.

T

MT

+”

-

-

-

-

-

-

-

-

-

-

Q

100

I

Figure 52. Effect of age on task complexity in RT and MT mechanisms.

Speed-Accuracy Relationships One manipulation which has been studied is the relationship between aging and the speed-accuracy trade-off. In this type of paradigm, the accuracy of a response is manipulated to determine how MT is affected. It has been suggested (Welford, 1982) that a part of the slowing which occurs with advanced age is due to the emphasis older adults place on accuracy of response. Additionally, Salthouse (1988) has suggested that older adults prefer to operate with a bias placed upon accuracy. However, a question to be asked is whether older adults are slower on purpose because of the need to achieve accuracy, or whether the accuracy is a result of slower cognitive motor abilities (Botwinick, 1984). Salthouse (1979) performed a series of experiments to test this speed-accuracy hypothesis, and found that subjects trade speed for accuracy in a near linear fashion. He suggested that older adults do regard accuracy as being more important; however, the observed age differences in speed were not totally accounted for by a speedaccuracy trade-off. Salthouse (1985) proposes that older adults are more concerned about making an error, and thus prefer to slow down to a movement speed where the chance of making an error is unlikely. Older adults are likely to commit errors of omission, that is, they fail to respond, for fear of being wrong (Botwinick, 1984). It

Aging and Cognitive-Motor Skills

220

143

I I I

200

-

180

-

160

-

Birren (1955) p Birren & Botwinick ! (1951) (words) /

(3.18)

-

/

Birren & Botwinick (1951) (digits)

I

140

-

120

-

TAPPING Miles (1931a) Nicholson

orted in Thorndike, et

100

I

20

I

I

I

I

I

30

40

50

60

70

al., 1928)

Age in Years Figure 53. MT deficits with age in simple tapping tasks, and a more complex task of handwriting. Plot is from Salthouse, (1985), "Speed of behavior and its implications for cognition," in J.E.Birren Kc K.W.Schaie (Eds.) Handbook of the p~ycholog~ of aging, volume 2 (p.402), New York: Van Nostrand Reinhold. Reprinted by permission.

may be that older adults simply need more certainty in a response before they actually make a decision about what to do. Smith and Brewer (1985) utilized a serial, four-choice RT task and found that older adults performed more slowly in RT and MT, and had lower error rates than young adults. These results were obtained in both correct RTs and in trials where errors were made. Botwinick (1984) implies that older people may be slower on purpose because they value accuracy. Thus, the results suggest an increased emphasis upon accuracy of response in older adults.

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Welford, Norris, and Shock (1969) conducted an experiment in which they examined the speed-accuracy trade-off in aiming movements. Subjects ranging in age from 20 to 79 years performed movements between two targets; the movement amplitude and target width was varied similar to the amplitude-target width relationships reported by Fitts (1954). They found that the factors determining the sense of position and of distance covered decline more with age than the decisional processes responsible for accurately contacting a target. It was also suggested that older adults compensate for the loss of speed by increasing accuracy in performing movements. Warabi, Noda, and Kato (1986), had subjects in their experiment (ages 20-67 years) follow a shift in target position with a laser beam spot operated by hand as quickly and as accurately as possible, They were interested in examining the speed-accuracy trade-off in motor responses among different age groups. Their results, unlike those found by Welford, et al. (1969), indicated that the process of moving quickly to the target remained unchanged with age, while closed-loop functions, or the processes responsible for accurately contacting a target changed significantly with age. Singleton (1954) performed an experiment in which subjects (20-70 years of age) moved a lever in four directions from a center position in a continuous fashion. Their results suggest that the overall differences in movement speed in older adults are due to the decision-making processes about altering or changing a movement rather than just producing slower movements. Singleton (1955) had young and older subjects move a lever from side to side as quickly as possible. Older adults were slower than young adults primarily at the points where they had to change direction, with less difference in the middle portion of the movement. Singleton's results indicated that older adults have difficulty in making decisions about movement changes. Welford (1977) describes how more complex movements are affected by increased age. In studies that examined handwriting, it was found that older adults were slower in writing speed, tracing figures, and copying digits. In fact, beyond age forty subjects were more concerned about accuracy rather than speed. In continuous movements or serial tasks, older adults tend to be as fast as young adults in the time spent producing the movement, but are much slower in the stationary portion, or the time deciding which movement to produce next (Singleton, 1954). Response Characteristics None of the experiments reported above which examined MT effects and/or those associated with speed-accuracy trade-offs in older adults considered how the movement was performed. It is quite possible that the movement patterns or response characteristics (kinematics or kinetics) produced by older adults in executing a movement are very different from those of young adults. Murrell and Entwisle (1960) conducted an experiment based on the premise that normal movement is composed of three phases: an acceleration phase, a steady speed, and a deceleration phase. They hypothesized that the kinematic patterns of older people might be different from those of young people. Two groups of subjects (ages 20-25 & 60-65) were required to make simple movements in a choice RT condition. An "ultra high-speed chrono-cyclographic technique" was utilized to examine the movement patterns. The results indicated that older subjects accelerated less rapidly than young subjects and displayed a short deceleration phase even though there were no significant MT differences.

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Goggin and Stelmach (1989) performed an experiment to determine if movement corrections would occur "online" or while the movement was being performed as advance information was varied. If subjects completely utilize advance precue information prior to movement initiation (during the R T interval) then h4T and the movement patterns should be unaffected. Based on the work of Murrell and Entwisle (1960) it was also hypothesized that the movement patterns (e.g., velocity, acceleration) between older and young adults would be different. Two groups of subjects (21-27 years and 66-77 years) performed long or short movements to the left or right on a digitizing tablet which recorded the horizontal and vertical coordinates of movement. Subjects were either provided no advance precue information, information about the direction (left or right) or the extent of the movement (long or short), or specific information about both parameters of movement. The data obtained by Goggin and Stelmach (1989) indicate that older subjects were significantly slower in RT, MT, and time to peak velocity. They also displayed smaller peak velocity and had a prolonged deceleration phase. Both groups of subjects carried the advance precue information into the execution phase of the movement; that is, higher peak velocity was produced when full advance information was provided, although MT was not affected by precue condition. The most important finding, however, was the result that suggested that older adults have difficulty in scaling velocity for long and short movements when amplitude is the only criterion of movement. Older subjects showed little difference in peak velocity between long and short movements, but yet took more time to reach peak velocity in long movements; the results for young subjects were just the opposite in that they showed little difference in time to peak velocity, with huge differences in peak velocity between long and short movements. Draper and Johns (1964) found similar results in patients with Parkinson's disease, in which they were unable to scale their velocity to match the amplitude of the movement. Corcos, Gottlieb, and Agarwal (1988) and Gielen, van den Oosten and Pull ter Gunne (1985) report that young subjects produced larger velocities in long movements but showed little difference in the time to reach peak velocity for long and short movements. Although Goggin and Stelmach (1989) indicated that older adults had difficulty scaling velocity to match movement amplitude, a concern was expressed that no component of spatial accuracy was present in the movements; subjects simply had to produce a 5 or 10 cm movement. Goggin (1989) conducted experiments to determine if this result would be apparent when movements had a high component of spatial accuracy. Young (22-30 years) and older (67-80) subjects produced aiming movements according to Fitts (1954), in which movement amplitude and target diameter were manipulated. The overall results of these experiments were similar to those reported by Goggin and Stelmach (1989) and indicated that older adults produced less peak velocity, displayed a slower rate of velocity production, and showed a prolonged deceleration phase. These effects can be seen in Figure 5.4. The finding of prolonged deceleration phases in older adults is different from the result found by Murrell and Entwisle (1960); it is possible that the accuracy components in the tasks of Goggin and Stelmach (1989) and Goggin (1989) caused older adults to spend more time in deceleration. In addition, older adults showed differences in peak velocity between long and short movements, but these differences were not as great as those produced by young subjects. Overall, these results seem to support the contention that older

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Time (ms) Velocity and acceleration profiles of young and older adults. Plot is from Goggin, N.L.(1989). "Akinematic analysis of age-related differences in the control of spatial aiming movements,"unpublished doctoral dissertation, University of Wisconsin. Figure 5.4.

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adults are less efficient in scaling velocity to match movement amplitude. Thus, the difficulty older adults have in scaling velocity to match movement amplitude may indicate a problem in controlling muscular force. Examination of the control of force (kinetics) is another means of studying the response characteristics, or how a movement is being performed. Vrtunski, Patterson, and Hill (1984) had young (mean age=28 years) and older (mean age=65 years) subjects perform button-press responses in order to examine the muscular forces applied and the respective time intervals. They found that young subjects to be more efficient in generating a response. In fact, older adults displayed less braking ability than young adults in the release segment of the button press and suggest that the synchronous relationship between agonist and antagonist muscles is poor in older adults. Vrtunski and Patterson (1985) used young and older adults (mean age of 27.7 years and 62.8 years, respectively) to perform an isometric button-press response to examine forces in a choice R T situation. The authors hypothesized that older adults would show changes in the velocity component (discontinuities) of a response. These investigators found that older adults showed discontinuities in the force trajectories when compared to young adults, with even more discontinuities present in more complex tasks. Stelmach, Teasdale, Phillips, and Worringham (1989) conducted an experiment to examine force production in young (18-31 years) and older adults (61-73 years) and Parkinson's disease patients (65.7 years). The subjects were instructed to produce a percentage (15, 30, 45, or 60) of their maximal force as quickly as possi?,le without making any corrections. The authors examined the relationship between force and force variability and whether this relationship was different in older adults and Parkinson's patients. The results indicated that older adults were significacely more variable (9.4%) than young adults (8.4%) in relative peak force. These data can be observed in Figure 5.5. Older adults also showed slightly more irregularities than young subjects in their rate of force production and took longer to reach peak force (vertical bars on graph). In addition, at higher force levels (higher percentage of maximal force) older adults show more variability in producing force. Causes of Slowing in Older Adults A variety of reasons, theories, and models have been postulated for the observed slowing which occurs with advanced age. Salthouse (1985) has described several plausible hypotheses which may account for the slowing that is apparent in older adults in both RT and MT mechanisms. One hypothesis, called input and/or output rate, postulates that there may be a slower rate of information transmission in older adults from the brain to the muscles or slowness in the rate from the perceptual mechanisms to the brain. This hypothesis deals with peripheral problems, or more specifically, with the sensory or motor delays that occur in information processing.

Several predictions to test the validity of this hypothesis have been proposed. One prediction states that the difference in time between young and older adults should be constant with increases in task complexity if the input and output rate remains constant (Cerella, 1985). As was mentioned previously, a few experiments (Griew, 1959; Jordan & Rabbitt, 1977) suggest that complexity has a different effect on young and older

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200 ms Figure 5.5. Point-to-point force variability profiles of young and older subjects (solid line indicates 15% of maximal force, dashed line 30%, dotted line 45%, and dashed-dotted, MI%) the vertical bars indicate the temporal location of the mean peak force. Plot is adapted from Stelmach, G.E.,Teasdale, N., Phillips, J. & Worringham, CJ., (1989), “Forceproduction characteristics in Parkinson’s disease,” Erpermenful bmin research, 76, 165-172. Reprinted by permission.

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adults. A second prediction states that if the slowing in older adults is due to input/output, then bypassing these processes should reduce differences between young and older adults. To test this prediction it is necessary to utilize EMG to fractionate RT, or P300 brain evoked potentials. Studies which have utilized these measures (Strayer, Wickens, Braune, 1987; Weiss, 1965) have shown that older adults are significantly slower in the central components of these measures than young adults, which suggests that age differences found in RT and RT components are primarily central in nature. Salthouse (1985) argues that the input/output explanation can only explain a small proportion of the slowing that occurs with advanced age because of the simplicity of tasks and the strength of stimuli used. Thus, it appears that peripheral factors only contribute slightly to the slowing in older adults (Cerella, 1985; Welford, 1985). A second hypothesis that Salthouse (1985) proposes for the slowing in older adults is what is termed "software differences." These differences are qualitative in nature and include inefficiency of control processes and strategy differences. There is a wealth of data (mentioned previously) to support the validity of this hypothesis with respect to advance preparation, task complexity, and speed-accuracy tradeoffs utilized as manipulations to examine the software differences. However, Salthouse suggests that software differences (due to an inability to isolate strategy differences) contribute only somewhat to the slowing in older adults.

The most plausible hypothesis suggested by Salthouse (1985) for the slowing with advanced age deals primarily with central mechanisms and is termed "hardware differences." It is suggested that the time required to perform internal operations in older adults is much greater than in young adults. One of the major theories which addresses the slowing in older adults is the "Neural Noise Hypothesis" proposed by Welford (1981, 1982, 1984% 1988). It is believed that processes in the neural system of an older adult take more time due to increases in the levels of "internal neural noise." This neural noise can occur in afferent pathways and in the brain (Welford, 1988). Welford proposes that the signal-to-noise ratio in the central nervous system in older adults is much smaller than the ratio in young adults due to increased noise, weaker signals, or a combination of the two. Welford (1982, p. 163) states "if the signal-to-noise ratio is low, performance is inaccurate, either because low signal levels cause errors of omission or because noise causes errors of commission." In order to compensate for the low signal-to-noise ratio in older adults, more time is taken to examine the signal and average out the noise (Welford, 1985). Thus, with the additional time (or unlimited time), older adults can have similar signal-to-noise ratios as young adults and can be as accurate in performance of a task. Welford (1982) also suggested that low signal-to-noise ratios can account for many central changes that occur with advanced age, including memory losses. Smith (1980) proposed that a signal is disturbed by neural noise with all possible responses being activated. With increased time and practice, the response that correctly matches the signal becomes focused. A buildup of activation occurs in the central nervous system, and when the activation reaches the critical level, the appropriate response is selected. Thus, when working with older adults it would be wise to use signals which are powerful to elicit the appropriate response.

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Another hypothesis which attributes the slowing in older adults to hardware differences is known as the Birren Hypothesis (as discussed in Botwinick, 1984 & Salthouse, 1980). This hypothesis states that the onset of all neural events becomes slower with advanced age; older adults can use the same behavioral processes, but are simply slower. A related hypothesis is the cycle-time hypothesis which attributes the slowing in older adults to all stages of the information processing system, indicating that slowing is a generalized phenomenon. Salthouse and Somberg (1982a) and Simon and Pouraghabagher (1978) utilized Sternberg methodology and have found that the time to perform within all stages of the information processing system increases with age. Bashore, Osman, and Heffley (1989) performed a meta-analysis of studies which utilized R T and P300 latency of the event-related brain potential to demonstrate slowing with advanced age. The results of their analysis indicate there is a general, proportional decline in cognitive processing which affects all processes. In addition, there may be some processes that also show additivity in decline. The authors suggest that in future experiments that use R T to infer speed of mental processing, P300 latency should also be examined since it is uninfluenced by measures which affect R T (e.g., complexity, speed-accuracy relationship). In spite of the evidence that supports the explanations proposed by Salthouse (1985), there is some data to suggest that older adults operate in a closed-loop fashion. Rabbitt (1982) has proposed that control mechanisms are either predictive or reactive. Predictive mechanisms would be those with which a person has the ability to initiate complicated motor patterns in anticipation of the changes which are about to occur. Reactive mechanisms would be those that utilize feedback to initiate complicated patterns of movements. Young adults are able to use either predictive or reactive mechanisms when necessary. Older adults lose the option of utilizing predictive mechanisms, and must rely on reactive mechanisms, thereby foregoing the typical number of control strategies which are available for use. It has also been suggested that older adults are slower in their performance of tasks because of a need for visual feedback and a reliance upon vision. Szafran (1951) performed an experiment in which he had young and older adults (ages 20-60) locate a target with a pointer under two conditions. In one condition, subjects had full vision of the limb and the display. In the second condition, subjects could see the display, but not the limb. Older subjects were significantly slower in initiating movements, but more importantly, older subjects were disproportionately slower than young subjects in locating the target when vision was not present. Additionally, older subjects turned their heads and bodies and even made postural adjustments in an attempt to help locate the target. Teasdale, Stelmach, and Bruenig (1989) performed an experiment in which they examined the relationship of vision and proprioception on postural stability. It was hypothesized that a reduction in these sensory mechanisms would have a detrimental effect on older adults. Older adults did show more postural instability than young but only when both vision and proprioception were altered. Thus, it appears that older adults may have more of a reliance on visual information than young people in producing and controlling movement especially if other sensory mechanisms are altered or absent.

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Additional causes of slowing in older adults have been proposed by Welford (1981, 1982, 1984a). He attributes the slowing to cautionary behavior, an increased emphasis upon accuracy of response, and monitoring of responses. He has suggested that older adults set higher criterion levels for responding (in terms of signal detection). Setting a high criterion level serves several purposes for an older adult. First, it may allow the older adult to be more accurate in his/her responding. Second, older adults may set higher criteria as a means of being deliberately cautious. An older adult may attempt to accumulate more data before acting or responding as a way of avoiding errors. Third, they may set a higher criteria, because they are not able to adjust or shift the criterion from moment to moment in order to create a balance between speed and accuracy. In fact, Rabbitt (1979) suggests that older adults will react faster and faster until they commit an error, and then will slow down significantly. Older adults are likely to keep their speed within a small range just below where an error may occur, which Rabbitt (1979) calls a "margin of safety". One suggestion proposed by Welford (1981) for the disproportionate slowing in older adults is that they spend more time monitoring their responses than young adults. Often times it is deliberate, but often it is unconscious or involuntary. It is a problem especially in continuous tasks; if an older adult is monitoring a response just made he/she is likely to be unable to process another signal. Welford (1982) suggests that older adults are less able to "suppress"the monitoring of responses and have a higher frequency of monitoring. The monitoring of responses may be an aspect of caution in the older adult's behavior and could be due to a decreased capacity. Bashore, et al. (1989), Botwinick (1984), and Cerella (1985) have proposed several models to account for the slowing of cognitive-motor processes in older adults. The first model is an Additive model in which it is proposed that age produces a constant increment in RT as task complexity is increased. According to this model, much of the slowness in RT is due to sensory-motor components, or transmission of information. A second model is the Multiplicative model which states that age causes a constant but proportional increase in RT when task complexity is increased. In other words, age interacts with task complexity which implicates the central nervous system in the observed slowing. The third model is called the exponential model in which age causes increases in RT as task complexity increases, but this increase is not at a constant rate. This model is not very different from the multiplicative model, and is limited by the number of levels of task difficulty that can be utilized. As was mentioned above, it is unlikely that the Additive model can account for the observed slowing since the model deals with peripheral mechanisms. Much of the data presented above support the Multiplicative model since the model deals with central mechanisms. Exceptions to Slowing with Age Although it is quite clear that RT increases with advanced age, there is data that suggest that increases in RT can be reduced or even prevented. The first line of evidence is that with additional practice older adults can reduce their RTs. In fact, Spirduso (1982) has reported that practice is much more beneficial to the older adult and both Salthouse (1985) and Welford (1988) have suggested that there is a decline in RT in older adults with extended practice. Rabbitt (1981) has found that the choice RTs of older adults rise more sharply as a result of task difficulty, but only in the very

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early stages of practice. He has suggested that with extended practice, older adults can show invariance in choice RT over a range of task difficulties. A second line of evidence indicates that if older adults are allowed to make vocal, rather than manual responses the RT differences between young and older adults are not as disproportionate (Salthouse, 1985; Salthouse & Somberg, 1982~).Finally, the third line of evidence indicates that maintaining physical fitness can help older adults. Spirduso (1982) has found that physical exercise can minimize and/or slow the rate of decline in some cognitive and physiological functions. She found that older adults who were physically trained were much faster than age-matched adults who were untrained. Although these three areas have been shown beneficial to the older adult, the age differences in RT between young and older adults cannot completely be eliminated (Fozard, 1981).

Conclusion The data presented demonstrate that the deficits in psychomotor performance of older adults are partially caused by slowness in RT and MT. Older adults display slowing in response preparation, response selection, response programming, and response execution. In addition, older adults display different response characteristics (e.g., velocity, acceleration, force) from young adults. Thus, it is likely that central mechanisms are responsible for the slowing, and that most of the slowing is general in nature and cannot be linked to a specific deficit. In fact, Salthouse (1985, p. 422) states "the controversy now is not whether peripheral or central mechanisms are responsible for the slowing-with-age phenomenon but rather which particular mechanism is the most fundamental." To fully understand the decline in motor performance that occurs with age it is necessary to examine factors such as physiological, social, health, and behavioral which all contribute to the aging process. In order to appropriately study motor behavior, according to Mortimer, Pirozzolo, and Maletta (1982), ones needs to consider the interaction of sensory and motor factors as well as the tasks being performed. References Bashore, T. R., Osman, A, Heffley, E. F. (1989). Mental slowing in elderly persons: A cognitive psychophysiological analysis. Psychology and Aging, 4, 235-244. Botwinick, J. (1984). Aging and Beltmior. New York: Springer Publishing Co. Brinley, J. F. (1965). Cognitive sets, speed and accuracy of performance in the elderly. In A. T. Welford & BE.en (Eds.) Behavior, Aging and the Nervous System (1-36). Springfield, I L Charles Thomas Publishers. CerelIa, J. (1985). Information processing rates in the elderly. PJrchological Bulletin, 98, 67-83. Clark, J. E., Lanphear, A. K., & Riddick, C. C. (1987). The effects of videogame playing on the response selection processing of older adults. Journal of Gerontology, 42, 82-85. Corcos, D. M., Gottleib, G. L., & Agarwal, G. C. (1988). Accuracy constraints upon rapid elbow movements. Journal of Motor Behavior, 20, 255-272. Draper, I. T., & Johns, R. J. (1964). The disordered movement in parkinsonism and the effect of drug treatment. Bulletin of Johns Hopkins Hospital, 115, 465-480.

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Falduto, L. L. & Baron, A. (1986). Age-related effects of practice and task complexity on card sorting. Journal of Gerontology, 41, 659-661. Fitts, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47, 381-391. Fozard, J. L. (1981). Speed of mental performance and aging: Costs of age and benefits of wisdom. In F. J. Pirozzolo & G. J. Maletta (Eds.) Behavioral Assessment and Psychopharmacology (pp. 59-94). New York: Praeger Publishers. Gielen, C. C., van den Oosten, K., & Pull ter Gunne, F. (1985). Relation between EMG activation patterns and kinematic properties of aimed arm movements. Journal of Motor Behavior, 17, 421-442. Goggin, N. L. (1989). A Kinematic Analysis of Age-Related Differences in the Control of Spatial Aiming Movements. Unpublished doctoral dissertation, University of Wisconsin. Goggin, N. L., & Stelmach, G. E. (1989). A kinematic analysk of precued movements in young and elderly subjects. Manuscript submitted for publication. Goggin, N. L., Stelmach, G. E., & Amrhein, P. C. (1989). Effects of age on motor preparation and restructuring. Bulletin of the Psychonomic Society, 27, 199-202. Gottsdanker, R. (1980a). Aging and the use of advance probability information. Journal of Motor Behavior, 12, 133-143. Gottsdanker, R. (1980b). Aging and the maintenance of preparation. Experimental Aging Research, 6, 13-27. Gottsdanker, R. (1982). Age and simple reaction time. Journal of Gerontology, 37, 342-348. Griew, S. (1959). Complexity of response and time of initiating responses in relation to age. American Journal of Psychology, 72, 83-88. Jordan, T. C., & Rabbitt, P. M. (1977). Response times to stimuli of increasing complexity as a function of aging. British Journal of Psychology, 68, 189-201. Kelso, J. A. S., Southard, D. L., & Goodman, D. (1979). On the coordination of twohanded movements. Journal of Experimental Psychology: Human Perception and Performance, 5, 229-238. Larish, D. D., & Stelmach, G. E. (1982). Preprogramming, programming, and reprogramming of aimed hand movements as a function of age. Journal of Motor Behavior, 14, 322-340. Marteniuk, R. G., MacKenzie, C. L., Jeannerod, M., Athenes, S., & Dugas, C. (1987). Constraints on human arm movement trajectories. CanadianJournal of Psychology, 41, 365-378. Mortimer, J. A., Pirozzolo, F. J., & Maletta, G. J. (1982). Overview of the aging motor system. In J. Mortimer, F. Pirozzolo, & G. Maletta (Eds.), Aging Motor System (pp. 1-6). New York: Praeger. Murrell, K. F., & Entwisle, D. G. (1960). Age differences in movement pattern. Nature, 185, 948-949. Rabbitt, P. (1979). How old and young subjects monitor and control responses for accuracy and speed. British Journal of Psychology, 70, 305-311. Rabbitt, P. (1981). A fresh look at changes in reaction times in old age. In D. Stein (Ed.) The Psychobiology of Aging: Problems and Perspectives (pp. 425-442). New York: Elsevier, North Holland.

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Rabbitt, P. (1982). Breakdown of control processes in old age. In T. M. Field, A. Huston, H. C. Quay, L. Troll, & G. Finley (Eds.) Review of Human Development (pp. 540-550). New York John Wiley & Sons. Salthouse, T. A. (1979). Adult age and the speed-accuracy trade-off. Ergonomics, 22, 811-821. Salthouse, T. A. (1980).. Age and memory: strategies for localizing the loss. In L. W. Poon, J. L. Fozard, L. S. Cermak, D. Arenberg, & L. W. Thompson (Eds.) New Directions in Memory and Aging (pp. 47-65). New Jersey: Lawrence Erlbaum Associates. Salthouse, T. A. (1985). Speed of behavior and its implications for cognition. In J. E. Birren & K. W. Schaie (Eds.) Handbook of The PsychorogY of Aging (pp. 400426). New York Van Nostrand Reinhold. Salthouse, T. A. (1988). Cognitive aspects of motor functioning. In J. A. Joseph (Ed.) Central Determinants of Age-Related Declines in Motor Function (pp. 33-40). New York: Annals of the New York Academy of Sciences, Vol. 515. Salthouse, T. A., & Somberg, B. L. (1982a). Isolating the age deficit in speeded performance. Journal of Gerontology, 37, 59-63. Salthouse, T. A., & Somberg, B. L. (1982b). Time-accuracy relationships in young and old adults. Journal of Gerontology, 37, 349-353. Salthouse, T. A., & Somberg, B. L. (1982~).Skilled performance: The effects of adult age and experience on elementary processes. Journal OfExperimental Psychology: General, 111, 176-207. Simon, J. R., & Pouraghabagher, A. R. (1978). The effect of aging on the stages of processing in a choice reaction time task. Journal of Gerontology, 33, 553-561. Singleton, W. T. (1954). The change of movement timing with age. British Journal of Psychology, 14, 166-172. Singleton, W. T. (1955). Age and performance timing on simple skills. International Association of Gerontology (Ed.) Old Age in the Modem World (pp. 221-231). London: Livingstone. Smith, G. A. (1980). Models of choice reaction time. In A. T. Welford (Ed.), Reaction Times. London: Academic Press, pp. 173-214. Smith, G. A., & Brewer, N. (1985). Age and individual differences in correct and error reaction times. British Journal of Psychology, 76, 199-203. Spirduso, W. W. (1982). Physical fitness in relation to motor aging. In J. A. Mortimer, F. J. Pirozzolo, & G. J. Maletta (Eds.) Aging Motor System (pp. 120-151). New York Praeger. Stelmach. G. E.. Amrhein, P. C., & Goggin, N. L. (1988). Age differences in bimanual coordination. Journal of Gerontology, 43, P18-23. Stelmach, G. E., Goggin, N. L., & Amrhein, P. C. (1988). Aging and reprogramming: The restructuring of planned movements. Psychology and Aging, 3, 15 1-157. Stelmach, G. E., Goggin, N. L., & Garcia-Colera, A. (1987). Movement specification time with age. Experimental Aging Research, 13, 39-46. Stelmach, G . E., Teasdale, N., Phillips, J., & Worringham, C. J. (1989). Force production characteristics in Parkinson's Disease. Experimental Brain Research, 76, 165-172. Stelmach, G. E., & Worringham, C. J. (1985). Sensorimotor deficits related to postural stability: implications for falling in the elderly. Clinics in Geriatric Medicine, I , 679-694.

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Strauss, M. E., Wagman, A. M., & Quaid, K. A. (1983). Preparatory interval influences on reaction time of elderly adults. Journal of Gerontology, 34 55-57. Strayer, D. L., Wickens, C. D., & Braune, R. (1987). Adult age differences in the speed and capacity of information processing: 2. An electrophysiological approach. Psychology and Aging, 2, 99-110. Szafran, J. (1951). Changes with age and with exclusion of vision in performance at an aiming task. Quarterly Journal of Experimental Psychology, 3, 111-118. Teasdale, N., Stelmach, G. E., & Bruenig, A. (in press). Postural sway characteristics of elderly persons under normal and altered visual and support surface conditions. The Journals of Gerontology: Psychological Sciences. Vrtunski, P. B., & Patterson, M. B. (1985). Psychomotor decline can be described by discontinuities in response trajectories. International Journal of Neuroscience, 27, 265-275. Vrtunski, P. B., Patterson, M. B., & Hill, G. 0. (1984). Factor analysis of choice reaction time in young and elderly subjects. Perceptual and Motor Skills, 59, 659676. Warabi, T., Noda, H., & Kato, T. (1986). Effect of aging on sensorimotor functions of eye and hand movements. Experimental Neurology, 92, 686-697. Weiss, A. D. (1965). The locus of reaction time change with set, motivation, and age. Journal of Gerontology, 20, 60-64. Welford, A. T. (1977). Motor performance. In J. E. Birren & K. W. Schaie (Eds.) Handbook of the Psychology of Aging (pp. 450-496). New York: Van Nostrand Reinhold. Welford, A. T. (1980). Memory and age: A perspective view. In L. W. Poon, J. L. Fozard, L. S. Cermak, D. Arenberg, & L. W. Thompson (Eds.) New Directions in Memory and Aging (pp. 1-17). New Jersey: Lawrence Erlbaum Associates. Welford, A. T.(1981). Signal, noise, performance, and age. Human Factors, 23, 97109. Welford, A. T. (1982). Motor skills and aging. In J. Mortimer, F. Pirozzolo, & G. Maletta (Eds.) Aging Motor System (pp. 152-187). New York: Praeger Publishers. Welford, A. T. (1984a). Between bodily changes and performance: some possible reasons for slowing with age. Experimental Aging Research, 10, 73-88. Welford, A. T. (1984b). Psychomotor performance. In C. Eisdorfer (Ed.) Annual Review of Gerontology and Geriatrics (pp. 237-273). New York Springer Publishing Co. Welford, A. T. (1985). Changes of performance with age: An overview. In N. Charness (Ed.) Aging and Human PerJormance (pp. 333-369). New York: John Wiley & Sons. Welford, A. T. (1988). Reaction time, speed of performance, and age. In J. A. Joseph (Ed.) Central Determinants of Age-Related Declines in Motor Function (pp. 1-17). New York: Annals of the New York Academy of Sciences, Vol. 515. Welford, A. T., Norris, A. H., & Shock, N. W. (1969). Speed and accuracy of movement and their changes with age. Acta Psychologica, 30, 3-15.

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6 Aging and Metacognitions Concerning Memory Function Eugene A. Lovelace Alfred University

The term metacognition is generally taken to refer to an individual’s cognition about cognition, what the person knows of cognitive processes employed in the encoding, storage, retrieval and use of information. The focus, as Wellman (1985) has noted, is not on how the person carries out these processes but rather on what the person knows and believes about these processes. People think about thinking, and this selfawareness includes beliefs about how, and how well, their mental activities function. They also attempt to assess current states and processes, i.e., they monitor their mental functions. The present chapter reviews some recent work addressing the general question of whether there are age-related changes in these metacognitive processes in late adulthood. The literature on metacognition and aging deals predominantly with metamemory, one‘s beliefs and knowledge about one‘s memory functioning. As originally conceived (e.g., Flavell, 1971; Flavell & Wellman, 1977) metamemory centered on knowledge about memory, e.g., knowledge about the memory demands of particular tasks and knowledge of the strategies appropriate to particular tasks. In current use metamemory is commonly taken to include memory beliefs or the notion of one‘s self-efficacy with respect to memory functioning. As Dixon and Hertzog (1988) have noted, metamemory “refers less to another level of processing than to another measurable variety of knowledge, beliefs, and perceptions”(p. 301) about memory and cognitive processes. (For further discussion of the relationship of metacognition to other types of cognitive processes see Perlmutter et al., 1987).

As Hultsch, Hertzog, Dixon and Davidson (1988) observed, there have been two broad approaches to the study of metamemory. The first has taken an individualdifferences psychometric approach and has relied heavily on questionnaires to elicit self-reports concerning the person‘s knowledge and beliefs about memory. The second approach has focused on self-assessment of memory function in specific memory tasks. This has typically entailed relating specific performance estimates by an individual to that person’s actual memory performance employing quasi-experimental paradigms. Hultsch et al. (1988) and Dixon (1989) provide good recent reviews of the questionnaire approach (see also Cavanaugh & Green, Chapter 7 of this volume). The present chapter, while covering both approaches, provides more extensive consideration of research employing the experimental approach.

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Metamemory is not a unitary thing, rather one needs to think of "varieties" of metamemory (Hultsch et al., 1988; Lovelace & Marsh, 1985). Hultsch et a1.(1988) suggested that there are four dimensions of metamemory: A ) Memory Knowledge -factual knowledge about memory tasks, processes and strategies; B ) Memory Monitoring -- self knowledge about current memory contents, states, and use; C) Memory Self-efficacy -- beliefs the individual holds about his or her memory abilities, strengths, or weaknesses; 0 ) Memory-related Affect -- emotional states generated by or associated with memory tasks. While the last of these four receives little consideration here, issues of age-related change in memory knowledge, memory monitoring, and memory self-efficacy will be considered in some detail. Specifically, the present chapter provides a review of key concepts and a selective review of the literature concerning age-related differences in the following areas of metacognition: (a) self-reports regarding perceptions of one's memory functioning; (b) pre-performance predictions concerning one's level of performance in particular memory task situations; (c) the monitoring of on-going memorial processes, including feelings-of-knowing, judgments-of-knowing, correctness of one's response, and reality monitoring. This review is followed by a brief section on relationships among the various types of metamemory. Knowledge of processing strategies is also a legitimate area of metacognition. Strategic processing is only briefly touched on in the present chapter since it receives substantial coverage elsewhere in this volume (Kotler-Cope & Camp, Chapter 8). Self-reports: Memory Perceptions

Loss of memory function has long been a part of the stereotype of changes characterizing old age. Memory complaints are known to be widespread among the aged, and they find memory failures more upsetting (e.g., Cavanaugh, Grady & Perlmutter, 1983; Hellebrandt, 1980; Hulicka, 1982; Poon, 1985). A great many interview and questionnaire studies have provided a nearly universal finding that the aged themselves confirm the stereotype, i.e., they report that they perceive themselves to have experienced a reduction in memory ability with aging (e.g., Dobbs & Rule, 1987; Herzog & Rodgers, 1989; Hultsch, Hertzog, & Dixon, 1987; Lovelace & Twohig, 1990; Perlmutter, 1978; Williams, Denney, & Schadler, 1983). Furthermore, older adults are more likely to believe in age-related memory decline than are younger adults (Dixon & Hultsch, 1983; Perlmutter, 1978).

As Cavanaugh and Perlmutter (1982) observed, "the notion of metamemory rests on the assumption that memory is amenable to inspection and analysis by the memorizer" (p. 12). A primary method of studying metamemory has been the use of self-reports. While many researchers have used questionnaires of unknown reliability or validity, several have devised systematic questionnaires to evaluate perceptions of memory function in adulthood and a few have taken steps to establish the reliability of their instruments (see Gilewski & Zelinski, 1986, for a recent review). Most notable in this regard are the Metamemory in Adulthood questionnaire (MIA) by Dixon and Hultsch (1983, 1984), the Metamemory Questionnaire of Zelinski, Gilewski and Thompson (1980) reduced to the Memory Functioning Questionnaire (MFQ) by Gilewski, Zelinski, Schaie, and Thompson (1983), the Inventory of Memory Experiences by

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Herrmann and Neisser (1978) reduced to the Shortened Inventory of Memory Experiences (SIME) by Herrmann (1979), and the Memory Self-Efficacy Questionnaire (MSEQ) by Berry, West and Dennehey (1989). All have been used to study agerelated changes in reported memory function. Some of the literature using these instruments, particularly the MIA, will now be considered (see also Hertzog, Dixon, & Hultsch, in press-b; Cavanaugh & Green, Chapter 7, this volume). There is a substantial body of research, primarily by David Hultsch, Roger Dixon, and Christopher Hertzog, employing the MIA instrument. From factor analyses of large sets of data, these researchers have identified 7 subscales or dimensions of metamemory being tapped by the items on this questionnaire. These subscales have been labelled Strategy, Task, Achievement, Anxiety, Capacity, Locus, and Change (Hultsch et al., 1988). In addition, two higher order factors have been identified (Hertzog, Dixon, Schulenberg, & Hultsch, 1987). The first of these they called a memory knowledge factor and the second a memory self-efficacy factor. While questions measuring the anxiety dimension loaded on both of the higher-order factors, the remaining six dimensions were separable. The three scales that loaded on the memory knowledge factor were Strategy (knowledge and use of information about one's remembering abilities), Task (basic memory processes and how most people would perform), and Achievement (the perceived importance of performing well on memory tasks). This higher-order factor of memory knowledge was found to be ageinvariant; the aspects of metamemory tapped by these three subscales appear to show little if any change with aging. For the higher-order factor of memory self-efficacy, however, they found significant age differences in the weights associated with this factor. The three subscales that were found to load on this factor were Capacity (prediction of memory performance), Locus (perceived personal control over memory ability), and Change (perception of susceptibility of memory to long-term decline). The Change subscale showed the strongest relationship to age; across seven samples they found that age accounted for between 13 and 37% of the variance in the Change subscale. A parallel finding is that the most consistent age difference on the MFQ is for a subscale called Retrospective Functioning which taps the person's perceptions of how their memory compares to their own memory when they were younger. In general the MIA appears to be somewhat more sensitive to age differences in self-perceptions of memory function than the MFQ (Hultsch et al., 1988). The data from several studies make it clear that "perceptions of change (decline) in memory and perceptions of reduced control over memory are more salient for the elderly" (Hultsch et al., 1988, p.71). While a more recent analysis (Hertzog, Hultsch & Dixon, 1989) has raised doubts about the stability of the memory knowledge factor, the memory self-efficacy factor has been consistently supported. Further, the Change subscale was found to load more heavily on this selfefficacy factor for older adults. Analyses of responses for large samples of individuals who completed both the scales of the MIA and the MFQ have provided evidence of convergent validity (Hertzog et al., 1989; Hertzog, Dixon, & Hultsch, in press). Both questionnaires yielded a higherorder factor, these factors showing a high degree of convergence (correlations near

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1.0); this common higher-order factor has been identified as global self-efficacy. (See Cavanaugh and Poon (1989) for a report of similar comparisons of the SIME and MIA instruments.) An assumption underlying much of the initial interest in metamemory was that increased knowledge about memory tasks or strategies could be expected to permit the individual to perform better in memory tasks, i.e., skilled memory performance may be dependent on metamemorial knowledge (Flavell & Wellman, 1977). Such a view predicts strong correlations between metamemory measures and actual performance in memory tasks. Self-report indices of memory function typically have not been found to show a strong relationship to actual memory performance, correlations of about .20 to .30 being common (Herrmann, 1982). Several authors have argued, however, that the perceptions the elderly hold about any change in memory function are of considerable interest, even if they do not represent veridical estimates of actual memory performance, since these perceptions may have a substantial impact in determining which behaviors an individual will choose to engage in or how they will perform memory tasks (e.g., Hultsch et al., 1987; Lovelace & Twohig, 1990; Poon, Fozard, & Treat, 1978; Thompson, 1980).

Gilewski and Zelinski (1986) suggested that what one finds regarding age differences in self-reported memory ability may depend on the way the question is asked. Cavanaugh (1986-87) has provided clear support for this idea. Comparing memory reports for 50 young and 50 older adults (Mages = 20 vs. 69) he found consistent agerelated decrements for general questions (e.g., How good do you feel your memory is?) but not for specific questions regarding a certain type of content (e.g., How often do you forget names?). The largest age differences were for questions assessing perceived change in memory over time (akin to the MIA subscale for Change and the MFQ scale of Retrospective Functioning). It seems likely that attempts to determine one global measure of perceived memory function have less utility than assessing perceived function for certain domain- or taskspecific memory abilities. Chaffin and Herrmann (1983) found self reported memory function to show increases, no change, or decreases with aging depending on the domain of activity. Hultsch et al. (1988) have noted that any self report measure of memory ability may be predictive of performance on some memory tasks but not others. They suggest stronger relations between metamemory measures and memory performance will be seen in memory tasks with higher ecological validity, at least for questionnaire measures of metamemory since these typically ask about everyday memory experiences. For example, self-reported memory function might predict text recall to a significant extent but not recall of lists of unrelated words (cf. Cavanaugh & Murphy, 1986). Related to this task-specific notion is the idea that a particular instrument might be a better predictor for one age group than another. For a text recall task Dixon and Hultsch (1983) found the pattern of correlations of subscales to memory performance to change with age. Similarly, Cavanaugh & Poon (1989), in regression analyses to determine the predictive value of component scales of the MIA questionnaire, found

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that although the Capacity subscale was a predictor for both young and old adults, the Strategy subscale was a predictor only for the young and the Change subscale only for the elderly. The relationship of the global self-efficacy factor to memory performance, and to other metamemory measures which tap more specific predictions of memory performance for particular tasks, has recently been explored by Hertzog et al. (in pressa). Their findings will be discussed in a later section of this chapter following description of some metamemory tasks involving those more specific predictions. It should be noted that while the concept of self-efficacy has been treated here as a cognitive variable, it may also be considered as a social or personality variable (Hultsch et al., 1988; Lachman, 1986). An increasing interest in the interface of cognitive and personality factors has recently developed (Cavanaugh et al., 1985; Cavanaugh, Morton & Tilse, 1989; West, Boatwright & Schleser, 1984; see also Gold and Arbuckle, Chapter 13, this volume). Lastly, with respect to self-reports of memory function, it is interesting to note that while questionnaires and interviews have revealed a nearly universal perception among the aged that memory ability declines with aging, very few healthy older adults feel that it poses any handicap in their everyday functioning (e.g., Lovelace & Twohig, 1990; Sunderland, Watts, Baddeley, & Harris, 1986). As Hultsch et al. (1988) observed, "although older adults perceive that their memory has declined from previously higher levels of functioning, they do not view this loss as a 'problem', either because their current level of functioning conforms to what they expect, or because incidents of forgetting do not seriously interfere with achieving everyday goals" (p. 85). It is clear from a substantial body of literature that the majority of older adults perceive themselves to have experienced some loss of memory function. The degree of this perceived change has shown modest correlations with memory performance measures, especially for more ecologically valid memory tasks such as text recall. Quite apart from the predictive validity of these self-reports for actual memory performance, the belief of loss of function may change the motivation of the individual to become engaged in memory-demanding situations. Specific Performance Predictions Consequences of Variation in Task or Processing This section deals with metacognitions based on knowledge of how memory works in general, as opposed to judgments of one's own current memory status for particular items. The variables to be considered here might be said to belong to the "memory knowledge" higher-order factor of the MIA (Hultsch et al, 1987) and to involve predominantly what Cavanaugh (1989) has called "systemic awareness".

In her dissertation research, Perlmutter (1978) found that most of her subjects "gave evidence of a broad range of knowledge about memory. For example, ...they thought it easiest to remember related, organized, interesting, understandable, concrete materials" (p. 336). These reports about how memory would depend on variation in

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materials were consistent with the effects of the variables in the memory literature and were unrelated to age group (20 vs. 60 years). It might be noted, however, that the form of the questions in her instrument was such that a ''yes'' bias in responding to questions would produce this apparent knowledge of memory effects. The Task subscale of the MIA contains items of this sort, and as noted above, this scale is part of the higher order factor of memory knowledge that was found to be unrelated to age. C o p e (1985) had young and old adults predict the number of words they would free recall from a list of 16 words if they were given a particular duration of presentation of the entire list. That duration was systematically manipulated. Both young and old adults showed their sensitivity to this task change by adjusting their predictions with changes in the duration of study time in a way that was appropriate, i.e., that paralleled later actual recall levels for various study times. Thus there appear to be no age differences in understanding the memorial consequences of variation in study time. Laboratory studies have shown that young adults may be insensitive to some task or processing variations which have substantial memory consequences. For example, Zechmeister and Shaughnessy (1980) had college students make ratings on a 7-point scale to predict later free recall of a list of words. The task variations included having words that were presented once vs. twice, and for those presented twice having the two occurrences be adjacent (massed practice) or distributed in the list. Prediction ratings were, as they should be, higher for words presented twice than for those presented only once. Ratings on massed vs. distributed practice items, however, showed predictions of better recall of the massed practice items whereas memory performance showed the usual superiority of distributed practice. Another example of insensitivity to variation in task processing concerns the effect of varying the orienting task so as to induce different levels of processing. Cutting (1975, Exp 1) had college students listen to a list of words and either check to see if the word contained the letter E (a low-level structural analysis) or make a pleasantness judgment for each word (a higher-level semantic analysis). After performing the orienting task for a list of 24 words subjects were asked to rate themselves on a 10point scale for "how well they might do if asked to recall the word that they just heard (p. 156). Actual performance on a subsequent free recall test showed the usual levelsof-processing effect: 61% of the words were recalled by those who performed the pleasantness judgment and only 38% by those who did E-checking. The subjects' predictions of performance ratings, however, showed no sensitivity to these memorial consequences of orienting task, in fact the E-checkers gave a slightly higher mean rating (5.6 vs. 5.2). In a subsequent replication, Cutting (1975, Exp 2) showed that Echeckers tended to overestimate their performance while pleasantness judges underestimated their recall level. A recent study by Shaw and Craik (1989) extends this line of investigation to compare young and old adults. Sixty words were presented to young (M = 19) or old (M = 69) adults who were to rate each word on an 11-point scale for the likelihood of success in later recalling that word. When each word was presented one of three types of descriptors was provided to influence the type of encoding processes for that item; these same descriptors later served as recall cues. For 20 of the words the encoding

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cues were letter cues (e.g., "starts with ic: ice"), for 20 rhymes (e.g., "rhymes with nice: ice"), and for the remaining 20 category/descriptive (e.g., "something slippery: ice"). There were substantial differences in recall performance as a result of different cue types (roughly 30%, 50% and 80% recall for letter, rhyme and category cues, respectively), but this performance difference was poorly predicted by both age groups. Even in this within-& design, where one might expect to maximize sensitivity to the manipulation, both young and old were relatively insensitive to the memory consequences; predicted levels of recall were about 55%, 63%, and 65%, respectively. This parallels Cutting's report of overestimate of recall performance following a structural orienting task and underestimation following a semantic task. The relative recallability of specific words was quite accurately monitored by both age groups; this type of monitoring will be discussed below in a section on Judgments of Knowing. A similar lack of sensitivity to the potent memorial consequences of using the strategy of interactive imagery for encoding word pairs was reported by Rabinowitz, Ackerman, Craik, and Hinchley (1982). Overall, recall was about 30% greater in an interactive imagery condition than for a condition in which subjects were simply instructed to remember the word pairs. Both young and old, however, gave performance predictions that differed very little for these two conditions. In that study both age groups were (equally) sensitive to a property of the materials, giving higher ratings for pairs that had stronger normative relatedness of the two words. That aspect of their study is described in more detail in the section on Judgments of Knowing. Studies involving variation of task or processing seem to show that people are more sensitive to memory-performance-related properties of the materials than they are to the memory consequences of different strategies or encoding operations. There is, however, evidence that young adults may be better able to accurately assess the relative memorial consequences of two types of processing once they have attempted recall of items processed in these ways. Brigham and Pressley (1988) conducted a study of the ability of younger and older adults to monitor the relative effectiveness of two types of memory encoding strategies, and to apply the results of that monitoring in their choice of a future strategy. While issues of age differences in spontaneous strategy use are dealt with elsewhere in this volume (Kotler-Cope & Camp, Chapter 8) the study of Brigham & Pressley is considered here since it is directly relevant to a question of age difference in ability to monitor differential memorial consequences of different strategies. The selection of an effective strategy depends on a general knowledge of conditions appropriate for particular strategies plus some "on line" metacognitions about the success one is experiencing in attempting to use a particular strategy (Pressley, Borkowski, & O'Sullivan, 1984; Pressley, Borkowski, & Schneider, 1987). Brigham & Pressley (1988, p.250) describe the general procedure for this study as follows: The paradigm involves exposing subjects to two strategies for accomplishing a learning task. One of the strategies is a more potent mediator of learning than the other, although the strategies are selected so that subjects are not aware of the relative potency of the two procedures at the outset of the experiment. Subjects

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are asked to make judgments about degree of learning with the two strategies, either before they have a chance to practice with the two techniques or after they study materials with the two techniques and take a test on the content studied. Subjects are also asked to select one of the two techniques for use on a similar task in the future. Monitoring of relative strategy potency is inferred if there is little awareness of relative strategy effectiveness before strategy practice, but substantial understanding following practice and testing that one of the strategies produces better performance than the other. The memory task in this study was to remember one-word definitions (common word equivalents) for rare English words. The two strategies were the generally more powerful “keyword strategy and a less powerful semantic context strategy. Forty young nonstudents (M = 33) and 40 older adults (M = 72) used the keyword strategy on half the items in a 22-word list and the semantic context strategy for the other half. The memory test was self-paced, item by item, and followed immediately after study of the list of words. Half the subjects at each age level judged the relative effectiveness of the two strategies both before and after performing the memory task while the other half made these assessments only after the memory test. More specifically, each subject indicated which strategy they would prefer to use to learn a list of vocabulary words, and how many of 11 words studied by each strategy in a 22-word list they believed they would recall. For those in the after-test only condition, these judgments were said to apply to another list to be learned. For those in the before-and-after condition, the number they would recall out of 11 on the after test was a postdiction, i.e., they were to indicate how many they believed they had gotten correct of the words studied by each strategy. Overall recall performance showed main effects of age, young recalling more than old, and of strategy, keyword items were recalled better than those studied using semantic context strategy. There was also a significant age X strategy condition interaction which resulted from a larger effect of strategy for the young than the old. The superiority of recall in the keyword condition to that in the semantic context condition was also significant, however, when considering data for the aged group alone. Before using the two methods, neither young nor old adults judged the keyword strategy to be more effective and neither group showed a preference for one strategy over the other. Predictions of the number that would be recalled were not related to strategy or to age. Thus, before engaging in the memory task, both groups were insensitive to the differential memorial consequences of these two strategies based simply on a description of each. For postdictions, however, the young gave significantly higher numbers for the keyword than for the semantic context strategy (Ms = 6.55 and 3.05) whereas the older subjects showed no significant difference, and actually gave slightly higher numbers for the semantic context strategy (3.30 vs. 3.40). This indicates that the old failed to monitor the differential recallability of items depending on strategy. (Note that this is a global judgment after the recall test is completed, not a direct measure of ability to identify the encoding operations used for individual items.)

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Given the failure of the aged to discriminate the relative numbers of items recalled for the two strategies, one would expect that the older subjects would not show a preference for using the keyword method. Indeed, Brigham and Pressley report that whereas the young subjects showed a marked shift toward selection of the strategy which had in fact produced the better recall for them, the aged showed almost no shift toward selection of the more effective strategy. Thus, at least for these particular induced encoding operations, young were clearly better able than older adults to monitor the memorial consequences after attempted recall, and thus were able to select the more effective strategy for future use whereas the aged could not. In summary, people seem to be sensitive to the memory consequences of some task or processing variables (e.g., degree of associative relatedness of items, study time) while showing little awareness of the consequences of others (e.g., orienting tasks inducing different levels of processing, interactive imagery, massing or distribution of practice of repeated items). These effects, however are unrelated to age. Studies to date have shown the elderly to display sensitivity to such manipulations in the same fashion as young adults. The study by Brigham and Pressley, however, suggests that the aged may not benefit to the same extent as young from the opportunity to learn the memorial consequences of different encoding operations. It is important to see if this finding can be replicated and extended. If so, we need to ascertain the source of this age-related deficit in strategic learning. Pre-performance Estimates of Memory

Here we are interested in the predictions individuals make about absolute performance levels they will show on a memory task which they have not yet attempted, This is an area in which it has been reported that there are age-related differences in metacognitive ability (e.g., Bruce, Coyne, & Botwinick, 1982; Coyne, 1985; Lovelace & Marsh, 1985; Murphy, Sanders, Gabriesheski, & Schmitt, 1981). In a study widely cited as evidence of age differences in metarnemory, Murphy et al. (1981) presented young and elderly adults with a series of pictures of common objects. Having established each subject’s span length for such pictures, they presented series that were subspan (span minus 2), span, or supraspan (span + 2) in length. Study time was set by the subjects themselves; they were instructed to study each series until they felt they would be able to recall it without error. The old gave themselves less time to study the series than did young subjects, yet performed more poorly. Both young and old lengthened their study time with longer lists, but the old provided themselves with proportionately less additional study time for longer lists. Whereas the young recalled more than 96% of the items correctly for all series lengths, the aged subjects dropped from 98% for subspan series to about 75% correct for supraspan series. Taking the relative study times as indices of the perceived difficulty of the task, the older adults seemed not to properly gauge the increase in memory demand with increasing series length. In this same experiment, Murphy et al. also had subjects make predictions of their memory span before attempting the picture series span task. The young predicted a larger span than did the old, means of 7.0 vs. 5.4. The overall error in prediction was found to be of similar magnitude for the young and old, however the direction was

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different. Despite predictions that were higher for the young than the old, the young underestimated and the old overestimated their actual performance on the picture span task. Actual mean spans were 7.7 for the young and 4.7 for the old, an age difference which is larger than that found in most studies of age and memory span. In a second experiment Murphy et al. included a forced-time condition in which the elderly were presented each series for the average time that the young had studied that series length in the first experiment. When forced to take the additional study time the aged performed as well as the young had in Experiment 1. Murphy et al. concluded that the aged displayed a deficit in monitoring for recall readiness. It must be noted that this study has been criticized on several grounds (Salthouse, 1982), most notably because the basic finding that the elderly will take less time than young adults in preparation for recall has not been supported in other studies, e.g., Bruce et al. (1982); Perlmutter, (1978); Perlmutter, Metzger, Nezworski, & Miller (1981). Bruce et al. (1982) asked young (18 to 31), young-old (60 to 69), and old-old (70 to 79) adults to attempt to recall four 20-word lists, one list representing each of the possible combinations of words that were high or low in frequency and high or low in imagery. Before seeing the actual lists, participants were shown 4-item samples of all four types of lists. With those samples present before them they were asked to predict the number of words they believed they could recall from a 20-word list of each type given an unlimited study time. The four lists were then presented, one list at a time, with individuals allowed to set their own study times. These authors report no significant differences in study times, although the actual time data were not presented. They did find, however, that as age increased the predicted number exceeded actual levels of recall. Considering the absolute values of both predicted and actual recall, there was only a small, nonsignificant age effect for predicted number but a sizeable age effect for actual recall. For young, young-old, and old-old, respectively, the mean numbers predicted were 11.42,10.02, and 9.24, while the mean actual recall levels were 11.60, 8.12, and 6.60. The general finding has often been described as a tendency for the aged to overestimate their memory performance. It is probably more accurate to say that they often underestimate the task difficulty. The point is that the aged are often less likely to have had recent experience with a similar memory task, especially if the young adults are current college students and the memory task involves memory for verbal materials recently encountered. For this reason the aged may simply lack proper "calibration" with respect to the task difficulty (Lovelace & Marsh, 1985). Furthermore, to say that the aged overestimate their memory ability does not adequately characterize all the literature since the aged occasionally underestimate their performance (e.g., Berry, West & Scogin, 1983; Camp, Markley, & Kramer, 1983). One indication that the elderly are not overestimating their memory ability so much as underestimating the task comes from the comments made by older subjects serving in studies where they predict higher levels of memory performance than they actually demonstrate. Both Bruce et al. (1982) and Lovelace and Marsh (1985) note that many of the aged participants verbalized concern that their memory was poor or that it had

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declined. These feelings were often voiced prior to attempting the memory task, and having such a sense of lowered memory self-efficacy is not consonant with having high estimates of one's ability. If the problem is one of calibration of the task difficulty then one might expect that repeated experience with the task or the provision of a normative frame of reference for performance on a given task would diminish age differences in prediction accuracy. Regarding improved calibration due to prior experience with the task, Lachman, Steinberg and Trotter (1987) had 47 elderly adults (M = 70, range 61 to 89) predict the number of words they would recall from a shopping list of 10 items prior to seeing the actual items. Following attempted recall of the first list each individual predicted the number he or she would recall on a second list with a shift of the category of items from groceries to gifts or vice versa. The accuracy of predictions was greater on the second trial, predominantly due to a reduction of some subjects' overestimation of the number they would recall. The calibration of prediction here might well occur due to subjects monitoring the correctness of their own responses, and so having knowledge of how many they managed to recall out of 10 on their first attempt. Monitoring for correctness of one's own responses will be considered in a later section. Note that ability to d o such monitoring effectively provides the individual with a salient frame of reference for their predictions of future performance on that task. Hertzog et al. (1989) have recently reported data where a large sample of adults aged 20 to 79 years were asked to predict the number of items they would remember and were provided with a normative frame of reference for performance on the task. Two recall tasks were employed, free recall of a categorized word list and text recall. After each task was described, but before memory materials were presented, individuals were asked to indicate how many items (words or idea units) they would recall and were told "It may help you to know that on this task the average person is able to remember about ..." The normative value they were given was not empirically derived for that group on that task but was simply the 50% recall level, 15 of 30 words or 25 of 50 idea units. Under these conditions they found increasing age to be associated with lowered predictions of performance level for both word and text recall tasks. This contrasts with several studies (e.g., Bruce et al., 1982; Lovelace & Marsh, 1985) where predicted levels are unrelated to age but actual performance of the aged falls short of predicted levels. The provision of the frame of reference by Hertzog et al. resulted in agerelated differences in predicted levels of performance which were then properly related to the age-related differences in actual recall. They concluded that under such conditions of a fixed frame of reference to calibrate predictions there was no evidence of age differences in the accuracy of those predictions. Other aspects of their study will be discussed below in a section on Interrelations of the Varieties of Metacognition. In summary, studies have often shown age differences in the accuracy of preperformance predictions of amount that will be remembered. Most commonly the predictions vary little with age, but actual performance levels are greater for the younger adults. While this has often been referred to as a tendency for the elderly to overestimate their memory ability, the argument was made here that this represents a calibration problem in which the aged underestimate the difficulty of the task. When specific (albeit bogus) information was provided about normative performance levels

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for a task, the aged predicted lower levels of performance than did the young (Hertzog et al., in press-a); under these conditions the accuracy of the predictions was unrelated to age. Memory monitoring

The term memory monitoring was introduced by Hart (1965, 1967) in conjunction with his studies of the feeling-of-knowing. As it is used here it deals with the cognitive ability to assess and evaluate current memorial contents and processes. It covers tasks which have been characterized by Cavanaugh (1989) as involving "epistemic" and "online" awareness. The memory monitoring tasks to be discussed here are "feelings-ofknowing" (including the tip-of-the-tongue phenomenon), "judgments of knowing," correctness of one's response, and reality monitoring.

Feelings of knowing. This variety of monitoring entails making judgments about the existence of a memory trace for an item or event which cannot be brought to consciousness at the moment. In Tulving and Pearlstone's (1966) terms one is monitoring for the "availability"of a trace in secondary memory when there has been a failure of "accessibility", i.e., when the search/retrieval process has not brought the item back into primary memory. It is, then, a monitoring during the attempted recall of an item for the contents of a memory trace that is currently inaccessible. The subjective experience is not all-or-none, but can exist in varying intensities, the more intense being termed a "tip-of-the-tongue" (TOT) state. William James (1890) provided an excellent description of the very active nature of the subjective component of the TOT state, something which Brown and McNeill (1965) likened to the feeling one has when on the brink of a sneeze. The phenomenon was first made amenable to laboratory study by Brown and McNeill. By asking college students to provide the word in question when they were read the definitions of very low frequency English words, they were able to induce significant numbers of TOT states. They sought to learn what the rememberer, when caught in the TOT state, could tell about the target item for which they were searching. They found substantial consistency in the sorts of information available when in the TOT state. For example, the person can often recall correctly the initial letter of the word sought, the number of syllables and stress pattern of the syllables, and other words that are known to be similar in sound or similar in meaning. The most important result of Brown and McNeill's demonstration of this monitoring of the contents of an unrecallable memory was to make it very clear that even familiar discrete units such as words are probably best thought of as represented in memory by some sort of constellation of features or elements. A memory trace is not a unitary thing, and some features may be retrieved when others are not. Failure to be able to access the phonemic or orthographic features characterizes the usual TOT state (see Burke & Laver, Chapter 10, this volume). A parallel development was the work of Hart (1965, 1967) initiating the literature on "feelings-of-knowing"(FOK). The basic paradigm employed by Hart involved asking an individual to recall something, then having them judge the probability that they

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would be able to properly recognize the correct answer for any item not correctly recalled. These FOK judgments were followed by a recognition memory test. Thus Hart had individuals attempting to monitor the availability of inaccessible items, and he was able to gain an indication of the validity of those judgments. Such monitoring, while not perfect, shows substantial predictive power, and FOK judgments can be made which reflect varying strength of the monitored trace. Higher levels of confidence in one's judgment that a trace is available are generally associated with higher probability of recognition of the items. While it has been suggested (Cohen, 1989) that the events occurring in TOT and FOK states may tell us little about what happens in fast, direct accessing that occurs most of the time, it is still of interest to see whether the metacognitive processes characteristic of these states show age differences. Lachman, Lachman and Thronesbery (1979) provided evidence that the accuracy of FOK judgments for information in semantic memory shows little or no relation to age. Twelve adults at each of three age levels, young (19 to 22 years), middle-aged (44 to 53). and elderly (65 to 74), were asked to attempt to answer 95 factual questions on a variety of topics. For questions that the individual could not correctly recall, FOK judgments were made on a 4-point scale, then multiple choice tests were given. The FOK ratings had predictive validity, i.e., higher FOK ratings were associated with higher probability of correct recognition. The slopes of the function relating probability of correct recognitions to FOK ratings were very similar for the three age groups. The oldest subjects were as accurate as the young in making the metacognitive judgment about the relative likelihood of later recognition of various items not currently accessible to recall memory (see also Lachman & Lachman, 1980). Butterfield, Nelson, and Peck (1988, Exp. 2) also compared the FOK judgments for general information questions for 54 college students and 36 elderly adults (60 to 93 years). The questions were taken from the Nelson and Narens (1980) norms. Questions continued until each subject had incorrectly answered, or failed to respond to, 12 items. Following the recall test subjects made both absolute and relative FOK judgments. For absolute judgments the person simply made yes/no predictions about success on a subsequent multiple choice recognition test. For relative judgments the 12 items were presented in all 66 possible pairings and subjects selected the question for which they were more likely to recognize the correct answer. To test the reliability of these judgments, the relative and absolute judgments were all repeated, with order of the tasks counterbalanced across subjects. Following the FOK tasks the 12 nonrecalled questions were presented in a 7-alternative forced-choice recognition test. Initial recall levels for those general questions were very similar for the two age groups; both had about 38% correct recalls prior to making 12 recall errors. Performance on the subsequent recognition test was better for the old (49%) than for the young (35%). Regarding the FOK judgments, retest reliability measures showed young and old to be equally reliable in making these judgments. Furthermore, the two age groups were about equally accurate in those FOK judgments; numerically, the accuracy was slightly better for the aged.

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The test-retest reliabliity of rankings showed that when two items received adjacent rankings on the first FOK ranking, the retest was at about chance (i.e., adjacent items might be reversed about half the time), but when 2 or more items intervened in the original rankings, the retest reliability of order of the pair was nearly perfect. This analysis of the fineness of discrimination among FOK judgments yielded very similar results for the young and old. Thus Butterfield et a1.k results strengthen and extend those of Lachman et al. (1979); with larger Ns they demonstrated that the FOK discriminations of young and old for general information (semantic memory) were similar in fineness, reliability, and accuracy. It should be noted that while many of the studies on FOK with young adults (college students) have employed episodic memory tasks, these studies of aging and FOK involved assessments of items in semantic memory. Age-related differences in memory performance are typically much greater for episodic than for semantic memory tasks. We do not yet know whether episodic memory tasks would also show Butterfield et a1.k finding, that the old are similar to the young in fineness, reliability and accuracy of FOK judgments. While systematic laboratory tests of the accuracy of FOK judgments have shown no age effects, this does not mean that feeling of knowing experiences occur with equal frequency in real life, or that the nature of the elements of the memory constellation accessible when in a FOK or TOT state is necessarily the same for young and old adults. Several recent studies suggest that there are age differences in TOT experiences (Burke, Worthley & Martin, 1988; Burke & Laver, Chapter 10, this volume; Maylor, in press-a, in press-b). A study of TOT states in everyday life was conducted by Burke, Worthley and Martin (1988) by having 30 young and 30 old adults (M ages = 20 vs. 70) keep structured diaries to record spontaneous TOT experiences over a four week interval. The older subjects recorded a greater number of TOT experiences, more than half again the number for the young. The old appeared to know less about the sound of the target words than did the young, and recorded fewer occurrences than the young of "blockers", familiar words repeatedly coming to mind that were semantically or phonemically similar to the target. Burke at al. also categorized the types of words involved in the TOTs into 3 categories: 1) Name of a person, place or movie; 2 ) name of an object; 3) non-object noun, adjective or verb. For both age groups the greatest number of TOTs were for people's names, mostly personal acquaintances. These accounted for a larger proportion of all TOTs for the old (68%) than for the young (56%). For the young, over 90% of the remaining TOTs were in the third category (non-object, adjective, or verb), whereas for the elderly 71% of the remaining TOTs were in the second category (name of an object). There was also some evidence that elderly may be less likely than young adults to employ any strategy to retrieve target words, relying instead on the item spontaneously popping into their mind. Burke et al. concluded that lexical retrieval processes are disrupted in old age, especially names, both proper nouns and object names. Lastly it should be noted that both groups were successful in ultimately retrieving the item for the majority of TOTs, with the percentage being slightly higher for the old than the young, 97.3% vs. 91.5%. (See

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Burke & Laver, Chapter 10 of this volume for an extended presentation of this program of research on TOTS and aging.)

To summarize, there is consistent evidence that the accuracy of FOK judgments for the contents of semantic memory are very similar for young and old adults. We do not know whether the accuracy of such judgments for episodic memory are similarly well preserved with aging. There is evidence that TOT states are qualitatively different as a function of age; older adults appear to have particular difficulty with lexical access to the names of objects, i.e., common nouns. Judgments of knowing. Whereas FOK and TOT states involve monitoring during attempted recall, jugments of knowing (JOK) represent an effort to assess, at time of study (encoding and storage), the likelihood of success in recalling individual items on a later memory test. The typical task here involves the subject rating each item (or ranking items) at time of study in an intentional memory task. The metacognitive skill of monitoring for relative future memorability of individual items is reflected in the extent to which items receiving higher ratings (rankings) on the JOK task are associated with higher memory performance levels on a later test.

Several studies have established that young adults can predict the relative performance for items on a later memory test with substantial accuracy (e.g., Groninger, 1976, 1979; King, Zechmeister & Shaughnessy, 1980; Lovelace, 1984a, 1984b). The proportion correctly remembered has typically been found to be a monotonically increasing function of the ratings the items received. Lovelace (1984b), for example, had college students study a list of 60 word pairs and rate each pair on a 7-point scale as to the likelihood of later recall of one member given the other. The conditional probability of recall of items receiving the lowest rating was .26. Successively higher ratings corresponded (almost linearly) to higher recall levels, and the recall probability for those items receiving the highest rating was 33. Studies that have compared young and old adults on this JOK task have shown no age-related deficit in the ability to monitor later retrievability during storage (Lovelace & Marsh, 1985; Rabinowitz et al., 1982; Shaw & Craik, 1989). Rabinowitz et al. (1982) had 24 young and 24 old adults (M ages 19 vs. 69) study a list of 50 word pairs, and rate each pair on a 10-point scale indicating how certain they were that they would later remember that pair. There were 42 critical pairs in the list, 14 pairs representing each of three levels of normative relatedness of the two members of the pair. The slopes of functions relating probability of recall to prediction ratings were very similar for young and old adults indicating equal accuracy for the two age groups in this variety of monitoring. Lovelace and Marsh (1985) employed a similar procedure, 60 pairs were studied and rated on a 5-point scale. They reported the same result: that the predictive accuracy of JOK ratings was similar for young and old adults. Rabinowitz et al. (1982) also reported that young and old adults were equally good at adjusting their JOK ratings for the relatedness property of the stimulus pairs. However, as noted in an earlier section, their study also contained a manipulation of processing strategy (interactive imagery) for which subjects of both ages proved to be relatively insensitive in their predictions.

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It is clear that the JOK predictions might be based on either a normative, shared component of the materials that make some items easy and others hard, or on an idiosyncratic knowledge of one's own particular encoding operations for a given item (ArbucWe & Cuddy, 1969; Lovelace, 1984a; Rabinowitz et al., 1982; Underwood, 1966). By use of a random yoking control procedure Underwood (1966) and Lovelace (1984a) have shown that for young adults there is a substantial idiosyncratic component to JOK ratings. An individual's ratings predict his or her own memory performance considerably better than they predict the performance of another subject. Both Rabinowitz et al. (1982) and Lovelace and Marsh (1985) found this to be equally true for their young and old adults. Thus the contribution of an idiosyncratic component to the monitoring of one's own encoding operations appears to be unrelated to age. In s u m m a r y , the old have consistently demonstrated an accuracy in distinguishing the relative memorability of items being studied which is very similar to that of young adults. This accuracy in predictions of relative memorability may occur despite age differences in the accuracy of perceived task difficulty or accuracy of the expected absolute performance levels. Finally, the idiosyncratic component of JOK ratings is present to the same degree for young and old adults, indicating a preservation with aging of the monitoring for relative effectiveness of one's own encoding operations. Correctness of one's response. In performing any memory task there is a decision step which follows retrieval, a step sometimes referred to as an "editing" process. This step amounts to a monitoring for the correctness of the retrieved information. In many situations one then has the option to respond overtly with the item retrieved or to remain silent, perhaps while re-initiating the search. In such tasks, where response omissions are possible, it has been suggested that there may be a criterion difference related to age in that the elderly may be more likely to withhold responses which would be correct (Arenberg & Robertson-Tchabo, 1977).

In some memory tasks, however, bias or criterion differences are precluded, and so one can assess the accuracy of attempts to discriminate right from wrong answers, i.e., the accuracy of monitoring the correctness of one's response. In the study by Lachman et al. (1979), discussed above in the section on FOK, they also asked their subjects to rate their confidence in the correctness of the choices they made on the 4-alternative multiple choice recognition test. The four levels of confidence were labelled "wild guess", "educated quess", "probably right", and "definitely right". The proportion correct selections for items assigned to each of these four levels of confidence were about .30 (slightly better than chance), .45, .60, and 37, respectively. The young, middle-aged and old showed very similar slopes, the proportions presented above being reasonably representative of all three age groups. In the study by Lovelace and Marsh (1985), cited above in the section on JOK, 20 young and 20 older adults (A4 ages 19 vs. 67) studied 60 unrelated word pairs. The memory test took the form of an associative matching task where subjects were given all 120 items, separated into pools of 60 left-hand and 60 right-hand members of pairs. They were asked to put them together in pairs as they believed they had occurred on the study list. When all items had been placed in pairs, subjects were then asked to indicate for each pair their confidence in the correctness of that pair on a 3-point

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scale: "a guess", "think it is correct", and "sure it is correct". The older subjects were more likely to use the "guess"rating than young, and less likely to use the "sure" rating. This was appropriate since the percentage correct matches was 50% for the young but only 30% for the old. The conditional probability of correct matches for those items rated at each of the three levels were, for young and old, respectively: guess, .04,-04; think correct, .44, .40; sure correct, .96,.91. Clearly the ability to discriminate the correctness of their responses, in this task where differential bias is ruled out, was quite similar for the two age groups. The pioneering study of Perlmutter (1978) also reported that young and old were equally accurate in monitoring the correctness of their responses. Both episodic word memory and semantic fact memory were tested by both recall and recognition measures. In all four combinations of material and form of test there were significantly higher confidence ratings for correct than incorrect responses, but no evidence that this ability was age-related. It should be noted, however, that under some conditions young and old adults may differ in monitoring the level of their performance on a memory task. Hanley-Dunn and McIntosh (1984) had 56 young and 56 older adults (Mages = 20 and 72) attempt to recall a list of 14 names, and then to indicate on a 5-point scale how well they felt they had performed: extremely well, good, average, below average, or poor. While there were no age-related differences in actual performance levels the old rated their performance significantly lower than younger adults. Since in this study they were not predicting actual absolute performance values it is very possible that the differential assessments of their performance have to do with differences in the way absolute levels of performance were mapped onto levels of the 5-point scale by the young and old. If so it is interesting to note that the aged, given the same actual level of performance appear to view that performance as less satisfactory. It is possible that they perceived the task to be much easier than it actually is.

To review, most studies have shown no age differences in the capacity to judge the correctness of one's own responses. The commonly observed tendency for the aged to withhold responses in some learning tasks is apparently less a problem of reduced ability to monitor relative likelihood of a response being correct than one of an increased criterion for the confidence necessary to provide an overt response. This suggests that the aged may actually be expecting or demanding more of the memory system than are younger adults. Reality monitoring. Another sort of monitoring concerns distinguishing between events occurring in the external environment from those only occurring as internal events, e.g., dreams, thoughts, images, plans. Failures of this monitoring with respect to immediate experience, such as hallucinations, are very rare. Failing to properly monitor whether a memory derived from external or internal events is widespread, however. For example, have you ever wondered, once in bed, whether you actually locked the front door or only thought about doing so before coming to bed?

One can think of failures of this sort of monitoring as a subset of a broader class of errors, those called "source forgetting". A recent paper by McIntyre & Craik (1987)

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indicates that there may be a marked disparity in the magnitude of age-related differences in two facets of memory: memory for items of information and memory for the source of that information. Their data showed a strong forgetting of source information for older adults, even when there is evidence of memory for the item. This finding is consonant with the observation that the elderly show reduced memory for contextual information in quite a range of situations (Burke & Light, 1981; Light, 1988). Johnson and Raye (1981) discuss the task of discriminating between internally derived and externally derived memories, referring to it as "reality monitoring". They suggest that monitoring for several attributes in the memory trace contributes to the basis for deciding on the source of the memory. If the source was an external event, sensory and contextual attributes will contribute a substantial portion of the constellation of attributes forming the memory trace. Although memory traces of internally generated events may have some of these attributes, and to a greater degree for individuals with better voluntary imagery (Johnson, Raye, Wang, & Taylor, 1979), they will generally be lacking in such sensory and contextual details. O n the other hand the memory trace for internally generated events will possess attributes denoting the cognitive operations performed in the internal generation of the event, e.g., reasoning, inferring, imaging. One implication of this is that tasks which require more complex or difficult mental operations should create stronger attributes of the generation operations as part of the memory trace and this should facilitate discriminating internal from external sources of the memory trace. Johnson, Raye, Foley and Foley (1981) found support for this prediction by manipulating the taxonomic frequency of the word to be generated in response to a category label and letter cue. Discriminating whether a word was provided by the researcher or self-generated proved easier for the more difficult word generation task, i.e., for the targets of lower taxonomic frequency.

Do the aged show a substantial loss of the ability to do reality monitoring? Recent studies by Hunt and his associates (Mitchell, Hunt & Schmitt, 1986; Guttentag & Hunt, 1988), Rabinowitz (1989), Cohen and Faulkner (1989), and Hashtroudi, Johnson, and Chrosniak (1989) have investigated this question. A common task for exploring this monitoring involves comparison of memory for items provided by the experimenter with those generated by the subject. By the use of cues, such as a category plus initial letter, one can control the items that will be self-generated. Slamecka and Graf (1978) demonstrated the wide range of conditions under which self-generated items will facilitate memory for those items. This memorial advantage is generally attributed to additional processing that occurs during generation such that the encoded trace is enriched or more distinctive. Mitchell et al. (1986) used a variation of this task in which a dozen college students and a dozen healthy, community dwelling older adults (Mages 22 vs. 66) were tested for their memory for 20 sentences which all took the form: The (subject) (verb) the (object), e.g., "The horse jumped the fence." Subjects alternately read complete sentences or read a sentence with a blank space for the object and generated a word to fill the blank. Recall for the objects of the sentences, cued by presentation of the

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sentence subjects, yielded the usual generation effect; both young and old recalled more of the words they had generated than words they read. There was a main effect of age, young recalling more than old, but age did not interact with the generation effect. After cued recall the subjects were shown 20 pairs of words, the subject and object from each of the 20 sentences, and asked to indicate for each pair whether it came from a sentence which they read (external source) or generated (internal source). Young were nearly always correct on these source decisions, mean greater than 97%. The aged were correct significantly less often, 85%. While this indicates that the aged may be poorer at making external/internal source discriminations, the authors noted that these effects were largely due to three of the 12 old subjects; when those three were excluded from the data set the mean for the aged rose to 94%. Clearly this study suffers from insensitivity on the source judgments due to ceiling effects. Rabinowitz (1989) has reported clear evidence that the elderly show benefits of selfgeneration, in both recognition and cued recall tasks, which are similar in magnitude to the benefits seen for young adults. In two experiments he had young and old adults (Mages 19 vs. 68) read words and generate words by completing word fragments. On a subsequent recognition test the individuals were to indicate whether those they recognized had an external source (were read) or an internal source (were generated). The older adults were consistently less accurate than the young in this discrimination of source. He concluded that "the two experiments were consistent in establishing large age-related differences in reality monitoring" (p. 266).

On the basis of findings from other studies in his laboratory Rabinowitz argued that this difference in discrimination was not based on differential storage of information about the mental operations during encoding nor age differences in retrieval of information about prior cognitive operations. He concluded that differential performance on the reality monitoring task may result from differences in the decision process -- namely, that the aged rely more on semantic, sensory and contextual attributes to make this discrimination than do young adults. He reached that conclusion in part because of a differential bias of the two age groups in responding. For old items that were correctly recognized the young showed a bias toward saying the item had been read whereas the older adults either showed no bias or a bias toward saying the word had been generated. A very common sort of reality monitoring entails discriminating planned or imagined actions from actions actually performed (see Kausler, Chapter 2, this volume, for further discussion). An early work of Kausler, Lichty, and Freund (1985), concerned with frequency judgments for planned versus performed actions, found that discrimination of planned actions from activities actually initiated was quite good for both young and old, and unrelated to age. Subsequent studies, however, suggest that there may indeed be some age differences in such discrimination.

Guttentag and Hunt (1988) presented young (A4 = 19) and older (M = 75) adults with a list of 24 simple actions, six from each of the following four categories: communicative gestures, tracing exercises, looking at an object, and touching body parts. After the subject read aloud a sentence describing the action, the experimenter instructed the person on a random half of the trials to perform that action, while on

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the other half of the trials the instruction was to imagine performing the action. On a subsequent surprise free recall for the set of 24 actions, the young recalled more than the old, 82% vs. 64%, and both groups recalled more actions they had actually performed than those imagined. While the interaction was not significant there was a tendency for the memorial benefit of performing over imagining to be greater for the young adults, 13% versus 8%. The subjects were then given the 24 actions and asked to tell for each whether the action was performed or imagined. These source judgments were quite accurate overall, but the young did identify the source correctly more often than the elderly, 90% vs. 80%. Again the aged had more difficulty with the reality monitoring task, and the age differences here might well be underestimated due to ceiling effects. Cohen and Faulkner (1989, Exp 1) also employed actions as the events for which source memory was assessed. Young, young-old, and old-old adults (Mages = 31, 65, 76, respectively) were presented with an array of everyday objects on a grid and then were given a pack of cards to turn over one at a time. Each card contained an action involving items on the board plus one of three commands: Perform, Watch, or Imagine. An example of an action is "Put the spoon next to the toothbrush." Given the command to Perform, the individual would carry out that action; if a Watch command, then the experimenter carried out the action while the subject watched. For an Imagine command the subject was instructed to look at the objects mentioned and imagine carrying out the action. After 36 actions had been experienced, and after a 10-min delay interval during which a magazine article was read, subjects received a printed checklist of the 36 actions randomly mixed with 18 new distractor actions. These new actions involved recombining old objects and actions in novel combinations that had not occurred in the original series. The task was to assign each action on the checklist to the proper source: performed, watched, imagined, or new. Both elderly groups made more errors reflecting confusion of source than did young adults. Specifically, the aged were more likely to give false alarms to the new actions on the list, and made more source errors on actions that were performed or imagined. "The old-old were less likely to claim that watched actions had been imagined, but more likely to claim that imagined actions had been watched and that watched actions had been performed. This pattern of errors reflects both failure to distinguish between internal and external sources and failures to distinguish between self- and othergenerated actions" (Cohen & Faulkner, 1989, p. 13). Cohen and Faulkner argue against interpreting this deficit in reality monitoring as due to quality of the memory trace (which would be an encoding deficit) or to differences in confidence. They favor an interpretation in terms of response bias with the elderly biased against deciding the action is new or only imagined, but biased toward saying the action was watched. They note that this bias to claim to have watched actions that have never occurred has implications for eyewitness testimony. In a second experiment they provide another demonstration of differential source forgetting. Young and old adults (A4 ages of 35 vs. 70) watched a 3 min videotape knowing that their memory for what they saw would be tested. Subsequently half the subjects of each age level read each of two 600-word story versions of the events on the video. One version was veridical whereas the second contained misleading (false)

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information about two critical incidents in the video. When given a recognition test and instructed to answer according to what they had seen in the videotape, the aged were more influenced by intervening misinformation than were the young. When not exposed to misleading information, the elderly remembered events from the film as well as the young, but when tested on events for which they had read a misleading account the aged were more likely to change their response. This reflects a deficit in monitoring between two external sources, the film and the written account. The relative ability of young and old to perform three different types of source monitoring was assessed by Hashtroudi, Johnson, & Chrosniak (1989). The three types entailed discrimination between externally and internally derived memories, between two types of internally derived memories, or between two types of externally derived memories. Equal numbers of young and old adults (A4 ages 20 and 70) were tested in one of four conditions. Thirty moderately common nouns were presented; they were spoken aloud by the experimenter and then experienced a second time in one of several ways depending on the condition. In the "say-listen'' condition subjects were asked to repeat half the words and to listen to a second experimenter repeat the other half. In the "think-listen'' condition subjects were told to imagine themselves repeating the word aloud for half the items and to listen to the second experimenter repeat the other half. In a "say-think condition subjects were to repeat some words aloud and to think of themselves repeating the others aloud. In a "listen-listen" condition there was a third experimenter present and the second and third experimenters each repeated half of the words aloud. All experimenters were females. The subjects were instructed to pay close attention to the words, but were not informed that there would be a memory test. A source monitoring test followed in which individuals were given a booklet of 60 words, 30 old and 30 new. They were to circle one of three letters beside each word to indicate the source of their second experience with each word. Depending on the condition they had experienced they receive the letter N for new (no prior experience in this task) along with two letters from this set: S for words they had said, H for those heard, and I for ones imagined. In the listen-listen condition the two additional letters required the subject to identify which experimenter had spoken the particular word.

Older adults were reported to discriminate internally from externally derived memories about as well as younger adults (say-listen and think-listen conditions) but were poorer than the young when discriminating within internal sources (say-think) or within external sources (listen-listen). The major point that Hashtroudi et al. drew from these data was that "older adults have a specific rather than a generalized deficit in remembering source information" (p. 110). Let us review the results of these three recent studies. Rabinowitz (1989) reported a consistent age-related deficit in reality monitoring and attributed this predominantly to decision factors or bias in selection of sources. Cohen and Faulkner (1989) also found evidence of reduction in accuracy of reality monitoring in the aged and similarly concluded that bias was the most likely cause. Hashtroudi et al. (1989) found no age differencesin source forgetting for conditions requiring discrimination between internal

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and external sources but did find age differences when the discrimination was within either internal or external sources. Which condition in the Hashtroudi et al. study should be taken to be the most appropriate assessment of reality monitoring? If one takes reality monitoring to be essentially a discrimination of internal vs. external sources of an action resulting in the memory trace, then one might argue that the say-listen and think-listen conditions represent reality monitoring. On the other hand, if one considers the discrimination between thoughts and plans versus overt actions to be central to reality monitoring then the say-think condition is a test of reality monitoring. The data make it clear that in this study the latter sort of reality monitoring did show age differences whereas the former did not. In terms of the internal vs. external source distinction of Hashtroudi et al., what are the discriminations being required in the Rabinowitz (1989) and the Cohen and Faulkner (1989) studies? It depends on whether one takes internal vs. external to be the source of the item or the nature of the person's actions. The subjects in the Rabinowitz study might be viewed as in a "say-say'' condition; regardless of whether the items were experimenter-provided or subject-generated the task was to say the item aloud and try to remember it. Thus, although in one sense the source of the word is external when the experimenter provides it and internal when the person generates it, one may view this as a "within-source'' discrimination since the response of the subject is overt in either case. Given the findings of Hashtroudi et al., one would expect age differences in ability to monitor whether the items were self-generated if these are both external tasks. The second study of Cohen and Faulkner, dealing with the influence of intervening information on the eyewitness recall involved discriminating the story from the video. This is clearly a discrimination between two external sources, thus the age differences seen there are consistent with Hashtroudi et al., but this study did not involve reality monitoring. The first study in Cohen and Faulkner includes two types of discrimination. In all cases the ultimate source of the particular actions was the stack of cards, an external event. Discriminations dealt with the conduct of the actions, however, involving distinctions to be made between actions of self and others and between actions imagined and those actually executed. The latter is a discrimination between two internal sources if you focus on who the actor is, but between internal and external if you focus on the domain of the action (covert vs. overt). Similarly the discrimination between actions of self and of others is between internal and external if one focuses on the actors, but between two external sources if one looks at domain (both are overt actions). Recall that Cohen and Faulkner found that the pattern of errors indicated age-related increases in failures of both these discriminations of self vs. other and of doing vs. imagining. It does not appear that one can adopt a single view regarding the terms internal and external sources that will permit a coherent account of the data of these three studies. There is, however, agreement among the three studies in two regards. First, all three provide additional evidence that the aged show greater difficulty with tasks requiring memory for contextual information, here dealing with some aspect of knowledge of the

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source of a memory trace. Secondly, the authors of all three studies conclude that the elderly do not show any decrement in ability to utilize attributes which reflect the cognitive operations encoded during initial experience. This is consistent with the conclusion above, from studies employing the random yoking procedure to study the idiosyncratic component of JOK ratings, namely that older adults appear to monitor their encoding operations as effectively as do younger adults. It is clear that under many conditions the aged show greater source forgetting than do young adults, including situations where the discrimination is between memories for imagined or planned events versus memories for actually experienced events. While arguments have been raised that point to the probable involvement of bias at the selection or decision step in source identification, the question of whether there is any age-related loss of encoding and utilization of information about the cognitive operations warrants more direct test. (See Hashtroudi, Johnson, & Chrosniak (1990) for recent evidence suggesting that older adults may fail to inhibit personal thoughts and feelings, and that this information may interfere with their memory for contextual details.) Light (1988) makes the following observations which are relevant to any discussion of source forgetting and aging. The patterns of spared and impaired memories in the aged suggest that automatic or implicit memory processes are relatively intact while explicit conscious retrieval of episodic information, such as context, shows age deficits (see Kausler, Chapter 2, this volume). Given the contextual retrieval deficit, the aged may have to rely more on implicit memory and, Light notes, "one suggestion that emerges from this work is that older adults might be encouraged to trust their memory even when they cannot identify the source of their knowledge" (p. 95). Their reliance on reconstructions that are based on general schemata and plausible inference will serve them well, however, only under conditions of a stable environment and the normal course of events. The failure to be able to monitor for source or contextual information as a way of having confidence in one's memory (Tulving, 1985) will become problematic when they must deal with anything "out of the ordinary". Interrelating Varieties of Metacognition While one can, on the basis of measurement operations alone, distinguish a number of different varieties of metacognition, the extent to which these are functionally separable remains an open question. Can one predict relative performance of individuals on one metacognitive task from knowledge of their performance on some other metacognitive task? At the present time there is surprisingly little empirical data relevant to this question. The exception to this is the extensive correlational analyses of the various subscales of self-report measures of metamemory (see Hultsch et al., 1988; Cavanaugh & Green, Chapter 7, this volume; and section above on Self-reports). We do not, however, have good data on how performance on different varieties of memory monitoring relate to one another, let alone how different aspects of selfreport, kinds of specific predictions, and types of monitoring are related. Finding dissociation of the effects of age on two types of metamemory provides an indirect indication that these varieties of metamemory are not strongly related. For example, Coyne (1985) reported that the accuracy of absolute pre-performance

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predictions of memory levels was age-related whereas the accuracy of the differential predictions of performance as a function of variation in study time was not related to age. A similar dissociation was reported by Lovelace and Marsh (1985). They had subjects make predictive ratings during study of later retrievability of individual items. While they found no age differences in the accuracy of predictions of the relative memorability of individual items, there were large age differences in the accuracy with which the use of the rating scale mapped onto later absolute performance levels. The absolute level of recall for the elderly was much lower than would be indicated by how they used the rating scale, whereas the recall level of the young was consistent with the level of the ratings they employed. Such findings suggest, as was indicated in a prior section, that while individuals may have fairly accurate metacognitions about the relative memorial consequences of various properties of the task, materials, or their encoding operations, they may lack accurate knowledge of the absolute memorial consequences of various types of encoding (e.g., levels of processing) or difficulty levels of tasks, i.e., they lack normative calibration for the task. Such dissociations do not indicate, of course, that the two varieties of metacognition have no processes in common. Indeed that would be very improbable. Instead, such dissociations may only indicate that there must be at least one process that the two varieties do not share, and that individual differences in performance of that process must be relatively uncorrelated with individual differences in any shared processes. The two substantial literatures dealing with aging and metacognition, global measures of self-efficacy and specific predictions in memory monitoring sorts of tasks, have developed largely independently, the former based on a psychometric approach to individual differences and the latter a laboratory approach of quasi-experimental designs with age treated as an independent variable. There has been little effort to study the relationship of memory beliefs to the accuracy of specific memory predictions. A notable exception is a recent study by Hertzog et al. (in press-a). They were interested in studying the relationship of performance predictions to other aspects of metamemory, particularly memory self-efficacy. While the MIA and MFQ questionnaires measure perceived global memory performance, prediction tasks may be thought of as calling for judgments of self-efficacy for a specific task situation. They propose that performance prediction is based on (a) accessing one's memory self-efficacy, (b) appraising the memory task, judging difficulty in terms of estimated normative performance levels, and (c) mapping one's self-efficacy onto a particular performance given the estimated normative distribution of performance levels. Inappropriate decisions at any of these three steps may result in inaccurate memory predictions. Within this framework the strength of the relationship of global self-efficacy measures to specific performance predictions hinges on two things: the extent to which there is commonality across subjects in the appraisal of normative distributions of task performance, and the degree of correspondence between individual differences in general memory self-efficacy beliefs and differences in self-efficacy beliefs for the specific task. One implication of the first of these is that apparent overconfidence by the aged in their memory ability might reasonably be viewed as a failure to accurately assess the normative distribution of performance levels on the task. This is, of course,

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essentially the argument made above in considering the calibration problem, i.e., that the overestimation of performance levels to which the aged are prone might better be viewed as an underestimation of task difficulty rather than overestimation of selfefficacy. In this study Hertzog et al. employed two recall tasks, free recall of word lists and free recall of narrative texts, each being performed three times. A large sample (N = 160) of adults, ranging in age from early 20s to late 70s, gave performance predictions prior to each of these six recall tasks. Since this model holds that lack of commonality in estimates of normative performance levels for the task can reduce the relationship of global self-efficacy measures to the accuracy of specific predictions, they attempted to minimize differences in such normative estimates by providing subjects with information about average performance levels for each task. As noted in an earlier section on Pre-performance Predictions, their subjects were told that, on average, people were able to recall half of the items, 15 of 30 words in a list or 25 of 50 ideas in a text. The two primary measures of memory self-efficacy were the Capacity and Change scales of the MIA and the Frequency of Forgetting scale of the MFQ. For both text and word recall tasks, increasing age was found to be associated with lower predicted performance levels. Assuming that subjects accepted the normative recall frame of reference they were provided, this provides evidence that older subjects have lower expected levels of recall on these two tasks. Predicted performance levels increased over trials, especially for the word list recall, but the magnitude of this change did not differ significantly for the two age groups. Performance predictions were significantly related to global self-efficacy measures, the Capacity and Change scales of the MIA and the Frequency of Forgetting scale of the MFQ, with correlations ranging from .20 to .41, all p s < .01. These correlations were greatest on the first trial and then declined slightly as might be expected if global self-efficacy makes a lesser contribution to these predictions as one develops greater task familiarity as a basis for judgments of task-specific efficacy. Most importantly, there were no age-related changes in the relationships seen between global measures and specific predictions. Actual recall performance was found to decline with increasing age for both the word list and text recall tasks. The performance of young adults was found to increase significantly across trials whereas the performance of the aged did not. For each of the two tasks, the accuracy of predictions increased across the three trials with that task, this being more true of the word list task. For text recall the correlation of predicted to actual number recalled increased over trials from .44 to .58 while for word list recall it improved from .24 to .62. For the word lists, even slightly higher correlations were found, however, when performance on trial N was related to the prediction on trial N + 1, indicating that subsequent predictions are based in part on the on-line monitoring of actual performance on the specific task on the prior trial. This was not seen for the text recall which may suggest that such monitoring is easier to do when the memory task is for discrete items than when it is for the number of ideas in text recall. Hertzog et al. concluded that there was no evidence of age differences in predictive accuracy, or in the relationship of specific performance predictions to global memory

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self-efficacy. It must be kept in mind, however, that these results were obtained in a paradigm in which the authors have intentionally precluded effects of age differences that might result from differential accuracy in the calibration of normative task difficulty. There remains a strong need for further exploration of the correlation or independence among the several varieties of metacognition. Such information is requisite for our broad understanding of cognitive functioning, and may prove especially productive as an area in which to consider individual differences in cognitive functioning, including age differences. Conclusions and Future Directions Early on in the study of metamemory it was proposed that effective use of memory relies on accurate assessments of the contents of one's knowledge store, plus reliable and valid monitoring of one's on-going memory processes. That is, that effective memory performance hinged on good metamemory skills. There is ample evidence of reduction in memory performance with aging, particularly for laboratory tasks tapping episodic memory. This led to the exploration of several varieties of metamemory in an attempt to identify metamemorial skills that deteriorated with age. On the basis of existing data it seems likely that one can rule out certain types of metamemory as contributory to age-related decline in memory performance. The online monitoring of the contents of knowledge store, even when currently inaccessible (FOK) appears to be well preserved, as does the ability to monitor, during storage, the effectiveness for future memory of particular encoding operations (JOK). In addition, the accuracy of the editing step in which one evaluates the correctness of items retrieved from knowledge store shows no evidence of age-related decline. The increased reticence to give overt responses in memory tasks, i.e., more omission errors for the aged, presumably reflects socio-cultural effects of increased concern about one's memory abilities as one grows older. Certain types of metamemory measures do frequently show age differences. When measures of memory self-efficacy are taken, particularly for global measures and measures of perceived change of memory ability, the aged typically respond in ways that indicate a perception of declining memory ability with aging. When the measures are directed at specific types of memory performance the magnitude of the age-related differences in perceived memory function are reduced, but the direction still implies reduced memory ability with aging. For some tasks this perception is accurate, for others it is not. We need more evidence of the extent to which the aged are able to accurately make these task-domain-specific discriminations with respect to memory self-efficacy. Another type of metamemory difference frequently reported involves an overestimation by the aged when trying to predict how they will perform on a memory task that they have not yet attempted. This overestimation of memory performance by the aged clearly runs counter to the reduced sense of memory self-efficacy discussed above. It was argued here that a more appropriate way to view these age differences in pre-performance prediction of absolute memory levels is to say that the aged

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underestimate task difficulty rather than overestimate their ability. This task calibration problem is probably related to age differences in familiarity and recency of experience with the sort of task components involved. It remains for future research to illuminate the complex interplay of these varieties of metamemory. How is it that aged individuals, who know what they know, know when they are encoding in more or less effective ways, and know when their memory retrieval attempts have been successful, still show some loss of reality monitoring and reduction in memory self-efficacy? To the extent that these result from relatively well preserved monitoring skills being applied to memorial processes that have been altered, what are the changes in processing that account for some metamemorial changes?

For example, do the age-related differences in reality monitoring simply reflect criterion changes as several authors have suggested, or do they result from changes in the nature of encoded traces, e.g., source forgetting due to a reduction in the complexity and richness of the associative context? To what extent are measures of global memory self-efficacy subject to differences that result from cultural stereotypes creating different demand characteristics for people of different ages? Are age effects reduced when the memory self-efficacy measures are task-domain-specific because this causes a shift from reliance on cultural sets to reliance on memory for one's specific past performance? How accurately can individuals, of any age, assess their cognitive strengths and weaknesses? How do they attempt such assessments, and what sorts of experience might improve their accuracy? To the extent that an individual's reported memory self-efficacy is not a valid measure of actual ability, what are the ways this perceived self-efficacy alters that person's behavior regarding cognitive tasks? And how is the answer to that question dependent on other stable trait characteristics of the individual such as personality, motivation, or cognitive style? Many of these questions are just beginning to be addressed, but they raise many of the issues for which we need answers if we are to develop a better understanding of metacognition and aging. References Arbuckle, T.Y., & Cuddy, L. L. (1969). Discrimination of item strength at time of presentation. Journal of Experimental Pychology, 81, 126-131. Arenberg, D., & Robertson-Tchabo, E. A. (1977). Learning and aging. In J. E. Birren & K. W. Schaie (Eds.), Handbook offhepsychology of aging (pp. 421-449). New York: Van Nostrand Reinhold. Berry, J. M, West, R. L., & Dennehey, D. M. (1989). Reliability and validity of the memory self-efficacy questionnaire (MSEQ). Developmental Psychology, 25, 701713. Berry, J. M, West, R. L., & Scogin, F. R. (1983, November). Predicting everyday and laboratory memory skill. Paper presented at meeting of the Gerontological Society of America, San Francisco, CA. Brigham, M. C., & Pressley, M. (1988). Cognitive monitoring and strategy choice in younger and older adults. Psychology and Aging, 3, 249-257.

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Aging and Cognition: Mental Processes, Self Awareness and lnteruentbns - Eu ene A. Louelace (Editor] 0 Elsevier Science Pub?kum B.V. North-Holland). 1990

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I Believe, Therefore I Can: Self-Efficacy Beliefs in Memory Aging ~~~

~

John C. Cavanaugh Elizabeth E. Green Bowling Green State University

Among the many classic stories we were told as children was one about a little railroad engine that had to pull a very long train over the mountain. Bigger engines mocked the little engine, and were convinced that the little engine had a well-developed delusion of grandeur. But when the time came, the little engine turned out not to be deluded at all. Instead, it became the Little Engine That Could. Its motto? "I think I can. I think I can." In a very different venue, Norman Vincent Peale has preached one consistent theme for many decades: the power of positive thinking. Negative thinking breeds doubt; doubt breeds inaction; inaction may breed guilt, failure, ridicule, and the like; and these fuel negative thinking, completing the circle. If you believe in yourself strongly enough, you can do anything.

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From children's literature to spiritual teaching, the message is the same if you believe you have what it takes, anything is possible. If you don't, nothing is. This chapter is merely another long discussion that makes the same point. Although it focuses on memory in old age rather than on trains, the point that what one says to oneself largely determines what and how well one does is no different. It is rare in psychology to stumble across such a pervasive phenomenon as the belief in oneself. We have been intrigued with its explanatory power for several years, and have argued strongly that it provides considerable insight into apparently contradictory findings in the literature on memory aging (Cavanaugh, 1989, in press; Cavanaugh, Kramer, Sinnott, Camp, & Markley, 1985; Cavanaugh & Morton, 1989; Cavanaugh, Morton, & Tilse, 1989). We are not alone; other researchers, too, have identified selfevaluations as one of the most important aspects of memory in older adults (e.g., Berry, West, & Dennehey, 1989; Hertzog, Dixon, & Hultsch, in press; Hertzog, Dixon, Schulenberg, & Hultsch, 1987; Hertzog, Hultsch, & Dixon, 1989; Hultsch, Hertzog, & Dixon, 1985; Hultsch, Hertzog, Dixon, & Davidson, 1988; Lachman, 1986). Indeed, a special section of Developmental Psychology (September, 1989) was devoted to the examination of self-evaluations (i.e., self-efficacy) in adults.

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The goal of this chapter is to provide some context for this exciting work. Current research on the role of self-efficacy evaluations represents an application of Bandura's (1986) theory. Consequently, we will begin with a review of what self-efficacy is. The next major section considers several constructs related to self-efficacy, and concludes with a discussion of how all of these ideas were brought into metamemory research. Third, we will concentrate on how self-efficacy is typically measured in memory aging research, summarize the available research, and critique this work. Finally, we will offer some speculations about where we believe we need to go, both in terms of new research questions and new theoretical frameworks. What Is Self-Eflicacy, That We Should Be Mindful of It? Picking up the theme of our opening paragraphs, we would like to borrow some points made by Bandura (1989a; see White, 1982). James Joyce's The Dubliners was rejected by 22 publishers before it was accepted. Gertrude Stein submitted poetry for roughly 20 years before one was finally accepted. Van Gogh sold a grand total of one painting throughout his life. Stravinsky was run out of town after the premier of L.e Sucre du Printemps (The Rite of Spring). Decca Records rejected the Beatles on the grounds that, "We don't like their sound. Groups of guitars are on their way out." More than a dozen publishers rejected a manuscript by e. e. cummings. Despite the problems, however, all these individuals persevered and became household names.

Are these just the exception? Apparently not. In his book Rejection, White (1982) argues that the most important characteristic about people who achieve prominence in their field is an unflappable belief in themselves and in the merits of what they are doing. This strong sense of self-belief allowed them to overcome such harsh criticism and rejection, and to persevere. And sometimes, one gets the chance to get even. When cummings's much-maligned manuscript was eventually published, the dedication (printed in all capitals) read: "WITHNO THANKS TO..." followed by the list of publishers who had rejected his work. (Imagine what fun it would be to print such a list for some of our own oft-rejected offerings!) While we are not suggesting that all people whose work is rejected frequently will eventually have name recognition equal to Van Gogh's (the present authors can vouch for that), we are saying that we can learn a valuable lesson from such individuals. This self-confidence, the belief that (with apologies to Descartes) "I believe, therefore I can," is the key not only to fame and fortune (perhaps), but also to good performance. The opposite stance, "I don't believe, therefore I can't,'' is also true, as long as one does not mind being unknown. Most of us have a good healthy dose of both beliefs that pervade our everyday lives. It is these beliefs that are referred to as self-efficacy. Simply put, self-efficacy refers to the degree of belief one has in his or her ability to mobilize the motivation, cognitive resources, and courses of action needed to exercise control over task demands (Bandura, 1986). Within this belief, however, is a call to action (Bandura, 1989b). That is, self-efficacy is not just a passive belief about some hypothetical future act; rather, it is a belief that then leads to behaving in particular ways. As we will see, the degree of confidence one has in oneself (i.e., degree of self-efficacy) concerning some future event becomes a motivator and

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regulator of behavior in the present (Bandura, 1989b). As Bandura (1989a) puts it, "a capability is only as good as its execution." Since first postulating self-efficacy as a key behavioral determinant in the late 1970s (e.g., Bandura, 1977), Bandura has refined the concept considerably. Self-efficacy acts primarily as an intervening or mediating process, such as motivation, affective arousal, and cognitive activities. Each of these will be considered in turn. Mediating Processes Two of the influences that self-efficacy has on behavior are determining how much effort to exert in a specific situation and deciding how long to persevere when faced with obstacles (Bandura, 1989a). Considerable research documents that people who have strong beliefs in their capabilities exert more effort and persevere longer when faced with challenges, whereas people with self-doubt reduce their effort and give up prematurely (e.g., Bandura & Cervone, 1983; 1986; Cervone & Peake, 1986; Jacobs, Prentice-Dunn, & Rogers, 1984). Additionally, people with high self-efficacy remain more efficient in their analytical thinking in complex decision-making tasks, whereas people low in self-efficacy are erratic in their analytical thinking (Bandura & Wood, 1989; Wood & Bandura, 1989). Quality of analytical thinking, in turn, is strongly related to degree of performance success. Having confidence in one's capabilities also influences how one visualizes the future and constructs anticipatory scenarios. People with high self-efficacyare more likely to visualize success scenarios that provide positive guides for performance and to rehearse these successful strategies cognitively. People with self-doubt tend to visualize failure scenarios and to dwell on how things will go wrong. This inefficacious thinking lowers motivation and effort, undermining subsequent performance. Several studies support these ideas; cognitive simulations in which people visualize successes enhances subsequent performance (e.g., Bandura, 1986; Feltz & Landers, 1983; Kazdin, 1978). Such visualization techniques are often used in the course of psychotherapy to help people change their self-belief system. As Bandura notes (1989b), practicing efficacious courses of action strengthens self-perceptions of efficacy, which in turn strengthens efficacious action. Even everyday successes require an optimistic sense of self-efficacy(Bandura, 1986). Everyday life is full of small rejections, obstacles, failures, frustrations, and the like. To succeed, people must overcome these roadblocks and the self-doubt that they engender. The secret, according to Bandura, is in how rapidly the person overcomes the normal, immediate self-doubt and regains their self-assurance. It is the resiliency of positive self-efficacy that matters (Bandura, 1989a). The anecdotes that opened the chapter and this section provide support for this idea. In addition to its cognitive influences, self-efficacy also affects the extent to which people experience stress or depression when faced with challenging situations. In this view, self-efficacy is similar to Lazarus and Folkman's (1984) process of appraisal in their transactional approach to stress and coping. In either approach, stress is not a property of an external event. Rather, it results from the relationship between one's

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perceived ability to cope and the aversive aspects of the event. Stress results when one thinks one is being challenged or threatened above one's ability to cope.

As in the cognitive domain, people who have high self-efficacy (i.e., believe that they have control over potential threats or challenges) are not disturbed by them. They do not define them as stressful. However, people who have low self-efficacy believe that they cannot cope with the same potential threats or challenges, and experience high levels of stress. Instead, they dwell on their inability to cope, viewing their environment as full of danger. Such thoughts interfere with functioning, resulting in high levels of distress (Bandura, 1988b, c; Lazarus & Folkman, 1984; Meichenbaum, 1977; Sarason, 1975). Research on the role of self-efficacy in perceived stress documents these points. People who believe themselves to be inefficacious in coping report greater distress and have higher autonomic (anxiety) arousal (Bandura, Reese, & Adams, 1982) and plasma catecholamine secretions (Bandura, Taylor, Williams, Mefford, & Barchas, 1985). Anxiety arousal is also affected by negative thoughts. People high in self-efficacy are better able to control apprehensive cognitions in order to lower anxiety arousal than are people with self-doubt (Kent, 1987; Kent & Gibbons, 1987). It is not the sheer occurrence of intrusive, negative thoughts that results in anxiety; rather it is the perceived inability to turn them off that is the major cause of distress (Bandura, 1989a; Salkovskis & Harrison, 1984). One can literally talk oneself into failing, and feel miserable about it at the same time! Such self-defeating musings may explain why older adults sometimes report higher levels of negative affect (distress) concerning their forgetting than younger adults do (Cavanaugh, Grady, & Perlmutter, 1983). Depression may also result from a self-perceived inability to attain personal goals (Bandura, 1988a; Cutrona & Troutman, 1986; Holahan & Holahan, 1987a, 1987b; Kanfer & Zeiss, 1983). When this perceived self-doubt involves interpersonal relationships, depression may result. This is due to a reluctance to cultivate satisfying relationships which help buffer the effects of chronic daily stressors (Holahan & Holahan, 1987). Perhaps the most unfortunate result of low self-efficacy with regards to handling stress is that such individuals are likely to make few changes, even in environments that provide lots of potential opportunities to make things better. Conversely, people with high self-efficacy use ingenuity and perseverance to figure out ways of exercising personal control even in environments containing few opportunities (Wood & Bandura, 1989). Finally, self-efficacy plays a major role in determining the kinds of activities we engage in and the environments we seek out (Bandura, 1989a). As noted above, people try to avoid activities and environments that tax their ability to cope, and seek out those that they believe they are capable of handling. Social influences, such as stereotypes, may play a role in reinforcing a particular set of self-beliefs (Bandura, 1986). For example, older adults' behavior in cognitively challenging situations such as memory tasks may be constrained by a self-belief that they cannot perform well, a belief inculcated by society. Such self-limitation of performance arises more often from perceived inefficacy than from an actual lack of ability.

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Bandura (1989a) emphasizes this point by arguing that any judgment of competence is a social construction, and that how society labels various levels of competence is extremely important. Thus, if society strongly adheres to a belief in age-related memory decline, then to the extent that older adults allow this belief to influence their self-efficacy, they will lower their motivation, effort, and perseverance, resulting in poorer performance and heightened distress. Because of the reciprocal nature of the system, these outcomes will serve to further weaken self-efficacy. Accuracy of Self-Efficacy: Is Ignorance Bliss? The point that poor performance may be more a function of self-doubt than actual lack of ability raises an important issue: To what extent should one's self-efficacy evaluation be an accurate reflection of one's true underlying ability? It is widely believed that being out of touch with one's underlying abilities results in dysfunction. Clearly, gross overstatement such as in delusions of grandeur are problematic. But where is the line? Is a slightly exaggerated sense of competence terribly detrimental? Should we always be accurate? It turns out that most of the time, when we err in self-evaluations, we overestimate our capabilities. But rather than being a problem, such overconfidence is usually a benefit (Bandura, 1989a). This outcome makes sense in view of our discussion. If selfefficacy beliefs always were based on just what people could do routinely, few (if any) of us would take on a serious challenge and exert the extra effort. Moreover, it turns out that so-called normals distort reality more often than anxious or depressed people (e.g., Abramson, Metalsky, & Alloy, 1989). Anxious and depressed people are actually the more realistic group. Neither group typically differs in their underlying ability to perform a particular task, but differ substantially in their beliefs about their ability. Specifically, nondepressed people believe themselves to be much more efficacious than depressed individuals even when both have identical skills. This difference between the realists (i.e., depressed and anxious individuals with substantial self-doubt) and the distorters (i.e,, nondepressed or nonanxious people with strong self-efficacy) is important. The realists are very much in touch with the way things are and may even adapt well, but are highly unlikely to do anything about their situation. These people will easily be discouraged when confronted with difficult circumstances or temporary personal setbacks. In contrast, people with a healthy overestimation of their efficacy are likely to take action and effect a change. Perceptions of incompetence despite underlying ability may also result from contextual factors (Bandura, 1989a; Cavanaugh & Morton, 1989). For example, attending to factors in tasks that are strange or new, rather than to what is familiar, may hinder performance. The mere presence of a highly confident individual may squelch one's self-efficacy. Being given a label which implies incompetence (such as "old person") often results in performance that is lower than what one is actually capable of doing. Offering unnecessary help to a competent person sends a message of incompetence which may suppress performance levels. Langer (1985) argues that mindlessness, not thinking about what one is doing, results from such contextual cues. Mindlessness typically lowers one's proficiency, which in turn may lower one's selfefficacy.

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Providing individuals with arbitrary performance standards or anchors, even when devoid of information concerning competence, influences self-efficacy (Cervone & Peake, 1986; Peake & Cervone, in press). Apparently, such anchors affect people level of performance motivation. That is, focusing people's attention to the difficult aspects of a task lowers self-efficacy, whereas focusing their attention on the doable aspects raises it. As self-efficacy increases through this manipulation, people persevere on the tasks longer even in the face of repeated failure. Clearly, self-efficacy beliefs are not static, and can be altered through rather benign-looking manipulations. In short, self-efficacy beliefs are often inaccurate reflections of true underlying abilities. Modest overconfidence is often advantageous, often resulting in greater motivation, effort, perseverance, and better performance. However, self-limiting beliefs, occurring when one's erroneous assessments underestimate true ability, lower these factors, and performance needlessly suffers. We will return later to this problem of self-limiting evaluations in the context of older adults' performance on memory tasks. A final note concerning the alteration of self-efficacy beliefs is needed. Considerable evidence suggests that some types of memory performance decline with age (e.g., Poon, 1985). If this is true, then it would seem reasonable to suggest that people's standards of judgment concerning their memory ability should change to reflect these underlying declines. Because it appears that people use temporal- or selfcomparative standards to produce positive self-evaluations while skills are improving, perceived competence may be better facilitated by social-comparativestandards when skills are declining (Frey & Ruble, 1989). Outperforming agemates contributes to the maintenance of positive self-efficacy, even when skills are declining.

As Bandura (1989a) notes, however, we must not push this idea too far. Although it works nicely when individuals can choose the grounds for competing with younger cohorts (such as runners racing in different events or age groups), social comparison may not be facilitative when one is forced to go head-to-head in situations in which one person's success is another's failure (such as in the corporate ladder). Frey and Ruble (1989) note that older people who maintain their cognitive abilities may also maintain their self-efficacy by exploiting age norms. Bandura's point is that those whose skills are truly declining would do well to avoid social comparison lest they learn the truth and realize that they are "below average." We will reconsider this advice later when we discuss the role of self-efficacy in intervention and remediation research. Critique

Without question, Bandura has helped reshape our thinking about self-evaluative judgments. We acknowledge that self-efficacy is a major theoretical construct that provides considerable insight into the processes by which people make such evaluations, and have even built this idea into our own theory (see below). But by the same token, self-efficacy theory alone is insufficient to account for all individual differences in self-evaluative judgments of memory and in the overall lack of large correlations between these judgments and actual performance. We are not alone in this assessment (see Hertzog et al., in press).

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One extremely important issue that is not well explained by self-efficacytheory is the genesis of what goes into a self-efficacyjudgment. That is, Bandura describes in great detail the "stuff' of self-efficacy judgments, but in our opinion does not provide sufficient detail concerning where and how this input originates. For example, we assume that making a judgment about one's present memory ability draws on both current aspects of the situation and one's past history of such judgments (among other things). Exactly how this mix of stored memories and current constructions create selfefficacy judgments is not well-articulated by the theory. To be sure, a complete answer goes well beyond the scope of this chapter (but see Cavanaugh, Feldman, & Hertzog, 1990). However, we will at least introduce a few of the other concepts we believe are needed for a more complete understanding of self-evaluations of memory. This discussion, comprising the next section, lays additional theoretical groundwork we believe is essential. Later, we will briefly summarize our integration of these ideas when we present our own framework. Concepts Related to Self-Effwacy

To this point, we have focused only on Bandura's notion of self-efficacy. However, there are several other closely related ideas that need to be considered. Although there are differences in the degree to which they overlap with each other and with selfefficacy per se, all have in common the notion that an important determinant of performance is some sort of self-evaluation. In this section, we will consider self theory, implicit theory, personal control, and attributions. We wish to acknowledge at the outset that we are not the first to attempt an integration of related social cognitive concepts in the context of memory aging. Rather, we view our efforts as extending previous theoretical and empirical reviews, especially those by Elliott and Lachman, (1989) and Hertzog, Dixon, Hultsch, and Davidson (1988). The present discussion draws from the more extensive analysis of these issues by Cavanaugh et al. (1990; see also Hertzog et al., in press). Self Theory Because self-efficacy reflects one's beliefs about one's abilities in a particular domain, it is logical to argue that it is one aspect of the self-concept. Markus and Wurf (1987) argued that self-conceptions both mediate and are mediated by other judgments and behaviors. Note that this description of the self-concept as a dynamic recursive process is very similar to Bandura's description of self-efficacy as a mediating and mediated process. Markus (1977) showed that people rate themselves on a wide variety of traits (e.g., "I am a good/poor rememberer."). When these self-evaluations are highly positive and the person reports that the trait is important to him or her, then the person is said to be schematic with respect to that trait. Schematic individuals show more efficient processing of relevant information in the trait domain (such as memory) and more elaborate processing of conflicting information in the domain. For example, if a person is schematic with respect to memory, he or she would readily process evidence that supports this belief (e.g., instances of successful remembering). Additionally, he or she would reflect on the meaning of conflicting information, that is, forgetting.

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Instances of forgetting are indicative of poor memory, and may lead to a questioning of the underlying belief that the person is really a good rememberer. Due to this pattern of processing, the schematic individual behaves much like an expert in a given domain (Druian & Catrambone, 1988). Schematic individuals also process information that conflicts with their self-evaluation more elaborately (e.g., Catrambone & Markus, 1987; Fong & Markus, 1982). The more efficient and elaborate processing demonstrated by the schematic individual closely resembles findings cited earlier for individuals with high self-efficacy. Most notable are the parallel findings that persons high in self-efficacy and persons who are schematic perform better than people who are neither.

Thus, it appears that there are several similarities between Markus's (1977; Markus & Wurf,1987) notion of schematicity and Bandura's notion of self-efficacy. Both are

relative to particular domains; that is, one would not normally expect a person to be schematic in everything from sexual prowess to skill in using mnemonics. Both influence performance, not so much directly but rather through mediated processes. Finally, both constructs emphasize the importance of individual differences. Specifically, people who are high in self-efficacy or who are schematic will perform quite differently than people who are low in self-efficacy or who are aschematic. Although not directly tied to Markus's notion of self-conception or schematicity, Sehulster (1981a, b; 1982) postulated a self theory of memory. This theory refers to a set of beliefs that a person holds about his or her memory abilities. On the basis of self-ratings of memory abilities in several domains, Sehulster (1981b) identified three factors or clusters of beliefs: beliefs about how good is one's memory for verbal material such as names, trivia, and jokes; beliefs about memories from one's personal past, such as childhood events and painful experiences; and beliefs about memory for information held for short periods of time, such as appointments and the location of objects. In a subsequent study, Sehulster (1981b) reported that beliefs about verbal memory varied along the dimensions of extraversion-introversion and social-unsocial. People high in self-belief tended to be extraverted and social. Beliefs about personal memories varied mainly along the dimensions of emotional-unemotional and socialunsocial. Persons high in this belief reported seeking emotional and social experiences. Beliefs about information needed for short time periods varied along the dimensions of extraversion-introversion and rational-nonrational. Persons who held strong beliefs here reported being in touch with their surroundings and liking logical games and puzzles. Sehulster (1982) also showed that these various types of beliefs are differentially related to ratings of different types of memories. Autobiographical memories are considered easier to retrieve, more certain and trustworthy, visually and auditorially vivid, richer in associations, and easily controlled compared to verbal memories or to memories concerning appointments or lost articles. Recall that Bandura strongly emphasized the domain specificity of self-efficacy. On the basis of Sehulster's research, there may be considerable within-domain variability in how we evaluate our abilities as well. This is important. Self-efficacy (or stronglyheld beliefs) could be high for some aspects of memory but not for others (e.g., "I can

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remember faces but I'm terrible at remembering names."). In the section on assessing self-efficacy, we will return to this issue. For now, suffice it to say that domain specificity may not be good enough. Personal Control and Attributions

One of the most widely used constructs in social cognition is personal control. Numerous theorists and researchers (e.g., Elliott & Lachman, 1989; Rodin, Timko, & Harris, 1985; Schulz, 1980) have documented that the extent to which one believes that outcomes are contingent on one's own behavior has considerable influence on social, cognitive, and physical functioning. Because several volumes are available that provide comprehensive reviews of the literature and theory (e.g., Baltes & Baltes, 1986; Fry, 1989), we will merely highlight some important ideas. Weiner (1985a, b) has convincingly argued that judgments about who or what is responsible for an outcome (termed locus) are independent of judgments concerning whether the outcome can be altered or affected in some way (i.e., termed control). That is, judgments are made pertaining to locus and control, not locus of control. The latter concept confounds internal locus with control and external locus with lack of control. Confusion over this distinction is a major source of problems in the research literature, a point we will emphasize later. In this chapter, personal control refers to whether you believe an outcome depends on something you do. It is in this sense that we wish to draw the parallels to self-efficacy. The distinction between locus and control is important in understanding the connection between personal control and self-efficacy. As defined by Bandura, selfefficacy refers to a judgment about one's own personal abilities. In this sense, selfefficacy consists of a series of beliefs of the form "It is true of me that ..." The nature of these beliefs limits the discussion to only those domains for which the individual has an internal locus. How could one truly believe that one has considerable ability (or a complete lack thereof) in some domain unless he or she believes it to be part of his or her own nature? Connecting self-efficacy to internal locus buys us a great deal. If we adopt the confounded locus of control approach, beliefs in a lack of ability due to increasing age or declining health become problematic. Researchers must treat them as external locus because such things are considered uncontrollable, despite evidence (Cavanaugh & Morton, 1988) that older adults themselves consider them to be internal. Separating locus and control relegates age- and health-related beliefs to their rightful (internal) position, yet allowing people to view them as uncontrollable. Whereas self-efficacy is invariantly related to internal locus, its relationship to control does vary with type of self-belief. Based on Bandura's definition, high selfefficacy implies a belief in the controllability of the outcome, whereas low self-efficacy implies a belief in the uncontrollability of the outcome. That is, people with high selfefficacy believe that the amount of effort they expend, for example, will have an effect on their performance, whereas people with low self-efficacy do not.

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In short, the connection between self-efficacy and personal control is this. High selfefficacy indicates internal-controllable judgments about one's ability or performance. Low self-efficacy indicates internal-uncontrollable judgments. When concretized in terms of memory, high self-efficacy indicates that people believe themselves to be good rememberers and that how they process information affects their performance. People with low self-efficacy for memory, however, perceive themselves to be poor rememberers who, despite their best efforts, have no direct control over how well they do. Implicit Theories of Cognition

Dweck and Leggett (1988) summarize an extensive line of research indicating that what one believes about the way one's cognitive system operates (one's implicit theory) strongly influences one's evaluations of performance. The basic differentiation between people is whether they hold an "entity"or a "skill" implicit theory of abilities. This differentiation has been applied to metamemory by Person and Wellman (1989), as well as to task performance by Wood and Bandura (1989). Individuals with an entity implicit theory believe that any ability is relatively fixed; those with a skill (or "incremental") implicit theory believe that ability may be modified with effort. Each implicit theory is associated with different types of goals: performance goals in the case of entity theory and mastery goals in the case of skill or incremental theory. Performance goals are aimed at gaining positive regard and avoiding negative outcomes; thus, when self-perceived ability is high or when positive feedback is obtained, the person shows high task persistence and seeks challenge. When self-perceived ability is low, "helpless" behavior, including negative affect and defensive attributions, leads to reduced effort, distraction, and withdrawal from the task. Mastery goals are associated with internal desire to learn; people with mastery goals show continued persistence, increased effort, alternative strategy selection, and other behaviors regardless of whether positive feedback from others is received or whether self-perceived ability is high or low. Although implicit theories and self-efficacy are defined somewhat differently, they have remarkably similar influences on behavior. Note that both are thought to influence goals, effort, motivation, and performance. Both are viewed primarily as mediating variables that operate in a dynamic, reciprocal system. And both have highly similar influences on how performance feedback is interpreted. These commonalities can be tied to other related ideas. First, those with skill or incremental theories also possess ideal-self schemas (Higgins, 1987, 1989) that emphasize the rewards of effort and persistence instead of (or in addition to) good task performance alone. Second, skill or incremental theories are associated with internal locus beliefs. Concerning entity implicit theories, individuals holding such beliefs show internal control, high self-efficacy, and corresponding positive affect toward the task only when self-perceived ability is high or success feedback is received. When the task is ambiguous or unfamiliar, persons with entity theories are likely to respond to "ability"vs. "chance" cues or other related manipulations. In contrast, people with skill or incremental theories should show high self-efficacy,internal control attributions, and positive affect across a wide range of tasks and feedback conditions, and should be less

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responsive to contextual manipulation. In other words, under some conditions one cannot tell the difference between persons holding entity beliefs and persons holding incremental beliefs. The only way to sort them out is to assess their beliefs in a variety of situations involving different types of feedback, carefully noting each person's response. Situation-specificityof beliefs would indicate entity implicit theories. Person and Wellman's (1989) preliminary data are consistent with these ideas. Other important issues, however, such as whether implicit theories change across the life-span and the degree to which implicit theories of memory are task- or situation-specific, remain to be investigated (see also Elliott & Lachman, 1989). What appears clear at this point is that individuals with entity theories avoid the negative affect created by (potential) failure to achieve their internal goal. In contrast, people holding skill or incremental theories have an offsetting source of positive affect the match between their effort and their corresponding ideal behavior. Internalcontrollable beliefs, in conjunction with positive affect, motivate strategy search; internal-uncontrollable beliefs and negative affect motivate withdrawal from the task. Translated to memory self-efficacy judgments, these points imply that people with entity theories are likely to perceive evidence for memory failures to reflect a pervasive problem which cannot be remediated, resulting in rather pessimistic self-evaluations. On the other hand, people with skill or incremental theories may perceive memory failures as something amenable to intervention, and may be less likely to base their self-evaluation on them; that is, they may be likely to provide rather positive evaluations despite personal evidence of forgetting.

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Indeed, implicit theories have important implications for memory training, a topic we will discuss in detail later (see also Elliott & Lachman, 1989). We expect that training memory strategies on one task would transfer to other tasks for people holding skill or incremental theories. We also expect these people's performance to be mediated by self-efficacy and the setting of internal goals, which motivates strategy search (cf. Cavanaugh, 1989; Cavanaugh et al., 1989). For individuals holding entity theories, successful performance using a given strategy on one task would not generalize to other tasks unless they had high memory self-efficacy impressions or received success feedback early in the task. Indeed, the finding that feedback often enhances strategy transfer (Borkowski & Cavanaugh, 1979) may reflect this process. Affect toward the self would be expected to result from effort and performance improvement for persons holding incremental or skill theories, but from performance relative to normatively mandated achievement levels for persons holding entity theories. This analysis supports the benefits stemming from "perceptions of control and optimism" (Langer, 1989; Scheier & Carver, 1985) in that generalized skill or incremental theory and its consequent perception of control, even if incorrect, will result in increased performance and positive outcomes most of the time. Thus, control training (Elliott & Lachman, 1989) or situations that make control perceptions more accessible (e.g., Cornelius & Caspi, 1986; Langer & Rodin, 1976) promote adaptive behavior. Even if control perceptions cannot be made chronic, providing contextual cues that increase the accessibilityof relevant self-schemas (e.g., by causing people with entity theories to perceive tasks as depending on abilities they believe they have in abundance) or by changing the evoked norms to create a belief in high ability (e.g., by

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providing specific standards of judgment) would create benefit. Training programs need to be sensitive to these issues. Memory Self-Efficacy and Recall of Personal Attributes Because self-evaluations of memory represents an aspect of the self, it follows that the retrieval of self-knowledge about memory is in principle no different than the retrieval of other types of personal attributes. Indeed, retrieval of personal attributes provides a way of integrating our previous discussions of self theory, implicit theories, and personal control with self-efficacy. Prior to describing this integration, however, it is important to establish why retrieval of personal attributes is related to self-efficacy. Upon reflection, it is clear that in order to make a judgment about one's competency and ability to affect an outcome, it is necessary to call to mind prior experiences. Recollection of these past successes and failures provides the data for making the self-efficacy judgment.

Ross (1989) argues that, in general, recall of personal attributes involves a two-step process: (1)determine one's present status on the attribute in question (i-e., determine one's present self-efficacy); and (2) invoke an implicit theory of stability or change in order to make a judgment. Memories that are consistent with one's beliefs or implicit theories are more accessible and have greater influence on the judgment. This is in line with our earlier arguments concerning chronically accessible schema. Additionally, ambiguous information is interpreted as supportive of one's beliefs. In short, one's implicit theory serves to organize memories into a pattern consistent with the theory. When individuals are faced with queries (generated personally or prompted by questionnaires) that require comparativejudgments concerning two different points in time, such judgments are typically biased in favor of consistency (e.g., Ross, 1989). That is, people tend to underestimate the degree of true change that has occurred over time. Individuals are biased to view their personal attributes now as emerging logically from the past in one connected path even though the events may not really have been connected (Cavanaugh, in press; Kagan, 1980). That is, people tend to impose coherence on event sequences even when evidence supporting such a view is lacking. On the other hand, individuals may also overestimate the degree of change that has occurred. Again, such errors stem from implicit theories. In this case, the implicit theory would be based on the assumption that certain attributes normatively change over time. For example, many adults believe in the concept of life stages or passages, and perceive that they experience fundamental change in some attributes across adulthood, even though there is objective evidence to the contrary (e.g., Costa & McCrae, 1980; Levinson, Darrow, Nine, Levinson, & McKee, 1978; Sheehy, 1981). In terms of metamemory, implicit theories that postulate age-related decrements in memory ability may be translated into self-judgments of declining performance, even in light of objective evidence to the contrary. Moreover, such implicit theories may also lead to inflated ratings of ability earlier in life (see Cavanaugh & Morton, 1988). McFarland, Turnbull, and Giltrow (1988) reported that older adults rate themselves as healthier and as having a better memory at age 38 than a group of 38 year-olds rate

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themselves currently. Even taking into account the influence of cohort effect in this cross-sectional study, these data are consistent with an interpretation based on implicit theories. That is, older adults making such ratings hold invalid theories of aging; they believe that health and memory both decline, so they adjust their ratings of their level on these variables in the past accordingly so as to show decline over time. In actual fact, however, the degree of change was much less. The point is that currently-held implicit theories of change profoundly influence judgments about oneself on personal attributes. Moreover, as the length of time between the actual event and the time of retrieval increase, the more retrieval is dependent on implicit theory-based construction in which present functioning serves as the benchmark. Such biases exist even when current status is deemphasized (e.g., Ross, 1989). These findings have important implications for assessing self-perceived change. Responses to such queries will be biased to the extent that the respondent is unaware of the true state of affairs, a relatively likely possibility in view of our earlier discussion about automaticity and awareness. In addition to these general biases, age differences have also been reported (Ross, 1989). Specifically, young adults tend to underestimate the stability of personal attributes, whereas middle-aged adults overestimate stability. Providing that this pattern is reliable, the overestimation of stability by middle-aged adults could present problems by the time these people reach old age. That is, if people assume no change when change actually happens, then when they are confronted with evidence of change (e.g., perceptions of increased forgetting), they may overreact and claim that they have changed (Lea,declined) more than they actually did. Such findings are often reported by researchers investigating self-evaluations of general memory ability in the elderly. Clearly, these effects need to be considered in interpreting age differences in self-reported changes in memory ability. Ross (1989) also emphasizes the importance of implicit theories in understanding how people recall personal attributes, but he differs from Dweck and Leggett (1988). Whereas Dweck and Legett view traits as either fixed (entity theories) or malleable (skill/incremental theories), Ross views some traits as more stable than others, emphasizing that stability does not mean permanently fixed. That is, Ross argues that people may believe that change can occur in normally stable traits under some circumstances, and that this change is not necessarily positive. For example, some individuals may believe that the ability to remember family members' names normally remains stable, but may change (decline) in some cases due to disease (e.g., Alzheimer's disease). Moreover, whereas Dweck and Leggett associate incremental theories with personal controllability and the potential for improvement, Ross decouples the concepts. For him, belief in the potential for change is not necessarily associated with personal controllability or with improvement. Finally, Ross also argues that one's implicit theory is likely related to one's personal experiences, and that implicit theories have both shared and idiosyncratic components. In terms of metamemory, Ross's points are that implicit theories about one's memory are more complex that Dweck and Leggett's stable-incremental dichotomy implies. Rather, they are more likely to reflect various combinations of personal attributions about the ability in question, how likely it is that the ability changes, and

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the context in which the judgment is made. This approach clarifies previous anomalies in the literature. For example, Cavanaugh and Morton (1988) found that some older adults expressed strong opinions in favor of changes in memory with age, but simultaneously stated that such change was outside of their control and that nothing they did would make any difference. By decoupling the notions of incremental implicit theories (leaving open the possibility that the change could be negative), controllability, and improvement we are able to parsimoniously account for these apparently contradictory data. Additional implications of a more complex view of implicit theories of memory are evident. To the extent that one's implicit theory is valid, personal recall of one's memory ability will be accurate. Such theories would be invoked at the time of retrieval if they are deemed salient and credible, and if they are accessible. Implicit theories are less likely to influence retrieval if specific memories are easily generated (accessible), or when respondents are forced to retrieve specific details of events. Note that these implications from Ross's (1989) work are highly similar to those discussed earlier in the social cognition literature. Summary

Although it may appear that the theoretical constructs presented in this section compete with self-efficacy theory, we do not accept this view. Rather, we consider them to be complementary, with each addressing similar but somewhat different aspects of self-evaluative judgments. Most important, we view these constructs to be operating at different points during the judgment process. Moreover, some of the constructs (e.g., self theory) seem to encompass more global ideas than others (e.g., implicit theories of memory). Consequently, we also believe that there is an implicit hierarchical relationship among these constructs that has not yet been fully appreciated in the metamemory literature. We believe that this hierarchical relationship cannot be ignored. In particular, we think that to the extent performance (and its subsequent evaluation) has implications for a broad aspect such as one's concept of self, the more likely it is that self-congruent evaluations will be incorporated to buttress the self, and self-incongruent evaluations will be rejected. On the other hand, to the extent that evaluations have only local implications, they may be incorporated in either case. Such outcomes should be demonstrable on metamemory questionnaires, and we have developed this argument elsewhere (Cavanaugh et al., 1990). For present purposes, we will confine our comments to a brief discussion of how these ideas became incorporated into metamemory theory. Self-Efficacy and Metamemory: Theoretical Integration

Self-efficacy and its related constructs have a central role in understanding memory performance, as has been clearly documented. What may be fuzzier, however, is the equally central connection between self-efficacy and the concept of metamemory. Metamemory involves a fundamental distinction between remembering and thinking about remembering (Cavanaugh & Perlmutter, 1982; Flavell & Wellman, 1977; Wellman, 1983). Thinking about memory, which is what has come to be called metamemory, involves a highly complex, dynamic process that mediates and is

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mediated by a host of other constructs such as personality, cognitive level, and social context (for more details, see Cavanaugh & Morton, 1989; Cavanaugh et al., 1989). Recall that self-efficacy is a similar process. The 1980s witnessed a major effort at integrating self-efficacy and metamemory. Much of the impetus for this empirical work came from three groups of researchers: Berry, West, and colleagues; Hertzog, Dixon, and Hultsch; and Lachman and colleagues. These researchers are responsible for the major assessment inventories currently available, which are reviewed below. Lachman and her colleagues have also been in the forefront of extending the self-efficacy-metamemoryconnection to memory training, a line of research we will also consider later. In order to place this research in context, however, we need to consider some of the major advances made in the measurement of metamemory itself. Dimensions of Metamemory One of the most difficult issues faced by metamemory researchers since the early 1970s has been its measurement. It is now well established that metamemory is a multidimensional construct that involves much more than establishing how well one thinks he or she remembers names, for instance. Indeed, much of the effort during the latter half of the 1980s was spent developing and validating multidimensional metamemory questionnaires (see Dixon, 1989; Gilewski & Zelinski, 1986). The successful development of these instruments led to further theoretical development of the construct of metamemory, forcing researchers to consider how many dimensions to it there are. Part of this reassessment was based on factor analytic work that we will summarize later. For the moment, we will consider a conceptual approach offered by Hultsch, Hertzog, Dixon, & Davidson (1988). Hultsch et al. (1988) identified four broad dimensions of metamemory. The first dimension, memory knowledge, reflects factual knowledge about memory tasks and processes. Examples of this dimension include knowing that recognition tasks are typically easier than recall tasks, and that memory strategies such as rehearsal or organization improve performance. The other three dimensions represent perceptions or self-evaluations of memory rather than factual knowledge. The second dimension, memory monitoring, involves self-awareness of how one typically uses memory as well as the current state of one's memory. For example, feeling-of-knowing judgments, online reports of strategy use, and evaluations of the accuracy of one's performance are all aspects of memory monitoring. The third dimension, memory-related affect, reflects the range of feelings and emotions related to, or generated by, memory situations. Examples here include depression, anxiety, and the positive and negative affect associated with particular memories. The fourth dimension, memory self-efficacy, is the one most relevant in the present context. Memory self-efficacy refers to one's sense of mastery in the memory domain. Examples here include beliefs about one's memory capacity, how much one's memory has changes, and the degree to which memory performance is under personal control. Most research on metamemory has focused on the first two dimensions, memory knowledge and memory monitoring. Only recently have investigations examined memory self-efficacy, and only a few researchers have considered memory-related

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affect. Interestingly, the bulk of the research in these latter two categories have been conducted by researchers interested in memory aging. We will consider much of this research in the next section. Including memory self-efficacy as a dimension of metamemory rests on a key assumption that we need to make explicit. This assumption is that much of metamemory consists of beZiefs that result from self-evaluations of one's abilities and performances (see Cavanaugh et al., 1989). Although this assumption is reasonable and appears to be fairly widely accepted (e.g., Herrmann, 1982; Hultsch et al., 1988), it is the case that it departs from the original exposition of metamemory (Cavanaugh & Perlmutter, 1982; Flavell & Wellman, 1977; see also Hultsch et al., 1988). Originally, metamemory referred to the memory knowledge dimension. As it became apparent that this view was too narrow, additional important aspects became included. Eventually, the need to view metamemory primarily as a set of beliefs began to emerge, and is now the most advantageous approach. Indeed, current models of the role of metamemory (Cavanaugh, 1989; Cavanaugh & Morton, 1989; Cavanaugh et al., 1989) emphasize the role of memory beliefs in understanding the dynamic role played by metamemory, as described below. Cavanaugh's Framework

The first attempt to build a reasonably complete theoretical framework of metamemory in the context of overall cognitive processing was by Cavanaugh (1989; Cavanaugh, Kramer, Sinnott, Camp, & Markley, 1985; Cavanaugh & Morton, 1989; Cavanaugh, Morton, & Tilse, 1989). His framework, depicted in Figure 7.1, was based on the desire to show how people are aware of different types of information about memory, how the self-evaluation aspect of self-knowledge fits in, and how the social context exerts a strong influence on the entire process. Because the model is described in detail elsewhere, we will simply point out some key aspects of it here. Cavanaugh's framework was the first to incorporate Herrmann's (1982) notion that responses to metamemory questionnaires constitute beliefs. Cavanaugh distinguishes memory knowledge (labelled metamemory in the model) from beliefs, and emphasizes the importance of the self-evaluative component. Both of these points are central to the arguments being proposed here. Self-efficacy clearly rests on a self-evaluative process, represents at core a set of personal beliefs, but is based in large part on underlying knowledge systems. Although Cavanaugh did not provide a detailed description of the nature of memory knowledge or of beliefs, it is apparent that the former is less open to cognitive biases (such as availability or representativeness) and other influences than is the latter. Moreover, there is a clear subjective evaluative aspect to beliefs; they are directly and indirectly influenced by personality factors and directly influenced by social context. A crucial aspect of the framework is that self-evaluations (self-efficacy, outcome expectations, and performance evaluation in the framework) are not conducted solely on the basis of direct input from content knowledge (knowledge base in the framework). Rather, content knowledge has a mediated relationship through beliefs. The importance of this mediated relationship cannot be overlooked. The framework implies that evaluations of ability are not done on the basis of retrieving objective

Fyre7.1. Conceptualmodel of self-evaluations of memory. Noiethat all aspectsinsidethe dotted box are influenoed by Executive Processing.and all in turn influence Executive Processing.Experience. and Social Context. (Reproducedfrom Figure 1 in Cavanaugh, J. C.. Morlon.K, R.. &Tase. C. S. (1989). Aself-evaluation tramwork for understanding everyday memory aging. In J. D. Sinnott (Ed.), Evmdav problem solvinq: Theorv and amlication (p. 274). Copyright 1989 by Praeger Publishers Reprinted with permission.)

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memory facts. Rather, they are conducted with information ("facts about memory") that have been exposed to more subjective, possibly biased and valenced, filters. Two points important in the present context are evident from this framework. First, it incorporates the various theoretical constructs discussed earlier. Specifically, the personality component incorporates aspects of self theory, the beliefs construct includes implicit theories and self-theory of memory, knowledge and experience both include prior judgments (stored personal attributes), and the various evaluation components all include aspects of personal control. Second, due to the number and complexity of mediated relationships between content knowledge and beliefs on the one hand and performance on the other, high correlations between the two sets of variables are unlikely unless these other constructs are taken into account. As Bandura emphasizes, self-efficacy's major direct effects are on effort and motivation, and not on performance. Indeed, correlations between self-evaluations of memory (as measured by metamemory questionnaires) have consistently been shown to be moderate at best, providing support for this supposition. Although much remains to be done in terms of explicating the constructs in the model, Cavanaugh's framework drives home the point that the path from self-efficacy to performance is quite crooked. In the next section, we will consider some of the attempts at taking the kinks out through measuring self-efficacy. Measuring Memory Self-Efficacy Given the proliferation of concepts that all involve the notion that what one believes about one's ability influences performance, it should be no surprise that there are a variety of ways that researchers have measured self-efficacy. These approaches range from single-item assessments to factor-analytic approaches, with all sorts of variations in between. Thus, our review will be selective rather than exhaustive, with the goal being to provide the reader with examples of each major approach to measurement. At the outset, it is important to note several things. As is clear from the above review, the concept of self-efficacy (as well as its related constructs) is complex and dynamic. As Bandura (1989, in press) emphasizes, self-efficacy is not a passive judgment, but a call to action. This point is important. As we will see, most researchers approach the measurement of self-efficacy from the former (passive judgment) view, ignoring the more dynamic aspects such as the effort and motivation it helps create. Consequently, it is difficult to provide firm conclusions about the adequacy of the various approaches to measuring self-efficacy and its relationship to performance. Throughout this section, we will point these problems out.

To begin, we would like to summarize Bandura's own approach to measurement, based on the notion that his approach represents an appropriate technique. Following that, we will consider three main approaches to measuring memory self-efficacy: single-items, memory self-efficacy scales, and factor analytic techniques. Bandura's Approach As reviewed earlier, Bandura argued that the level and strength of self-efficacy alters behavior. Perceived self-efficacy influences the level of performance by enhancing

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intensity and persistence of effort. Bandura used self-efficacy scales to measure people's belief in their capabilities to fulfill different levels of task demands within the psychological domain selected for study. When assessing self-efficacy, Bandura (e.g., Bandura, Cioffi, & Taylor, 1988) obtains separate measures of the magnitude, strength and generality of perceived efficacy. Participants are given a list of tasks, specific to the domain of behavior under study, and instructed to designate which tasks they believe they could perform. For each task, individuals rate the strength of their expectations on a 100-point index ranging in intervals of ten. The index ranges from high uncertainty, through intermediate values of certainty, to complete certainty. Strength of self-efficacy is computed by summing the judgments across tasks and dividing by the total umber of performance tasks, yielding an average level of efficacy for the domain. A generality measure is obtained by asking people to rate the level and strength of their perceived self-efficacy in performing the overall task. On the surface, Bandura's procedure is reminiscent of the performance prediction paradigm (Cavanaugh, 1989). In performance prediction, individuals are asked to predict how well they are likely to perform a particular task prior to actually doing it. A common manipulation in this paradigm is either allowing participants to see the task or not and to assess the effects this has on the discrepancy between predicted and actual performance. Despite the use of prediction in both paradigms, there are several important differences between the two. First, Bandura's paradigm involves having individuals rate their expected performance on a variety of tasks, many of which are not actually performed. In the performance prediction paradigm, individuals only predict performance on tasks they actually perform. Second, the nature of the prediction assessment itself differs. Whereas Bandura provides anchors for performance as well as for confidence, the performance prediction paradigm requires people to generate the level of performance (e.g., number of items they think they will recall) and only rarely assesses confidence (Cavanaugh, 1989). Finally, the performance prediction paradigm does not assess the generality of the prediction, whereas such measurement is an integral part of Bandura's paradigm. In the remainder of this section, we will review other approaches to the measurement of self-efficacy. We will begin with single-item methods, followed by specific self-efficacy questionnaires and factor analytic techniques. Single-Item Approaches

One fairly common way that investigators have adapted Bandura's approach to memory research is to ask people to rate their ability to perform the memory task at hand. This technique has been used for several years, although it was discussed mainly as a way to assess metamemory (see Cavanaugh & Perlmutter, 1982). More recently, Rebok and Balcerak (1989) used a single self-efficacy judgment in their investigation of memory self-efficacy and performance differences in young and old adults. Memory performance was assessed with the use of serial-word and digital-recall tasks. Memory self-efficacy was assessed with a one item self-efficacy scale for each task. Participants were asked to rate the strength of their expectations to recall 12 words or 12 digits in their exact serial order. The rating scheme consisted of a 100-point probability scale,

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ranging in 10-point intervals from 10 (not sure) to 100 (real sure). The highest number circled on the scale was used as a measure of self-efficacy strength. The participants were also asked to predict the number of items they felt they could recall. However, no confidence rating was taken on this measure. The number of items the subject expected to recall was used as a measure of self-efficacy level. The difference between self-efficacy level and actual performance was considered to be a measure of prediction inaccuracy. Rebok and Balcerak found that prediction inaccuracy did not vary with age or mnemonic training. However, self-efficacy varied with age; younger adults had higher self-efficacy strength scores than older adults. Bandura (1989b) argues that Rebok and Balcerak's (1989) one-item self-efficacy measure yields a curtailed distribution. He points out that such one-item measures do not provide a sensitive measure to overall efficacy because they do not distinguish between individuals who judge themselves inefficacious to perform the most difficult memory task but who differ in their perceived efficacy for less demanding tasks. Rebok and Balcerak used a moderately difficult task; in this case, there is no way to tell if the people who judged themselves inefficaciouswould have evaluated themselves differently at a lower level of task difficulty. A second problem with single-item assessments is that they provide little information about the generality of perceived self-efficacy, one of the aspects measured in Bandura's own approach. That is, single-item assessments only address efficacy vis a vis the task at hand, and provide little information about other similar tasks. The problem here is similar to the individual differences issue above. It is possible that people perceive themselves to be inefficacious (or efficacious) with certain types of content but not others. Who among us has not said something like "I'm good at faces but terrible with names?"

Undoubtedly, single-item assessments of self-efficacy provide valuable information that could form the basis for additional research. But by themselves, they give us little insight into the general issue of how people view themselves as rememberers and how these beliefs influence subsequent performance. Bandura (1989b) argues that by using self-efficacy scales with task difficulty gradations, the measure would be more sensitive to variations in perceived memory self-efficacy. Memory Self-Efficacy Scales A second approach to measuring self-efficacy involves constructing a domain-specific scale. Two examples of this approach will be described here: the Memory SelfEfficacy Questionnaire and the Personality in Intellectual Contexts Inventory.

Memory Self-Efficacy Questionnaire. Berry, West, and Dennehey (1989) took Bandura's (1989b) point to heart and created the Memory Self-EfficacyQuestionnaire (MSEQ). Sample items from the MSEQ are presented in Table 1. The MSEQ incorporates Bandura's methodology because it utilizes the multiple situation, task difficulty gradation technique. The MSEQ consists of ten memory tasks for which individuals assess their memory ability. The ten memory tasks are word recall, recall of directions for drawing a path through a maze, digit recall, picture recall of line drawings, remembering a sick friend's grocery list, three telephone numbers from a telephone

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directory, the locations of objects placed around a room, and two filler tasks, photographs and errands. Table 7.1 Sample Items From the Memory Self-Efficacy Questionnaire (MSEQ) For the Grocery Task 1. If I heard it twice, I could remember 12 items from a friend's grocery list of 12 items, without taking any list with me to the store. No Yes 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2. If I heard it twice, I could remember 10 items from a friend's grocery list of 12 items, without taking any list with me to the store. No Yes 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 3. If I heard it twice, I could remember 8 items from a friend's grocery list of 12 items, without taking any list with me to the store. No Yes 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 4. If I heard it twice, I could remember 5 items from a friend's grocery list of 12 items, without taking any list with me to the store. No Yes 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 5. If I heard it twice, I could remember 2 items from a friend's grocery list of 12 items, without taking any list with me to the store. No Yes 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Adapted from Appendiv in Berry, J. M., West, R. L., & Dennehey, D. M. (1989). Reliability and validity of the Memory Self-Efficacy Questionnaire. Developmenfal Pgchology, 25, 713. Copyright 1989 by the American Psychological Association. Reprinted with permission.

Five different levels of difficulty are listed for each of the tasks with the most difficult level listed first and easiest level last. Respondents indicate whether they could perform the task at the specified leveling by circling 'yes' or 'no'. For example, if the task were to remember 12 words, individuals would first be asked if they believed they could remember 12 words, then 10 words, then 8 words, and so on down to 4 words. For each level of difficulty that an individual responded 'yes' to, he or she is then asked to give a confidence rating for his or her ability to perform at that level. The format of the confidence ratings follows Bandura's scheme, with a 100-point probability index ranging in intervals of ten. The sum of 'yes' responses with at least a 20% confidence rating, yields the self-efficacy level score (SEL). The two filler tasks do not contribute to this sum. The SEL is interpreted as a reflection of the individual's assessment of his or her basic memory skill level. The average of the confidence ratings across the five levels of difficulty within each task yields a self-efficacy strength (SEST) score. Psychometric properties of the MSEQ were assessed in three studies by internal structure estimates, criterion-related validity, and alternate versions of the scale. The first study used the original MSEQ. Coefficient alpha estimates for each of the eight scales (the filler scales were omitted from this analysis) were quite high. The eight scales were then subdivided into two groupings; typical laboratory tasks and everyday

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tasks. These two sets of tasks were then presented to participants. SEL scores and SEST scores both predicted performance on everyday tasks. Laboratory task performance, however, could not be significantly predicted from the SEL scores or the SEST scores. In a second study, the anchor points of the task difficulty levels were varied under the hypothesis that how scales are anchored will influence both self-efficacy level and strength. The original format, with greatest difficulty listed first, was compared to two other formats. The first alternative format used a random presentation of difficulty levels within tasks. The second format presented the difficulty levels in an ascending hierarchical format from easiest to most difficult. Six of the eight tasks showed a significant format effect; only the Map and Maze tasks were not affected by the ordering of the items. The random format produced inconsistent responses across type of task. Higher SEL scores were found on the original descending format compared to the ascending format. From these results, Berry et al. concluded that the original descending format provides a more accurate measure of SEL and SEST scores. They caution, however, that more research is needed to determine whether these alternate formats have any influence on subsequent performance (cf., Cervone & Peake, 1986). Berry et a1.k third study was designed to examine age differences on an alternate MSEQ scale (same descending format as the original, but with different tasks), examine the relationship between pre-performance and post-performance efficacy judgments, and compare self-efficacy predictions with single task-specific predictions. The correlations between pre- and post-performance efficacy scores were moderate to high. Older adults had slightly higher test-retest estimates. Consistent with the results obtained in the first study, SEL and SEST scores were a better predictor for performance on everyday tasks. Finally, there was a significant age effect for SEL scores, but the effect for age on SEST scores only approached significance. Berry et al.'s research demonstrates that it is possible to construct a psychometrically sound measure of memory self-efficacy. The MSEQ appears to be a reliable and valid index of developmental and individual differences in people's self-evaluations of their memory ability. The scale has moderately good predictive validity, although this varies with the timing of measurement. That is, correlations of SEL and SEST scores are higher when obtained after task performance than when obtained prior to performance. Such findings fit well with previous research, however (Cavanaugh, 1989), and should not be viewed as a flaw in the scale. Although younger adults endorse higher expected levels of performance than oIder adults (reflected in SEL scores), age differences were not apparent in SEST scores. This finding emphasizes the need to separate the measurement of skill level from confidence ratings (Bandura, 1989b). Although Berry et al.'s scale is a major improvement over single-item measures, it nevertheless misses an important aspect of self-efficacy. Bandura (1989b) notes that they, along with most other researchers, refer to their measures as tapping performance prediction. Bandura argues that performance prediction connotes that self-efficacy serves only to predict some future performance in a passive way. As noted earlier, however, self-efficacy is not a passive construct. Persons varying in self-efficacy deal with tasks in fundamentally different ways.

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Placed into the context of the MSEQ, individual differences on SEL and SEST should map onto individual differences in how individuals approach and perform memory tasks. Although Berry et al. (1989) did not test this, some supportive data have been reported. Berry (1987) showed that people with efficacious beliefs are active producers of good memory performances through differential allocation of attentional and other cognitive resources. Self-efficacy beliefs in her research participants were put into action; they made things happen. We will return to this issue later when we consider directions for future research. Personality in Intellectual Contexts Inventory. Lachman and colleagues (i.e. Lachman, Baltes, Nesselroade & Willis, 1982; Lachman, 1983; Lachman & Leff, 1989) have

addressed the individual differences in memory performance from a personality ability paradigm. Her work has been concerned with perceived control and intellectual functioning in the elderly; that is, whether personal control beliefs are related to individual differences in cognitive aging (Lachman & Leff, 1989). Assessment of intellectual abilities and personality dimensions were used to study change in cognitive aging over time. Of particular interest here is the Personality in Intellectual Aging Contexts (PIC) Inventory, used to measure perceived control (Lachman, 1983).

The PIC assesses older adult's self-evaluations of their intellectual aging. Although the PIC scales are conceptually derived from six parent personality scales, they are specific to the domain of intellectual functioning. The parent personality scales are Levenson's (1974) multidimensional Locus of Control measure (tapping internal, chance and powerful others aspects of locus of control), a trait measure of Achievement selected from Jackson's (1974) Personality Research Form, an anxiety measure from the State Anxiety Scale (Cattell & Nesselroade, 1974) and finally a measure of Morale in the context of aging derived from a subscale of Attitudes Toward Own Aging (Lawton, 1975). These transcontextual personality dimensions served as models for conceptualization of a more context-specific personality dimension assessment. The parent personality dimensions can be viewed as a more general conceptualization,while the PIC focuses more on how these personality dimensions are narrowly concerned with intelligence and aging. The PIC was designed to cover self-assessments of intellectual competence and beliefs and attributions about intellectual functioning both in everyday activity and laboratory created situations. Items on the PIC include content related to intellectual aging and one of the following six dimensions: locus of control (internal, chance, powerful others); achievement; anxiety; and morale. The three locus of control scales are concerned with how one evaluates ones capabilities and attributes control over intellectual processes. The achievement scale is concerned with the importance associated with intellectual competence. Affective reactions to intellectual tasks, assessed via the anxiety scale and the morale or attitude toward own aging subscale, deals with perceived change in intellectual competence over time. The scale originally contained 158 items; however, following item analyses examining items with high interscale or interitem correlations or low intrascale correlation, the scale was reduced to 72 items (12 items on each subscale). The 72 items are randomly ordered across subscales and presented in a skpoint likert type scale with responses ranging from Strongly Agree to Strongly Disagree. Further analyses, examining

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correlations between the parent personality dimensions and the PIC context-specific dimensions, demonstrated convergent validity for the PIC. Coefficient alphas for the six subscales ranged from .76 to .91, and five month test-retest stabilities ranged from .74 to .88 (Lachman, et al, 1982). A multitrait-multimethod analysis approach was taken based on the relationship between the parent dimensions and the contextspecific dimensions. The resulting correlation matrix was consistent with the expected pattern of relationships. This agreement was evidence for both discriminant and convergent validity of the PIC. Assuming that the PIC measures the personality dimensions of interest, the next question is how these personality dimensions tie into cognitive aging. Lachman and Leff (1989) assessed personal control beliefs and intellectual aging in a five year longitudinal study. The study was interested in how these personal control beliefs may affect individual differences in intellectual aging. Subjects were administered intellectual assessment measures along with personality measures, specifically the PIC, during 1981 and then five years later in 1986. Three scales from the PIC were used; the internal control scale, the chance control scale and the powerful others control scale. The internal control scale reflecting how much the subjects agreed with statements concerning their intellectual competence on everyday cognitive tasks and their ability to learn or to succeed through effort. Examples of items on this subscale are: I could remember important telephone numbers if I practiced them; If I studied a map carefully, I could figure out how to get around in a strange place. Chance control assessed to what degree cognitive decline in later years is inevitable. For example, My crossword puzzle skills will go downhill, even if I keep doing puzzles. The extent to which one sees other people as better able to do things or whether one is dependent on others to solve cognitive problems in later life is assessed through the powerful others control subscale. For example, I would have to ask a salesperson how much I would save with a 20% discount. Each scale contained 12 items with a respective scoring range of zero to 60. The findings suggest that there were no changes found in intellectual functioning, generalized control beliefs or perceived internal control over intelligence in the elderly across the five year period. Sense of internal control remained stable across time, while beliefs in powerful others control significantly increased. The ratings of belief in inevitable decline in cognitive ability over time did not significantly increase over time. Interpretation of these results points to the idea that one's sense of personal self-efficacy is maintained over time, while also recognizing that dependency on others to help with cognitive tasks occurs. Overall control beliefs did not affect intellectual performance (Lachman & Leff, 1989). Although the PIC appears to be a reliable measure of perceived ability, whether it is a valid measure of self-perceptions is another issue. Recall that Weiner (198Sa, b) pointed out that one must be careful not to confound locus and control when measuring self-evaluations. We argue that these variables are confounded in the PIC, and lead to some problems in interpreting PIC scores. For example, belief that intellectual aging is inevitable is considered to be a chance attribution in Lachman's scheme, meaning that the locus of causality is external to the individual. However, when directly asked, individuals do not express this external locus; on the contrary, they believe it to be an internal one (cf. Cavanaugh & Morton, 1988). We believe that such

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troublesome categorizations come from the confounding of locus and control on the PIC, and not from a flaw in the underlying logic. Consequently, we believe that the PIC could prove to be a very useful approach, provided that continued scale development research is conducted to unconfound locus and control. Factor Analytic Approaches An early criticism of self-report metamemory questionnaires was that they lacked reliability and validity. Indeed, until the early 1980's memory questionnaires had been developed by cognitive psychologists with minimal background in questionnaire methodology, and often were constructed and used with meager supporting evidence for their construct validity (Gilewski & Zelinski, 1986; Herrmann, 1982). Dixon (1989) identified several key issues that metamemory questionnaires must address in order to correct these problems, including the adoption of a coherent theoretical stand on the number of separate aspects or dimensions of metamemory assessed by any questionnaire (see also Dixon & Hertzog, 1988; Gilewski & Zelinski, 1986).

In the early 1980's, two approaches for improving the quality of memory questionnaires emerged. One focused on memory complaints and problems, while the other focused on personal ratings of various aspects of memory functioning. Each approach is represented by several questionnaires, but two (one in each approach) have received the most psychometric research: the Memory Functioning Questionnaire using the memory complaint approach, and the Metamemory in Adulthood Instrument using the memory rating approach. Considerable work has been done using the factor analytic approach. Because a comprehensive review of this research is beyond the scope of this chapter, we intend to provide a taste of this literature to pique the reader's curiosity. For more information, we recommend the review chapter by Hertzog et al. (in press). The Memory Functioning Questionnaire (MFQ) (Gilewski & Zelinski, 1986) is a 64-item revision of Zelinski, Gilewski, and Thompson's (1980) 92-item Memory Questionnaire. There are seven dimensions to the MFQ: a single item General rating of memory problems, Retrospective Functioning (current compared to past memory ability), Frequency of Forgetting (how often do specific items present a problem), Reading (how often do specific problems of memory arise for written materials [e.g. magazines, books]), Remembering Past Events (memory for temporally remote events), Seriousness (seriousness of problems created by memory lapses), and Mnemonics (frequency of use of techniques to aid remembering). The Frequency of Forgetting scale is the longest and most important MFQ scale. Gilewski, Zelinski, Schaie, and Thompson (1983) explored the factor structure of the MFQ and obtained a three factor solution. An overall general frequency-of-forgetting factor accounted for 70% of the variance. The second factor contained items from the Seriousness scale, while the third factor had high loadings from items on the Retrospective and Mnemonics scales. The best validated scale deriving from the memory rating tradition is Dixon & Hultsch's (1983b) Metamemory in Adulthood (MIA) instrument. The MIA is a 120-item questionnaire measuring eight dimensions of metamemory: Strategy

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(knowledge about basic memory processes), Capacity (perception of one's ability to perform on specific tasks), Change (perception of the degree of stability of memory with increased age), Anxiety (feelings of stress related to memory performance), Activity (degree of participation in other everyday activities thought to influence memory ability), Achievement (perceived importance of having a good memory), and Locus (perceived control over memory ability), Dixon and Hultsch (1983b) reported that six out of the eight scales were reliable and factorially valid. Those items expected to load on each of the diverse scales did so. Hertzog, Dixon, Schulenberg, and Hultsch (1987) hypothesized that inherent in the MIA scale may be higher-order structure with the different scales loading on the different factors. Data on seven MIA subscales from six separate studies were combined for cross validation purposes. The Activity scale was dropped from the analysis due to poor internal consistency, thereby leaving seven scales to be analyzed. The data were divided into three age samples, corresponding to young, middle-aged, and elderly adults. Hertzog et al. first hypothesized that three higher-order factors existed on the MIA: general memory knowledge, beliefs about memory (self-efficacy), and memory-related affect. Exploratory factor analysis was used to assess the number of factors found among the three groups. One and two factors solutions did not adequately fit the data, thereby supporting the initial hypothesis of a three factor solution. Strategy and Task scales loaded on the Knowledge factor. Capacity and Change formed the Self-Efficacy Beliefs factor. The Memory-Related Affect factor consisted of the Achievement, Locus, and Anxiety scales. Simultaneous confirmatory factor analyses techniques, using LISREL, were then employed to test the three factor solution. This time more restrictions were placed on the data. That is, the number of factors was predetermined as well as which attributes should load on which factors. This analysis provided a test of whether the postulated factor structure fit the data, and whether that same factor structure can be found across different measures and/or different populations. Contrary to expectation, the three factor solution did not adequately fit the data because the Memory-Related Affect factor was ill-defined. Consequently, the three factor model was abandoned. Further analysis yielded a two factor solution; however, there was a residual covariance between Achievement and Anxiety. The two factors were labeled Knowledge and Self-Efficacy Beliefs. In this solution Locus, Achievement, and Anxiety scales related to both factors. The two factor solution was accepted as adequate, so the equality of this factor pattern was tested across the age groups. The two factor structure was not equal across groups, apparently indicating that the MIA was tapping different constructs across the three age groups. Follow-up analysis pointed out that this discrepancy across the three groups was related to the Self-Efficacy factor. In this new analysis, the Knowledge factor was held constant over the three groups while the Self-Efficacy factor was allowed to vary. This new model appeared to fit the data adequately.

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In short, the hypothesis of higher-order factors of the MIA subscales appears to be plausible. The Knowledge factor is measured by the Strategy and Task scales, while the Self-Efficacy Belief factor reflects Capacity and Change. The three remaining scales, Achievement, Locus, and Anxiety, loaded on both factors.

As stated earlier, the MIA and MFQ were designed specifically for use with adult of all ages. One important psychometric property of these scales, therefore, is their potential sensitivity to age differences. The differences were not only expected but obtained, at least in the initial assessment of the psychometric properties of these scales (e.g., Dixon & Hultsch, 1983b). Cross validation using different age groups showed sensitivity to age differences on several of the MFQ scales (e.g., Gilewski et al., 1983) as well as on the MIA Task, Capacity, Change, and Locus, with the largest differences found on the Change scale (Dixon & Hultsch, 1983b; see also Cavanaugh & Poon, 1989). Once these initial analyses documented the higher-order factor structures, replicating them became an important issue. Unfortunately, even though virtually identical procedures were employed, failures to replicate the initial pattern of age differences on these two scales have been reported. For example, Hultsch, Hertzog, and Dixon (1987) in an attempt to replicate earlier findings on the MIA, found age differences for Capacity, Change, and Locus scales, but found no differences for Task. Hertzog et al. (1987) attempted to replicate the two factor solution in an independent sample. The results showed consistency with regards to the Self-Efficacy Beliefs factor but failed to replicate the Knowledge factor. The inability to replicate was not limited to the MIA alone. Johnson and Anderson (1988) also encountered difficulty attempting to replicate the factor structure of the MFQ. In a subsequent study reviewed below, Hertzog et al. (1989) revised the best-fitting factor structure to accommodate these findings. A second major issue concerning these measures concerns their predictive validity. In the present context, predictive validity refers to the relationship between these metamemory questionnaires and memory task performance. From a very naive standpoint, an individual's self-report of memory ability should be closely related to an individual's performance on a memory task. However, researchers have consistently shown that this expected high correlation is virtually never obtained; indeed, correlations between metamemory scales and performance are modest at best (e.g., Cavanaugh & Murphy, 1986; Cavanaugh & Poon, 1989; Dixon, Hertzog & Hultsch, 1986; Dixon & Hultsch, 1983a; Zelinski, et al., 1980). Some investigators even report weak to non-existent relationships (e.g., Sunderland, Watts, Baddeley, & Harris, 1986; West, Boatwright, & Schleser, 1984). Some have postulated that this failure to correlate metamemory questionnaires with memory performance stems from poor questionnaire construction (Herrmann, 1982). Undoubtedly, predictive validity is an important aspect of scale construction. However, its use as the major determinant of the validity of a scale is questionable (see Cavanaugh et a1 [1990] and Hertzog et al. [in press] for related arguments). To the extent that metamemory questionnaires tap aspects of self-efficacy, and to the extent that metamemory itself is a dynamic construct, then the expectation of high

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correlations between scores on such scales and performance is mistaken. As argued in earlier sections of this chapter, processes such as metamemory and self-efficacyserve as mediating and mediated constructs. Consequently, one would expect modest correlations between measures of self-efficacy per se and performance unless one takes the other mediating processes into account. The point here is that predictive validity is not the most appropriate benchmark of validity for measures of self-efficacy or metamemory. Rather, we should adopt a construct validity benchmark, and determine whether the scales in fact measure what we think they do. Much of the work involved in this approach concerns demonstrating convergent and discriminant validity, which is precisely the approach adopted by Hertzog and colleagues. Convergent and Discriminant Vulidution of the MIA and MFQ. Convergent validity refers to the degree that two instruments, in this case the MIA and MFQ, both of which contain multiple scales, are actually measuring the same underlying constructs. Although the two measures do not overlap completely (e.g., the MIA measures affect more than the MFQ), it is hypothesized that there is one underlying construct evident in both. Hertzog, Hultsch, and Dixon (1989) undertook the task of exploring the convergent validity of these two measures. In previous work reviewed above, Hertzog et al. (1987) reported that Capacity, Change, Anxiety, and Locus scales of the MIA load on a dimension they labeled Memory Self-Efficacy. Hertzog et al. (1989) hypothesized that the same construct was being measured by the Frequency-ofForgetting scale of the MFQ.

Two samples, drawn from different populations, were administered both the MIA and MFQ. Convergent validity was evaluated using confirmatory factor analysis techniques via LISREL. Three questions were addressed: (1) Is the Memory Self-Efficacy factor in the MIA also found in the MFQ? (2) Does the same factor pattern appear in the two different samples? (3) Are equivalent factor structures found in different age groups? Analysis of the MFQ suggested that its global rating, retrospective functioning, frequency of forgetting, and remembering past events tap interrelated aspects of memory self-efficacy. Further analysis showed that the correlation between the Memory Self-Efficacy factor of the MFQ and the Memory Self-Efficacy factor of the MIA was almost perfect. This is strong evidence that both questionnaires measure memory self-efficacy. Hertzog et al. (1989) also postulated that a second factor, Memory Knowledge, was present. However, a two factor solution was not congruent with the data. Instead, the analyses indicated four distinct factors aside from the two memory self-efficacy factors: an MFQ Reading Self-Efficacy factor (with high loadings on the scales addressing problems remembering novels and problems remembering newspapers and magazines), a Strategy factor (with high loadings on the Strategy scale of the MIA and the Mnemonics scale of the MFQ), an Affect factor (tapped by the Achievement scale of the MIA) and a Change factor (tapped by Change and Locus scales of the MIA and the Retrospective Functioning scale of the MFQ).

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Correlations between the factors help clarify these findings, and can be seen in Table 2. The Change factor correlated with both MSE factors in the expected direction. High correlations were found between the MSE factors and the MFQ Reading Self-Efficacy factor. Correlations between the two MSE factors and the other factors were relatively low. This pattern demonstrates two things. First, it shows that there is a relationship between different aspects of self-efficacy,but that this relationship is far from perfect. This finding supports the view that measurement of self-efficacy needs to be done within specific domains; a general self-efficacy scale will not serve a useful purpose. Second, memory self-efficacy is a distinct construct from other aspects of metamemory. This finding strongly supports the view that metamemory is a multidimensional construct (Cavanaugh, 1989; Cavanaugh et al., 1989; Hultsch et al., 1988). Table 73 ~

Factor Correlations Between MIA and MFQ Scales in Four Groups Factors MSEMIA,

MSEMFQ

MSEM,, MSE, MSEMm, MSE, MSE,, Strategy MSEMm,Strategy MSE,, Strategy MSEM,, Affect MSEM,, Affect MSE,, Affect Strategy, Affect MSEM,, Task MSEM,, Task MSE,, Task Strategy, Task Affect, Task MSE,,, Change MSEMm,Change

Victoria (55-78 yrs) 88 67 74 -19 -12 -11 -10 09 -06 32 09 05 01 08 32 37 32

Annville (55-78 yrs) 96 73 75 -37 -21 -07 -21 -15 -16 29 10 20 23 09 18 64 49

Victoria (20-55 yrs) 1.04a 74 81 -26 -23 -12 -13 -01 01 27 00 06 14 27 18 34 17

Annville (20-55 yrs) 84 59 78 -18 -15 -14 -17 11 -03 38 10 19 24 12 27 13 11

Note. MSE = Memory Self-Efficacy; MIA = Metamemory in Adulthood scale; MFQ = Memory Functioning Questionnaire; RD = Reading. Decimals are omitted. a Estimated covariance, when rescaled, was greater than 1.0. Reprinted from Table 9 in Hertzog, C., Hultsch, D. F., & Dixon, R. A. (1989). Evidence for the convergent validity of two self-report melamemory questionnaires. Developmenfal Pq~chology,25, 696. Copyright 1989 by the American Psychological Association. Reprinted with permission.

The next question addressed was whether the factor pattern of the first sample was similar to the factor pattern obtained in the second sample. The question is answered via simultaneous confirmatory factor analysis. Results of this analysis showed that the

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same factor structure could be found in both samples, further bolstering the robustness (and validity) of the results. In the third test of convergent validity, Hertzog et al. (1989) divided each of the two samples into two age groups and compared the factor structures. Surprisingly, there were no significant differences found in the factor loadings across the age groups. However, before dismissing the idea that age differences are non-existent, it is beneficial to compare the factor correlations. Significant age group differences were found in the correlations of the Change factor with the Memory Self-Efficacy factors. Perceived change was more highly correlated with self- efficacy in the older groups. Hertzog et al. (1989) also examined the discriminant validity of the MIA and MFQ. Through multivariate analytical techniques, they compared the metamemory questionnaires with a variety of personality measures, general locus of control measures, and psychological distress measures. The personality factors under examination were Neuroticism, Extraversion, and Energy, all derived from the Jackson Personality Research Forms and the Jackson Personality Index. The locus of control measures yielded a general control score. The psychological distress measures were Well-Being, Depression and Anxiety. Correlations between these additional measures and the two Memory Self-Efficacy factors of the MIA and MFQ yielded some very interesting results. The two MSE factors correlated roughly .5 with General Self-Efficacy. Hence, it can be said that although memory self-efficacy is related to general self-efficacy, it is still distinct. The MFQ-Memory Self-Efficacy factor correlated more highly to factors of Neuroticism and psychological distress and well-being than did the MIA-Memory Self-Efficacy factor. A significant residual correlation was found between the MFQ's Global rating and the Neuroticism and well-being factors, independent of the correlation with overall MFQ-Memory Self-Efficacy. The results obtained from this study are interesting in two respects. First, Memory Self-Efficacy appears to differ from basic personality dimensions. Although there are relationships among Memory Self-Efficacy and some of these factors, they were largely minimal. This result supports Cavanaugh's (1989; Cavanaugh et al., 1989) view that personality and self-efficacy represent two distinct constructs. Second, these results suggest that the MFQ, with its emphasis on memory problems, reflects the influence of Neuroticism and Psychological Well-Being more than the MIA. This finding should not be totally unexpected based on the premise that these two measures come from two different approaches to memory self-report. Moreover, the connection between measures of complaints and personality provide psychometric documentation for Cavanaugh, Grady, and Perlmutter's (1983) finding of a strong affective component to self-reports of memory failures, especially among older adults. In sum, the pioneering work of Hertzog and colleagues convincingly shows that memory self-efficacy is a real construct that can be measured reliably and validly. Their efforts have documented that self-efficacy needs to be assessed within specific domains, that it is distinct from personality as well as other psychosocial variables, and that format of the question may matter. We believe that their work is the most significant advance in the validation of self-evaluation instruments over the past

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decade, and should be viewed as a role model for other investigators. As we note later, we think that their approach should be adapted and expanded by researchers interested in developing scales targeted specifically to memory self-efficacy. Applying Memory Self-Efficacy: Mnemonic Training

To this point, we have reviewed the meaning and measurement of self-efficacy and related constructs. However, even psychometrically perfect measures are useless unless the constructs they measure have some practical utility. In the case of memory selfefficacy, this utility has been explored most in the context of mnemonic training. Traditionally, mnemonic training emphasizes acquiring and using memory strategies such as organization or imagery, or improving concentration and attention (Borkowski & Cavanaugh, 1979). This view assumes that the reason for poor memory performance is entirely due to ineffective and inefficient memory strategies. Indeed, much of the literature on memory aging documents the lack of use of effective strategies by the elderly (Hultsch & Dixon, 1990; Poon, 1985). As should be apparent by now, however, this view of mnemonic training is incomplete (Borkowski & Cavanaugh, 1979). Successful mnemonic training requires not only instruction of strategies, but also a clearly defined rationale. This rationale involves recognizing how self-conceptions or self-evaluations influence the selection and use of mnemonic strategies. As noted earlier in this chapter, such self-evaluations, manifested as self-efficacy, exert powerful influences on effort and motivation, which in turn are thought to heavily influence the selection of strategies (see Cavanaugh, 1989; Cavanaugh et al., 1989). Because we have already reviewed the major evidence supporting the influence of self-efficacy, we will limit our discussion here to applying these results to mnemonic training in the elderly. Also, because Elliott and Lachman (1989) provide a good review of the nuances involved in this application, we will only highlight their major points. Much of our own contribution, therefore, consists of critiques and suggestions for additional research. Linking Self-Efficacy and Mnemonic Training Elliott and Lachman (1989) developed a working framework for building self-efficacy into mnemonic training programs. Their premise is straightforward: "to help the elderly deal effectively with memory problems, one must orient them toward a constellation of adaptive thought, affect, and behavior" (Elliott & Lachman, 1989, p. 345). This constellation, we argue, encompasses memory self-efficacy. Thus, in order for a mnemonic training program to be effective, it must include a component that addresses older adults' negative self-evaluations of their memory. Elliott and Lachman's premise agrees with inferences drawn from Cavanaugh's (1989; Cavanaugh et al., 1989) model. Recall that he postulated a central role to selfefficacy that largely influenced effort, motivation, and the like, which are the major determinants of strategy selection. Extrapolating a bit, it stands to reason that

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interventions that target key influential variables should be more powerful than interventions that target outcome variables. Consequently, interventions aimed at only training mnemonics should be less effective than interventions aimed at underlying beliefs as well as strategies. This prediction is supported by many studies that show little or no maintenance or generalization of trained mnemonics when such strategies are presented alone (e.g., Anschutz, Camp, Markley, & Kramer, 1987). It is important to note that Elliott and Lachman (1989) do not propose replacing training mnemonics with self-efficacy training. In fact, evidence suggests that mnemonic training, even when done well, has little influence on self-evaluations such as self-efficacy (e.g., Rebok & Balcerak, 1989; Scogin et al., 1988). Training on both aspects must proceed together. Lachman and her colleagues have begun investigating such combined training. In a preliminary study, Lachman and Dick (1987) showed that older adults who received persuasive attributional information in conjunction with training in the method of loci showed greater increases in recall confidence and made more adaptive attributions about their performance on a transfer task than older adults receiving either strategy training alone or no training. Unfortunately, improvement in confidence was not matched by performance; memory performances in the strategy plus attribution and strategy only groups were equivalent. In a subsequent study, Weaver and Lachman (1989) improved the attribution training procedure, included a young adult comparison group, and examined maintenance of training over a one-week interval. The method of loci was the strategy trained. Three groups within each age level were included: strategy plus attribution, strategy only, and no training. Attribution training consisted of several components, including providing adaptive attributions during training by comparing pretest to training performance, information about the controllability of memory, and several other issues (e.g., memory decline in old age is not inevitable, strategies improve performance, remembering and forgetting are due to many factors, and that people tend to be biased to dwell on failures rather than on successes). Prior to performing the task, individuals predicted how many items they would remember, as well as completing a modified version of the MSEQ. One striking finding from this study was that memory performance and self-efficacy improved over time, regardless of training condition. People in the strategy only group improved most from pretest to first posttest, but this advantage was gone by the one week follow-up. Younger adults performed better and had higher self-efficacy strength than older adults, but no age differences were found in performance prediction accuracy. In an attempt to account for the improvement in self-efficacy, Weaver and Lachman conducted additional analyses based on differences on demographic variables. They found that young-old (M = 64 yrs) participants showed greater improvement in memory performance and self-efficacy than old-old adults (M = 73 yrs). Strangely, however, selfefficacy improved more in young-old adults in the strategy only condition. Additionally, they noted that individuals scoring lower on depression became more accurate in their performance predictions.

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Weaver and Lachman's (1989) study shows clearly that the dynamics of the selfefficacy-performance relationship are complex. Contrary to expectation, providing a set of attributions in addition to mnemonic training did not significantly add to performance. In some cases, it actually hurt. They note that one problem may have been the timing of attribution training; that is, it may need to begin prior to mnemonic training rather than afterward as in their study. We agree with this assessment. Perhaps the main advance made by Weaver and Lachman is to suggest that cognitive training research has much to gain from the clinical literature. Like Cavanaugh (Cavanaugh & Morton, 1989; Cavanaugh et al., 1989), they argue that memory training needs to include cognitive restructuring similar to that used in cognitive therapy (e.g., Beck, Rush, Shaw, & Emery, 1979; Ellis & Harper, 1975). Close inspection of these clinical techniques reveals a strong emphasis on the central role of personal beliefs in determining behavior that is highly similar to the arguments raised earlier in this chapter. Cognitive developmental research is only now becoming aware of the utility of principles developed years ago by clinicians (cf. Kramer & Bopp, 1989). We strongly support such cross-fertilization, viewing it as the primary way to continue advancing the field. Where Do We Go From Here?

The 1980s have seen major shifts in the understanding of cognitive aging. The inclusion of the construct self-efficacy as an important variable in research is but one indication of the shifts. Clearly, we need to continue researching these ideas, as well as to keep searching for new ones. In this concluding section, we will provide some issues that we believe will emerge as key ones during the 1990s. Theory Development

Earlier we described several theoretical constructs we believe are important for understanding self-evaluations of memory. Although self-efficacy figured prominently in this discussion, additional notions are needed as well. We believe much more attention needs to be paid to translating these constructs from semi-connected ideas to complete theoretical frameworks. Cavanaugh's efforts notwithstanding, we have precious little theoretical guidance for research on self-evaluations of memory. From our perspective, this should be the major focus of work in the near future, if for no other reason than to bring closure to this fragmented field. Where should this work begin? Perhaps the first step is understanding the cognitive processes underlying the responses people make on metamemory questionnaires. To date, we have little knowledge about what goes through people's minds when they are asked to give ratings of their memory. Work on this topic is proceeding (see Cavanaugh et al., 1990), but much more emphasis is needed. Second, we need to develop better frameworks concerning the interrelationships among the various constructs discussed in this chapter. For example, how implicit theories of memory and personal control beliefs interact needs to be thought through. In any event, we need to spend far more time thinking about how it all fits together rather than reloading our empirical shotguns. Only then will we be in a position to conduct the kind of theorydriven research needed to advance the field.

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Measuring Self-Efficacy Earlier, we reviewed three main approaches to the measurement of self-efficacy in memory aging research. We believe that continued progress on this issue is needed. Scales such as the MSEQ need to be refined, as well as expanded to other content areas. Continuation of factor analytic work in the Hertzog, Dixon, and Hultsch tradition could be expanded to include these more specialized scales rather than omnibus metamemory instruments. Important as these research efforts will be however, they do not exhaust the possibilities. To us, the most important methodological issue is to develop measures of the rest of Bandura's notion of self-efficacy. that is, we have focused to date primarily on the prediction side of the construct. We have ignored effort, motivation, and the like, the very variables Bandura (e.g., 1989a, b) views as essential to understanding self-efficacy in the first place. Arguably, without such measures, we have not really been studying self-efficacy, but rather the passive prediction that Bandura clearly distinguishes from self-efficacy. Consequently, we must develop indicators of effort, motivation, and other activities that put predictions into action and influence strategy selection and performance. We recognize that such efforts will be difficult. Indeed, Borkowski and Cavanaugh (1979) alluded to similar needs, but few investigators heeded their call. Although indicators such as task persistence are less than ideal, they at least provide a place to start. We also believe that increased use of observational techniques, interviews, talkaloud approaches, and naturalistic inquiry methods could be exploited for additional measures. While it is likely that no one method will be perfect, combining the benefits of several approaches will minimize the limitations and provide converging information about the constructs. Development of such measures would also provide a better test of theoretical frameworks that incorporate self-efficacy (e.g., Cavanaugh et al., 1989). Applying Self-Efficacy The second research area we think will emerge involves applying self-efficacy not only to understand cognition in older adults, but also as the basis for intervention in a wide variety of arenas. We have already witnessed an increasing interest in basing memory training programs on self-efficacy. As training research continues to become more sophisticated, we believe that researchers will look increasingly to cognitive therapeutic approaches for guidance. Caution needs to be exercised, however. While cognitive restructuring clearly works, how it is implemented needs careful attention. As currently practiced in memory training research (e.g., Weaver & Lachman, 1989), restructuring focuses on getting participants to attribute memory failures to controllable variables such as effort. Thus, people who perform poorly on a recall task would be trained to believe that by exerting more effort they will improve their performance. Most of the time this is true; using a mnemonic such as the method of loci takes increased effort, but this effort pays off in better performance.

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Our caution is that we must not lose sight of the fact that for some older adults, poor performance may not be remediable by effort (i.e., mnemonics) alone. Given the undeniable evidence that secondary memory performance declines in old age (e.g., Poon, 1985), there may well be structural limits on how much improvement we can expect. Indeed, most training studies still find that mnemonic strategies do not completely eliminate the differences between older and younger adults. To date, we have not considered the possible ramifications of restructuring someone's belief system who is already performing to capacity. What happens to the person who, despite the best of training, cannot improve performance, but who is now (as the result of training) is making self-statements that he or she is not exerting enough effort? The solution, we believe, is this. We need to understand the difference between remediable performance problems and intractable performance problems. We need to be sensitive to the potential drawbacks of getting people to make internalcontrollable self-evaluationsbefore we blanketly instruct experimental training groups. In short, we need to tailor the instructional approach to the person, much the same way that good clinicians tailor their therapeutic approach to fit the client. For example, the typical cognitive restructuring approach may be entirely appropriate to a normal, healthy older adult. But for the person with brain damage from a stroke, this approach may be inappropriate; indeed, one may want to get this individual to make self-statements that focus on the disease as the cause of poor performance if necessary in order to emphasize that not all aspects of performance are controllable. Our point is that we need to conduct research that refines our ability to individualize intervention. We pointed out earlier that such things as implicit theories have profound consequences for everything from what tasks one chooses to where one looks for rewards. We suggest using such known constructs to sort individuals into groups holding similar beliefs, and compare these groups. Only in this way will we be in a position to understand the true role of self-efficacy in memory aging. Averaging across people holding vastly different beliefs (as is done routinely now) only obscures the issue. Summary and Conclusions

In this chapter, we have reviewed theory and research relating to self-efficacy. We showed that several other constructs related to self-efficacy are also useful, and that all of them are necessary in order to understand metamemory. Unfortunately, as our review of the methodological literature showed, few researchers have developed strong indexes of self-efficacy, and even fewer have attempted to build theoretical frameworks incorporating the empirical work. Much remains to be done. We believe that there is much to be gained by examining how self-efficacy has been applied to other content areas. In this way, we may be able to avoid some of the pitfalls and make important advances in the near future. We are convinced that self-efficacy and its related constructs will prove to be the key to understanding memory performance in the elderly. We hope that researchers will accept the challenge to test our belief.

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Sunderland, A., Watts, K., Baddeley, A. D. M., & Harris, J. E. (1986). Subjective assessment and test performance in elderly adults. Journal of Gerontology, 42,376384. Weaver, S.L,& Lachman, M. E. (1989, August). Enhancing memory self-conceptions and strategies in young and old adults. Paper presented at the annual meeting of the American Psychological Association, New Orleans. Weiner, B. (1985a). An attributional theory of achievement motivation and emotion. Psychological Review, 92, 548-573. Weiner, B. (1985b). "Spontaneous" causal thinking. Psychological Bulletiq 97,74-84. Wellman, H. M. (1983). Metamemory revisited. In M. T. H. Chi (Ed.), Trends in memory development research (pp. 3 1-51). Basel: Karger. West, R. L., Boatwright, L. K., & Schleser, R. (1984). The link between memory performance, self-assessment, and affective status. Experimental Aging Research, 20, 197-200. White, J. (1982). Rejection. Reading, M A Addison-Wesley. Wood, R.,& Bandura, A. (1989). Impact of conceptions of ability on self-regulatory mechanisms and complex decision making. Journal of Personality and Social PsyChOlO~,56, 407-415. Zelinski, E. M.,Gilewski, M. J., & Thompson, L. W. (1980). Do laboratory tasks relate to self-assessment of memory ability in the young and old? In L. W. Poon, J. L. Fozard, L S. Cermak, & L.W. Thompson (Eds.), New directions in memory and aging (pp. 519-544). Hillsdale, NJ: Erlbaum.

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Memory Interventions in Aging Populations Susan Kotler-Cope Louisiana State University Cameron J. Camp University of New Orleans

To outline current models of memory and the way they relate to aging would require more space than is available for this chapter (and indeed, would require a chapter to itself -- e.g. Chapter 1 of this volume). The focus of this chapter is on memory interventions. As such, we will assume that the reader has a working knowledge of models of memory and the more general research in the aging and memory literature. Several excellent reviews of that literature have appeared over the years (e.g. Craik, 1977, 1984; Hultsch & Dixon, 1990; Poon, 1985), to which the reader is referred. Given the ubiquity of memory complaints and actual deficits associated with aging and disorders commonly found in elderly adults, it appears that there is a great need for techniques that can improve the elderly person's memory functioning in everyday life. However, it might also seem that such techniques would be doomed to fail, given the number of aspects of memory functioning and processes related to memory functioning that appear to be impaired in older adults. Many factors need to be taken into consideration in devising effective memory interventions for elderly individuals. Treat, Poon, Fozard, and Popkin (1978) have provided guidelines for establishing effective cognitive skills programs and Poon, Fozard, and Treat (1978) have outlined a comprehensive intervention program for memory problems at a time when memory intervention research with elderly subjects was in its "infancy." Our prescriptions follow basically the same outline, but we have attempted to incorporate more recent findings from research on aging and memory as well as more recent recommendations for designing memory interventions in both normal and pathological aging populations (Camp & McKitrick, 1989, in press; Glisky & Schacter, 1986). Features of Effective Memory Interventions for Older Adults When designing a memory intervention for an elderly individual, it is first necessary to determine if the change in memory ability reported by the older adult represents a true functional deficit in memory or is due to some other factor (e.g., drug side-effects). Multimodal, comprehensive assessment of physical, cognitive, and affective functioning through subjective and objective measures is necessary. Such assessments have become important in most instances where Alzheimer's Disease is suspected, and may become standard in making a diagnosis of Age Associated Memory Impairment -- AAMI (Crook et al., 1986).

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Next, keeping in mind the etiology of the deficit (e.g., dementia, cerebrovascular accident, etc.), the program should attempt to capitalize on presumed and observed intact functions and to compensate for non-intact functions (e.g. see Nebes, 1989, for a review of impaired and spared aspects of semantic memory in Alzheimer's Disease). An effective memory intervention program should also take into account the general findings from research on the memory abilities of normal elderly individuals (see Craik, 1984;Kausler, 1989,and Chapter 2 of this volume). The recommendations that follow are based on these general findings. Because many older adults do not spontaneously develop organizational strategies or spontaneously engage in elaborative or deep encoding, both of which would help them remember more efficiently, techniques that promote the development of these processes must be employed or provided directly by the trainer (see Craik, 1984). Elderly trainees should be directed to attend to many aspects of the stimuli the multimodal attributes of the stimulus, the unique, specific, subtle, and detailed information as well as the typical, global, salient, and general characteristics. Stimulus attributes deemed irrelevant or unusual may be readily ignored by elderly individuals. Efforts should be made, however, to avoid overtaxing the apparently limited attentional and working memory capacity of older learners. Presenting too much instruction and information, presenting it at a rate that is too fast to be processed adequately, or creating situations that would require simultaneous processing of information already in working memory and new, incoming information should be avoided. Attempts to reduce the amount of effortful processing and increase the amount of automatic processing by providing adequate encoding aids and retrieval supports would also be helpful.

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Allowing elderly subjects to self-pace the presentation of stimuli, to engage in sufficient (perhaps additional) practice time, to use familiar, meaningful, and highly discriminable stimulus materials, to respond in a cued recall or recognition rather than free recall format, and to be provided with a good match between encoding and retrieval cues would be expected to enhance learning. Implicit learning and procedural learning, which are relatively intact in older individuals, may be enhanced by motoric enactment of the to-be-remembered information. The formation of semantic memories may be facilitated by additional practice or rehearsal of episodic events. The probability of remembering to carry out certain actions can be increased with the use of external, rather than internal strategies. Finally,care should be taken to provide a supportive learning environment for the elderly individual where he or she has a sense of control over the learning situation and is allowed to experience a feeling of achievement and competence. If older participants can make choices regarding the types of information to be learned and/or the types of mnemonics they feel comfortable using, chances for a successful intervention increase. An individualized memory intervention program should match the unique features of the person (e.g., skill level, personality, cognitive style) to the type of materials (e.g., meaningful or familiar) and techniques (e.g., type of task) employed. Elderly individuals should be informed that the acquisition of new cognitive skills may be arduous initially and require frequent and long-term practice to maintain their new skills. Additional support will probably be necessary to prevent the elderly individual from becoming discouraged and giving up.

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Granted, this prescription for an effective memory intervention program represents an ideal situation. However, as the review of mnemonic techniques presented in the next section will reveal, many memory interventions that seem to fall short of this ideal appear nevertheless to be effective in helping elderly people remember what they need to remember (Yesavage, 1985). Classification and Description of Mnemonics There are several ways to classify types of mnemonics, i.e. memory aids. Each classification system has a distinctive theoretical basis which, in turn, affects how the mnemonic technique may be implemented at the practical level. We will describe these classification systems and give examples of the types of mnemonics associated with these systems. We will then describe the types of mnemonics used spontaneously by older adults, followed by a review of research which involved utilizing mnemonics as interventions with older populations. Organizational and Encoding Mnemonics Bellezza (1981, 1987) has proposed a system which classifies mnemonics into two types. Organizationalmnemonics "organize and interrelate new information in memory so that it can be later recalled" whereas encoding mnemonics are used "to transform low-imagery, abstract material into a more memorable form before an organizational mnemonic is used to store the information in memory" (Bellezza, 1987, p.35). This classification system, then, assumes an hierarchical arrangement of mnemonic devices. The hierarchical aspect of mnemonic processing has rarely been addressed in the memory intervention literature. Failure to take into account the hierarchical order of mnemonic components may explain the failure of some memory interventions. Bellezza has also described properties of the mental cues that are essential if the mnemonic is to function effectively. Consfrucfibilityrefers to the ease and reliability with which a mental cue can be generated during learning and retrieval. This is related to the principle of encoding specificity (Tulving 2% Thomson, 1973) and the degree to which the encoding and retrieval cues should be as similar as possible. For example, the method of loci mnemonic will not be effective if the loci are not wellmemorized in the first place. Associability refers to the ease with which the mental cues can be associated with the to-be-remembered (TBR) material. More vivid composite images enhance retention better than less vivid images. Mental cues already related to, or more often associated with, the TBR material are more easily associated with one another. For example, "car" is more readily associated than "apples"with the locus "garage." Familiarity of stimulus materials and techniques was described earlier as an important factor in enhancing elderly subjects' performance on memory tasks. Discriminability refers to the fact that cues should be distinctive enough that they are not easily confused with one another. For example, loci "placed mentally far apart or word lists placed on different colored backgrounds enhance discriminability and facilitate learning (Bellezza, 1983, 1986). Emphasizing the salience of encoding cues was discussed earlier as a method of enhancing encoding by elderly subjects. Invertibility refers to a "bidirectional association between a mental cue and the new information associated with it" (Bellezza, 1987, p.38). The association may be encoded one way but should be able to be recalled in the other direction. For example, in

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paired-associate learning, the initial presentation may be "fiscal-money" but recall conditions may present "money,"rather than "fiscal" as the cue. Bidirectional encoding facilitates learning. This aspect has not been explicitly examined in the memory intervention literature, Bellezza's other essential properties of mental cues have been supported by the research on memory abilities of elderly people.

Process and Fact Mnemonics Higbee (1985) has recently proposed a distinction between process and fact mnemonics. Fact mnemonics provide specific information directly in a way that duplicates the information contained in the mnemonic. For example, "30 days hath September" is a mnemonic that contains the important to-be-remembered information within the mnemonic rhyme. Process mnemonics are used to generate information from the mnemonic and are helpful in "remembering rules, principles, and procedures - the processes underlying problem-solving"(p. 407). For example, the rule "i before e, except after c" describes a procedure for remembering how to correctly arrange certain letter combinations but does not give all the examples of the rule. Fact mnemonics are specific devices that provide information about what is remembered whereas process mnemonics are general techniques that provide information about how information is remembered. Within both of these systems, meaningfulness, association, organization, attention, and visualization are essential principles and similar to some of those qualities described by Bellezza (1981, 1987). This separation of mnemonic strategies into two types appears to follow the distinction between declarative and procedural. Fact mnemonics involve remembering facts about something or the "something"itself, for example, the name of a person or the location of an object. Like declarative knowledge, knowing that is acquired through verbal rehearsal or other processes that are deliberate, slow, conscious, and communicable. Process mnemonics involve remembering how to do something, for example, a sequence of steps to carry out a procedure. Like procedural knowledge, knowledge about how is processed more automatically, quickly, and less consciously and is less communicable. However, while declarative knowledge may become procedural knowledge with repeated practice, fact mnemonics and process mnemonics remain distinct. As we have mentioned, procedural memory may be more intact in elderly people than declarative memory. This raises the question whether or not process mnemonics would have greater efficacy as a memory intervention technique with older people than would fact mnemonics. Internal and External Mediators The most common method for classifying mnemonics is in terms of internal or external mediators. Internal mediators are those that exist inside one's mind and usually are employed to facilitate learning. The method of loci, pegword, first letter cueing, rehearsal, concentration, imaging, elaborating, associating, reconstructing, grouping, and so forth are examples of internal mnemonic techniques (Cavanaugh, Grady, & Perlmutter, 1983). Internal mediators appear to involve more effort to learn and use than external mediators. However, it should be kept in mind that all mnemonics involve the deliberate, effortful application of a strategy, at least initially. With repeated practice, they may become more automatic.

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External mediators are those that exist outside one's mind and usually function as reminders to do something. Lists, diaries, calendars, calculators, timers, memos, placing a note or object in a distinctive location, or something as simple as a string tied around the finger are common examples of external mnemonic devices (Wilson & Moffat, 1984b). External mediators appear to involve less effort to learn to use than internal mediators (Dixon & Hertzog, 1988). If external mediators are easier to use and involve less of the elderly person's limited cognitive resources than internal mediators, one would expect to find that elderly individuals rely on external mediators more often than internal mediators. Studies of the use of mnemonics in the elderly reveal that, although this is generally the case, older subjects have preferences for different types of mediators in different situations (Poon, Walsh-Sweeney, & Fozard, 1980; Winograd & Simon, 1980). Spontaneous Use of Mnemonics by Normal Elderly Individuals

As mentioned above, external mediators appear to be used more frequently arid spontaneously by elderly people than do internal mediators (Cavanaugh et al,, 1983; Harris, 1978, 1980). In a study by Jackson, Bogers, and Kerstholt (1988), elderly and young subjects were given a series of questionnaires about memory lapses, how they would try to remember past information and to do things in the future, and a memory aids frequency questionnaire. Responses on the questionnaires revealed that elderly subjects had used and would use more external aids for remembering to engage in future activities, but neither external nor internal aids more frequently for remembering past events. Elderly subjects appear to rely less on their own memory systems and attempt to keep their lives in order by relying on external aids. In a study by Weinstein, Duffy, Underwood, MacDonald, and Gott (1981), elderly subjects were asked to describe the strategies they would use to help them remember information for either everyday tasks or laboratory tasks involving traditional experimental materials. The number of strategies generated by subjects presented with the everyday tasks was much larger than the number of strategies generated for the experimental tasks. These findings suggest that the learning situation and the type of material to be remembered may significantly affect the number of strategies deemed useful by the elderly person. Several studies have investigated the actual spontaneous use of mnemonics by elderly subjects in daily life and in experimental situations. An early study by Hulicka and Grossman (1967) compared the performance of younger and older subjects on a verbal paired-associate learning task in two conditions: one in which no mnemonic instructions were given and another condition in which instructions in the use of visual mediators were provided. Without instructions, elderly subjects spontaneously developed significantly fewer mediators and performed more poorly than younger subjects. When provided with instructions, however, elderly subjects showed more improvement than younger subjects, although their performance did not equal that of the younger subjects. Treat, Poon, and Fozard (1981) also compared younger and older subjects' performance on a similar word pairs task with a no instructions condition, experimenter-generated imagery instructions, and a self-generated mnemonic instructions condition. Older subjects were able to develop successful strategies on their own when given enough practice such that their performance on the task equalled

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the performance of the younger subjects in the imagery instructions group by the third session. In a n effort to discover what strategies, if any, are spontaneously employed by naive, "control" subjects in mnemonic training studies, Camp, Markley, and Kramer (1983b) asked elderly subjects to predict their performance on a simple memorization task, to memorize a short list of common words, and then to report on the strategies they used to accomplish the task. Many of these subjects spontaneously used fairly sophisticated mnemonic techniques that induced deeper levels of processing. In a second part of the study, subjects were asked to describe what mnemonic strategy they intended to employ in an upcoming memorization task. Following performance on the task, they were questioned about the actual strategy they had used. Although most of the subjects employed the same strategy they had predicted they would use, over one-third used a different strategy than they had intended. The authors hypothesized that certain qualities of the stimulus (e.g., "low" vs. "high imagery words) may have "induced" those subjects to initiate a change toward a more effective strategy. This supports the findings by Weinstein et al. (1981) where qualities of the stimulus materials or situation may dictate what type of mnemonic strategy is used. Another implication of this study is that it is important to know what strategies subjects use on their own before beginning training in another mnemonic technique. Elderly trainees may be reluctant to give up what they feel is an already effective or at least more comfortable and familiar strategy and may "revert" to their old strategies while being trained in new ones. In addition, subjects in the "control" group may spontaneously use mnemonic strategies that could enhance their performance (Camp, Markley, & Kramer, 1983a). These potential confounds could interfere with the accurate evaluation of the efficacy of memory intervention techniques. Mnemonics Training in Normal Elderly Individuals Most of the memory intervention research has focused on examining the effectiveness of internal mediators, rather than the efficacy of external mediators with elderly individuals. This may be due to the fact that external mediators appear to be more easily, frequently, and spontaneously used by elderly individuals. Thus, elderly individuals may not need instruction in methods they already use. Internal mediators require more intensive training efforts to learn. Therefore, more research is devoted to the acquisition of internally mediated skills. Many of the methods used in internal strategy research are adopted from or based on well-established paradigms from research in cognitive psychology, which give a pre-existing structure in which to carry out research. There have also been few studies that compare the relative efficacies of internal mediators and external mediators for a single type of material or learning situation. We will first discuss the research on internal mediators as mnemonics, and follow that with a discussion of research on external mediators. Internal Mediators Verbal mnemonics. There are few studies that have examined the efficacy of purely verbal mnemonic strategy training with normal elderly subjects. A study by Hellebusch (1976) that compared the effects of the peg-word mnemonic, sentence generation, and

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first-letter strategies on learning demonstrated that both the peg-word (a visual mnemonic) and sentence-generation techniques resulted in improved performance by elderly subjects. The lack of studies examining the effect of verbal mnemonic strategies is curious, considering that a) research suggests that verbal abilities tend to be quite well-preserved in elderly adults (probably better-preserved than nonverbal or visuospatial abilities); b) when they use mnemonics spontaneously, older adults tend not to use mental imagery and may develop verbal strategies more spontaneously (Camp, et al., 1983b; Wood & Pratt, 1987); and c) older people in general experience difficulty in producing and remembering visual images and imagery associations (Hulicka & Grossman, 1967; Mason & Smith, 1977; Paivio, 1971; Poon, Walsh-Sweeney, & Fozard, 1980; Winograd & Simon, 1980). In studies that have demonstrated the success of imagery mnemonics, perhaps it is not the addition of verbal techniques, such as semantic elaboration, that enhances visual encoding, but that instructions to use imagery enhances material that is essentially and initially verbally processed or encoded. It may also be the case that most verbal mnemonics are what Bellezza (1981) refers to as "single-use"mnemonics where information is made into a rhyme (e.g., "thirty days hath September. "), a memorable acronym (e.g., "ROY G BIV" for the colors of the rainbow), or other first letter cueing structure (e.g., "On old Olympic towering top . . " representing the first letter of each of the cranial nerves). These mnemonics may have limited applicability in that their use is restricted to a limited amount of information for a specific purpose. Thus, it may not seem practical to teach elderly people skills that will be restricted and less generalizable. However, strategies such as first-letter cueing, sentence generation, and story mnemonics (Bower & Clark, 1969) are organizational mnemonics that could be applied to a variety of situations and materials. It would be interesting to see if such verbal mnemonics could be adapted to new situations and new information encountered by the elderly in their daily lives (e.g., remembering a medication regimen by making up a rhyme about it).

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Visual mnemonics. Mnemonics training with elderly subjects using visual imagery techniques have generally produced positive results. A review of some early memory intervention studies by Poon, Walsh-Sweeney, and Fozard (1980) revealed that visual imagery mnemonic techniques were successful in facilitating elderly subjects' performance on the acquisition and immediate recall of information about items and paired-associate learning in 14 out of 17 studies. The visual mnemonic techniques that have resulted in significantly better performance than use of other techniques (e.g., semantic encoding alone or verbal organizational strategies) are interacting imagery instructions and bizarre interacting imagery cartoons (Thomas & Ruben, 1973), self-generated images (DeLeon, 1974; Treat, 1977; Treat & Reese, 1976), novel visual mediators (Kahn, Zarit, Hilbert, & Niederehe, 1975; Zarit, Gallagher, Gamer, & Walsh, 1977), and others reviewed below. Pegword mnemonics. Although there are numerous visual mnemonic techniques available (Bellezza, 1981, 1982, 1983, 1984, 1986; Higbee, 1977), only the pegword mnemonic, method of loci, and a few bizarre or interacting imagery instructions have been examined in memory intervention studies with elderly subjects. One of the apparently least frequently-used or least studied techniques is the pegword mnemonic. This method requires the trainee to first learn a number-word rhyme, similar to the

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following: "one is a bun, two is a shoe , . , and so forth (Higbee, 1977). Once this rhyme has been memorized, the to-be-remembered items are then paired with the word in the rhyme (e.g., bun-car, shoe-window, and so forth) and the trainee is asked to form an interacting image of the two words to enhance encoding. Studies by Smith (1975) and Mason and Smith (1977) demonstrated that the pegword mnemonic technique did not result in significantly greater recall for elderly subjects trained in this method compared to elderly subjects who received no training. However, a study by Hellebusch (1976) showed that both the pegword and sentence-generation, but not first-letter cueing techniques, enhanced recall of word lists for both younger and older subjects after a delay of 3 minutes. These gains were maintained by younger subjects but not by elderly subjects when all subjects were tested for recall two weeks later. A more recent study by Wood and Pratt (1987) employed the pegword mnemonic technique with subjects in four age groups. Subjects were given explicit and careful instructions in how to use the "one-bun" pegword rhyme and asked to associate a common saying such as, "An apple a day keeps the doctor away," with each mnemonic stimulus. The authors reasoned that using more "ecologically valid stimuli (e.g., the sayings) rather than seemingly irrelevant lists of words would make the task more meaningful for the elderly subjects. The results showed that elderly subjects were able to benefit from training in the pegword system just as much as younger subjects but that their performance was not improved enough to equate their level of performance with any of the younger subject groups. Furthermore, an interview conducted four months after the training revealed that half of the subjects used the pegword mnemonic very little while the other half did not use it at all in their daily lives. The problem of maintenance of mnemonic devices after training is a recurrent one in the memory intervention literature. In their reports of what strategies they actually used to remember the material, only 8% of the subjects reported using mental imagery (the least frequently used strategy) with the most frequently- reported strategy involving the selection of key words from the sayings and rehearsing them (41%).

These results are similar to those of Camp et al. (1983b) who found a low rate of imagery use and discrepancies between the mnemonic strategy reported and the actual strategy used by the subjects. These findings also seem to challenge our assumptions about what kind of processing is actually being used by the subjects. Even though subjects are instructed to emphasize and rely on visual imagery, and may even believe they are doing so, verbal mediation may be involved to a greater extent than has been previously recognized. Method of loci. The method of loci is another predominantly visual mnemonic that has been used since ancient times to help organize information for later recall. In this technique, the trainee develops an ordered list of locations, usually based on a familiar setting, such as rooms in his or her house or on landmarks along a well-known route, such as the path from his or her home to a neighbor's home. Once this list of locations is learned perfectly, the trainee then creates a visual association between the first location and the first to-be-remembered on the list. This process continues until each item has been "placed in order at a location. To recall the list of items, the trainee takes a mental "walk" through the house or along the route and retrieves the image of each item from its location. Several studies have demonstrated the effectiveness of this technique for enhancing recall in elderly subjects.

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A study by Robertson-Tchabo, Hausman, and Arenberg (1976) demonstrated that elderly subjects could easily acquire the method of loci mnemonic and that their free recall performance improved significantly over training trials compared to control subjects' performance. The authors attributed the success of the technique in their study to the fact that the locations were generated by the subject rather than being provided by the experimenter and were therefore more familiar to them. Familiarity of locations was hypothesized to have reduced the load that simultaneously learning the mnemonic technique and the associated information would have placed on the elderly subjects' cognitive processing capabilities. Recall performance on the posttest transfer task was not significantly better than performance on the pretest for most subjects, although subjects who were given explicit instructions to use the method they had used during the training did show some transfer of training in their performance on the posttest. The authors suggested that a review of the list of loci before presenting the new to-be-associated information may have enhanced transfer of the skill to the new learning situation. This cueing to remember the strategy would be particularly critical if the newly-acquired skill were to be applied to learning in everyday situations.

Two variations on the method of loci study described above were carried out by Yesavage and Rose (1983, 1984b). In the earlier study, subjects received a combination of concentration training and mnemonic training using the method of loci in order to facilitate learning of word lists. Amount of improvement from the pretest to the final posttest in both immediate and delayed recall in the list-learning and paired-associate learning situations was significantly greater for those subjects who received concentration training before mnemonic training. Thus, the positive effects achieved with mnemonic training alone may be enhanced when the elderly subject's ability to concentrate is improved through training. It should be noted that the use of mnemonics such as the method of loci focuses a large amount of attention on target items, at least when the strategy is initially being learned and implemented. Part of the improvement in memory performance associated with using such mnemonics might be due to this increased focusing of attention, independent of the effects of imagery per se. In addition, older adults with reduced attentional resources (Craik, 1977, 1984) may be reluctant to use such mnemonics if they view the cognitive effort associated with utilizing the mnemonic as too high. (We would like to thank Robert Markley for bringing this idea to our attention.) In another study by Yesavage and Rose (1984b) one group of elderly subjects was trained in the traditional method of loci technique using familiar loci, such as their houses. Another group received the same instruction in the construction of loci sites and associations but was additionally instructed to judge the pleasantness or unpleasantness of each image association they made. Based on previous research, the authors hypothesized that performing these affective judgments would result in more elaborate, deeper encodings of the visual stimuli and thereby enhance performance by making the associations more accessible to later recall. Results of this study showed that the additional nonredundant elaboration significantly improved the effectiveness of the mnemonic as subjects in this condition demonstrated better recall than subjects in the "Loci Only" condition. Interestingly, however, subjects trained only in the

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traditional method did not significantly improve their performance. The authors attribute the discrepancy between their findings and those of Robertson-Tchabo et al. (1976) to the fact that their subjects received two days less training than the Robertson-Tchabo et al. subjects and that the subjects in the Yesavage and Rose (1984b) were instructed and tested in groups (see also Rose & Yesavage, 1983). The need for elderly subjects to receive extensive practice when learning new information or techniques and the need to take into account individual differences in amount of time to learn new information or skills, imagery skill, modality processing preference, and cognitive style have been mentioned earlier in this paper (see also Robertson-Tchabo, 1980). We will discuss the implications of these shortly. Two studies by Anschutz, Camp, Markley, and Kramer (1985,1987) have investigated the use, maintenance, and generalization of the method of loci mnemonic with elderly subjects. In the earlier of the two studies, elderly subjects were given a free recall pretest for words, then trained in the method of loci using associated words high in meaningfulness, concreteness, and imagery. A week later, subjects were taken grocery shopping and asked to remember personal grocery lists using the mnemonic technique they had learned previously. This situation was intended as a test of training maintenance and generalization from an "academic" learning situation to a more ecologically-valid setting. Another trip to the grocery store was repeated four weeks later, followed by another free recall posttest four weeks after the second grocery trip and similar to the pretest. Results showed that elderly trainees were able to generalize their training in the method of loci mnemonic learned originally with words to a more ecologically-valid setting with a high rate of success. However, continued maintenance and further generalization of the training was successful for only a few subjects. Most of the subjects modified the original mnemonic strategy after the first shopping trip. These subjects subsequently demonstrated no decline in the recall of items during the second shopping trip but large decrements in the free recall posttest, which the authors attribute to the subjects' failure to adhere to the original strategy. The second study (Anschutz et al., 1987) was performed almost three years after the first study and involved 9 of the 10 original subjects. Free recall and recognition testing for two word lists, one new list and one list of words used in the original study, revealed that many subjects recognized words used in the original study. Most subjects were also able to recall most of the loci from the original study but less than half used the technique to learn the new word lists in the follow-up study. In addition most subjects had not continued to use the strategies in their daily lives nor did they try to generalize the training to other learning situations. The authors attributed the subjects' failure to maintain use of the technique to lack of social support for the use of such strategies and to a general lack of compliance with following instructions, similar to that found with medication non-compliance. Alternately, it could be that elderly subjects are not very good at self-monitoring their performance and the efficacy of mnemonic techniques. They may not have adequate metacognitive ability to determine whether or not a technique is still effective or how to self-correct their strategies if they are not working properly (Poon, Walsh-Sweeney, & Fozard, 1980). Another factor that may mitigate against the maintenance of mnemonic training in elderly subjects concerns their motivation. If older adults are not concerned about their

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memory problems or don't believe their abilities can be altered, their motivation to continue using special techniques for remembering may decrease (Cavanaugh & Morton, 1988). Subjects in the study described above reported, however, that they felt the training was helpful, but this could have reflected their wish to please the experimenters and be socially appropriate (i.e. show appreciation for the attention they had received). It has been our experience that older research participants enter into experiments expecting (and sometimes demanding) high levels of social interaction with experimenters. Older adults may therefore feel "obligated" in such a "social" setting to utilize mnemonic training, but feel released from the obligation outside of the experimental context. The issue of subjects' attitudes toward and knowledge about the efficacy of mnemonic techniques appears to be a critical one in the maintenance of training benefits.

Face-name mnemonics. Much of the research on imagery mnemonics has been devoted to teaching elderly people to remember face-name combinations. This skill has obvious ecological validity. Inability to associate a name with a face is one of the more common memory complaints among the elderly (Zelinski, Gilewski, & Thompson, 1980). Yesavage and his colleagues have performed several studies training elderly subjects in the use of imagery for learning face-name combinations. Yesavage and Rose (1984a) trained elderly, middle-aged, and young subjects in a name-face mnemonic technique developed by McCarty (1980). This technique employs a sequence of steps for recalling an individual's name when presented with his or her face. The encoding stage proceeds as follows: The first step involves identifying a distinctive prominent facial feature (such as a mouth), then developing a "concrete, high-imagerytransformation of the person's name" (e.g., "Whalen"becomes "Awhale"), then forming an interactive mental image that associates the prominent facial feature with the transformation of the name (such as whale in the person's mouth) (p.197). Retrieval of the person's name also involves several steps: Identifying the prominent facial feature, using that feature as a retrieval cue for the image association, reconstructing the name transformation from the image association, and finally decoding the name from the name transformation. The most problematic step identified by McCarty (1980) was remembering the visual image association once the prominent facial feature had been identifed. Recall for names was assessed in all three groups before and after training. Significant differences between all age groups were found on both the pretest and posttest, with younger subjects recalling the greatest number of names associated with faces and elderly subjects recalling the least number of names when presented with faces. There were no significant differences in the amount of improvement between groups, although there was a trend suggesting that elderly subjects improved the least. Nevertheless, this study demonstrated that elderly subjects can benefit from the use of visual mediators and specific mnemonic strategies in learning to associate names with faces. Another study by Yesavage (1983) involved training two groups of elderly subjects in the face-name mnemonic technique described above. One group of subjects received additional instruction in techniques to improve their visual imagery abilities prior to being instructed in the mnemonic technique. The group that received the combination mnemonic and imagery training performed significantly better than the

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group that received the mnemonic training alone. This finding suggests that training in visual imagery can improve memory for names only when it facilitates organization, such as that provided with the mnemonic training (Paivio, 1971). In another study of face-name learning, Yesavage, Rose, and Bower (1983) employed a methodology similar to that described earlier where semantic elaboration was used to enhance encoding of loci (Yesavage & Rose, 1984b). Subjects were trained in the visual imagery technique developed by McCarty (1980), and used in the study by Yesavage (1983) and Yesavage and Rose (1984a). Subjects in one group were given no training in how to remember the names associated with faces. Both experimental groups were given the standard mnemonic technique described above. They were given the mnemonic components by the experimenter on the first day of training (experimenter-mnemonic) and presented with an opportunity to generate their own mnemonic components (self-mnemonic) on the second day of training. In addition, one of the experimental groups was asked to judge the pleasantness or unpleasantness of the visual image associations. Single cue and multiple cues were presented in immediate and 48-hour delayed recall trials. Results of this study show that both experimental groups of subjects who were instructed in the interactive imagery mnemonic technique performed better than the control subjects and that subjects who received the additional instructions to make affective judgments about the interactive image performed better than subjects who were not given those additional instructions. These differences were even more pronounced in the delayed recall trials and in the self-mnemonic instruction group. The results also support the finding by McCarty (1980) that retrieving the image association is the most difficult step in the mnemonic. The authors attribute the greater power of the interactive imagery + affective judgment training to "qualitative changes in the memory trace" (p.202), not to additional time spent making affective judgments of the images while they were in working memory nor to additional structural supports or reminders to use the specific mnemonic, as Robertson-Tchabo et al. (1976) had suggested. It is still unclear at this stage, however, which qualitative changes are related to the effectiveness of a mnemonic technique. Combined visual and verbal mnemonics. It should be clear from the review of the visual mnemonic training studies that many of the more successful training methods do not employ strictly visual imagery but incorporate verbal mediators, such as semantic orienting or elaboration instructions, in addition to imagery. At some point, it becomes difficult to distinguish the effects of visual mediators from verbal mediators. Additional studies that compare the relative efficacies of different visual and verbal strategies would be helpful in designing more effective treatment programs. Unfortunately, there appear to be few studies that have attempted such a comparison.

External mediators

As discussed earlier, most of the memory intervention research with normal elderly subjects has concentrated on the development of internally mediated strategies, In addition to the reasons for a dearth of research on external mediators discussed above, it may be conjectured that perhaps elderly people are reluctant to be seen relying on stacks of notes, strings tied to their fingers, or objects placed in strange locations in

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their houses - obvious reminders of their "impaired memorial abilities. Certainly this resistance to using external aids has been a problem in the cognitive rehabilition of brain-injured individuals (Wilson & Moffat, 1984b). It may be even more difficult for a relatively intact individual to admit that he or she has a "disability" (e.g., impairment in memory functioning) that requires remediation. This attitudinal variable could account for the lack of training studies using external aids. There is no point in training someone to use something they are not motivated to continue using. In addition, fostering a supportive and accepting social environment may be a prerequisite to the effective, widespread use of external mnemonics by older adults. Caregivers who wish to deny that a memory problem exists may resist the use of such aids by their memory-impaired relatives. Acceptance of mnemonic aids should take place within families and society. Another reason for a lack of studies on external mediators relates to the relative recency of studies on everyday memory in elderly individuals. Everyday memory functioning is undoubtedly complex and multivariate. Tasks, processes, and other components of everyday memory are still being identified. It is also difficult to provide adequate simulations of everyday memory situations in the lab (Baddeley & Wilkins, 1984) or to study everyday memory in situ. While internal mediators have been shown to have a positive effect on elderly subjects' performance on some everyday memory tasks (name-face and grocery list learning), external mediators seem to be particularly relevant to memory functioning in everyday situations. There are numerous studies of the use of external aids in non-elderly subjects which could be replicated with elderly subjects. There are also proposed guidelines for the successful construction of external aids that could be examined in experimental settings (Harris, 1978). Studies that have compared external and internal mediators in everyday memory are also rare. McEvoy and Moon (1988) designed a comprehensive multiple strategy training program to teach skills for improving elderly subjects' memory functioning in everyday situations. Name-face recall training consisted of training subjects to use imagery mnemonics, to associate the new person's name with the name of someone they already knew (to associate new information with old knowledge), to repeat the new name in conversation over increasingly longer periods of time (distributed practice), and to review the names of people they knew but saw infrequently just before meeting with them (priming). For remembering non-routine appointments, subjects were instructed in the use of external aids and trained how to review appointments before they occurred. For remembering routine tasks, external and internal aids were employed. Checklists were used to remind subjects what tasks had to be done and to record their being carried out. Subjects were also taught to associate routine tasks with the occurrence of appropriate and specific environmental events, such as watering plants after a gardening show on television (to increase cue specificity). Everyday situations that involve spatial orientation include remembering where the car is parked and which door was used to enter a store. Subjects were taught to use visual and verbal information to remember landmarks and other information about their location (cue redundancy) and to look behind them in order to view their return routes from the returning rather than the incoming perspective. They were also taught to analyze a new environment before going into it by relying on maps and general knowledge about how buildings and towns were typically arranged (relating new information to old information). Practice was given with a walking tour

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of a building. Training to facilitate locating objects consisted of storing objects in a function-related location, marking frequently-misplaced objects with bright colors (to increase salience), and using a central location for objects that must be put down temporarily but then used again in a short time. Concentration training involved teaching subjects to review verbal material (e.g., a story) as it was presented, to relate the material to existing schema, and to try to predict upcoming events in the material in order to focus their attention on the task. Subjects were also administered a memory questionnaire designed to measure the frequency of occurrence of the above situations as well as ability to keep track of a conversation, learn new information or skills, keep several things to do in mind, and recal1 old information (such as word-finding). The results of the study revealed that the memory-skills training resulted in a reduction in the self-reported occurrence of four problem areas - names and faces, appointments, routine tasks, and spatial orientation. It is interesting to note that training in the four areas that improved used combined multiple strategies - internal and external, verbal and visual - in varying combinations. Subjects did not appear to suffer from processing capacity overload, but rather seemed to benefit from the nonredundant training that involved multiple processing and response systems. Unfortunately, outcome measures relied solely on self-report, which may limit the interpretability of these results. The relationship between self-report of memory problems and objective task performance is complex (see Kotler-Cope, 1990). Future studies that use multiple strategies similar to the study by McEvoy & Moon (1988) should include both self-report and more objective measures of performance. External mediators are most often employed in situations where the individual needs to remember to perform an important activity. Harris (1978) recommends that in order to be most effective, such cues should be: a) given close to the time of the required action; b) active rather than passive (e.g. an alarm going off rather than a string tied around a finger); and c) specific to the task it is intended to facilitate. A problem with external mediators is that most do not contain all of these features. Studies investigating the effectiveness of training on prospective memory (Harris & Wilkins, 1982; Pajurkova & Wilkins, 1983; Wilkins, 1986; Wilkins & Baddeley, 1978) are rare, at least with elderly people as subjects. While there are several studies of prospective memory abilities in older adults (see Sinnott, 1986,1989; West, 1988) none of these have attempted specifically to train subjects to enhance their abilities to remember to do things. It would seem that this important aspect of everyday memory functioning would warrant more attention in the memory intervention literature. Further Considerations An honest appraisal of the effects of memory interventions in older populations to date would have to conclude that the effects of such interventions have been, at best, modest. While mnemonics are sometimes touted as means of creating a ‘‘super memory” by entrepreneurs who sell memory training courses or books to the general public, gains made by older adults in experimental interventions often have been small in overall magnitude. In addition, older adults generally cease to use the training outside of experimental contexts. What can account for these findings?

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Older adults are not unique in thew reactions. Cavanaugh (1986) and Smith (1986) reported results from studies in which adults from different occupations as well as researchers in the field of memory and aging were surveyed regarding the types of mnemonics these individuals personally used and recommended. The overwhelming majority (regardless of background and expertise in mnemonics) preferred the use of external aids, and recommended these as well. Internal mnemonics were utilized with a surprisingly low frequency. The general reason for this was assumed to be the high levels of cognitive effort necessary to execute most internal mnemonics, especially with regard to mental imagery. Therefore, the fact that older adults do not use mnemonic training when they leave a lab and return home is not surprising. Many, if not most, researchers in the area appear to do the same thing. Such behavior may not reflect the "failure" of older research participants, but rather a "failure" of the experimental techniques. Faced with such a possibility, researchers invested in the use of internal mnemonics may have to deal with the choice of either learning to apply experimental techniques more effectively or searching for new techniques. Testing the limits. Kliegl, Smith, & Baltes (1989) described a framework for the study of cognitive plasticity -- the degree to which cognitive functioning can be influenced, (for example, through training). They listed three levels of performance and latent potential: baseline performance, baseline reserve capacity, and developmental reserve capacity. Baseline performance refers to what participants do under standardized conditions of assessment. Baseline reserve capacity is the level of performance which could be achieved if conditions of assessment were optimal, without any attempt to alter the cognitive and motivational repertoire of respondents. They view baseline reserve capacity as the current maximum performance potential (i.e. plasticity). Developmental reserve capacity involves assessment after interventions designed to optimize cognitive and motivational potentials. In order to test developmental reserve capacity, Kliegl et al. recommended the general methodology of "testing the limits" (p. 247). In their experiment, Kliegl et al. trained older and younger adults to use the method of loci, To maximize cognitive and motivational potentials in their older participants, only healthy, high verbal individuals were used. Familiar locations around the city (West Berlin) served as loci, and training took place across a maximum of 26 sessions in small groups. Participants were not rushed and could take breaks for socializing, etc. during training. Their results indicated that older adults have a sizable developmental reserve capacity. "Old adults, on average, were able to repeat 32 of 40 words they had seen only once at a self- paced presentation rate" (p. 250). By varying the presentation rates of stimuli, they found that age differences were greater at the highest levels of performance (i.e. at slower rates of stimulus presentation) than at lowest levels of performance (with faster presentation rates). They concluded that "effects of aging may be more clearly identifiable at performance levels near the upper limit of reserve than at baseline performance" (p. 255).

It may be that many mnemonic intervention studies using older adults have not attempted to measure developmental reserve capacity. Even Kliegl et al. note that the performance level of their older participants may not yet represent an asymptotic level.

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Their study demonstrates two important points: a) memory interventions with older adults thus far may not have been designed to help older adults reach maximum potential, and the level of performance available to older adults may be much greater than is currently believed; and b) testing the limits methodologies can yield important theoretical as well as pragmatic results.

An admonition. If the goal of memory interventions in older populations is to utilize developmental reserve capacity, many benefits can be gained. This is true regarding substantial increases in performance levels as well as enhancement of the theoretical sophistication underlying our research. To date, attempts to achieve very high performance levels for memory in older adults have been relatively few. This is not because such performance levels cannot be achieved. Rather, perhaps we have set too low a criterion for success, e.g. statistical significance rather than "clinical" significance. In part, this may be due to a reluctance on the part of both experimenters and older research participants to engage in the extensive training necessary to tap developmental reserve capacity, especially for interventions requiring a large amount of cognitive effort. Maintenance and generalization of training. Finally, we would briefly like to address the problems of maintenance and generalization of training. It is in these areas that research results have thus far been most discouraging. Two points on this issue need to be made. If interventions designed to tap developmental reserve capacity were more widely utilized, perhaps we would find maintenance and generalization of training more readily. Generalization may be more difficult to achieve than maintenance, but again we first need to develop more effective interventions before addressing this issue. Even if training utilization remains relatively specific with regard to tasks or domains of information, lack of generalization does not render training useless if the target area of training is important to everyday functioning. Memory interventions for individuals who do have impairments which can severely impair their everyday functioning will be discussed next. Memory Interventions and AtTective Status in Elderly Populations Depression and anxiety The relationship between affective status, subjective memory complaints, and objective performance on memory tasks is a complex one. In depressed elderly individuals (as measured by self-report scales), complaints of poor memory appear to be more related to depression and less to actual performance on memory tests. A study by Zarit, Gallagher,and Kramer (1981) examined the effects of two types of training programs on memory task performance, level of depression, and number of memory complaints in elderly subjects. Memory complaints were assessed using a 12-item interview rating scale developed by the authors. Depression was assessed using the Zung Depression Scale (Zung, 1965). All subjects were given these subjective measures before and after training. Training for one group consisted of receiving instruction in a variety of organizational, mediational, and integrative strategies with visual and verbal components that could be used to compensate for information processing deficits. The other subjects were assigned to the "growth"group and received training in how to develop more effective concentration and personal and

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interpersonal skills, such as problem-solving, assertiveness, and relaxation skills. Memory tasks consisted of recall for a phone number and shopping list, unrelated items, personal lists of pleasant and unpleasant activities, lists of pleasant and unpleasant general activities, name-face pairs, recognition of material from a paragraph, and forming questions based on material in the paragraph. All subjects were tested for their memory performance before, during, and after training. The results of this study revealed that memory performance improved and the number of subjective complaints about memory decreased for both subject groups. Decrease in scores on the depression measure was not associated with an improvement in objective test performance but a greater decrease in depression was associated with the "growth" training. These results suggest that training designed to change elderly subjects' selfperceptions and affective status may do so without substantially influencing objective performance on memory tasks. Another study by Zarit, Cole, and Guider (1981) examined the effects of training in task-specific memory strategies or participation in a current events discussion group on memory task performance and subjective memory complaints of elderly subjects. All subjects were presented with a different memory task each session. These tasks involved learning a 10-item list of grocery items or daily activities, 10 name-face pairs, a list of 10 unrelated items, and a long prose passage. Recall was assessed prior to training and immediately after training on each task and after all 4 sessions had been completed. Memory training consisted of providing subjects with a different mnemonic strategy for each of the 4 tasks. For example, in learning a list of unrelated items, subjects were taught how to create novel images that connected the words. Subjects in the current events group were reassured that the memory lapses revealed in their initial screening evaluations were not signs of senility and that discussing current events would improve their memory abilities. Memory complaints were assessed before and after training with the 1Zitem interview rating scale used in the Zarit, Gallagher, and Kramer study (1981). Subjects in the memory training group improved their performance on two of the memory tasks, the list of related items and list of unrelated items, but there was no difference in amount of improvement between the two subject groups' performance on the other two memory tasks. An extension of this study involved comparing subjective memory complaints and memory task performance by a group receiving similar task-specific memory training instructions and a waiting list control group. Subjects in the memory training condition improved their recall on three of the four tasks, related items, name-face pairs, and unrelated items. However, when an additional recognition format was used for the tasks, there were no differences in performance between the training and no training groups. Subjective memory complaints decreased for training group subjects and increased slightly for control subjects, but there was no significant correlation between memory complaints and task performance. Overall, these results corroborate those of the Zarit, Gallagher, and Kramer (1981) study. While memory training may improve performance on memory tasks, this improvement appears to be independent of a decrease in subjective complaints. The authors propose that training subjects in groups has a beneficial effect in reducing their concerns, and thus complaints, about memory failures.

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A similar conclusion was also reached by Scogin, Storandt, and b t t (1985). Two groups of older adults differing in the degree of their complaints about memory difficulties were given a self-paced, self-taught program designed to enhance memory skills through the use of four popular mnemonic techniques (method of loci, novel interacting images, categorization, and chunking). Their performances on various memory tasks, number of memory complaints, and scores on a depression inventory were compared to those of a no-treatment control group. Results revealed that the individualized, self-taught memory training program resulted in improved performance on memory tasks but did not have an effect on the subjects' evaluations of their memory abilities or degree of depression. High complaint and low complaint individuals differed only in the degree of memory complaint and were otherwise comparable in their memory task performance and depression scores. These authors concur with Zarit, Gallagher, and Kramer (1981) that memory complaints do not necessarily reflect actual memory deficits or depression but refer to negative attitudes or beliefs that elderly individuals may have about their cognitive abilities. Treatment of memory problems in depressed elderly patients might, therefore, emphasize changing the patient's concerns and expectations about his or her memory functioning. Herrmann (1982) has proposed that inaccurate self-perceptions about one's memory abilities can result in a failure to use appropriate or effective strategies or to use strategies that are inappropriate for a given task (see also Cavanaugh and Green, Chapter 7, this volume).

A study by Hill, Sheikh, and Yesavage (1987) examined the effect of memory training on memory task performance self-confidence and actual task performance in elderly subjects. Subjects in the experimental condition received training in the visual imagery mnemonic for remembering name-face combinations previously developed by Yesavage and Rose (1983) while control subjects received no such training. Prior to the pre-test and post-test, all subjects were asked to rate their confidence in their ability to recall the names of unfamiliar faces. Results showed that the effect of mnemonic training itself did not significantly improve the confidence ratings of subjects who received the training. However, the trained subjects became more accurate in their ability to assess changes in their memory abilities and to judge future task performance more accurately. Reducing subjects' performance anxiety has also been found to have an effect on the success of memory training. Yesavage and colleagues (Yesavage, 1984; Yesavage & Jacob, 1984; Yesavage, Sheikh, Tanke, & Hill, 1988) have performed several studies designed to assess the effect of relaxation training on memory training in elderly individuals. In one study (Yesavage, 1984), one half of the subjects received mnemonic training designed to enhance name-face recall (Yesavage et al., 1983) while the other half received progressive muscle relaxation training prior to receiving the same mnemonic training. A measure of state and a measure of trait anxiety were administered before, during, and after training. Results revealed that although both groups showed improvement in name-face recall after mnemonic training, improvement was significantly greater for subjects who had received the pre-training relaxation instructions. While trait anxiety scores did not change significantly over the three testing sessions for either group, state anxiety scores for the relaxation + mnemonic training group decreased while scores for the mnemonic training only group

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increased. Learning a complicated mnemonic (or any new skill for that matter) and subsequently having one's performance evaluated can be anxiety-provoking. Anxiety can interfere with one's ability to concentrate and that can in turn inhibit the learning of a mnemonic as well as interfere with recall. Relaxation training can therefore serve as a very effective adjunct to mnemonic training. These results were replicated in a similar study by Yesavage and Jacob (1984) which showed that subjects who showed the greatest improvement in face-name recall following mnemonic and relaxation training also exhibited the largest decreases in state anxiety and cognitive interference and the largest increase in attentional abilities. The importance of individual differences in ability to respond to different types of training was demonstrated in the study by Yesavage, Sheikh, Tanke, and Hill (1988). Before receiving training in the name-face mnemonic described in earlier studies (Yesavage et al., 1983), elderly subjects received pre-training in either progressive muscle relaxation or verbal elaboration training. Subjects were also administered the WAIS Vocabulary subscale and the Spielberger State-Trait Anxiety Inventory. Results showed that subjects who scored relatively high on the verbal intelligence test benefited more from the mnemonic t verbal elaboration training than the other training combination, and that subjects who scored high on the state anxiety measure benefited more from the mnemonic + relaxation training. This study stresses the importance of taking into account individual differences in initial skill and affective status variables when assessing the effects of different types of training on elderly individuals. Memory Interventions in Elderly Populations with AAMI and Alzheimer's Disease Age-Associated Memory Impairment

Very few studies have investigated the efficacy of memory intervention techniques with elderly people who have Age-Associated Memory Impairment. Perhaps this is due to the relative recency of this diagnostic category. A study by Sheikh, Hill, and Yesavage (1986) examined the effects of different types of pretraining on the long-term outcome of training in an imagery-based mnemonic designed to enhance name-face recall (Yesavage et al., 1983) in patients diagnosed with AAMI. Subjects were randomly assigned to one of six groups, three "treatment" and three "control" groups. Subjects in one of the treatment groups were given pretraining in verbal elaboration techniques while those in the second treatment group were given pretraining in visual imagery elaboration techniques. Subjects in the third treatment group received pretraining in progressive muscle relaxation techniques. All subjects in the treatment conditions received their pretraining prior to being instructed in the name-face mnemonic technique described in an earlier study by Yesavage et al., 1983. Subjects in one of the control groups were given "nonspecific"pretraining in the form of a series of lectures on memory and aging prior to receiving the name-face mnemonic instructions. Subjects in the second control group participated in the nonspecific pretraining lecture series but received no mnemonic training. They were simply instructed to practice learning the name-face pairs. Subjects in the third control group were put on a waiting list and received neither the pretraining nor mnemonic instructions. All subjects were tested for name-face recall performance before pretraining (baseline), after pretraining, immediately following training (posttest), and

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6 months later. Results revealed that at posttest, the groups who received training in the mnemonic technique (e.g., the three with specific and the one with nonspecific pretraining) demonstrated significantly more improvement over baseline performance than the control groups who did not receive mnemonic training. This finding provides additional evidence in support of the efficacy of this particular visual imagery mnemonic technique in improving name-face recall in memory-impaired older adults. At 6-month follow-up evaluation, subjects who received specific pretraining instructions in addition to the mnemonic training (e.g., the "treatment"groups) recalled significantly more names than subjects who received the nonspecific pretraining with and without mnemonic training (e.g., subjects in the "control" conditions). Interestingly, there were no significant between-group differences in recall found for the three types of pretraining. There was also no significant difference between the control group that received nonspecific pretraining with mnemonic instruction and the no-mnemonic group at 6 months. Wait list control subjects were not evaluated at 6-month follow-up. These findings suggest that although visual imagery mnemonic training may enhance recall of name-face pairs by elderly people with AAMI over a relatively short period of time, specific and active pretraining, and not nonspecific pretraining, appears to be an essential ingredient for the maintenance of the mnemonic training gains over time. Why different types of pretraining techniques appear to be equally effective is a critical issue for future research in mnemonic training. Alzheimer's Disease There have been relatively few studies on memory interventions with Alzheimer's Disease (AD) patients. Perhaps researchers and clinicians are reluctant to devote efforts in investigating whether or not the cognitive deficits that accompany this inevitably deteriorating illness are remediable. Nevertheless, there are a handful of memory intervention studies that have examined the capability of AD patients to learn new information. (For an extensive review of this literature, see Camp & McKitrick [in press] and Glisky & Schacter, 1986). One study by Zarit, Zarit, and Reever (1982) examined the effects of different types of training on elderly individuals with early symptoms of dementia. Subjects received training in either visual imagery, problem solving, or practical methods for managing daily-life problems caused by memory loss and then were given a variety of memory tasks. The subjects' everyday functioning was evaluated by their caregivers before and after the subjects received training. Although there was some improvement in memory task performance, these gains appeared to have little functional relevance and practical applicability to memory functioning in daily life. The ability of an AD patient to learn a task of relevance for everyday situations name-face associations - was examined by Hill, Evankovich, Sheikh, and Yesavage (1987). After obtaining baseline data on his ability to remember name-face pairs, a 66-year-old male patient diagnosed as having Dementia of the Alzheimer's Type (DAT) was taught the 3-step visual imagery mnemonic described by Yesavage, Rose, and Bower (1983). The patient was then instructed how to apply the mnemonic to help remember two name-face pairs. An increase in the retention duration of name-face pairs (achieving durations of 4 to 5 minutes) was evident immediately following instructions to apply the mnemonic technique and at one month follow-up.

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In addition to moderately increasing retention intervals, there are several additional points about this study. The effects of the training could have been enhanced by asking the patient to recall the mnemonic before each training session. It has been proposed that failure to remind subjects to use the specific mnemonic may result in decreased performance despite thorough training (Robertson-Tchabo et al, 1976). The third step in the mnemonic, image association, was the most difficult for the patient to recall spontaneously. This finding is consistent with another study (Yesavage, 1983) and suggests that imagery pretraining may enhance an AD patient's recall of the entire mnemonic without prompting. Finally, although there is strong evidence that this mnemonic technique is effective for learning name-face pairs (Yesavage et al, 1983; Yesavage & Rose, 1984a), the increased retention duration found in this study may not be attributed to the effects of the mnemonic alone. During the testing phase, the patient was presented with the photo of the face and asked to recall the associated name. This procedure was repeated several times with the interval between presentations increasing in one-minute increments. During the time between presentations the experimenter engaged the patient in casual conversation unrelated to the task in order to prevent rote rehearsal of the new material. This procedure is similar to that described by Bjork (1979, 1988; Landauer & Bjork, 1978), referred to as the "spaced retrieval" technique. The effectiveness of the spaced retrieval method as a mnemonic technique is attributable not to repetition practice alone but to practice in retrieval (Baddeley, 1984; Moffat, 1984; Rabinowitz & Craik, 1986). Recent research by Camp (1989; Camp & McKitrick, in press; McKitrick & Camp, 1989) has demonstrated that the spaced retrieval technique can be applied to facilitate new learning and long-term retention in persons with AD. In these studies, spaced retrieval training consisted of progressively increasing the amount of time between recall of a target association, such as remembering the name of a caregiver. The initial recall interval was 5 seconds, then 10, 20, 40, and 60 seconds, with expansions by increments of 30 seconds after that interval following successful recalls. If the subject was unable to recall the name of the caregiver, the correct answer was provided and the next recall interval was decreased to the longest previous interval during which recall was successful. As in the study by Hill, Evankovich, Sheikh, and Yesavage (1987), the trainer engaged the trainee in casual conversation or other unrelated activities during the retention intervals to prevent rehearsal of the name. Initial research findings indicate that individuals with AD who could not previously retain new associations for 60 seconds can successfully recall information presented via spaced-retrieval for intervals up to 5 weeks. It should be noted that there are individual differences in maximum length of retention interval, maintenance of the association over time, and generalizability of the training, (e.g., from photographs to the real people pictured in the photos). But the success of the first studies using this technique is very encouraging. The reason for the success of the intervention lies in its attempt to tap developmental reserve capacity. Spaced-retrieval training takes place in a social setting, often the participant's home. Time during recall intervals is spent chatting or playing games, so that training sessions take on the characteristics of a social visit. In addition, the use of a shaping technology in a memory task creates a large number of successful recalls, which is

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extremely reinforcing to individuals with memory deficits. The positive affect associated with the "visits" (e.g., training sessions) is maintained even when participants forget from session to session exactly what the experimental procedures involve. The task also utilizes those cognitive capacities that are most spared in AD. The task appears to involve implicit memory (see Schacter, 1987 for an extensive review of research on implicit memory), perhaps in the form of repetition priming -- an ability which is spared for an extensive period in the course of AD (see Nebes, 1989). Learning appears to occur without much expenditure of cognitive effort, to the extent that the ease of learning is obvious even to the participants. One trainee remarked, after a successful recall trial, "Don't worry, I'm not going to forget." In memory-impaired older adults, effective interventions simply cannot require a huge expenditure of cognitive effort (Camp & McKitrick, in press; Glisky & Schacter, 1986). Such interventions are more likely to increase frustration and anxiety rather than motivate participants. Instead, it will be necessary to capitalize on spared or less impaired abilities. Implicit memory tasks seem ideally suited for these interventions, and a variety of techniques such as shaping or fading (vanishing cues) can be used to access implicit memory (e.g. Glisky & Schacter, 1987; Glisky, Schacter, & Tulving, 1986). The goals of interventions in these populations may be to teach domain-specific knowledge (Glisky & Schacter, 1986) rather than to enhance memory ability per se or effect generalization. It would also seem evident that the use of external cues may be even more important for interventions in impaired populations. A problem with this approach is that memory-impaired individuals may forget how to use such cues, or forget where cues are located in everyday environments. The second author is currently engaged in pilot studies in which we are attempting to train individuals with AD, via spaced-retrieval, to use a strategy for utilizing an external memory aid -- a calendar. If such a strategy can be learned and appropriately executed, the impact of the intervention will be maximized, since entries on the calendar can be changed from day to day and afford great flexibility in what the client can "remember." Finally, we should note that any successful intervention developed with pathological populations can be effectively utilized by normal adults. The authors can attest that practicing the retrieval of newly-learned names over increasingly long intervals at parties enables one to remember the names of new acquaintances when saying goodnight. Are Memory Interventions Really Worth the Effort?

If increasing deficits in cognitive functioning appear to be an inevitable consequence of aging (Poon, Fozard, Cermak, Arenberg, & Thompson, 1980) and if the decline in cognitive functioning is assumed to be related to the deterioration of certain neuroanatomical structures and neurochemical processes (Gispen & Traber, 1983; Petit, 1982; Samuel, Algeri, Gershon, Grimm, & Toffano, 1983; Woodruff, 1982), can we realistically expect mnemonic training to be effective in elderly populations? Is there a sufficient rationale for making such attempts? We would answer this question in the affirmative. Rationales for attempting to teach aging adults cognitive skills stem

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from the fields of adult developmental psychology, cognitive psychology, clinical neuropsychology, and rehabilitation psychology (Kotler-Cope, Blanchard-Fields, & Gouvier, 1988). Research in adult developmental psychology has clearly delineated several qualitative and quantitative characteristics unique to older adults' cognitive functioning (Labouvie-Vief, 1985). Some have argued that these differences are often conceptualized as deficits or signs of deterioration only when compared to the criteria applied to younger populations (Labouvie-Vief, 1985). However, some of these changes may actually represent a process of adaptation by the aging adult to specific ecological demands encountered in adulthood and may not necessarily represent markers of deterioration in cognitive functioning (Bjork & Bjork, 1988; Camp, 1988). For example, Labouvie-Vief & Schell (1982) suggest that with aging, higher-ordered units of meaning transcend low-structured detail in importance. This informationprocessing "preference" may serve an adaptive function for adults. Observed differences may not, therefore, be absolute deficits, and different patterns of information processing may be adaptive at different developmental stages. An important question yet to be answered is "At what point do the differences cease to be adaptive?" The functional significance of such differences and whether or not they are identified as deficits by elderly individuals will be important issues in the development of effective memory intervention techniques. Learning when differences are not deficits and when not to intervene may be important goals for our maturing discipline. Research in cognitive psychology, clinical neuropsychology, and geropsychology has demonstrated that there is no single course of decline in memory functioning common to all aging adults (Schaie & Labouvie-Vief, 1974). There is a high degree of intraand interindividual variability in cognitive functioning in older adult populations which appears to increase with age (Rabbitt, 1983). In addition, memory is not a unitary ability and while specific features of memory functioning may change or decline with age, others may not. As the relative contributions of certain variables change with age (Hertzog, 1985), individual differences will become an important consideration when interpreting evaluation results and developing effective interventions. An additional factor that Poon and Fozard (1980) have identified is that it is difficult to determine at this point how much of the observed age-related decline in memory functioning is directly attributable to changes in neurological structure in normal elderly people. Research in behavioral neurology and related fields suggests that "functional deficit and structural deficit are not necessarily coextensive" (Goldstein & Shelly, 1981, p. 68). even in individuals with known neural pathology such as dementia or traumatic brain injury. Memory impairments in aging individuals may be due to a variety of etiologies, such as Age-Associated Memory Impairment, Alzheimer's Disease, Parkinson's Disease, cerebrovascular accidents, anxiety, or depression. For each of these diagnostic entities, there may be specific patterns of deterioration in memory abilities (Butters et al., 1988)where some aspects of memory functioning may be relatively intact. Mnemonic training programs may teach individuals to compensate for deficit functions by capitalizing on intact functions.

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Finally, the rapidly expanding field of rehabilitation psychology is founded on the belief that functional deficits due to physiological damage may be overcome through a variety of strategies, such as cognitive rehabilitation or behavioral compensation (Gazzaniga, 1978; Laurence & Stein, 1978). An increasing amount of empirical evidence from several areas of research with head-injured patients, accumulated over the past 10 years, suggests that successful remediation of specific cognitive deficits may occur with the application of appropriate techniques (Gouvier, Webster, & Blanton, 1986; Wilson, 1987; Wilson & Moffat, 1984a). There is also some evidence indicating that the course and speed of deterioration in certain cognitive functions in aging adults may be altered through cognitive stimulation and training programs (cf. the ADEPT program, Baltes & Willis, 1982). The growth in the population of elderly individuals in our society will necessitate an expansion of our knowledge about normal and abnormal geriatric functioning in a variety of spheres. We will need to know how to help elderly individuals with many types of problems, not the least of which are problems with memory which can limit the elderly individual's successful participation in society. Enhanced independent functioning and better quality of life for elderly adults and their significant others may be possible through the development of effective strategies for coping with deficits, such as the acquisition of mnemonic techniques. These are worthwhile goals. Thus, there seems to be abundant social justification and increasing research evidence that supports the application of memory interventions to aging populations. As emerging treatment outcome studies in the clinical psychology literature suggest, the optimum outcome of any treatment for a given individual appears to be predicated on a good match between specific features of the client's disorder and the type of treatment interventions employed (McKnight, Nelson, Hayes, & Jarrett, 1984). In this chapter, we have attempted to describe the types of memory interventions employed to date, to describe the features of memory interventions which seem to be effective, and to encourage the use of high levels of performance as goals for interventions. It is our hope that this chapter has been of benefit to those interested in designing such interventions. References

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Kotler-Cope, S., Blanchard-Fields, F., & Gouvier, W. D. (1988). Adult cognition: Neuropsychological evidence and developmental models. In J. M. Williams & C. J. Long (Eds.), Cognitive approaches to neuropsychology (pp. 287-306). New York Plenum Press. Labouvie-Vief, G. (1985). Intelligence and cognition. In J. E. Birren & K. W. Schaie (Eds.), Handbook of the psycholop of aging (pp. 500-530). New York Van Nostrand Reinhold. Labouvie-Vief, G., & Schell, D. A. (1982). Learning and memory in later life. In B. B. Welman (Ed.), Handbook of developmentalpsycholo@ @p. 828-846). Englewood Cliffs, NJ: Prentice-Hall. Landauer, T. K., & Bjork, R. A. (1978). Optimum rehearsal patterns and name learning. In M. M. Gruneberg, P. E. Morris, & R. N. Sykes (Eds.), Practical aspects of memory (pp. 625-632). London, UK Academic Press. Laurence, S., & Stein, D. G. (1978). Recovery after brain damage and the concept of localization of function. In S. Finger (Ed.), Recovery from brain damage: Research and theory (pp. 369-407). New York Plenum Press. Mason, S. E., & Smith, A. D. (1977). Imagery in the aged. Experimental Aging Research, 3, 17-32. McCarty, D. (1980). Investigation of a visual imagery mnemonic device for acquiring face-name associations. Journal of Experimental Psychologv: Human Learning and Memory, 6, 145-155. McEvoy, C. L., & Moon, J. R. (1988). Assessment and treatment of everyday memory problems in the elderly. In M. M. Gruneberg, P. E. Morris, & R.N. Sykes (Eds.), Practical aspects of memory: Current research and issues (vol. 2) (pp. 155-160). Chichester, UK. John Wiley & Sons. McKitrick, L. A., & Camp, C. J. (1989, August). Name and location learning in DAT with spaced-retrieval. Paper presented at the annual convention of the American Psychological Association, New Orleans, LA. McKnight, D. L., Nelson, R. O., Hayes, S. C., & Jarrett, R. B. (1984). Importance of treating individually assessed response classes in the amelioration of depression. Behavior Therapy, 15, 315-335. Moffat, N. (1984). Strategies of memory therapy. In B. A. Wilson & N. Moffat (Eds.), Clinical management of memory problems (pp. 63-88). London, U K Croom Helm Ltd. Nebes, R. D. (1989). Semantic memory in Alzheimer's Disease. Psychological Bulletin, 106, 377-394. Paivio, A. (1971). Imagery and verbal processes. New York: Holt, Rinehart, & Winston. Pajurkova, E. M., & Wilkins, A. J. (1983). Prospective remembering in patients with unilateral temporal or j?ontal lobectomies. Paper presented at the Sixth European Conference of the International Neuropsychological Society, Lisbon. Petit, T. L. (1982). Neuroanatomical and clinical neuropsychological changes in aging and senile dementia. In F. I. M. Craik & S. Trehub (Eds.), Aging and cognitive processes (pp. 1-31). New York: Plenum Press. Poon, L. W. (1985). Differences in human memory with aging: Nature, causes, and clinical implications. In J. E. Birren & K. W. Schaie (Eds.), Handbook of the psychology of aging (pp. 427-462). New York: Van Nostrand Reinhold Company.

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Poon, L. W., & Fozard, J. L. (1980). Epilogue: New directions in memory and aging research. In L. W. Poon, J. L. Fozard, L. S. Cermak, D. Arenberg, & L. W. Thompson, (Eds.)., New directions in memory and aging (pp. 545-550). New York Lawrence Erlbaum Associates. Poon, L. W., Fozard, J. L., & Treat, N. J. (1978). From clinical and research findings on memory to intervention programs. Experimental Aging Research, 4, 235-253. Poon, L. W., Fozard, J. L., Cermak, L. S., Arenberg, D., & Thompson, L. W. (Eds.). (1980). New directions in memory and aging: Proceedings of the George A. Talland Memorial Conference. New York Lawrence Erlbaum Associates. Poon, L. W., Walsh-Sweeney, L., & Fozard, J. L. (1980). Memory skill training for the elderly: Salient issues on the use of imagery mnemonics. In L. W. Poon, J. L Fozard, L. S. Cermak, D. Arenberg, & L. W. Thompson (Eds.), New directions in memory and aging: Proceedings of the George A. Talland Memorial Conference (pp. 461-484). Hillsdale, NJ: Lawrence Erlbaum Associates. Rabbitt, P. M. A. (1983). How can we tell whether human performance is related to chronological age? In D. Samuels, S. Algeri, S. Gershon, V. E. Grimm, & G. Toffano (Eds.), Aging of the brain (pp. 9-18). New York: Raven Press. Rabinowitz, J. C., & Craik, F. I. M. (1986). Prior retrieval effects in young and old adults. Journal of Gerontology, 41, 368-375. Robertson-Tchabo, E. A. (1980). Cognitive-skill training of the elderly: Why should "old dogs" acquire new tricks? In L. W. Poon, J. L. Fozard, L. S. Cermak, D. Arenberg, & L. W. Thompson (Eds.), New directions in memory and aging: Proceedings of the George A. Talland Memorial Conference (pp. 511-518). Hillsdale, N J Lawrence Erlbaum Associates. Robertson-Tchabo, E. A., Hausman, C. P, & Arenberg, D. (1976). A classical mnemonic for older learners: A trip that works! Educational Gerontology, I, 215-226. Rose, T. L., & Yesavage, J. A. (1983). Differential effects of a list-learning mnemonic in three age groups. Gerontology, 29, 293-298. Samuel, D., Algeri, S., Gershon, S., Grimm, V. E., & Toffano, G. (Eds.). (1983). Aging of the brain. New York: Raven Press. Schacter, D. L. (1987). Implicit memory: History and current status. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 501-518. Schaie, K. W., & Labouvie-Vief, G. (1984). Generational versus ontogenetic components of change in adult cognitive behavior: A fourteen-year cross-generational sequential study. Developmental Psychology, 10, 305-320. Scogin, F., Storandt, M., & Lott, L. (1985). Memory-skills training, memory complaints, and depression in older adults. Journal of Gerontology, 40, 562-568. Sheikh, J. I., Hill, R. D., & Yesavage, J. A. (1986). Long-term efficacy of cognitive training for Age-Associated Memory Impairment: A six-month follow-up study. Developmental Neuropsychology, 2, 4 13-421. Sinnott, J. D. (1986). Prospective/intentional memory and incidental everyday memory: Effects of age and the passage of time. P~yclzologvand Aging, 1, 110-116. Sinnott, J. D. (1989). Prospective/intentional memory and aging: Memory as adaptive action. In L. W. Poon, D. C. Rubin, & B. A. Wilson (Eds.), Everyday cognition in adulthood and old age (pp. 352-369). New York: Cambridge University Press. Smith, A. D. (1975). Aging and interference with memory. Journal of Gerontology, 30, 319-325.

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Smith, A. D. (1986, August). Implications for basic memoy research: practicing what we preach, Paper presented at the annual convention of the American Psychological Association, Washington, D. C. Thomas, J. C., & Ruben, H. (1973). Age and mnemonic techniques in paired associate learning. Paper presented at the Gerontological Society meeting, Miami, FLA. Treat, N. J. (1977). Age, imagery, focused attention, and egocentkm in paired-associate learning. Unpublished doctoral dissertation, University of West Virginia. Treat, N. J., & Reese, H. W. (1976). Age, imagery, and pacing in paired-associate learning. Developmental PsychoZogy, 12, 119-124. Treat, N. J., Poon, L. W., & Fozard, J. L. (1981). Age, imagery, and practice in paired-associate learning. Experimental Aging Research, 7, 337-342. Treat, N. J., Poon, L. W., Fozard, J. L,& Popkin, S. J. (1978). Toward applying cognitive skill training to memory problems. Experimental Aging Reseurch, 4, 305-319. Tulving, E., & Thomson, D. M. (1973). Encoding specificity and retrieval processes in episodic memory. Psychological Review, 80, 352-373. Weinstein, C. E., Duffy, M., Underwood, V. L., MacDonald, J., & Gott, S. P. (1981). Memory strategies reported by older adults for experimental and everyday learning tasks. Educational Gerontology, 7,205-213. West, R. L. (1988). Prospective memory and aging. In M. M. Gruneberg, P. E. Morris, & R. N. Sykes (Eds.) Practical aspects of memory: Current research and issues (Vol. 2) (pp. 119-125). Chichester, UK: John Wiley & Sons. Wilkins, A. J. (1986). Remembering to do things in the laboratory and everyday life. Acta Neurologica Scandinavica, 74 (Suppl. 109), 109-112. Wilkins, A. J., & Baddeley, A. D. (1978). Remembering to recall in everyday life: An approach to absentmindedness. In M. M. Gruneberg, P. E.Morris, & R. N. Sykes (Eds.), Practical aspects of memory (pp. 27-34). London, UK: Academic Press. Wilson, B. A. (1987). Rehabilitation ofmemory. New York The Guilford Press. Wilson, B. A., & Moffat, N. (1984a). Clinical management of memory problems. Beckenham, UK: Croom Helm Ltd. Wilson, B. A., & Moffat, N. (1984b). Rehabilitation of memory for everyday life. In J. E. Harris & P. E. Morris (Eds.), Everyday memory, actions and absentmindedness (pp. 207-233). London, UK: Academic Press. Winograd, E., & Simon, E. W. (1980). Visual memory and imagery in the aged. In L. W. Poon, J. L. Fozard, L. S. Cermak, D. Arenberg, & L. W. Thompson (Eds.), New directions in memory and aging: Proceedings of the George A. Talland Memorial Conference (pp. 485-506). Hillsdale, NJ: Lawrence Erlbaum Associates, Wood, L. E., & Pratt, J. D. (1987). Pegword mnemonic as an aid to memory in the elderly: A comparison of four age groups. Educational Gerontologyl 13, 325-339. Woodruff, D. S. (1982). Advances in the psychophysiology of aging. In F. I. M. Craik & S. Trehub (Eds.), Aging and cognitive processes (pp. 29-53). New York Plenum Press. Yesavage, J. A. (1983). Imagery pretraining and memory training in the elderly, Gerontology, 29, 271-275. Yesavage, J. A. (1984). Relaxation and memory training in 39 elderly patients. American Journal of Psychiatry, 141, 778-781.

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Current Issues in Cognitive Training Research Sherry L. Willis Pennsylvania State University

As a relatively new research area within psychogerontology, cognitive training studies have generated considerable interest and discussion. To date, a number of studies have demonstrated that behavioral interventions are effective in significantly improving, on average, the community dwelling elderly's performance in a variety of cognitive domains, including face-name memory (Yesavage, Lapp & Sheikh, 1989), problem solving tasks (Denney, 1982), and fluid intelligence abilities (Willis, 1987). However, there remains considerable debate regarding the interpretation of these findings and their implications for our understanding of intellectual development in later adulthood (Donaldson, 1981; Kliegl& Baltes, 1987; Willis, 1987). In this chapter, we will examine five issues of current concern in the cognitive training literature. We will begin by reviewing some empirical data related to each issue and then consider the implications of the issue for our broader understanding of adult cognition in old age. 'lko Research Programs on Cognitive Training

In discussing these five questions, we will refer primarily to two programs of research

on cognitive training. The designs of these research programs are shown in Figure 9.1. The first data base is from the Adult Development and Aging project (ADEPT) begun by Paul Baltes and Willis in 1976 (Baltes & Willis, 1982; Willis, Blieszner & Baltes, 1981; Willis & Nesselroade, in press). Over 500 subjects have participated in 5 studies focusing on the modifiability of fluid intellectual performance. Three studies involved training of cognitive strategies related to the fluid abilities of Figural Relations or Inductive Reasoning. The remaining two studies involved practice (without feedback on performance and with no strategy training), on Figural Relations and Inductive Reasoning abilities (Hofland, 1981; Hofland, Willis & Baltes, 1981). Each study involved a pretest-treatment-posttest, control group design. Following the pretest, cognitive strategy training subjects participated in 5 one-hour training sessions focusing on cognitive strategies related to the target ability. Posttests were conducted one-week, one-month and six-months following training to examine maintenance of training effects. In 1986-87, a seven-year follow-up was conducted to examine long-term training effects (Willis & Nesselroade, in press). Preparation of this chapter was supported by research grants AGO3544 and AGO5304 from the National Institute on Aging.

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I 1984

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Figure 9.1. Design of the Adult Development and Enrichment Project (ADEPT) training studies (top), and training phase of the Seattle Longitudinal Study (bottom).

The second research program is part of the Seattle Longitudinal Study (SLS). Although the SLS was begun in 1956 (Schaie, 1983), the training phase of the study started in 1984 (Schaie & Willis, 1986; Willis & Schaie, 1986). Longitudinal data on older SLS subjects were examined over the fourteen-year period (1970-84) prior to training. Subjects' performance on two abilities, Inductive Reasoning and Spatial Orientation, was classified as having remained stable or having declined over the fourteen-year interval. In 1984, subjects received cognitive training on either Inductive Reasoning or Spatial Orientation. Subjects classified as having declined on only one of the abilities received training on that ability. Subjects classified as having remained stable or having decline on both abilities were randomly assigned to one of the training programs. Subjects received five one-hour training sessions, individually conducted in their homes. A posttest followed training. Description of Cognitive Training Procedures

The development of the training materials and the format of the training sessions was similar for each of the abilities trained, although the cognitive strategies emphasized in training and the training tasks differed by ability. We will briefly describe the

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Spatial Orientation training program to illustrate these procedures. Spatial Orientation ability involves speed and accuracy in mentally rotating abstract drawings in two-dimensional space. We employ Spatial Orientation ability in tasks, such as reading road maps or interpreting floor plans. In a test assessing this ability, the subject is shown a target drawing and must identify which of six drawings could be rotated to look like the target (Thurstone & Thurstone, 1949). The six drawings are at 45, 90, 135, 180, 225, 270, and 315 degree angles, and some are mirror images of the target. Several cognitive strategies that facilitate spatial orientation performance were identified via a review of the research literature (Cooper & Shepard, 1973; Egan, 1981; Kail, Pellegrino, & Carter, 1980). The following strategies were emphasized in training: 1) Progression from manual to mental rotation. Poor performers frequently physically turn the page to view the stimuli at various angles, rather than mentally rotating each drawing. Physical rotation is inefficient in that it take time; also since the subject often forgets what the target object looks like, he/she may repeat the physical rotation multiple times. The training procedures progress from activities in which physical rotation is permitted to those requiring mental rotation. 2) Progression from familiar to abstract stimuli. Training exercises in the early sessions involve stimuli that are familiar and meaningful to the subject. For example, subjects are asked whether they know their right hand from their left hand, and then perform an exercise in which they identify left versus right hands drawn at various angles. Use of familiar stimuli reduces the memory load during mental rotation, and also facilitates our demonstration of the use of other cognitive strategies (e.g., focusing on features of the stimulus) described below. Later training exercises involve more abstract stimuli. 3) Use of verbal labels for abstract stimuli. Subjects are encouraged to generate verbal labels for abstract stimuli. The verbal label is generated by the subject, rather than the trainer. Again, use of verbal labels reduces the memory load during mental rotation, and helps the subject identify key features of the stimulus to attend to during mental rotation. 4) Attending to key features of the target stimulus. Proficient performers attend to two or more features of a figure and determine the spatial position of these features, compared to their position on the target stimulus. During training, subjects are taught to identify key features and to attend to these features during mental rotation. 5) Use of common names for angle rotations. Many older subjects, particularly women, have not had geometry as part of their formal education, and are unfamiliar with the terms for the degrees of rotation (e.g., 45,180). In training, these angles of rotation are discussed with reference to the face of a clock (e.g., 3 o'clock position), or the portion of a pie (e.g., quarter of a pie). These strategies are emphasized in the training exercises included in the training material. The training program for each ability involved 5 one-hour training sessions, conducted in small groups or individually. A training booklet involving several training exercises has been developed for each of the five training sessions. The training exercises emphasize the cognitive strategies discussed above. Each exercise begins with several exemplar problems for which the trainer models use of the cognitive strategies to solve the problem. Then the subject practices using the strategies to solve several additional problems in the exercise; the trainer gives feedback regarding correctness of the answer and proper use of the strategies.

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Data from the ADEPT and SLS training studies are beginning to permit us to examine a number of issues related to training. The longitudinal data from the SLS study allows us to examine antecedents of training and to evaluate training effects within a developmental perspective. The ADEPT follow-up study conducted in 1986-87 is beginning to provide data on the long-term effects of training.

QUESTION I: Have Most Older Adults Declined on Fluid Abilities? Therefore, Does Training Involve Simply the Remediation of Pre-existing Cognitive Performance Levels ? Since virtually all training studies have focused on cognitive processes and abilities that show early normative patterns of decline, there has often been the assumption that training improvement reflects primarily a remediation or reactivation of previous cognitive skill levels. However, our data indicate that the asssumption that training involves solely the reactivation of pre-existing skills is too simplistic and is not completely accurate. Two issues must be considered: 1) Not all older adults have suffered age-related decline. For subjects experiencing no prior cognitive decline, training gain represents improvement beyond prior performance levels, not a reactivation of prior skill level; 2) The behavioral changes associated with training effects are not a simple reversal of the behaviors associated with age-related decline. That is, training gain is not a mirror-image of the behavioral change associated with decline. Data from the SLS training study deal with these two issues (Schaie & Willis, 1986; Willis, 1987; Willis, in press). SLS subjects' performance over the fourteen year period prior to training was classified as having remained stable or having declined for spatial and reasoning abilities (Figure 9.2). Only 22% of the subjects had declined on both abilities; 47% had not declined on either ablity; approximately 15% had declined on one of the abilities but not on the other. These data indicate that there are wide individual differences in the timing and rate of age-related decline, even when considering fluid abilities. These data should not be interpretted as evidence against the reality of age-related decline, but only to demonstrate the importance of individual differences in the timing of decline (Schaie, 1990). If training researchers studied only subjects 80 years of age and older, then they could accurately assume that most of their subjects had experienced some age-related decline. However, training subjects are generally the young-old, that age period in which there are the widest individual differences in patterns of decline. Figure 9.3 shows performance patterns (i.e., total number of items answered correctly) for stable and decline subjects trained on Inductive Reasoning (Willis, in press). In 1970, fourteen years prior to training, stable and decline subjects were performing at the same level on reasoning ability. In 1984 prior to training, the performance of subjects who had declined was significantly below that of subjects showing no prior decline (stables). Training resulted in significant performance gain for both stable and decline subjects. However, the nature of training effect is qualitatively different for the two groups (Willis, in press). For decliners, training was

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4 1

0 15% DECL INE: SPACE 16% DECL I NE: REf3SON 22% DECLINE ON BOTH 47% STABLE ON BOTH

Figure 92. Proportion of Seattle Longitudinal Study training subjects whose performance on Inductive Reasoning and Spatial Orientation abilities were classified as having remained stable or having declined over the 1970-1984 interval prior to training.

effective in returning their performance to the 1970 score level; that is, after training decline subjects were answering correctly, on average, the same number of problems as they had in 1970. On the other hand, for stable subjects training reflects improvemenr in performance beyond their 1970 level; stable subjects were answering correctly, on average, substantially more problems than they had in 1970. Examination of the raw mean scores for stable and decline groups at various occasions before and after training, as shown in Figure 9.3, provides little information on the nature of these changes in performance. Does training result in a simple reversal of age-related decline? It is accurate to say that there is a reversal of the change on the test score. After training, the average score for decliners is comparable to their 1970 score level (Figure 9.3). However, our analyses suggest that the nature of the behavioral changes associated with age-related decline are not identical to the behavioral changes associated with training. On the left-hand side of Figure 9.4, the bars show the magnitude of age-related change in Inductive Reasoning over the fourteen years (1970-84) prior to training for stable and decline subjects; the bars on the right-hand side of the figure shows the pre-posttest training gain for the two groups. The shading of each bar shows that part of the total change score associated with a change in accuracy and that part associated with a change in speed of problem

Sheny L. Willis

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Figure 93. Mean scores on Inductive Reasoning ability for Seattle Longitudinal Study training subjects classified as stable and decline. Scores are shown at four occasions: Prior to training (1970, 1977); at pretest (1984-PR); and at posttest (1984-FT).

solving'. A change in accuracy reflects a change in the proportion of attempted items that were answered correctly (Willis & Schaie, 1988). The change in speed reflects that part of the total change score that is associated with a change in the number of items attempted and that cannot be attributed to a change in accuracy. There was an age-related decline of approximately seven points for decliners over the fourteen years prior to training (left-hand side of Figure 9.4); approximately half of the decline reflects a drop in accuracy with the remaining half associated with a decline in speed of problem solving. That is, as they decline, subjects attempt fewer problems and are less accurate in the problems they do attempt. However, the training gain for 'The total change score (e.g., 1984 rights minus 1970 rights) can be partitioned into that part associated with change in accuracy and the remaining part resulting from a decline/gain in problem solving speed (e.g., decline/gain in number of attempted items). The accuracy change score was derived by first computing the expected score at time two (e.g., 1984), if accuracy had remained at the time one level (e.g., 1970). The expected score was computed by multiplying the time two (1984) attempts by the time one accuracy rate (e.g., 1970 rights divided by 1970 attempts). The accuracy change score equals the observed time two (e.g., 1984) score minus the expected score. A speed change score was computed by subtracting the accuracy change score from the raw change score.

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decliners (right-hand side of Figure 9.4) resulted primarily from an increase in accuracy. In contrast to the pattern of change associated with age-related decline involving both a drop in accuracy and speed of problem solving, most of the training improvement is a gain in accuracy. Thus, although the magnitude of the mean change scores associated with age-related decline and training gain are comparable, the nature of the behavioral changes differs for the two phenomena (see also training effects for Spatial Orientation ability, Willis & Schaie, 1988). What is the nature of behavioral change for the stable subjects? Since the magnitude of age-related change from 1970-84 is negligible, we will focus on the nature of the training gain for stable subjects. First, stable subjects answered substantially more problems correctly than they did in 1970; that is, their test score after training was higher than their score in 1970, on average (Figure 9.3). Second, and more importantly, they were performing at a higher level of accuracy after training than they did in 1970 (right-hand side of Figure 9.4). Most of the training gain was associated with increased accuracy rather than increased speed of problem solving (Figure 9.4). Thus, training effects for stable subjects involved not only an improvement in level of performance beyond their 1970 score, but also increased accuracy.

Figure 9.4. Changes in accuracy and speed of problem solving on Inductive Reasoning ability for Seattle Longitudinal Study training subjects classified as stable and decline. Change scores are shown: Prior to training (1970-1984); and Pre-posttest change.

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QUESTION 11: Are Training Effects Narrow? Should Training Transfer Be Expected to Occur at the Level of Second-order Latent Constructs, Such as Gf?

Perhaps one of the most debated issues in cognitive training research is the issue of training transfer. Some have suggested that training effects are narrow and reflect little more than "teaching the test" (Donaldson, 1981). The issue of training transfer must be considered from the perspective of Cattell's (1971) hierarchical model of fluid and crystallized intelligence. At the lowest level of the hierarchy, cognitive functioning must be assessed through behavioral observations, sometimes called tests. Test scores can be dimensionalized into common and unique variance. Unique variance is specific to a particular measure, reflecting the specific content or format of a test. The common variance of a test is that portion of the variance that is common or shared with other tests that measure the same ability. The common variance shared among tests measuring the same ability is represented within the hierarchical model as a first-order latent construct, better known as a primary mental ability (PMA). First-order constructs can also be partitioned into that related to unique and common variance. The common variance shared among first-order constructs is represented as a second-order construct. Second-order latent constructs such as fluid and crystallized intelligence reflect the variance shared among primary abilities (first-order constructs). Now, at what level should one expect to find training effects? Since we have targeted our training efforts at a specific primary ability, we maintain that training effects should be found at the level of first-order latent constructs, and indeed, this is the level at which training effects have been reported (Schaie, Willis, Hertzog & Schulenberg, 1987; Willis & Schaie, 1986). Training effects at the level of a first-order construct (Lg., at level of a primary mental ability) are broader than "teaching the test." If training only involves "teaching the test," then training effects should be specific to one test. However, if training impacts the common variance shared by all tests of an ability, then training effects should be demonstrated at the primary ability, or first-order construct level. Some of our colleagues (Donaldson, 1981; Hayslip, 1989b) have argued that training effects at the level of first-order constructs are too narrow. They maintain that training on one primary ability should result in transfer to other abilities this would result in transfer at the level of a second-order construct (e.g., Gf). However, this argument appears in conflict with current research on cognitive processes. It is well documented that the cognitive components or processes associated with a particular cognitive ability or skill are quite specific and are distinct from the processes associated with another skill or ability. Therefore, since our training programs have emphasized components and strategies specific to a given ability, there is little reason to expect that training focusing on the components associated with one ability (e.g., inductive reasoning) should result in improvement on another ability (e.g., spatial orientation).

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QUESTION III: Are Interventions Focusing on Performance Factors as Effective as Cognitive Strategy Training 3 There is no question but that cognitive functioning is influenced by both cognitive factors and performance factors. The question then arises whether interventions directed at performance factors are as effective as cognitive strategy training in improving intellectual behavior. Let's begin with definitions of cognitive versus performance factors. We will take what some will consider a rather narrow view of cognition, for the purposes of this discussion. Cognitive factors are limited to those intellectual components, skills and processes that are said to underlie a particular ability or intellectual dimension; they are considered intrinsic to the ability. For example, cognitive strategies such as mediators and mnemonics are intrinsic or specific to memory ability (Poon, 1985); utilization of salient rule patterns is associated with inductive reasoning ability (Kotovsky & Simon, 1973); mental rotation skill is specific to spatial orientation ability (Cooper & Shepard, 1973). In contrast, performance factors influence performance on one or more abilities or skills, but are not considered to be an intellectual component or process specific to the ability (Denney, 1980; Willis & Baltes, 1981; Willis, Cornelius, Blow & Baltes, 1983). Performance factors that have been studied include general response speed, affective dimensions (e.g., anxiety, morale, depression), motivational factors (e.g., achievement motivation, intrinsic/extrinsicreinforcement), and attitudinal factors (self efficacy, locus of control). Since affective and attitudinal factors, such as self efficacy, do involve thoughts, they have sometimes been described as cognitions, thus complicating the use of the term "cognitive." In addition, the distinction has recently been made between general and task- or domain-specific attitudinal and affective factors, such as intellectual self efficacy and health self efficacy (Lachman, 1986). Such context- or domain-specific attitudes or beliefs are considered to be specific to a particular ability or cognitive dimension. However, we consider these attitudinal and affective factors, both general and domain-specific, to influence mental ability perjomzance, but not to be intrinsic intellectual components or processes underlying a particular ability in the same manner as mnemonics or cognitive strategies. A number of studies have examined the relative effectiveness of interventions focusing on performance factors versus cognitive strategy training. Our interpretation of findings from these studies is that treatment conditions focusing solely on performance factors are generally not as effective as those involving training on cognitive strategies specific to the ability. Support for this position is based on both correlational and experimental data.

First, although the correlations between performance factors, including domain-specific control beliefs, and fluid ability performance are often found to be statistically significant, given large enough sample sizes, the correlations are typically modest (compared with the magnitude of intercorrelations among ability factors), generally in the range of .20 to -40 (Lachman & Leff, 1989; Willis, 1988). The correlations between fluid abilities and anxiety measures are even more modest (Hayslip, 1989b). Moreover, domain-specific control beliefs have not shown to be

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significant predictors of age-related change in fluid or crystallized intelligence dimensions in old age (Lachman & Leff, 1989; Willis & Jay, 1990). Second, most experimental studies have reported modest or no effect on ability functioning of treatments focusing solely on performance factors. No significant improvement in cognitive performance was reported for interventions focusing solely on the performance factors of monetary reinforcement (Denney, 1980; Hoyer, Labouvie, & Baltes, 1973); noncontingent social reinforcement (Mergler & Hoyer, 1981), additional planning time (Denney, 1980), social contact (Willis et al, 1983), or morale (Yesavage, 1983). Hayslip (1989a, 1989b) has recently argued that treatments focusing on performance factors, such as anxiety reduction procedures, are as effective as cognitive strategy training. However, the Hayslip research (1989b) suffers from a number of limitations that affect interpretation of the findings. First, subjects in the anxiety reduction condition also received practice on the cognitive strategy training materials, confounding the two treatments. Second, Hayslip's claim of a training effect for the stress innoculation group was demonstrated only immediately after training; greater durability of training effects was shown for the cognitive strategy training than for the stress innoculation. Third, Hayslip found no reduction in anxiety level following the stress innoculation procedure, bringing into question whether changes in cognitive performance could be attributed to the stress reduction intervention. Indeed, in a number of instances a negative relationship was found between cognitive gain and reduction in anxiety (Hayslip, 1989b). A major limitation of much of the previous research on interventions focusing on performance factors has been the lack of a direct test of the intervention's effectiveness. That is, in many studies, change on the target variable (e.g., anxiety, morale, self efficacy) has not been directly assessed. However, if improvement in cognitive functioning is to be attributed to the performance factor, then change in the performance factor needs to be demonstrated, and a relationship needs to be shown between change in the cognitive ability and change in the performance factor. To Hayslip's credit, he did examine pre-posttest change in anxiety, but found little relationship between change in anxiety and in cognitive performance.

Some of the most systematic experimental research on the effect of performance factors on memory performance has been conducted by Yesavage and colleagues (Yesavage, 1983; Yesavage, Lapp & Sheikh, 1989). In a number of studies, Yesavage has examined the effect on face-name memory recall of various non-cognitive treatments (e.g., imagery enhancement, relaxation, morale) alone and in combination with specific training on mnemonic strategies. While performance factor interventions alone have had modest or no effect on face-name recall, these treatments do appear to be useful when administered in combination with specific mnemonic strategy training. Such interventions appear to boost or enhance the effectiveness of the strategy training in some instances. Thus, based on the Yesavage research, it appears that combinatorial training involving treatments focusing both on cognitive strategies specific to the ability and on domain-specific performance factors may yield the maximum improvement in cognitive functioning.

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As a final note, Yesavage's most recent research also suggests that there appear to be wide individual differences in the effectiveness of treatment on a particular performance factor. For example, only subjects high in anxiety were found to benefit from a combination of relaxation and strategy training. Thus, combinatorial training programs may need to be "tailored to the specific needs of the individual in order to maximize the effects. QUESTION IV: Is Practice as Effective as Cognitive Strategy Training in Improving Cognitive Performance ?

Some have maintained that older subjects can generate their own strategies if given the opportunity to practice, and that cognitive strategy training produces no greater effects than practice alone. Teaching of cognitive strategies may even be confusing to the subject. This perspective is based largely on the assumption that intervention involves the activation of pre-existing cognitive skills. It is certainly true that almost any exposure of elderly subjects to cognitive problem solving tasks results in some improvement (Hofland et al., 1981). Even practice associated with pre- and posttesting raises performance on a variety of measures for both training and control groups. On the one hand, such practice effects are encouraging, since it supports the notion of plasticity in intellectual performance in old age. On the other hand, it forces the researcher to examine more carefully what are the specific benefits from particular types of intervention (e.g,. practice versus cognitive strategy training conditions). We must begin by differentiating between procedures employed in cognitive strategy training and those traditionally involved in practice. A practice condition traditionally has involved the subject attempting to solve a number of problem solving tasks without feedback regarding the correctness of the response and with no instruction on strategies or techniques that are useful in solving the problem. Furthermore, the difficulty level of tasks included in a practice condition have traditionally been randomly ordered, so that the problems did not proceed from simple to more complex tasks.

In comparing the effects of cognitive strategy training versus practice, it is important to consider not only the increase in number of correct items, but also level of accuracy (Willis, in press). As a function of practice, subjects can speed up their behavior, thereby attempting more problems and increasing the number of problems answered correctly. However, an increase in the number of correct answers does not necessarily reflect an increase in accuracy. Accuracy level may increase, decrease or remain constant, even though there is a significant increase in number of correct answers. We have found cognitive strategy training to be particularly effective in increasing level of accuracy in both the ADEPT and SLS studies. Accuracy is important for both theoretical and practical reasons. Increased accuracy suggests that the older adults are utilizing the cognitive strategies emphasized during training. We believe that subjects' utilization of these strategies and the resulting increase in accuracy are important to the maintenance of cognitive training effects. Accuracy is also very important to the

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older adult from a practical perspective. Mistakes or errors due to incompetence or carelessness can be very costly, even deadly, to the older adult. Many older adults recognize this and much of their efforts are devoted to maintaining a high level of accuracy, even if it involves avoiding activities where there is potential for error. Therefore, training that demonstrates potential for increased accuracy is particularly important in old age. In our ADEPT research, we have compared level of accuracy for cognitive strategy training and practice conditions on the fluid ability of Figural Relations. Figure 9.5 shows the proportion of problems answered correctly under strategy training versus practice conditions on Figural Relations ability. After 5 strategy training sessions, 69% of the items attempted were answered correctly. In contrast, after 5 practice sessions only 50% of items were answered correctly. Even after 10 practice sessions, the accuracy level (53%) was below that for the cognitive strategy training group after five training sessions. In recent research by Baltes and colleagues (Baltes, Kliegl & Dittmann-Kohli, 1988), cognitive strategy training was also found to result in higher levels of accuracy on Inductive Reasoning ability than a practice condition involving the same number of hours of practice as cognitive training. However, there was no difference in accuracy between the strategy training and practice conditions on the Figural Relations ability. However, it was noted that the Baltes et al. sample was a more advantaged sample than the ADEPT subjects, and scored about one-half standard deviation above the ADEPT sample at pretest. For more advantaged subjects, such as in the Baltes et al. study, a practice condition may be effective for some less complex abilities, such as Figural Relations. Some Figural Relations problems have been shown to be able to be solved by a visual inspection of the stimulus (e.g., the gestalt), rather than by logical reasoning strategies. Several recent studies (Baltes, Sowarka, & Kliegl, 1989; Blackburn, Papalia-Finlay, Foye & Serlin, 1988; Hayslip, 1989a) have employed a different type of self-instruction or practice condition than that traditionally included in the literature on practice effects. We believe that comparisons reported in these studies between self-guided practice and cognitive strategy training need to be interpretted with considerable caution. In all of these studies the practice condition involved use of an adaptation of the ADEPT cognitive strategy training materials, but with little or no trainer direction. Thus, the nature of the practice materials was qualitatively different from the types of materials traditionally employed in a practice condition. That is, the cognitive strategy training materials were designed similar to programmed instructional materials. The training problems were sequentially ordered to introduce the subject to cognitive strategies and problem solution rules in a systemmatic manner. Problems within the materials were ordered in terms of difficulty level. Thus, it would be expected that advantaged subjects would be able to engage in self instruction as they progressed through the materials, as has been demonstrated with other programmed instructional materials. Furthermore, in two of the studies (Blackburn et al., 1988; Hayslip, 1989a), subjects were given the answers to practice problems, thus giving them feedback regarding the correctness of their responses; traditionally, no feedback on problems has been given under practice conditions. This type of self guided instruction condition

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275 FIGURflL RELATIONS TRflINING

CFR PRACTICE: 5 SESSIONS

0 5% SKIPS

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45% URONGS H 58% RIGHTS

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Figure 9.5. Proportion of attempted Figural Relations ability test items that were correct, wrong, or omitted. Figures are shown for: Training subjects after five training sessions; Practice subjects after five practice sessions; and Practice subjects after ten practice sessions.

represents a midpoint in a continuum from cognitive strategy training and the traditional practice condition. While findings from these studies contribute to our understanding of the antecedents of cognitive functioning, we consider this self guided instructional condition to provide much more support to the subject than is provided in the traditional practice condition, and results of the studies need to be interpreted accordingly.

QUESTION V: Are There Long-term Effects for Cognitive Training? The durability of training effects over time is an important issue when examining the implications of training within a developmental perspective. The concern is not only that significant improvement in older adults cognitive performance can be demonstrated immediately after training, but whether training interventions are durable and have implications for patterns of longterm cognitive development. In the first phase of cognitive training research, the maintenance of training effects was assessed one week, one month, and six months after training. In several studies in our own laboratory and that of others, training effects were found to be maintained six months following training (Willis et al, 1981; Willis, 1987).

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As it has become possible to follow training subjects for multiple years after initial training, we have begun to examine the longterm impact of training on subjects' subsequent cognitive development. We have recently reported findings from a seven-year follow-up of ADEPT subjects trained on the fluid ability of Figural Relations (Willis & Nesselroade, in press). Subjects received initial training in 1979, with subsequent booster training sessions in 1981 and 1986. Significant training effects at each of the three occasions indicated that subjects were able to continue to profit from cognitive interventions as they advanced from young-old to old-old age. Moreover, training subjects even into their late seventies and early eighties continue to perform at a level significantly above their baseline level (prior to training). Examination of subjects' performance at the individual level, indicated that 64% of the training group's performance was consistently above baseline, compared to 33% of the control group. Thus, findings from our follow-up studies suggest that during the late seventies and eighties, during a period in which widespread cognitive decline would be expected, multi-phased cognitive interventions are effective in maintaining older adults' level of performance significantly above their baseline performance, seven years previously. Summary and Discussion

In this chapter we have discussed five issues of concern in current research on cognitive training in elderly samples. Perhaps the most notable outcome of the training research to date has been the repeated demonstration of plasticity in older adults' cognitive functioning. Plasticity in cognitive functioning has been exhibited not only by the numerous studies reporting significant improvement in older adults' performance via brief behavioral interventions, but also by studies showing that for some healthy older adults' reliable declines in cognitive performance can be remediated through cognitive training. Findings from training research have provided a new and broader framework in which to consider the average or normative levels of performance reported in single occasions studies of cognitive aging. Based on the findings of training research, one can expect that one half to two-third of the subjects in a descriptive (single occasion) study are capable of performing at a statistically significantly higher level than they exhibit at a single occasion of measurement. Our studies suggest that many healthy older adults' can perform at a level one-half to three-quarters of a standard deviation above their average or normative level of performance. At the same time, some words of caution and reservation are needed. First, the research on cognitive training has focused largely on relatively healthy older adults, living independently in the community. Participants have typically been the young-old, rather than the old-old and very old, since the young-old are more likely to volunteer for almost any type of behavioral research. There has been less cognitive training research with institutionalized or cognitively impaired elderly; these populations are more likely to include the old-old and very-old. However, fortunately interest in this type of research is growing, given the changing demographics of our society, and the need to foster the maintenance of independence into advanced age.

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Second, the number of cognitive abilities and processes that have been subjected to training interventions is relatively limited. To date the abilities that have received the most intensive study are those that longitudinal research indicates show the earliest onset of decline (e.g., fluid abilities). Further research is needed on abilities and skills, such as crystallized intelligence, that tends to remain intact until advanced old age. Such research is important for theoretical and applied reasons. From the perspective of developmental theory, it is important to compare the relative range of plasticity in intact as well as more vulnerable cognitive dimensions. Interestingly, it may prove more difficult to enhance performance on more intact abilities to the degree exhibited for abilities showing earlier decline. Although there have been virtually no direct training studies of crystallized abilities, several studies have shown smaller retest or practice effects for intact abilities such as vocabulary than for fluid abilities (Baltes et al., 1988; Willis et al., 1981). From an applied perspective, fostering the maintenance and enhancement of intact abilities and skills may be particularly useful in order to compensate for irreversible loss in other cognitive domains due to neurological impairment. Three themes for future research emerge from the discussion of the five issues considered in this chapter. First, future training research must give greater attention to individual differences. Many previous training studies have reported effects in terms of mean scores, with standard deviations serving as the only indication of variability in training effects. However, our own research has demonstrated the importance of distinguishing between training as the remediation of decline, versus training as the improvement of the performance of subjects experiencing no previous decline. Furthermore, recent research on performance variables (e.g., anxiety, self efficacy) affecting cognitive functioning suggests that future training research needs to target interventions specifically to the strengths or deficits of the individual. To be maximally effective, training procedures need to be "tailored" to the individual. Furthermore, study of individual differences is important in furthering our understanding of the antecedents of cognitive change and of the predictors of training effectiveness. For example, more study is needed of the relationship between chronic disease and the medications prescribed for these diseases and cognitive change and responsiveness to training interventions. The second theme to emerge focuses on further study of the nature of the behavioral changes reflected in training and practice effects. We have begun to examine training effects reflecting changes in accuracy versus speed of problem solving. Baltes and colleagues (Baltes et al., 1988) have examined the relationship between item difficulty level and training effects. However, further research is needed to examine the nature of the behavioral changes occuring in training for subjects who have declined v e m those who have not declined. The nature of behavioral change occurring under different treatment conditions also needs further study. Finally, the effects of training interventions need to be examined within the ongoing development and aging of the individual. That is, training effects need to be examined within a lifespan perspective. Our recent follow-up studies of ADEPT training research suggest that training and intermittent booster sessions are effective in sustaining higher levels of functioning even into advanced old age. We believe that it is the examination of training interventions from a lifespan framework that will

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provide the most fruitful information on the utility of training for fostering and enhancing the quality of life for older individuals and thereby for society at large.

References Baltes, P. B., Kliegl, R., & Dittman-Kohli, F. (1988). On the locus of training gains in research on the plasticity of fluid intelligence in old age. Journal of Educational Psychology, 80,392-400. Baltes, P. B., Sowarka, D., & Kliegl, R. (1989). Cognitive training research on fluid intelligence in old age: What can older adults achieve by themselves? P~chology &Aging, 4, 217-221. Baltes, P. B. & Willis, S. L. (1982). Enhancement (plasticity) of intellectual functioning in old age: Penn State's Adult Development & Enrichement Project (ADEPT)(1982). In F. I. M. Craik & S. E. Trehub (Eds.), Aging and cognitive processes (pp. 353-389). New York: Plenum Press. Blackburn, J. A., Papalia-Finlay, D., Foye, B. F., & Serlin, R. C. (1988). Modifiability of figural relations performance among elderly adults. The Journals of Gerontology, 43, P87-89. Cattell, R. (1971). Abilities: Their structure, growth, and action. New York: Houghton Mifflin. Cooper, L. & Shepard, R. (1973). Chronometric studies of rotation of mental images. In W. G. Chase (Ed.), Visual information processing (pp. 75-96). New York: Academic Press. Denney, N. (1980). The effect of manipulation of peripheral noncognitive variables on the problem-solving performance of the elderly. Human Development, 23, 268-277. Denney, N. (1982). Aging and cognitive changes. In B. B. Wolman (Ed.), Handbook of developmental psychology. Englewood Cliffs, NJ: Prentice Hall. Donaldson, G. (1981). Letter to the editor. Journal of Gerontology, 36, 634-636. Egan, D. (1981). An analysis of spatial orientation test performance. Intelligence, 5, 85-100. Hayslip, B. (1989a). Alternative mechanisms for improvements in fluid ability performance among older adults. Psychology and Aging, 4, 122-124. Hayslip, B. (1989b). Fluid ability training with aged people: A past with a future? Educational Gerontology, IS, 573-595. Hofland, B. F. (1981). Practice effects in the intellectual perjormance of the ekierh: Retesting as an infervention strategy. Unpublished doctoral dissertation. The Pennsylvania State University, University Park, PA. Hofland, B., Willis, S. L. & Baltes, P. B. (1981). Fluid intelligence performance in the elderly: Intraindividual variability and conditions of assessment. Journal of Educational Psychology, 73, 573-586. Hoyer, W., Labouvie, G., & Baltes, P. B. (1973). Modification of response speed and intellectual performance in the elderly. Human Development, 16, 233-242. Kail, R., Pellegrino, J., & Carter, P. (1980). Developmental changes in mental rotation. Journal of Experimental Child Psychology, 39, 102-116.

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Kliegl, R. & Baltes, P. B. (1987). Theory-guided analysis of mechanisms of development and aging through testing-the-limits and research on expertise. In C. Schooler & K. W. Schaie (Eds.), Cognitivefunctioning and social structure over the life course (pp. 95-119). Nonvood NJ: Ablex. Kotovsky, K. & Simon, H. (1973). Empirical tests of theory of human acquisition of concepts for sequential patterns. Cognitive Psychology, 4, 399-424. Lachman, M. (1986). Personal control in later life: Stability, change, and cognitive correlates. In M. M. Baltes & P. B. Baltes (Eds.), The psychology of control and aging. (pp. 207-236). Hillsdale NJ: Erlbaum. Lachman, M. & Leff, R. (1989). Perceived control and intellectual functioning in the elderly: A 5-year longitudinal study. Developmental Psychology, 25, 722-728. Mergler, N. & Hoyer, W. (1981). Effects of training on dimensional classification abilities: Adult age comparisons. Educational Gerontology, 6, 135-145. Poon, L. W. (1985). Differences in human memory with aging: Nature, causes, and clinical implications. In J. E. Birren & K. W. Schaie (Eds.), Handbook of the psychology of aging, 2nd ed. (pp. 427-462). New York: Van Nostrand Reinhold. Schaie, K. W. (1983). The Seattle Longitudinal Study: A 21-year exploration of psychometric intelligence in adulthood. In K. W. Schaie (Ed.), Longitudinal studies of adult psychological development (pp. 64-135). New York: Guilford. Schaie, K. W. (1990). Intellectual development in adulthood. In J. E. Birren & K. W. Schaie (Eds.), Handbook of the psychology of aging, 3rd ed. (pp. 291-310). New York: Academic Press. Schaie, K. W. & Willis, S. L. (1986). Can decline in adult intellectual functioning be reversed? (1986). Developmental Psychology, 22, 223-232. Schaie, K. W., Willis, S. L., Hertzog, C. & Schulenberg, J. E. (1987). Effects of cognitive training on primary mental ability structure. Psychology and Aging, 2, 233-242. Thurstone, L. L. & Thurstone, T. G. (1949). Examiner manual for the SRA Primary Mental Abilities Test. Chicago: Science Research Associates. Willis, S. L. (1987). Cognitive training and everyday competence. In K. W.Schaie (Ed.), Annual review of gerontology and geriatrics Vol. VII, (pp. 159-188). New York Springer. Willis, S. L. (1988, April). Myths of cognitive training research. Paper presented at the Second Cognitive Aging Conference, Atlanta, GA. Willis, S. L. (in press). Contributions of cognitive training research to understanding late life potential. In M. Perlmutter (Ed.), Late life potential. Washington, D C Gerontological Society of America. Willis, S. L. & Baltes, P. B. (1981). Derivation of gerontological training research from the Gf-Gc theory of intelligence: A reply to Donaldson and some critical observations. Journal of Gerontology, 36, 634-638. Willis, S. L,Blieszner, R. & Baltes, P. B. (1981). Intellectual training research in aging: Modification of performance on the fluid ability of Figural Relations. Journal of Educational Psychology, 73, 4 1-50. Willis, S. L., Cornelius, S. W., Blow, F. & Baltes, P. B. (1983). Training research in aging: Attentional processes. Journal of Educational Psychology, 75, 257-270. Willis, S. L. & Jay, G. (1990). Reciprocal relutionships between fluid and crystallized intelligence and intellectual control beliefs. Unpublished manuscript. University Park P A Pennsylvania State University.

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Willis, S. L. & Nesselroade, C. S. (in press). Longterm effects of fluid ability training in old-old age. Developmental Psychology. Willis, S. L. & Schaie, K. W. (1986). Training the elderly on the ability factors of spatial orientation and inductive reasoning. Psychology and Aging, I , 239-247. Willis, S. L.& Schaie, K. W. (1988). Gender differences in spatial ability in old age: Longitudinal and intervention findings. S a roles, 18, 189-203. Yesavage, J. (1983). Imagery pretraining and memory training in the elderly. Journal of Gerontology, 29, 217-275. Yesavage, J. A., Lapp, D. & Shiekh, J. I. (1989). Mnemonics as modified for use by the elderly. In L W. Poon, D. C. Rubin & B. k Wilson (Eds.), Eveyduy cognirion in adulthood and late life. New York: Cambridge University Press.

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10 Aging and Word Retrieval: Selective Age Deficits in Language Deborah M. Burke Pomona College Gary D. Laver The Claremont Graduate School

Older adults' increasing difficulty in remembering well known names and vocabulary words is proverbial ("I'll never forget what's-her-name"), and figures prominently in self reports of their memory problems (Cohen & Faulkner, 1986; Sunderland, Watts, Baddeley, & Harris, 1986). Nevertheless, it is often stated that verbal ability is preserved in old age (e.g., Botwinick, 1984) and that episodic, but not semantic memory exhibits age-related impairment (Mitchell, 1989). This distinction is consistent with the well established pattern of differential age-related decline in tasks involving new learning versus old knowledge, or fluid versus crystallized abilities (e.g., Horn & Cattell, 1966; Light & Burke, 1988; Salthouse, 1982,1988). But how are these research findings to be reconciled with older adults' reports, such as that of one person who after being tested in one of our language comprehension experiments suggested, "If you want to study something really interesting, find out why I cannot remember the name of my friend of 20 years when I go to introduce her"? In this chapter, we argue that this apparent paradox is because research showing preserved verbal abilities in older adults involves primarily language comprehension, for example, the verbal subtests of the Wechsler Adult Intelligence Scale (Botwinick, 1984), whereas older adults' complaints involve language production. We describe current models of the knowledge representations and processing mechanisms involved in accessing conceptual and lexical information in memory. This theoretical description clarifies the difference between retrieval processes involved in the comprehension and production of language. We review findings that validate older adults' complaints about one aspect of production, namely, their declining word finding ability and we describe a series of studies on the "tip-of-the-tongue'' phenomenon that illuminates the locus of word retrieval deficits. We postulate a n age-related impairment in a basic cognitive mechanism, namely, the transmission of priming among memory representations (Burke, Worthley, MacKay, & Wade, 1989; MacKay & Burke, in press) which can account for the greater vulnerability of older adults' language production processes compared to comprehension processes. Portions of this paper were presented at the Meeting of the Psychonornic Society in Atlanta, November, 1989. This research was supported by grant AGO2452 from the National Institute on Aging.

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Interactive Activation Models of Language and Memory Current models of memory for conceptual and linguistic knowledge postulate a vast network of pathways connecting representational units called nodes. Nodes are organized into a semantic system which represents word meanings and a phonological and orthographic system which represents word sounds and spellings (e.g., Baddeley, 1982; Collins & Loftus, 1975; Dell, 1986; MacKay, 1987; McClelland & Rumelhart, 1981). Figure 10.1 illustrates a portion of the representation offrirbee in the semantic and phonological systems within an interactive activation model of speech production similar to that of Dell (1986) and MacKay (1982, 1987). Lexical nodes store neither phonological nor semantic information. Rather, they are connected in the phonological system to hierarchically organized nodes which represent syllables, phonological compounds and features, and in the semantic system to nodes which represent aspects of meaning. Representational nodes in the phonological and semantic system are types, not tokens, because they are connected to all lexical nodes whose words include that component. Activation of nodes to threshold causes retrieval of information and may occur in one system independently of the others. Activation spreads along connections within and between systems to related nodes increasing subthreshold levels of excitation, called priming, which increases the availability of the information at these nodes. Within the Node Structure Theory of MacKay (1987), retrieval of a word sound occurs only when nodes at the lowest level, i.e., phonological features, have been activated to threshold. The sequence of mental processes differs for speech production (the translation of thought into language) and comprehension. The same nodes, however, are involved in production and comprehension. Production involves activation of semantic nodes and the top-down spread of priming to phonological nodes corresponding to the relevant word. Comprehension involves the activation of phonological or orthographic nodes and the bottom-up spread of priming to semantic nodes corresponding to the relevant meaning. Evidence for priming in the semantic system comes from studies of word recognition; responses are faster, for example, in the lexical decision task for words preceded by semantically related rather than unrelated words (e.g., Meyer & Schvaneveldt, 1971; Neely, 1977). Bottom up processing of the prime word triggers a spread of priming within the semantic system to the node for the target word increasing its availability. Priming within the phonological and orthographic systems (which is involved in production) is demonstrated by facilitation of lexical decision responses for words preceded by orthographically and phonologically related prime words, e.g., bribe-tribe (Hillinger, 1980; McNamara & Healy, 1988; Meyer, Schvaneveldt, & Ruddy, 1974; Shulman, Hornak, & Sanders, 1978) or only phonologically related words, e.g., eight-mate (Hillinger, 1980). Priming may occur automatically without engaging attention both for semantically related (e.g., Neely, 1977) and phonologically related words (Hillinger, 1980; Humphreys, Evett, & Taylor, 1982). According to MacKay's Node Structure Theory, the strength of connections between nodes, called linkage strength, determines the rate and amount of priming transmitted between them (MacKay, 1987). Linkage strength determines whether or not a node is activated to threshold and how rapidly. Thus, linkage strength is an important

283

Aging and Word Relrievai

SEMANTIC SYSTEM

Propositional Nodes

FRISBEES ARE MADE

FRISBEES ARE

OF PLASTIC

FFSBEE (noun)

Lexical Nodes

Syllable Nodes

Phonological Nodes

LABIODENTAL

Feature Nodes

Figure 10.1. An example of the representation offrisbee in the semantic and phonological systems in an interactive activation model.

determinant of what information becomes available. Linkage strength has been used to explain the effects of recency and frequency of node activation on accessibility of information, and to explain the effects of aging on language production (Burke et al., 1989). According to this Transmission DeficitHypothesis, linkage strength declines with age so that the transmission of priming is decreased (Burke et al., 1989; MacKay & Burke, in press).

W h y are retrieval processes involved in language production, but not comprehension, affected by age-related declines in linkage strength? The answer within the present model is that semantic priming spreads between nodes for semantically related words through a rich network of connections and summates at the lexical node; that is, most

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related words have a number of dimensions of shared meaning so that priming is transmitted over a number of connections to a lexical node. In contrast, phonological priming spreads from a single lexical node to a number of syllable and phonological nodes (see Figure 10.1). Thus phonological priming diverges from a lexical node and semantic priming converges on a lexical node. A deficit in transmission of priming would have a greater effect when priming diverges. Comprehension and Production Processes in Older Adults

This theoretical distinction between comprehension and production processes is consistent with empirical research findings suggesting that these two types of processes are affected differently by aging. Studies of young and older adults have indicated a pattern of generally spared comprehension processes but declining production processes with increasing age. The transmission of semantic priming, involved in comprehension, appears to be unaffected in old age; the magnitude of semantic priming effects is at least as large for older as for young adults in lexical decision (e.g., Bowles & Poon, 1985a; Burke, White, & Diaz, 1987; Chiarello, Church, & Hoyer, 1985; Howard, McAndrews, & Lasaga, 1981) and in word naming tasks (Balota & Duchek, 1988; Cerella & Fozard, 1984). Indeed, Laver and Burke (1990) report that a meta-analysis of these studies reveals a slightly larger semantic priming effect for older adults. Further, the time course for the transmission of priming in word recognition appears to be age invariant (Balota & Duchek, 1988; Burke et al., 1987; but see Howard, Shaw, & Heisey, 1986). The speed of retrieving semantic information corresponding to words shows only a general age-related slowing (Madden, 1985; Mueller, Kausler, & Faherty, 1980). Consistent with this preservation of semantic activation processes involved in comprehension, older adults show little decline in tests of comprehension, except under conditions where their ability to remember new information is exceeded (Burke & Harrold, 1988; Light & Albertson, 1988). Age-related declines in memory for new information is a consistent finding in the aging literature (e.g., Burke & Light, 1981; Salthouse, 1985) and thus age-related deficits in comprehension are no surprise when tests require retention of new information for correct performance (as in, for example, Cohen, 1979; Light, Singh, & Capps, 1986). There is no evidence that age deficits in such comprehension tests reflect age differences in retrieval of semantic information in memory (Burke & Harrold, 1988). In contrast to comprehension, there is evidence for word production deficits in old age, independent of memory for new information. First, there are age-related decreases in the accuracy of naming pictures of objects or actions (Albert, Heller, & Milberg, 1988; Borod, Goodglass, & Kaplan, 1980; Harrold, 1987; Nicholas, Obler, Albert, & Goodglass, 1985; Van Gorp, Satz, Kiersch, & Henry, 1986), and increases in the time to name pictures (Mitchell, 1989; Thomas, Fozard, & Waugh, 1977; but see Poon & Fozard, 1978). At least some of these age-related deficits are in retrieval of phonological information because the age difference decreases for naming accuracy with phonological cues (Nicholas et al., 1985), and for naming latency when the picture is preceded by its name (Thomas et al., 1977).

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Older adults are also slower and less accurate than young adults in producing a target word corresponding to a definition (Bowles & Poon, 1985a). On timed fluency tests, older adults usually produce fewer words starting with a specified letter or in a specified category than do young adults (Burke & Light, 1981; Borod et al., 1980). Word Production Deficits and the Tip of the Tongue State The tip of the tongue state (TOT), in which a person is unable to remember a well known name or other word, is an error caused by a deficit in the retrieval of phonological information. Despite the frequency of complaints about this problem, it has received relatively little theoretical treatment since the landmark article by Brown and McNeill in 1966. Nevertheless, naturally occurring speech errors are an important source of information about the architecture of the system underlying language production (e.g., Dell, 1986; Garrett, 1980; MacKay, 1987). Many speech errors occur within the phonological rather than the semantic system, e.g., coat thrutting for throat cutting. TOTs are a good example of the vulnerability of phonological retrieval: we know a word's meaning, but are unable to access the desired word even though we are absolutely certain we "know" it and can often report partial phonological information such as the first letter (Brown & McNeill, 1966; James, 1890/1981). During a TOT for 973x33 in Figure 10.1, information within the semantic system is activated, but the transmission of priming within the phonological system goes awry so that there is insufficient priming for retrieval of the word's phonology (Burke et al., 1989). Thus, TOT states are particularly interesting in the present context because they offer a fairly pure case of a phonology retrieval failure. The proposal that the cause of lexical retrieval failure is a deficit in transmission of priming has been suggested by several investigators (Bowles, Obler, & Poon, 1989; a h e n & Faulkner, 1986; MacKay & Burke, in press; Yaniv & Meyer, 1987) and has been named the Transmission Deficit Hypothesis (Burke et al., 1989). It is consistent with the finding that ratings of feeling of knowing (FOK) for the target in unanswered questions reflect the level of priming at target nodes in memory; the higher the FOK, the faster the response times to targets presented for lexical decision (Yaniv & Meyer, 1987) and the shorter their perceptual identification times (e.g., Goodglass, Wingfield, & Wayland, in press; Nelson, Gerler, & Narens, 1984). Further, FOK ratings are positively correlated with probability of target recognition (e.g, Nelson et al., 1984) or recall (e.g., Gruneberg & Monks, 1974; Read & Bruce, 1982). The other most frequently mentioned mechanism for lexical retrieval failure is the Inhibition Hypothesis that retrieval of the TOT target is "blocked or inhibited by another more common word that persistently comes to mind (Baddeley, 1982; Jones, 1989; Jones & Langford, 1987; Reason & Lucas, 1984; Roediger & Neely, 1982). The main source of evidence for this hypothesis is that semantically related prime words slow retrieval of target words corresponding to definitions (Brown, 1979) and phonologically related primes increase the frequency of TOTs for target words corresponding to definitions (Jones, 1989; Jones & Langford, 1987). However, such effects may not be due to direct inhibition between prime and target because they are eliminated with methodological changes such as informing the subject of the prime-target relation (Bowles & Poon, 1985b; Roediger, Neely & Blaxton, 1983).

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We investigated the contribution of inhibition and transmission deficits to young and older adults' word retrieval failures in a series of studies on the TOT phenomenon. These studies confirm that word retrieval is an increasing problem in old age, and provide evidence that this reflects age-related changes in the transmission of phonological priming. Age Differences in Naturally Occurring Tip of the Tongue States

Our initial study of TOTs was a naturalistic study (Burke et al., 1989), following in the tradition of studies of naturally occurring speech errors. We asked 50 young (M = 19.4 years), 30 midage (M = 38.7) and 50 older (M = 71.0) adults to use structured diaries to record spontaneous TOTs as they occurred during a four week interval in their everyday life. This technique allowed us to examine the characteristics of TOT states, such as partial information available about the inaccessible word and the nature of the TOT words themselves, while avoiding the constraints of a laboratory study where potential TOT targets are selected by the experimenter. Subjects recorded a total of 686 TOT experiences, of which 95% were resolved in one way or another, giving us a corpus of 653 words which were the TOT targets. As can be seen in the top of Table 10.1, we found that midage and older adults had significantly more TOTs than younger adults. Table 10.1 Mean Number and Percent of Naturally Occurring and Lab Induced TOTs

Study

Age

Mean number of TOTs

%afrprecall

trials that were TOTs

Naturally Occurring TOTs (Burke et al., 1989, Study 1)

Young Midage Old

3.9' 5.4 6.6

Lab induced (Burke et al., 1989, Study 2)

Young Old

9.9b 11.9

17% 31%

Lab induced (Burke & Laver, 1989)

Young Old

8.9' 11.6

34% 44%

'Mean number during a four week interval bMean number for 100 questions 'Mean number for 50 questions

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The corpus of TOT targets from the diary study revealed that, not surprisingly, the words had very low frequency of occurrence. There were 207 TOT targets which were not proper names, and of these only 39% were listed in Francis and Kucera (1982). Those that were listed had a very low median frequency of occurrence, namely 5. Proper names accounted for about 68% of all TOT targets and of these from one third to one half, depending on age, were names of acquaintances. As can be seen in Table 10.2, these acquaintances were people who had been known for some time, with the duration depending on age, but who had not been contacted recently. Thus, TOT targets can be described as words which occur infrequently in the language or as the names of people who have not been contacted recently, especially for older adults. Table 10.2 Mean Numbers of Naturally Occurring TOTs for Acquaintance Names

Age

Number of TOTs

Young Old

109

43

Length of Acquaintance" 1.1 17.7

Recency of Contact" 0.3 4.0

"In years

Subjects also recorded alternate words that came persistently to mind while they were in the TOT state. As can be seen in Table 10.3, the percent of trials on which alternates occurred decreased significantly with age. These persistent alternates were related to the targets in very consistent ways. They were generally phonologically and semantically similar to the target, and they were virtually always in the same grammatical class as the target. This syntactic class regularity is seen in other lexical speech errors where the error word is virtually always in the same class as the intended word. Partial information about the target that was accessed such as its number of syllables and initial phonemes also decreased significantly with age, as seen in Table 10.3. Age Differences in Experimentally Induced Tip of the Tongue States Because naturalistic data may be subject to reporting biases, we attempted to replicate our diary study findings with laboratory induced TOTs. In extensive pilot testing, we developed a set of 100 questions that were the most successful in inducing TOTs and that had only one correct answer (Le., there were no synonyms for the target). The answers were single words in one of five categories: 1. proper names of famous people, 2. place names, 3. object names, 4. abstract nouns, 5. adjectives and

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Table 10.3

Percent TOTs with Persistent Alternates and Mean Number of Accessed T O T Characteristics

Age

Percent TOTs with Persistent Alternates

Young Midage Old

66.7 57.9 48.3

Examples:

Mean Number of Accessed TOT Target Characteristics 2.1 1.9 1.5

Alternate lobotomy vibrator charity

TOT Target dichotomy blender chastity

verbs. For example, we would ask, "What do you call the weapon used by the gauchos of South America to entangle the legs of cattle and other animals?" (BOLA),or "What is the name for a person who collects postage stamps?" (PHILATELIST). This technique was used by Brown and McNeill (1966). These questions were presented one at a time on a computer and subjects indicated whether they knew the answer, did not know the answer, or had the answer on the tip of their tongue. A request to guess the target appeared on the screen. If the subject typed in an incorrect answer or no answer, a multiple choice test was presented with the target and three alternatives. The results for 21 young (M = 20.0 years) and 21 older adults (M = 71.3) were assessed for absolute number of TOTs and for proportion of TOTs in questions that could not be answered initially (TOTs and Don't Know responses). TOTs were included in the analysis only when the target was correctly recognized in the multiple choice test. The frequency of TOTs for the 'wrong' word, as indicated by an incorrect response on the multiple choice test, did not differ by age. These trials were excluded because they do not necessarily represent lexical retrieval failure as there was no evidence that the subject knew the target word. As can be seen in the second study of Table 10.1, older adults had a higher absolute number of TOTs than young adults, although the age effect was only significant for one category of target, proper names. However, when TOTs were calculated as a proportion of trials where the target could not be produced, older adults had a higher proportion of TOTs in all target categories except abstract nouns. These results confirm that word retrieval, especially of well known names, is impaired in older compared to young adults. Further, we replicated the age difference in the occurrence of persistent alternates found in our diary study. With induced TOTs, young adults had 4 times more alternates come to mind than older adults.

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These results on persistent alternates are problematic for the Inhibition hypothesis because alternates decreased with age whereas TOT frequency increased with age. If alternates cause TOTs by blocking the target, then some other mechanism must be postulated for explaining why TOTs increase with age; and this or another mechanism must explain also why alternates decrease with age. Further, in this and previous studies, on at least one third of the TOTs no alternates come to mind, so it is unclear what could be blocking the target. These results seemed more compatible with the Transmission Deficit hypothesis. That is, we propose that age as well as infrequent and nonrecent use reduces the transmission of priming from semantic representations to representations of phonological information necessary for word production. The reduction in priming decreases the amount of phonological information that becomes available thus accounting for our finding that the number of target characteristics retrieved declines with age. Further, within this model, alternates are the result of priming by the target word, and the model predicts that they, too, should decline with age as we have observed. The interactive activation model developed to account for these data is described in detail in Burke et al. (1989). Age Differences in Priming of Tip of the Tongue Targets In an effort to further test the Transmission Deficit hypothesis we used a clever technique developed by Yaniv and Meyer (1987) in which word recognition is compared for TOT targets and control words. According to the Transmission Deficit hypothesis, TOTs occur because of weak connections between semantic and phonological nodes which reduce the spread of priming thereby impairing the retrieval of the word sounds. TOT targets would be primed and thus recognized more rapidly than control words, but this priming effect would be reduced for older compared to young adults, According to the Inhibition hypothesis, another more common word persistently comes to mind and directly inhibits the TOT target. This hypothesis predicts that TOT targets should be recognized more slowly than control words, and this effect should be stronger for older adults. The method involved a hybrid task in which subjects were asked TOT inducing questions. Each question was followed by a series of lexical decision trials in which one stimulus was the word that answered the preceding question. Yaniv and Meyer reported that on trials where subjects could not answer the initial question, they made faster lexical decisions for the target when they had indicated being in a TOT state and having a strong "feeling of knowing" (FOK) for the answer compared to when they indicated "don't know" (DK) and low FOK. Yaniv and Meyer argue that this facilitation of lexical decision for inaccessible but known target words is caused by semantic priming resulting from the processing of the question. Reading the question causes an automatic spread of priming to nodes of items related to the question, including the target word. This is consistent with our hypothesis that TOT targets are primed, though inaccessible. Yaniv and Meyer's results are also consistent with the hypothesis of Nelson et al. (1984) that the subjective experience of feeling of knowing is mediated by priming of the target. Yaniv and Meyer found that as FOK increased in the TOT or Don't Know state, lexical decision latencies to the target decreased. Applying the Transmission Deficit hypothesis that aging reduces the transmission of

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priming, we would predict that in Yaniv and Meyer's hybrid task, older adults would have smaller facilitation of target words following a TOT and less accurate FOK than young adults. Thirty young adults and 34 older adults were tested with the sequence of events shown in Figure 10.2. There were 50 trials each consisting of a general knowledge

4-1 I

Please press RETURN for a question. I ~

~

~~

~~

I I

What is the name of the nylon fabric that h a s two pieces which stick to each other and is used as a fastener?

+ + + +

Type K CRETURN) if you Know the answer. Type D (RETURN) if you Don't Know the answer. Type T W3TURN) if the answer is on the Tip of your Tongue.

Please rate your feeling of knowing the word: lo 1...2...3...4...5...6...hi 7

If you typed K,please type the answer.

+

Prepare for the Word-Nonword task. Burter

Li Thatch

1- '

Please press RETURN for another question.

I

Figure 10.2. Sequence of events in a trial in the hybrid task including TOT inducing questions and lexical decisions.

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question followed by 6 lexical decisions. Subjects were to respond either that they knew the answer, did not know it, or were having a tip of the tongue experience. Regardless of the subject's response, the 6 stimuli for lexical decision always included the target, which answered the previous question, and a control word matched with the target on number of letters. We developed two pools of questions each with 50 different questions and targets. Each pool was used with half of our subjects. The control words came from the pool of questions not seen by a subject. Table 10.4 shows the percent of each type of response to the TOT question and Table 10.1, at the bottom, shows mean number of TOTS. Consistent with our previous findings on age differences in induced TOTs, older adults had significantly more TOTs relative to Don't Know responses, although the difference in absolute number of TOTs did not reach significance. Table 10.4 Percent of Each Response Type to TOT Inducing Questions Responses Age Young Old

Know 48

47

Don't Know

34 29

TOT 18 23

Young and older adults' lexical decision RT for control and target words is shown in Figure 10.3 for each type of response to the preceding question. We included in this analysis only TOT trials where the subject correctly recognized the TOT word on a multiple choice recognition test given after all 50 trials. This eliminated trials in which the subject had in mind a word other than the target. Because particular target words do not appear equally in the various conditions, RTs for each target were adjusted by subtracting the deviation of mean RT for that word from the grand mean RT for all words. This was done separately for each age group. Collapsing across age groups, the interaction of question response type by target/control was significant. The triple interaction with age was not significant indicating similar patterns for both age groups. As can be seen in Figure 10.3, RT was faster for targets compared to controls after "know" responses and marginally faster after TOT responses. There was no difference after Don't Know responses. For Know responses, this is a repetition priming effect because the subject had just generated the target word on correct trials and then had typed it on the screen. Consistent with this, when subjects responded Know and then typed in an incorrect word, lexical decision RT did not differ for targets and controls (these RTs are not included in Figure 10.3).

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Young ss

c.

1000

E

Word Type

Y

I-

U

El-

900

m

U

Q

c

TARGET

u)

z 800

700

Know-Correct

TOT-Correct

Don't Know

Response Type

1200

Older Ss

,

I

Word Type

k

El-

K

m

Know-Correct

TOT-Correct

TARGET

Don't Know

Response Type Figure 10.3. Mean adjusted lexical decision RT for young and older adults as a function of response to question and lexical decision word type. Targets were answers to the preceding question and controls were unrelated to the question.

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Young and older adults show the same pattern of differences between control words and target words, but these differences appear less pronounced for the older adults. Indeed, if the TOT state is a consequence of a deficit in transmission of priming so that the target word is inaccessible but partially activated, then we would expect faster RTs for targets in a TOT state compared to a Don't Know state. As can be seen, this is true for young but not for older adults. We also performed an analysis suggested by Yaniv and Meyer (1987) in which Don't Know and TOT responses were combined with feeling of knowing to yield what they called a latent accessibility score. The FOK rating provided additional information about the subjective feeling of the accessibility of targets that could not be recalled. The categories for this are shown in Table 10.5. The low accessibility category included Don't Know responses with low FOK. The high accessibility category included TOT responses with high FOK. The medium accessibility category included everything else, namely Don't Know with high FOK and TOT with low FOK. Table 10.5 Criteria for Three Latent Accessibility Categories Catenow

Resvonse

FOK

Low-Access (1) Mid-Access (2)

Don't Know Don't Know TOT-Correct TOT-Correct

1, 2, or 3 4, 5, 6, or 7 1, 2, 3, or 4

High-Access (3)

5, 6, or 7

Figure 10.4 shows that the inclusion of FOK allows further differentiation of RT for the young lexical decision RTs for targets are faster as accessibility increases while control RTs are unaffected by accessibility. Because subjects' responses fall into the various response categories in unequal numbers, standard ANOVA analysis is not possible. Instead, it is necessary to tag each individual response with a response type code, pool the responses from all subjects and analyze these data with a multiple regression. In this way, both main effects and interactions can be tested even though ANOVA techniques are not applicable. The interaction of latent accessibility and word type (i.e., control or target) as tested with a multiple regression was significant for the young adults, t(1027) = - 2 . 7 5 , ~< .01, replicating Yaniv and Meyer. For older adults the latent accessibility by word type interaction was marginally significant, r(1327) = -1.89, p < .06. But, as can be seen in Figure 10.4, this is because control RTs increase with latent accessibility while target RTs are unaffected. Although targets are faster than controls with high accessibility, there is not the systematic decline in target RTs as accessibility increases as was the case for young adults. Consistent with this, gamma correlations between latent accessibility and target RT were significantly different from zero for the young (mean gamma = -.23, t(28) = -4.24, p < .OOl), but not the old (mean gamma = -.05, t(28) = -.78, p > .2),

--

D.M.Biirke arid G.D.Laver

294

Young Ss

1200 1100

9

E

Word Type

v

l-

a

ElTARGET

800 Med Access

Low Access

High Access

Latent Accessibility

Older Ss 1300

A

2

1200 Word Type

Y

+ a al c

am TARGET

1100

u)

a

s a

1000

900

Low Access

Med Access

High Access

Latent Accessibility

Figure 10.4. Mcan adjusted lcxical decision RT for young and older adults as function of latent accessibility and lcxical decision word type.

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We also calculated gamma correlations between FOK on a trial and the response to the question on that trial (Know, Don't Know, or TOT) separately for each age group. For Know responses we calculated correlations between FOK and whether the correct word was produced. The gamma correlations did not differ significantly by age and were .67 and -57 for young and older adults, respectively. Thus both groups were likely to give high FOK for questions that they were able to correctly answer and low FOK for questions where they recalled an incorrect word. That is, FOK was consistently related to accuracy when subjects thought they knew the response. Gamma correlations between FOK and a TOT or Don't Know response were .90 and .59 for young and older adults, respectively. Thus, young adults consistently gave high FOK with TOTS (where they later correctly recognized the response) and low FOK with Don't Know responses. Older adults, however, showed a much smaller correlation, and the age difference is significant, t(60) = -2.79, p < .01.

Thus not only were older adults' lexical decision RTs for targets unaffected by latent accessibility, but also their responses of TOT or Don't Know to the questions were less consistently related to FOK, compared to young adults. These results are consistent with the hypothesis that aging is associated with a deficit in transmission of priming leading to less facilitation of lexical decision for TOT targets and less accurate FOK responses. Before enjoying this support for our hypothesis, we realized that the older adults' data suggested, as is often the case with lexical decision, that the RT may reflect some strategies on the part of the subjects. We suspected a strategic component for two reasons. First, in interviews at the end of the sessions, our subjects reported looking for the target among the lexical decision stimuli. Indeed, one elderly subject was eliminated from the study because she was responding 'yes' only if the lexical decision item answered the previous question, instead of making the requested word/nonword decision. Second, the lexical decision error rate was highest in the TOT condition when the subject had the incorrect word in mind. This would occur if the subject had the strategy of looking for the target among the lexical decision stimuli. We thought the possibilities of strategies warranted further exploration so we constructed new questions that were unrelated to our original targets. We ran a second group of 20 young subjects using these questions and the old lexical decision targets. These subjects had exactly the same task as those in the original experiment, but now the lexical decision stimuli bore no relation to the questions. In Figure 10.5 we see data combined from the original experiment and this second group which we called true controls. RTs are shown for Know and TOT responses. The true control RTs are at least 150 ms faster than the original control RTs. This suggests that when the answers to the questions are included among the lexical decision stimuli, subjects are engaging in mental processes beyond those necessary for the lexical decision alone.

Thus, although the pattern of age differences in this hybrid task is consistent with an age-related transmission deficit, the possibility remains that strategies rather than semantic priming contributed to the observed effects. It is clear that the hybrid lexical decision task needs modification before priming effects can be interpreted as the result of automatic processes.

D.M.Burke and G.D.Laver

2%

Young Ss

1000

Word Type

E

Y

F

EXP.CONTROLS

900

a

TRUECONTROLS

'0 0

c

EXP. TARGETS

u)

=a a

800

700 Know-Correct

TOT-Correct

Response Type

Figure 10.5. Young adults' lexical decision RT as a function of response to question (Know or TOT), lexical decision word type, and whether lexical decision words included the answer to the question. Exp. targets are words that answered the question, exp. controls are words unrelated to the question, and true controls are words unrelated to the question and in a separate experiment where all lexical decision words were unrelated to the questions.

Conclusions We have presented empirical evidence that supports older adults complaints about their increasing difficulty in retrieving well known words during speech production. Our naturalistic data on TOTS suggests that the occurrence of a TOT is related to frequency and recency of word use, and to the age of the user. Our laboratory research confirmed the age difference in TOT frequency, particularly for proper names. Findings in both studies are consistent with the Transmission Deficit hypothesis that transmission of priming decreases with aging. That is, older adults report less information about TOT targets and have fewer persistent alternate words come to mind compared to young adults. This reduction in the availability of information related to TOT targets would be expected if the transmission of priming declined with age. We attempted to compare young and older adults' level of priming of TOT targets by measuring facilitation of targets in a lexical decision task. Young adults show more effect of the accessibility of the target on RT and their FOK was a stronger index of accessibility. Both these findings are consistent with an age-related reduction in priming, but conclusions based on the RT data are weakened by the possibility of strategy based facilitation effects.

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Within this theoretical framework, older adults' difficulty in accessing phonological information corresponding to words they "know" is perfectly consistent with their preserved ability to access meaning corresponding to words. We have attempted to clarify the difference between the retrieval processes involved in comprehension and production of language in order to identify the locus of word retrieval deficts. This approach suggests the futility of characterizing age-related changes in terms of a spared or impaired memory system, for example, semantic memory. Rather, specification of explicit models of mental structure and processes allows a better understanding of even everyday memory problems, such as the TOT phenomenon. References Albert, M.S., Heller, H.S., & Milberg, W. (1988). Changes in naming ability with age. Psychology and Aging, 3, 173-188. Baddeley, A.D. (1982). Domains of recollection. Psychological Review, 89, 708-729. Balota, D.A., & Duchek, J.M. (1988). Age-related differences in lexical access, spreading activation, and simple pronunciation. Psycholosy and Aging, 3, 84-93. Borod, J. C., Goodglass, H., & Kaplan, E. (1980). Normative data on the Boston Diagnostic Aphasia Examination, Parietal Lobe Battery, and the Boston Naming Test. Journal of Clinical Neuropsychology, 2, 209-215. Botwinick, J. (1984). Aging and behavior. New York: Springer Publishing Company. Bowles, N.L., Obler, LK., & Poon, L.W. (1989). Aging and word retrieval: Naturalistic, clinical and laboratory data. In L.W. Poon, D.C. Rubin, & B.A. Wilson (Eds.), Everyday cognition in adulthood and late life (pp. 244-264). Cambridge: Cambridge University Press. Bowles, N. L., & Poon, L. W. (1985a). Aging and retrieval of words in semantic memory. Journal of Gerontology, 40, 71-77. Bowles, N. L., & Poon, L. W. (1985b). Effects of priming in word retrieval. Journal of Expenmental Psychology: Learning, Memory, and Cognition, 11, 272-283. Brown, AS. (1979). Priming effects in semantic memory retrieval processes. Journal of Experimental Psychology: Human Learning and Memory, 5, 65-77. Brown, R., & McNeill, D. (1966). The "tip of the tongue" phenomenon. Journal of Verbal Learning Behavior, 5, 325-337. Burke, D. M., & Harrold, R. M. (1988). Automatic and effortful semantic processes in old age: Experimental and naturalistic approaches. In L. L. Light & D. M. Burke (Eds.), Language, memory, andaging (pp. 100-116). New York Cambridge University Press. Burke, D.M., & Light, L.L. (1981). Memory and aging: The role of retrieval. Psychological Bulletin, 90, 5 13-546. Burke, D.M., White, H., & Diaz, D.L. (1987). Semantic priming in younger and older adults: Evidence for age constancy in automatic and attentional processes. Journal of Experimental Psychology: Human Perception and Performance, 13, 79-88. Burke, D.M., Worthley, J., MacKay, D., & Wade, E. (1989). On the rip offhefongue: What causes word finding impairments in young and older adults? Manuscript submitted for publication. Cerella, J., & Fozard, J.L. (1984). Lexical access and age. Developmental Psycholosy, 20, 235-243.

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Chiarello, C., Church, K. L., & Hoyer, W. J. (1985). Automatic and controlled semantic priming: Accuracy, response bias, and aging. Journal of Gerontology, 40, 593-600. Cohen, G. (1979). Language comprehension in old age. Cognitive PsychologS 11, 412-429. Cohen, G., & Faulkner, D. (1986). Memory for proper names: Age differences in retrieval. Britkh Journal of Developmental Psychology, 4, 187-197. Collins, A.M., & Loftus, E.F. (1975). A spreading-activation theory of semantic processing. Psychological review, 82, 407-428. Dell, G. S. (1986). A spreading-activation theory of retrieval in sentence production. Psychological Review, 93, 283-321. Francis, W.N., & Kucera, H. (1982). Frequency analysis of English usage: Lexicon and grammar. Boston: Houghton Mifflin. Garrett, M. F. (1980). Levels of processing in sentence production. In B. Butteworth (Ed.), Language production: Vol. 1. Speech and talk (pp. 177-220). London: Academic Press. Goodglass, H., Wingfield, A., & Wayland, S.C. (in press). The nature of prolonged word search. Brain and Language. Gruneberg, M. M., & Monks, J. (1974). "Feeling of Knowing" and cued recall. Acta Psychologica, 38, 257-265. Harrold, R. M. (1987). Object naming in Alzheimer's Disease: What is the cognitive defcit? Unpublished doctoral dissertation, Claremont Graduate School, Claremont, CA. Hillinger, M. L (1980). Priming effects with phonemically similar words: The encoding-bias hypothesis reconsidered. Memory and Cognition, 8, 115-123. Horn, J.L., & Cattell, R.B. (1966). Age differences in primary mental ability factors. Journal of Gerontology, 21, 210-220. Howard, D. V., McAndrews, M. P., & Lasaga, M. I. (1981). Semantic priming of lexical decisions in young and old adults. Journal of Gerontology, 36, 707-714. Howard, D. V., Shaw, R. S., & Heisey, J. G. (1986). Aging and the time course of semantic activation. Journal of Gerontology, 41, 195-203. Humphreys, G.W., Evett, LJ., & Taylor, D.E. (1982). Automatic phonological priming in visual word recognition. Memory & Cognition, 10, 576-590. James, W.(1890/1981). The Principles of Psychology, (Vol. 1). Cambridge: Harvard University Press. Jones, G.V. (1989). Back to Woodworth: Role of interlopers in the tip of the tongue phenomenon. Memory and Cognition, 17, 69-76. Jones, G. V., & Langford, S. (1987). Phonological blocking in the tip of the tongue state. Cognition, 26, 115-122. Laver, G., & Burke, D.M. (1990). Meta-analysis of differentialpriming effects in young and older adults. Paper presented at the third Cognitive Aging Conference, Atlanta, GA. Light, L L, & Albertson, S. A. (1988). Comprehension of pragmatic implications in young and older adults. In L. L. Light & D. M. Burke (Eds.), Language, memory, and aging (pp. 133-153). New York: Cambridge University Press.

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Light, L. L., & Burke, D. M. (1988). Patterns of language and memory in old age. In L. L. Light & D. M. Burke (Eds.), Language, memory and aging (pp. 244-271). New York Cambridge University Press. Light, L.L., Singh, A., & Capps, J.L. (1986). The dissociation of memory and awareness in young and older adults. Journal of Clinical and Experimental Neuropsychology, 8, 62-74. MacKay, D. G. (1982). The problems of flexibility, fluency, and speed-accuracy trade-off in skilled behavior. Psychological Review, 89, 483-506. MacKay, D. G. (1987). The orgMization ofperception and action: A theory for language and other cognitive skills. New York: Springer-Verlag. MacKay, D.G., & Burke, D.M. (in press). Cognition and aging: A theory of new learning and the use of old connections. In T. Hess (Ed.), Aging and cognition: Knowledge organization and utilization. Amsterdam: North Holland. Madden, D.J. (1985). Age-related slowing in the retrieval of information from long-term memory. Journal of Gerontology, 40, 208-210. McClelland, J.L., & Rumelhart, D.E. (1981). An interactive model of context effects in letter perception: Part 1. An account of basic findings. Psychological Review, 88,375-407. McNamara, T.P., & Healy, A.F. (1988). Semantic, phonological, and mediated priming in reading and lexical decisions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 398-409. 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., & Ruddy, M. G. (1974). Functions of graphemic and phonemic codes in visual word recognition. Memoy and Cognition,2,309-321. Mitchell, D. B. (1989). How many memory systems? Evidence from aging. Journal of Experimental Psychology; Learning, Memory, & Cognition, IS, 31-49. Mueller, J.H., Kausler, D.H., & Faherty, A. (1980). Age and access time for different memory codes. Experimental Aging Research, 6, 445-449. 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. Nelson. T.O.,Gerler, D., & Narens, L. (1984). Accuracy of feeling-of-knowing judgments for predicting perceptual identification and relearning. Journal of Experimental Psychology: General, 113, 282-300. Nicholas, M., Obler, L., Albert, M., & Goodglass, H. (1985). Lexical retrieval in healthy aging. Cortex, 21, 595-606. Poon, L.W., & Fozard, J.L. (1978). Speed of retrieval from long-term memory in relation to age, familiarity, and datedness of information. Journal of Gerontology, 33, 711-717. Read, J. D., & Bruce, D. (1982). Longitudinal tracking of difficult memory retrievals. Cognitive Psychologv, 14, 280-300. Reason, J. T., & Lucas. D. (1984). Using cognitive diaries to investigate naturally occurring memory blocks. In J. E. Harris & P. E. Morris (Eds.), Everyday Memory Actions and Absent-Mindedness (pp. 53-70). London: Academic Press. Roediger, H.L., & Neely, J.H. (1982). Retrieval blocks in episodic and semantic memory. Canadian Journal of Psycliology, 36, 213-242.

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Roediger, H. L,Neely, J. H., & Blaxton, T. A. (1983). Inhibition from related primes in semantic memory retrieval: A reappraisal of Brown's (1979) paradigm. Journal of Experimental Psychology: Learning, Memory, and Cognition, 9, 478-485. Salthouse, T.A. (1982). Adult Cognition. New York: Springer-Verlag. Salthouse, T.A. (1985). A theory of cognitive aging. Amsterdam: North-Holland. Salthouse, T.A. (1988). Initiating the formalization of theories of cognitive aging. Psychology and Aging, 3, 3-16. Shulman, H. G., Hornak, R., & Sanders, E. (1978). The effects of graphemic, phonetic, and semantic relationships on access to lexical structures. Memory and Cognition, 6, 115-123. Sunderland, A, Watts, K.,Baddeley, A. D., & Harris, J. E. (1986). Subjective memory assessment and test performance in the elderly. Journal of Cerontologv, 41, 376-384. Thomas, J.C., Fozard, J.L., & Waugh, N.C. (1977). Age-related differences in naming latency. American Journal of Psychology, 90, 499-509. Van Gorp, W. G., Satz, P., Kiersch, M. E., & Henry, R. (1986). Normative data on the Boston Naming Test for a group of normal older adults. Journal of Clinical and Experimental Neuropsycholog, 8 702-705. Yaniv, I., & Meyer, D.E.(1987). Activation and metacognition of inaccessible stored information: Potential bases for incubation effects in problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 187-205.

Aging and Cognition: Mental Processes, SelfAwareness and Interventions - E ene A. huelace Editor) 0 Elseuier Science A#shers B.V. (North-HollandJ,1990

11 The Way Reading and Listening Work A Tutorial Review of Discourse Processing and Aging

Elizabeth A. L. Stine Brandeis University

Of all the cognitive activities in which we engage over a lifetime, none has more relevance for intellectual and social functioning than the comprehension, memory and production of language. There is no age at which these skills become outdated or useless, and no modern-day cohort that is not daily bombarded with spoken prose and written text. Much of the language we encounter requires a prompt reply, and the consequences of responding inappropriately or belatedly to such a message, be it telephone gossip or a fat manilla envelope from the IRS, can be severe. Given the diversity (but not universality) of cognitive declines that are associated with aging (6. Salthouse, 1982), it is important for us to understand how language processing is achieved as we grow older. In the next few pages I hope to accomplish two goals: (1) to describe and justify a somewhat "generic" model of discourse processing synthesized from the current cognitive literature, and (2) to discuss how parametric variation in this model might be able to account for age differences in discourse processing. I have adopted Simon and Schuster's (1967) The Way Things Work as my own model for describing the processing architecture of the language processing system, hoping that the reader will come away with a sense of language being operated on by a complex but coordinated system of component processes. What will remain then is to consider what happens to this machinery as we age. A Synthetic Model of Discourse Processing

In the past fifteen years, there have been a number of rather comprehensive proposals for how language is processed (Aaronson & Scarborough, 1977; Baddeley, Logie, Nimmo-Smith, & Brereton, 1985; Daneman & Carpenter, 1980; Graesser, 1981; Just & Carpenter, 1980; Kintsch, 1988; Kintsch & van Dijk, 1978). While these models differ in emphasis, there is a remarkable degree of consensus on the general constellation of cognitive processes that would be necessary to make a language processing machine work. To some extent, the research problem is a relatively Preparation of this chapter was supported by grant R29 AG08382. I am grateful to Tom Hess, Susan Kemper, and Art Wingfield for their thoughtful comments on a previous draft of this manuscript.

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well-defined one: how is it that we can process such a small amount of information at any point in time, and yet be able to comprehend and remember extended and complicated passages of discourse; in other words, how is it that at this present moment I can know so little, but can understand and learn so much? The solution to this puzzle must be that there is some part of the cognitive system that interprets small successive subsets of the incoming message sequentially in time. This limited capacity system must not only be able to represent each subset of information rapidly, but it must represent this information in such a way as to be able to integrate it with the information contained in previous and successive subsets. Within this framework, language processing is thought to involve an array of component processes that are conducted within this "working memory" (cf. Just & Carpenter, 1980; Baddeley et al., 1985). This array of processing operations has been conceptualized as having a "vertical" arrangement in the sense that raw sensory input is fed from the bottom, up through working memory, while knowledge filters from the top, down through working memory to provide a framework for structuring the information held there. The notion of "bottom-up" processing implies that there must be some point at which there is a representation that is a verbatim, more or less literal reproduction of the input. The "top-down" influence of knowledge, on the other hand, implies that there must also be a point at which there is a representation that is more abstract and semantically based. In spite of this general agreement on the principal functions of working memory, different slants have been taken on modelling and testing the concept. Modelling Working Memory

I think it is fair to say that there are three reigning conceptualizations of working memory in the current literature, (1) the Kintsch and van Dijk model, (2) the Baddeley model, and (3) the Daneman and Carpenter model. Because our more generic model will draw so heavily on these ideas, each of these will be briefly summarized. Kintsch and van Dijk's Conceptualization of Working Memory The working memory described by Kintsch and van Dijk (e.g., Kintsch & van Dijk, 1978; Kintsch & Vipond, 1979; van Dijk & Kintsch, 1983) is a limited capacity (short-term) buffer in which the semantic representation of discourse is assembled. Van Dijk and Kintsch (1983, pp. 352-356) are explicit in assigning both storage and processing functions to this buffer which compete for processing capacity. Earlier instantiations of this model (e.g., Kintsch & van Dijk, 1978) are more algorithmic in character with language being processed in cycles determined by syntactic structure. Each chunk is represented as a sequence of propositions, or idea units, consisting of a predicate (i.e., a relational term) and one or more arguments (i.e., simple or propositional concepts to be related). For example, "The young boy rolled the red ball down the deserted street so his dog would chase it" would be represented as:

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(YOUNG BOY) (ROLL BOY BALL) (RED BALL) (LOCATI0N:DOWN P2 STREET) (DESERTED STREET) (PURPOSES0 P2 W ) (CHASE DOG BALL)

Organization among these units is represented in terms of a hierarchical "coherence graph" which shows interrelationships among propositions, with coherence assumed to be based on propositions sharing arguments (e.g., BOY, P2, BALL, STREET). The proposition that is most central to the meaning is placed at the head of this coherence graph (e.g., in this case, probably, P2), and propositions sharing an argument are P1, P2-->P3, P2-- > P4, P2-->P6); propositions sharing subordinated to it (e.g., P2--> arguments with this group are placed at the third tier (e.g., P4--> P5 and P6--> P7), and so on. A subset of these propositions, the size of which depends on individual differences in buffer capacity, are held in working memory, as a new cycle is processed. Those that are higher in the hierarchy and the most recent (i.e., the "leading edge" of the coherence graph) are the items most likely to be held over (in this case, perhaps P2--> P6-- > P7). In the new cycle, the segment is again represented propositionally and this new set of propositions is connected to the coherence graph held over from the previous input cycle. There are three parameters in the model to account for individual differences among subjects, texts, and task requirements: (1) the maximum number of propositions that can be input on any one cycle, (2) the number of propositions that can be held over from cycle to cycle, and (3) the probability that a proposition will be reproduced at retrieval given that it is held over in a processing cycle (to account for different effects of retrieval requirements (e.g., recall or summarize) and storage conditions (e.g., retention interval)). The processing operations of this model are explicit enough that computer simulations have been conducted to show "recall" patterns similar to those of college students (Miller & Kintsch, 1980). More recent explications of this model (e.g., van Dijk & Kintsch, 1983; Kintsch, 1988) accentuate the strategic nature of these processes. Thus, van Dijk and Kintsch have recently placed more emphasis on the possibility that the semantic representation of any given text is not unique, but rather dependent upon the knowledge base and goals of the reader or listener, Such strategic operation is dependent upon a "control system," which supervises processing within working memory, guides searches through long-term memory, and activates procedural knowledge about language processing. Strategic control of discourse processing also entails more on-line effort at meaning. That is, there is less emphasis on the end of the processing cycle as the processing locus of organization and coherence construction, but rather the view is that the discourse processor is continually creating working hypotheses about meaning, coherence, and inference. It should be noted that the Kintsch and van Dijk model is essentially a model of the construction of meaning, or the semantic representation of discourse. As such, it takes the elements of meaning, i.e., propositions, as theoretical primitives, without an account

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of how propositions themselves arise in discourse processing. Thus, although syntax is not explicitly considered in any detail in this model, syntactic processing could be assumed to function in the assignment of thematic roles (i.e., how lexical items are encoded as predicates and arguments), as well as to signal how much information is permitted into the input buffer on each cycle. This general model of discourse processing has provided the framework for much of the work on language processing in the last decade. The research tactic for testing its validity has relied mainly on using the model to generate relative response probabilities (e.g., what propositions would be most likely to be held over for more processing cycles, and therefore, recalled; what concepts would be most likely to be retained in working memory at a given point in processing, and therefore, verified more quickly) for texts of different characteristics (e.g., propositional density, length, topic signalling, context), and comparing the generated model behavior with actual reader or listener behavior (e.g., recall probabilities, response latencies). Baddeley's Conceptuakation of Working Memory

Baddeley (1986) has proposed both a general (WMG) and a more specific (WMS) model of working memory. Generally speaking, working memory is "the temporary storage of information that is being processed in any range of cognitive tasks" (p. 34). His more specific model of working memory, which describes the particular functions that a general working memory would have to have in order to simultaneously store and process information, is a tripartite system, consisting of two peripheral slave systems (an articulatory loop and a visuo-spatial scratchpad) and a central executive. The articulatory loop is a speech-based system that is "maintained and refreshed by the process of articulation, i.e., the "inner voice," with an adjunct phonological store that maintains an acoustic image, i.e., the "inner ear." The visuo-spatial scratchpad is a vision-based system used to generate and manipulate visual images. The central executive has perhaps been explored less thoroughly than its slave systems, but it has been characterized by Baddeley (1981, 1986) as a limited capacity attentional system with supervisory functions, "capable of selecting strategies and integrating information from several different sources" (Baddeley, 1986, p. 225). The research tactic used to test and develop this model has relied heavily on the dual task paradigm in which the subject performs some language, memory, or reasoning task while simultaneously engaging in some simple secondary task. The secondary task (e.g., holding three to six digits in memory, repetitive articulation of a single word, or brightness judgments) is selected so as to be easy enough to produce very few errors and to theoretically load one of the slave systems. The effects of the demands of the secondary task are then observed in the primary task. The fact that even these simple secondary tasks generally reduce the performance level of the primary task is taken as evidence for a general working memory system (WMG). Furthermore, inferences about the structure of the working memory system are based on the particular pattern of disruptions seen in primary task performance. Daneman and Carpenter's Conceptualization of Working Memory

The earlier model of working memory developed by Daneman and Carpenter

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(Daneman & Carpenter, 1980, 1983) was a unitary system of limited capacity having storage and information manipulation functions, not unlike Baddeley's WMG model. These storage and processing functions were assumed to compete for resources such that greater processing demands would be expected to disrupt storage. The research tactic used to test this notion relies on the assumption that there are individual differences in the efficiency of the processing operations in working memory, and therefore, individual differences in the functional capacity of working memory. In other words, readers or listeners who are more efficient in language processing operations will have more working memory capacity available for storage. To assess these individual differences, Daneman and Carpenter developed a "reading span test" and a "listening span test." In these tasks subjects are presented a set of sentences and instructed to listen to or read each sentence (to assure that subjects are effectively processing the sentences, they are sometimes asked to read sentences aloud or to give true-false judgments), and at the end of the set of sentences to recall the last word of each sentence. Thus, the more processing capacity it takes for an individual to process each sentence, the less likely it is that recall will be accurate. The set size of the sentences progressively increases, and the maximum set size that the subject can successfully complete is taken as his or her working memory span. Consistent with prediction that this ability is a necessary component of effective language processing, Daneman and Carpenter (1980) showed that there were moderate to high correlations between working memory span and such indicators of reading comprehension as Verbal SAT, and the abilities to answer fact questions and to identify pronominal antecedents. Note that the methodological approach is the converse of that taken by Baddeley. While Baddeley's technique is to minimally load the storage component assuming that sensitivity to load will show up in the processing component, the Daneman and Carpenter approach is to require some minimal level of processing assuming that sensitivity to processing will show up in the storage component. More recently, Daneman and her colleagues (Daneman & Green, 1986; Daneman & Tardif, 1987) have questioned the notion of a unified, "monolithic" working memory. Their approach was to develop some nonverbal span tests (i.e., a math span and a spatial span) and examine intercorrelations among verbal and nonverbal spans as well as their predictive validity of reading comprehension and verbal ability. The data have suggested considerable process-specificity, causing these researchers to recant the assumption of a unitary working memory, and in a revised model they have proposed the existence of a language-specific system whose function is to represent and manipulate symbolic information. Even within this language-specific system, however, there may be specialization for comprehension and production, since another variant on the span measures requiring production, the "speaking span" (Daneman & Green, 1986), has been found to be differentially predictive of a vocabulary production task whereas the reading span was differentially predictive of vocabulary comprehension. The accumulating evidence for modularization of process within working memory prompted Daneman and Tardif (1987) to question the need for a general purpose processor in a model of working memory. Finally, the necessity of defining WM in terms of trade-offs between storage and processing functions has even been called into question by the finding that performance on the processing component of the span

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tasks is predictive of facility with language processing (even when there is no memory component). For all of these reasons, Daneman and Tardif (1987) have proposed that there may be a number of processors distinguished by function, e.g., language vs. spatial processing, that act as both information stores and information manipulators within WM (see Monsell (1984) for other arguments for modularity of process within WM). Language Processing Functions in Working Memory

With this foundation on the various conceptualizations of working memory, we are prepared to consider in a more detailed way the functions of working memory in language processing. For this specific purpose, we could define WM as that point in the information processing system at which input is mapped onto knowledge, or in other words, as that point at which input is organized in terms of what is already known. When such a mapping occurs, it is said that the input is "comprehended" or "understood." The array of processes necessary to accomplish this function are illustrated in Figure 11.1. This model, a slightly elaborated synthesis of the three conceptualizations just discussed, bears more than a passing resemblance to its multi-store ancestors (cf. Eysenck, 1984). The general principle here is that information is processed within a succession of function-defined modules that are themselves hierarchically arranged. In order for input to be mapped onto knowledge, the input must be transformed such that its format is consistent with the format of the knowledge net, or as Fodor (1983) has put it, "what perception must do is to represent the world so as to make it accessible to thought" (p. 40). Thus, the format of the information changes as it is processed through the cognitive system such that modules that perform their functions at the front end of the system represent the information in a more verbatim format, while modules that perform their functions at the back end represent the information in a more semantically based format. Language processing is accomplished rapidly by virtue of the fact that these subsystems are able to work in parallel, so for example, preliminary phonemic decoding of one part of a spoken message begins even while an earlier part of the message is being semantically represented. Additionally, bottom-up decoding of input is facilitated by top-down predictions (cf. Just & Carpenter, 1980). The Processing Architecture of Working Memory Phonological and Orthographic Coding. Early in processing, auditory or visual input is decoded into phonemic or orthographic representations. If the language input consists of written symbols, it must be orthographically coded; if the input is spoken language, there must be some phonemic representation. But given the modality-specificity of these codes, how critical is it for them to be activated in working memory during language comprehension?

There is considerable evidence that even in silent reading, phonemic information is automatically accessed, at least, momentarily. For example, in a lexical decision task, it takes longer to reject nonwords when they are phonologically "legal" (strig, barp) or phonologically illegal but pronounceable (i.e., gratf, lamg) than if they are unpronounceable (e.g., likj, crepw) (Rubenstein, Lewis, & Rubenstein, 1971, Experiment 1).It also takes longer to reject nonwords when they are homophones with

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Sound Wave Figure 11.1. Schematic model for the cognitive functions involved in discourse processing.

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real words (e.g., trate, brane) (Rubenstein, et al., 1971, Experiments 2 and 3). On the other hand, lexical decisions for words are facilitated when they are primed by homophonous words (Davidson, 1986). In a Stroop paradigm, color naming latencies for target words have been found to be longer when they are auditorily or visually primed by phonologically similar words than when they are primed by dissimilar words, suggesting that automatically activated phonological codes were interfering with color naming (Tanenhaus, Flanigan, & Seidenberg, 1980). Consistent with this is a report by Martin (1978), showing the Stroop effect in a card sorting task to be disrupted by irrelevant articulation. Thus, it is fairly clear that regardless of input modality, a phonological code is activated, implicating the articulatory loop in a wide range of discourse processing activities (for a more comprehensive summary of the evidence for the role of phonological coding in language processing, the interested reader is referred to a recent review by Patterson and Coltheart (1987)). It is tempting to assign phonological coding a critical role in the language processing system since phonological coding is phylogenetically and ontogenetically prior to orthographic coding (i.e., we learn to understand spoken language before we learn to read it), and to speculate that phonological representation may have some privileged status in working memory processing by virtue of this fact. This certainly may be (cf. Patterson & Coltheart, 1987), of course, but there is also evidence that orthographic coding is automatically activated during auditory processing (Tanenhaus, et al., 1980), suggesting rather that it may be that multiple codes are activated in working memory. Such a view is consistent with Kintsch's (1988) suggestion that working memory is "promiscuous" in activating information. That is, the modus operundi of the working memory system is to have available anything that has the slightest chance of being useful. That which is not needed fades quickly; that which is used by other processing components remains activated (cf. Till, Mross, & Kintsch, 1988). Thus, it may be that while phonological and orthographic codes are both available early in processing (regardless of input modality), ontogenetic primacy may dictate that the phonological code is more useful to the system for transformation into a semantic representation. Articulatory Loop. Following Baddeley's (1986) lead, I have placed the phonological store within the articulatory loop in our synthetic model of Figure 11.1. The existence of some sort of acoustic based component in short-term memory has been acknowledged for some time (Conrad, 1964). It is needed to explain such phenomena as phonemic similarity effects (i.e., that confusions in immediate memory tend to be acoustic in character, and that word lists are harder to remember if they contain phonemically similar items than if they contain phonemically dissimilar items) and word length effects (i.e., that it is harder to remember a list of longer words than a list of shorter words presumably because longer words take longer to subvocally rehearse) (cf. Baddeley, 1986; Baddeley, Vallar, & Wilson, 1987).

The articulatory loop is probably not simply a unitary acoustic store, but it is argued to consist of an articulatory control process and a phonological store (cf. Baddeley, 1986). The phonological store (i.e., the "inner ear") holds a quickly fading acoustic image. The input for this store can either come from an auditory stimulus, or from the articulatory control process (i.e., the "inner voice"). Such a distinction is needed to explain relatively simple phenomena like the fact that rhyme and homphony judgments do not appear to be disrupted by irrelevant articulation (Baddeley & Lewis, 1981), and

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rather complex phenomena like the following: When language input is visual, the phonemic similarity and word length effects are destroyed by irrelevant articulation (“articulatory suppression”),but when input is auditory these effects remain strong in the face of articulation. So visual input requires an articulatory control process to create an acoustic image for the phonological store; if that articulatory process is busy, phonological coding is disrupted. If input is auditory, however, input into the phonological store does not require an articulatory process for transduction, so it doesn’t matter how this process is otherwise occupied. In on-line processing of extended discourse, the most recent input equivalent to about a sentence or two (Glanzer, Dorfman, & Kaplan, 1981) is represented in this articulatory loop. Thus, it is assumed here that the articulatory loop serves as the input buffer for the most recent discourse segment (cf. Kintsch & van Dijk, 1978). The fact that such such a verbatim representation is retained in WM is probably not just an epiphenomenon of processing, but rather an important component enabling on-line comprehension. For example, access to the verbatim signal enables repairs in lexical access of an ambiguous homophone which has at first been misinterpreted (Danernan & Carpenter, 1983), or repairs in parsing in light of subsequent context, for example, in the case of a ”garden path” sentence like, “The horse raced past the barn fell.” If articulatory loop processing is disrupted, then comprehension is likely to fail (Glanzer et al., 1981).

Visuo-SpatialScratchpad. While the visuo-spatial scratchpad is probably used mainly for storing and manipulating pictorial and spatial input (cf. Baddeley, 1986), spatial representation appears to function in some kinds of language processing. For example, the ability to comprehend information from television programming is moderately correlated with spatial ability, perhaps because of the necessity of integrating across input modalities (Pezdek, Simon, Stoeckert, & Kiely, 1987). Another example is the comprehension of linear syllogisms for which comprehension may involve spatial representation (Huttenlocher, 1968). Central Executive. In our synthetic model, it is assumed that it is within the central executive that the semantic representation of discourse is constructed. This is accomplished by accessing both declarative knowledge of lexical and higher-order concepts as well as procedural knowledge about how to understand language (e.g., how to parse, how to organize and integrate ideas into a coherence graph; how to use context to infer the meaning of novel or ambiguous words). Thus, it is the central executive that does the real “work of working memory of transforming the verbatim representation of language into its semantic format. As such, the notion of a “central executive” is probably best thought of as a first approximation toward a description of a whole collection of processing modules. For example, there must be a processor which selects parsing sites, thus regulating input cycles into the articulatory loop. In fact, neuropsychological evidence suggests some independence of function of syntactic analysis from semantic analysis (Bradley, Garrett, & Zurif, 1980). Additionally, there must be processors which organize propositions into a coherent representation, select those to be retained for the next cycle, and then integrate successive subsets. On-line reading data suggest that organization within processing cycles is a cognitively distinct process from integration between processing cycles (cf. Aaronson & Scarborough, 1977)

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Certainly, the richness of the knowledge net in part determines which propositions will be selected as higher-order propositions (cf. van Dijk & Kintsch, 1983; Miller, 1985). Furthermore, the effectiveness of language processing strategies are probably influenced at least to some extent by how much they are practiced (cf. Rice & Meyer, 1986). Thus, experience with the world and with language processing no doubt contributes to how information is structured in the central executive (see Hess (in press) for a review). Distinctive Attributes of the Synthetic Working Memory Model

This model of working memory has three properties which are important from the standpoint of enabling the model to generate testable hypotheses and to be falsifiable. Iterative Processing in Cycles. As we have seen, one important feature of how the WM system operates is that it processes information in cycles, such that only a small piece of the message is processed at a time. The analogy has been made between language processing and a sausage machine, which processes each sausage link, and then shuttles it to be combined with the entire sausage string (Frazier & Fodor, 1978).

What are the linguistic "sausages"? That is, how much linguistic meat does the sausage machine process at one time, and how does it know when it has received enough to make a complete sausage? In an early experiment, Jarvella (1971) presented subjects with connected speech which was interrupted for immediate recall. Passages were constructed such that the content of the speech was controlled but the location of the syntactic boundary was manipulated, as in the following examples: [A] The tone of the document was threatening. Having failed to disprove the chatges, Taylor was later fired by the President. [B] The document had also blamed him for having failed to disprove the charges. Taylor was later fired by the President. Even though the words were exactly the same, subjects' verbatim recall was higher when the words were within the present constituent than when they were from the previous constituent. From these data Jarvella concluded that "[bloth the sentence being listened to and the immediately heard clause of spoken connected discourse appear to be differentiated from previous speech as accessible units in memory" (p. 415). Furthermore, when these "accessible units" are eliminated from working memory by a distractor task between sentences, comprehension is slowed; when the formation of these "accessible units" is hindered by a distractor task between sentences, comprehension is reduced (Glanzer et al., 1981, Experiments 2 and 3). More recent experiments have also suggested that syntactic structure plays an important role in the parsing, or segmentation, in on-line language processing. In the measurement of on-line reading time, extra reading time is allocated to the ends of sentence and clause boundaries and at the beginnings of story episodes, suggesting that these are points of organizational processing (cf. Aaronson & Scarborough, 1977; Haberlandt, 1984). Similar findings have been reported using the technique of spontaneous segmentation (Wingfield & Butterworth, 1984) in which subjects are asked to listen to spoken language on a tape recorder and to stop it whenever they like in

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order to accurately recall the speech segment by segment. The primary locations of segmentation points in this paradigm are sentence and clause boundaries (Wingfield & Butterworth, 1984). Thus, there is considerable evidence that parsing sites are determined to a large extent by syntax. This still leaves the problem of how much meat gets processed in a sausage cycle. In the Kintsch model, this is a flexible parameter (depending in part on the demands placed by syntax) which might comfortably range from about 7 to 12 propositions (cf. Kintsch & van Dijk, 1978). Using a computer simulation, the model has been able to explain about 36% of the variance in the probability of propositional recall among college students (Miller & Kintsch, 1980). Consistent with these data is the finding by Glanzer et al. (1981, Experiment 1) that subjects in cued recall task during listening are able to recall verbatim the past 1 1/2 to 2 sentences. Sometimes, comprehension will fail simply because the syntactic structure requires that the central executive include too much in a single cycle, such as in the case of some complex sentence embeddings, e.g., ”The architect the lawyer the professor met married moved to France.” Processing Resources. The WM system is assumed to be driven by “processing resources” (e.g., Norman & Bobrow, 1975). The availability of these resources is limited, though there are individual differences in the quantity of resources available and in how they are allocated to different functions. The different functions of working memory vary with respect to the extent to which they consume resources. For example, the operations of the central executive are thought to be more effortful, thus requiring the expenditure of a lot of resources (Baddeley, 1986). Within the present framework, we might propose that the degree to which processing resources must be expended by the central executive is proportional to the extent of discrepancy between the input and the contents of the knowledge net. In other words, if it requires few transformations of the input in order for it to have some point of connection with the knowledge net (e.g., reading words, or naming pictures), mapping may occur relatively automatically with very little work performed by the central executive, and few processing resources are required. If, on the other hand, extensive transformation is needed by central executive functions for mapping to occur (e.g., reading a Shakespearean sonnet), then more resources are required.

So the WM system is limited not only in the amount of information that can be processed in one cycle (as we saw earlier), but also in the processing resources that are available to perform this processing. These limitations are conceptually independent but interrelated. In this regard, we might draw the analogy between how working memory processes a passage of discourse and how a car “processes“ a large group of passengers from one side of town to the other. (We will make the added constraint that within this group of passengers are several groups of friends who insist on not being separated, although the very large groups of friends will consent to being divided into subgroups for their trip.) Some cars are large, some are mid-sized, and some are compact. A car that has a larger capacity will be able to transport more people at one time than a car with a smaller capacity (and we may even speculate that a group transported all at one time might be more socially cohesive); if some of the people are very large, the car will be

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able to transport fewer at one time. As cars vary in capacity, they also vary in the efficiency with which they consume "processing resources" (i.e., gasoline): some cars can travel for longer distances on a gallon of gas than others. (For the moment, let's assume that our cars of different sizes are equally fuel efficient.) Finally, we assume that the distance through which the passengers have to be transported is analogous to the disparity between the input and the knowledge net; i.e., input that requires many transformations for mapping is analogous to a group of passengers having farther to go. Within such a metaphor, it is easy to separate out "resources" and "capacity." If the car is out of processing resources, it doesn't matter what its capacity is; the efficiency of transporting passengers will be curtailed until more resources are available. Similarly, if the capacity of the car is small, the number of passengers transported per trip will be small, no matter how full the gas tank is, or how fuel efficient it is. A car that has a longer distance to go in order to transport its passengers will not feel a strain on its capacity, but it will consume more resources. The consumption of processing resources, however, is related to capacity. If the car is loaded with passengers, it will consume more processing resources in its trip than if there are few passengers. We might even observe a distance by number of passengers interaction on resource consumption such that longer distances are a particular drain on resources when the car is full of passengers. Similarly, we might expect to observe a distance by fuel efficiency interaction such that fuel inefficient cars would consume especially more fuel on longer trips. Organization within Working Memory. Given the limitations in capacity, an important use of resources is to sort out what information should remain in working memory to receive further processing. In other words, a primary resource consuming activity of working memory is to organize input such that resource limitations may be effectively allocated within given capacity limitations. Several sources of evidence, e.g., the levels effect (e.g., Kintsch & Keenan, 1973; Kintsch & van Dijk, 1978) and the fact that subjects tend to remember the same particular items from extended discourse (Rubin, 1978,1985) suggest that in fact information from discourse is organized in memory and that there is some degree of consistency across individuals with respect to the qualitative nature of organization. Additionally, there is evidence to suggest that this is an encoding phenomenon. For example, in a concept verification task during discourse comprehension, subjects are able to more quickly verify propositional arguments that would have theoretically been held over according to the Kintsch and van Dijk (1978) model (Fletcher, 1981). A Caveat or Two

Comprehension, Memory, and Production

The emphasis here has been on explaining the input side of discourse processing. A more complete account would, of course, have to include a description of how something that is comprehended is subsequently remembered (i.e., reproduced and/or reconstructed) and represented in language production. Certainly, the interrelationships among comprehension, memory, and production are complex, and the neglect of these topics reflects on space limitations rather than on their importance.

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A Question of Representation: Wiat is the Best Metaphor?

For better or worse, the present discussion has relied heavily on a multistore memory account, resting on a computer metaphor (cf. Eysenck, 1984). This of course, is not the only metaphor available, and with the rise of connectionism in cognitive psychology, there has been increasing use of the nervous system as the metaphoric vehicle (e.g., McClelland & Rumelhart, 1985). Within such a framework, knowledge is represented as an associative network: access to this knowledge base is described in terms of activation of particular nodes of this network; learning and memory is represented as changes as the strengths of connections among nodes. Even though discourse across the "pardigmatic divide" is difficult (cf. Kuhn, 1970), the use of phrases such as "activated in working memory" suggest that we are already slipping between metaphorical systems. To be sure, the conceptual work of synthesizing these two perspectives has already begun (Schneider & Detweiler, 1987). We might attempt a crude translation in the present case by assuming that "nodes activated in the network is analogous to "information being held in working memory." Each metaphor has its own points of clarity and ambiguity. For example, the network metaphor allows for the possibility of nodes varying in their levels of activation at different temporal points in processing (e.g., Till, Mross, & Kintsch, 1988). This is surely an advantage since it is probably naive to conceptualize information as being either "in" working memory or "not in" working memory. We could suggest an ad hoc remedy by assuming that "in working memory" is a stochastic process, but that is a somewhat more cumbersome way of representing something that comes naturally to the network metaphor. On the other hand, with the network metaphor it is more difficult to conceptualize complicated interrelationships among idea units. It is by no means impossible since we could represent any relationship between nodes by assigning a high associative strength (cf. Kintsch, 1988); it is simply more cumbersome. There are several groups of phenomena within the discourse processing realm, e.g., those associated with articulatory loop processing and parsing strategies, that are more easily dealt with in the working memory framework. For these reasons, the synthetic model presented here has been cast in terms of working memory processing guided by the information stored in an associative net. The Aging Discourse Processor

It has been proposed by several researchers in cognitive aging that it might be possible to understand language processing difficulties in later adulthood in terms of limitations in working memory processing (e.g., Cohen, 1979; Kemper, 1988; Light & Anderson, 1985; Stine & Wingfield, 1987; Zacks & Hasher, 1988), herein called the "Working Memory - Discourse Hypothesis." As we have seen, however, putative "working memory" functions seem to run rampant in the cognitive system. One may well question whether such a proposal does any more than point to the proverbial haystack with assurances that the needle is in there somewhere. Indeed, one may well question whether or not such a proposal is simply a vacuous explanation (cf. Salthouse, 1988). Consider the alternative meanings of a Working Memory-Discourse hypothesis. Are working memory functions generally slower or less efficient with age? Are selective working memory functions slower or less efficient? For example, is the capacity of the work space (central executive) smaller? Or is the storage capacity of

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the slave systems smaller? Are processing resources limited such that more "room" is taken up by processing? These questions are important not only in understanding the effects of aging on cognition, but in exploring the utility of various conceptualizations of working memory. Research Tactics It is possible to distinguish two different research strategies that have been used to test the hypothesis that age differences in discourse memory are due to age-related differences in working memory processing. In the in vivo approach, it is assumed that working memory limitations will be apparent in greater sensitivity to conditions in which processing load is increased. Thus, subjects are asked to comprehend and/or remember discourse, and performance is examined as a function of conditions which make processing more difficult (e.g., by manipulating characteristics of the text or the task). Larger age differences under high load conditions are taken as support for the hypothesis that working memory deficits contribute to that effect. (In a more specific version of this strategy, the discourse processing task may be manipulated in some way as to theoretically increase the difficulty of a particular kind of working memory process.) I have called this strategy "in vivo" because it assesses the involvement of working memory processing in the discourse processing situation itself. For example, Kemper's (1988) work showing that older adults have particular difficulty in processing complex syntactic forms, Zacks and Hasher's (1988) work showing that the elderly have particular difficulty in drawing unexpected inferences, Light and Capps' (1986) work suggesting that older adult have greater difficulty in understanding pronouns when the pronoun is widely separated from its antecedent, and research in our own lab (Wingfield, Stine, Lahar, & Aberdeen, 1988) showing that memory span differentially decreases among the elderly as simultaneous language processing requirements increase all illustrate the in vivo approach to testing the working memory hypothesis.

Not all work has been supportive, however. For example, Zelinski (1988) has argued that younger and older adults are similar in their ability to integrate antecedents and anaphors even when they are widely separated in text. Such data are important because they show that even if there are working memory limitations in later adukhood (as inferred from other studies), they do not always have an impact. Thus, there is a difficulty with this research strategy: it is easier to find supporting evidence than negative evidence (not unlike with much psychological research (Sternberg, 1988, pp. 8-10)); while finding an age by load interaction implicates working memory, the failure to find an age by load interaction raises the question of whether or not load was sufficient to tax the working memory system. The in vitro strategy, on the other hand, involves isolating the putative working memory processes in a "psychometric test tube," as it were, and then determining whether this set of processes can account for individual differences in performance in the discourse processing situation. In other words, this research tactic relies on specifying the cognitive components thought to be responsible for age differences, and then developing an instrument that is independent of criteria1 discourse processing performance which embodies these components. First, if this instrument is predictive of discourse processing performance, there would constitute evidence that the contents of this psychometric test tube are, in fact, processes that are also involved in discourse

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processing. Furthermore, if a high correlation between age and discourse processing performance is reduced to nonsignificant levels when the effects of the psychometric test tube are statistically controlled (partialled out), then evidence is provided in support of the hypothesis that those processes were in part responsible for the age differences. In our lab, we have found a Daneman and Carpenter (1980) style listening span task to be predictive of recall for short speech strings and sentences (Stine & Wingfield, 1987) and for extended passages of discourse (Stine, Wingfield, & Myers, 1990; Stine & Wingfield, 1989), and to account for at least some of the age variance in performance (Stine & Wingfield, 1987; Stine et al., 1990), particularly when passages are shorter and simpler (Stine & Wingfield, 1989). Again, not all research has been supportive of the Working Memory-Discourse Hypothesis. For example, while Light and Anderson (1985) found older adults to do more poorly than the young on a reading span test, this could not account for age differences in discourse memory, and Hartley (1986) found no age differences at all on a reading span measure. As in the previous case, these negative findings using the in vitro research strategy are important because they suggest that we have not yet adequately specified the cognitive operations in our psychometric test tube to account for age differences in discourse memory. In future research of this kind it will be important to specify more carefully (and/or manipulate) the nature of the sentences (how comprehensible are they? how much central executive transformation is necessary to achieve mapping with the knowledge net?) and the target words (how easily do they fit on the articulatory loop?). Thus, the Working Memory-Discourse Hypothesis has been useful in accounting for age differences in discourse memory in a limited way; more importantly, the working memory model has proven fruitful in generating testable hypotheses. For these reasons, I would argue that the Working Memory-Discourse Hypothesis has been a good first approximation toward explaining the pattern of age differences in discourse processing. Theories of working memory and of discourse processing, however, have now advanced to the point that we could perhaps develop working memory hypotheses with greater specificity, and begin to consider the developmental course of particular working memory functions and their impact on age differences in discourse processing. With this goal in mind, I will consider evidence for age effects on different aspects of working memory functioning. This effort will no doubt be premature, and it is tempting to say less so as not to be proven wrong (by the time we are finished the reader may wish that I had been more strongly tempted), but at this point it perhaps better to articulate the issues even at the (desirable) risk of later revision. The Effects of Age on the Discourse Processing System Peripheral Processing

There is no doubt that there are sensory deficits in both the visual and auditory modalities, which are due to age-related changes in both the peripheral and central nervous systems (Wine & Schieber, 1985; Olsho, Harkins, & Lenhardt, 1985). In terms of the representation in Figure 11.1, then, it is fairly certain that input into working memory is likely to be in some way degraded among elderly adults. What is less clear

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is how important this lack of sensory fidelity really is for processing language in everyday contexts. A moderate high tone hearing loss, which can be rather pronounced for frequencies greater than about 3000 to 4000 Hz (depending on exposure to noise), is typically observed among older listeners. The energy distribution of speech is most heavily weighted by frequencies below 3000 Hz, and speech syllables from which frequencies greater than 3000 Hz are filtered is still highly intelligible (Licklider & Miller, 1951). For these reasons, a high tone loss in itself would not be expected to be devastating for communicative abilities. Additionally, language understanding outside of the laboratory usually takes place in an information-rich environment. The extent to which this complexity facilitates or hinders processing may well depend on the kind of processing being performed and the nature of the contextual complexity. There is some evidence to suggest that older adults make particular use of context in interpreting lower level information which might be perceptually degraded in both the auditory (Cohen & Faulkner, 1983; Wingfield, Aberdeen, & Stine, 1989) and visual domains (Madden, 1988). Phonemic and Ortliograpltic Coding Relatively little research has directly addressed the question of whether there are age differences in phonemic or orthographic coding. Elias and Hirasuna (1976) reported similar patterns of buildup and release from proactive interference for visually presented rhyming words for younger and older adults, suggesting that young and old alike automatically accessed the phonological code in working memory. In a lexical decision task, Madden (1983) found that primes that were "graphemically" (i.e., orthographically) similar to target words had similar effects on the lexical decisions times of young and elderly subjects, suggesting that young and old alike access the orthographic code. Further, Madden and Greene (1987) concluded that the speed of accessing the orthographic code does not vary with age. There is some evidence, however, that there may be age differences at this level of processing. Mueller, Kausler, and Faherty (1980) reported that older adults took differentially longer to make homophony decisions (e.g., do "hear" and "here" sound alike?) than to make decisions about physical identity or category membership about printed word pairs, suggesting that the elderly may have more difficulty in making acoustic-identity decisions. (It should be noted that this task is different from the others in having the subject making conscious choices about the sounds of words, whereas in the other experiments this was not made overt, which may be an important difference.) A couple of recent experiments address age differences in orthographic coding in the context of connected discourse. Madden (1988) had subjects perform a lexical decision task in which words were degraded by the embedding of asterisks within the targets. Additionally, these targets were the sentence-final words of sentences that were either predictive of the target word or not. The lexical decisions of older adults were particularly slowed by the degraded targets, prompting Madden to conclude that older adults are slower than younger adults in extracting feature level information. On the other hand, older adults benefitted more than the young from the presence of a

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constraining context when targets were degraded, suggesting that the context of discourse may ameliorate slowing in orthographic coding. In a recent experiment in which I measured word-by-word reading times (Stine, in press), I found that young and old allocated reading time similarly as a function of word length, again suggesting age-similarity in on-line orthographic coding in the context of naturalistic discourse processing. For a more thorough analysis of age differences in coding at this level, see Burke and Laver, Chapter 10, this volume. Articulatory Loop One of the most well replicated phenomena in cognitive aging is the fact that the span of immediate memory for information that must be simply held and reproduced remains relatively unimpaired well into later adulthood (cf. Craik, 1977; Salthouse, 1982; Wingfield et al., 1988). Such a task which requires minimal transformation or semantic processing of the stored information is what is traditionally termed a "primary memory," or "short-term memory" task. Because the articulatory loop is the slave system of working memory that is theoretically responsible for the simple storage of verbal information (Baddeley, 1986), we might might want to summarily dismiss this component of working memory as an important source of age differences. Several long-standing anomalies as well as recent data, however, suggest that even though the capacity of the articulatory loop may remain stable, the efficiency of the articulatory control process may be somewhat diminished in later adulthood. First of all, while digit span usually shows little change during adulthood, letter span and word span declines are somewhat more prevalent (see Craik, 1977; Botwinick, 1984 for reviews), suggesting that the likelihood of age differences increases with the complexity of the materials to be processed (Wingfield et al., 1988). Craik's (1977) summary of the literature more than a decade ago still holds: "["lo the extent that a 'short-term memory' task reflects [primary memory] functioning only..., no age decrements will be observed; to the extent that short-term retention involves a large [secondary memory] component...older subjects will perform less well" (p. 387). For example, in a digit span task items are drawn from a small (n = 9 or lo), well-defined set such that top-down processing could facilitate item decoding; in a letter span task, items are drawn from a somewhat larger (n = 26), but still well-defined set; and in a word span test, items are drawn from an extremely large ill-defined set in which top-down processing is not likely to facilitate decoding. The point here is that it may be that the capacity of the articulatory loop is fairly constant over age, but that the operations through which the articulatory control process (the "inner voice") accesses linguistic units and transduces them for the phonological store (the "inner ear") may be less efficient in later adulthood. Evidence for such a proposal has been reported in a recent study by Kynette, Kemper, Norman, & Cheung (1989). These researchers examined the relationship between articulation rate and memory span for younger and older adults using a paradigm developed by Hulme, Thomson, Muir, and Lawrence (1984). Hulme et al. (1984) showed that among children and adults there was a substantial relationship between the rate at which words could be repeated and recall performance for short word lists; furthermore, age increments in recall could be accounted for in terms of

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increments in speech rate. Taking speech rate as an index of articulatory loop processing, Hulme et al. concluded that subjects could recall as much as they could say in a fixed interval -- about one and a half seconds -- a value which did not significantly vary as a function of age. Thus, Hulme et al. argued that is it not the capacity of the articulatory loop which increases in childhood but rather the rate at which items can be stored on the articulatory loop, and that it is this change in the rate of articulatory processing that is responsible for increases in memory span during childhood. Following this logic, Kynette et al. measured the speech rate and the memory spans of younger and elderly adults. The articulation rates of older adults were slower with the durations of both words and pauses being longer for this group (this finding is consistent with a report by Salthouse (1980) suggesting that older adults are slower than the young in covert rehearsal). As would be expected, the memory spans of the older adults were also reduced relative to those of younger adults. Most interesting was the finding that both age groups showed a similar relationship between articulation rate and memory span with a loop duration parameter similar to that reported by Hulme et al. Thus, Kynette and her colleagues suggest that while the duration (capacity) of the articulatory loop does not change with adult age, older adults are slower in storing information onto the loop, which may contribute to age differences in memory span. There is some evidence that age deficits in simple articulatory loop processing may impact on memory for extended discourse. First, in a recent experiment in our own lab (Stine & Wingfield, 1989), we found that simple word span was a significant predictor of memory for sentence- and paragraph-length prose passages and that this variable could to some extent account for age differences in prose memory. Also, older adult have been shown to have particular difficulty in resolving ambiguous pronouns when extraneous material intervenes between the pronoun and its antecedent (Light & Capps, 1986). Such data would be consistent with the notion that older adults have less efficient articulatory loop processing: less extensive verbatim information would be held, making it more difficult to resolve ambiguous pronominal references.

Vkuo-spatial Scratchpad While there has been substantial research suggesting that older adults are impaired in spatial processing (see Smith & Park, Chapter 3, this volume), the role of visual or spatial processing in age differences in discourse has not been extensively investigated. What sparse evidence there is suggests that when language processing necessitates spatial coding, age differences may be particularly apparent. In a recent experiment, we (Stine, Wingfield, & Myers, 1990) examined the recall of spoken information from television news programs either alone or when supported by the visual track. Unlike in the case of the young, the recall of the spoken information by the elderly was not increased by the presence of the visual information. To the extent that integrating information across modalities may require spatial processing ability (Pezdek et al., 1987), these data are suggestive of a role of spatial processing deficits in age differences in discourse memory. A second piece of evidence comes from an experiment by Light, Zelinski, and Moore (1982, Experiment 3), who presented subjects with linear syllogisms such as:

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David was taller than Bob. Bob was taller than James. James was taller than Ron.

D > B B > J J > R

(T or F?) David was taller than Ron.

D > R?

True/false items were either directly stated as premises or were inferences from the premises. Additionally, the presentation order of the premises was manipulated (e.g., as given above vs. J > R; D > B; B > J). As intuition might suggest, it is possible (though certainly not necessary) that such language problems may be solved by means of spatial representation (Huttenlocher, 1968). For example, in the above problem, you might image Bob below David on hearing the first premise, James below Bob on hearing the second, and Ron below James on hearing the third. Verification of the true/false question would rest on simply checking to see whether or not David was positioned above Ron in your image. If the order of the premises was rearranged as in the case of J > R ; D>B; B>J, it would be more difficult to create an image since there is no way to connect the two disparate pieces of information from the first two premises -- until the presentation of the third premise. Light et al. (1982) showed that older adults had particular difficulty in solving such linear syllogisms when the order of the premises was of the second type, suggesting that such a pattern of results was supportive of the hypothesis of age-related working memory deficits in later adulthood. We might also say that such results would be consistent with the hypothesis that older adults have difficulty in manipulating the spatial arrangement of the terms of the syllogisms. One caveat with such a conclusion is that this presupposes that the visuo-spatial scratchpad is responsible for quite a lot of information manipulation. Rather, it may be more parsimonious to consider this a province of a spatial processor within the central executive, or alternatively, to partition the central executive such that all of its processing functions go with modality specific modules (Daneman & Tardif, 1987). It is probably premature to argue a model to that level of precision. The point here is that there is some tentative evidence suggesting that spatial processing deficits may impact on language processing among the elderly. Certainly, a firmer statement of this point must await further research. Central Executive Processing

Baddeley (1986) has suggested that this is a major locus of age deficits in information processing, citing numerous studies (including some investigating discourse processing) showing an exaggeration of age differences in tasks which involve "either complex manipulation of the material, or time-sharing between information from two sources" (p. 245). Consistent with Baddeley's description of the central executive as the "rag bag," I have attributed quite a lot to it in language processing, including parsing, organization and thematic role assignment, integration across input cycles, and supervisory control of on-line allocation of resources and of off-line repairs. Given this diversity of functions, how are we to evaluate the hypothesis that the central executive is to a large extent responsible for age differences in discourse processing? A deficit in central executive processing would predict that older adults processing

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discourse would have general deficits in syntactic analysis and parsing, the semantic analysis of discourse (e.g., in terms of a propositional representation), and in the supervisory control of resource allocation. While some data are consistent with such an account, there are enough qualifications to make the value of such a heuristic questionable. With respect to syntactic processing, there is every indication that the parsing strategies of younger and older adults are quite comparable in normal discourse (Stine, in press; Stine, Wingfield, & Poon, 1989; Wingfield, Lahar, & Stine, 1989), even when the rate at which this must be accomplished is very fast (Wingfield & Stine, 1986). Similarly, in many ways the semantic analysis of language appears to be consistent across the life-span. For example, younger and older adults show similar ordinal relationships in propositional recall probabilities (Stine & Wingfield, 1988; in press). Furthermore, an unqualified central executive deficit would predict that recall for propositionally dense text would be disproportionately low for older adults, which does not appear to be the case (Stine, Wingfield, & Poon, 1986). Perhaps, the most compelling evidence against a general central executive deficit is that such an explanation would seem to entail a deficit in strategic control of discourse processing, and to preempt the use of adaptive or compensatory strategies by older adults in processing language, neither of which appears to be the case. For example, older adults appear to use reading strategies that are remarkably similar to their younger counterparts (Stine, in press), and they appear to be able to adapt their discourse processing strategies so as to differentially rely on processing mechanisms that would be less susceptible to disruption by limitations in processing resources (Stine & Wingfield, 1987). Nevertheless, there appear to be certain aspects of what we might think of as central executive processing that have been noted to show deficits. For example, to the extent that older adults have difficulty in making inferences (Zacks & Hasher, 1988; Cohen, 1981; Cohen & Faulkner, 1984), in processing complex syntactic forms (Kemper, 1988), and in organizing discourse under load (Stine & Wingfield, 1988, in press), they would appear to be less effective in central executive processing. Toward a resolution of these contradictions, consider what it is that the central executive is doing in terms of segmenting and organizing input (of varying complexity) within each cycle, holding some subset, and subsequently integrating it with new input (which itself may vary in complexity and coherence with previous input). In the on-line reading experiment to which I have referred earlier (Stine, in press), I have found evidence that the existence of age differences in these putative central executive processes depends on on-line processing demands. More specifically, while younger and older adults were quite similar in how they allocated time to organize information within processing cycles, younger adults were more likely than older adults to allocate time to integrate information between processing cycles, suggesting that structuring information becomes difficult for older adults when simultaneous memory load becomes great (which is essentially the generic definition of working memory with which we started). The same conclusion can be drawn from an examination of relative memorability for texts of varying processing loads. Art Wingfield and I (Stine & Wingfield, 1988, in

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press) have recently argued that older adults are especially less likely to remember the more memorable elements of a text (often the more important or "higher level" ones) when processing loads are greater. Such results are impossible to explain in terms of simple differences in the sizes of younger and older adults' central executive work spaces. If older adults simply had smaller work spaces, then they would be more likely to lose the less memorable items as processing load increased (cf. Spilich, 1983; Stine & Wingfield, in press). Such does not appear to be the case. Rather, we must assume that it is organizational processing which is hampered as processing load is increased. Also a preservation of the size of the work space coupled with less efficient organization under load would explain why overall recall is not particularly disrupted among older adults by informationally dense prose (Stine et al., 1986), but why recall of more memorable items is (Stine & Wingfield, 1988). Thus, one way to characterize the aging of the central executive is that with age its computational power is more likely to be disrupted by storage requirements. This proposal is somewhat different from a simple WMG model in that it does not really allow the question "Are age differences located in storage, processing, or both?" (cf. Kausler, 1989). Rather, it is more like our car analogy. As the car gets older, its capacity does not change: if a car is mid-sized when it is young, it will be mid-sized when it is old. With age, however, the car is likely to become less fuel-efficient such that a passenger load will have a greater effect on fuel consumption for an older car than for a younger one. So while a new car may be easily able to drive across town with lots of people, an older car may have to carry fewer people in order to conserve processing resources.

On the other hand, there is a growing body of evidence suggesting that aging of the central executive is also characterized by a reliance on previously acquired knowledge and strategies. In our lab, we (Stine & Wingfield, 1987) have found that in the recall of nonsense word strings, older adults are more likely to reconstruct in recall so as to give sense to the word strings; in spite of the fact that this in itself represented an error, the use of this strategy was predictive of overall better performance in the task. Adams, Labouvie-Vief, Hobart, and Dorosz (1990) have also reported that older adults are more likely to represent narrative discourse in terms that are personally relevant rather than in propositional terms. Furthermore, Hess (in press) has argued that older adults rely on relevant schematic information in processing discourse, and Reder, Wible and Martin (1986) have suggested that older adults differentially rely on the assessment of plausibility in discourse memory. Older adults may minimize the impact of deficits in computational efficiency by making particular use of their knowledge of language structure. For example, Fullerton and Smith (1980) have suggested that older adults particularly rely on syntactic structure to discriminate between "given" information (which has already received some processing) and "new" information (which is signalled as the relevant information for the current processing cycle). This could be a useful strategy since it enables the listener or reader to efficiently allocate processing resources to that which will probably be the most important for understanding successive discourse. Similarly, it has been noted in our lab and elsewhere that in speech processing older adults

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appear to place particular reliance on the prosodic contour of speech (Cohen & Faulkner, 1986; Stine & Wingfield, 1987; Wingfield, Lahar, & Stine, 1989). Again, this strategy could be useful in facilitating parsing and highlighting important information without a large expenditure of processing resources. Conclusions: From a Cognitive Model to a Developmental One The starting point of our discussion on the effects of aging on discourse processing was to consider the way any human discourse processing system works. There has yet to be presented compelling evidence suggesting that the fundamental principles of discourse processing among the young are not equally valid for the old. This is important since it highlights the preservation of language processing functions into later adulthood. The approach taken here has been to examine the language processing system and the kinds of changes that would have to occur in such a system to explain the presence or absence of age differences that have been observed under varying processing demands. As Salthouse (1982) has argued, the danger of this tactic toward explanation is that we will be left with a checklist of processes, some showing age-sensitivityand others not, with no unifying developmental principle. On the other hand, by starting with a cognitive model that is fairly well understood, emergent principles of aging have also been readily accessible. The model I have focussed on is a working memory in which raw input is interpreted in terms of previously acquired knowledge. If we can take the essence of aging to be a sort of race between biology and experience, our cognitive model of working memory becomes a developmental one. On the one hand, the biological consequences of aging often entail that the raw input of discourse will be to some extent degraded and that the processing operations which represent and transform this input will be reduced in processing efficiency. Insofar as storage capacity and computational capacity can be disentangled, we have reviewed evidence which would suggest that there is a relative preservation of storage capacity in later adulthood at the level of the articulatory loop and the central executive. Furthermore, the computational power needed to perform linguistic analysis is largely intact under ordinary circumstances. Age deficits that do exist can tentatively be explained in terms of a decreased efficiency of processing under high load conditions. On the other hand, the experiential consequences of aging entail that this processing will be done within the context of a large base of declarative knowledge about the world and of procedural knowledge about how language processing works (cf. Wingfield & Stine, in press). In other words, assuming that we may take working memory as the processing locus of the mapping of input onto knowledge, the knowledge base which the older adult brings to bear on the task is likely to be more extensive than that of an otherwise comparable younger adult. In particular, the older adult who has been committed to his or her own aging as a "race between biology and experience" is likely to have a very rich knowledge net through which to interpret discourse, thus ameliorating the effects of processing limitations.

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12 Adult Age Ditrerences in Traditional and Practical Problem Solving Nancy W. Denney University of Wscomin at Madison

The purpose of this chapter will be to review research on the effect of aging on problem-solving performance. Research with both traditional, abstract problem-solving tasks as well as research with practical, everyday tasks will be reviewed. Intervention research aimed at facilitating problem-solving performance will also be considered. And, finally, professional creativity and productivity will be explored. Traditional Abstract Problems

A number of investigators have studied the performance of adults of different ages on a variety of traditional, abstract problem-solving tasks. The vast majority of this research indicates that older adults perform less well than younger adults. Piagetian Tasks

Research with various Piagetian tasks indicates that there is a decline after middle age in performance on most tasks, with the possible exception of conservation problems. Such age differences have been reported on tests of egocentrism (Bielby & Papalia, 1975; Comalli, Wapner, & Werner, 1959; Looft and Charles, 1971; Rubin, 1974; Rubin, Attewell, Tierney, & Tumolo, 1973), animism (Dennis & Malinger, 1949), moral reasoning (Bielby & Papalia, 1975), classification (Annett, 1959; Denney, 1974; Denney & Lennon, 1972), multiple classification (Denney & Cornelius, 1975), class inclusion (Denney & Cornelius, 1975) and various formal operational tasks (Tomlinson-Keasey, 1972). While the results of the above studies indicate that elderly adults do not perform as well as younger adults on the vast majority of Piagetian tasks, the results of studies of conservation are much less clear cut. In several studies young adults were found to exhibit more conservation than older adults. Papalia (1972) reported that elderly adults performed less well on tests of substance, weight, and volume conservation than college students and middle-aged adults, although she found no age differences in conservation of number. Rubin, Attewell, Tierney and Tumolo (1973) reported that older adults performed less well than young and middle-aged adults on twodimensional space, number, substance, weight, and continuous quantity conservation problems. Sanders, Laurendeau, and Bergeron (1966) found that elderly individuals conserved less on a conservation of surfaces test than did young or middle-aged individuals.

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On the other hand, there is evidence that older adults may not lose their ability to conserve. For example, there are studies in which elderly adults obtained perfect scores on weight (Storck, Looft, & Hooper, 1972) and number (Papalia, 1972) conservation. And, Rubin (1976) reported that virtually all of his elderly subjects exhibited conservation of both continuous and discontinuous quantity. Further, Rubin (1975) reported being unable to locate elderly individuals who did not conserve even when testing adults of low education levels, low socio-economic class and living in institutions. Selzer and Denney (1980) also found that elderly adults performed as well on tests of conservation of substance, weight and volume as middle-aged adults. And, Papalia-Finlay, Blackburn, Davis, Dellmann, and Roberts (1980-81) also reported being unable to find many elderly subjects who could not conserve. Thus, while there appear to be definite age differences on most types of Piagetian tasks, it appears that conservation may be the exception. Denney (1982a) has suggested that elderly adults may tend to maintain their ability to conserve because conservation is an ability that would ordinarily be employed by most adults in their daily activities. Thus, conservation may be maintained while many of the other Piagetian abilities may decline as a result of lack of practice or exercise. Concept Learning Tasks

Performance on concept learning tasks has typically been found to decline with increasing age. On these tasks the subjects are presented with stimuli that vary in a number of dimensions and are told, with each presentation, whether the stimulus is a positive or negative example of the concept. From this information the subjects are supposed to determine what the concept is. Studies by Arenberg (1968), Brinley, Jovick and McLaughlin (1974), Carpenter (1971), Flicker, Ferris, Crook, and Bartus (1986), Hartley (1981), Hayslip and Sterns (1979), Hoyer, Rebok, and Sved (1979), West, Odom, and Aschkenasy (1978), and Offenbach (1974) have also demonstrated that elderly adults require more trials to determine what the concept is than younger adults. Older individuals also tend to perform less well on tasks that require that they change the concept or strategy that they have been using during an experimental session. Heglin (1956) found that older adults have more difficulty overcoming set in problems similar to the Luchins water-jar problems than do younger adults. Likewise, Wetherick (1965) found that elderly adults were less able than younger adults to change an established concept in a typical concept learning task even when they were given feedback indicating that the solution they were using was no longer correct. Rogers, Keyes, and Fuller (1976), Botwinick, Brinley and Robbin (1939, Nehrke (1973), and Witte (1971) have also reported that elderly adults have difficulty shifting from one concept to another. In summary, the concept learning research indicates that it takes older adults more trials to determine what the concept is in a typical concept learning experiment. The research further indicates that it also takes older adults longer to shift from one concept to another.

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Verbal and Nonverbal Search Tasks Research also indicates that there are age differences on a variety of "search" tasks in which the subject is supposed to find the stimulus or stimulus arrangement that is designated as correct from a variety of other alternatives. The subject selects sets of stimulus alternatives and is told, with each selection, whether the correct stimulus or stimulus arrangement is included in the selected set. The object is to find the correct solution in as few set selections as possible. Research with nonverbal tasks in which the subject selects stimuli from, and receives feedback from, a mechanical device indicates that performance decreases with age during the adult years (Arenberg, 1974; Jerome, 1962; Young, 1966, 1971). Similar decreases in problem-solving efficiency have been found with age on the Twenty Questions task in which the subject asks questions and receives verbal feedback from the experimenter (Denney & Denney, 1973; Denney & Denney, 1982; Kesler, Denney & Whitely, 1976; Hartley & Anderson, 1983a; 1983b). So, on both verbal and nonverbal search tasks, older adults require more set selections and are, therefore, less efficient in solving search problems than younger adults. Verbal Reasoning Tasks Elderly adults have also been found to perform less well on tests of verbal reasoning. Bromley (1957) found that older adults performed less well than young adults on proverb interpretation tasks. Friend and Zubeck (1958) also found older adults to perform less well on their test of "critical thinking" which is a test of verbal reasoning. Similarly, Morgan (1956), Nehrke (1972), and Wright (1981) also found older adults to perform less well than younger adults on tests of logical reasoning. Visual and Spatial Tasks Age differences have also been found with a variety of visual and/or spatial problemsolving tasks. For example, age differences have been found on tasks in which individuals are asked to put together either two-dimensional or three-dimensional puzzles when presented with the component parts. Parks, Klinger, and Perlmutter (1988-89) presented both young and elderly adults with two different types of puzzles. On the easier puzzle they were presented with a group of ten magnetic men, a magnetic bar and a stand. They were told to be creative and develop their own designs. On the more difficult puzzle they were presented with wooden blocks in the shape of a cube. The cube was then taken apart and the subjects were asked to reassemble it. Younger adults completed more of both the easy and the difficult puzzles than elderly adults. Salthouse (1987) presented young and older adults with a number of computerpresented block design problems. In standard block design problems the subjects are presented with a number of colored blocks and a picture of a design. They are asked to put the blocks together to make the design. Salthouse adapted this task so it could be presented on a computer screen. The blocks were converted to two-dimensional figures. But, the task was essentially the same. Performance on this task was found to decrease with age; the older adults were slower and less efficient than the younger adults.

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A similar age effect was obtained by Weinman (1986) with maze problems, A series of mazes were presented to adults between the ages of 20 and 70. Older adults were slower and made more errors than younger subjects. O n matrix problems similar findings have been obtained. For example, Harber and Hartley (1983) presented young and elderly adults with matrix problems in which they had to select from a number of alternatives the one that would complete a matrix with an empty cell. Performance decreased with age. Rebok (1981) found similar results in a study of middle-aged and elderly women. And, finally, on perceptual problem-solving tasks age-related declines have also been obtained. Lee and Pollack (1978) presented adults between the ages of 20 and 79 with Witkin's Embedded Figures Test in which subjects are asked to find simple geometric shapes that are embedded in more complex geometric shapes. They found that older adults solved fewer items and required more time to solve the items than younger adults. Similar results have also been obtained with the Matching Familiar Figures test which tests the ability to select from an array of alternatives the one that matches a standard figure exactly. Denney and List (1979) found that between the ages of 30 and 80 both the number of errors and the response latencies increased with age. Coyne, Whitbourne and Glenwick (1978), however, found an increase in errors but not a corresponding increase in response latency with age. Kleinman and Brodzinsky (1978) conducted a similar study with a haptic matching procedure; they also found that elderly adults made more incorrect choices. In summary, these results suggest that there is a decline with age in performance on a variety of perceptual and spatial problem-solving tasks. Creativity Test Performance A number of studies have been conducted to investigate creativity in adults of different ages. Many of the measures of creativity involve problem-solving abilities and, as a result, are considered relevant to this review. Guilford (1967) reviewed a number of studies showing a decline in flexibility, fluency and originality after the age of 30 or 40. Jaquish and Ripple (1981) studied 218 adults between the ages of 18 and 84 years of age on measures of fluency, flexibility and originality of thought. They found that performance increased up to middle age and decreased thereafter. More recently, McCrae, Arenberg and Costa (1987) found a similar curvilinear age function. They gave 825 men between the ages of 17 and 101 six measures of divergent thinking. The same tests were administered again to 278 of the men six years after the first test. Both cross sectional and longitudinal comparisons indicated an increase in performance up to the age of 40 and a decrease thereafter.

Some studies, on the other hand, have indicated that performance on measures of creativity declines with age during the adult years rather than increasing up to middle age and then decreasing. For example, Alpaugh and Birren (1977) measured divergent thinking in 111 teachers ranging in age from 20 to 83. They used Guilford's tests of redefinition, originality, adaptive flexibility, and ideational fluency. Preference for complexity was measured with the Barron-Welsh Art Scale. Older subjects preferred less complexity and performed less well on the Guilford tests of originality, flexibility

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and fluency measures. Although the authors apparently only performed a regression analysis that would detect a linear age function, it is apparent from the graph presented in their article that creativity increased from 20 to 30 and then declined thereafter. Thus, their results may not be that different from those reported by Guilford and McCrae, Arenberg, and Costa. Likewise, Alpaugh, Parham, Cole, and Birren (1982) studied the performance of 61 women who were either young (20-38) or old (60-83) on three standardized measures of creativity and a creative writing project. They included the measure of creative writing as a more ecologically valid measure of creative output. They used Guilford's Uses of Objects, Consequences, and Plot Titles to measure originality, ideational fluency and spontaneous flexibility. English professors rated the creativity of the stories written by the subjects. Comparisons of the two age groups indicated that the younger subjects performed better than the older. Since only two groups were studied, they would not have been able to detect a curvilinear age function. However, Ruth and Birren (1985) performed a study in which a curvilinear age relationship could have been, but was not, detected. They studied well-educated young (25-35), middle-aged (45-55) and elderly (65-75) men and women in Finland. They used two verbal (Uses of Objects and Just Suppose) and two nonverbal (Patterns and Inkblots) tests of creativity. The tests were scored for three subcomponents of creativity: fluency (total number of answers), flexibility (different categories, reflected in the answers) and originality (uniqueness of response). Age differences were found in all three of the above components of creativity -- with performance decreasing with increasing age. Bromley (1956) presented the Shaw Test of creative thinking to individuals in four age categories -- 17-35,35-51,51-66,and 66-82. He also found that the youngest group was able to produce the most creative solutions. In summary, it appears that there are definitely age-related declines in performance on measures of creativity during the adult years. The only question appears to be when the age decline begins. Some studies indicate that the decline may begin after early adulthood (e.g., Bromley, 1956; Ruth & Birren, 1985). Others indicate that it might not begin until as late as 40 (e.g., Jaquish & Ripple, 1981; McCrae, Arenberg, & Costa,1987). Summary: Abstract Problems

In summary, the results of research with Piagetian tasks, concept-learning tasks, search tasks, reasoning tasks, and visual/spatial tasks all indicate linear declines in performance with increasing age during the adult years. Research on creativity also indicates decline with age. However, some studies reflect decline occurring after early adulthood while others do not reflect such change as occurring until as late as 40. Intervention Research Since performance on traditional problem-solving tasks has been found to decline with age during the adult years, a number of investigators have developed intervention

techniques in an attempt to facilitate problem-solving performance in adults. Some of the intervention techniques have involved direct cognitive training on the ability of

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interest. Others have involved indirect, noncognitive interventions attempts to modify some factor other than the ability of interest in the hope that a change in that factor may, in turn, have a beneficial effect on the cognitive ability. Many of these intervention techniques have been found to be successful in improving problem-solving performance. Those that have proven to be the most successful are ones that involve a direct cognitive intervention on the particular ability under investigation. Cognitive Training Research

Piagetian Tasks. Some of the intervention studies have focussed on performance on Piagetian tasks. Hornblum and Overton (1976) attempted to train elderly women to conserve by providing them with feedback contingent on their responses to conservation problems. The control subjects were given the same problems but were not given feedback as to the correctness of their answers. On the posttest significantly more conservation responses were given in the feedback condition than in the control condition. Papalia-Finlay, Blackburn, Davis, Dellmann, &Roberts (1980-81) attempted to replicate this finding but were unable to find more than a coupIe of elderly women who did not spontaneously conserve on their pretest. This finding, that most elderly adults conserve, is consistent with the earlier-reported findings of both Rubin (1975; 1976) and Selzer and Denney (1980). However, it does appear that when elderly adults do not conserve on their own, they can be taught to do so with contingent feedback. With respect to the Piagetian abilities that show more decline during the adult years, the evidence of modifiability in response to training is very clear. For example, Schutz and Hoyer (1976) found that feedback facilitated the performance of elderly individuals on a spatial egocentrism task. Denney (1974) was able to increase the use of similarity classification in older adults by exposing them to a model that classified according to similarity.

Concept Learning Tasks. Training effects have also been obtained with concept learning. Crovitz (1966) found that performance could be facilitated on concept learning tasks with a modeling procedure. Sanders, Sterns, Smith and Sanders (1975) compared four different experimental treatments. In the reinforced training condition the subjects got tokens for correct responses in addition to verbal feedback, strategy hints and memory cue cards. In the training condition subjects got everything mentioned above, minus the tokens. In the practice condition, if the subjects did not solve a problem, they were given the solution before they went on to the next problem. In the control condition the subjects were only given the pre- and posttests. Performance increased significantly in both the reinforced training and training conditions but not in the practice and control conditions. Sanders and Sanders (1978) tested the same subjects used in the Sanders et al. (1975) study one year later. They presented the subjects with a bidimensional problem (when the subjects were originally trained on a unidimensional problem) and found that the training effects were both durable and transferred to the bidimensional task. Using similar training techniques Sanders, Sanders, Mayes, and Sielski (1976) also found significant training effects in elderly females.

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Verbal and Nonverbal Search Tasks. A number of training studies have also been conducted with verbal and nonverbal search tasks. For example, Denney and her colleagues (Denney and Denney, 1974; Denney, Jones, & Krigel, 1979) found that the performance of elderly individuals could be increased on the Twenty Questions task if the subjects viewed a model using an efficient strategy. And, Young (1966) found that direct instruction in strategy use could be used to facilitate the performance of elderly adults on a nonverbal search task. Abstract Reasoning Tasks. Training effects have also been obtained with abstract reasoning tasks. Labouvie-Vief and Gonda (1976) used both a strategy training and an anxiety reduction training condition to facilitate performance on an inductive reasoning task in elderly adults. They reported that both the strategy training and anxiety reduction groups performed better than the control group. Likewise, Plemons, Willis and Baltes (1978) used a strategy training technique to improve the performance of elderly adults on a figural relations task. Their training consisted of eight one-hour sessions. Similar training effects have been reported in other studies of older adults in which the same training technique was employed (e.g., Baltes, Dittmann-Kohli & Kliegl, 1986; Blieszner, Willis, & Baltes, 1981; Hofland, Willis, & Baltes, 1981; Willis, Blieszner, & Baltes, 1981). In the above studies, the training procedure that was employed extended over a number of training sessions and consisted of a number of hours of instruction. However, Denney and Heidrich (1990) demonstrated that significant improvement in performance could be obtained on the Raven Progressive Matrices in only one brief training session. They included young, middle-aged and elderly subjects and found that the training effect did not differ as a function of age. Rebok (1981), however, did find an interaction between age and training in a similar study. He studied the performance of middle-aged and elderly women on matrix problems. He gave some of his subjects verbal feedback as to the correctness of their responses and others no feedback. He found that feedback facilitated the performance on both his middle-aged and elderly subjects on the easier matrices. But on the more difficult matrices only the middle-aged subjects benefitted from the training. Summary. It appears that a variety of intervention techniques can be used to successfully facilitate performance on problem-solving tasks. The techniques that have clearly been demonstrated to be successful on a variety of tasks include (a) providing individuals with models that use effective strategies on problem-solving tasks, (b) providing subjects with instructions in the use of effective strategies, or (c) providing subjects with feedback contingent on their performance. Noncognitive Training Research

The intervention techniques described so far were designed to give subjects direct training in the cognitive ability under investigation. Most of these studies show that strategy modeling, direct strategy instruction, and contingent feedback facilitates problem-solving performance. There have been other intervention studies that did not involve direct training on the specific cognitive ability of interest but that have involved more indirect intervention techniques. Most of these studies were conducted because of the belief that older adults' poor problem-solving performance may be a result of noncognitive factors rather than a result of cognitive deficits. For example, it has been suggested that older adults may perform less well on problem-solving tasks because

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they are slower, lack motivation, lack self-confidence, and so forth. If it were true that elderly adults perform less well than younger adults for some noncognitive reason, then changing the noncognitive factor should result in improved performance on the problem-solving task. Response Speed. A number of researchers have attempted to change problem-solving performance by changing speed of response. For example, Hoyer and his colleagues (Hoyer, Hoyer, Treat & Baltes, 1978-79; Hoyer, Labouvie, & Baltes, 1973) tried to improve performance on a number of intellectual abilities by increasing response speed. These studies are relevant in that some of the intellectual abilities are spatial and verbal reasoning tasks. Reinforcing response speed did not, in fact, result in improved performance on the intellectual measures.

Further, Denney (1982b) has conducted a series of studies in which she has attempted to change elderly individuals' performance on the Matching Familiar Figures test. The dependent variables on this task are response latency and number of errors. Denney attempted to both increase and decrease the subjects' response latencies by direct instruction, modelling, and forcing the subjects to respond after either shorter or longer latencies. None of these procedures affected the subjects' response latencies with one exception. When the subjects were forced to respond very quickly during training, they tended to respond more quickly on the posttest. The subjects who were forced to respond more quickly also had a higher error rate on the posttest than the other subjects. Denney also tried to modify the error rate by way of strategy training but the strategy training also had no effect on error rate. Thus, it may be that the Matching Familiar Figures test measures an ability that is less responsive to training than some of the other abilities. This could be because it taps perceptual ability more than actual reasoning. Other variables. Denney (1980) conducted a series of noncognitive intervention studies with the Twenty Questions task. In one study she attempted to increase elderly adults' motivation by allowing them to earn money based on their performance. The more efficient their performance, the more money they would earn. However, she found that the subjects who received money for efficient performance did not perform any better than the control subjects who did not receive money.

Denney (1980) also attempted to manipulate self confidence in a study in which the elderly were presented with a series of matrix problems before being given the Twenty Questions task. The subjects in the experimental condition were told that they had performed extremely well on the matrix problems while the subjects in the control condition, who were given the same problems, were not given any feedback. Both groups were then given the Twenty Questions task. No difference in performance was obtained between the two groups, Denney (1980) also tested the hypothesis that elderly adults might not perform as well on the Twenty Questions task because they do not take the time to think about the task demands and to formulate an efficient strategy. Thus, she gave an experimental group of elderly adults a forced three-minute delay after the instructions were given before the subjects were allowed to begin asking questions. The subjects

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in the control group were allowed to begin asking questions immediately following the instructions. Again, no difference was obtained in performance between the two groups.

Summary. While intervention research indicates that performance on problem-solving tasks can be facilitated by strategy modeling, strategy instruction, and contingent feedback, it also indicates that it may not be possible to facilitate problem-solving performance with more indirect, noncognitive interventions. A number of studies indicate that increasing response speed may not be a n effective method for augmenting problem-solving performance. And, other studies have failed to find that manipulations intended to increase motivation, self confidence and strategy planning are successful in changing problem-solving performance. So, while a variety of cognitive interventions are effective, noncognitive interventions, at least so far, have not proven to be effective. Conclusions

As was mentioned previously, intervention research was undertaken primarily to determine whether the relatively poor performance of elderly adults on traditional problem-solving tasks could be improved. Some researchers assumed that the performance of elderly adults was relatively low because of disuse and that, with training, their performance deficit could be counteracted. Many of these researchers trained elderly adults and, when their performance increased as a result of training, concluded that they had corrected the age deficit. However, the studies in which young and\or middle-aged adults were also included do not support this view. These studies do not suggest that training eliminates the age differences typically found in problemsolving performance. Rather, they indicate that if their performance is not at ceiling, young and middle-aged individuals either benefit as much, or more, from training than elderly adults. Given these findings, it no longer seems reasonable to conclude that training eliminates an age-related deficit. Rather, it seems clear that direct, cognitive training improves performance in all age groups. Practical Everyday Problems In recent years there has been a rather large increase in the number of studies aimed at investigating practical, everyday problem solving. In general, these studies indicate a somewhat different developmental trend than that obtained in studies of performance on traditional problem-solving tasks. Whereas the studies of traditional, abstract problem-solving tasks indicate that performance tends to decline in a fairly linear fashion after early adulthood, most of the studies of practical, everyday problem solving indicate an increase in performance up to the middle-adult years and a decrease thereafter. Two types of studies have been conducted to determine whether different performance is obtained with more realistic problems -- problems in which more reallife-like stimuli are used with traditional problem-solving tasks and problems in which the entire problem-solving task is new and realistic.

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Traditional Problems with Realistic Stimuli Arenberg (1968) conducted a study in which he used more realistic stimuli with a standard concept learning task. Rather than using abstract geometric figures, he used foods as his stimuli. The subjects were presented with "meals" composed of three foods each. The subjects were told that one of the foods was poisoned and it was their task to figure out which one it was. For each meal, the subjects were told whether a person who supposedly ate the meal "lived" or "died. "Lived", of course, indicated that the poisoned food was not included in the meal and "died' indicated that it was in the meal. Arenberg found that older adults could solve the poisoned food problems somewhat more easily than they could solve the traditional concept learning problems. However, even on the poisoned food problems younger adults performed better than older adults. Sinnott (1975) obtained similar findings with a formal operations task. She presented her subjects with both a problem that required the subject to make all possible binary combinations of items and a problem that required the subject to deal simultaneously with several proportional relationships. She used both traditional stimuli and familiar, realistic stimuli with both types of problems. While the performance of both younger and older adults was facilitated by the use of the realistic stimuli, age differences were not eliminated. In fact, if anything, the age differences were increased by the use of familiar stimuli. Harber and Hartley (1983) compared performance on the Raven Progressive Matrices with performance on a similar matrix task with more realistic stimuli. The use of the realistic stimuli facilitated the performance of younger adults but not the performance of older adults. The older adults performed less well than the younger adults on both tasks. Although the results are not entirely consistent, these studies suggest that individuals of all ages perform better on traditional problem-solving tasks when attempts are made to make the stimuli and feedback more meaningful and realistic. But, the studies further indicate that the use of more realistic stimuli does not eliminate the age differences that are typically found with traditional problem-solving tasks. In fact, in some cases age differences were found to be greater with the more realistic stimuli. The use of more realistic stimuli seems to facilitate the performance of young adults more than the performance of older adults. In the studies in which more realistic stimuli were used with traditional problemsolving tasks only young and elderly subjects were included. As a result, it is not possible to determine whether the age function was linear or nonlinear. However, in the studies in which entirely new types of problems were employed, more age groups have typically been employed and, as a result, a more precise portrayal of the age function has been obtained. Practical Problem-Solving Tasks With practical problem-solving tasks, performance has typically been found to increase up to middle age and decrease thereafter. This trend has been reported in

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a series of studies conducted by Denney and her colleagues. In the first study Denney and Palmer (1981) presented individuals between 20 and 80 with a set of nine practical, real-life problems to solve. These problems dealt with a variety of different situations including problems related to the weather, parenting, consumer issues, and legal issues. The following is an example of one of the problems:

Let's say that one evening you go to the refrigerator to get something cold to drink. When you open the refrigerator, you notice that it is not cold inside, but rather, is warm. What would you do? Denney and Palmer found that middle-aged subjects -- the 40 and 50 year olds -- were able to generate more safe and effective solutions to such problems than either younger or older subjects. The results of the first study could have occurred because the problems were unintentionally biased in favor of middle-aged adults. As a result, in a second study, Denney, Pearce and Palmer (1982) devised three different sets of practical problems. One set was developed to be biased in favor of young adults, a second set was developed to be biased in favor of middle-aged adults, and a third set was developed to be biased in favor of elderly adults. That is, each of these sets of problems were intended to be composed of problems that the relevant age group would have had more experience with than would the other two age groups. The following are examples of the young, middle-age and elderly adult problems, respectively: Let's say that a young man who is living in an apartment building finds that the heater in his apartment is not working. H e asks his landlord to send someone out to fix it and the landlord agrees. But, after a week of cold weather and several calls to the landlord, the heater is still not fixed. What should the young man do? Let's say that a middle-aged woman is frying chicken in her home when, all of a sudden, a grease fire breaks out on top of the stove. Flames begin to shoot up. What should she do?

Let's say that a 60-year-old man who lives alone in a large city needs to go across town for a doctor's appointment. He cannot drive because he doesn't have a car and he doesn't have any relatives who live near by who could drive him. What should he do? All three sets of problems were presented to individuals between the ages of 20 and 80. Performance on the young adult problems decreased with age while performance on both the middle-aged and elderly problems increased up to middle age and decreased thereafter. Since the elderly adults did not perform better than the other age groups on the problems that were developed to give them an advantage, Denney and Pearce (1989) conducted a third study. For the third study they recruited elderly adults to help them make up practical problems in order to guarantee that the problems would be biased in favor of the elderly adults. They gave the set of problems that were developed by

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elderly adults to individuals between the ages of 20 and 80. Again, they found that middle-aged adults performed better on these problems than either young or elderly adults. Thus, it appears that, over all, there is a tendency for middle-aged subjects to perform better on the types of everyday problems that were employed by Denney and her colleagues. Similar findings have been obtained by a number of other investigators. For example, Demming and Pressey (1957) presented subjects with tests of practical information. They found performance on these tests increased up to the 30s or 40s and then declined. Rimoldi and Vander Woude (1969) presented subjects with both a practical problem-solving task and two abstract problem-solving tasks. They found an increase in performance after the late teens, followed by a decrease in performance in the late 50s and beyond on all three tasks. Heyn, Barry and Pollack (1978) also presented young, middle-aged and elderly males and females with a series of everyday problems, half of which were masculine in orientation and half feminine. Their middle-aged subjects solved more of the problems than either their young or elderly subjects, which did not differ from each other. Gardner and Monge (1977) also presented adults between the ages of 20 and 79 with tests of practical information about the world. They, too, found that performance tended to increase up to middle age and decrease thereafter. Thus, although there are some exceptions (e.g., Cornelius and Caspi, 1987), it appears that most studies of practical or everyday problem solving indicate that performance increases from early to middle-adulthood and declines sometime thereafter. This finding suggests that the experience adults gain in dealing with a variety of different situations during the adult years allows them to perform better on practical kinds of problems than young adults who have had less experience with such problems. This finding also suggests that experience counteracts some of the age-related decline that is apparent in performance on more novel, abstract types of problems. However, the fact that elderly adults do not perform as well as middle-aged individuals even on problems developed to give them an advantage suggests that even experience cannot completely nullify the effects of aging. Eventually age-related decline appears to limit the performance of elderly adults. Summary: Practical Everyday Problems The research in which more realistic stimuli were used with traditional problemsolving tasks indicates that although performance is facilitated with the use of more realistic stimuli, age differences are not eliminated. Rather, younger adults have been found to outperform older adults even when more realistic stimuli are used. The research with nontraditional, practical problem-solving tasks, on the other hand, indicates that performance may increase up to middle age and decrease thereafter. Presumably this trend is a result of the additional experience that middle-aged individuals have obtained. Thus, it appears as if the developmental trends for the two types of research -- traditional problems with more realistic stimuli and nontraditional problems -- may differ. However, since none of the studies of traditional problems with realistic stimuli used more than two age groups -- young and older adults -- it is not possible to determine how middle-aged individuals would perform on such tasks.

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Thus, it may be that the developmental trends on the two types of problem-solving tasks may not differ. It is possible that performance on both types of tasks would increase up to middle age and decrease thereafter. Professional Creativity A number of studies of professional creativity are relevant to problem solving during the adult years. These studies deal with age functions of creative problem solving in one's professional activity. These studies generally indicate that creative production tends to increase fairly rapidly to a peak in the early 30s or 40s and then to decline in a more gradual manner, depending on the particular field. Earlier peaks are observed in areas such as poetry writing, pure mathematics and theoretical physics. Later peaks are often obtained in areas such as novel writing, history, philosophy and medical research (Simonton, 1988).

Lehman (1953) studied the relationship between age and professional creativity very extensively. He looked at the age at which scholars, who became famous for their work, made their most significant contributions. Lehman reported that the ages varied from field to field but that they typically fell between the ages of 30 and 39. Others have obtained similar findings. Zusne (1976) studied psychologists; he found that the mean age at which famous psychologists published their most significant contributions was 39. Manniche and Falk (1957) reported that the average age at which Nobel Prize winners published the research for which they received their prizes varied from 33.9 for physicists to 43.8 for medical scientists. Elo (1965) found that world-class chess players tend to peak in chess playing ability at about the age of 36. Simonton (1988) pointed out that professional productivity also increases during the early adult years, peaks at about the same age as professional creativity, and then decreases with age. Simonton proposed the "constant-probability-of-success model" to represent the relationship between creativity and productivity. According to this model "creativity is a probabilistic consequence of productivity". That is, Simonton suggests that the proportion of the total number of works produced that are judged to be truly significant remains the same across the adult years. While young and older adults both produce less and make fewer significant contributions, the proportion of their works that are judged to be truly significant remains constant. Summary: Professional Creativity Thus, the results of the research on both professional creativity and productivity indicate similar developmental trends. Both indicate increases up to the 30s or maybe even 40s and decreases thereafter. Overall Summary and Conclusions The research on performance on traditional problem-solving tasks clearly indicates that there is a decline in performance across the adult years. Such a decline has been reported with almost all traditional problem-solving tasks. There is, however, one exception. While clear age-related declines have been obtained with most Piagetian

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tasks, the results with respect to conservation are not so clear. While some researchers have reported conservation deficits in elderly adults, a number of investigators have not found conservation deficits in the elderly. This suggests either that older adults do not lose the ability to conserve or, if they do, that the loss is not as great as in other abilities. Denney (e.g., 1982a) has suggested that performance on most of the traditional problem-solving tasks declines with age, in large part, because those types of abilities are not often exercised during the adult years; she further suggests that the ability to conserve may not be lost to the same extent in the later adult years because older adults actually use their ability to conserve quite frequently in their daily lives. This conception does seem to provide a plausible explanation for why conservation abilities are related to age in a different manner than are the other abilities tapped by traditional problem-solving tasks. The results of studies on the various components of creativity also indicate that performance on these types of tasks may exhibit a different age function than performance on the other traditional problem-solving tasks. While many studies indicate a linear decline in performance on tests of creativity with age, others indicate that performance may increase up to about 40 and then decrease. Given this discrepancy, more research will be needed to clarify the relationship between performance on creativity tasks and age. Further, in order to determine whether the relationship betwe'en age and performance differs for traditional problem-solving tasks and the problem solving involved in creativity tasks, it would be helpful if the two types of problems were presented to the same subjects. It is possible that the discrepancies in the results of earlier studies were a result of differences in the subject populations. In order to determine for sure that different abilities follow different developmental functions, both types of tasks should be presented to the same subjects. Since performance on traditional problem-solving tasks exhibits age-related decline during the adult years, a number of intervention studies were conducted to determine whether the relatively poor performance of older adults could be improved. This research clearly demonstrates that it is fairly easy to facilitate performance on such problem-solving tasks by either modeling the use of an efficient strategy, giving direct instruction in the use of an efficient strategy, or giving feedback contingent on effective responses. Given that it is so easy to facilitate performance, it is clear that the decline observed in performance does not necessarily reflect a decline in absolute ability. Rather, we must conclude that the typical performance obtained on those tasks is usually not indicative of an individual's absolute ability. Many individuals clearly have the ability to perform at a higher level than they do spontaneously. One might be tempted to conclude that older adults tend to perform below their ability level because they are not often called upon to use problem-solving abilities in their daily lives. However, since the intervention research indicates that the performance of both young and older adults can be facilitated rather easily with training, we must conclude that neither young adults nor older adults are typically performing up to their maximum ability -- rather both groups appear to be capable of performing much better than they typically do on their own. While the direct, cognitive training techniques have proven to be very effective in facilitating problem-solving performance, other methods have proven to be less effective. Attempts to increase problem-solving performance by increasing older

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adults' motivation, self-confidence, and strategy planning time have not resulted in the desired improvement in performance. Further, attempts to change problem-solving performance by changing response speed have also been unsuccessful. Thus, at least with the research conducted so far, it appears as if the most effective way to improve problem-solving performance is with training aimed at facilitating the cognitive processes required by the tasks. This would also suggest that less-than-optimal problem-solving performance is a result of cognitive factors rather than a result of noncognitive factors such as lack of motivation, self confidence, and taking enough time to plan one's strategy. However, it is, of course, possible that the noncognitive experimental manipulations employed so far have simply not been effective ones and that if more effective noncognitive manipulations were employed, they might yield better Performance. On practical problem-solving tasks, the developmental trend typically differs from that obtained with traditional problem-solving tasks. Performance on more realistic problems appears to increase up to the middle-adult years and decrease thereafter. However, there may be an exception. In the studies in which more realistic stimuli have been employed with traditional problem-solving tasks, only age decline has been reported. But, since middle-aged individuals were not included in those studies, it is not possible to determine whether middle-aged adults would have performed better than the young adults. Otherwise, the research fairly consistently indicates that on tasks that tap everyday problem-solving ability, middle-aged individuals perform better than young adults. This age function is obtained with performance on laboratory problem-solving tasks as well as in both creativity and productivity in professional work. Middle-aged individuals presumably perform better than young adults on practical problem-solving tasks because they have had more experience with practical problemsolving situations in general and the experience they have had has a beneficial effect on their ability to perform on such tasks. Research also indicates that problem-solving performance is, in part, determined by experience with the particular type of task involved. Young adults tend to perform better than older adults on problems that young adults would be likely to encounter in their everyday lives while middle-aged adults tend to perform better on problems that they would be likely to encounter. This is consistent with Denney's (e.g., 1982a) view that experience very significantly affects the developmental functions obtained with different types of cognitive performance. However, while young and middle-aged individuals are able to perform better than other age groups on problems similar to those they might have to deal with in their daily lives, the same does not hold for elderly individuals. On problems developed to give elderly adults the advantage, middle-aged individuals perform better than older subjects. The fact that the performance of elderly adults has not been found to surpass that of young and middle-aged adults when an effort has been made to bias the practical problems in their favor suggests that there are age-related limits on problemsolving performance. This finding is consistent with Denney's (e.g. 1982a) view that age-related declines in neurophysiological functioning eventually become severe enough to actually interfere with, and thereby, limit performance during the later adult years.

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When this occurs the beneficial effects of experience are not great enough to overcome the detrimental effect of the neurophysiological decline. According to this viewpoint, neurophysiological functioning actually begins to decline after early adulthood but the decline is not great enough to actually counteract the effects of experience until the later adults years. During the later adult years declines in performance will be obtained regardless of the opportunity for practice of the particular abilities in question. In summary, there is age-related decline evident in all types of problem-solving abilities. However, the decline is greatest, and begins earliest, in those abilities tapped by traditional, abstract problem-solving tasks that are not usually exercised during the adult years. Abilities that are exercised more frequently, such as practical problem solving and professional achievements, tend to exhibit improvement up to middleadulthood and decline thereafter. Thus, it appears that both age and experience affect problem-solvingperformance. Age tends to be associated with declines in performance while experience facilitates performance. During the middle-adult years the positive effects of experience can counteract the negative effects of aging. However, eventually, during the later adult years, the effects of aging set limits on performance that even the effects of experience cannot overcome. References

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13 Interactions Between Personality and Cognition and Their Implications for Theories of Aging Dolores P. Gold Tannis Y.Arbuckle Concordia University

Current research on the development of personality and cognition during adulthood increasingly reflects a theoretical emphasis on contextual approaches. These approaches make the assumption that environmental contexts are important determinants of the development both of personality and of cognitive abilities. In turn, to at least a certain extent, individual characteristics of personality and cognition are believed to influence the selection both of contexts and their effects (Lerner & BuschRossnagel. 1981; Lerner, Hultsch & Dixon, 1983). The processes underlying development are thus viewed as a lifelong series of mutually-causal interactions between the individual and the environment. These theoretical approaches emphasize the variability or plasticity of development, although the continuity of development is also well recognized (Atchley, 1989). Adult characteristics are regarded not as the inevitable outcome of a developmental trajectory determined earlier in life, but are thought of rather as reflecting and shaping the environmental exchanges in which the individual engages in an attempt to maintain a sense of continuity throughout life (Datan, Rodeheaver & Hughes, 1987). With this emphasis, life-span developmental psychologists are unlikely to commit "the ontogenetic fallacy", that is, ignoring or giving only token recognition to the "profoundly interactive nature of self-society relations and the complexity and variability of social environments" (Dannefer, 1984, p. 100). Thus, a great deal of current research has studied the embeddedness of individuals in their environments and how this reflects what they are and affects what they will become. The literature on contextual variables has emphasized person-environment interactions, but in addition, some research has also been devoted to studying how within person variables can also provide contextual influences. That is, psychological functions are not studied in isolation but their interaction with and influence on each other is examined. Within this broader research area there has been a small amount of research on the question of how personality and cognition influence each other (Nesselroade, 1986). Clinicians and parents have always believed that the extent to which an individual behaves intelligently is as much a question of how personality determines the degree to which intellectual ability is used as it is a question of how much intellectual ability the individual has. However, psychologists have only recently recognized that, although the intellectual capacities we end up with in old age are largely determined by the intellectual abilities we started with, how intelligent we are in old age is also partly a question of what personality traits we started with. The

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converse thesis, that cognitive processes influence the development of personality in old age, has also been advanced, although this position has tended to be more narrow, focussing on the effects of cognitive competence on specific beliefs about the self. The elucidation of personality-cognition interactions has both theoretical and applied aims. Understanding the influence of personality factors can help explain the wide range of cognitive function among the elderly. This is clearly a desirable objective since most studies of aging and cognition fail to explain the bulk of variation across individuals. On a practical level, knowledge of how personality variables mediate cognitive function can assist in the design of interventions intended to help maintain or improve cognitive function in elderly populations (Lachman, 1986b), by making the interventions more responsive to salient individual differences in personality variables. In this chapter, we will first review the empirical findings on the relations between personality traits and cognitive abilities, focussing on the nature and extent of these relations throughout adulthood. Next, we will consider possible causal influences of each on the other. We shall briefly examine the relative stability of personality traits and cognitive abilities over the adult lifespan and the way in which any differences in the extent of age-related changes would influence the nature of causal relations between them. A detailed examination of the extensive bodies of research literature examining the stability and change in cognition and personality across the adult lifespan is beyond the scope of this paper. In any case, the research on age-related influences on cognitive function in the adult lifespan is dealt with elsewhere in this book. To conclude, we will propose a heuristic model specifyingthe direct and indirect relationships between personality and cognition. There are certain other issues that space limitations will not permit us to discuss. Our focus is on normal development and hence, we are specifically excluding the consideration of catastrophic onslaughts on cognitive function which lead to personality disorganization and deterioration, such as Alzheimer's disease or cerebral vascular trauma. We are also excluding issues of interest only to researchers in personality or cognition that have no bearing on the relations between the two areas, such as the question of the emergence of qualitatively distinct as opposed to quantitative changes in traits or abilities in old age, except inasmuch as they are relevant to the question of the relations between the two. Relations Between Personality and Cognition Are there any consistent patterns of relations between cognition and personality that emerge from the results of the studies in this area? A pattern that emerges in sequential and longitudinal as well as cross-sectional designs and that is replicated with different measures of the personality traits and cognitive abilities involved would be regarded as a robust finding. Readers will quickly discover, if they are not already aware of it, that robustness in this area necessarily can only refer to the persistence of the reported associations between personality and cognition and not to their magnitude. Magnitude of effect is in any case difficult to assess precisely because of the great variation across studies in the particular set of variables retained for analysis, in the methods of analysis used and in the types of statistics reported. Therefore, in reporting the statistically significant associations that have been found between

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personality traits and performance on cognitive measures, we will attempt only a rough classification of the magnitude of these associations, evaluating the size of effect as low when correlation coefficients are below .40, or moderate when coefficients range from .40 to .60. We note that no study has reported what could be considered high levels of association between personality and cognitive variables. Researchers studying relations between personality and cognition have developed and examined a wide variety of personality constructs reflecting different theoretical orientations. The literature on personality traits has long been recognized as containing many different terms for traits that clearly overlap substantially in meaning. Factor analytic approaches to the study of personality traits, however, have demonstrated that five broad factors account for most of the variance in personality descriptions (Norman, 1963; Digman & Inouye, 1986; Borkenau, 1988). These five factors may be described somewhat differently by the personality theorists working within the factor analytic approach, but the various factors defined by different researchers can be grouped together on the basis of their similarities into the five broad factors and appear to be central dimensions of personality (Costa & McCrae, 1986). The five factors represent continuums on the dimensions of introversion-extraversion, emotional stability or neuroticism, openness to experience, agreeableness, and conscientiousness. The dimension of introversion-extraversion refers to the sociability and introspectivenessof individuals, including such aspects as reserved-outgoing, quiettalkative, impulsivity-deliberateness. Descriptors of the neuroticism factor refer to the emotional life of an individual, specifying such polar opposites as stable-labile, calmworrying,eventempered-temperamental,self-satisfied-self-pitying and hardy-vulnerable. Openness to experience encompasses such terms as imaginative, original, curious and preferring variety as opposed to down-to-earth, conventional, incurious and preferring routine. The factor of agreeableness includes aspects such as suspicious-trusting, stingy-generous,critical-lenientand irritable-good natured. Conscientiousness includes such personality characteristics as hardworking,well organized, ambitious and punctual versus their opposites, lazy, disorganized, aimless and procrastinating. Although there may be some disagreement about the interpretation of aspects of these five central factors, there are sufficient data supporting their stability and strength for us to adopt this conceptual taxonomy in presenting the literature on the relations between personality traits and cognitive abilities. The particular cognitive abilities examined in this research field have been even more diverse than the personality traits studied. Because no theory has dealt in any systematic way with possible relationships between personality and cognition, there have been no criteria to guide the selection of cognitive measures, and hence, there has been little consistency across studies in the tasks and measures used. It is true that, as a general rule, longitudinal studies of intellectual change have used standardized psychometric measures of intelligence. However, the various longitudinal studies that have examined relations between personality and cognition have all used different intelligence tests to measure cognitive abilities, thus reducing the comparability of outcomes across studies. A further problem with the use of intelligence tests is that such tests tend to provide information about cognitive functioning that is relatively nonspecific and therefore potentially less valuable for

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understanding relationships with personality than that which would be provided by more finely-tuned measures, Cross-sectional research on personality-cognition interactions has typically used laboratory tasks to assess cognitive abilities. Such tasks provide more finely-tuned measures but their very specificity increases the difficulty of comparing outcomes across studies and therefore of identifying patterns of relationships between cognitive abilities and personality traits. Despite the lack of strict comparability among measures, there has been a tendency in both the longitudinal and cross-sectional studies to classify cognitive measures in terms of whether they reflect fluid or crystallized intellectual processes. Fluid intelligence refers to those intellectual processes which are relatively independent of experience or acculturation, while crystallized intelligence refers to those intellectual processes which reflect specific training and general cultural experience (Horn, 1982). To the extent that it is possible, we will adopt this dichotomy in discussing personalitycognition interactions in the expectation of gaining some insight into the question of whether certain facets of cognition are more sensitive to personality dimensions than are others. Central Personality Traits and Cognition Introversion-extraversion. The two earliest studies examining this trait were carried out as part of the Iowa State longitudinal project (Owens, 1966; Schoenfeldt, 1973). The subjects were 96 men who had participated in all three testing occasions of the study, 1919, 1950 and 1961. On all three testing occasions, intelligence was measured by the Army Alpha Examination. In addition, at the 1961 interview subjects completed a life experience inventory that had been developed specifically for the study. Factor analysis of the inventory data yielded five factors, one of which was labelled introversionextraversion. The items loading on this factor were largely related to the social interactions of the individual, although items indicating tenseness, poor health and low energy level were also associated with greater introversion. Owens (1966) found that change in Army Alpha scores between 1950 and 1961 had a low association with introversion-extraversion; with subjects identified as more extraverted showing a more negative change in total Alpha score over the 11 years. Change in scores from 1919 to 1950 was not related to extraversion. Schoenfeldt (1973) used the five life experience factors to classify 84 subjects into seven different subgroups and then compared these subgroups in terms of their Army Alpha performance at each test occasion. He found that the only factor which discriminated significantly among the subgroups was socioeconomic status, with all significant group differences in test scores being between the two groups that were lowest in socioeconomic status and the other five groups. The two subgroups that had the highest and second highest mean scores on the extraversion factor performed worst and best respectively on the Army Alpha on all three test occasions.

Costa, Fozard, McCrae and Boss6 (1976), working with data on 969 men aged 25 to 82 in the Normative Aging Study (Bell, Rose & Damon, 1972), compared groups of subjects classified by age and personality characteristics. Personality characteristics, including introversion-extraversion, were defined based on cluster analysis of the responses to the items on the Cattell 16 PF Scale (Cattell, 1973). At all age levels extraverts performed more poorly than introverts on tasks that loaded on a Pattern

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Analysis Capability factor, identified by Costa et al. as a measure of fluid intelligence. In contrast, extraverts and introverts did not differ on tasks loading on an Information Processing Ability factor, identified as a measure of crystallized intelligence, nor did they differ on tasks loading on a manual dexterity factor. Costa et al. did not present specific information on effect size, but noted that the relation between pattern analysis and extraversion was small but significant, even after controlling for education and socioeconomic status. Arbuckle, Gold and Andres (1986) studied 285 women and men aged 65 to 93 and found, using multiple regression, that extraversion, measured by the Eysenck Personality Inventory (EPI, Eysenck & Eysenck, 1968), was negatively associated with scores on a composite index of episodic verbal memory, derived from measures of digit span, free recall of semantically related words and story recognition. Again, the magnitude of the association was low, although it was statistically significant even after controlling for other contextual variables including education and age. A longitudinal follow-up was made of Canadian male army veterans, examining intellectual change and continuity across 40 years from enlistment measures of intelligence obtained during WW I1 when the recruits had a mean age of 25 to intelligence at age 65. Low negative associations between extraversion, measured by the EPI and both nonverbal (picture completion, picture anomalies, paper formboard) and verbal (arithmetic, vocabulary, verbal analogies) subtests on the Canadian Army M Test of Intelligence and Aptititude were reported (Andres, Gold, Arbuckle, Schwartzman & Chaikelson, 1988; Gold, Andres, Arbuckle, Schwartzman & Chaikelson, under review). The verbal subtests were identified as measures of crystallized intelligence while the nonverbal subtests were identified as measures of fluid intelligence. The finding that extraversion had similar correlations with both types of subtests contrasts with Costa et a1.k (1976) finding that extraversion was related only to measures of fluid intelligence. As is the case throughout this literature, however, the tasks used to measure crystallized intelligence in the two studies were sufficiently different that the different outcomes may simply reflect the particular tasks used. Finally, Field, Schaie and Leino (1988a) rated previously obtained interviews in the Berkeley Older Generation Study for extraversion, defined in terms of the rated frankness, talkativeness and excitability of the interviewees. They found no relation between extraversion defined in this way and Wechsler Adult Intelligence Scale scores (WAIS, Wechsler, 1955) at either of two interviewing occasions. They also did not find any predictive relations either between extraversion in 1969 and WAIS scores in 1983 or between WAIS scores in 1969 and extraversion in 1983. In summary, studies that have defined introversion-extraversion on the basis of standardized personality measures have generally reported a low negative association of extraversion with cognitive functioning. In contrast, studies where extraversion was rated primarily on social interactions as reflected in interview or life experience data have generally not found an association with cognitive functioning. In discussing the results of their experiment, Costa et al. (1976) argued that it was probably the introspective capacity of introverts, not their social orientation, which had a facilitative effect on pattern analysis performance. That conclusion is consistent with the general pattern of positive and negative results and also with current theories relating

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introversion-extraversion to genetically-based differences in brain function (McCrae & Costa, 1984; Eysenck, 1982; Stelmack, 1981).

Emotional stability-neuroticism. The dimension of emotional stability-neuroticism has been linked to cognitive performance in a variety of non-clinical studies. Costa et al. (1976) reported that at all age levels anxious subjects, defined as those who scored above the mean on an Anxiety-Adjustment dimension, performed more poorly on the three cognitive factors of pattern analysis, manual dexterity, and information processing. After adjustment of the cognitive scores for education and socioeconomic status, anxiety still had a small negative association with pattern analysis and manual dexterity scores, but had no significant association with information-processing scores. Higher levels of neuroticism, as assessed by the EPI, were negatively associated with performance on the composite memory index used by Arbuckle et al. (1986). The magnitude of the association was low but significant even after controlling for other contextual variables. The Canadian veterans study (Gold et al., under review) also reported that neuroticism, again as assessed by the EPI,had a low negative association with current performance on both the verbal and nonverbal subtests of the Canadian Army "M" Test. Cavanaugh and Zuidema-Murphy (1986) used the General and Today versions of the Multiple Affect Adjective Checklist and the Speilberger State-Trait Anxiety Inventory in a comprehensive assessment of the effects of emotional state on memory in young and old adults. At both age levels performance on measures of word list and prose recall had a low negative association with measures of anxiety, hostility and depression as assessed by the two versions of the checklist, but was not significantly associated with state or trait anxiety as measured by the inventory. Contrary to the generally negative effects of anxiety on accuracy of performance on cognitive measures, Costa and Fozard (1978) found positive associations between aspects of emotionality and speed of responding on cognitive tasks. With age differences partialled out, nine out of twenty bivariate correlations between indices of emotionality and speed of verbal response in recognition memory and picture naming were significant at a low or moderate level, while one out of eight correlations between indices of emotionality and motor response speed on a reaction-time measure was significant at a low level. Faster responding by the more emotional subjects was attributed to their presumed higher level of arousal (Costa & Fozard, 1978). All the above studies used questionnaire measures of personality. In contrast, Hayslip (1988) used the Holtzman Inkblot Technique to assess personality dimensions in a sample of elderly individuals. One personality factor, labelled as sampling anxiety over ideational sufficiency, was identified as reflecting trait-like anxiety, ego threat and defensiveness. This factor loaded on the crystallized intelligence measures of vocabulary and abstruse analogies, but in the unexpected direction. Greater anxiety, perceived threat and defensiveness were associated with higher levels of crystallized intelligence. Hayslip interpreted this finding as possibly indicating that highly anxious individuals build up their accumulated knowledge as a defense against the threat posed

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by aging. However, the unusual direction of the association plus the uniqueness of the measure of personality used in this study suggest the need for replication of the findings. The clinical literature also reports a negative association between neuroticism and intellectual functioning. Nunn et al. (1974) classified a sample of community-dwelling men and women aged 65 and over as normal or neurotic on the basis of psychiatric assessments. They found that, after controlling for other potentially relevant predictors such as age, gender, social class, social activities and physical health, neurotics had lower scores than normals on the WAIS. The effect size was low, but WAIS scores were the single most important discriminating variable in terms of explained variance between the two groups. A six-year follow-up replicated this basic result and further showed that those individuals whose diagnosis changed from normal to neurotic over the period had tended to have lower initial WAIS scores for the normal group while those changing from neurotic to normal had tended to have higher initial WAIS scores for the neurotic group. These results led Nunn et al. to suggest that the direction of causality in the relationship was from intelligence to personality, with lower intelligence leading to poorer coping skills, poorer adjustment and thus a higher risk of neurosis. Another clinical study by Kleban, Lawton, Brody and Moss (1976) found that initial levels of anxiety or neuroticism were low positive predictors of decline in level of functioning in a sample of cognitively impaired women aged 70 to 94. Patients who had been rated as being emotionally stable at the outset showed little further impairment in level of functioning, based on behavioral assessments made four times weekly over a two-year period. In summary, most studies have found that over the adult age range greater emotionality or neuroticism is associated with poorer outcome on measures of cognitive functioning. Some research (Nunn et al., 1974; Gold et al., under review) indicates a generalized negative association of neuroticism with all intellectual abilities measured; however, other studies suggest that crystallized intellectual functions and speed of verbal responding may be less susceptible to the negative effects of neuroticism and may even be facilitated by higher anxiety levels (Costa & Fozard, 1978; Hayslip, 1988). This diverse pattern of effects is reminiscent of Berlyne's (1960) hypothesis that a given level of arousal can have positive effects on cognitive functioning at low levels of task complexity and negative effects at higher levels of task complexity. Openness. Openness to experience has been less frequently examined in relation to cognitive performance. Costa et al. (1976) reported that at all age levels men scoring above the mean on the openness to experience dimension had higher scores on measures of information processing and pattern analysis. After adjustment for education and socioeconomic status, the effect of openness was significant only for performance on pattern analysis. Costa and Fozard (1978) found that openness to experience was a low to moderate positive predictor on a subset of their measures of response speed on cognitive tasks. Examination of their results reveals that for each of the tasks they used, forced-choice motor response, verbal recognition memory, and picture-naming, greater openness was associated with faster responding on the task

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variant that placed the least demand on memory, but was unrelated to response speed under more demanding conditions.

No other studies have examined the personality trait of openness, but some studies have examined a construct that appears conceptually similar. The EPI includes several items which sample reluctance to admit behaviors that most people in actuality do perform. This "lie scale", interpreted variously as a measure of defensiveness, reluctance to admit flaws or adherence to socialized norms, seems thus to represent a lack of openness to experience. Arbuckle et al. (1986) reported that lie scale scores had a low negative association with performance on their memory index after adjustment for education and age. Similarly, the Canadian veterans study (Gold et al., under review) reported that lie scale scores had a low negative association with current performance on the verbal subtests of the Canadian Army M test, although not on the nonverbal subtests. To summarize, the limited number of studies in this area suggest that openness to experience can be a positive predictor of cognitive performance, particularly when task demands on processing capacity are relatively low. Agreeubleness. Only one study (Field, Schaie & Leino, 1988a) has examined the relation of cognitive function to the personality domain of agreeableness. Ratings of agreeableness were based on a reanalysis of the 1969 and 1983 interviews in the Berkeley Older Generation Study. Field et al. found that agreeableness had a low positive correlation with WAIS performance scores in 1983 and that change in WAIS performance scores from 1969to 1983predicted change in agreeableness. While these data suggest an association between agreeableness and cognitive functioning, they obviously need replication before any conclusions can be drawn. Furthermore, the extent to which agreeableness, as defined by Field et al., is consonant with other conceptualizations of the agreeableness construct should be determined. Field et al. described the dimension of agreeableness, as they had defined it, as being an approximation of the construct of ego integrity (Erickson, Erickson & Kivnick, 1986), although Field et al. believed that their measure did not capture the full complexity of Erikson's construct. Conscientiousness-carelessness.The last major personality trait, conscientiousness, has, as far as we can ascertain, not been studied in relation to cognition. Because conscientiousness, as defined by personality theorists, might be expected to increase the amount of effort expended on cognitive processing, it would clearly be of interest to examine it in the context of manipulations of task demands.

Conclusions: Central traits and cognition. From the review of the empirical findings on the relations between the central personality traits and cognition, several conclusions can be drawn. First, of the relatively few studies that have been published in this area, most have found some significant associations between personality dimensions and cognitive abilities, even after adjusting for demographic variables. However, the communality of variance has been low.

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Second, where associations have been found, studies that have compared different age groups have generally reported that the magnitude of association did not vary with age. Possible changes in magnitude of associations between personality traits and cognitive functions have been less frequently examined in response to other demographic variables such as gender and socioeconomic status. One exception (Arbuckle et al., 1986) reported that the relationships found between personality variables and memory did not differ with sex or education. Third, the studies which have not found significant relationships or which have found relationships that change with age have tended to be the ones where the personality trait has been defined on the basis of life experience or interview data rather than on the basis of standardized personality measures (Field et al., 1988a; Owens, 1966; Schoenfeldt, 1973). Finally, in studies which have examined relationships between a personality dimension and a number of cognitive measures, usually only a subset of the measures has been significantly related to the trait, although the general trend within each study has typically been fairly consistent across the different measures. The great variety of measures and the limited number of studies make it premature to draw firm conclusions about whether this variation across measures signifies any underlying pattern in the relations between central personality traits and cognitive functions. It does appear that, in the case of neuroticism, its emotional arousal component may have both positive and negative relations to cognitive functioning. Specifically,whereas neuroticism is predictive of poorer performance on most cognitive measures, the high arousal component appears to facilitate performance of well-learned verbal responses, particularly when speed, not accuracy, is the response measure. With respect to the other three traits for which there are data available, people who are more introverted, more open to experience and more agreeable perform a little better on most ability measures. These traits appear to be associated with better cognitive functioning in general rather than having a differentiated pattern of relationships depending upon the specific cognitive ability involved. However, considering the relatively small number of studies in the area and the fact that, within any given study, not all cognitive measures showed the same degree of sensitivity to the personality trait, this generalization should be considered a hypothesis for further testing rather than an established finding. Although the literature provides clear evidence of relationships between central personality traits and cognitive abilities, there is a further question that can be raised regarding the interpretation of these relationships. With the exception of introversionextraversion, the other central personality dimensions, neuroticism, openness to experience, agreeableness and conscientiousness, can be characterized as relating to personal and social adjustment. The results of the studies can be interpreted as indicating that people who are more successful at developing a well-adjusted personality are also better at developing cognitive competence. However, the results could also be interpreted as indicating that more cognitively competent people present themselves on personality assessments in more desirable ways. This latter possibility raises the question of whether the observed relationships between the central traits and cognition are not simply artifacts. In answer, it should be noted that the presence of a social desirability set cannot readily explain the association between cognition and introversion-extraversion since neither pole of this dimension is necessarily considered as being more desirable than the other. Nor, in view of the variety of measures,

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designs and assorted controls for education, occupation and, in some cases, social desirability, that have been used in the various studies, can the argument that the relationship is artifactual be considered to be convincing. Peripheral Personality Traits and Cognition Researchers have also examined the relations between cognitive abilities and three traits that are not central constructs in personality theory, field independencedependence, flexibility-rigidityand perceived locus of control. These traits are typically characterized as reflecting personality dimensions, although it could be argued that they would be more appropriately classified as cognitive style variables or even as alternative indices of cognitive abilities. Thus, there are two questions to be posed about this research literature, first, the relations of these traits to cognition and second, their validity as personality constructs.

Field independence-dependence. Hooper and colleagues (Hooper, Hooper & Colbert, 1984; Hooper, Hooper, Colbert & McMahan, 1986) examined the relationships between the performance of adults aged 17 to 68 on a wide range of tasks, most of which were measures of fluid intellectual abilities, and four personality dimensions, field independence-dependence, locus of perceived control, self-esteem and tolerance for ambiguity. The cognitive measures included tests of verbal and visual memory, a number of measures of logical reasoning ability and the Raven Progressive Matrices (Raven, 1962). The only personality variable that was consistently related to performance on the cognitive measures was field independence-dependence, as assessed by the Embedded Figures Task developed by Witkin, Cox, Faterson, Goodenough and Karp (1962). Greater field independence predicted better performance on all measures except vocabulary. The correlations between field independence and cognitive measures were low to moderate, being larger for the Raven Progressive Matrices and for some of the logical reasoning measures.

Flexibility-rigidity.The Seattle Longitudinal project has been a source of data for the study of relations between flexibility-rigidityand cognitive abilities, (e.g., Schaie 1983a; 1984). The measure of flexibility is the Test of Behavioral Rigidity which contains measures of speed of written response under no interference and interference conditions as well as a true-false self-assessment of flexible behavior, flexible attitudes and social responsibility. From this test, factor scores are computed for three dimensions, usually referred to as psychomotor speed, motor-cognitive rigidity or flexibility, and personality-perceptual rigidity or attitudinal flexibility (Gribbin, Schaie & Parham, 1980; Johnson, Lunneborg & Schaie, 1987; Schaie, 1984). With respect to concurrent relations between flexibility and cognitive abilities, Schaie (1983a) reported that for both men and women the three flexibility measures were positively and significantly correlated with the scores on most dimensions of the SRA Primary Mental Abilities test (Thurstone 8z Thurstone, 1949). The magnitudes of the associations were moderate for psychomotor speed and motor cognitive flexibility and somewhat lower for attitudinal flexibility. In time-lagged comparisons with the data combined across cohorts and periods, flexibility, as indexed by scores on the three factors at Time 1, was found to be a moderate positive predictor of performance 21 years later on measures of verbal meaning, inductive reasoning, number and word

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fluency, with the multiple regression coefficients ranging between .52 and .61 (Schaie, 1984). When specific types of flexibility were considered, the particular type of flexibility that had the strongest association with later performance on measures of both flexibility and cognitive functioning differed depending on what part of the lifespan was being considered. For example, in cross-lagged correlations of flexibility scores at age 35 with cognitive measures and flexibility scores at age 49, only attitudinal flexibility emerged as a predictor, being significantly predictive of spatial orientation and inductive reasoning abilities and of motor-cognitive flexibility at age 49. In contrast, in cross-lagged correlations at age 63, with the same measures and the same time interval, only motor-cognitive flexibility emerged as significant, predicting the cognitive abilities of number and word fluency and the flexibility dimension of perceptual motor speed at age 77 (Schaie, 1984). One question that can be asked about the Seattle project is whether the variation in statistically significant relationships across cognitive measures with personality signifies any underlying pattern in the relations between the different dimensions of flexibility and cognitive functioning. Although in most studies done by Schaie and his colleagues only a subset of the cognitive measures has been found to be significantly correlated with a given flexibility dimension, the general trend of better performance being associated with greater flexibility has been fairly consistent across the different measures. Thus, greater flexibility appears to be associated with generally better cognitive functioning over the adult age range.

Locus of control. The construct of perceived locus of control has evolved from a unidimensional construct (Rotter, 1966) to a multidimensional one, assessing internal attributions of responsibility, belief in chance determinants of outcomes and belief that powerful others determine outcomes (Levenson, 1974). Studies examining the relationship between perception of locus of control and ability have reported inconsistent results. People with an internal locus of control have been reported to have higher scores on measures of verbal intelligence (Brown & Granick, 1983; Gold et al., under review), but Arbuckle et al. (1986) found no association between locus of control scores and verbal memory measures. Sebby and Papini (1987) reported a low positive association between internal locus of control and formal reasoning, while essentially no association was found between the same measures by Hooper et al. (1984). Lachman (1983; 1986a; 1986b) has argued that traditional generalized measures of locus of control and cognition, assessed as separate domains (transcontextual measures), are relatively ineffective in specifying the relationships between them and that what is needed are measures specifically designed to sample their interface (contextual measures). Thus, Lachman and her colleagues have developed the Personality in Intellectual-Aging Context Inventory (PIC), which assesses the beliefs of individuals about their own intellectual competence within the three domains of internal responsibility, chance, and powerful others, as well as within three other domains, achievement in cognitive work, anxiety associated with intellectual performance, and attitude toward their own aging. Lachman, Baltes, Nesselroade and Willis (1982) found that, whereas the transcontextual measures of personality (locus of control, achievement, anxiety and

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morale) showed generally insignificant associations with factor scores on the four ability dimensions of general reasoning, memory span, crystallized knowledge and perceptual speed, the contextual PIC measures of personality variables were more strongly and consistently related to cognitive performance. Greater internality on each of the three PIC measures of powerful others, achievement, and anxiety, was a positive predictor of performance on all four cognitive ability measures. Scores on the PIC internal and attitude toward own aging scores were positive predictors of general reasoning, memory span and crystallized knowledge while chance scores were negative predictors of general reasoning and crystallized knowledge. The effect sizes varied from low to moderate with the coefficients ranging from .25 to .54. In later research, Lachman (1986a) found similar results. For both college students and older adults aged 64 to 91 the generalized measures of internality, chance and powerful others did not account for significant proportions of the variance in two measures selected as indices of fluid and crystallized intelligence, respectively. For the older adults, but not the students, however, the corresponding PIC measures significantly increased the explained variance for both outcome measures, the effect size being moderate in both cases. Cornelius and Caspi (1986) studied the same two outcome measures in relation to other PIC dimensions. The fluid ability measure correlated with PIC factor scores assessing intellectual self-efficacy and both the fluid and crystallized ability measures correlated with concern about intellectual aging.

Validity as personaliw measures. The three constructs, field independence, flexibility, and domain-specific locus of control, have two characteristics in common. First, their concurrent and predictive relations with cognitive abilities are generally stronger and more consistent than is true of either the central personality traits or the generalized locus of control measures. Second, as operationally defined, all three constructs contain cognitive elements. Field independence, as measured by the Witkins test, is considered in other contexts to be a measure of spatial ability, not personality (e.g., MacLeod, Jackson & Palmer, 1986; McGee, 1979). Consistent with the idea that field independence is a cognitive measure, not a personality measure, Hooper et al. (1984) found no relationship between it and their other personality measures, self-esteem, locus of perceived control and tolerance for ambiguity. Flexibility, as assessed by the Test of Behavioral Rigidity, is in part measured by performance on what would normally be considered cognitive tasks. Further, at least one of the three dimensions identified by the test, psychomotor speed, is a commonly used measure of informationprocessing, as in Costa and Fozard (1978). Finally, a measure of locus of control that is restricted to individuals’ attributions about their own intellectual abilities has an obvious cognitive component. In many respects the type of self-analysis required by the PIC measures is similar to the kind of self-appraisals of cognitive processing that is demanded by the measures of metamemory developed by cognitive researchers (see Lovelace, Chapter 6 of this volume). Although each of these traits does have cognitive aspects which could serve to explain their relatively strong correlations with cognitive measures, they nonetheless each also have personality aspects. Attitudinal flexibility, for example, is measured by selfassessment items, comparable to standard personality inventories (Johnson et al., 1987). The question of how these constructs relate to more typical measures of personality is largely unknown, since, with the exception of field independence, we

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have no data showing how their measures correlate with central personality traits. As the literature has evolved they are increasingly being referred to as cognitive style variables rather than personality variables, but this terminology in some ways only serves to disguise the fact that what we really are dealing with here are specific examples of personality-cognition interactions, such as self-attributions or beliefs (cf. Cavanaugh & Green, Chapter 7 of this volume). As such, they point the way for developing a more general model of personality-cognition interactions. Our guess is that these so-called peripheral personality traits are related to central personality traits in specific ways that have yet to be identified. Aging and Personality-Cognition Interactions

In 1976 Costa et al. speculated that any associations between personality and cognition would be minimal in young adulthood but might tend to increase with age. Although various studies have examined developmental changes in personalitycognition interactions, there is still no clear answer to the question of whether any of the associations that exist between personality and cognition change with age during the adult years. Considering first the central personality traits, the majority of studies have found no evidence of changes with age in the relationships between these traits and cognitive functioning. The data from cross-sectional comparisons of different age groups have been particularly consistent in this regard. The associations between personality variables and cognition appear to be stable over adulthood (Costa & Fozard, 1978; Arbuckle, Gold & Andres, 1986; Cavanaugh & Zuidema-Murphy, 1986). Evaluation of the findings from longitudinal studies is more complex because one must consider changes in concurrent trait-ability correlations across age levels and in predictive associations across different stages of adulthood. The two longitudinal studies that have examined relationships between central traits and cognitive abilities have had inconclusive outcomes, reporting evidence of age changes in some relationships but not in others (Owens, 1966; Field et al, 1988a). Until the positive findings can be replicated in longitudinal studies using standardized personality measures, however, the best supported conclusion appears to be that the direct associations between central traits and cognition remain relatively stable with age. Age appears to be of somewhat greater importance for the peripheral personality traits, with the exception of field independence where the relations between personality and cognition appear to be similar at all adult ages (Hooper et al., 1984). In the case of flexibility we have already reported the different patterns emerging between different types of flexibility and abilities over the adult age range and that the finding that flexibility promotes intellectual functioning appears to be sensitive to age variance. Internal locus of control within the intellectual domain also appears to have agespecific relations with cognition, with internality on the PIC predicting the performance of older adults but not younger ones (Lachman, 1986a). Internality, as assessed by general measures of locus of control does not appear to be affected by age, but PIC scores appear to be age sensitive, with the direction of change depending on the specific domain of the PIC subscale. However, Cornelius and Caspi (1986) found that partialling out the effects of ability and generalized locus of control scores eliminated any significant age-related changes in PIC scores. Lachrnan argues that for the elderly

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perceived control varies, within the same time frame, across specific areas of functioning. Consequently, the strength of association between locus of control and cognition would vary with the age and type of measure used, being strongest for elderly people whose locus of control is assessed by a differentiated measure. Causality of Relations Between Personality and Cognition Cognitive Style Variables

Because the most extensive work on personality-cognition interactions over the lifespan has been with the cognitive style dimensions of flexibility and locus of control, it is not surprising that this literature has also provided the most research on possible causal directions in these relationships. Flexibility. With respect to flexibility, time-lagged correlations between flexibility scores at Time 1 and cognition scores 14 to 21 years later are more likely to be statistically reliable than are those between cognitive measures and subsequent flexibility scores (Schaie, 1983a). These data thus suggest that the direction of causality is from personality, or at least cognitive style, to cognition. Other analyses of the Seattle data set, while not invalidating this conclusion, suggest that this picture is oversimplified and that much of the effect of flexibility on cognitive functioning may be indirect, supporting an interactive model in which mutually causative agents interact. Specifically, people who are behaviorally flexible in their early adult years are believed to develop lifestyles which tend to facilitate the practice and development of intellectual abilities (Gribbin, Schaie, & Parham, 1980). The differential development of intellectual functioning elicited by various environments, in turn, serves to explain some of the variation among individuals in the maintenance and rate of decline of cognitive skills in early old age (Schaie, 1984). Schaie’s contention that flexibility is a requirement for the creation of early and mid-adult occupational and lifestyle contexts that promote intellectual development during the later adult years indicates that flexibility may be particularily important at early stages of the adult lifespan. Presumably, flexibility requires years of practice to produce beneficial effects on intellectual maintenance. At older age levels, however, attitudinal flexibility seems to have only a weak effect upon maintaining a complex lifestyle but does appear to be important as a mediator in helping individuals who are experiencing high levels of stress cope with health problems, which in turn predict intellectual decline (Field, Schaie & Leino, 1988b). It appears that flexibility in the early years has an indirect positive effect on subsequent intellectual development through the creation of a complex lifestyle that leads to practice in cognitive skills while flexibility in later years has an indirect positive effect on intellectual development by acting as a buffer variable that helps mitigate the negative effects of health problems and stress on daily activities and lifestyle.

Locus of control. Lachman (1983) examined the causal implications of cognitionpersonality interactions from a somewhat different theoretical perspective. Her research falls within the approach of the cognitive theory of personality (Thomae, 1970), which argues that perceived change rather than actual change is the best

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predictor of behavior, and within the social cognitive theoretical approach of Bandura (1989) with its emphasis on self-efficacy beliefs as determinants of motivation, behavior and affect. Lachman used causal modelling techniques in studying change over a two-year period in measures of general locus of control, domain-specific locus of control as indexed by the PIC, and cognitive abilities. The only two dimensions that exhibited sufficient differential change over the two years to be used in the assessment of causality were the cognitive dimension of memory and the PIC dimension of intellectual self-efficacy. As defined by the PIC, intellectual self-efficacy is a composite factor that reflects perceived intellectual competence, perceived control over one's intellectual functioning and desire to maintain one's intellectual competence. The results of causal modelling for memory scores indicated that the best predictor of Time 2 memory was Time 1 memory: adding either the generalized or the domainspecific locus of control measures did not improve the fit of the model. The results of causal modelling for intellectual self-efficacy revealed that scores at Time 2 were best predicted by a model that included Time 1 measures of fluid ability and generalized internality in addition to the Time 1 efficacy scores. Individuals who had higher scores initially on the measures of fluid intelligence and internal locus of control changed in a more positive direction in perceived efficacy than did those who scored lower initially. Thus, both ability and perception of oneself as being in control were necessary to retain and improve a positive sense of intellectual efficacy in old age, or, in other words, a cognitive dimension and a general locus of control dimension both appeared causally related to change in a cognitive style dimension. In contrast, neither the general locus of control dimensions nor the cognitive style dimensions predicted change in the cognitive measure of memory.

Central Personality Traits Although the question of direction of causality has been considered in studies of interactions between the central personality traits and measures of cognitive functioning (Costa et al. 1976; Costa & Fozard, 1978; Arbuckle et al., 1986; Gold et al., under review), there has not been the same type of systematic testing of different causal models. In an early attempt to develop a rationale for examining causal relations between personality and cognition, Costa et al. (1976) speculated that individuals' enduring personality characteristics would affect their cognitive style of coping and dealing with everyday problems of obtaining, using and remembering information and consequently, these characteristics would mediate cognitive functioning. Thus, the direction of causality was, for Costa et al., from personality to cognition. As an example, they argued that introversion was associated with better performance on their pattern analysis task because the introspective capacity of more introverted individuals assisted them in making the complex judgments involved in pattern analysis which requires the making of comparisons of current perceptions against internal schema. Similarly, they argued that openness to experience led to better pattern analysis performance because a greater willingness to accept and process new experiences would have beneficial effects on the development of information-processing ability. However, because of the

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lack of a strong and consistent pattern of relations in their findings, Costa and his colleagues cautioned against expecting a simplistic translation of personality into performance on measures of cognitive function. Stability of central traits and cognitive abilities. In considering possible causal relations between personality and cognition, one should consider not only the few studies specifically examining causal relations between cognition and central personality traits, but also the studies of the stability of personality and cognition across the adult lifespan. That is, any normative age-related or developmental changes in function in personality and cognition that occur in the adult years should also be considered when conceptualizing possible causal relations between the two over the course of the adult lifespan. If the two domains remained constant in their functions over time, any causal relations between them would be extremely difficult to untangle. However, changes with advancing age in the continuity of either personality or cognition could very well alter the relations that exist between the two. Thus, the existence of age-related changes in the continuity of personality or cognitive function might provide a basis for testable predictions about their associations which would clarify some of their causal relations.

Dominant views concerning the stability of personality have varied in the recent past (Moss & Susman, 1980). At the risk of over-simplifying the many possible types of personality traits and the various developmental, cohort and cultural influences upon them (Schaie & Parham, 1976), we believe that, due primarily to the impressive results of longitudinal studies of personality, (Conley, 1984, 1985; Costa & McCrae, 1986; Costa, McCrae & Arenberg, 1980; McCrae & Costa, 1982; McCrae & Costa, 1984; Stevens & Truss, 1985;), the prevailing view strongly emphasizes the stability of central personality traits, such as neuroticism and introversion-extraversion, for which there appear to be some genetic basis (Plomin, Pedersen, McClearn, Nesselroade & Bergeman, 1988). Although there is evidence for cohort effects in personality scores (Stevens & Truss, 1985; Woodruff & Birren, 1972), the research evidence largely emphasizes continuity of these personality traits. For example, Aldwin, Spiro, Levenson and Boss6 (1989) reported that for the male subjects in the Boston Normative Aging Study, psychological symptoms were basically stable, with the rate of change being one new symptom every 28 years. In contrast to the relative stability of central personality traits over adulthood, cognitive abilities tend to decline with age. The amount of decline observed is typically less in longitudinal comparisons, but even in longitudinal studies declines on psychometric measures of intelligence have been reported to begin in the late 50s and to reach substantial magnitudes by the late 70s and early 80s (Schaie, 1983a; 1989). Certain cognitive functions, such as fluid abilities, effortful processing and explicit memory, may be more age-sensitive than others, such as crystallized abilities, automatic processing and implicit memory, although much of the supporting research on these task differences has been cross-sectional in design and has typically involved subjects under 75 years of age in the oldest group. Longitudinal data indicate that although decline in fluid abilities, for example, begins at an earlier age, crystallized abilities decline precipitously in advanced old age (Schaie, 1983b).

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Although the mean performance on cognitive measures tends to decline with age, test-retest reliabilities tend to be high for psychometric data. For example, an examination of stability of performance on the Canadian Army M Test showed significant decline over 40 years in total scores coupled with a test-retest reliability of .76 (Schwartzman, Gold, Andres, Arbuckle, & Chaikelson, 1987). Data such as these suggest that decline in cognitive functions with age is relatively ubiquitous across individuals, although there is also good evidence from longitudinal work that the extent of decline varies widely across individuals and that a substantial minority of elderly individuals remain stable or even improve on measures of cognitive functioning, (e.g., Field et al. 1988a,b; Jarvik and Bank 1983). Thus, our reading of the literature indicates that at the older age ranges normal individuals generally retain their basic personality features, responding emotionally and socially in ways consistent with their past patterns of response. In contrast, most normal healthy older individuals show declines in speed of intellectual performance, declines in fluid abilities and, to a greater or lesser extent, some decline in crystallized abilities. Central Personality Traits and Cognition: a Causal Model

We propose the following model of personality-cognition relations in old age, incorporatingthe central personality variables of introversion-extraversion, neuroticism, openness to experience, agreeableness and conscientiousness. We hypothesize that most of these traits have both direct and indirect effects on cognition (See Table 13.1). This model reflects the assumption that both central personality traits and cognitive abilities help determine the characteristic ways that individuals engage in the environment in which they find themselves and create environmental contexts that are as favorable to them as possible. In addition, since the biological and environmental determinants of personality and cognition are believed to differ in many ways, the amount of variance shared between them should be small. Effects of cognitive function on personality are believed to occur primarily relatively early in life, especially within the educational system, when individuals receive systematic social feedback about their cognitive competence. The ramifications of such feedback on the development of self-concept, self-efficacy and scholastic and occupational ambition and avocational interests are manifest during the late childhood and adolescent years. Similarily, social feedback about and self-evaluation of an individual's cognitive performance in old age, a stage of life when many people are apprehensive about possible declines in memory and other cognitive skills, can have similar results on self-efficacy, in the manner postulated by Lachman (1983). The central personality traits, with their large emotional expressive components and possible biological bases, are believed to be less influenced in adulthood by feedback about cognitive competence. However, with increasing age, as changes in central nervous system processes and the lessening demand of environmental contexts result in some decline of cognitive performance, we believe that personality will become of increasing importance in determining cognitive performance, especially since these traits appear to be stable and thus have years in which their cumulative effects on cognition can emerge. These effects appear to be general, that is, at the older age

Table 13.1 Causal Model of Central Personality Trait Influence on Cognition* Personality Trait

Direct Effects

Introversion

1.

2.

3. Neuroticism

1.

2.

3.

greater introspective capacity resulting in more thorough processing greater tolerance for repetition less distractable in attending negative affect and emotional lability reduce attention ego centeredness reduces ability to be task-centered high arousal increases speed of response at lower levels of task complexity

Indirect Effects

Cognitive Performance

1. lesser sociability

The direct effects should facilitate cognitive abilities; the indirect effects are mixed

resulting in receiving less social stimulation and poorer social problem-solving skills 2. less dependence on social support in stressful circumstances 1. less satisfaction with

2.

3. 4. 5.

social support received less social support received poorer socioeconomic status lesser life satisfaction poorer health

Reaction time may be facilitated; other cognitive abilities detrimentally affected

Table 13.1 (continued) Personality Trait

Direct Effects

Openness to Experience

1.

2.

greater receptivity to and practice in processing a variety of stimuli extreme openness can lead to distractability

Indirect Effects

Cognitive Performance

1. greater flexibility

cognitive abilities in general facilitated

in processing new information 2. larger knowledge base

1. more likely to be able

Agreeableness

to have positive social interactions, resulting in receiving more social stimulation and social support Conscientiousness

*

1.

more effort and care resulting in more thorough processing in cognitive tasks

1. more ability to organize relevant knowledge; that is, a better development of “learning to learn” skills with correspondingly reduced dependence on contextual support in encoding

e

ii

cognitive abilities in general facilitated

cognitive abilities in general facilitated

To facilitate the clarity of predictions, we have discussed the personality factors in relation to only one pole of the dimensions.

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levels specific cognitive skills do not appear to be differentially linked to personality traits, the one possible exception being the link between cognitive performance and the emotional arousal component of neuroticism. Rather these personality traits appear to have a general facilitative or detrimental effect on cognition. The effects of personality traits on cognitive abilities can be not only positive or negative, they can also be direct or indirect. Certain aspects of personality are believed to lend themselves directly to more adequate cognitive performance. For example, the more conscientious individual should approach cognitive tasks with more care and attention, with this greater effort resulting in better performance. Personality traits are also believed to have indirect effects on cognition, as mediated by social, adjustment and occupational factors. Thus, the model we are proposing incorporates contextual interactive features and specifies that people with certain personality traits are more or less likely to create and retain supportive environments that maintain good cognitive function and either retard or accelerate the decline of cognitive abilities with age. In addition, personality traits can affect, both directly and indirectly, the development of adaptive selective optimization compensatory functions (Dixon & Bakes, 1986; Salthouse, 1988) which are important in off-setting declines in cognitive abilities that reflect the aging of biological capabilities. Throughout the mid-adult years, the relative stability of function in both cognition and personality, with their largely different determinants, should result in a pattern of central personality variables having consistent, but small, direct effects on cognitive function. However, the indirect effects of stable personality variables on cognitive functioning as individuals age and begin experiencing age-related declines in cognitive abilities should become increasingly accumulative and more important. These same personality traits will probably make their possessors more or less likely to benefit from interventions, both formal institution-based programs and self-initiated, informal efforts. Consequently, the central personality variables will act as mediators on the modifiability of agerelated declines. This line of thinking reflects prior theoretical and empirical work by personality researchers. Introversion-extraversion is regarded as being largely genetically based and is assumed to reflect differences in neurological processing (McCrae & Costa, 1984; Eysenck, 1982; Stelmack, 1981). Introverts are primarily differentiated from more extraverted individuals by lesser sociability and impulsivity and by a greater capacity to be absorbed by their own thoughts and ideas. Extraverts are believed to be more distractable and should perform less adequately on cognitive measures which require persistence and error checking and should be less tolerant of the repetitious practice necessary for the development of certain skills. Consequently, the greater introspective ability of more introverted individuals should lead to more adequate depth of processing, greater persistence and attention in cognitive work. Extraversion should have an indirect positive effect on cognitive performance through the greater sociability of extraverted individuals, which should lead them to engage more actively in a greater variety of social activities, resulting in their receiving more social stimulation. Their greater familiarity with and ease in such situations should lead them to become more adept at social problem-solving compared to more introverted people. But this positive effect of extraversion, although it can make them more receptive to group interventions designed to improve cognitive performance, can also become a negative one inasmuch as extraverts are more dependent upon social support in

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maintaining morale (Duckitt, 1984) and thus, in the absence of social support, are potentially more vulnerable to the depressing effects of stressors, which in turn can impair cognitive function. Neuroticism is a trait that is expected to have, with few exceptions, negative direct effects on cognitive function. The generation and experiencing of negative affect and the emotional lability (Costa & McCrae, 1986) which characterize higher levels of neuroticism even at non-clinical levels, are not conducive to the maintenance of attention and motivation necessary for the development and retention of cognitive abilities. The greater self-preoccupationof neurotics should be a distracting influence, reducing their ability to focus their energies and efforts on task demands. The higher levels of arousal associated with neuroticism may directly lead to faster responding in certain situations involving minimal response competition and, based on Hayslip's (1988) findings, may have an indirect positive effect on cognition by leading individuals to compensate more thoroughly with knowledge-based skills for declining abilities to learn, However, the indirect effects of neuroticism should primarily result in the experience of a more difficult old age. The greater anxiety and poorer adjustment of more neurotic individuals should lead them to experience more life stress and to cope less well with it. Other indirect effects of neuroticism, mediated by interpersonal relations and social functioning, should also be negative. Higher levels of neuroticism have been found to predict lesser satisfaction with social support, family, friends, lower life satisfaction, poorer health and poorer socioeconomic status later in life (Costa, McCrae & Norris, 1981; Gold et al., in review), which have been found to be negatively associated with cognitive performance. Openness to experience should have consistently positive direct and indirect effects on cognition, although it is possible that extremely high scoring individuals might be too distractable to concentrate effectively on task performance. At more moderate levels, the greater practice in processing a variety of stimuli should directly enhance cognitive performance, while such practice should also increase intellectual and behavioral flexibility and the individual's accumulation of knowledge. Both flexibility and knowledge should, in turn, help to maintain the cognitive performance of older individuals. The construct of openness to experience appears to us to be conceptually similar to Schaie's construct, flexibility. This is an empirical question that should be examined and, if the results do indicate a substantial overlap between the two constructs, then the findings of the Seattle project concerning flexibility could be interpreted as substantiating the importance of openness to experience for cognitive performance. Agreeableness does not appear to have any direct effects, either positive or negative, on cognitive performance. Rather, whatever influence it has on cognition appears to be positive but to result through indirect processes. More agreeable people are more likely to be successful in their habitual interactions with other people and to have more extensive social networks. Consequently, their cognitive performance should benefit from the extra social stimulation they receive compared to their less agreeable peers. In addition, more agreeable people should also benefit by receiving more adequate social support from their friends and families in high stress situations, which should help to maintain their cognitive skills by mitigating the negative experience of stress.

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Conscientiousness should have positive direct and indirect effects on cognitive skills. The greater conscientiousness of individuals should result in better performance in most task situations, particularly in situations and on tasks that demand more cognitive effort and attention. In addition, more conscientious individuals should also benefit indirectly by being more likely to use elaborative processing to organize and maintain relevant information, thus making it more accessible to them later. For example, older conscientious individuals should be more likely to develop the use of more extensive cues as aids to memory and task performance than less conscientious people. Because there is a considerable literature relating age-related declines in memory to "decreased efficiency in verbal elaboration, visual imagery, organization and failure to spontaneously process 'deeply"' (Poon, 1985, p. 443), studies of how the personality trait of conscientiousness relates to elaborative processing at different age levels could be of great interest. The model obviously has many testable hypotheses and it is hoped that it will generate research in the area. Of particular interest would be studies examining the efficacy and magnitude of the indirect effects, mediated by lifestyle and environmental contexts, of central personality traits on cognitive function at various age levels. Such research has not been done. Conclusions

Certain personality traits have consistent, but not large, associations with general cognitive performance. It is not surprising that the effects of personality on cognition are relatively small, since cognitive functioning is multidetermined and personality effects are just one set among many other factors. The effects of central personality traits appear to be nonspecific inasmuch as, with the exception of neuroticism, the pattern of relations does not appear to vary in any systematic way with the cognitive abilities studied. In this respect the personality trait studied seems to be of greater importance than the cognitive ability. The direct effects of personality traits on cognition appear to be consistent across the adult age range, while the indirect effects are expected to increase with age. An exception to this prediction are the effects of behavioral flexibility on cognition which require the creation of a lifestyle to mediate them. Consequently, behavioral flexibility seems to be of most importance in the early and mid-adult years. The model of causality that we have proposed emphasizes a unidirectional flow, but the model should be viewed as supplemented by the interactive effects of less central personality variables, the cognitive style variables of internality and flexibility. Our model helps to explain the enormous range of individual differences in cognition found in normal elderly people, but does not explain personality variability. More research is required not just to test the model but also to determine if personality-cognition relations function in the same way for women and men. Few studies have examined this issue even though the differences in the life span ' development histories of males and females result in their having somewhat different patterns of personality traits and different functional relations with age (Schaie, 1983a), which can lead to different effects on cognition. For example, at younger ages the

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associations between personality and cognitive performance appear to be somewhat different for males and females (Barton, Cattell & Silverman, 1975; Ozer, 1987). Finally, it must again be emphasized that a vast variety of measures have been used in both the personality and cognitive domain. Some researchers have used cognitive measures with established ecological validity but little specificity. Other research has been done using laboratory measures of cognitive abilities which have limited, if any, psychometric data available. Similarly, there has been limited comparability of personality measures used by different researchers. An important priority in this area of research is to establish the extent of overlap, both conceptually and empirically, within the various measures of personality and cognition. References Aldwin, C.M., Spiro 111, A., Levenson, M.R., & BossC, R. (1989). Longitudinal findings from the normative aging study: 1. Does mental health change with age? Psychology and Aging, 4, 295-306. Andres, D., Gold, D., Arbuckle, T., Schwartzman, A. & Chaikelson, J. (1988, November). The effects of introversion-extraversion on intellectual functioning in aging men. Presented at the Annual Meeting of the Gerontological Society of America San Francisco. Gold, D., & Andres, A. (1986). Cognitive functioning of older people Arbuckle, T.Y., in relation to social and personality variables. Psychologv and Aging, 1, 55-62. Atchley, R.C. (1989). A continuity theory of normal aging. The Gerontologist, 29, 183190. Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44, 1175-1184. Barton, K., Cattell, R.B., & Silverman, W. (1974). Personality correlates of verbal and spatial ability. Social Behavior and Persona/i&, 2, 113-118. Bell, B., Rose, C.L., & Damon, A. (1972). The normative aging study: an interdisciplinary and longitudinal study of health and aging. Aging and Human Development, 3, 5-17. Berlyne, D.E. (1960). Conflict, arousal and curiosity. New York: McGraw-Hill. Borkenau, P. (1988). The multiple classification of acts and the big five factors of personality. Journal of Research in Personality, 22, 337-352. Brown, B.R., & Granick, S. (1983). Cognitive and psychosocial differences between I and E locus of control in aged persons. Experimental Aging Research, 9, 107-110. Cattell, R.B. (1973). Personality and mood by questionnaire. San Francisco: JosseyBass. Cavanaugh, J.C., & Zuidema-Murphy, N. (1986). Personality and metamemory correlates of memory performance in younger and older adults. Educational Gerontologv, 12, 385-394. Conley, J.J. (1984). Longitudinal consistency of adult personality: Self-reported psychological characteristics across 45 years. Journal of Personality and Social Psychology, 47, 1325-1333. Conley, J.J. (1985). Longitudinal stability of personality traits: A multitraitmultimethod-multioccasionanalysis. Journal of Personality and Social Psychology, 49, 1266-1282.

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Cornelius, S.W. & Caspi, A. (1986). Self-perceptions of intellectual control and aging. Educational Gerontology, 12, 345-357. Costa, P.T., Jr. & Fozard, J.L. (1978). Remembering the person: Relations of individual difference variables to memory. Experimental Aging Research, 4, 291304. Costa, P.T. Jr., Fozard, J.L., McCrae, R.R., & B o d , R. (1976). Relations of age and personality dimensions to cognitive ability factors. Journal ojGerontology, 31, 663669. Costa, P.T., Jr. & McCrae, R.R. (1986). Personality stability and its implications for clinical psychology. Clinical Psychology Review, 6, 407-423. Costa, P.T. Jr., McCrae, R.R., & Arenberg, D. (1980). Enduring dispositions in adult males. Journal of Personality and Social Psychology, 38, 793-800. Costa, P.T., Jr., McCrae, R.R., & Norris, A.H. (1981). Personal adjustment to aging: Longitudinal prediction from neuroticism and extraversion. Journal ojGerontology, 36, 78-85. Dannefer, D. (1984). Adult development and social theory: A paradigmatic reappraisal. American Sociological Review, 49, 100-116. Datan, N., Rodeheaver, D., & Hughes, F. (1987). Adult development and aging. Annual Review of Psychology, 38, 153-180. Digman, J.M & Inouye, J. (1986). Further specification of the five robust factors of personality. Journal of Personality and Social Psyckology, 50, 116-123. Dixon, R.A., & Baltes, P.B. (1986). Toward life-span research on the functions and pragmatics of intelligence. In R. J. Sternberg & R. K. Wagner (Eds.), Practical intelligence: Nature and origins of competence in the everyday world, (pp. 203-235). London: Cambridge University Press. Duckitt, J. (1984). Social support, personality and the prediction of psychological distress: An interactionist approach. Journal of Clinical Psychology, 40, 1199-1205. Erickson, E. H., Erickson, J. M., & Kivnick, H. Q. (1986). Vital involvement in old age. New York: W. W. Norton. Eysenck, H.J. (1982). A model for intelligence. New York: Springer. Eysenck, H.J. & Eysenck, S.B.G. (1968). Manual: Eysenck personality inventory. San Diego, C A Educational and Industrial Testing Service. Field, D., Schaie, K.W., & Leino, E.V. (1988a, November). Continuity in intellectual functioning: The role of personality. Presented at the meeting of the Gerontological Society of America, San Francisco, CA. Field, D., Schaie, K.W., & Leino, E.V. (1988b). Continuity in intellectual functioning: The role of self-reported health. Psychologv and Aging, 3, 385-392. Gold, D., Andres, D., Arbuckle, T., Schwartzman, A., & Chaikelson, J. (Under Review). Early and concurrent predictors of intelligence in elderly men: A study of World War I1 Veterans. Gribbon, K., Schaie, K.W. & Parham, I. (1980). Complexity of life style and maintenance of intellectual abilities. Journal of Social Issues, 36, 41-46. Hayslip, Jr., B. (1988). Personality-ability relationships in aged adults. Journal of Gerontologv, 43, 79-84. Hooper, F.H., Hooper J.D. & Colbert, K.K. (1984). Personality and memory correlates of intellectual functioning: Young adulthood to old age. New York Karger. Hooper, J.O., Hooper, F.H., Colbert, K., & McMahan, R. (1986). Cognition, memory, and personality in elderly students. Educational Gerontology, 12, 219-229.

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Horn, J.L. (1982). The theory of fluid and crystallized intelligence in relation to concepts of cognitive psychology and aging in adulthood. In F.I.M. Craik & S . Trehub (Eds.) Aging and cognitive processes, (pp.237-278). New York: Plenum. Jarvik, L.F., & Bank, L. (1983). Aging twins: Longitudinal psychometric data. In K.W. Schaie (Ed.) Longitudinal studies of adult psychological development (pp.64135). New York: Academic Press. Johnson, D.A., Lunneborg, C.E., & Schaie, K.W. (1987, November). Attitudinal flexibility and intellectual functioning in the elderly. Presented at the 40th Annual Scientific Meeting of the Gerontological Society of America. Washington, DC. Kleban, M.H., Lawton, P. M., Brody, E.M., & Moss, M. (1976). Behavioral observations of mentally impaired aged: Those who decline and those who do not. Journal of Gerontology, 31, 333-339. Lachman, M.E. (1983). Perceptions of intellectual aging: Antecedent or consequence of intellectual functioning? Developmental Psychology, 19, 482-498. Lachman, M.E. (1986a). Locus of control in aging research: A case for multidimensional and domain-specific assessment. Psychology and Aging, I , 34-40. Lachman, M.E. (1986b). The role of personality and social factors in intellectual aging. Educational Gerontology, 12) 339-344. Lachman, M.E., Bakes, P., Nesselroade, J.R., & Willis, S.L. (1982). Examination of personality-ability relationships in the elderly: The role of the contextual (interface) assessment mode. Journal of Research in Personality, 16, 485-501. Lerner, R.M., Hultsch, D.F., & Dixon, R.A. (1983, April). Contextualism and the character of developmental psychology in the 1970’s. Presented at the meeting of the Section of History, Philosophy and Ethical Issues of Science and Technology of The New York Academy of Sciences, New York. Lerner, R.M., & Busch-Rossnagel, A. (1981). Individuals as producers of their development: Conceptual and empirical bases. In R.M. Lerner & N.A. BuschRossnagel (Eds.), Individuals as producers of tlzeir development: A life-span perspective, (pp. 1-36). New York: Academic Press. Levenson, H. (1974). Activism and powerful others: Distinctions within the concept of internal-external control. Journal of Personality Assessment, 38, 371-383. MacLeod, C.M., Jackson, R.k, & Palmer, J. (1986). On the relation between spatial ability and field dependence. Intelligence, 10, 141-151. McCrae, R.R., & Costa, P.T., Jr., (1982). Self-concept and the stability of personality: cross-sectional comparisons of self-reports and ratings. Journal of Personality and Social Psychology) 43, 1282-1292. McCrae, R.R., & Costa, P.T., Jr., (1984). Emerging lives, enduring dispositions: Personality in adulthood. Toronto: Little, Brown. McGee, M.G. (1979). Human spatial abilities: Psychometric studies and environmental, genetic, hormonal, and neurological influences. Psychological Bulletin, 86, 889-918. Moss, H.A., & Susman, E.J. (1980). Longitudinal study of personality development. In O.G. Brim, Jr., & J. Kagan (Eds.), Constancy and change in human development, (pp. 530-545). Cambridge, Mass. Harvard University Press. Nesselroade, J.R. (1986). Selection and generalization in investigations of interrelationships among variables: Some commentary on aging research. Educational Gerontology, 12, 395-402.

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14 Intellectual Abilities and Age: Concepts, Theories and Analyses Walter R. Cunningham UniversiQ of Floridu Adrian Tomer Pennsylvania State University

The study of intellectual abilities in later life continues unabated. It has traditionally been the most highly researched area in the field of adult development and aging. This review notes considerable progress but also an increasing awareness of the complexity and subtlety of intellectual functioning in older adults. Broad, unqualified assertions such as general decline or general stability have clearly been rendered obsolete by recent developments. It is necessary to qualify such conclusions carefully with respect to the age level, kind of ability considered as well as the operationalization of the ability in question. Most intellectual abilities show considerable stability for most of the adult life-span. However, it is clear at this point that there are substantial declines for a few abilities that begin well before old age, that many intellectual abilities begin to show highly reliable, though usually small declines in the decade of the 60s, and that only a very selected set of variables show stability past age 70. While many descriptive studies continue, there is an increasing interest in studies focussing on issues pertinent to particular theoretical viewpoints. We discuss several theoretical proposals in the first section, including speed viewpoints, fluid and crystallized intelligence, stage theories and Sternberg's triarchic theory. A problem in this area, however, is the fact that to a greater or lesser extent, most of the theoretical formulations reviewed here are in fact substantially "borrowed" from the literature of young adulthood or childhood. It is the exception rather than the rule that theoretical formulations guiding research in this area are germane to genuinely gerontological perspectives on intellectual functioning. Studies of both structural characteristics and changes in level are moving away from prefabricated test batteries such as the WAIS or the PMA, and consist increasingly of selected multiple indicators for ability factors for which there is knowledge of factor structure in both young and old. Further, many new factors which have been well replicated in the young are being studied for the first time in the old. The boundaries of knowledge of different abilities continues to widen, and have already moved well beyond what is possible with brief prefabricated instruments. Some work is now beginning to appear documenting individual differences in change over time and also

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possible antecedents of early decline. Ecological validity (including issues of plasticity and reversibility) as well as recent developments and trends in multivariate methodology are also considered. Theories

Speed Explanations for Changes in Intellectual Functioning It is important to make a distinction between a theory of intelligence that considers speed of information processing to be an essential aspect of intellectual behavior and a speed interpretation of age-related changes in intellectual functioning. Given the validity of a speed view of intelligence and the extremely robust and general findings about slowing of information processing with increased age (e.g., Salthouse, 1985a), a "speed hypothesis" regarding age changes seems to follow. It is possible, however, to propose and to hold a speed hypothesis quite independently of a more general view of intelligence as (mainly) speed of information processing. We start by presenting briefly some of the concepts and types of evidence related to a speed view of intelligence. We then proceed to an examination of speed models (or rate of processing models) as explanations of age changes in intellectual functioning and evaluate the empirical evidence related to these models and their possible future developments.

The speed view of intelligence. A variety of models which consider mental speed to be a fundamental aspect of intelligent behavior have been developed in the last twentyfive years (for a review see Eysenck, 1987). An example is the model developed by Eysenck (1982, 1987). According to this model individual differences in intelligence are accounted for primarily by differences in mental speed which is the speed with which cognitive functions are performed (possibly identical to speed of information processing). Other aspects of intelligent behavior are recognized in the model, namely persistence and error checking. Persistence refers to the tendency to continue search processes in problem solving activities. Error checking seems to reflect standards of accuracy in judging solutions. Both these latter aspects are considered to be related to personality whereas speed is viewed as cognitive (and ultimately genetic) in nature. Other similar models emphasize the importance of speed of information processing in limited capacity systems which lose information through processes of decay, forgetting, etc. These types of models tend to leave out an account of intelligent behavior variables such as knowledge or strategy. A more comprehensive view of intelligence including these two aspects and the "mechanics of information processing" was proposed by Hunt (1978). The speed view of intelligence is based on substantial empirical findings, the main part of which deals with the relationship between choice reaction time as a measure of mental speed and psychometric intelligence (e.g., Jensen, 1982; Vernon, 1983). Without engaging in an evaluation of this literature, we would like to point out that there is a continuing debate regarding the interpretation of these findings (e.g., Marr & Sternberg, 1987). A recurring topic in this debate is the possibility that variables such as motivation, attention and strategy play an important role in choice reaction time performance as they may play indeed an important role in psychometric

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intelligence. Additional arguments question the role of speed in very complex intelligent behavior in which, for example higher-order planning and other metacomponents may play an important role (Sternberg, 1981, 1985; see also Hertzog, 1989; Horn 1985).

The speed hypothesis. A speed hypothesis as an explanation of cognitive change in older adults was formulated by Birren (e.g., Birren, 1964, 1965; Birren, Woods, & Williams, 1980) and further elaborated by Salthouse (1985b). The hypothesis maintains that there are changes at the level of CNS and, as a result, there is slowing down with age in a variety of behaviors and a decline in intellectual functioning in general. What the theory tries to explain is a) consistencies between changes over time in measures of speed and b ) decrements in some cognitive functions in older age. In its simplest form a speed hypothesis would assume the existence of one fundamental process or mechanism at the level of the CNS. More complex formulations would allow for multiple mechanisms. A direct testing of point a above should be based on a factor analysis of longitudinal changes in measures of speed assumed to reflect to some extent the basic information processing rate. A less direct approach would consider changes in the structure of speed measures over time. Given the validity of a speed hypothesis and given the existence of interindividual differences in rates of change we can expect increases in covariances among the speed factors over time (e.g., Hertzog, 1985, 1987). There is evidence for this type of increase in covariances (eg., Cunningham, 1980). More difficult is the question of a unifactorial versus a multifactorial view of the processes involved (e.g., Salthouse, 1985b). Cerella's findings (Cerella, 1985; Cerella, Poon, & Williams, 1980) seem to suggest the existence of at least two factors, one peripheral and one central. For reasons of parsimony it seems that one has to accept the existence of only two factors, one of them being a central factor. This view fits the simple form of the speed hypothesis. These findings were obtained regressing old subjects' scores obtained in a variety of tasks and experiments on young subjects' scores obtained in the same tasks and experiments. On the other hand, factor analytic studies evidenced the existence of a multitude of speed factors (e.g., Hertzog, Raskind, & Cannon, 1986; Tomer, 1989; White and Cunningham, 1987). Hertzog, Raskind and Cannon (1986) found non-uniform differences in the covariance structure of two aged groups suggesting a multitude of mechanisms at work.

Additional evidence for the existence of multiple mechanisms at the central level is based on relationships between intellectual speed measures and other intellectual functioning (Hertzog, 1989; Tomer, 1989). According to the speed hypothesis cognitive changes with age should disappear or be greatly reduced when controlling for speed of information processing (Salthouse, 1985b). Typically speed was measured using the perceptual speed tasks but also reaction time and card sorting tasks (Tomer, 1989; White and Cunningham, 1987). Measures of intellectual functioning included tests of fluid intelligence, such as tests which measure inductive reasoning (Hertzog; 1989; Horn, Donaldson, & Engstrom, 1981; Stankov, 1988; Tomer, 1989) but also measures of other abilities such as numerical facility or spatial abilities (e.g., Hertzog, 1989). These studies have used age groups which differ considerably in range and background. Different methods have been used to control statistically for speed: semipartial correlations,partial correlations, multivariate regression and commonality analyses, and

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structural equation models. The results indicate a reduction in the relationship between age and cognitive functioning. These reductions in relationship vary somewhat, ranging from a "substantial"reduction which still leaves a significant amount of variance in the cognitive functioning explained by age (Horn, Donaldson, & Engstrom, 1981), to very large reductions leaving only significant but small (Hertzog, 1989) or nonsignificant portions of variances unaccounted for by differences in speed (Tomer, 1989). These results have been interpreted as providing support for a speed hypothesis. The findings are also consistent with other results of increased covariances between speed factors and fluid intelligence with increased age (e.g., Cunningham & White, 1983). Other interpretations are, however, possible (Horn, 1982; Horn, Donaldson, & Engstrom, 1981; Hertzog, 1989). In particular the respective roles of attention and intellectual speed in accounting for intellectual decrements still need to be elucidated (Horn 1982; Horn, Donaldson, & Engstrom, 1981; Stankov, 1988). The issue of one versus multiple mechanisms of speed has been investigated trying to determine if there is more than one speed mechanism accounting independently for cognitive performance. Hertzog (1989) found that Perceptual Speed and Answer Sheet Speed predicted significantly and independently the primary ability scores. Similarly, Tomer (1989) using a structural equation modeling approach found figural perceptual speed and choice reaction time to mediate independently the relationship between age and fluid intelligence. It is important to emphasize that changes in speed alone are not able to account for all changes in cognitive functions. For example, verbal abilities that tend to remain stable or to increase with age are shown to be (even more) strongly positively related to age when controlling for speed (e.g., Horn, Donaldson, & Engstrom, 1981; Stankov, 1988; Tomer, 1989). Also, Salthouse reviewing the literature regarding the relationship between age, speed and memory concluded that there was only limited support for the speed hypothesis in this context. These limitations show the need for more comprehensive models which might include additional explanatory variables besides speed and which might also include several interdependent abilities such as fluid and crystallized intelligence (Tomer, 1989). A complex model of theoretical and heuristic importance including adjustments and adaptations as well as strategy and information variables was proposed by Salthouse (1985b). The Theory of Fluid and Crystallized Intelligence The theory of Fluid and Crystallized intelligence, propounded by Cattell in the early 1940s and subsequently expanded and refined (Cattell, 1987) is a theory of pivotal importance for adult development and aging. It represents an attempt to reconcile the multiple factor theory of Thurstone with the original concept (though not the details) of Spearman's theory of "g". The theory consists of two major aspects: a structural component involving a particular organization of intellectual abilities into a hierarchical model of test variables, primary or first order factors and also second and higher order factors, with a "g" at the top of the hierarchy, just above the two higher

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order factors of Fluid and Crystallized Intelligence (see Cunningham and Brookbank, 1988 for more detailed development). Many theories of intellectual or cognitive functioning do not imply strong predictions with regard to age and intellectual functioning. A major exception to this is the theory of Fluid (often abbreviated Gf) and Crystallized (Gc) Intelligence. As is well known, the theory explicitly predicts different developmental trajectories for these higher order factors: increase or at least stability for Gc and decline for Gf. Empirical evidence supports some aspects of the theory better than others. There is considerable evidence supporting the structural predictions of the theory. Also, the developmental trajectory for Gc is well supported by both cross-sectional and longitudinal studies. Further, cross-sectional studies clearly support the predictions of negative relationships with age for Gf. The primary locus of intellectual tension concerns the lack of correspondence between decline predictions and the longitudinal change data from a variety of studies. Originally, it was suggested that Gf peaked in the early twenties and declined precipitously thereafter. Recently, more modest claims have been made, e.g., that declines are detectable in the forties (e.g., Horn and Donaldson, 1976). Owens (1966) reported a statistically significant decline for one subtest of the Army Alpha: Number Series completion between ages 50 and 60. Schaie (e.g., 1983) has also reported relatively slight declines for both Inductive Reasoning and Space from the Primary Mental Abilities Test developed on the basis of Thurstone’s program of factor analytic research. Also, in unpublished work in my own laboratory (e.g., Cunningham, 1988) with a battery of inductive reasoning measures, statistically significant but relatively small declines have been found in the 60s. Thus, the longitudinal data from a number of sources are not consistent with the magnitude or timing originally predicted by the theory of fluid and crystallized intelligence. How is this to be understood? Should the theory be considered wanting with regard to this key issue? There are several lines of defense that are possible. The first is to question the longitudinal methodology as it is typically applied in actual longitudinal studies. Horn and Donaldson (1976) have questioned certain aspects of longitudinal studies, and have been particularly critical of interpretations of longitudinal data. A simple statement of the argument is that since various influences (selective attribution, repeated measures, inconsistencies in testing conditions) almost always are biased against detecting statistically significant declines, it is logically possible to question whether longitudinal data can ever convincingly support a conclusion of stability rigorously generalized to the population at large. Similar concerns and issues have been raised by other researchers who prefer cross-sectional data (e.g. Salthouse, 1985b) as well as those who do longitudinal studies but urge considerable caution in the interpretation of such data (Siegler and Botwinick, 1979; Birren, Cunningham & Yamamoto, 1983). Certainly, one can seriously question how the longitudinal method, with the restriction in range implicit in the typical two to seven year follow-ups, and biased as it is by repeated measures and selective attrition can ever provide convincing evidence for stability. A much more convincing approach would be to take crosssectional data, identify the sources of cohort differences, employ a statistical control (with path or regression analyses) for such influences, and then show that resulting

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partial correlations between chronological age and various ability variables are not significantly different from zero. Of course, a valid identification of important operational variables related to cohort differences depends on an incisive theoretical analysis of exactly what cohort differences are, which has still not occurred even though various reviewers have from time to time commented on the obvious need for such an analysis (e.g., Cunningham, Birren, & Yamamoto, 1983). Another issue concerns the operationalization of Gf. Perhaps inductive reasoning and space tasks are too contaminated with knowledge and experience (Gc) and that new, more novel or better controlled tasks (with regard to previous experience) are needed. A third issue concerns individual differences in intra-individual decline. It could be that there are substantial declines occurring in some individuals, but that these individuals are differentially less likely to persist in longitudinal studies (the perception of lessened ability is probably troubling) and hence selective attrition accounts for the lack of the detection of large declines (e.g., Horn & Donaldson, 1976). From this viewpoint, decline may be pronounced at some point in life, but the timing of such decline may vary widely across individuals, and that decline itself is a prime cause of dropout in longitudinal studies. A troubling aspect of this argument is that the key data are inevitably those that are not available. There is a "phantom data" quality to this argument. This aspect renders it relative immune to empirical test. In fairness, it must be said that the argument is internally consistent, and has the potential heuristic value of drawing attention to the need for a more searching analysis of decline with regard to these variables. Given these points of view, the possibility of decline in more representative samples

on variables widely assumed to be related to the construct of fluid intelligence is a source of continuing intellectual tension among researchers in this area. Stage Theories of Cognitive Development

Schaie's proposal. Schaie (1977-78) proposed a tentative scheme based on five cognitive stages, one corresponding to childhood and adolescence and the other four corresponding to young adulthood, middle age and old age. The stages are denoted as acquisitive, achieving, responsible, executive and reintegrative. The transition from stage to stage is determined by a change in experience and a process of adaptation involving a reformulation of goals of cognitive behavior, gains in some abilities and losses in other abilities. In the acquisitive stage of childhood and adulthood a main goal is the acquisition of skills. These skills need to be used to achieve independent functioning in the achieving stage, to assume responsibility for other people and to formulate long-term goals in the responsible stage or to integrate complex hierarchical relationships in the executive stage. Fluid abilities are assumed to lose at least part of their relevance in these stages of middle age. Finally, in the reintegrative stage of old age the "why" of the search for meaning of life replaces the "how" of the previous stages, presumably with a further loss of interest in pure, playful, fluid thinking. Schaie's scheme implies that, to assure ecological validity, new ways for measuring intelligence are needed during the life span according to the developmental stage of the individual.

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Labouvie-Viefproposal. A scheme for a developmental theory similar in some ways to Schaie's was proposed by Labouvie-Vief (1982). The Neo-Piagetian model assumes that behavior is being restructured beyond the formal stage of Piaget's theory. Three levels of logic are assumed to correspond to these regulations in adulthood: an intrasystemic level corresponding to the stage of formal operations, an intersystemic level and an autonomous level. At the first intrasystemic level, formal structures are achieved which are applied in the subsequent postformal stages. At the intersystemic level, relativity of truth to a system of reference is realized, and the adult is able to coordinate different systems and to commit himself to a point of view while realizing the impossibility of a complete justification of his commitment. In the next stage of autonomy final conclusions of the former relativity are achieved. Even formal laws are at this level viewed according to their regulative function in respect to the self and the other. Labouvie-Viefs model is an interesting synthesis of cognitive and ethical theory. It assumes that adults continue their cognitive growth to reach post-formal stages. It also implies that decline as evidenced by psychometric tests may be no more than a gross misinterpretation based on the application of criteria relevant to adolescents and young adults to older individuals who have outgrown the formal stage. A counterargument to this type of model is that, possibly, the same abilities are applied to different goals and to different problems during the life span. A college student may be interested in logical puzzles and paradoxes whereas his older counterpart may be more interested in the meaning of life or in the well-being of his family. It is still possible, however, that being creative, formulating problems and making progress in their solution require the application of a common set of processes. There is still a scarcity of empirical evidence relevant to proposals such as Schaie's, Labouvie-Viefs or alternative proposals of postformal operational models of intelligence (but see Labouvie-Vief, 1985; Baltes, 1987). Some of the questions regarding the validity of measures of intelligence at various points during adult development are discussed in a later section.

Stemberg's triarckic theory. Sternberg's triarchic theory (Sternberg, 1985) has been applied to an understanding of intellectual change by Sternberg and his colleagues (Berg and Sternberg, 1985). The three parts of the theory are 1) a contextual aspect relating intelligence to the environment, 2) a componential aspect concerned with cognitive processing, and 3) an experiential part concerned with the experience of the individual. The contextual part of the theory considers intelligent behavior as adaptation to a sociocultural context which is likely to change during the life span. There is evidence indicating that everyday competence is considered to be more indicative of intelligent behavior at older ages (Berg and Sternberg, 1985; Williams, Denney, & Schadler, 1983), a result which presumably fits the more important role played by everyday problems in the life of older individuals. An application of the componential part of Sternberg's theory (or, for that matter, of any other componential approach) to an understanding of cognitive change with age involves an examination of the different components as age functions. Berg and Sternberg (1985) have reviewed the evidence regarding metacomponents and performance components. They found evidence for deficits in most metacomponents,

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for example in the ability to define the problems that need to be solved, to allocate attentional resources efficiently and to monitor one's solution effectively. On the other hand, Berg and Sternberg reported evidence suggesting that metacomponents may improve in older adults with continuing practice, possibly to the point of cancellation of age deficits. A review of performance components in the area of spatial information processing suggests deficits in all three components: encoding, combination and comparison, and response components. These results seem to generalize to other tasks. One notable exception is verbal functioning. Berg and Sternberg do not discuss the evidence for age deficits in knowledge-acquisition components, namely in selective encoding, selective combination and selective comparison. These components are used to acquire knowledge or in reaching insights in novel situations (Davidson and Sternberg, 1984; Sternberg, 1985). The stability of crystalized intelligence may suggest that these components do not decline until advanced ages. This stability, however, may only indicate relatively early application during the life span of the knowledgeacquisition components and automatization of the respective behaviors. Findings about semantic memory (e.g., Meyer and Rice, 1981) seem to indicate stability of these components, at least in a context of prose comprehension. Additional investigation should consider the application of these components in various contexts, including various degrees of novelty. Finally, the experiential part of Sternberg's triarchic theory claims that intelligence is best measured using "optimal" degrees of novelty or familiarity and looking at performance in these circumstances and at the ability to automatize this performance with extended practice. Declines in various fluid abilities may suggest that older adults decline in intelligence. However, the interpretation of these findings is complicated by the possibility that fluid tasks may be more novel to older adults than to younger adults and in any case they appear more novel to them (Cornelius, 1984). For this reason, Berg and Sternberg have emphasized the importance of assuring that tasks are equally relevant and novel across age groups. A problem with this approach is that the ability to deal with novelty is probably differentially important at different ages. It is not clear then how one should control for the amount of novelty across ages and ultimately to what extent age comparisons are meaningful, given these problems. It seems that one price paid by very comprehensive theoretical models of intelligence such as Sternberg's triarchic theory or postformal theories of adult development is the inability to make direct comparisons between individuals of largely different ages in terms of their intellectual functioning. Structural Changes White and Cunningham (1987) studied the structure of highly speeded tasks such as Choice Reaction Time, Sternberg Reaction Time and Card Sorting in two crosssectional samples of 150 young (18 to 33 years) and 150 old (58-73years) adults. For the young sample, a three factor solution was fully satisfactory. The factors corresponded exactly to the three types of task demands in the variables studied. In the old, however, a surprising result occurred. Neither a three or a four factor solution was satisfactory. It was necessary to rotate jive factors to achieve a satisfactory fit. One of the extra factors appeared to be an artifact of order. The other factor was substantively more interesting: a figural component split off from the Sternberg RT factor so that there was one factor encompassing symbolic and verbal material and a

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new factor for figural stimuli. This factor may reflect an emergence of greater individual differences in perception of figural material in old age. Naturally, this result is exactly the opposite of what one would expect from a de-differentiation hypothesis (e.g., Cunningham, 1980). Another puzzling development was the results of Tomer's dissertation (1989). He failed to find increases with age in factor covariances in cross-sectional comparisons for a battery of highly speeded tests. Although there might be selection effects in the younger sample (noted elsewhere in this review) and longitudinal comparisons might be more enlightening, this finding, in conjunction with the White and Cunningham (1987) results, has produced a decline in complacency regarding structural stability with age. In particular, it is possible that increased covariances among factors are most consistently found in test batteries with both high speed demand and difficulty level variance. It is possible that the often found increased factor covariances stem from increased associations between relatively pure measures of speed and measures consisting of difficult items in power tests or at least tests where there is significant power related variance. Further, the White and Cunningham results underscore the important possibility that individual differences in new dimensions may emerge in later life. Some of these may be simple artifacts of visual acuity or other extraneous variables such as arthritis and Parkinson's disease. These conjectures clearly require further study. It appears that researchers should continue to study factor invariance in the ability area. This is particularly true for factors not previously employed in studies of the intellectual performance of older adults. Changes in Level

One of the problems in studying abilities in a longitudinal context is that the time period of follow-up is often not long compared with the full age range of adulthood or even old age. For example, a common period of follow-up is seven years, but this amounts to a considerable restriction in range compared with what is common place in cross sectional studies. Thus, a failure to detect declines could result from the restriction in range of age change implicit in almost all longitudinal studies. For example, Schaie and Hertzog (1983) found more reliable evidence for decline (beginning in the decade of the sixties) with a fourteen year interval than had previously been found with a seven year interval. In this context, the report of Sands, Terry and Meredith (1989) is important because it covers far greater age ranges than the bulk of the studies of the literature, even though only WAIS scores were available. Two groups of subjects originally participating in the Berkeley Growth and Guidance Study and the Oakland Growth Study were studied on several occasions. The first was followed from ages 18 to 55, and the second from ages 48 through 61 years. A number of interesting item analyses were performed which revealed fascinating details of changes in performance over time. Also, at the subtest level, highly significant and consistent declines were found for the Digit Symbol measure after age forty. Cunningham (1988) reported preliminary results from a broadly focused study which gathered descriptive data on a large number of intellectual ability variables, blood pressure, subjective health and life-style items, and several measures of memory in

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both 150 young and 150 elderly subjects. This study is referred to as the Intelligence and Memory Study (I & M). The 150 older subjects ranged in age from 58 to 73 years with a mean of 65.4 years at the initial time of testing in 1980/81. There were 98 females and 52 males. Average education was 15 years. Eighty subjects were retested in 1987/88. A lengthy battery of psychometric and cognitive tasks were administered. Most of the latter tasks were administered via computer. Data were gathered on multiple indicators for factors of Verbal Comprehension, Inductive Reasoning, General Reasoning, Symbolic Perceptual Speed, Figural Perceptual Speed, Choice Reaction Time, Sternberg Reaction Time (with Verbal and Symbolic materials), Sternberg Reaction Time (with Figural materials), Associative Memory (word lists), Associative Memory (symbols), and Card Sorting.

All eleven factors showed statistically significant longitudinal declines. The largest losses were registered by Figural Perceptual Speed. This was consistent with earlier results from the Florida Study (e.g., Cunningham, 1989). This factor was showing declines of approximately three quarters of a standard deviation when averaged across the two cohorts. This was a much larger decline than was seen for any other factor.

The next largest decline (.4 SD) was seen for Sternberg Reaction Time for Figural Stimuli. A slightly smaller decline was seen for Card Sorting. Declines of about .3 SD were found for Symbolic Perceptual Speed and Associative Memory for SymbolicLists. Small (less than .25 SD) but statistically significant declines were found for Verbal Comprehension, Inductive Reasoning, General Reasoning, Choice Reaction Time, Sternberg Reaction Time for Symbolic Stimuli and Associative Memory for Word Lists. Cohort differences were much in evidence. For ten factors, these cohort differences favored the earlier born cohort. Only Choice Reaction showed a better performance for the younger cohort, although the difference was virtually negligible. For several of the factors (Verbal Comprehension, General Reasoning, Sternberg Reaction Time for Words and Symbols and Associative Memory for Symbol Lists), the cohort difference was approximately the same size as the age changes. On the other hand, seven factors showed age declines of more than twice the cohort difference. As in the Florida Study, Figural Perceptual Speed showed a large age change to cohort difference ratio (5.9 to 1). All of the factors evaluated are showing declines by the late sixties. For most variables, these declines are relatively small. (The Verbal factor in I&M has several speeded tests.) In a global way, these data are consistent with other recent results (e.g. Schaie 8i Hertzog, 1983). Some factors appear to begin declining from the early sixties or even younger. Figural Perceptual Speed and Sternberg Reaction Time (Figural Stimuli) showed relatively large declines. Figural Perceptual Speed was orderly with respect to cohort but Sternberg Reaction Time was not.

The cohort differences require some comment. Usually empirical differences across cohorts favor later born cohorts and are interpreted as results of cultural drift. It has sometimes been argued that highly speeded measures show the opposite effect, favoring earlier born cohorts. In these data, the earlier born cohort at time 1 is almost

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always performing on a higher level compared with the later born cohort at time 2 for all factors analyzed (e.g., whether they are speeded or not). We are therefore inclined to consider the possibility of a selection/volunteer effect. The older cohort is somewhat better educated and may be intellectually more active. This study evaluated data for eight separate higlzly speeded factors. Considerable variety and texture to patterns and magnitude of age changes and cohort differences was found. An earlier paper (White & Cunningham, 1987), documented the presence of several separate factors of highly speeded abilities. These results on age changes extend earlier findings by again emphasizing the complexity of speeded performance with respect to age (Cunningham, 1988). The results of the I&M study taken in conjunction with the results of the previous Florida Study suggest that the largest and most consistent declines in intellectual functioning concern speed of perception and decision-making in responding, and that these declines are largest for figural material. Other variables such as Sternberg R T (Words/Symbols), Card Sorting and Symbolic Perceptual Speed, showed very consistent but smaller declines while the majority of unspeeded intellectual abilities including Verbal Comprehension, General Reasoning, Inductive Reasoning showed rather small declines. Additional work focused on antecedents of decline and was an extension of the longitudinal I&M study. The purpose was to evaluate additional antecedents beyond the demographic and health rating information described above. Extensive questionnaire item data have been gathered pertaining to health and lifestyle. We have carried out item analyses to construct small but reliable indices of different aspects of life-style which may influence intellectual change. Taking into account item analyses of reliability and substantive hypotheses, general health rating items, item reports of neurological symptoms, patterns of exercise, and depression items appeared promising. We have also taken measures of blood pressure and have item data on medication. Time 1 data (n = 150) has been used to construct variables and to evaluate cross-sectional path models. Selected variables showing adequate internal consistency and which appeared promising were used to extend results. Preliminary work with composites of cardiovascular items, exercise items, neurological/mental items and blood pressure indicated no significant relationship to Figural Perceptual Speed. A composite of Alcohol use items showed a non-significant trend. However, one facet of depression was found to have very interesting properties. This preliminary work is described next. An extremely plausible antecedent of decline is depression. Depression is the most common adjustment related complaint of elderly persons. It appears to be more common in younger cohorts and therefore is likely to be an even greater concern for the elderly in the future (Merman & Weissman, 1989). It is generally believed to be associated with both quantitative and qualitative deficits in cognitive functioning across the life-span (Weingartner, Cohen, Murphy, Martello, & Gerdt, 1981; Jorm, 1986).

Depression is, however, complex, both in terms of behavior and also with regard to clinical diagnosis (Marsella, Hirschfeld & Katz, 1987). There appear to be a number

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of facets of depression, both in terms of diagnostic subcategories and also in terms of psychometrically oriented factor analytic studies (merman, 1987). Hypotheses concerned with depression as an antecedent of intellectual decline could consider general depression or some facets of depression. The study was intended to evaluate in a preliminary way the possibility that depression may play an important antecedent role in intellectual decline in clinically normal, community residing elderly. Subjects were 146 older participants in the Intelligence and Memory Study who had compIete data available for the variables of interest. They ranged in age between 58 and 73 years. The depression indicators were based on the following preliminary analysis. Bearing in mind that the psychometric structure of depression is only little explored (e.g. Lewinsohn & Rohde, 1987), a preliminary factor analysis (principal components rotated by the Varimax criterion) was carried out on ten items believed to reflect depression from the Florida Health Survey (Schwab & Schwab, 1983). Neither a general factor or even a two factor solution was consistent with the data. For the older subjects, a clear three factor solution emerged reflecting two items concerned with Worry, five items reflecting cognitive or Memory Complaints, and three items reflecting Dysphoria. Our initial work focused on Dysphoria (hereafter referred to as Depression) as the most likely candidate to be an antecedent of decline. Latent variables included in the path analysis were Figural Perceptual Speed with three indicators (Parts 1 and 2 of Identical Pictures from the ETS Kit of Factor Referenced Tests-Ekstrom, French, & Harman, 1976-- and the Perceptual Speed subtest of the Guilford Zimmerman Aptitude Survey-Guilford & Zimmerman, 1948) and three indicators of depression (self rating items of low spirits, concern about the future and whether things seem worthwhile). The three rating items had an alpha reliability of .62. Observed variables included age, educational attainment, sex, and a single rating of subjective health. Data analysis consisted of path analysis techniques, using LISREL for parameter estimation and significance tests. Exogenous variables were age, sex and education. The primary endogenous variable was a latent variable of Figural Perceptual Speed with three indicators. Intermediate endogenous variables were Depression and Subjective Health. The a priori model allowed paths from all three exogenous variables to all three endogenous variables. Direct paths were also allowed from Health to both Depression and Figural Perceptual Speed. In Model I, no path was allowed from Depression to Figural Perceptual Speed. The focus of the analysis concerned whether or not there was a significant gain in fit when a single additional path was allowed from Depression to Figural Perceptual Speed. This additional path was allowed in Model 11. Results were very encouraging. In general, both the chi-square and goodness of fit indices were quite satisfactory for both models. However, the key hypothesis involved a one degree of freedom contrast between the two models. Model I1 was found to be superior, since one less degree of freedom resulted in a gain of 7.71 chi-square points, which is significant at the .01 level. In Model 11, the standardized path coefficient between Depression and Figural Perceptual Speed was -.29, which was very slightly

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larger than the coefficient from Age to Figural Perceptual Speed, which was -.25. As expected, Subjective Health was a significant predictor of Depression, although Age was not significantly related to Subjective Health. This latter finding probably reflects the fact that the volunteer sample was unusually healthy. Age was also significantly related to Depression, as expected. Age, Education and Sex were all significantly related to Figural Perceptual Speed, again as was expected. The major conclusion was that the Dysphoria facet of depression is a significant predictor of Figural Perceptual Speed. Further, Dysphoria is actually a slightly better predictor of Figural Perceptual Speed than is Age. Also, the fact that Age is significantly related to Depression further underscores the potential importance of depression in intellectual decline. Clearly, these results indicate further study is needed with larger samples and more elaborate operationalizations of Depression. Similar analyses carried out with two other facets of depression (Cognitive Complaints and Worry) did not show significant relationships to Figural Perceptual Speed. An important qualification of the study concerns the measurement of Depression. Note that the indicators for depression were three simple rating items with appropriate distributions and an Alpha reliability coefficient of .62. Given the modest nature of the available indicators, no claims can be made that Depression has been measured in a complete or comprehensiveway. However, it is obvious (given the significant path coefficients from both Age and Subjective Health to Depression, and the further significant path from Depression to Figural Perceptual Speed), that even this very simple operationalization of Depression is reliably related to other variables, and thus merits further study.

Interindividual Differences in Patterns of Change The existence and magnitude of interindividual differences in patterns of cognitive change is of theoretical as well as of practical importance. The fact that considerable cohort differences have been demonstrated may suggest that heterogeneous patterns of aging are common (Willis & Baltes, 1980) and that modifiability or plasticity (e.g., Schaie & Willis, 1986) are possible. This section presents some of the relevant empirical results, especially results based on the Seattle Longitudinal Study (e.g., Schaie, 1983) and on the Florida Longitudinal Study (e.g., Cunningham 1989). Other studies, in particular the Duke Longitudinal Studies (e.g., Siegler, 1983) and the Bonn Longitudinal Study of Aging (e.g., Schmitz-Scherzer & Thomae, 1983) have also analyzed differential patterns of change but their findings would not appreciably modify the general picture presented here. Stabilities in Longitudinal Studies One way to measure differences in intraindividual change is to calculate the amount of variability in difference scores of individuals obtained on the basis of comparable successive occasion. Schaie (1983) used this criterion for the five Primary Mental Abilities (Verbal Meaning, Space, Reasoning, Number and Word Fluency) measured

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on 4 occasions over a 21 years period in the Seattle Longitudinal Study. Over periods of 7 years for ages 25 to 74 he found standard deviations of the order of .4 to .8 relative to the standard deviations of the base scores. The variability tended to decrease for samples of subjects in their 80s. The importance of the intraindividual variability can be expressed also by dividing an average change obtained for a group by the standard deviations of the difference scores. In these terms the intraindividual differences seemed to be more important for all the abilities with the exception of Verbal Meaning for ages up to the sixties and became less important in the late seventies. Verbal Meaning showed a pattern of relative importance of intraindividual changes for mid-adult ages. Of course the relative importance expressed in this way is somewhat ambiguous as a small score may reflect either small overall changes (as it is the case with Verbal Meaning in middle years) or a large standard deviation in difference scores. An investigation of training effects of participants from the Seattle Study (Schaie & Willis, 1986; Willis & Schaie, 1986) tended to reinforce this notion: almost half of the subjects exhibited no decline on Space Ability and on Inductive Reasoning over a period of 14 years. The criterion for reliable decline was a drop of 1standard error during the 14 years.

A large variability in change scores does not necessarily imply low correlations over time of the ability variable with itself. While perfect stability in this sense does not imply lack of variability in patterns of change, less than perfect stability does imply interindividual differences. Several analyses performed on a sample from the Seattle Longitudinal Study indicated, however, that the stability of intellectual abilities over time is very high (Hertzog & Schaie, 1986, 1988). The investigators used longitudinal structural equation models to analyze stability of scores over a 14 year period during which measurements were made on three occasions for three age groups (young, middle age, old age). Three general intelligence g factors were hypothesized to determine scores on the five subtests of Thurstone’s Primary Mental Abilities at each one of the three occasions. The intercorrelations between the general factors at two successive occasions (7 years) were above .9 indicating high stability. It was not possible to determine in the same straightforward way the stability of the 5 abilities because of the lack of multiple indicators. However, an estimation of an additional model in which specific factors were defined corresponding to these abilities across time could provide some indications on this point. The residuals of the observed variables in this model have a variance which reflects both unreliability of the measures and individual differences. Many variances were at the level of what could be expected on the basis of estimated unreliability alone. High stability is therefore suggested by these findings. Several unique variances were, however, relatively high. Thus, unique variances--over .4--were found, in the old group for the space ability, at first and second occasion.

In the framework of the Florida Study (e.g., Cunningham, 1989) thirty tests of intellectual ability factors representing ten ability factors were administered to 1116 senior citizens recruited from 30 cities throughout Florida. Three batteries were administered, each one to about one third of the participants. One battery consisted of easy but highly speeded tests, a second battery consisted of tests of medium

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difficulty and intermediate time requirements and a third one consisted of relatively difficult less speeded tests. Several tests were administered to all participants to evaluate comparability. About 53% of the participants were retested after seven years. The factorial structure was found to be largely invariant across time (Cunningham, Smook, & Tomer, 1985a,b). Our main interest in this section is in the results regarding stabilities. These were estimated in several models with education and sex as exogenous variables, possibly affecting the intellectual factor at time 1 and at time 2, and with subjective health possibly affecting the intellectual factor at time 1 and at time 2, and with subjective health possibly affecting the intellectual factor at time 2. Six of the ability constructs had stabilities of .9 and higher. Two constructs (Sensitivity to Problems and Semantic Redefinition) evidenced lower stability coefficients--.81 and .89. Symbolic Perceptual Speed and Figural Perceptual Speed showed stabilities of $3 and .74. The last result seems especially interesting considering the fact that Figural Perceptual Speed is relatively age-sensitive and cohort-insensitive (e.g., Cunningham, 1989). In a different study Figural Perceptual Speed was found to mediate especially well the relationship between Age and Fluid Intelligence (Tomer, 1989). It suggests that there are interindividual differences in age-related processes involved in intellectual change.

In conclusion, the results from longitudinal studies indicate in general a remarkable stability of cognitive functioning over periods of 7 years or so. However, this result should be qualified in several ways: 1. Even high stabilities are compatible with individual differences in development, especially over longer periods of time. 2. Some cognitive abilities such as Figural and Symbolic Perceptual Speed and Spatial ability may be less stable than other abilities. 3. The amount of interindividual variability in change may be greater in middle and young-old age, and smaller in old-old age. Antecedents of Interindividual Differences in Patterns of Change Several findings throw some light on the issue of antecedents of (differential) change. In the Florida Study (Cunningham, Smook, Tomer, 1986) educational level was found to predict some of the change in Figural Perceptual Speed over a period of 7 years. Differential patterns in change of the verbal subscale of WAIS according to educational background were reported from the Bonn Longitudinal Study (SchmitzScherzer & Thomae, 1983). Schaie (1983) also found an advantaged SES to be related to maintenance of cognitive performance. These findings are consistent with similar findings from the Bonn Longitudinal Study and from the Duke Longitudinal studies (Siegler, 1983). Health variables such as cardiovascular disease and arthritis have been also found in these longitudinal studies to be related to cognitive changes. Differential changes in intellectual functioning may be also related to personality traits and states, and to changes in these (Hayslip, 1988; Lachman, 1983; Lachman & Jelalian, 1984; Mason & Rebok, 1984; McCrae and Costa, 1985), although a causal interpretation remains rather elusive (Hayslip, 1988). In the same vein, the preliminary findings obtained by the first author (see the section above on Changes in Level) suggest the possibility that

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changes in depression may precede some changes in cognitive functioning. (See Gold & Arbuckle, Chapter 13 of this volume, for further discussion of the relationships between personality and cognition.) Many questions relating to the relationship between these antecedents and the cognitive variables are still open. In particular they include: 1. What relationships are reciprocal and what relationships are causative? 2. What are the mechanisms responsible for the relationship? 3. Over what periods of time do these mechanisms act before producing noticeable effects? Fluctuations in Cognitive Functioning in Relation to Age The results mentioned above deal mainly with stability in the sense of covariance stability (Hertzog & Nesselroade, 1987) measured over relatively long periods of time. Some findings (Horn, 1972) suggest that there are short term fluctuations in intellectual functioning. In Horn's study 14 primary abilities were measured on 10 occasions over a period of 5 days for 106 adults. Analyses conducted to reveal traits have found 4 factors; Fluid Intelligence, Crystallized Intelligence, General Visualization and Fluency. Analyses conducted to determine states (using pooled within subject covariances) have indicated some fluctuations, especially in the last two factors but also in three reasoning tasks which served as measures of Gf and Gc. The magnitude of the state factors in terms of their power to explain variance was considerably lower in the analyses for states than in the analyses for traits. The finding of reliable short period fluctuations is, however, especially important in light of new emphasis (Nesselroade, in press) on the need for integration between long term and short term variability. Interesting questions would be whether older subjects fluctuate more than younger subjects, whether there are significant interindividual differences among older subjects in short term fluctuations, and if and how are these short term fluctuations related to long term changes and to long term differences in development. The Ecological Validity of Changes in Intellectual Functioning Longitudinal and cohort sequential studies of intelligence suggest that the magnitude of declines in intellectual performance over extended periods of time is sufficient to be of practical importance. Thus Schaie & Hertzog (1983) found declines from some PMA abilities beginning in the fifties and rates of between one-third and one-half standard deviation after age 60 over an interval of 14 years. These data suggest mean decrements of one standard deviation for a variety of psychometric abilities over a period of two decades or so. The decrements of linguistic functions such as verbal comprehension start later but it is possible that they proceed at a rather steep rate in the seventies (e.g., Cunningham, 1989). Remembering the existence of interindividual differences in the rate of change, these data suggest that a significant proportion of the general population, undergo decrements of one standard deviation and above in a variety of intellectual functions during the period of late middle age and old age. Developmental and ecological changes with increased age have prompted researchers to raise justified questions and concerns regarding the validity of psychometric measures (e.g., Labouvie-Vief, 1985; Schaie, 1978; Willis & Schaie, 1986) in old age. Following Hertzog (1989) and Schaie (1978) we can divide these questions into two

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classes: 1. Questions regarding the validity of intellectual measure in old age in the sense of the relationship between the measure and the construct or genotype it is supposed to measure and 2. Questions regarding the validity of the construct or genotype itself in the sense of its relevance to life situations in the stage of later life. The first concern may be justified either on a developmental and/or an ecological basis. Problems posed to the elderly may embody assumptions which are plausible in the case of younger subjects but are not correct for the older subjects who may construct the problems in a different way (Labouvie-Vief, 1985). It was also suggested that older subjects may be less motivated to take tests and solve "logical problems" designed for their younger counterparts (Labouvie-Vief, 1982) or that, possibly, tests emphasize speed to an extent which is unfair to the elderly subject. These concerns, however, have not been corroborated by data. If psychometric measures had lost their construct validity so that they actually measured different things than the constructs they were designed to measure we should have obtained large changes in intellectual structure over time. This however was not the case, as a large number of studies (e.g., Cunningham 1980, 1981; Hertzog and Schaie, 1986) have suggested the existence of the same factors over the adult life span (configural invariance) and even, in some cases, the maintenance of the same factor pattern weights (metric invariance). Findings of increased covariances among factors with increased age may be readily explained by the existence of individual differences in the rate of cognitive slowing (Cunningham, 1981; Hertzog, 1985). Consistent with these findings of structural invariance Hertzog (1989) has found no age by speed interaction in regression analyses in which perceptual speed and answer speed measures (in addition to background variables) served as independent variables predicting composite measures based on tests from the Educational Testing Services Kit and PMA abilities or on PMA abilities. The same result was replicated in this study for 3 out of 4 PMA abilities, the sole exception being Verbal Meaning. The second concern questions the validity of the constructs themselves. Thus Labouvie-Vief (1980) proposed to interpret fluid-type deficits as indicating either that fluid forms of reasoning constitute a developmentally prior mode of adaptation or that these modes of reasoning are highly specialized and not important in older age, or both. This line of argument seems to be rendered plausible by empirical findings which suggest that there is an increased importance of pragmatic, practical problem solving in later life. Thus, Berg & Sternberg (1985) examining intuitive notions of intelligence at various ages have found that everyday competence is perceived to be more important at 50 years and at 70 years of age than at 30 years. It is important however to add that even for the age of 70 an important factor characterizing concepts of intelligent behavior was a composite factor of fluid and crystallized intelligence. One way to evaluate these arguments is to examine the evidence of age related change in performance on more "ecologically valid" tasks and on relationships between performance on these tasks and traditional measures of intellectual functioning. Some of the studies (e.g., Denney and Palmer, 1981) reported a quadratic relationship of practical abilities with age, with performance reaching a peak in the forties and fifties and declining thereafter. (See Denney, Chapter 12 of this volume, for further discussion.) Increases continuing in old age were reported by Cornelius and Caspi (1987) who mentioned the possibility that their Everyday Problem Solving Inventory

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might involve too low a level of complexity to detect decrements. The same study found modest (around .3) but significant correlations among the practical abilities on one hand, and Verbal Meaning and Letter Series, on the other hand. Another study which examined the relationship between fluid and crystallized intelligence and practical intelligence as measured by an ETS Basic Skills Test was reported by Willis and Schaie (1986). Confirmatory factor analysis showed that practical intelligence has a primary loading on the fluid intelligence factor and a secondary loading on crystallized intelligence. The Basic Skills test score correlated .83 with Figural Relations (a measure of fluid intelligence) and .78 with vocabulary. The study of practical intelligence is extremely difficult, mainly because of problems of ecological validity of the everyday life tasks used to measure practical intelligence. To the extent that it is possible to draw conclusions on the topic discussed here, namely the validity of conventional tests of intellectual functioning in old age, it seems that there is no solid evidence to substantiate the argument of irrelevance of these abilities in older age (Labouvie-Vief, 1982). At most it is possible that practical intelligence is a distinct form of intelligence with a distinct developmental pattern (Baltes, 1987). Even then we can expect processes related to traditional forms of abilities to be involved in practical intelligence as well. It is possible of course, that a low level of complexity encountered in many everyday life problems make decrements in these abilities relatively unimportant in respect to the everyday life tasks. A longitudinal study of practical and psychometric intelligence is needed to answer this question. A related question which also may be addressed by such a study would consider not only processes of adjustment implied by a view of intelligence as ability to adapt (Sternberg, 1985) but also processes of shaping the environment and selecting the environment. A particular interest is the possibility that older adults modify their environment to compensate for intellectual decrements. It is plausible that the general question regarding the importance of intellectual decrements in older age has no general answer. For an adult interested in maintaining a high level of intellectual activity, being for example involved in activities of reading and writing, decrements of the type measured by traditional tests might be of some threat in the long run. This makes questions regarding the trainability of intellectual abilities in old adults to be of a practical as well as a theoretical interest. Two aspects of this issue, plasticity and reversibility, will be discused in the next section. Plasticity and Reversibility of Intellectual Functioning in Old Age Plasticity There are several results suggesting the possibility of modifying--improving--abilities in old subjects. This evidence includes inductive reasoning (Baltes, Dittmann-Kohli & Kliegl, 1986; Blieszner, Willis, & Baltes, 1981; Schaie & Willis, 1986), figural relations (Baltes, Dittmann-Kohli & Kliegl, 1986; Willis, Blieszner, & Baltes, 1981), spatial orientation (Schaie & Willis, 1986), and attention (Baltes & Willis, 1982). Some of the results obtained in these studies suggest, however, that the amount of transfer to other tests of Figural Relations was either small (Willis et al, 1981) or narrow and limited to tests similar to the one used in training. The best evidence

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suggesting modifiability at a factor or construct level includes inductive reasoning and spatial ability. In a somewhat different context--of problem solving--training effects have been obtained in the areas of conservation tasks (Hornblum and Overton, 1976), in the area of concept-identification (Sanders, Sterns, Smith & Sanders, 1975; Sanders & Sanders, 1978) and in the 20 questions task (Denney, 1984). Moreover, transfer to more complex identification tasks has been shown by Sanders & Sanders (1978), indicating a change at the ability level. It seems therefore that modifiability of intellectual abilities by training and even, possibly, by practice alone (cf. Baltes & Willis, 1982) has been demonstrated for some psychometric and for some problem solving abilities. There are intriguing questions concerning the amount of plasticity, the rate at which changes can be accomplished and how these parameters change during the life span. The relatively small number of intervention studies which include young and old groups (e.g., Heglin, 1956; Hoyer, Hoyer, Treat & Baltes, 1979, Young, 1966) make risky a generalization of this issue. However the evidence was found to be sufficient by Denney (1984) to include in her theory of exercised and unexercised ability a decreasing gap between the two kinds of abilities corresponding to decreasing plasticity with increasing age. The testing-the-limits approach (e.g., Baltes 1987) addresses the issue of boundaries of plasticity by intervening to identify latent reserve capacities. While this strategy has shown again the existence of sizeable plasticity, it has evidenced also the greater plasticity of younger adults, at least in the area of using efficiently mnemonic skills. An answer to the question of change in plasticity with increased age is complicated by the fact that the comparison is being made among groups of adults belonging to different cohorts and having different initial amounts of practice of the trained ability. Long term longitudinal studies including interventions with respect to a variety of abilities may provide a definitive answer to the question of intraindividual variability in plasticity. Reversibi I i ty An important question is whether age declines in intellectual functioning can be reversed (or prevented in the first place) and to what extent is the reversal possible. It might seem that positive results in training studies imply a positive answer to this question. However, the question is complicated, among other things, by the difficulty of distinguishing empirically between improvements which reflect an increased exercise of cognitive abilities and improvements which reflect a real reversal of age-induced changes in the "developmental reserve capacity" (Baltes, 1987) or in the "exercised potential" (Denney, 1984).

To illustrate this point consider Schaie and Willis's (1986) training study. In this study a cognitive training technique was applied to some of the participants in the Seattle Longitudinal Study who evidenced decline in PMA scores on either inductive reasoning or spatial ability over a period of 14 years. In 40% of cases the decliners reached post-training scores as high or higher than their base level scores measured at the beginning of the 14 years period. This may be considered as "remediation of decline" in 40% of the cases. Moreover, the researchers demonstrated that the gains were at the factor level and that they occurred in the training group (spatial ability or

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inductive reasoning). The question arises whether the remediation in this case was obtained due to an exercise of a previously unexercised ability, thus without modifying the potential, or whether it represented a more fundamental remediation of the decline in the potential itself. The fact that a large proportion of the stable individuals who were not trained improved as well, is in fact consistent with the hypothesis that the change was not in the underlying potential. The somewhat lower proportion of the percentage of subjects improving from training in the stable category could be expected assuming that those people exercised the relevant abilities to a greater extent than the decliners did. This by no means should detract from the importance of the results obtained in this study but it points to some unsolved problems of interpretation. It is possible that the application of the testing-the-limits methodology may help to investigate not only the boundaries of plasticity but also the limits of reversibility in intellectual changes. Methodological Developments

The tools available to the researcher interested in the study of intellectual change with age are continuously increasing in number and in sophistication. We review here some of the more commonly used methods emphasizing the ways they may be used in an analysis of interindividual differences in patterns of change. Autocorrelation Linear Structural Models

Models of this type have been analyzed by Joreskog and others (e.g., Joreskog, 1979; Joreskog & Sorbom, 1988) and applied in longitudinal studies of intelligence (e.g., Cunningham & Birren, 1980; Hertzog & Schaie, 1986). In these models abilities measured at different times using several indicators are assumed to be autocorrelated over time at the factor level. Commonly, to achieve satisfactory fit, residuals of indicators of abilities are also assumed to autocorrelate over time. Usually centered scores have been used in models but occasionally means have been modeled as well (Hertzog and Schaie, 1988). The magnitude of autocorrelations of abilities over time provides an indication of stability. Interindividual differences are reflected in lower intercorrelations. In fact there is a strong rationale for modeling means in this context. First, questions regarding the stability of means (to distinguish from covariance stability, Hertzog & Schaie, 1988) are of substantive interest. In addition, the model to be estimated typically has implications in terms of means, such that modeling should allow a better estimation of all parameters and a better test of the whole model (Hayduck, 1987, chapter 9). Rather than correlating errors over time it is possible to consider each measure as influenced (in addition to the common factor) by its own orthogonal specific factor which is stable over time (Joreskog, 1971). Such an approach may produce some interesting results in an analysis of stability. The errors at the level of the specific measures would reflect in this case lack of reliability and individual differences in patterns of change which affect the specific measures. Should the variances of these residuals be of the magnitude of the lack of reliability, that would suggest minimal individual differences (Hertzog and Schaie, 1986) and high stabilities of the specific measures.

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Multivariate Panel Models These models can be probably best understood in relation to a simplex (autoregressive) model (Joreskog, 1970). In such a model a latent variable at time ti is determined by the variable at time ti.l and by a unique factor. The latent variable is measured at any time ti with one indicator. The simplex model allows the estimation of several parameters which may serve as measures of stability: Beta coefficients for the relationships between two consecutive latent variables and variances of changes in the latent variable between two waves (Werts, Linn and Joreskog, 1977). More complex models would involve multiple indicators and multiple latent variables (Alwin, 1988; Campbell, 1988; Joreskog, 1979). A further generalization will consider higher order autoregressive models including paths from variables at time ti to variables at time ti+t. Models including autoregressive processes may be used to measure stability of latent variables conceived as traits which are relatively stable over time and of latent variables conceived as states which are relatively unstable over time (Kenny and Campbell, 1989). Causal models may also be allowed to relate individual differences in patterns of change to background variables such as health, education or SES. These models have been analyzed by Joreskog (1979) but have not been extensively used in the study of intellectual changes (for an application see Cunningham, Smook, & Tomer, 1986). The background variables are assumed to affect the ability variables at time 1, time 2, etc. Of particular interest is the magnitude of the coefficients for the path from the exogenous variables measured at an earlier time t to the ability construct measured at a later time. However, it was argued (Rogosa & Willett, 1985; Rogosa, 1988) that the magnitude of these coefficients may change drastically with a change in the initial time. A growth curve approach based on individual time paths was advocated for the measurement of change and for the study of correlates of change (Rogosa, Brandt, & Zimowski, 1982; Rogosa & Willett, 1985). Latent Growth Curves Latent growth curve models have been developed and described by Meredith, McArdle and their colleagues. (McArdle, 1986, 1988; McArdle & Epstein, 1987; Meredith & Tisak, 1990). In a simple model of this kind a developmental process is represented by a latent variable--"curve"-- affecting an observed variable at different times. The parameters for the paths relating the latent variable to the observed variable may change from time to time. The model has implications in terms of means and variances of the raw variables, in addition to implications for covariances, and therefore should be fitted to a Moment Matrix including information about means (e.g., McArdle 1988). The factor scores on the curve variable reflect over-time pattern similarity to the overall group curve. Of special interest in our context is the individual variation around the group curve which is estimated by the model. Also a Growth Curve Reliability at each occasion indicates the proportion of variability in the score of the observed variable at the respective occasion accounted for by the latent curve. An increase with time would thus suggest an increased ability to predict an individual's change trajectory.

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The simple model above may be transformed into a path model by allowing background variables to affect the latent curve (McArdle & Epstein, 1987). In this case a significant coefficient for this path would suggest that the background variable may account to some extent for different patterns in growth. Latent growth curves may be formulated to include more than one latent variable, for example one representing general Level and one representing general Shape (McArdle, 1986). They also may be applied to cases where several factors with multiple indicators are repeatedly measured (McArdle, 1988). In this case one has to integrate a common factor longitudinal model with a curve model. For example, curves may be based on factors, rather than on observed variables to generate a Curveof-Factor-Scores model. Alternatively, curves may be defined at the level of indicators and factors may be based on curve scores (McArdle, 1988). In general, latent curve modeling appears to be promising as models of change which also may help to define and quantify better interindividual differences in patterns of change and may be used to analyze results obtained in sequential designs (Meredith & Tisak, 1990). Structural Equation Models with Random Parameters Commonly, parameters in a structural equation model are considered to be fixed across individuals--the main reason for Rogosa's criticisms. At most they may be considered to vary across different populations (Joreskog, 1971). Recently, however, structural equation models with random parameters have been formulated (e.g., Goldstein & McDonald, 1988; Muthen, 1989, Muthen & Satorra, 1989). In this case, parameters of equations defined for a certain level (within a group or individual) may be treated as random variables. This type of model, although still being in a rather incipient phase seem to be especially appropriate for longitudinal analyses (Muthen, 1989). Different occasions within a given individual will constitute one (low) level of analysis and the interindividual variability a second level of analysis. Concluding Thoughts A wide variety of concepts and theoretical viewpoints have been reviewed. A number of issues and problems have been identified and evaluated. It is apparent that the complexity of intellectual functioning and age is being probed to an increasingly greater extent. Today, there is less of an emphasis on whether or not there is decline and a much greater emphasis than has been the case in the past on questions like what variables are showing larger declines, beginning at what ages, or what the precursors of decline are. It is becoming increasingly apparent that intellectual functioning must be considered within the context of a variety of individual difference variables. Examples include not only personality variables (such as Dysphoria), but functional variables (such as health status and visual acuity) as well. Points of intellectual tension between various theories and existing data continue to motivate interesting research in this area. The relentless development of linear structural equation modeling and related new developments such as latent growth

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Joreskog, K. G. (1979). Statistical estimation of structural models in longitudinal developmental investigations. In J. R. Nesselroade & P. B. Baltes (Eds.), Longitudinal research in the study of behavior and development (pp. 303-351). New York: Academic Press. Joreskog, K. G., & Sorbom, D. (1988). LISREL 7: A Guide to the program and its application. Chicago: SPSS, Inc. Jorm, A. F. (1986). Cognitive deficit in the depressed elderly: a review of some basic issues. Australian and New Zealand Journal of Psychiatty, 20, 11-22. Kenny, D.A., & Campbell, D. T. (1989). On the measurement of stability in overtime data. Journal of Personality, 57, 445-481. Herman, G. L. (1987). The nature of depression: Mood, symptom, disorder. In k J. Marsella, R. M. A. Hirschfeld,, & M. M. Katz (Eds.), The measurement of depression (pp. 3-19). New York: Guilford Press. Herman, G. L., & Weissman, M. M. (1989). Increasing rates of depression. Journal of the American Medical Association, 261, April 21, 2229-2235. Labouvie-Vief, G. (1980). Beyond formal operations: Uses and limits of pure logic in lifespan development. Human Development, 23, 141-161. Labouvie-Vief, G. (1982). Dynamic development and mature autonomy: A theoretical prologue. Human Development, 25, 161-191. Labouvie-Vief, G. (1985). Intelligence and cognition. In J. E. Birren and K. W. Schaie (Eds.), Handbook of the psychology of aging (2nd ed., pp. 500-530), New York: Van Nostrand Reinhold Company. Lachman, M. E. (1983). Perception of intellectual aging: antecedent or consequence of intellectual functioning? Developmental Psychology, 19, 482-498. Lachman, M. E., & Jelalian, E. J. (1984). Self-efficacy and attributions for intellectual performance in young and elderly adults. Journal of Gerontology, 39, 577-582. Lewinsohn, P. M., & Rohde, P. (1987). Psychological measurement and depression: overview and conclusions. In A. J. Marsella, R. M. A. Hirschfeld, & M. M. Katz (Eds.), The measurement of depression (pp. 240-266). New York: Guilford Press. Marr, D. B. & Sternberg, R. J. (1987). The role of mental speed in intelligence: A triachic perspective. In P. A. Vernon (Ed.), Speed of information processing and intelligence (pp. 271-294). NJ: Ablex Publishing Company. Marsella, A. J., Hirschfeld, R. M. A., & Katz, M. M. (Eds.). (1987). The measurement of depression. New York: Guilford Press. Mason, C. E., & Rebok, G. W. (1984). Psychologists' self-perceptions of their intellectual functioning. International Journal of Behavioral Development, 7, 255266. McArdle, J. J. (1986). Latent growth within behavior genetic models. Behavior Genetics, 16, 163-200. McArdle, J. J. (1988). Dynamic structural equation modeling with repeated measures data. In J. P. Nesselroade, & R. B. Cattell (Eds.), Handbook of multivariate experimental psychology (2nd ed., pp. 561-614). New York Plenum. McArdle, J. J., & Epstein, D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58, 110-133. McCrae, R. R., & Costa, P. T. (1985). Updating Norman's "Adequate Taxonomy": Intelligence and personality dimensions in natural language and in questionnaires, Journal of Personality and Social Psychology, 49, 710-721. Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107-122.

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Meyer, B. J. F., & Rice, G. E. (1981). Information recalled from prose by young, middle, and adult readers. Experimental Aging Research, 7, 253-268. Muthen, B. 0. (1989). Latent variable modeling in heterogeneous populations. Psychometnka, 54, 557-585. Muthen, B., & Satorra, A. (1989). Multilevel aspects of varying parameters in structural models. In D. Bock (Ed.) MultileveZ analysis of educational data. (pp. 87-99), San Diego: Academic Press. Nesselroade, J. R. (in press). The warp and the woof of the developmental fabric. In R. Downs, L Liben, & S. Palermo (Eds.) Visions of deveZopment, the environment and aesthetics: The legacy of Joachim F. WohlwilL Hillsdale, NJ: Lawrence Erlbaum. Owens, W. A. (1966). Age and mental ability: A second adult follow-up. Journal of Educational Psychology, 57, 311-325. Rogosa, D. (1988). Myths about longitudinal research, In K. W. Schaie, R. T. Campbell, W. Meredith, S. C. Rawlings (Eds.), Methodological issues in aging research (pp. 171-209). NY: Springer Rogosa, D. R., Brandt, D., & Zimowski, M. (1982). A growth curve approach to the measurement of change. Psychological Bulletin, 92, 726-746. Rogosa, D. R., & Willett, J. B. (1985). Understanding correlates of change by modeling individual differences in growth. Psychometnka, 50, 203-228. Salthouse, T. A. (1985a). Speed of behavior and its implication for cognition. In J. E. Birren & K. W. Schaie (Eds.), Handbook of the psychoZogy of aging (2nd ed., pp. 400-426). New York: Van Nostrandt Reinhold. Salthouse, T. A. (1985b). A theory of cognitive aging. Amsterdam: North Holland. Sanders, J. C., Sterns, H. L., Smith M., & Sanders, R. E. (1975). Modification of concept identification performance in older adults. Developmental Psychology, 22, 824-829. Sanders, R. E., & Sanders, J. C. (1978). Long-term durability and transfer of enhanced conceptual performance in the elderly. Journal of Gerontology, 33,408412. Sands, L., Terry, H., & Meredith, W. (1989). Change and stability in adult intellectual functioning assessed by Wechsler item responses. Psychology and Aging, 4,79-87. Schaie, K. W. (1977-78). Toward a stage theory of adult cognitive development. International Journal of Aging and Human Development, 8, 120-138. Schaie, K. W. (1978). External validity in the assessment of intellectual development in adulthood. Journal of Gerontology, 33, 695-701. Schaie, K. W. (1983). The Seattle Longitudinal Study: A 21-year exploration of psychometric inteligence in adulthood. In K. W. Schaie (Ed.), Longirudinal studies of adult psychological development (pp. 64-135), New York The Guilford Press. Schaie, K W., & Hertzog, C. (1983). Fourteen-year cohort-sequential analyses of adult intellectual development. Developmental Psychology, 19, 531-543. Schaie, K. W., & Willis, S.L. (1986). Can decline in adult intellectual functioning be reversed? Developmental Psychology, 22, 223-232. Schmitz-Schemer,R., & Thomae, H. (1983). Constancy and change of behavior in old age: Findings from the Bonn Longitudinal Study on Aging. In K. W. Schaie (Ed.), Longitudinal studies of adult psychological development (pp. 191-221), New York The Guilford Press.

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15 Cognitive Aging: A Summary Overview Eugene A. Lovelace Alfred University

The chapters of this book deal with three facets of cognitive aging. One is an attempt to understand underlying cognitive processes, and to discover the ways in which these processes show age-related change, both quantitative and qualitative. A second facet concerns the impact of other traits of the individual on cognitive functioning. These include personality, beliefs, self-evaluation, self-knowledge and conscious awareness of these processes in the monitoring of cognitive activities. The third involves exploration of some possibilities for effective interventions that will permit an improvement of cognitive functioning for older adults, and perhaps at least delay, if not prevent, cognitive decline with aging. Conceptual Frameworks As noted by Stine (Chapter ll), the theoretical framework for current research efforts in cognitive aging is a mixture of concepts from revised versions of the modal multistore models and from associative network models. Within the multistore framework the concept of primary or short-term memory (STM) as a single, largely passive, limited quantity store has been supplanted by a more active, multiple storage conception of working memory. In the earlier framework, consensus held that little of the age-related decline in cognitive functioning was to be seen in STM abilities (e.g., Craik, 1977), but rather the processes of storage and retrieval from secondary or longterm memory (LTM) were responsible for most adult age differences (e.g., Poon, 1985). There was a clear appreciation, however, that when STM is not taken as simply storage (indexed, for example, by memory span), but rather requires the active manipulation of the contents of STM, age effects become much more pronounced. In terms of current conceptions, much of cognitive aging is believed to be due to agerelated change in working memory, specifically a loss of efficiency in the operation of the central executive processing component.

From the perspective of associative network models, while there is considerable evidence for age invariance in spreading activation, aging has been related to selective alteration in the ease of activating certain types of nodes (Burke, Chapter 10). There are a number of issues related to research strategy that are important to keep in mind in the conduct and interpretation of research on cognitive aging. For example, most often the interest is in central cognitive processes, and care must be taken to see that any decrements in functioning are not due to more peripheral processes, to deficits in sensory input (or even changes in the purely motor components). That is,

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we need to know whether the deficits in performance are truly due to "resource" limitations rather than to "data" limitations (Norman & Bobrow, 1975). An issue for research strategy concerns whether you are interested in age-related difference in current levels of cognitive functioning or in potential levels. Efforts to assess the magnitude and durability of training in cognitive functioning (see Kotler-Cope & Camp, Chapter 8, and Willis, Chapter 9), and the "testing-the-limits" approach to studying cognitive plasticity in old age, as well as the study of true expertise in older adults, all reflect the growing importance of this distinction, and the developing emphasis on potential performance levels. Much of the recent research on cognitive functioning has been in the mold of "the new mental chronometry" where differences in response times, or relations holding among response times, are taken as indices of the structure of underlying processing events. Such use of time measures, and the assumptions regarding the processes they are presumed to index, require special consideration when aging is a variable of interest, since response slowing is one of the more universal changes characterizing the performance of aged adults (see Goggin & Stelmach, Chapter 5). The slowing itself has been proposed by some to be the primary general determinant of cognitive decline in aging (Birren, 1965; Salthouse 1982, 1985a). A second general determinant, which has figured heavily in many recent accounts of cognitive aging, centers on the allocation of attentional or processing resources. The limited capacity aspect of human information processing is often held to derive from the limitation of processing resources (e.g., Kahneman, 1973). Age-related reduction in cognitive processing ability is then related to reduction with aging in such attentional or processing resources (e.g., Burke & Light, 1981; Craik & Byrd, 1982). It is important to note that there is a heavy demand for these resources made by conscious, deliberate information processing tasks ("effortful"tasks), while little or no demand for such resources is assumed to be made by "automatic"processing. Thus, according to this general model, one should find sizeable age effects on tasks involving effortful processing and little if any age differences on automatic tasks (e.g. Hasher & Zacks, 1979, 1984). Another recurring view regarding the origins of age-related cognitive deficit holds that the magnitude of age effects seen on any cognitive task is primarily a function of the complexity (or difficulty) of the task. This task complexity relation to age effects is most often provided as a summary description characterizing properties of tasks in relation to cognitive aging rather than as an explanatory model. Clearly the greater complexity of the task might be taken to involve a greater number of cognitive processes, each carried out more slowly by the aged, thus magnifying age effects, or greater complexity might be taken to involve more demand for attentional/processing resources. Thus this general observation of enhanced age effects with increased complexity is readily accommodated by either a slowing or a processing resources model of cognitive aging. Performance on nearly all cognitive tasks might be expected to be enhanced by practice, i.e. by experience with components of the task or very similar events. Since greater age is generally associated with greater experience, and so more practice with

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task relevant processes, the benefit of experience should create age differences favoring the older adult. There may well be another, concurrent, effect; as people grow older and amass wisdom from experience they also suffer biological consequences of aging. Aging is then, as Stine observes (Chapter ll), a race between biology and experience. Denney (1982) provides a good discussion of this viewpoint. If one assumes that the benefits of experience diminish with repeated exposure (the usual negatively accelerated learning curve) then the benefits of experience will show the greatest effects from young to middle-aged adults. If one then also assumes the cumulative consequences of biological aging on neurophysiological functioning to show accelerating effects beyond middle age, then one arrives at Denney's general model of age-related improvement in cognitive functioning until somewhere in middle-age after which point the decline due to progressive loss of neural capacity more than outweighs the small gains from additional experience. While such models make some broad predictions and permit an integrated summary of a number of findings, there is an increasing understanding that broad, general assertions about the stability or decline of cognitive functioning with aging, even of a particular type such as problem solving or language processing, will ultimately prove insufficient. The nature of cognitive aging effects hinges on the interactions of properties of the stimulus materials, other personal characteristics of the individual (besides age), and characteristics of the task, most particularly the nature of the memory operations involved. As noted below, patterns of age effects have been found to depend critically on other properties of the individuals, e.g., level of verbal skill, education level, socio-economic status, and amount of prior practice with the task. Ultimately, effective theorizing about cognitive aging will have to reflect these extensive contextual interactions. In addition to the domain- or task-specific nature of aging effects, we need to make our theorizing more sensitive to personal trait factors. Moderating the relationship of age to cognitive performance on the basis of factors such as self-efficacy or personality type, while making the relationship more complex, should permit more complete understanding (more accurate predictions). These personal variables may be found to account for some of the wide variations in cognitive performance in older adults, as well as provide additional information that could enhance the design of effective, personalized intervention programs. The absence of such a theoretical framework, grounded in specific contextual interaction, is part of the reason why there is such wide variation in tasks and tests employed in the study of cognitive aging. Such diversity may have merit in its own right, in that it can eventually help to establish the boundary condition of cognitive aging effects. However, it reflects the lack of clear criteria for the selection of appropriate task, test, and subject samples, due to the absence of very specific hypotheses. The bulk of the current research in cognitive aging remains primarily descriptive or provides empirical test of a broad prediction about a single variable. In these cases, of course, we sum across a series of different contextual properties and these tend to increase the within-condition variance and reduce our sensitivity to true age effects. The extent to which one can successfully use an approach in which aging effects are conditionalized on other factors depends, of course, on what we know about these

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other factors, and how well they can be measured or specified. For example, in the w e of personal traits, it might hinge on the accuracy and reliability with which the structure of personality can be specified and individual differences measured. This issue of measuring the structure of personal traits (e.g., personality) or task characteristics (e.g., types of intelligence) is also important in its own right in the study of cognitive aging. Evidence of the stability of such structure across ages in cross sectional studies, and over time in longitudinal studies, can clearly provide an important indication of whether any age-related changes observed are likely to be differences in kind or in degree. The remainder of this chapter attempts a concise recapitulation and synthesis of the major points in earlier chapters. Generalizations are made here without the extensive reference documentation provided in earlier chapters. For more details the reader is directed to those earlier discussions by parenthetical author plus chapter, e.g., "(Kausler, Chapter 2)",it being understood that these refer to chapters of the present volume. Cognitive Aging: Mental Processes This section provides a review of major points of this volume regarding some of the basic underlying cognitive processes. Attention Deliberate, conscious cognitive activity requires attention. A popular conception of attention is as a pool of resources available to support these deliberate or "effortful" cognitive processes (Kahneman, 1973). Some information processing occurs "automatically" with little or no conscious intention and places minimal demand on these attentional resources. Whereas conscious, effortful processes must be undertaken serially (sequentially), it is typically assumed that automatic processes, since they do not draw on this resource pool, can occur concurrently or "in parallel". Since Sternberg (1969) introduced the new mental chronometry, the common way to distinguish processes that occur serially from those that occur in parallel involves comparing the times required to carry out the mental operations involved in those processes. Plude and Doussard-Roosevelt (Chapter 4) note that there is reason to believe that, under some conditions, older adults are more cautious; thus, slower decision times on their part might result from some speedlaccuracy trade off where they were being cautious not to make errors and in doing so increased their times. Three lines of evidence presented by Plude and Doussard-Roosevelt indicate this speed/accuracy trade off is not a problem for the sort of visual search tasks that form the major focus of their review of attentional processes. In those tasks, automatic parallel processing versus effortful serial processing is inferred from the slopes of set size functions. The decision time is independent of the number of items present in an array to be scanned, thus producing a zero slope and implying automatic processing, for certain kinds of search tasks, e.g., is there a BLUE item in the array? When the task cannot be performed on the basis of simply detecting the presence of a single feature, but rather requires indentifying the conjoint

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presence of two or more features, a substantial positive slope is typically obtained; the greater the number of items in the array the longer the decision time indicating that the task required focussing attention on items sequentially. In the two-stage Feature Integration Theory of Treisman (1988; Treisman & Gelade, 1980) the detection or "extraction" of features can occur very quickly, automatically, and in parallel over the whole visual field. Thus if the feature BLUE is present anywhere in the field it is immediately detected, regardless of the number of items in the array. The second stage involves the "integration"or conjoining of the sets of features that characterize each item, e.g., color and shape. This integration of components is held to require selective or focal attention allocated serially across the distribution mappings of detected features. Are there adult age differences in either or both of these stages, feature extraction or feature integration? Plude and Doussard-Roosevelt (Chapter 4) review this literature and present a series of their own studies, which lead them to the following conclusions. Selective attention deficits, i.e., interference of non-targets on target detection performance, are not seen for young or old adults when the task involves detection of a single feature. The registration of a color, for example, occurs as a preattentive, parallel process; the slopes of functions relating decision times to display set size are near zero for young and old. When the task requires the integration of two features, e.g. color plus shape, then focal attention is required and substantial positive slopes are found for the display set size function. The slope is considerably greater for the old than for young adults on this feature integration task, indicating an age deficit at this sort of selective attention. Plude and Doussard-Roosevelt note that it is probably not display set size per se which produces this effect but rather the relationships between features of the targets and non-targets. This age effect in selective attention may represent differential spatial localization abilities; the differential slope is reduced by providing cues to the spatial location of the target item, and this is true when attention, but not visual fixation, is shifted to that location where the target event will occur. With respect to divided attention, the data have generally shown greater decrements from division of attention for old than for young adults. Plude and Doussard-Roosevelt conclude that you see this age effect when feature integration is emphasized, but not when the tasks involve simply feature extraction. If control of the display is given to the individual, so speeded processing is deemphasized, the elderly show compensation for the divided attention decrement. They conclude that the divided attention deficit is found when the task involves both feature integration and a speed set for performance. Further, the aged appear able to ignore irrelevant stimuli provided those stimuli are spatially distinct from the location of focal attention. Their interpretation is that this age effect is best viewed as another instance of the general finding that increasing task complexity magnifies age differences. With respect to sustained attention, or vigilance, one can distinguish between difference in target detection performance level vs. the rate of decrement in performance with the passage of time. The literature shows age effects are more often seen in performance level than in rate of decline in performance, suggesting minimal loss of ability to sustain performance level over time. Two other findings also fail to show clear evidence of age effects: a) if the percent targets in training are not the

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same as the percent targets on the vigilance test, one gets a generalization decrement, but this decrement is of the same magnitude for young and old; b) generalization decrements that result from stimulus degradation are about the same for young and old. The age differences in performance level seen in an initial session do not decline when additional practice with the task is provided. In fact, older adults seem to show a deficit for the development of automaticity. There are two varieties of automaticity, that which is innate, or “pre-wired, and that which is acquired by practice. The former is independent of practice and is seen in the feature detection processes of Treisman’s model. The other sort occurs in situations where sufficient practice with stable task parameters can lead to rapid, parallel processing, e.g., the work of Shiffrin and Schneider (1977) where set size effects were eliminated when the mapping of items onto target or non-target categories was constant, but not when it was varied. A recent review (Plude & Hoyer, 1985) indicated that age effects are generally greater for varied mapping conditions than for consistent mapping. This may be best viewed as an age deficit in working memory, for which the load is greater in varied than in consistent mapping, rather than as facility of the aged for automatizing the task. More recent work of Fisk and his colleagues (e.g., Fisk, McGee, & Giambra, 1988), comparing performance on varied versus consistent mapping across a large number of training sessions, shows the age effect to eventually be much greater on the consistent mapping, suggesting again a deficit for automaticity in the aged. It must be pointed out, however, that there is good evidence that cognitive activities that have been automatized prior to late adulthood may be maintained at high levels (see the literature on expertise, e.g., Charness, 1985; Salthouse, 1984). For theoretical reasons there has been a strong interest in the issue of automaticity and aging. This topic is re-visited below in the section on memory and aging. Memory and Aging

Types of memory. There is extensive evidence now for differential effects of age as a variable on different forms of memory tasks. For example, age effects are much greater for episodic than semantic memory (e.g., Smith, 1980) although there is evidence for age-related decrements for some semantic memory tasks (Light & Burke, 1988). For episodic memory, age effects are much greater for recall than for recognition tests (e.g., Craik & McDowd, 1987). The preceding generalizations refer to explicit rather than implicit memory tasks (Schacter, 1987). In explicit memory tasks the individual had an intention, at time of the experience, to remember something, and, at time of test, consciously attempts to retrieve that memory trace. Implicit memory tasks occur, on the other hand, when there was no knowledge at time of experience that memory will be tested, and so no intention to remember the experienced event, coupled with a test situation in which the person is not asked to attempt to remember. Instead the test situation asks the individual to attempt some task where the response may be altered by virtue of the prior experience, even in the absence of any conscious recollection of that prior experience. For example, having seen a list of words presented, when shown the initial few letters of a word and asked to complete it with the first word that comes to mind,

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the likelihood of various completions are altered. The individual has an increased probability of using one of the words recently seen, even though there is no awareness on the part of the individual that this is occurring. The recently encountered item appeared to have been primed for easier activation. About two decades ago it was discovered that organic amnesics, who were much poorer than age-matched normal controls at intentional episodic memory tasks, performed very similarly to the controls on a word stem completion task. There has been increasing interest in whether this implicit memory might be unrelated to age. Kausler (Chapter 2) reviews these research efforts. While some have considered implicit memory to be a system separate from episodic memory and semantic memory, Kausler assumes that it is a variety of episodic memory where performance is determined by the similarity of encoding processes at time of encounter with those processes occurring at time of implicit memory test. It is assumed that the advantage that a greater encoding of contextual information may confer on young adults in a recall task will be lost in such tests where there is no conscious recollection of the prior episode. The evidence for this lack of age effects on implicit memory tests is modest. Comparisons of young and old on implicit tests typically show small, nonsignificant differences, in contrast to larger, statistically reliable differences for the age variable in studies employing an explicit memory condition. The small differences on the implicit tasks do, typically, favor the young. As Light & Burke (1988) observe, however, these small age differences may reflect the fact that these tasks contain some small component of explicit memory. As Kausler notes, the absence of age effects on implicit memory tests is of considerable importance to contemporary cognitive theorizing. While one cannot accept the null hypothesis of age insensitivity for implicit memory with certainty at this time, it is clear that any age effects on such tests are very small indeed. One implication is that there may be automatic encoding of those features necessary to perform the implicit memory test, and that such automatic encoding may be unrelated to age.

While the storage capacity of primary or working memory may show minimal age effects, when active manipulation of the information is required, or "the processing functions are loaded", age effects in working memory are sizeable (e.g., Dobbs & Rule, 1989; Salthouse, Mitchell, Skovronek, & Babcock, 1989; Wingfield, Stine, Lahar, & Aberdeen, 1988). Working memory has often been seen as the limited capacity bottleneck in information processing. In this regard the age differences in working memory may be taken as support for the concept of an age-related decline in attentional/processing resources as the major determinant of cognitive aging. There is, however, considerable evidence that working memory may not be a unitary construct but rather there may be a need to view it as task- or domain-specific (see Stine, Chapter 11). One role of working memory is as gate-keeper for admission of material to generic memory; it is where selective attention resides. Working memory might become less selective in its processing functions, and so less efficient, with aging. Hasher and Zacks (1988) have made such a proposal, that the main cause of cognitive aging is a reduced

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efficiency of working memory due to age-related decline in the function of inhibitory control mechanisms. This allows more irrelevant information to enter working and secondary memory in older adults. Automaticity. One of the criteria which Hasher and Zacks (1979) proposed for distinguishing between "effortful" and "automatic" encoding processes was whether the process was age sensitive for various adult ages. Automatic processes, they argued, should not show age-related changes in performance.

There are three issues raised by a theoretical model of this sort. To prevent circularity one needs a behavior-independent criterion for whether tasks involve automatic, as opposed to effortful, processes. Thus one's theorizing must specify the sort of processes a priori. Hasher and Zacks suggested several types of information that they believed would be automatically encoded, including frequency of occurrence, spatial location, and temporal order of events. A second issue concerns the fact that the sort of evidence one needs to support the model is the absence of age effects, raising the issue of when it is appropriate to accept the null hypothesis. Consistency in direction of effects, and magnitude of mean differences take on greater importance than statistical significance tests, since there are a variety of ways that "real effects" may be missed in statistical tests. The third issue concerns how to arrange the tasks so as to minimize the possibility that supposedly automatic tasks will involve some amount of effortful processing, thereby introducing age effects. The proper control here would appear to be a truly incidental memory task in which at time of original encoding the individual has not even been forewarned that any memory test will occur. Kausler's extensive review of the literature on frequency, spatial location, and temporal order provides little support for Hasher and Zack's assumption that encoding of the characteristics are automatic, and as such would be unrelated to age. For both spatial location and temporal order, when tests for this information were unexpected, older adults showed average performance deficits of about 25% relative to the performance levels of young adults when tested on memory for these features. The magnitude of these age effects is not very different from that seen when individuals know they will be tested on such features, or that observed in clearly effortful tasks such as paired-associate learning. For frequency of occurrence measures, however, the average deficit relative to performance levels of young adults was only about 5%, and the differences often statistically nonsignificant. Even here, however, the direction of the effects quite consistently favors the young over the old, yet these small differences may well reflect the contamination of some minimal effortful component of task performance. In addition to these three attributes (frequency, location, and temporal order), Kausler cites evidence for significant age effects in memory for a number of other "non-content" attributes of material, when tests for memory of such attributes were unexpected. These include sex of voice, upper or lower case of print, modality (visual vs. auditory) of experiencing an item, color of pictures, and lateral orientation of pictures. This deficit the aged show in memory for such non-content attributes is

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consistent with the notion that older adults may well remember that item information which is central to the intentional, effortful task, while failing to encode as effectively as young adults the other peripheral features of the situational context (e.g., McIntyre & Craik, 1987). Memory for activities or actions constitutes another category of experience where the memory of young and old have been assessed on an unexpected memory test. In Kausler's review it appears that the result hinges on what form the memory test takes. When asked to recall actions they have performed, older adults show a 20 to 30 percent deficit relative to the recall performance of the young. On recognition tests, however, little or no age effect is seen. While absolute performance levels are high in the recognition tests making it possible that this is an artifact of an insensitive index, the fact that performance is near perfect even for very lengthy series suggests that the information is automatically being encoded. It seems likely that performance on the recall task may require that greater amounts of contextual information have been coded as part of the memory trace, and so the reduced capacity of the aged for this contextual encoding produces the retrieval deficit. fiuospatiaf memoty. Are there differential age effects for explicit memory tasks that rely on visual codes versus those that rely on verbal codes? From their review of the literature, Smith and Park (Chapter 3) conclude that the evidence for greater aging effects for visuospatial than for verbal tasks is not compelling. Many have interpreted the lesser decline on verbal subtests than on the performance subtests of the WAIS intelligence test as an indication that the aged show a greater performance decrement on perceptual/motor, or visuospatial, tasks than on predominantly verbal tasks. But as Smith and Park point out, there are other equally plausible interpretations, such as greater emphasis on time constraints on the performance subtest. Also, the resource demands on working memory are generally greater for performance than verbal tests, i.e., in order to complete the performance tasks more ongoing processing of information is required.

If the aged are less able to utilize visuospatial information then the benefits of adding visual features to verbal materials should be greater for young than for old adults. The observed performance increments, however, are of about equal magnitude for young and old. When visual stimuli are employed that are readily capable of linguistic coding, e.g., simple line drawings of common objects, memory performance is better for the picture form than for the word, the "picture superiority effect". The magnitude of this effect appears unrelated to age, thus failing to confirm the hypothesis that older adults cannot effectively use visuospatial information. When complex scenes are the to-be-remembered events, and recognition memory is tested shortly after presentation of the scene, Park and her colleagues found no age effect. It must be noted that this does not result from insensitivity of the test due to ceiling effects, since performance was well below the ceiling with neither hit rate nor d' showing significant age differences. On later memory tasks, however, at 1to 4 weeks delay, the young did perform better than older adults. Since the immediate test showed no age differences it may be that these age effects on delayed tasks reflect differential difficulty gaining access to the trace later rather than a deficit in encoding of the visual information. Pezdek (1987) reported an interesting finding, however, that

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even on a recognition test without delay, substantial age effects were seen if the task required yes-no judgments as to whether the presented item was the original versus a variant that involved only a slight change. Some visual stimuli are not readily coded linguistically. For abstract drawings age effects are typically found, but Smith and Park note that it is not clear that the magnitude of these age effects are any greater than those seen on some verbal tasks. Bartlett and Lesli (1986) have studied faces, also hard to verbally encode, and found that the information coded by older adults appears to be qualitatively different than that of younger adults. The elderly code information which is face-specific but not view-specific. That is, the young appear to have encoded more information which captures the exact orientation of the face. If the recognition memory task requires such view-specific information, e.g., the foils are the same faces in different orientation, the older adults perform more poorly than the young. However, if the test contains all new orientations for the "old"faces, i.e., view-specific information is now irrelevant, then the age effect disappears. One might say that the young remembered the details of a picture of a person better, but the older adults remember the appearance of the person just as well. Clearly, in terms of the real world application, view-specific information has little utility. The idea that age effects increase in magnitude in relation to task complexity is consistent with Bartlett and Lesli's findings if one assumes the view-specific task to be a more difficult discrimination. Similarly, Pezdek's test of same scene versus slight variant is a more difficult discrimination, and even the introduction of a delay in the tests of scenes by Park and her colleague made the task more difficult. Recall, however, that the absence of an age effect for the easier task was not because the task was so simple that performance was near perfect. In this case the scenes represent meaningful patterns for which the individual may have some well-developed encoding operations which supported the encoding both at study and at time of test. Thus there would be stability of the encoded representation. To the extent that these modes of representing the contents of the scenes are highly practiced and approach being automatic, and given the nature of the recognition test, which as Kausler notes can approach an implicit memory test, the absence of age effects on immediate test is not surprising. The findings, taken collectively, support Smith and Park's two major conclusions: a) older adults do use visuospatial processes in representing their experiences for later memory, and b) there is no strong evidence that, relative to young adults, they show a particular deficit in such processes; at least memory performance based on visuospatial information does not show markedly greater age effects than seen for verbal material. Motor Activity

Slowed response time is one of the widely replicated effects of aging. Goggin and Stelmach (Chapter 5 ) review the attempts to discover the components of motor control that are age sensitive and discuss proposed causes. They cite cognitive-motor deficits in response planning, in programming and reprogramming movements, and in the control of movement execution.

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When older adults are given advance information, so that they can readily prepare for the signal, age effects in reaction time (RT) are greatly diminished. From this it can be argued that the slower RT normally seen does not simply reflect a general slowing of the central nervous system, but may instead be due to less effective control processes. Increases in response uncertainty increase RTs, since the response characteristics cannot be prepared prior to the signal to respond, and older adults show a disproportionate increase. The duration of precue interval is critical, the aged benefitting less than the young if the interval is too long or too short; at 13 s older adults appear to have difficulty maintaining preparation, and at 500 ms the aged are less able to rapidly restructure their response. Accurate advance information about a movement permits response programming prior to the signal. If that information is occasionally inaccurate, on those occasions the individual must reprogram, i.e., change their planned movement, at time of the signal. While there may be some age-related decline in preparing a specific movement parameter, higher-level processes such as reprogramming seem to be carried out very similarly by young and old adults. When response complexity is increased, the age effect increases. For example, the elderly find it disproportionately harder to control or coordinate 2 asymmetrical movements. Once a movement has begun, the time to execute the response, called movement time (MT), is generally longer for older adults. It appears that there is less difference in the steady, continuous middle portion of a movement than in the acceleration and deceleration phases. In particular, in complex movements the aged appear to need more time to redirect from one movement to another, perhaps indicating greater difficulty programming the subsequent movement while executing another. The changes in acceleration and deceleration are seen in kinematics analyses which suggest that the aged have more difficulty in scaling velocity to match the amplitude of movement, i.e., less effective muscular control. Salthouse (1985b) has distinguished two broad classes of causes of the slower responses, "software" and "hardware" differences. Hardware differences refer to changes in time required for internal operations of the brain to occur. The two candidates for hardware explanations are neural noise and the Birren hypothesis. Increased neural noise has the effect of lowering the signal to noise ratio and so weakening the effective neural signal to which the person is responding (Welford, 1988). According to the Birren Hypothesis the onset of all neural events is slowed. Software differences refer to an inefficiency of older adults in control processes or strategic differences. There is much evidence for such strategic or control deficits in the aged, but in the final analysis both the software and hardware accounts will be needed to explain age-related slowing. One variety of software explanation (Rabbitt, 1982) distinguishes predictive from reactive modes. In the predictive mode individuals initiate complicated movement patterns in anticipation of events about to occur, whereas in the reactive mode such movements are initiated only in response to feedback from ongoing events. The young are said to be able to operate in both predictive (open-loop) and reactive (closed-loop) modes, whereas the aged are more likely to operate predominantly in the reactive mode.

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The effects of practice also suggest that the age differences in RT are not due to fixed and inevitable change in neural functioning. High levels of physical fitness are associated with minimal age effects, and appear to slow the rate of decline in cognitive and physiological functioning. Furthermore, practice may be more beneficial to old than to young adults in reducing RTs (Spirduso, 1982). There is some evidence that the age differences in the rate of rise in RT with increasing task complexity may be true predominantly in early stages of practice. It is possible that the lesser age effect for vocal responses than for manual responses also reflects a greater level of continued practice or skill with the response mode. Language

The effects of language on cognitive functioning are very extensive and complex. For a detailed review of these effects the reader is directed to the recent volume by Light and Burke (1988). The consideration of language in the present volume is largely restricted to the discussion of a general model of discourse processing (Stine, Chapter ll), and a particular problem of language use that appears to increase with aging, the inability to think of a word one wishes to use in conversation (Burke and Laver, Chapter 10). As Burke and Laver note, the common claim that language abilities represent a cognitive function that is little affected by aging is too broad a generalization. While speech comprehension, as indexed by performance on a traditional vocabularly test, may show little age effect until very late in one's life (terminal drop), it is a widespread complaint of older adults that they have greater word finding difficulty. That is, they more frequently experience tip-of-the-tongue (TOT) states where they are unable to say a word they wish to use in conversation. Bowles and Poon (1985) have shown that older adults do have greater difficulty getting from the definition of a low frequency word to saying the word. Lovelace and Coon (1990) found that there is an age-related asymmetry of moving between a word and its definition. Whereas older adults performed better than young adults on a traditional forward vocabulary test, the young did best on a reverse test (given the definition and asked to provide the word) for the very same set of words.

Burke and Laver present additional evidence regarding this difficulty in lexical retrieval. In a study for which participants kept structured diaries to record naturally occurring TOT states, middle-aged and elderly adults had greater numbers of TOTs, the majority of these being words of low frequency of occurrence and names of people they had not been in contact with recently. Two laboratory studies by Burke and Laver, where TOTs were induced by asking questions aimed at eliciting low-frequency words, also showed greater numbers of TOTs for old than for young adults. Their data also indicate that older adults can report fewer characteristics of the target word when in the TOT state, and also that the frequency with which an alternative word persists in coming to mind during the TOT state is less for old adults. This last point is relevant to a consideration of alternative causal explanations of TOTs. One account, an inhibition hypothesis, says that the occurrence of an alternative word coming to mind at time of attempted access of the intended (target) word produces a blocking that prevents access. This is a straightforward interpretation from

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the perspective of interference theories of forgetting. Note that if older adults have more TOTS they should, according to this hypothesis, experience persistent blockers with greater frequency, but they do not. An alternative account, favored by Burke and Laver, they term the fransmksiondeficit hypothesis. In a network model, the strength of the linkages between nodes is held to decline with increasing age, therefore the transmission priming related nodes is decreased. Presentation of a word for comprehension involves activation of several nodes at the orthographic or phonological level which converge on the appropriate semantic node. Because of this convergence of a number of linkages, the collective activation is adequate despite some weakening of individual linkage strength. In speech production however, the initial activation is of a semantic node and this spreads through linkage that diverges moving to nodes for the phonological elements. The TOT state is, then, a phonology retrieval deficit. It happens more often in the elderly due to weakened priming effects that result from reduced strength of network linkages. The results of their laboratory studies clearly favor the transmission deficit hypothesis over the inhibition hypothesis. The central issue that Stine's (Chapter 11) model of discourse comprehension deals with is how a limited capacity system, which can process so little at any one moment in time, can comprehend and remember complicated passages of discourse. This is accomplished in this model by an array of component processes within working memory. Interpretation of small segments of incoming information is handled sequentially but this information is then integrated with prior information, the analogy used is of production of individual sausages that are then passed on to become part of the larger chain. Stine provides a good discussion of the components of working memory, and of the necessary vertical connections of the central executive to the working memory stores at the lower levels containing representations that are near verbatim copies of the input energy, and to procedural knowledge (needed for parsing, etc.) and declarative knowledge (e.g., lexicon) at higher levels. It is assumed that subsystems can operate in parallel so as to speed the operation of the whole system. A major function of the central executive is to organize the input so as to maximize the effective allocation of processing resources. Age decrements in discourse processing are held to result from decreased efficiency of working memory, although Stine observes that the particular ways in which the functioning of working memory changes have yet to be elucidated. The effects of age on peripheral processes (e.g., sensory decline) and on central executive processing are discussed, along with the relative lack of age effects on phonemic and orthographic codes, the articulatory loop and the visuospatial scratchpad. The essence of aging was likened to a race between the detrimental effects of biological senescence and beneficial effects of experience. The losses with aging may be in reduced precision and efficiency of the encoding of sensory input, and some loss of efficiency of the central executive under conditions of high processing loads. The benefits of experience are to enhance the base of both procedural and declarative knowledge, and so to facilitate the top-down elements of discourse processing by heightened levels of language skills. Intelligence and Problem Solving

Does intelligence decline with aging? As Cunningham and Tomer (Chapter 14)note, any broad general conclusion about the stability or decline of intelligence is

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inappropriate. The results of recent research indicate that the answer to such a question depends on the kind of ability one takes as intelligence, and the particular measures taken, as well as the age range one considers. Some abilities already show substantial decline in middle adult years, some show smaller declines starting in the 60s and 70s, and a few abilities appear to often remain stable past 70. As more has become known about different ability patterns and the factor structure of intelligence for young and old adults, there has been a lessened use of brief, existing instruments in favor of selecting several indicators for each of those factors one wishes to study. The sorts of ability measures employed, of course, are influenced by one's theories of intelligence and aging. Cunningham and Tomer discuss the following theories: speed of information processing, fluid versus crystallized intelligence, stage theories of cognitive development, and Sternberg's triarchic theory. The view that mental speed is a fundamental aspect of intelligence is distinquished from a speed hypothesis of cognitive age change (referred to above as the Birren hypothesis). If such age changes result from change in physiological functioning of the nervous system, which in turn derives from the aging process and is occurring faster for some people than for others, the covariance of speed measures should increase in repeated measures over time as people age. There is clear evidence to support this prediction. The fluid versus crystallized view of aging and intelligence has been modified over the years. While the crystallized component, Gc, is still assumed to be stable, or perhaps to increase across most of adulthood, there has been some change in the age of onset, and the rate, of decline in Gf. The reduction in performance on tasks tapping fluid intelligence is now said to begin in the 40s or 50s rather than the 20s, and the decline is less precipitous than once believed. One issue of interest is whether some tasks presumed to tap fluid intelligence might contain some component of Gc, reducing the observed decline. The stage theories of Schaie and Labouvie-Vief assume that the important roles of cognitive behavior undergo qualitative changes throughout the adult life, implying that intelligence needs to be measured and interpreted differently depending on the stage (age) of the individual. As Cunninghan and Tomer note, there is little data relevant to testing such models. In the context of Sternberg's triarchic theory the notion is raised that competence on everyday tasks may be taken to be more indicative of intelligence by people as they grow older. Berg and Sternberg (1985) note age-related deficits in "metacomponents" such as defining problems, efficient allocation of attentional resources, and monitoring one's solution. Sternberg makes the argument that intelligence is best measured using "optimal" degrees of novelty or familiarity, looking at both performance levels and the ease with which the processes might become automatic with continuing practice. Cunningham and Tomer observe that it is very difficult to know that you have tasks that are equally relevant or novel across age groups. And more importantly, the ability to deal with novelty itself may be of differential importance at different ages.

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The important question of whether the structure of intelligence is stable throughout adulthood has received only limited study to date. A couple of recent findings suggest that there may be emergence of some additional relevant dimensions as the population tested is older. Several studies show that age-related deficits in intellectual functioning are particularly tied to visual-perceptual and speeded decision abilities, e.g., Figural Perceptual Speed measure, Sternberg RT for figural stimuli. Recall, however, that Smith and Park (Chapter 3) conclude that memory for visuospatial information is not differentially age-related. In considering possible antecedents of intellectual decline, one facet of depression, dysphoria, has been shown to be a significant predictor of Figural Perceptual Speed, the cognitive performance measure most strongly related to aging. Age has, of course, frequently been reported to be related to depression. Two other facets of depression, a cognitive complaints factor and a worry factor, were not found to be related to cognitive decline. There has been increasing interest in the issue of the ecological validity of age declines on measures of intelligence, and an attendant interest in practical intelligence or problem solving. Studies suggest an increase in perceived importance of such pragmatic intelligence and problem solving skill in later life. But, it remains hard to establish the ecological validity of practical measures of intelligence. In her review of aging and problem solving, Denney (Chapter 12) adopts a framework (Denney, 1982)that assumes age-related decline in neurological functioning which begins in early to middle adulthood, but with an accelerating rate in late adulthood. The effects of this physiological decline may be modulated by experience. From early to middle adulthood the benefits accruing from experience may outweigh the, then gradual, decline of neurophysiologicalfunctioning, whereas in late adulthood the decline becomes severe enough so that the benefits of experience cannot compensate and performance declines with aging. In comparing the effects of adult age on traditional abstract problems with that on practical everyday problems, Denney reports different patterns of performance change. For the great majority of abstract problems that have traditionally been studied in laboratory research, performance declines from early to late adulthood. For concept identification problems the age effects are particularly pronounced when the task requires a change of concept or strategy, the elderly appearing to have difficulty overcoming the particular set, once established. One exception to the general finding that performance on abstract problems declines with adult age is the inconsistent data on Piagetian conservation tasks. In several studies these tasks showed little or no age effects. Denney suggests that this may be due to daily applicability of the principles underlying these tasks, so that the older adults were very familiar with, and skilled at, this sort of problem. The typical pattern seen with performance on practical everyday problems, unlike the monotonic decline of abstract problems, is one of improvement from young to middle-adult age and then a subsequent decline in late life. In terms of Denney's framework, this difference in pattern is readily explained. From young to middle-aged adulthood performance will increase if the individual has sufficient gain from

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experience to offset the gradual biological decline. For practical everyday problems most adults have this benefit of experience whereas for the abstract problems they do not. Attempts to reduce the superiority of young to old adults on traditional tasks by posing the problems in terms of more realistic stimuli have not met with much success. All age groups do better when the tasks are thus modified, as a result the age difference was not reduced. When Denney and her colleagues involved adults of different ages in the selection of age-appropriate real world problems for young, middle-aged, and old adults, later tests with other individuals revealed that young did best on the "young" problems, and the middle-aged did best on "mid-adult"problems, but the older adults were not best on any problems. Apparently the added benefit of the experiences between mid-adult and old age were not sufficient to offset the physiological aging effects, so even on "older" problems the middle-aged adults outperform the old. This age pattern seen with practical problems seems to parallel the age effects seen in studies on professional creativity, i.e., creativity peaks in late 30s or early 40s and then shows gradual decline. This would follow if the creative act required the same sort of processes that are involved in the practical problems. It should be noted, however, that pure productivity levels (amount of output) of professionals shows the same pattern of greatest output in mid-adult years. As Simonton (1988) has observed, the creative peak around 35 to 40 would then occur if one simply adopted a "constant probability of success" model. If a set proportion of an individual's work were to be so good as to be judged creative, and this is unrelated to age, the greatest numbers of creative works will be produced by those most productive, i.e., 35 to 40. Cognitive Aging, Personality and Self-Awareness

Although cognitive researchers have historically showed little interest in personality and self concepts, recently there is a growing conviction that these variables may prove very important moderators of cognitive relationships (e.g., Cavanaugh, 1989; Cavanaugh 8t Murphy, 1986; Lachman, 1986). It seems a natural extension for a cognitive researcher who is asking how cognitive functions are influenced by one personal characteristic, adult age, to ask about the influence of other characteristics of the person. Personality and Cognitive Aging

The relationships of various personality traits to cognitive functioning are mentioned in several chapters of the present volume and discussed in detail by Gold and Arbuckle (Chapter 13). They note that, although intellectual capacities we display in old age depend on the level of intellectual abilities we possessed as young adults, how intelligent we are in old age is also partly a question of what personality traits we have possessed in adulthood. It has long been assumed that environmental contexts were important in determining the development of cognitive abilities and personality. Some have espoused the idea that aspects of one's personality and of one's intellectual abilities influence the selection of contexts, and to some extent the effects those contexts have. This sort of framework, while emphasizing the individuality or plasticity

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of development, conceives of the environment and personal characteristics in mutually-causal interaction throughout life. (See Dannefer 8t Perlmutter, 1990, for a recent theoretical discussion of the interaction of biological, environmental, social and cognitive factors during adulthood.) The cognitive measures in this literature have more often been psychometric measures in longitudinal studies and laboratory tasks in cross-sectional studies, a common thread being a tendency to classify the tasks as tapping fluid versus crystallized abilities. Gold and Arbuckle present personality measures in terms of the "big 5" central measures of introversion-extraversion, emotional stabilities (neuroticism), openness to experience, agreeableness, and conscientiousness. Introversion, when indexed by standard personality test measures, has been found to show a weak positive relation to cognitive functioning, and greater emotionality (greater neuroticism) is associated with poorer cognitive functioning. For the remaining three there is too little data to make any strong assertion. In general, where personality is related to cognitive functioning, the magnitude of that relationship has not been found to vary with age. The direction of effects of a personality trait tend to be connected across a range of cognitive measures, even when these effects are not consistently significant. This suggests that the effects of the personality trait are related to general cognitive functioning rather than to specific processes or abilities. The evidence suggests strong stability of personality traits across adult ages, whereas the majority of cognitive measures show decline, especially those with speed components or tapping fluid abilities. Despite the decline, retest reliabilities tend to be high for psychometric data. For example, one test-retest at a 40 year interval (Schwartzman, Gold, Andres, Arbuckle, & Chaikelson, 1987) showed a correlation of .76. While this would suggest the experience of decline is nearly universal, some longitudinal studies show considerable inter-individual variability with some individuals showing little if any decline in the 60s and 70s. The differential rate of aging across individuals may account for this latter finding. Measures of field dependence-independence, of flexibility-rigidity, and of locus of control are referred to by Gold and Arbuckle as "peripheral"personality traits. These are also often referred to as "cognitive style" factors, and when relating these measures to performance on cognitive tasks, it maybe a matter of correlating two cognitive measures. In general, field independence predicts better cognitive functioning, but this relationship does not appear to be age-related. Measures of flexibility-rigidity, in the Seattle Longitudinal Study (Schaie, 1983), show flexibility to be positively related to cognitive measures (Primary Mental Abilities) but there were age-related differences in the pattern of which of three flexibility measures was the best predictor. In early adulthood an attitudinal flexibility measure provided prediction of cognitive performance whereas for older adults a motor-cognitive flexibility predicted best. Global (trans-contextual) measures of locus of control have shown mixed results, but domain-specific measures such as Lachman's PIC show that greater internality is related to better cognitive performance, and there is a suggestion that this effect may be stronger for older adults. With respect to the causality in these cogntive/personality relationships, Gold and Arbuckle reason as follows. In early life, childhood and during schooling, cognitive

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abilities may have a substantial bearing on the development of one's self-concept and self-efficacy, and that again in late life social feedback about, and self-evaluation of, one's own cognitive performance can impact self-efficacy. But during most of adulthood, they argue, the central personality traits, which have large emotional expressive components and potential genetic bases, are less influenced by cognitive functioning. Throughout adulthood, however, the central personality traits influence cognitive functioning. Furthermore, in late life, the senescent changes in nervous system function, coupled with reductions in cognitive demands of the environment, result in an increased influence of personality on level of cognitive functioning. These effects are viewed not as determinants of specific skills or processes but as having a general facilitative or detrimental effect on cognition. A thoughtful and detailed account of potential direct and indirect effects of variation in each of the central personality traits is provided by Gold and Arbuckle. The reader is directed to Table 13.1 for a summary of these effects.

Self-Efficacy and Cognitive Aging Rarely have measures of self-concept, self-evaluation or beliefs about one's abilities been given much weight in cognitive theorizing, with the exception of some of the work on metamemory. Cavanaugh and Green (Chapter 7) make a strong case for the necessity of taking such variables into account. In their view self-efficacy is a major determinant of cognitive functioning, and changes in self-efficacy may account for a substantial portion of the age-related changes in cognitive performance. With respect to memory changes with aging, they feel that what one says to oneself strongly influences performance, both what one will attempt to do and how well one will succeed. Older adults have typically bought into a cultural stereotype of declining competence which lowers self-efficacy, particularly a decline in memory functioning specifically, which lowers memory self-efficacy. Self-evaluation of cognitive performance may be faulty and contribute to lowered self-efficacy. Given the expectation of decline of cognitive functioning during adulthood, older individuals are likely to overestimate their own earlier competence, and so provide an inflated benchmark against which to assess current performance levels. Thus they may perceive a greater decline of cognitive functioning than has actually occurred. Cavanaugh and Green explore the relationships between self-efficacy and self theory, implicit theory of cognitive function, and notions of locus of control. In the case of the latter concept they argue for the separation of locus from control, i.e., that the original measures of locus of control really confound two separate dimensions. The aged tend to view any perceived memory problems as internal and uncontrollable, i.e., a property of their person that is the inevitable consequence of the passage of time. A complex but comprehensive conceptual model of self-evaluations is provided by Cavanaugh and Green in Figure 7.1. In this model personality traits influence efficacy judgments directly, as suggested by Gold and Arbuckle, and indirectly via beliefs, which gives a very subjective quality to these judgments. Self-efficacy is also influenced by knowledge evaluation and task evaluation, thus memory self-efficacy can be expected

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to be task- and materials-specific. My memory self-efficacy then, is not the single global judgment which Hultsch, Hertzog, Dixon and Davidson (1988) proposed as one of the dimensions of metamemory. It is rather modulated by my evaluation of my experience with this or related tasks, and by my familiarity with the subject matter, i.e., whether I have developed processing strategies for such a task and have a substantial knowledge base with respect to the subject matter. The major direct effects of self-efficacy are on effort and the motivational component, and only in this way, indirectly, on performance. Cavanaugh and Green note that the modest correlations typically found between metamemory measures and memory performance are consistent with such indirect effects of memory self-efficacy. Aging and Metamemory

Metamemory is one area of cognitive research which has explicitly dealt with self-knowledge and beliefs about one's own cognitive functioning. Lovelace (Chapter 6) reviews the literature on metamemory and aging. Hultsch et al. (1988) suggest four dimensions to capture the issues with which metamemory research deals. The first, memory knowledge, concerns factual knowledge about various memory tasks and processes. This is rather general or abstract knowledge that might be applicable to any other adult, i.e., it does not have the clearly personal, self-referential quality of the three remaining dimensions. Memory monitoring is a dimension concerned with assessing the current contents of memory, and the effectiveness of particular memorial operations. Memory self-efficacy centers on the beliefs one holds about his or her own memory abilities, strengths and weaknesses. The last is the dimension of memoryrelated affect which involves the emotions or feelings induced by memory situations or associated with memory tasks. In general, with regard to the memory knowledge dimension, most adults have a fairly accurate sense of the memorial consequences of many specific variations in memory tasks, or in the to-be-remembered materials, e.g., the effects of study time, repetition of experience, existing knowledge base, recall versus recognition tests. An early assumption of metamemory researchers was that performance would be directly related to accuracy of such metamemorial knowledge, the cognitive decline in aging being hypothesized by some to be attributable to poorer metamemorial skills. These notions have not found strong support. The correlations between memory knowledge measures and performance are only on the order of .30. There are some variations in processing, such as depth manipulations, that have substantial effects on performance levels but which have only slight effects on the individual's beliefs regarding the memory consequences. This failure to accurately judge memory processes is equally true of young and old (Shaw & Craik, 1989). A failure to apply strategies in performance, despite indicating when questioned on a metamemory task that the strategy would help memory, points to the lack of a strong linkage of such memory knowledge to one's own memory performance. Here age differences are seen, the lack of application of strategies acknowledged to be beneficial, e.g., organization and imagery, being a more common occurrence for old adults than for young. This failure to engage in effective strategic processing is, in fact, a major cause of poorer performance in older adults. The evidence from a number of studies that older adults underestimate task difficulty represents another age-related difference in memory

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knowledge. In Cavanaugh's model presumably this calibration error would influence performance via efficacy judgment's effects on effort and outcome expectations. If this error occurs for older adults because they are less familiar with the task, then the experiences would influence knowledge base in that model, and so impact task evaluation. In general people seem more sensitive to memory-performance-related properties of the materials than to the memory consequences of different strategies or encoding operations. With respect to the monitoring dimension of metamemory, the bulk of the data supports a general conclusion that there are no appreciable age-related changes in the monitoring of many of one's on-going memory processes, such as feelings-of-knowing, judgments-of-knowing, and correctness of one's response (but see Burke & Laver, Chapter 10 for an exception). There is some evidence, however, that older adults have somewhat greater difficulty with reality monitoring tasks, an effect which may simply reflect some response bias, but which may also be related to poorer memory for the source of information in older adults (e.g., McIntyre & Craik, 1987). Issues related to the memory self-efficacy dimension were discussed above. Suffice it to say that memory self-efficacymeasures, particularly when global rather than taskor domain-specific, consistently indicate a belief that memory ability is declining with age. Such beliefs are accurate for some tasks but not for others. Beliefs about changes in memory function, even if not veridical, are of interest because of the potential impact they have on the behaviors the individual will choose to engage in, or how they will perform a memory task. While the belief in memory decline with aging is nearly universal, very few healthy older adults see that it poses any problem for their daily functioning (e.g., Hultsch et al., 1988; Lovelace & Twohig, 1990; Sunderland, Watts, Baddeley, & Harris, 1986).

To date we have relatively little data directly comparing measures across the different metamemory dimensions. However, the dissociations, age effects appearing for some measures but not for others, suggest that any conception of metamemory as a unitary process is inappropriate. Intervention: When, Why, How,for Whom? While we cannot now provide a complete answer to the questions posed in the above heading, we can identify some of the issues and provide some tentative conclusions. Clearly the answers are interdependent, i.e., none of the questions is readily answered independent of consideration of the others. In the present volume, the chapters by Kotler-Cope and Camp (Chapter 8) and Willis (Chapter 9) deal directly with cognitive training programs. Portions of several other chapters touch on these issues, most notably those of Cavanaugh and Green (Chapter 7), Denney (Chapter 12), and Cunningham and Tomer (Chapter 14). It is clear that the cognitive behavior of older adults shows plasticity, "training has been found to be effective in both remediating cognitive decline and improving performances of subjects exhibiting no prior decline" (Willis, 1989, p. 553). Denney (Chapter 12) observed that it is so easy to facilitate the performance of older adults on problem solving tasks, via modeling strategies, strategy instruction, or contingent feedback, that the observed decline in performance clearly

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does not directly reflect decline in absolute ability. A striking finding, however, is that the training benefits are often as large or larger for young than for old adults. One issue for remediation has to do with whether the decline of cognitive function is taken to reflect loss of competence or simply a change in performance which may have come about for any of several reasons. For example, changes in life style that have resulted in reduced social interactions and reduced stimulation may lower cognitive function, as may other non-cognitive factors such as anxiety, reduced self-confidence, or apathy associated with sub-clinical depression. Reciprocally, of course, as Cunningham and Tomer (Chapter 14) point out, the importance of cognitive decline in old age depends in part on the life style one has been leading, or intends to lead, i.e., it depends on the cognitive demands of one's chosen life activities. For activities highly valued and frequently practiced, performance may be best sustained. As Kotler-Cope and Camp note, apparent cognitive deficits might indicate qualitatively different patterns of information processing, reflecting processing preferences rather than deficits. But there is evidence that an adaptation of some older adults to cognitive decline is to become more and more selective with respect to the intellectual activities they engage in, putting more time and effort into those activities where the higher performance levels are still possible. The work of Baltes and his colleagues on "selective optimization with compensation" is important in this regard (e.g., Baltes, Dittman-Kohli, & Dixon, 1984; Baltes & Willis, 1982). If the decline is viewed as a performance factor due largely to lack of recent challenge by, or experience with, the particular sort of task/situation, this falls in the category of a "getting rusty" hypothesis. One implication of this is that familiarization or experience with the task should bring about differentially greater benefits for older than for young adults. The data on this appear mixed. While Berg and Sternberg (1985) indicate that practice helps the elderly more than the young on metacomponents of some intelligence measures, several cognitive training studies have shown nearly equal improvement in performance for young and old (see Denney, Chapter 12, and Willis, 1989, and Chapter 9). When is it desirable to attempt cognitive intervention? With whom? Complaints of memory decline are nearly universal among old adults; are all of these individuals to be included in the target group when considering memory interventions? Most of these individuals report that any memory decline they have experienced poses no problem for their daily activities. Interventions can have very different goals, the prevention of cognitive decline of specific abilities, in which case one might envision trying to target the population broadly, versus therapeutic interventions for individuals for whom cognitive decline, or their belief about cognitive decline, is a cause for concern. Where the decline involves an affective change, such as anxiety or depression, some training to address the affective state may yield memory benefits (e.g., Yesavage, 1984). On the other hand, training to change the aged's self perceptions and affective states may accomplish that goal without substantially influencing objective memory task performance (Zarit, Gallagher, & Kramer, 1981). And conversely, memory performance may be improved while neither affective state nor memory self-efficacy are changed (Scogin, Storandt, & Lott, 1985).

428

EALovelace

For individuals with clear cognitive decline, remediation may not even be a realistic goal. For those with neuropathologies such as senile dementia, as Backman (1989) notes, any interventions might be a very different sort, focusing on external contextual support. Even in the absence of such known pathology, some individuals are likely to show little or no benefit from the cognitive training. What are the ethical considerations of having induced expectations that then cannot be met? Will this negatively impact on their self-evaluation, further lowering self-efficacy? Does this confirm their fears that the decline is inevitable; that they are beyond help? As Cavanaugh and Green (Chapter 7) note, if the training was intended to raise self-efficacy, so the basic message may be "you can if you believe and make the effort", a controllable, internal attribution, the impact of failure to improve may be particularly devastating to one's self concept. Clearly, great care must be exercised in the selection of individuals for whom cognitive remediation is a realistic goal, and here the validity of our screening assessment procedures may leave something to be desired. Kliegl, Smith, and Baltes (1989) have examined cognitive functioning in adulthood from a framework of "testing the limits" of cognitive plasticity. The level at which the individual performs when given a cognitve task, the "baseline" performance, is taken to be less than what the person could achieve with optimal conditions, the current maximum performance potential, or "baseline reserve capacity". The purpose of training is then to increase their cognitive as well as non-cognitive (e.g., motivational) potentials to the maximum. This concept of what their performance could become when cognitive skills and motivation reached their peak is their "developmentalreserve capacity". The data make it clear that older adults have very sizable developmental reserve capacity, for example the capacity to recall 32 of 40 words studied just once with a proper mnemonic (see Kotler-Cope and Camp, Chapter 8, for more on this study). This bodes well for the potential success of cognitive training programs. It is not, however, that young adults perform near their maximal achievable level whereas the aged possess that same maximum but have fallen away from it. Young adults' current performance levels are far below their maximum possible levels. Indeed, the developmental reserve capacity of young is often greater than that of old adults, despite the young having higher current baseline performance. That is, the effects of aging are more pronounced at performance levels near the upper limit of the reserve capacity than they are when baseline performance is considered. Thus, although this work shows that substantial improvement of cognitive functioning of older adults may be possible, it also suggests that such training benefits decline with aging. Denneyk (1982) general model would predict a reduction of training benefits the older the sample. Whereas young and middle-aged adults benefit from experience to an extent that more than offsets progressive biologically-based cognitive decline, for the aged the neurophysiological senescence reaches a point where benefits of experience cannot compensate for the loss. Presumably at that point the developmental reserve capacity is so diminished that training effects would become nil. This implies that interventions may be most successful in early old age (cf., Baltes, 1987). Cohort comparisons for the Seattle Longitudinal Study suggest that remediation was accomplished least readily for the oldest cohort of these elderly participants (Willis, 1989).

Summary Overview

429

Since the benefits of practice have sometimes been reported to be greater for old than for young (e.g., Berg & Sternberg, 1985; Spirduso, 1982), the relative benefits for the two groups must depend on task and person parameters in ways we need to understand more fully. As many have noted (e.g., Backman, 1989; Kotler-Cope and Camp, Chapter 8), it is important to individualize training programs and attempt to build on that which is most preserved in an attempt to compensate for that which has declined. In addition to considering cognitive abilities in this perspective, however, it may be important to consider both cognitive and non-cognitve personal traits in deciding whether, or what sort of, remediation program is appropriate for that individual. Certain cognitive abilities, such as verbal skills, may have particular importance for the sorts of interventions likely to be most effective, and personality traits are likely to function as mediators of the extent to which age-related cognitive declines can be remediated by training. Gold and Arbuckle (Chapter 13), in discussing the relation of personality to cognitive interventions, suggest that personality traits are likely to affect the development of selective optimization compensatory functions that can serve as an effective means of coping with cognitive decline. An additional, indirect effect of personality traits lies in the way certain traits make an individual more or less likely to act so as to create and retain supportive environments that can preserve cognitive functioning (see also Gribbin, Schaie, & Parham, 1980). A major task of future research on cognitive interventions will be to address the complex inter-relations of various sorts of training programs. There already exists evidence to support certain assertions. Whereas non-cognitive training by itself will often fail to influence cognitive performance (e.g., Zarit et al., 1981; Denney, Chapter 12), given certain personal traits such training may prove beneficial (e.g., Yesavage, Sheikh, Tanke, & Hill, 1988). More importantly, the joint effects of motivational or affective training plus cognitive training are likely to exceed those of cognitive training alone (see discussion by Kotler-Cope and Camp, Chapter 8, especially the work of Yesavage and colleagues). Cavanaugh and Green argue that effective training programs aimed at age-related decline should contain attributional or self-evaluation training along with, and probably prior to, that for specific cognitive skills such as mnemonic training. In this regard the clinical techniques used in cognitive therapy designed to restructure inappropriate beliefs or cognitions may prove particularly useful.

Details regarding features of effective memory intervention and cognitive training procedures are provided by Kotler-Cope and Camp (Chapter 8) and by Willis (Chapter 9). With regard to the evaluation of the effectiveness of such programs, two points should be made. First, in addition to the traditional criterion of statistical significance, such training should have as a goal achieving what Kotler-Cope and Camp refer to as "clinical significance" as well. That is, training aimed at greater increases in performance levels, exercising the individual's developmental reserve capacity. The second point concerns what measures we should take, and when, as our index of the effectiveness of the training. The issues raised are whether the effects of training show generalization and are maintained over time. While there is increasing agreement that these should be major goals of intervention programs, they are often not evaluated, and the evidence has been mixed when they were. Whereas Willis (1989; Chapter 9) cites a number of studies that have found beneficial effects of

EALovelace

430

training on follow-up tests at intervals up to 6 months, many training studies show little or no maintenance, the training effects being context-dependent or social-obligationdependent (e.g., Anschutz, Camp, Markley, dc Kramer, 1985, 1987).

To what degree should the benefits of cognitive training be expected to generalize? Cognition is not a unitary process, but rather a collection of relatively separable, albeit often interacting, abilities. The training is not intended to be task-specific, but to enhance the cognitive abilities that are entailed in performing the particular task. Thus one would expect generalization to other tasks for which these same abilities are salient. If interindividual variation across sets of tasks have been factor analyzed, one has an indication of the structure of the tasks. Training on a particular task should strengthen performance on those other tasks that load on the same factor(s) as the training task. But the improvement should not generalize beyond these tasks, i.e., one needs to see that there is enhanced performance within the factor but not across factors. In this way one can begin to specify the abilities enhanced by a particular cognitive training program, knowing that the effects are not via some broader influence on performance. On the other hand, if the training is not aimed at specific cognitive abilities, but at the modification of important non-cognitive components susch as affective states (e.g., reduction of anxiety or depression) or self-evaluations (e.g., self-efficacy), then an effective training effect would be expected to show broad effects across tasks representing a great many different cognitive factors. Joint training of self-efficacy or relaxation along with training of cognitive abilities has been advocated to maximize the performance gains by the trainee (e.g., Cavanaugh and Green, Chapter 7). While this is appropriate from the perspective of the benefits of a real-world intervention, such joint training makes it very difficult to determine the effectiveness of individual components of such an intervention "package". As a research endeavor one needs separate conditions for the various training components singly and in various combinations. Concluding Comments

My global impression, from the concepts and literature discussed in this volume, is that we have made substantial progress in the past decade in our understanding of cognitive aging. The picture is at once clearer and more complicated. The moves toward more contextualist views, and toward an appreciation of the need to simultaneously consider properties of the task, the materials, the environment, and the person create more complexity for our research efforts but surely give our conceptualizations greater potential validity. The study of cognitive aging has been advanced both descriptively, so that the conditions where aging deficits are likely and those where little or no age effects may be seen are better differentiated, and theoretically, where the dissociations of age effects for various conditions have begun to influence basic cognitive theorizing. While the predominant model of cognitive aging has centered on the inevitable decline, three positive themes recur. One is that within any domain of cognitive activity some abilities are notably well preserved to advanced age; for many cognitive

431

Summay Overview

tasks there are failures to see age-related decline. Secondly, interindividual variability in cognitive functioning is great, so that some individuals show little decline even on fluid abilities commonly thought to show large age deficits. And thirdly, most healthy, community-dwellingelderly, at least until very old age, continue to show plasticity and can benefit from cognitive training, or even improve simply by practice on cognitive tasks. A major challenge for us, in terms of the application of our knowledge, lies in the development of techniques of assessment that are adequate to the task of identifying the patterns of personal traits and sets of cognitive abilities for which particular interventions can prove effective in sustained, meaningful ways. References

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Lovelace, E. A., & Twohig, P. T. (1990). Healthy older adults' perceptions of their memory functioning and use of mnemonics. Bulletin of the Psychonomic Society, 28, 115-118. McIntyre, J . S., & Craik, F. I. M. (1987). Age differences in memory for item and source information. Canadian Journal of Psychology, 41, 175-192. Norman, D. A., & Bobrow, D. (1975). On data-limited and resource-limited processes. Cognitive Pgchology, 7,44-64. Parasuraman, R. (1987, November). Aging and sustahed attention. Paper presented at the National Institute on Aging Conference on Aging and Attention, Bethesda, MD. Pezdek, K. (1987). Memory for pictures: A life-span study of the role of visual detail. Child Development, 58, 807-815. Plude, D. J., & Hoyer, W. J. (1985). Attention and performance: Identifying and localizing age deficits. In N. Charness (Ed.), Aging and human perjtormance (pp. 47-99). London: Wiley. Poon,.,.J W. (1985). Differences in human memory with aging: Nature, causes and clinical implications. In J. E. Birren & K. W. Schaie (Eds.), Handbook of the psychology of aging (2nd ed., pp. 427-462). New York: Van Nostrand Reinhold. Rabbitt, P. M. A. (1982). How do old people know what to do next? In F. I. M. Craik & S.E. Trehub (Eds.), Aging and cognitive processes (pp. 79-98). New York: Plenum. Salthouse, T. A. (1982). Adult cognition: An experimental psychology of human aging. New York Springer-Verlag. Salthouse, T.A. (1984). The skill of typing. Scientific American, 250, 128-136. Salthouse, T. A. (1985a). A theory of cognitive aging. Amsterdam: North-Holland. Salthouse, T.A. (1985b). Speed of behavior and its implications for cognition. In J. E. Birren & K. W. Schaie (Eds.), Handbook of thepsychology of aging (2nd ed., pp. 400-426). New York Van Nostrand Reinhold. Salthouse, T. A, Mitchell, D. R., Skovronek, E., & Babcock, R. L. (1989). Effects of adult age and working memory on reasoning and spatial abilities. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 507-516. Schacter, D. L. (1987). Implicit memory: History and current status. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 501-518. Schaie, K. W. (1983). The Seattle Longitudinal Study: A 21-year exploration of psychometricintelligence in adulthood. In K. W. Schaie (Ed.). Longitudinalstudies of adult psychological development, (pp.64- 135). New York: Guilford. Schwartzman, A, Gold, D., Andres, D., Arbuckle, T., & Chaikelson, J. (1987). Stability of intelligence: A forty year follow-up. CanadianJournal of Psychology, 41, 244-256. Scogin, F., Storandt, M., & Lott, L. (1985). Memory-skills training, memory complaints, and depression in older adults. Journal of Gerontology, 40, 562-568. Shaw, R. J., & Craik, F. I. M. (1989). Age differences in predictions and performance. Psychology and Aging, 4, 131-135. Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: 11. Perceptual learning, automatic attending, and a general theory. Psychological Review, 84, 127-190. Simonton, D. K. (1988). Age and outstanding achievement: What do we know after a century of research? Psychological Bulletin, 104, 251-267.

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435

Author Index

Aaronson, D., 301,309,310 Aberdeen, J. S., 314, 316, 413 Abramson, L. Y.,193 Ackerman, B. P.,163 Adamowicz, J. K, 51 Adams, C., 321 Adams, N. E., 192 Agarwal, G. C., 145 Alba, J. W., 35, 45 Albert, M. L., 110 Albert, M. S., 53, 284 Albertson, S. A,, 60,284 Aldwin, C. M., 366 Algeri, S., 252 Alloy, L. B., 193 Alpaugh, P.K., 332,333 Alwin, D. A., 399 Amrhein, P. C., 138, 140, 141 Anderson, J. A., 11 Anderson, J. R., 69 Anderson, J. W., 331 Anderson, N. S., 215 Anderson, P.A., 313, 315 Andres, D., 355, 363, 367, 423 Annett, M., 329 Anschutz, L., 220, 240, 430 Arbuckle, T. Y.,172, 355, 356,358, 359, 361, 363, 365, 367, 423 Arenberg, C., 48,71,79, 113, 172, 239, 252, 330-333, 338,366 Aschkenasy, J. R., 330 Atchley, R. C., 351 Atkeson, B. M., 102 Atkinson, R. C., 2, 4, 99, 103 Attewell, P.,329 Attig, M., 34, 35, 41 Auday, B. C., 46 Azari, N. P.,46 Babcock, R. L., 20, 74, 413 Backman, L., 57, 429

Baddeley, A. D., 8, 20, 161, 215, 243, 2A4, 251, 281, 282,285, 301, 302, 304,308,309,311, 317,319,426 Bahrick, H. P.,7 Bahrick, P.O., 7 Balcerak, L. J., 207,208, 220 Balota, D. A., 10, 42, 284 Baltes, M. M.,197 Baltes, P. B., 100, 197, 211, 245, 254, 263, 271, 272,274,277,335, 336,361,370,385, 391,396,397,427, 428 Bandura, A., 190-194, 198,206-208, 210,222 365 Bank, L., 367 Barchas, .I. D., 192 Baron, A., 141 Barry, J. R., 340 Bartlett, J. C., 54, 77-78, 82, 83, 416 Barton, K., 373 Bartus, R. T., 33, 330 Bashore, T. R., 150. 151 Beck, A. T., 221 Bell, B., 105,354 Bellezza, F. S., 233, 234,237 Benson, P.E., 98 Berg, C. A., 385,395,420,427,429 Bergeman, C. S., 366 Bergeron, J., 329 Berkovsky, K., 73,81, 85, 87 Berlyne, D. E., 357 Berry, J. M., 159, 166, 189, 208-211 Bespalec, D., 49 Bielby, D. D., 329 Birren, J. E., xiv, 18, 21, 125, 126, 332,333,366, 381, 383,384,398,408 Bjork, E. L., 253 Bjork, R. A., 251, 253

436

AUTHOR INDEX Blackburn, J., 274, 330, 334 Blanchard-Fields, F.,253 Blanton, P. D., 254 Blaxton, T. A., 6, 59, 285 Blieszner, R., 263, 335, 3% Block, R. A., 38 Blow, F., 271 Boatwright, L. K.,161,215 Bobrow, D. G., 100,311,408 Bogers, H., 235 Boies, S. J., 97 Bopp, M. J., 221 Borkenau, P.,353 Borkowski, J. G., 163, 199, 219,222 Borod, J. C., 284, 285 Bosse, R., 354, 366 Botwinick, J., 7, 18, 19, 22, 70, 142, 143, 150,151, 165, 281,317,330,383 Bower, G. H., 8,60,76,77, 237, 242,250 Bowles, N. L., 20, 21, 284, 285, 418 Bradley, D. C., 309 Brandt, D., 399 Braune, R., 99, 139, 149 Brelsford, J., 43 Brereton, N., 301 Brewer, N., 143 Brigham, M. C., 163 Brinley, J. F., 137, 330 376 Brody, E. M., 357 Brodzinsky, D. M., 332 Bromley, D. B., 331, 333 Brookbank, J. W., 383 Brown, A. S., 60,285 Brown, B. R., 361 Brown, R., 168,285,288 Bruce, D., 285 Bruce, P.R.,52, 165-167 Bruenig, A., 150 Burke, D. M., xiv, 22, 29, 42, 126, 170, 174, 281, 283-286, 289, 408, 412,413,418 Busch-Rossnagel,A., 351 Buschke, H., 43 Butterfield, E. C., 97, 169 Butters, N., 253 Buttenvorth, B., 310, 311 Byrd, M., 20, 113, 126, 408

Camp, C. J., 166, 189, 204,220, 231, 236-238,240,250253,430 Campbell, D. T., 399 Campbell, R. T., 399 Cannon, C. J., 381 Capps, J. L., 60,284,314,318 Carpenter, P.A., 301, 302, 304-306, 309,315 Carpenter, W. L., 330 Carter, J. E., 110 Carter, P., 265 Carter, R. C., 104 Carver, C. S., 199 Caspi, A., 199, 340,362, 363, 395 Catrambone, R., 1% Cattell, R. B., 211, 270, 281, 354, 373,382 Cavanaugh, J. C., 20, 158, 160, 168, 189, 192, 193, 195, 197, 199,200, 202-204, 207, 210, 212, 215, 217-219, 221,222,234,235, 241, 245, 356, 363, 422 Ceci, S. J., 76 Cerella, J., 22,99, 100, 102, 106, 109, 113, 119, 125-127, 147, 149, 151, 284,381 Cermak, L. S., 252 Cervone, D., 191, 194,210 Chaffm, R., 160 Chaikelson, J., 355, 367, 423 Chance, K.E., 77 Charles, D. C., 329 Charness, N., xiv, 21, 22, 122, 412 Cherry, C., 97 Cherry, K. E., 52,53,73, 81,85,87, 92 Cheung, H., 317 Chiarello, C., 60, 284 Chromiak, W., 35 Chrosniak, L. D., 54, 174, 177, 179 Church, K. L., 284 Cioff, D., 207 Clancy, S., 113, 122 Clark, E., 78 Clark, J. E., 140 Clark, M. C., 237

437

AUTHOR INDEX Cohen, G., 169, 174, 176-178, 281, 284,285,313,316, 320, 322 Cohen, G. D., 33 Cohen, R. L., 56, 57 Cohen, R. M., 389 Colbert, K. K., 360 Cole, K. D., 247, 333 Collins, A. M., 9, 282 Coltheart, V., 308 Comalli, P.E., 40,329 Conley, J. J., 366 Conrad, R., 308 Coon, V. E., 418 Cooper, L., 265, 271 Corcos, D. M., 145 Corkin, S., 113 Cornelius, S., 329 Cornelius, S. W., 199, 271, 340, 362, 363,386,395 Costa, P. T., 200, 332, 333, 353-357, 362, 363, 365, 366, 370, 371, 393 Cotton, B., 46,47 Cowan, N., 8, 10 Cox, R. B., 360 Coyne, A. C., 162, 165, 179,332 Craig, A., 123 Craik, F. I. M., xiv, 5, 20-22, 39, 41, 54, 71, 83, 85, 113, 114, 121, 126, 162, 163, 171, 173, 231, 232, 239, 251, 317, 407, 408, 412, 415, 425,426 Crook, T., 33, 78, 231, 330 Cross, H. A., 46 Crovitz, E., 334 Crowder, R. G., 47 Csapo, K., 70 Cuddy, L. L., 172 Cunningham, W. R., 18, 381-384, 386-389, 391-395, 398,399 Cutrona, C. E., 192 Cutting, J. E., 162 DAgostino, P. R., 75 Dallas, M., 59 Damon, A., 354 Daneman, M., 301,304-306,309, 315, 319

Dannefer, D., 351, 423 Darrow, C., 200 Datan, N., 351 Davidson, B. J., 308 Davidson, H., 157, 189, 195, 203, 227,425,432 Davidson, J. E., 386 Davies, D. R., 97, 123 Davies, G., 77 Davis, E., 330,334 Davis, R. T., 35,44,46,56, 57 Day, J., 35, 49 DeLeon, J. L. M., 237 Dell, G. S., 282, 285 Dellmann, M., 330,334 Demming, J. A., 340 Dennehey, D. M., 159, 189,208, 24N Denney, D. R., 331 Denney, N. W., 158, 263, 271, 272, 329-332, 334-336, 339, 342, 343, 385, 395, 397, 409,421, 428 Dennis, W., 329 Derrick, W. L., 113 Detweiler, M., 313 Diaz, D. L., 29, 284 Dick, M. B., 57, 220 Digman, J. M., 353 Dismukes, K.,102 Dittmann-Kohli, F., 274, 335, 396, 427 Dixon, R. A., 157-160, 189, 195,33, 213-217, 219, 222, 231, 235, 351, 370, 425,427 Dobbs, A. R., 20, 158,413 Dodson, J. D., 123 Donaldson, G., 263, 270, 381-384 Dorfman, D., 309 Dorosz, M., 321 Doussard-Roosevelt, J. A., 105, 107112, 118 Downing, C. J., 108 Draper, I. T., 145 Druian, P.,196 Duchek, J. M., 42, 284 Duckitt, J., 371 Dudley, W. N., 81-85 Duffy, M., 235 Durso, F. T., 39, 70 Dweck, C. S., 198, 201

AUTHOR INDEX

438 Egan, D., 265 Egeth, H.,103,106 Ekstrom, R. B.,390 Elias, C. S.,316 Elias, J. W.,1, 103,118 Elias, M.F., 1 Elias, P. K.,1 EUiott, E., 195,197,199,219,220 Elliott, J. M.,41 Ellis, A., 221 Ellis, H., 77 Ellis, N. R., 36 Elo, A. E., 341 Emery, G.,221 Engstrom, R., 381,382 Entwisle, D.G.,144,145 Epstein, D.,399,400 Erber, J. T., 7 Erickson, E. H.,358 Erickson, J. M.,358 Erlick, D.E., 34 Ernst, W., 105 Evankovich, K.D.,250,251 Evett, L.J., 282 Eysenck, H. J., 355,356,370,380 Eysenck, M.,123,306, 313 Eysenck, S. B. G.,355 Faherty, A., 284,316 Falduto, L. L., 141 Falk, G.,341 Farnilant, M.E., 113 Faterson, H. F., 360 Faulkner, D., 174,176-178,281, 285, 316,320,322 Feldman, J., 195 Felts D.L., 191 Ferris, S. H., 33,78,330 Field, D.,355,358,359,363,364, 367 Fisk, A. D.,113,122, 124,412 Fitts, P. M.,144,145 Flanigan, H. P.,308 Flavell, J. H., 157,160,202,204 Fletcher, C. R., 312 Flexser, A. J., 6 Flicker, C., 330 Fodor, J. A., 306 Fodor, J. D., 310 Foley, H.J., 174 Foley, M.A., 174 Folkman, S., 191,192

Fong, G.T., 1% Foye, B. F., 274 Fozard, J. L., 105,138,152 160,231, 235,237,240,252, 253,284,354,356, 357,362,363,365 Francis, W. N., 287 Frazier, L.,310 French, J., 390 Freund, J. S.,34-37,41,54,57,175 Freund, R., 43 Frey, K. S., 194 Friend, C. M.,331 Fry, P.S., 197 Fuller, B. J., 330 Fullerton, A., 75,321 Gabriel-Byme, J., 115, 117 Gabriesheski, A. S., 165 Gallagher, D.,237,246-248,427 Garbart, H.,106 Garcia-Colera, A., 137,139 Gardner, E. F., 340 Garner, W. R., 99,102 Garrett, M.F., 285, 309 Gaylord, S.A., 73 Gazzaniga, M.,70 254 Gebhard, P.,49 Gelade, G.,11, 101,102,411 Gerdt, C., 389 Gerler, D., 285 Gernsbacher, M.,54,83 Gershon, S., 33,252 Giambra, L.,98, 122,124.,412 Gibbons, R., 192 Gielen, C. C., 145 Gilewski, M.J., 158,160,203,213, 215,241 Gilleard, C., 36,51 Gilmore, G.C., 103 Giltrow, M.,200 Gispen, W.H.,252 Glanzer, M.,29,31,80,309-311 Glenberg, A. M.,36,47 Glenwick, D.S.,332 Glisky, E. L.,231,250,252 Goggin, N.L., 136-141,145,146 Gold, D., 355-358,361,363,365, 367,371,423 Goldstein, A. G.,?7 Goldstein, G.,253 Goldstein, H., 400

AUTHOR INDEX Gollin, E.S., 52, 92 Gonda, J. N., 335 Gonzalez, E.C., 39 Goodenough, D. R., 360 Goodglass, H., 284,285 Goodman, D., 141 Gorman, W., 54,83 Gormican, S., 101, 105 Gott, S. P., 235 Gottleib, G. L., 145 Gottsdanker, R., 137 Gounard, B. R., 80,81 Gouvier, W. D., 253,254 Grady, J. G., 158, 192, 218, 234 Graesser, A. C., 301 Graf, P., 174 Granick, S., 361 Green, C., 36 Green, D. M., 123 Green, I., 305 Greene, H. A., 316 Greene, R. L., 38, 47,56,58 Greenwood, P., 123 Gribbin, K.,360,429 Griew, S., 140, 147 Grimm, V. E.,252 Groninger, L. D., 171 Grossman, J. L., 75, 235, 237 Gruneberg, M. M., 285 Guider, R. L., 247 Guilford, J. P.,332, 333, 390 Guttentag, R. E., 174, 175 Haberlandt, K., 310 Hakami, M. K.,29,34-36,41,56,57 Hanley-Dunn, P., 173 Harber, K. D., 332, 338 Harbluk, J. L., 10 Harker, J. O., 79 Harkins, S. W., 82, 315 Harman, M., 390 Harper, R. A., 221 Harris, J. E.,161, 215, 235, 243, 244,281,426 Harris, S., 197 Harrison, J., 192 Harrold, R. M., 284 Hart, J. T., 168 Hart, R. P., 82 Hartley, A., 117 Hartley, A. A,, 330-332, 338 Hartley, J. T., 315

439 Hasher, L., 10,20, 31, 32, 34-38, 41, 43, 45, 46,49, 89, 98,313, 320,408, 4l3,414 Hashtroudi, S., 54, 174, 177 Hausman, C. P., 239 Hayduck, L. A., 398 Hayes, S. C., 254 Hayslip, B., 270-272, 274, 330, 356, 357,371,393 Healy, A. F., 282 Heffley, E.F., 150 H e g h , H. J., 330, 397 Heidrich, S. M., 335 Heisey, J. G., 29, 284 Hellebrandt, F. A., 158 Hellebusch, S. J., 236,238 Heller, H. S., 284 Henry, R., 186, 284 Herlitz, A., 57 Herman, J. F., 52 Herrmann, D. J., 159, 160, 204,213, 215, 248 Hertzog, C., 157-159, 161, 167, 168, 180, 181, 189, 194, 195, 203, 213-218, 222, 235, 253, 270, 381, 382, 387, 388, 392, 394, 395, 398, 425 Herzog, A. R.,158 Hess, T. M., xiv, 42, 310, 321 H e p , J. E.,340 Higbee, K. L., 234, 237,238 Hi&s, E. T., 198 Higgins, J. N., 42 Hilbert, N. M., 237 Hildebrandt, W. G., 106 Hilgard, E.R.,8 Hill, G. O., 147 Hill, R. D., 60,248-251,429 Hillinger, M. L., 282 Hinchley, J. L., 163 Hinrichs, J. V., 43, 46 Hinton, G. E.,11 Hintman, D. L., 34, 38 Hirasuna, N., 316 Hirschfeld, R.M. A,, 389 Hobart, C. J., 321 Hofland, B. F., 263,273,335 Holahan, C. J., 192 Holahan, C. K.,192

AUTHOR INDEX Hoimgren, J. E., 99 Hood, J., 52 Hooper, F. H., 330 Hooper, J. D.,360-363 Hooper, J. O.,360 Horn, J. L.,281,381-384,394 Hornak, R., 282 Hornblum, J. N.,334,397 Howard, D.V.,29,60,284 Howell, S., 79 Hoyer. F. W.,336,397 Hoyer, W.J., 21,22,60,97-103, 113, 114,118,122, 126,272,284,330, 334,336,397,412 Hughes, F., 351 Hulicka, I. M.,75, 158,235,237 Hulme, C.,317,318 Hultsch, D.F.,21,76,157-161,179, 189,195,203,204, 213-217,219,222, 231,351,425 Humphreys, G. W., 282 Hunt, E.,380 Hunt, R. R., 41,174,175 Hurry, S.,?7 Huttenlocher, J., 309 Inman, V., 79,87 Inouye, J., 353 Jackson, D. N., 211 Jackson, J. L.,235 Jackson, R. A,, 362 Jacob, R., 248,249 Jacobs, B., 191 Jacoby, L.L.,6, 59 James, W.,97,101,168,285 Jaquish, G.A,, 332,3336 Jarrett, R. B., 254 Jarvella, R. J., 310 Janik, L. F., 367 Jay, G.,272 Jelalian, E.J., 393 Jenkins, J. J., 20.69 Jensen, A., 380 Jerome, E.A., 331 Johns, R. J., 145 Johnson, D. A,, 360,362 Johnson, J. W.,215 Johnson, K. S.,77

Johnson, M. K., 40,54,70,174,177, 179 Jones, F. W.,335 Jones, G.V.,285 Jonides, J., 38,39 Jordan, T.C.,141,147 Joreskog, K. G.,398-400 Jorm, A. F.,389 Joula, J. F.,99 Jovick, T.J., 330 Just, M. A., 301,302, 306

Kagaa, J., 200 Kahn, R. L.,237 Kahneman, D.,98,408,410 Kail, R., 265 Kanfer, R., 192 Kaplan, B., 309 Kaplan, E., 53,284 Karlsson, T.,57 Karp, S. A., 360 Karttunen, M., 49 Kato, T.,144 Katz, M.M.,389 Kausler, D.H., xiv, 14, 18,29,30, 33-37,41,44-48. 53-59,99, 100, 175, 232,284,316,321 W i n ,A. E.,191 Kean, M.,57 Keenan, J., 312 Keitz, S. M.,80,81 Kelso, J. A. S., 141 Kemper, S.,313,314,317,320 Kenny, D.A., 399 Kent, G., 192 Kerstholt, J., 235 Kesler, M. S., 331 Keyes, B.J., 330 Kieley, J., 117 Kiely, J., 309 Kiersch, M. E.,284 Kimble, G. A., 43,46 Kinchla, R. A., 118 King, J. F., 171 Kintsch, W.,301-303,30&313 Kivnick, H.Q.,358 Klapp, S . T., 10 Klatzky, R., 3,8, 10, Kleban, M. H., 357 Kleinman, J. M.,332 Klerman, G.L.,389,390

441

AUTHOR INDEX

Kliegl, R., 245, 263,274,335,3%, 428 K h e , D., 102 f i n e , D. W.,100,315 Klinger, E.,331 Klisg P.,53 Knopf. M.,57,58 Kolers, P.A., 58 Kotler-Cope, S., 244,253 Kotovsky, K,271 Kramer, D. A., 189,204,221 Kramer, J. J., 166,220, 236,240, 430 Kramer. N, 237,26248,427 ICrigel, S.H.,335 Kucera, H.,287 Kuhn, T.S.,2,313 Kynette, D.,317,318 Labouvie, E.W., 18 Labouvie, G.,272,336 Labouvie-Vief, G.,253,321,335, 385, 394-3% Lachman, J. L., 97, 169,170,172 Lachman,M. E.,161, 167,189,195, 197,199,211,212, 219-222,271,272, 352,361-364,367, 393,422 Lachman, R.,97, 169 Lafronza, V. N., 53,n Lahar, C. J., 314,320,4U Landauer, T.K.,251 Landers, D.M., 191 Langer, E.J., 193,199 Langford, S., 285 Lanphear, A. K, 140 Lapp, D., 263.272 Larish, D. D., 140 Lasaga, M. I., 284 Laurence, S., 254 Laurendeau, M., 329 Laver, G., 284 Lawrence, A., 317 Lawton, M.P.,211 Lawton, P.M., 357 Lazarus, R.S., 191,192 Lee, J. A., 332 Leff, R., 211,212,271,272 Leggett, E.L., 198,201 Lehman, E.B.,54 Lehman, H. C., 341

Leino, E.V., 355,358,364 Lenhardt, M.L,315 Lemon, M. L., 329 Lerner, R. M., 351 Lesli, J. E.,78,83,416 Levenson, H.,211,361 Levenson. M.R., 366 Levinson, D. J., 200 Levinson, M., 200 Levy, J., 70 Lewinsohn, P. M., 390 Lewis, s. s.,306 Lewis. v., 308 Lichty, W..29,33,35-37,44,46,54, 56-58,175 Licklider, J. C. R.,316 Liddle, C.L., 36,39 Light, L. L., xiv, 22,29,42,50-53, 60,92,126,174, l79,281,284,3U315,318,319,408, 412,413,418 Linn, R. L., 399 List, J. A,, 332 Lockhart, R. S., 5, 39, 41 Loftus, E. F.,4,9,282 Loftus, G.R., 4 Logie, R., 301 Looft, W.R., 329,330 Lorsbach, T.C., 113 Lott, L., 248,427 Lovelace., E.A., 158,160,161, 165-167,171, 172, 180,418,426 Lowenthal, M. F., 56 Lucas, D.,285 Lunneborg, C. E., 364 Lutz, R.,51 Lyman, B. J., 100 MacDonald, J., 235 MacKay, D. G., 281283,285 Mackworth, N.H.,108 , MacLeod, C. M.,362 Madden, D. J., 98, 102,105,112, 113,126, 284,316 Maki, R. H., 34 Maletta, G. J., 152 Malinger, B., 329 Mandler, J. M., 35,49-51 Manniche, E.,341 Marcel, A. J., 10

AUTHOR INDEX

442 Marcus, N., 103 Markley, R. P., 166,189,220,236, 240,430 Markus, H., 195, 1% Marr, D. B., 380 Marsella, A. J., 389 Marsh, G. R., 73, 127, 158, 165-167, 171, 172, 180 Martello, J., 389 Martin, J., 170, 321 Martin, M., 308 Martinez, D. R., %,57 Mason, C. E., 393 Mason, S. E., 75-78, 231, 238 Mayes, G. J., 334 Maylor, E. A., 170 McAndrews, M. P., 284 McArdle, J. J., 399, 400 McCarthy, M., 78 McCarty, D., 241, 242 McClearn, G. E., 366 McClelland, J. L., 11, 282, 313 McCormack, P. D., 43,51,92 McCrae, R. R.,Zoo, 332,333.353, 354,356,366.370, 371,393 McDonald, R. P., 400 McDowd, J. M., 21, 22, 71, 114, 121, 412 McEvoy, C. L., 243.244 McFarland, C., 105, 200 McGee, M. G., 362 McGee, N. D., 122,412 McIntosh, J. L,173 McIntyre, J. S., 21, 54, 83, 173, 4l5, 426 McKee, B.,200 McKillip, J., 49 McKitrick, L. A., 231, 250-252 McKnight, D. L., 254 McLachlan, D., 10 McLaughlin, L. M., 330 McMahan, R., 360 McNamara, T. P., 282 McNeill, D., 168, 285, 288 Mefford, J. N., 192 Meichenbaum, D. H., 192 MelIinger, J. C., 54 Meredith, W.,387, 399-400 Mergler, N., 272 Metalsky, G. L., 193 Metzger, R., 43, 166

Meyer, B. J. F.. 20,71, 310,386 Meyer. D. E.,9,282,285,289,293 Milberg, W.,284 Miller, G. A., 3, 316 Miller, J., 116 Miller, J. R., 43, 166, 303, 310, 311 Mitchell, D. B., 22, 23, 29, 60, 174, 281,284 Mitchell, D. R., 20,74, 413 Mitchell, S. A., 36 Moffat, N., 235, 243, 251, 254 Monge, R. H.,21,340 Monks, J., 285 Monsell, S., 306 Moon, J. R., 243, 244 Moore, M., 319 Moore, T. E., 52 Morgan, A. B., 331 Morrell, R., 81, 82, 84, 85 Mortimer, J. A., 152 Morton, K. R., 161, 189, 193, 197, 200,202-204,212, 221,241 Mosmvitch, M., 10 Moss, H. A., 366 Moss, M., 357 Mross, E. F., 308, 313 Mueller, J. H., 284, 316 Muir, C., 317 Murdock, B. B., 2, 46 Murphy, D. L., 389 Murphy, D. R., 60 Murphy, L. J., 115, 117 Murphy, M. D., 36,39,165, 166 Murphy, N. Z., 160,215,422 Murrell, K.F., 144, 145 Muthen, B., 400 Myers, S. D., 315, 318 Narens, L., 169, 285 Naveh-Benjamin, M., 29, 38, 39, 50, 52 Nebes, R. D., 53, 102, 232,252 Neely, J. H., 282, 285 Nehrke, M. F., 330,331 Neidhardt, E., 57, 58 Neisser, U., 36,101,159 Nelson, R. 0..254 Nelson, T.O., 169, 285,289 Nesselroade, C. S.,263, 276 Nesselroade, J. R., 18, 100, 211, 351,361,366,394

AUTHOR INDEX Nestor, P., 123 Nenvorski, T., 43, 166 Nicholas, M., 284 Niederehe, G. A., 237 Nilsson, L. G., 57 Nimmo-Smith, I., 301 Nissen, M. J., 113 Noda, H., 144 Norman, D. A., 100,311,408 Norman, S., 317 Norman, W. T., 353 Norris, A. H., 144, 371 Norris, M. P., 33 Nunn, C., 357 ONeill, B. J., 75 OSullivan, J. T., 163 Obler, L. K., 110, 284,285 Odom, R. D., 330 Offenbach, S. I., 330 Ogden, W. C., 113 Olsho, L. W., 315 Osman, A., 150 Ostby, R. S., 34 Osterlind, P. O., 57 Overton, W. F., 334, 397 Owens, W. A., 354,359,363,383 Ozekes, M., 36,51 Ozer, D. J., 373 Pachella, R. G., 99 Paivio, A., 69, 70, 75, 237, 242 Pajurkova, E. M., 244 Palmer, A. M., 339,395 Palmer, J., 362 Palmer, R. L., 36 Papalia, D. E., 329, 330 Papalia-Finlay, D., 274, 330, 334 Papini, D. R.,361 Parasuraman, R., 97, 98, 123-125 Parham, I., 333,360,364,366,429 Park, D. C., 51-54, 73, 80-85, 87, 89, 92 Parks, C. W., 331 Pasko, S., 49 Patterson, K.,308 Patterson, M. B., 147 Paullin, R., 42 Peake, P. K.,191, 194,210 Pearce, K. A., 339 , Pearlstone, Z., 5, 168 Peck, V., 169

443 Pedersen, N. L., 366 Pellegrino, J., 265 Perhutter, M., 43, 51, 53, 157, 158, 161, 166, 173, 192, 202, 204,207, 218, 234,331, 423 Person, D. C., 198, 199 Peterson, M. A., 40, 98 Petit, T. L., 252 Pezdek, K,51,82-84, 87,309, 318, 415 Phillips, J., 147, 148 Phillips, P. L., 47, 48, 56 Pirozzolo, F. J., 152 Plemons, J. K.,335 Plomin, R., 366 Plude, D. J., 21, 97-105, 107-115, 117, 118, 122, 126, 412 Podolski, S., 105 Pollack, R. H., 102, 332, 340 Poon, L. W., xiv, xv, 20-22, 113, 126, 158, 160, 194, 215, 219, 223,231,235, 237, 240, 252, 253, 271, 284,285, 320, 372,381,407, 418 Popkin, S. J., 231 Posner, M. I., 97, 98, 113 Pouraghabagher, A. R., 138, 150 Pratt, J. D., 237, 238 Prentice-Dunn, S., 191 Pressey, S. L., 340 Pressley, M., 163 Prill, K.,114 Puckett, J. M., 30, 34, 35, 41, 53 Puglisi, J. T., 51, 54, 80-85, 87, 89, 92 Pull ter Gunne, F., 145 Quaid, K.A., 137 Quilter, R. E., 98, 124 Rabbitt, P., 101-103, 105, 141, 147, 150, 151,253, 417 Rabinowitz, J. C., 54, 163, 171, 172, 174, 175, 177, 178, 251 Rankin, J. L., 59 Raskind, C. L., 99, 381 Raven, J. C., 360

AUTHOR INDEX

444 Ray, D., 78 Raye, C. L., 174 Read, J. D., 285 Reason, J. T., 285 Rebok, G. W., 207, u)8,220,330, 332, 335,393 Reder, L. M., 321 Reese, H. W., 75, 100, 237 Reese, L., 192 Reever, K. E., 250 Reeves, C. L., 36 Reige, W. H., 79, 87 Rice, G. E., 20, 71, 310, 386 Richards, B., 52 Riddick, C. C., 140 Rimoldi, H. J. A., 340 Ripple, R. E., 332, 333 Rissenberg, M., 29, 31, 80 Robbin, J. S., 330 Roberts, P., 330, 334 Robertson, E. A., 21 Robertson-Tchabo, E. A., 113, 172, 239, 240,242,251 Rodeheaver, D., 351 Rodgers, W. L., 158 Rodin, J., 197, 199 Roediger, H. L., 6, 58, 59, 285 Rogan, J. D., 114 Rogers, C. J., 330 Rogers, R. W., 191 Rogers, W., 122 Rogoff, B., 51, 92 Rogosa, D., 399 Rohde, P.,390 Rose, C. L., 354 Rose, K. C., 45 Rose, P. M., 40 Rose, R. T., 42 Rose, T. L., 60,76, 77, 239-242, 248, 250,251 ROSS,M., 200-202 Rotter, J. B.,361 Rowe, E. J., 39, 75 Royal, D., 81,82 Ruben, H., 237 Rubenstein, H., 306,308 Rubenstein, M. A., 306 Rubin, D. C., xiv, xv, 312 Rubin, K.,329, 330, 334 Ruble, D. N., 194 Ruddy, M. G., 282 Rule, B.G., 20, 158, 413

Rumelhart, D. E., 11, 282, 313 Rundus, D., 43 Rush, A. J., 221 Ruth, J., 333 Rybarczyk, B. D., 82 Salkovskis, P. M., 192 Salthouse, T. A., xiv, xv, 3, 12, 16, 19-22, 30,31, 34, 35, 41, 44,45, 49, 53-55, 71, 73, 74, %, 99.1w 114-116, 118,121, 122, 125-127, 135, 138, 142, 143, 147, 149-152, 281, 284, 301, 313, 317, 318, 322, 331, 370, 380-383,408,412, 413,417 Samuel, D., 252 Sanders, E., 282, Sanders, J. A. C., 334 Sanders, J. C., 334, 397 Sanders, R. E., 36,38-41,46,334, 397 Sanders, S., 329 Sandler, S. P.,57 Sands, D. C., 57 Sands, L., 387 Sanft, H., 45 Sarason, I. G., 192 Satorra, A., 400 Satz, P.,284 Saults, J. S., 30, 35, 44,45, 55 Scarborough, H. S., 301,309, 310 Schacter, D. L., 10, 49, 59, 231, 250, 252,256,412 Schadler, M., 158, 385 Schaie, K.W., xiv, 158, 213, 253, 264,266,268,268270,355,358,360, 361,364,366,372, 383,384,387,388, 391-398,423,429 Schear, J. M., 53 Scheier, M. F., 199 Schell, D. A., 253 Schieber, F., 315 Schleser, R., 161, 215 Schmidt, H., 108 Schmidt, S. R., 6

AUTHOR INDEX

Schmitt, F. A., 165,174 Schmitz-Scherzer, R.,391,393 Schneider, W.,10,11, 21,98, 1U, 121,122,124,163, 313,412 S h o r e , M.M.,75 Schoenfeldt, L. F., 354,359 Schonfreld, D.,21, 1U Schroeder, K., 57 Schdenberg, J. E.,159,189,214, 270 Schulman, A. I., 50, 51 Schulz, R., 197 Schulz, R. W.,47 Schutz, R.N.,334 Schvaneveldt,R. W.,9, 282 Schwab, J. J., 390 Schwab, M.E.,390 Schwartzman, A.,355,367,423 Scialfa, C.T.,100,102 ScOgin, F., 166,220,248,427 Sebby, R.A., 361 Seegmiller, D.,35 Sehulster, J. R., 1% Seidenberg, M.S.,308 Sekuler, R., 102 Selzer, R.,330,334 Serlin, R. C.,274 Sharps, M.J., 52, 92 Shaughnessy,J. J., 162,171 Shaw, B.F.,221 Shaw, R. J., 29, 162,171,425 Shaw, R. S.,284 Sheehy, G., 200 Sheikh, J. I., 248-251,263,272,429 Shelly, C.,253 Shepard, R.,265,271 Shepherd, J., 77 Shiffrin, R. M.,2,4, 10, 11, 21,98, 121,412 Shock, N. W.,144 Shulman, H.G.,282 Siegler, I. C.,383,391,393 Sielski, K.A.,334 Silberstein, M.,7 Silverman, W.,373 Simon, E.,73,74,T',80,81,87 Simon, E.W.,235,237 Simon, H., 271 Simon, J. R., 138, 150 Simon, S., 309 Simonton, D. K.,341,422

445

Simpson, G. B.,113 Singh, A., 29,60,284 Singleton, W.T.,144 Sinnott, J. D.,189,204,244, 338 Skovronek, E.,20,74,413 Slamecka, N. J., 174 Smith, A. D.,20, 21,53,69,71, 73, 75-77,80-85,87, 88.92, m,238, 245,321,412 Smith, G. A., 143,149 Smith, J., 245,428 Smith, M.,334,397 Smith, S. M.,36 Smook, G., 393,399 Sokolov, E.N.,98 Solso, R. L.,97 Somberg, B. L.,99, 114-116,l38, 150,152 Sorbom, D., 398 Southard, D.L., 141 Sovacool, M.,51,80,81,83.84,92 Sowarka, D.,274 Spilich, G.J., 321 Spirduso, W.W.,151,152,418,429 Spiro, A., 366 Stankov, L., 113,381, 382 Stark, H.A., 59 Stein, D. G., 254 Steinberg, E.S., 167 Stelmach, G.E.,135-141,145,147, 148,150 Stelmack, R. M.,356,370 Sternberg, R.J., 314,380,381,385, 386,395,396,420, 427,429 Sternberg, S., 410 Sterns, H.L.,334,397 Stevens, D.P.,366 S h e , E.A.L.,313-318,320,321, 322,413 Stoeckert, J., 309 Storandt, M.,248,427 Storck, P.A.,330 Strauss, M. F.,38, 137 Strayer, D. L.,99, l39, 149 Sullivan, C.,46 Sunderland, A.. 161,215,281,426 Surwillo, W.W.,124 Susman, E.J., 366 Sved, S. M.,330 Swanson, N.G.,47

AUTHOR INDEX

446

Swets, J. A., 123 Szafran, J., 150 Tabor, L,76 Tanenhaus, M.K., 308 Tanke, E. D., 248,249, 429 Tardif, T., 305,306,319 Taylor, C. B., 192, 207 Taylor, D.E., 282 Taylor, T., 174 Teasdale, N., 147,148,150 Terry, H., 387 Thomae, H.,364,391,393 Thomas, J. C., 237, 284 Thomas, J. L., 52 Thompson, K., 38 Thompson, L. W., 127, 158, 160, 213, 241, 252 Thornson, D. M.,6,233 Thornson, N., 317 Thorley, W., 77 Thronesbery, C., 169 Thurstone, L. L., 265, 360 Thurstone, T. G., 265, 360 Tierney, M., 329 Till, R., 54, 83, 308, 313 Th e, C. S., 161, 189, 204 Timko, C., 197 Toffano, G., 252 Toglia, M.P.,43, 46 Tomer, A., 381, 382, 387, 393, 399 Tomlinson-Keasey,C., 329 Townsend, J., 106, 119 Traber, J., 252 Trask, F. P., 43, 46,47 Treat, N. J., 75, 160, 231, 235, 237, 336,397 Treisman, A. M.,11, 98, 101, 102, 104,108,411 Trotter, S. D., 167 Troutman, B. R., 192 Truss. C. V.. 366 Tulving, E., 4-7, 10, 29, 59, 168, 179, 233, 252 Tumolo, P., 329 Turnbull, W., 200 Twohig, P. T., l58,160, 161,426 Tzeng, 0. J. L., 46,47 Underwood, B. J., 34,43,46, 172 Underwood, V. L., 235

Vaidya, C., 113 Vallar, G., 308 van den Oosten, K, 145 van Dijk, T. A., 301-303,309-312 Van Gorp, W. G., 284 Vander Woude, K. W., 340 Verillo, R. T., 98 Verillo, V., 98 Vernon, P.A., 380 Vipond, D., 302 Virzi, R. A., 106 Vitiaa, J. R., 39 von Wright, J. M.,49, 50 Vrtunski, P.B., 147 Waddell, K.J., 51, 92 Wade, E., 281 Wagman, A. M., 137 Walsh, D., 237 Walsh-Sweeney, L.,235237,240 Wapner, S., 40,329 Warabi, T., 144 Warren, L. R., 36 Warren, R. M., 12 Warren, R. P., 12 Warrington, E. K., 7, 10, 33 Watts, K., 161, 215, 281, 426 Waugh, N. C., 284 Wayland, S. C., 285 Weaver, S. L.,220-222 Webster, J. S., 254 Wechsler, D., 355 Weiner, B., 197, 212 Weingartner, H., 38 Weinman, J., 332 Weinstein, C. E., 235, 236 Weiskrantz, L., 10, 33 Weiss, A. D., 149 Weissman, M.M., 389 Welford, A. T., 3, 22, 135, 137, 141, 142, 144, 149,417 Wellman, H., 157, 160, 198, 199, 202,204 Wells, J. E., 46 Werner, H., 40,329 Werts, C. E., 399 West, R. L., 33, 159, 161, 166,208, 209,215,244,330 Wetherick, N. E., 330 Whitbourne, S. K., 332 White, H., 29, 284 White, J., 190

447

AUTHOR INDEX

White, N. A., 381,382,386,387, 389 Whitehouse, P.,33 Whitely, S. E.,331 Wible, C.,321 Wickens, C. D.,99, 115, l39, 149 Wilkins, A. J., 243,244 Willett, J. B.,399 Williams, D. M.,22,126,381 Williams, K. W., 39 Williams, M. V.,21,381 Williams, S. A,, 158,385 Williams, S. L.,192 Willis, S. L.,211,254,263,264,266, 268-273,211,335, 361,391,392,394, 396,391,426-429 Wilson, B. A., xiv, xv, 235,243,254, 308 Winblad, B.,57 Wingfield, A., 285, 310,311, 313-318,320-322, 413 Winocur. G.. 10 Wmograd, E., l3,14,V,79-81,87, 235,237 Wise, J. L., 36 Witkin, H. A., 360 Witte, K. L., 34-37,41,330 Wittlinger, R.P.,7 Wohlwill, J. F., 101 Wood, L. E.,237,238 Wood, R., 191,192,198 Woodruff, D.S., 98,121,252,366 Woods, A. M., 21,381 Woodward, S., 110 Worringham, C.J., 135, 147,148 Worthley, J., 170,281 Wright, L. L., 103,118 Wright, R. E.,34,35,331 Wurf, E.,195,1% Yamamoto, K.,383,384 Yaniv, I., 285, 289,293 Yap, E.C.,40 Yerkes, R. M.,I23 Yesavage, J. A., 60, 76,11,233, 239-242,248-251, 263,212,421,429 Yntema, D. B., 43,46,47 Young, M.L.,331,335,397 Young, R. K.,48

Zacks, J. T., 106 Zacks, R.T., 10,20, 31,32,34,43, 45,46,49, 89,98, 106,313,314,320, 408,413,414 Zajonc, R. D., 10 Zarit, J. M.,2.50 zarit, S. H.,237,246-248,2.50,427, 429 Zechmeister, E. B., 49,50,162 Zeiss, A. M.,192 Zelinski, E.M.,50-53,92,158,160, 203,213,215,241, 314,319 Zimmerman, J., 43,46 Zimmerman, W. S., 390 Zimowski, M., 399 Zubeck, J. P.,331 Zuidema-Murphy, N.,356,363 Zung, W. K.,246 Zurif, E. B., 309 Zusne, L.,341

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449

Subject Index Alzheimer's disease - memory intervention in, 250-252 AAMI, 33 mnemonic training, 249-250 ADEPT program, 263,265266,274-276 Age difference vs. age change, 17 Aging - definition of, 15-16 - - chronological, shortcomings of, 15-16 - demographic changes, 1, 254 - differences in rate of, 3,423 - functional age, 15 - incipient disease, vs., 15 Amnesics - preserved implicit memory, 33 Attention (see also visual search) arousal, 98, 123 - Yerkes-Dodson principle, 18,357 - capacity, 98, 1l3-122,124 - divided, 20, 114-121,411 - feature-integration theory of, 11, 101-102, 127,411 - focused, 11, 102-113 - methodological issues, 99-100 - pre-attentive 11, - selectivity, 97,102-113,411 non-target interference effects, 103 shifting, 113 - sustained, 123-125 - resources, 9,20-21,126-127,408 Automatic processes (see encoding, and processes) Awareness (see also metamemory), 8,10 Cognitive training (see also mnemonics), 263280,426-430 - breadth of effects, 269-270,3%397 - generality, 246, 269-270,429-430 maintenance, 246,275-276,429-430 non-cognitive performance factors, 270272,335-337,427-428 - practice effects, vs., 273-275 - problem solving, 333-335,397 - training procedures, 264-265

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- remediation vs. enhancement, 266-269, 397-398 Complexity effect (see task difficulty) Creativity (see problem solving) Dementia (see Alzheimer's disease) Depression intelligence and, 389491,394 memory and, 246-249 self-efficacy and, 192 Discourse processing, 301-313 aging and, 313-322,419 central executive in, 319-322 peripheral process, 315-316 working memory stores, 316-319 Working Memory-Discourse Hypothesis, 314-315 model of, 301-313 developmental aspects of, 322 language processing, 306-313 working memory in, 302-306 Distributed processing, 11 Encoding acquired automaticity, 121-122,125,412 automatic vs. effortful, 31,38-58,121-122, 125,414-415 distinctiveness,6,49-50 dual code hypothesis, 69 environmental support, 85 orienting task, 5 spatial information, 53,89-92 specificity, 6,83 temporal information, 45-49 Familiarity, 22 Field dependence (see personality, peripheral traits) Flexibility-rigidity (see personality, peripheral traits) Generalization research findings, 17-18 cognitive training, 246,269-270 Goals for study of cognitive aging, 23 Imagery (see memory for viusuospatial information) Information processing approach, 2-3

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450 Intelligence, 379-401 age changes, 379-398,420-421 antecedents of, 389-391,393-394 cohort effects, vs., 388-389 depression as predictor, 389-391,394 ecological validity of, 394-3% levels, 387-391 longitudinal studies, 391-393 patterns of change, 391-393 plasticity, 396-397 reversibility, 397-398 methodological developments autocarrelational structural models, 398-399 latent growth curves, 399-400 multivariate panel models, 399 structural equation models, 400 short-term fluctuations, 394 structural changes, 386-387 theories fluid vs. crystallized, 382-384 speed explanations, 38O-f382 stage theories, 384-386 practical, 395-396 Interactionkt framework, 19-20,69-71,204-206, 351-352,409-410 Judgments frequency, 30-32,34-38 of knowing (see metamemory) temporal, 32,42-43 Knowledge declarative vs. procedural, 7,234,322 Language (see also discourse processing) interactive activation model, 282-284 language comprehension, 281,284,312 language production,281,284-286,312 word production deficit (see also tipof-the-tongue),284-286 Levels of processing, 5, 162-163,425 Lexical access (see tip-of-the-tongue) Locus of control (see personality, peripheral traits) Memory age dissociations, 30-31 activation of, 8-9,313 affective status and, 246-249 availabilityvs. accessibility, 5 compensation, 22,254,427,429 complaints, 22,158,231,246-248,427 episodic, 6,14,29,30-33,71, 170,412 implicit, 10-11,33,59-61,232,412-413

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SUBJECT INDEX

- interference, 6,285 - interventions, 23, 231-254

- affective states, 246-249 - external mediators, 242-244 - effective features, 231-233 - - internal mediators, 236-242 - - utility of, 252-254 -

- long term (secondary), 4,7,9,407 - monitoring (see metamemory) - multistore model, 3-7,313,407 - network theories of, 9,282-2244,313,407 - proceduralist view of, 58 - remote (tertiary), 7 - retrieval, 2,6,21,42 - sensory stores, 3 - semantic, 6,14,29,70-71,170,412 - short term (primary), see also working memory, 3,407

- type of test, importance of, 21,57-58,71, 83-84,412,415 Memory for, abstract drawings, 79-80 activities, 55-58,175-176,415 others'actions, 56 color, 89 complex scenes, 81-88 contextual information, 83, 173-174 faces, 77-79,241-242,416 age differences in view-specific detail, 78-79 frequency information, 34-42 adult age differences, 34-42 line drawings, 80-81 picture superiority effect, 80-81 non-content attributes, 34-54,414 personal attributes, 200-202 source of information (see also reality monitoring), 54, 173-179 spatial location, 49-53,89-92 adult age differences, 51-53 temporal information, 42-49 adult age differences, 43-45 visuospatial information, 69-93,415-416 Metacognitions, 157,179-182 Metamemory, 157-158,202-204,425-426 correctness of one's response, 172-173 dimensions of, 158-159,203-204 feelings-of-knowing,168-171 judgments-of-knowing, 171-172 evidence of idiosyncratic component, 172

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451

SUBJECT INDEX

Metamemory (cont.) pre-performance estimates, 165-168 - calibration hypothesis, 166-167 - questionnaire data, 158-161 - reality monitoring, 173-179 relations among varieties, 179-182 self-efficacy and, 202-206 - stategies, 163-165 - subjective loss of function, 22, 158,161 variation in task or encoding operations, 161-163 Methodological issues - cohort effects, 1617,388-389 cross-sectional analysis, 16, 100-101 - longitudinal approach, 16-17 measurement of intelligence, 398-400 - selective attrition, 17 - speed-accuracy trade-off, 99-100 Mnemonics - essential properties, 283 - internal vs. external mediators, 234-235 - organizational & encoding, 233 - process vs. fact, 234 preference for external aids, 245 spontaneous use, 235-235 - training, 236-246 - - combined with training to reduce affect, 248-249,272,427 - external mediators, 242-244 - - internal mediators, 236-242 - maintenance and generalization,246, 429-430 Monitoring of memory (see metamemory) Motor control, 135-155,416-418 - measures (RT,MT), 135 - monitoring, 151 open vs. closed loop, 150 - hypothesized need for visual feedback, 150 MT, response characteristics, 144-147 - MT. speed-accuracy relationship, 142-144 RT, response complexity, 140141 RT, response preparation, 137-138 RT, response programming, 140 RT, response selection, 138-140 Network theories (see memory) Nonmgnitive factors, 18-19 depression and anxiety, 192,246-249 learning/performance distinction, 18 training of, 270-272,335-337

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Personality - five broad factors, 353-354 - relation to cognition, 352-364 - aging and, 363-364, 422-424 - causality Of, 364-372 - causal model for central traits, 367-372 central personality traits, 354-360 - peripheral traits (cognitive style variables), 360-363,423 Personal control, 197-198(see also personality, peripheral traits) Plasticity - fluid abilities, 266-269 - of intelligence, 3%-397 and testing the limits, 245-246,397,408, 428 Practice age benefits, 151,397,409 - vs. cognitive training, 272-275 Priming (see also memory, activation of) - convergence/divergence in, 282-284 - phonological, 285-286 - semantic, 284-285 Problem solving, 329-344,421-422 - creativity and, 332-333,341,422 - everyday (practical), 337-341 intervention research - concept learning, 334 - Piagetian tasks, 334 reasoning, 335 - search tasks, 335 - noncognitive interventions, 335-337 traditional (abstract) - concept learning, 330 Piagetian, 329-330 reasoning, 331 - search tasks, 331 - visual-spatial tasks, 331-332 Processes - attentional (see attention) - automatic, 10-11,21,31-32,98,408,410, 414 - central executive (see also working memory), 8 - controlled (see effortful) - data-limited, 100,408 - effortful, 11, 21,31, - serial vs. parallel, 11, 101-102,108-109, 410 Top-down/bottom-up, 12,302,3%

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452

SUBJECT INDEX

Processing capacity (see also working memory capacity) limitation on capacity, 2,4,97 Reaction time (see motor control) Rehearsal, 4 elaborative vs. maintenance, 5 Representation imagery instructions, 75-77 secondary memory - age differences, 22 visuospatial vs. verbal, 69-88 psychometric data, 70-74 - split-brain patients, 70 Research strategies cross sectional/longitudinal, 16-17 in vivo/in vitro, 314-315 Seattle Longitudinal study, 264-269,360-361, 391-392,428 Self-efficacy, 159-161,190-193,424-425 - accuracy of. 193-194 - and coping with stress, 191-192 and implicit theories of cognition, 198-200 and metamemory, 202-206 and personal control, 197-198 - separation of locus and control, 197 and self theory, 195-197 memory self-efficacy, 180-181,uK)-202 - application in mnemonic training, 219221,222-223 measurement of, 206-219,222 Self theory (see self-efficacy) Spatial localization, 32 - cues in visual search, 109-1l3 Speed factor in intelligence, 380-382,389 generalized slowing, 126, 135,150,408 - slowing factor, 147-151 - exceptions, 151-152 - input/output hypothesis, 147-149 - neural noise hypothesis, 149 - software vs. hardware, 149 speed-accuracy relationship, 99-100,142144,410 age-related criterion shift, 151 Task difficulty, 22,83,121,125,408,416 Tip-of-the-tongueexperience, 168,170-171, 281,418-419 - age differences - diary study, 286-287 laboratory (induced), 287-295 priming of targets, 289-295

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- inhibition hypothesis, 285

- transmission deficit hypothesis, 285 Visual search, 103-109 eccentricity of targets, 107-108 spatial cuing, 109-113 Visuospatial memory, (see memory for, visuospatial information) Volition, 9 Word retrieval (see tip-of-the-tongue) Working memory, 8 age effects, 20, 407,413,414 central executive, 4 309-310 age defiats, 319-322 models of, 302-306,313 - - Baddeley’s, 304 - Daneman and Carpenter’s, 304-306 - - Kintsch and van Dijk’s, 302-304 organization within, 312 - processing resources, 311-312 - role in language, 306-312 articulatory loop, 8, 308-309,317-318 - phonological and orthographic coding, 306-308,316-319 - visuo-spatial scratchpad, 8,309,318319 - stores as slave systems, 8

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