Advances in Cognitive Science, Volume 2

Advances in Cognitive Science Volume 2

ii    Advances in Cognitive Science

Advances in Cognitive Science Volume 2

Edited by

Narayanan Srinivasan Bhoomika R. Kar Janak Pandey

Copyright © Narayanan Srinivasan, Bhoomika R. Kar and Janak Pandey, 2010 All rights reserved. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage or retrieval system, without permission in writing from the publisher. First published in 2010 by Sage Publications India Pvt Ltd B1/I-1, Mohan Cooperative Industrial Area Mathura Road, New Delhi 110044, India www.sagepub.in Sage Publications Inc 2455 Teller Road Thousand Oaks, California 91320, USA Sage Publications Ltd 1 Oliver’s Yard, 55 City Road London EC1Y 1SP, United Kingdom Sage Publications Asia-Pacific Pte Ltd 33 Pekin Street #02-01 Far East Square Singapore 048763 Published by Vivek Mehra for Sage Publications India Pvt Ltd, typeset in 10/13 pt. ITC Stone Serif by Star Compugraphics Private Limited, Delhi and printed at Chaman Enterprises, New Delhi. Library of Congress Cataloging-in-Publication Data Available

ISBN:  978-81-321-0444-5 (HB) The Sage Team:  Rekha Natarajan, Meena Chakravorty, Amrita Saha and Trinankur Banerjee

Contents List of Figures List of Abbreviations Preface

ix xv xvii

Section I  Learning and Memory Introduction Chapter 1 Study of Basic Associative Processes Contributes to Our Understanding in Cognitive Science J. Bruce Overmier and John M. Holden Chapter 2 Minimizing Cognitive Load in Map-based Navigation: The Role of Landmarks Kazuhiro Tamura, Bipin Indurkhya, Kazuko Shinohara, Barbara Tversky, and Cees van Leeuwen Chapter 3 Quantitative and Qualitative Differences between Implicit and Explicit Sequence Learning Arnaud Destrebecqz Chapter 4 Behavioural Study of the Effect of Trial and Error versus Supervised Learning of Visuo-motor Skills Ahmed, Raju S. Bapi, V. S. Chandrasekhar Pammi, K. P. Miyapuram and Kenji Doya Chapter 5 ACE (Actor–Critic–Explorer) Paradigm for Reinforcement Learning in Basal Ganglia: Highlighting the Role of the Indirect Pathway Denny Joseph, Garipelli Gangadhar, and V. Srinivasa Chakravarthy

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7

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vi    Advances in Cognitive Science Section II  Perception and Attention Introduction

93

Chapter 6 Peripersonal Space Representation in Humans: Proprieties, Functions, and Plasticity Elisabetta Làdavas and Andrea Serino

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Chapter 7 A Neurophysiological Correlate and Model of Reflexive Spatial Attention Anne B. Sereno, Sidney R. Lehky, Saumil Patel, and Xinmiao Peng

104

Chapter 8 Effects of Emotions on Selective Attention and Control Narayanan Srinivasan, Shruti Baijal, and Neha Khetrapal

132

Chapter 9 Modelling Neuropsychological Deficits with a Spiking Neural Network Eirini Mavritsaki, Glyn W. Humphreys, Dietmar Heinke, and Gustavo Deco

150

Section III Time Perception Introduction Chapter 10 Continuity of Subjective Experience Across Eye Movements: Temporal Antedating Following Small, Large, and Sequential Saccades Kielan Yarrow

175

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Chapter 11 Duration Illusions and What They Tell us about the Brain Vani Pariyadath and David M. Eagleman

196

Chapter 12 Implicit Timing Trevor B. Penney, Latha Vaitilingam, and Siwei Liu

207

Chapter 13 Localization and Dynamics of Cerebral Activations Involved in Time Estimation: Studies Combining PET, fMRI, and EEG Data Viviane Pouthas

224

Contents   

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Section IV  Language, Cognition, and Development Introduction Chapter 14 Effects of Remediation on Auditory Temporal Processing in Dyslexia: An Overview Bhoomika R. Kar and Malini Shukla

241

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Chapter 15 Brain Networks of Attention and Preparing for School Subjects Michael I. Posner and Bhoomika R. Kar

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About the Editors and Contributors Subject Index Name Index

270 278 283

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List of Figures Chapter 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9

Illustration of behavioural stream and three theories of action of reinforcers. SD = discriminative stimulus, R = response, SR = reinforcer Illustration of a transfer-of-control procedure Latency to perform an avoidance response as a function of stimulus type and test day Illustration of a standard discriminative conditional choice task employing common outcomes, and a similar task employing differential outcomes Performance in a bi-conditional discrimination task under differential and common outcomes procedures Illustration of the transfer-of-control procedure employed by Kruse et al. (1983) Illustration of an experimental design for showing inter-problem transfer of control of choice Illustration of a stimulus equivalence training procedure similar to that employed in Joseph et al. (1997) Data on the short-term working memory of normal older men and older men with Korsakoff’s disease

8 10 11 12 13 14 16 17 20

Chapter 2 2.1

2.2

2.3 2.4 2.5

2.6

Examples of map displays used in Experiment 1: The global landmark conditions (global–inside: A, global–outside: B, and without–global landmark: C) Example sequences of displays used in Experiment 1 (Aligned and Misaligned versions of a global landmark inside two local landmark conditions) Mean response speed and standard errors for the interaction of the global landmark and Alignment of the test display in Experiment 1 Mean response speed and standard errors for the interaction between local landmark and alignment test display conditions in Experiment 1 Mean response speed and standard errors for the interaction between the arrangement of landmarks (fixed versus random), number of local landmarks, and the Alignment of the test display in Experiment 1 Example of maps used as stimuli in Experiment 2

27

28 30 32

34 36

x    Advances in Cognitive Science 2.7 2.8

Mean response speed and standard errors for the interaction between type of landmark and Alignment of the test display in Experiment 2 Mean response speeds and standard errors between Arrangement and type of landmark conditions in Experiment 2

38 39

Chapter 3 3.1

3.2 3.3

Mean reaction times during the 15 blocks of the SRT task plotted separately for participants trained with a 0 ms or 1000 ms RSI. Block 13 is the transfer block during which another sequence was used. Mean recognition scores in RSI 0 and RSI 1000 conditions when either a constant (CST) or variable (VAR) RSI was used at test. RTs recorded for old and new fragments presented in the recognition task to participants trained with a 0 ms or a 1000 ms RSI and plotted separately for fragments presented at test with either a constant (CST) or variable (VAR) RSI

48 50

51

Chapter 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7

Sequence Learning Tasks Summary snapshot of a subject (AS) from “mild-learning” group of 1 × 12 experiment Summary snapshot of a subject (AS) from “mild-learning” group of 2 × 6 experiment Summary snapshot of a subject (ST) from “mild-learning” group of 1 × 12 experiment Summary snapshot of a subject (JY) from “mild-learning” group of 2 × 6 experiment Summary snapshot of a subject (BN) from “continued-learning” group of 1 × 12 experiment Summary snapshot of a subject (BN) from “continued-learning” group of 2 × 6 experiment

58 62 63 65 66 67 68

Chapter 5 5.1 5.2 5.3 5.4 5.5 5.6

ACE Architecture Simple muscle model system (a) Architecture of the actor network (b) The 2D arm and the targets to be reached Architecture of the critic network Architecture of the explorer (a) STN–GPe neuron pair illustrating the excitatory and inhibitory connections, (b) Network model of STN–GPe loop with lateral connections

73 74 75 75 76 78

List of Figures    5.7

5.8

5.9 5.10

5.11 5.12

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Dynamics of the STN–GPe Loop: Three characteristic patterns of activity in the STN–GPe layer – (a) Uncorrelated activity, (b) Travelling waves, and (c) Clustering Snapshots of STN activity for various values of DNe: (a) DNe = 50; observed E‑dim ~ 96, (b) DNe = 20; observed E-dim ~ 48, (c) DNe = 5; observed E-dim ~ 15. There is a consistent decrease in E-dim with decreasing DNe Output of the Critic for different targets, with the x–y plane representing [ga, gb] values and the z-axis representing the Value, Q (a) The dynamics of the model before learning, E-dim = 93, (b) The dynamics of the model after learning for eight epochs, E-dim = 57, (c) The dynamics of the model at the end of learning, E-dim = 5 The relation between norepinephrine (DNe) in the STN–GPe layer and d Changes due to dopamine reduction

79

81 83

85 86 87

Chapter 7 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 7.10 7.11 7.12 7.13 7.14

Typical reflexive spatial attention task used to elicit IOR Typical behavioural results obtained in a reflexive spatial attention task Behavioural results of monkeys in a reflexive spatial attention task Activity of SC neurons during a reflexive spatial attention task Stimulus repetition suppression effects in the ventral steam during a serial recognition task Stimulus repetition suppression effects in the ventral steam during delayed match-to-sample tasks Schematic localization of visual pathways in the macaque brain Schematic diagram of the fixation task (one location, eight shapes) Activity of AIT and LIP shape selective neurons during a passive fixation task with repeated stimulus presentations within a trial Repetition suppression effects in AIT and LIP Repetition suppression effects in LIP across six blocks of trials Proposed network model of reflexive spatial attention Simulated outputs of the model at three different CTOAs during a reflexive attention task Simulation of reflexive spatial attention and the influence of shape

106 107 109 110 112 114 115 116 117 118 119 120 121 123

Chapter 8 8.1 8.2 8.3 8.4

Search times for sad and happy schematic faces in a detection task Search times for sad and happy schematic faces in a discrimination task The magnitude of flanker compatibility N 100 component (90 to 140 ms; frontal sites) depicts increased amplitude for happy target faces compared to threatening target faces

136 137 143 144

xii    Advances in Cognitive Science 8.5 8.6

N2 component (220 to 260 ms; central midline sites) is locked to stimulus onset and reflects processing of conflict ERN component (60 to 90 ms; central midline sites) is locked to onset of response and reflects processing of errors

144 145

Chapter 9 9.1 9.2 9.3 9.4 9.5 9.6

The architecture of the sSoTS model The mean correct reaction times (RTs, in ms) for the unlesioned (dotted lines) and lesioned versions (solid lines) of sSoTS The mean percentage miss responses for the lesioned versions of sSoTS (data from Simulation 1) Example displays from Simulation 2 The mean correct RTs (ms) from Simulation 2 (a) The mean correct RTs (ms) and (b) the mean percentage miss responses for Simulation 3 (variation in the NMDA parameter)

153 160 161 163 164 167

Chapter 10 10.1 10.2

Schematic of the experimental task in saccade and control conditions Time estimation data

184 186

Chapter 11 11.1 11.2 11.3

The debut effect Repeated stimuli subjectively proliferate less than random stimuli Proposed repetition suppression diagnostic tool

198 201 203

Chapter 12 12.1 12.2 12.3 12.4 12.5

SOA Polyrhythmic Sequence Mean Stop-RT (ms) averaged across 20 participants in a bimodal single SOA experiment from our lab Performance on two SOA Polyrhythmic Sequence Examples of typical passive oddball paradigms used in mismatch negativity (MMN) studies of interval timing

209 211 212 214 217

Chapter 13 13.1 13.2 13.3 13.4 13.5

Contingent Negative Variation (CNV) Generalization gradients Peak latency Mean CNVs Three hypothetical models of the neural mechanisms for timing

226 227 228 229 230

List of Figures    13.6 13.7 13.8 13.9

Attention conditions Estimation of Activations due to length of duration Waveforms Time course activity

xiii 231 233 235 236

xiv    Advances in Cognitive Science

List of Abbreviations AIT

anterior inferotemporal cortex ANOVA Analysis of Variance ANT Attention Network Test AR Adapted Response ARP Associative Reward Prediction BG Basal Ganglia BOLD Blood Oxygenation LevelDependent CBCS Centre for Behavioural and Cognitive Sciences CS Cognitive Science CO Common Outcomes CNV Contingent Negative Variation CFFT Critical Flicker Fusion Threshold CTOAs Cue–Target Onset Asynchronies DO Differential Outcomes DRD4 Dopamine 4 Receptor Gene DLPFC Dorso–Lateral Prefrontal Cortex EEG Electroencephalography ERN Error-Related Negativity ERPs Event-Related Potentials fMRI functional Magnetic Resonance Imaging GPe Globus Pallidus externa GPi Globus Pallidus interna IOR Inhibition of Return IFG Interior Frontal Gyrus ISI Inter-Stimulus Interval

LIP LED MMN MOBS mRT NE OP PREP PA PSE PPC PMC pre-SMA RBF RT RF RL thr RT RSI RBD SOC SAIM SRT SCRs SLI sSoTS SOA S-R Stop-RT

Lateral Intraparietal Cortex Light-Emitting Diode Mismatch Negativity Modified Binary Search modulatory Response Time Norepinephrine Omission Potential PASS Reading Enhancement Program Performance Accuracy Point of Subjective Equality Posterior Parietal Cortex premotor cortex pre-Supplementary Motor Area Radial Basis Function Reaction Time Receptive Field Reinforcement Learning relative threshold Response Time Response-Stimulus Interval Right Brain Damaged Second Order Conditional Selective Attention for Identification Model Serial Reaction Time Skin Conductance Responses Speech Language Impairment spiking Search over Time and Space Stimulus Onset Asynchrony Stimulus-Response Stop-Reaction Time

xvi    Advances in Cognitive Science SNc SNr STN–GPe

Substantia Nigra pars compacta Substantia Nigra pars reticula Subthalamic Nucleus and Globus Pallidus externa

SC STG TD TOJ VTA

Superior Colliculus Superior Temporal Gyrus Temporal Difference Temporal Order Judgement Ventral Tegmental Area

Preface

C

ognitive Science is an interdisciplinary enterprise interfacing with psychology, neuroscience, computer science, philosophy, and linguistics. Cognitive science seeks to answer many fundamental and long-standing questions about the nature of mind and mental processes. In the last few decades, it has established itself as a truly interdisciplinary science. Given the current advances, it is expected that it will become even more interdisciplinary. Cognitive Science is not yet a flourishing discipline in India. Recently, the Department of Science and Technology of the Government of India has designated Cognitive Science as a fourth pillar of knowledge along with nano-, bio-, and information technologies and has started major research initiatives in Cognitive Science. Under the UGC Scheme of Universities with Potential for Excellence, the University of Allahabad was selected for developing “Behavioural and Cognitive Sciences” as an Island of Excellence. As a follow-up, the University established the Centre of Behavioural and Cognitive Sciences (CBCS) in 2002, for providing education of merit and distinction in line with new developments and challenges, as a constructive opportunity for advancement of scientific knowledge through basic and applied research and teaching as well as outreach programmes. The objectives of the academic programme are to provide comprehensive training and prepare the students for a professional/research/academic career, to develop a richer understanding of mental processes and neural mechanisms underlying cognition using behavioural, computational and neurophysiological techniques. The faculty and students at the Centre are involved in research programmes pertaining to vision, attention, perception, linguistics, cognitive neuroscience, consciousness, cognitive disorders, cognitive modelling and human computer interactions. There is a strong emphasis on research projects and exposure to various theoretical and experimental studies in Cognitive Science. The Centre and the University provide an ideal environment for study and research in Cognitive Science. The first International Conference on Cognitive Science at the Centre of Behavioural and Cognitive Sciences was held in December 2004 followed by the Second International Conference on Cognitive Science on December 10 to 12, 2006. The mission of the conferences was to explore the truly interdisciplinary nature of cognitive science and create awareness of cognitive science among the interested students and researchers. The conferences served as the meeting point for scientists from interfacing disciplines like psychology, neuroscience, computer science, linguistics, and philosophy. The selected papers of the first conference are published by SAGE, New Delhi in a book titled “Advances in Cognitive Science: Volume 1”. The first volume consisted of twenty-seven chapters organized

xviii    Advances in Cognitive Science into six sections (Cognitive Processes, Cognitive Neuroscience, Computational Modelling, Culture and Cognition, Cognitive Development and Intervention, and Consciousness). The Second International Conference in 2006 comprised three keynote lectures, twenty-three oral presentations and thirty-four poster presentations. The conference was inaugurated by Prof. K. Ramakrishna Rao, Chairman, Indian Council of Philosophical Research, Delhi, and chaired by Prof. R. G. Harshe, Vice-Chancellor, University of Allahabad. The keynote lectures were presented by three prominent experts in cognitive science: Prof. Bruce Overmier, University of Minnesota, USA, Prof. Ira Noveck, Centre National de la Recherche Scientifique, France, and Prof. James Georgas, University of Athens, Greece. There were oral sessions and symposia on cognitive neuroscience, computational modelling, language and cognition, time perception, mind and consciousness, and attention. In addition to faculty members from various institutions in India and abroad, doctoral research scholars and master students attended the conference. Based on initial review of abstracts and papers, the editors requested the selected authors to submit full papers for the volume. All the editors reviewed the papers and fifteen contributions were selected for publication in the current volume. The contributors are senior as well as young cognitive scientists from various countries including USA, Canada, UK, France, Belgium, Italy, Japan, Spain, Singapore, and India. The volume contains research articles addressing the challenges faced in cognitive science requiring cross-linking of different interfacing disciplines like psychology, neuroscience, and computer science. The recent findings from cognitive science presented in the volume will serve as a useful resource for scientists working in the area. The volume represents a good sample of the current trends in major sub-disciplines in cognitive science. It contains four sections: (a) Learning and Memory, (b) Vision and Attention, (c) Time Perception, and (d) Language, Cognition, and Development. The first section focuses on basic cognitive processes of learning and memory including simple associative processes, spatial memory, sequence learning, and implicit learning. The second section focuses on vision and attention with contributions on multisensory spatial perception, basic attentional processes, and modelling of attention. The third section contains four chapters focusing on time perception. The fourth section on language, cognition, and development includes two chapters on both normal and abnormal development especially focusing on language development and related cognitive abilities. We would like to acknowledge the efforts and support of office staff especially M. P. Srivastav, Puneet Srivastav, and Shabeeh Abbas who have contributed to the conference and preparation of this volume. We would like to thank all the colleagues for their enthusiastic support. We would like to thank all the CBCS students as well as research scholars from the Department of Psychology who worked very hard for the conference. We thank Sage Publications for bringing out this volume. Narayanan Srinivasan Bhoomika Kar Janak Pandey

Section I

Learning and Memory

2    J. Bruce Overmier and John M. Holden

Introduction

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earning and memory are critical for adaptive behaviour. Learning refers to acquisition of information and skill. Memory is a process by which learned information is stored for later use. The chapters in this section focus on different aspects of learning and memory. One basic aspect of learning and memory is associative learning that is based on the assumption that ideas and experiences can be linked to each other to enhance the learning process. Reinforcement learning involves activation of brain mechanisms that increase the likelihood that response will occur. Overmier and Holden’s chapter focuses on associative processes that underlie choice and decision-making, which can be influenced by simple associative mechanisms. Theories like associative theories of learning, stimulus-response theory, Mowrer’s twoprocess theory, and modern expectancy theory are discussed. The authors propose that the expectancies of reward control choice behaviour and may probe separate memory mechanisms. They also argue that animal studies on learning have implications for the role of associative processes in the treatment of disorders like Down’s syndrome, and alcohol related dementia. Overmier and Holden have discussed about the relevance of the study of associative processes for understanding human cognition and behaviour in the context of choice behaviour and decision-making. An important aspect of learning and memory is their role in spatial navigation. Navigational patterns depend on the learning and formation of cognitive maps (Solso et al., 2005). The chapter by Tamura and colleagues discusses the role of learning landmarks in navigation decision. They have discussed the results of two experiments designed to study how landmarks facilitate map reading. Such an inquiry also gives an insight into the mental processes that are involved in mental navigation decisions in the context of mental rotation and reorientation. Left-right orientation is an important factor in navigation. The authors have investigated the differences between local and global landmarks related to their efficacy in facilitating navigation decisions. Authors discuss the effect of misalignment between the orientation of the map and the navigator on navigation decisions and the role of landmarks in reducing the misalignment. They also find that global landmarks facilitate navigation decisions but not the local landmarks and that the global landmarks provide orientation cues to facilitate left right orientation. The study has implications for improving navigation by adding more global landmarks that facilitate mental rotation and reorientation. Distinctions about how we learn and retain information have been made in terms of implicit and explicit acquisition of information. Implicit learning has been characterized as a passive process, whereas explicit learning, as an active process where people seek

4    Advances in Cognitive Science out the structure of any information that is presented to them. Experimental and neuroimaging studies suggest that implicit and explicit learning, and memory operate through distinct mechanisms (Kluwe et al., 2003; Gazzaniga et al., 2002). Research on implicit learning and memory has embodied three fundamental issues in cognitive science namely consciousness, mental representation and modularity of the cognitive system. The chapter by Destrebecqz raises methodological and theoretical issues associated with implicit learning. The focus on the notion of implicit learning with respect to unconscious learning has been discussed in the context of the sequence learning studies based on serial reaction time tasks. Tasks that have been used in earlier studies to study implicit learning or to measure conscious knowledge were actually dependent on both implicit and explicit components. Fluency, for example, constitutes a potential bias in conscious knowledge assessment, for it may reflect implicit rather than explicit influences. The author has discussed these issues on the basis of the results of a sequence learning experiment in which the implicit influence of perceptual and motor fluency was controlled in a recognition task. Results of this experiment were found to be consistent with the idea that slowing the pace of the learning phase increases explicit knowledge acquisition. The dissociation between priming and recognition as shown by previous studies were not replicated in this study. The authors do not argue for two independent learning systems; rather they argue for the interaction between cortical (primarily involving the anterior cingulate) and subcortical (primarily involving the striatum) structures. The authors conclude that conscious knowledge affects performance and influences behaviour and do not support the notion that sequence learning is based on unconscious learning mechanisms. Skill learning involves the acquisition and improvement of mental or physical abilities through practice and human skill learning has been studied extensively including behavioural and neuroimaging studies (Perez et al., 2007; Grafton et al., 1998; Hikosaka et al., 1996). Brain bases of implicit and explicit learning have been studied with neuroimaging studies focusing on medial temporal structures for explicit learning and memory and basal ganglia for implicit learning. Skill learning studies have focused on issues such as inter-manual transfer, chunking and co-articulation effects. The chapter by Ahmed and his colleagues focuses on mechanisms of skill learning. But the effects of learning paradigm adopted during sequence learning, such as explicit guidance and trial and error, are not specifically investigated. The authors designed a task to tap supervised learning and trial and error learning. The authors propose that the time course of chunk formation could be a viable measure for demarcating a subject’s extent of learning. Learning by reinforcement has been investigated in the context of the neural mechanisms focusing on the neural pathways that modulate the reward information. Basal ganglia and its connections have been identified as the prominent neural substrate for reinforcement learning. The chapter by Joseph, Gangadhar and Chakravarthy highlights the role of basal ganglia in exploration, a component of reinforcement learning. They present a comprehensive model of basal ganglia involving every nucleus of basal ganglia

Learning and Memory   

5

and the anatomical substrates of the various components of reinforcement learning have been discussed. The model is being proposed to identify and explain the neural basis for exploratory behaviour. The chapter describes the architecture of the proposed model with its three components, actor, critic and explorer. In the model, out of the three components, “Actor” represents the sensorimotor cortical pathway, “Critic” represents the cortico-striatal pathway and “Explorer” represents the subthalamic nucleus-globus pallidus pathway. The model was trained to perform a simple behaviour, that is, to learn to reach the target. In all, the model explains the neural basis for the acquisition of motor skill and highlights the dynamic nature of the neural circuit, which enables the organism to respond to changes in the environment. The implications of this model with respect to disorders like Parkinson’s disease have also been discussed.

References Gazzaniga, M. S., R. B. Ivry, and G. R. Mangun. 2002. Cognitive Neuroscience. New York: W. W. Norton & Company. Grafton, S. T., E. Hazeltine, and R. B. Ivry. 1998. “Abstract and effector-specific representations of motor sequences identified with PET”, Journal of Neuroscience, 18, 22: 9420–428. Hikosaka, O., S. Miyachi, K. Miyashita, and M. K. Rand. 1996. “Learning of sequential procedures in monkeys”, in J. R. Bloedel, T. J. Ebner, and S. P. Wise (eds), The Acquisition of Motor Behaviour in Vertebrates, pp. 303–17. Cambridge: MIT Press. Kluwe, R. H., G. Luer, and F. Rosler. 2003. Principles of learning and memory. Boston: Birkhauser Verlag. Perez, M. A., S. Tanaka, S. P. Wise, N. Sadato, H. C. Tanabe, D. T. Willingham, and L. G. Cohen. 2007. “Neural substrates of intermanual transfer of a newly acquired motor skill”, Current Biology, 17: 1896–902. Solso, R. L., M. K. MacLin, and O. H. MacLin. 2005. Cognitive Psychology. New York: Pearson Education Inc.

6    J. Bruce Overmier and John M. Holden

Chapter 1 Study of Basic Associative Processes Contributes to Our Understanding in Cognitive Science* J. Bruce Overmier and John M. Holden

Introduction

W

e would like to illustrate, from our work and that of our colleagues, a basic research finding that shows that the cognitive processes presumed to underlie choice and decision-making can be dramatically influenced by simple associative mechanisms. Moreover, we want to show that this same basic animal research can be translated into applications with human patients and that such translation is taking place today. It is a long and complicated story, but not uninteresting because it reflects how our psychological science is self-correcting and how with that self-correction come new insights and new treatment options. In this story, we shall go from learning theory to the animal laboratory, to tests with normal persons, to applications with clients. We will skip some of the steps and details, but all the links are there. Let us begin our research presentation with some reflections on early theory and its transformation. The behaviouristic associationism that so dominated Western research and thinking in the first half of the 20th century springs from the research and theorizing of Thorndike (1911). Thorndike argued that learning was the development of associations between a stimulus (environment) and a response (action) that was “stamped in” because the sequence was followed by a reinforcer (see Figure 1.1A). For Thorndike, the reinforcer was a catalyst

* Study of Basic Associative Processes Contributes to Our Understanding in Cognitive Science. Supported by grants from NSF and NICHHD to the Center for Cognitive Science, University of Minnesota.

8    J. Bruce Overmier and John M. Holden Figure 1.1 Illustration of behavioural stream and three theories of action of reinforcers. SD = discriminative stimulus, R = response, SR = reinforcer

establishing the stimulus-response (S-R) learning, but the reinforcer was not itself part of what was learned (see Figure 1.1B). According to the theory, it really did not matter what the particular reinforcer was—or even if the same reinforcer was used all the time—the behaviour in question just had to be reinforced. One fascinating thing about this theory is its dominance despite the fact that it conflicts with our private conceptions of “why” we do things; introspection suggests that we do them to get to a particular goal, rather than as goalless automatons. Nonetheless, Thorndike’s theory—with Spence’s (1937) extension—was very successful in accounting for many observed phenomena of learning and choice behaviour and made interesting predictions (for example, both transposition and when it would fail). Theorists like Tolman (1945) tried to incorporate learning about goals (“cathexes”) into the then-current theories of learning. They were not very successful in this in their time, but they did get later theorists thinking about the functions of reinforcers and the outcomes of choices. Perhaps the best known of these attempts is Mowrer’s two-process theory (1947). In part, Mowrer invoked this theory in an attempt to explain avoidance behaviour—behaviour which prevented the occurrence of an aversive event (for example, a rat learning to jump a barrier in a shuttle-box in order to avoid an electrical shock, the upcoming presentation of which was signalled by a tone). The theoretical question of interest was, since the result of a successful avoidance response was a non-event (for example, the tone is turned off and there is no delivery of shock), what was motivating avoidance behaviour?

Basic Associative Processes   

9

The two-process theory invokes a classically conditioned mediating state between the stimulus and the response. Mowrer argued that behaviour was the product of two parallel learning processes. The first was a Pavlovian association between the stimulus (environment) and the scheduled outcome event that established an anticipatory state (the anticipatory state standing between the environment and action was thought to motivate behaviour). The second was a Thorndikian strengthening of the response either by the reinforcer outcome or a change in the outcome-based anticipatory state. Thus, in the example mentioned above, the subject learns two things: (a) the tone signals upcoming shock, and thus the tone evokes an unpleasant anticipatory state (that is, fear) through classical conditioning; and (b) making the response of jumping the barrier turns off the tone and reduces the fear, thereby reinforcing that instrumental response. There is another way to talk about this theory and what it accomplished. One of the basic dissatisfactions with Thorndikian S-R behaviourism was that it clashed with our own “causal explanations” as to why we do things. That is, we think we do things in order to get certain goals or achieve certain outcomes, while the Thorndikian view has us as goalless automatons, who do not know why we do things. In one sense, Mowrer’s theory and its extensions brought the goals back into the picture. The anticipatory motivational state, brought about through Pavlovian conditioning, was based on the goal. Moreover, this motivational state mediated between the stimulus and the behavioural act. That is, we are talking about anticipations of outcomes “causing” the behavioural act (see Figure 1.1C). Mowrer thought that the key property of these mediating anticipations was one of “energizing” behaviour in a relatively non-specific manner. Tests of this idea—that it was a Pavlovian-conditioned mediating state that was generating the behaviour—used what is called a transfer of control design that had three phases (see Figure 1.2): 1. An Instrumental Phase: in which an instrumental response was learned. 2. A Pavlovian Phase: in which a stimulus–outcome relation was learned. These first two phases can be in either order. 3. A Test Phase: in which the Pavlovian cognitive science (CS) was presented in the instrumental context. The question of interest was whether the Pavlovian CS could evoke or control the instrumental response. Aspects of this two-process theory are still popular today especially as they account for relations among trauma, fears, phobias, and avoidant defensive behaviours. For Mowrer, the key property of the anticipatory state was as a behavioural mediator that provided non-specific motivation for actions. Figure 1.3 contains data from an experiment that indicates that a separately established “fear-evoking” CS can immediately evoke the trained instrumental response—even after the original discriminative stimulus has been extinguished.

10    J. Bruce Overmier and John M. Holden Figure 1.2 Illustration of a transfer-of-control procedure

Some years ago within this Mowrerian tradition taught by R. L. Solomon, the primary author was led to ask: “Are the conditioned fears of different things different?” Not quantitatively different, as in Mowrer’s theory, but rather, qualitatively different? And if so, what would be the implications of the qualitative difference? At the same time, our colleague Milton Trapold, one of Kenneth Spence’s students, asked a similar question about the then hypothesized “fractional anticipatory responses” that Hull (1951) and Spence (1956) had argued antedated rewards as a result of a conditioned association between the discriminative stimulus and the reward. Together, we theorized that the hypothesized, association-based, conditioned anticipatory mediating state was not merely motivating as Mowrer suggested but rather guiding the selection of the behaviour. That is, we speculated that the mediator had cue properties (Trapold and Overmier, 1972). Indeed, we thought these cue properties likely to be more important than any motivational properties (see Figure 1.1D). Although the idea was not entirely new (for example, the “sg” in Hull’s proposed “rg-sg” mechanism), we pushed the idea to its logical conclusion. For example, we argued that the conditioned mediating state was specific to the particular reinforcer or “outcome” anticipated and that it was as distinctive as that outcome. We even argued that it was possible—even likely—that the mediator had only these “cue” properties, rather than motivating properties. For this reason, we actually referred to the conditioned anticipatory state as an expectancy—with the quasi-cognitive connotations intended. Now, this little change in thinking may not seem significant, but we propose to show that it is quite significant for research and practice. So, how would one test this new conception about the possible cue properties of conditioned anticipatory mediating states or, as we called them, “expectancies”? If expectancies of outcomes have cue properties, then we should be able to show that the

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Figure 1.3 Latency to perform an avoidance response as a function of stimulus type and test day

Source: From Bull, J. A. and Overmier, J. B. 1968. Transfer of control of avoidance is not dependent upon the maintenance of the original discriminative response. Proceedings 76th Annual Convention of the American Psychological Association. Note:

Each point represents 40 responses.

supposed cue properties can guide behaviour. The best task in which to show the existence of cue properties is the conditional discriminative choice task. An example would be in a T-maze or Skinner’s operant chamber with two or more alternative responses. Let us describe the traditional way that instrumental discriminative choice learning tasks are structured, using S for discriminative stimulus, R for choice response, and O for the outcome event. Then, we will contrast that traditional method with our test for cue properties of expectancies of particular reinforcers—a test procedure that we call the differential outcomes (DO) procedure. In the traditional conditional discriminative choice task (see Figure 1.4A), in the presence of one stimulus, S1, choice of a response to the left, R1, results in the usual common reinforcer—perhaps a sweet pellet for a rat; choices of the R2 yield no reinforcer. In the presence of S2, choices of a response to the right, R2, also results in getting a reinforcer and it is the same sweet pellet reinforcer, while now choices of the R1 response yield nothing. Note that following either discriminative stimulus, correct choices produce the same common reward. We call this the common outcomes (CO) procedure because the reward is common to either correct choice. And, typically, animals, children, even college students can learn conditional discriminations this way—although when the stimuli are complex, not always easily.

12    J. Bruce Overmier and John M. Holden Figure 1.4 Illustration of a standard discriminative conditional choice task employing common outcomes, and a similar task employing differential outcomes

In our proposed DO procedure (see Figure 1.4B), the organism is required to learn exactly the same S-R relations. That is, the choice problem that must be solved is identical. But, in contrast, there is a difference after the choosing is done. The difference is that each type of correct stimulus-response relation is followed by its own, unique reward. Thus, in the DO procedure, in the presence of one stimulus, S1, choice of a response to the left, R1, results in one reinforcer—perhaps an unsweetened pellet, while in the presence of S2, choices of a response to the right, R2, result in getting a different reinforcer—one unique to that response, perhaps sweet water. That is, correct discriminative choices following the different discriminative stimuli produce different rewards—rewards unique for each association—hence our label DO. Why is this apparently trivial design feature important? In the CO procedure, the organism only has the presence of the discriminative stimulus to guide its choice. In contrast, in the DO procedure, if there are unique, specific, anticipations or expectations of rewards or outcomes, and if these expectations of these different rewards have cue properties, then the organism has these extra cues from the expectancies to guide the choices as well.

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In essence, we are asking: What is in the organism’s “mind” at the time of choice? Is it thinking retrospectively of the recent discriminative stimulus, is it thinking prospectively of the expected reward? Or is it thinking perhaps of both? Functionally, if the organism has more than one source of guiding information, then it should learn faster and better. Let us now compare rates of learning under these two different training paradigms (see Figure 1.5). Comparisons of groups learning in conditional discriminative tasks wherein one group was trained using CO procedure and the other trained using DO procedure reveals that the DO procedure produces significantly faster learning—and commonly to a higher asymptote (Overmier, Bull, and Trapold, 1971; Trapold, 1970). Several experiments using different species of animals from birds to horses and different kinds of reinforcers have confirmed this new phenomenon (for example, Edwards et al., 1982; Miyashita et al., 2000). Yet, it is a basic fact completely unanticipated within the traditional Thorndikian behaviourist tradition (and, as yet, is also rarely noted in texts). Of course, there are a number of ways that this procedural difference could induce the differences in rates of learning. But, we argued that it was the Pavlovian conditioned association between each discriminative stimulus and its distinctive outcome that was responsible. To show that it is the Pavlovian conditioned mediator that controls choosing Figure 1.5 Performance in a bi-conditional discrimination task under differential and common outcomes procedures

14    J. Bruce Overmier and John M. Holden requires a somewhat different experiment—a variant on the transfer of control design in which we can separate out the Pavlovian relation to isolate its choice controlling function. In this three-stage transfer of control experiment (outlined in Figure 1.6), we (Kruse et al., 1983) began by training a conditional discriminated choice using DO such that each cue-choice sequence resulted in a different, unique outcome like that just described. Then in a second stage, which took place outside of the choice arena, we took a new neutral stimulus and associated it with one of the two reinforcers. Finally, in the third test stage, the animal was returned to the choice situation and probes of the Pavlovian CS were introduced. This was a test of the CS’s power to directly induce the animal to make the specific choice for the signalled outcome—even though such choices had never before occurred in the presence of the CS. If the particular outcome with which the CS was associated were irrelevant, then choosing should be random. On the other hand, if the CS elicits a specific expectancy which in turn has unique response-cueing properties, then the CS should result in the CS inducing the animal to make the choice response that had previously produced that specific outcome in the original discriminative training. Such choices we would call “correct”.

Figure 1.6 Illustration of the transfer-of-control procedure employed by Kruse et al. (1983)

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We found that the Pavlovian stimulus, in the presence of which the animal had never before made any choice responses, immediately and reliably substituted for the instrumental discriminative cue to elicit the outcome-specific “correct” choice responses. This is consistent with the view that embedded simple Pavlovian associations in conditional discriminated choice tasks can and do guide choices. We recognize that most readers are likely cognitive or clinical psychologists and wonder what this can tell you about humans and patients. So let us address this question. Recall that a very large part of learned human behaviours are in fact conditional discriminative choices. Deciding on the correct name for the person standing before you is a conditional discriminative choice. So is deciding daily proper clothing. For example, in the northern United States where we live, when choosing our clothing for the day, we always first check to see what the temperature is. The weather is the discriminative stimulus and choices of clothing must be conditional upon that stimulus. Wrong clothing choices can lead to death—and do each year. This illustrates how the DO applies to clothing choices. Does the DO procedure have a facilitating effect on learning by humans? In our lab at Minnesota, we have tested this (Maki et al., 1995), and colleagues around the world (for example, Estevez et al., 2001) have confirmed our findings. We have found that in nearly every task we have tested, using DO facilitates learning or performance—sometimes very modestly, sometimes dramatically—depending on the task difficulty and the particular outcomes used. This is true for normal five–six year old children learning to point to correct pictures or learning symbolic relationships. And, as we will note later, it is even true for persons who have learning disabilities. The experiments with humans are more complicated conditional discriminative choice experiments than the ones we have illustrated with animals, but they are essentially the same. Estevez, Fuentes and their associates (Estevez et al., 2003) have extended tests of this teaching method to adults with Down’s Syndrome. The Down’s clients have exactly the same pattern of greater success in learning using the DO procedure. This success with learning-disabled populations has been found by other groups as well. But does this parallel effect in humans mean that the same simple Pavlovian associative processes underlie the enhanced choice behaviour? Well, we can apply the same transferof-control paradigm as in the animal experiments to test this. First, children are trained on a conditional discriminative choice task, either with CO or with DO. Then, new stimuli are separately and selectively paired with the outcomes in a Pavlovian procedure. Finally, the children are tested to determine if the Pavlovian “CSs” will selectively control the choosing behaviour of the children. The results are straightforward. Data from these experiments comparing rates of learning by the children on conditional discriminative choice tasks trained either with the traditional CO procedure with learning under the new DO procedure show that the DO method yielded faster and better learning than the CO procedure. Moreover, when tested with the Pavlovian signals for ability to control outcome specific responses, the children trained under DO made specific choices with great accuracy,

16    J. Bruce Overmier and John M. Holden while those trained with CO performed at chance. Moreover, just as in our experiment with rats, here too the Pavlovian stimuli evoke specific selective choices of the “correct” response—the response that would produce the expected outcome (Maki et al., 1995). As a variation of the Pavlovian transfer, we can also use the conditioned expectancy model to show inter-problem transfer of control of choice (see Figure 1.7). Some would prefer to describe this as learning equivalences or categories. But here, it is based on signalled outcomes. After learning two different discriminations using the same outcomes, we test for a “crossover” of control, by presenting the subjects with the samples from one problem and the choice stimuli from another. We would expect that the sample from one problem would evoke responding for the choice stimulus from the other problem associated with the same outcome. The results indicated that there was substantial interproblem transfer based on the signalled outcome. This effect is true for children and for animals. Figure 1.7 Illustration of an experimental design for showing inter-problem transfer of control of choice

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Acquired stimulus equivalence (Sidman, 1985) is essentially a form of complex conceptual category learning in which new untrained controlling relationships emerge. Subjects are taught a series of conditional discrimination problems in order to establish separate stimulus categories. For example, in Figure 1.8, in the course of teaching successfully four separate conditional discriminations, our subjects are explicitly taught these relationships between sample stimuli and choice alternatives such that when S1 is presented (S1→S3) and continuing with training in which S1→S3, S3→S5, S5→S7, and S7→S9, while S2→S4, S4→S6, S6→S8, S8→S10. This should lead to the establishment of separate stimulus categories—S1, S3, S5, S7, and S9 should belong to one category, whereas S2, S4, S6, S8, and S10 should belong to another. The Prader–Willi syndrome is an eating disorder that is accompanied by mental retardation. In my laboratory, we have extended this paradigm to teaching sets of acquired stimulus equivalences to patients with Prader–Willi syndrome. The clients were actually trained on a succession of four conditional discriminations, each with two cues and four alternative choices. Each pair is taught after the prior pair is mastered. Testing for transitivity and ‘symmetry’ involves testing of stimulus control of choice alternatives that are in the chain ‘but are relations that were not directly trained’. These are sometimes referred to as emergent relations. For example, if such training has been successful in establishing stimulus categories, then we should see the emergence of such untrained associations as S1→S5, S3→S7, S5→S9, S1→S7, S3→S9 or even S1→S9 on the one hand, and S2→S6, S4→S8, S6→S10, S2→S8, S4→S10 and S2→S10. These are all examples of a kind of emergent relationship called ‘transitivity’, and based on previous research, Figure 1.8 Illustration of a stimulus equivalence training procedure similar to that employed in Joseph et al. (1997)

Note: Correct choices are marked with an asterisk (*).

18    J. Bruce Overmier and John M. Holden we should expect to find less transitivity as the ‘nodal distance’ between stimuli in a class increase (for example, S1→S7 involves greater nodal distance than S1→S5). What’s more, we should also see the emergence of ‘symmetrical relationships’; for example, S9→S7, S7→S5, S5→S3, or S3→S1 on the one hand, and S10→S8, S8→S6, S6→S4, and S4→S2. Again, the learning and mastery of such equivalence relations by the learning-impaired clients with Prader–Willi syndrome is dramatically more accurate when they were taught using DO than with CO (Joseph et al., 1997). Interestingly, in the DO training conditions, accuracy of transitivity and emergence of equivalences is independent of nodal distance along the chain of possible relations, while in contrast, CO training results in decreasing accuracy as nodal distance increases. Thus, these adult retarded clients not only learned the basic relations faster when taught using DO, but they showed more reliable generative use of the new relational equivalences. We have begun work on demonstrating that we may well use the DO procedure to teach useful basic life skills to clients with Down’s syndrome. We have used newspaper symbols for cues for the selection of items of apparel that they should take with them to their workshop. Correct choices of weather-appropriate clothing received unique token reinforcers exchangeable for unique items. The early results from this new teaching method have been very promising suggesting this is a useful training tool in the real world. But, have we learned all we can from our animal experiments? No. We can gain more. Given that outcomes are important in learning, perhaps they are important for memory as well. Consider that if animals have to learn a conditional discriminative task but are not allowed to make choices until some time after the discriminative stimulus is removed, then how do they choose? This simple, delayed choice procedure is the prototypic way for testing short-term working memory. In the traditional CO procedure, participants have only their memory of the stimulus to rely on. However, if we use the DO procedure with such a delayed choice task, there is an additional source of information or cueing: The expectancy of the reinforcer could help to bridge the time delay gap because Pavlovian conditioned responses typically persist until the typical time of reward. Does the DO procedure prove and assist in such memory tasks? The answer is a resounding “yes”. Let us describe sample experiments, first with pigeons, then with patients. Consider an example of a conditional symbolic discriminative choice task for pigeons arranged for testing short-term working memory function. First, a colour is presented for a few seconds in the centre of a display panel in front of the pigeon. Then the colour cue is removed. After a variable delay (on the order of any where from zero to eight seconds), the bird must choose between two alternatives presented, one on each side of the display panel. Here, the choice is between alternatives of a vertical line and a horizontal line. If red is remembered, the vertical is correct; if green is remembered, then horizontal is correct. Correct choices are reinforced. The delay between the cue and the opportunity to choose is the “memory load”. If we arrange the sequence of events such that the reinforcer is the same for both selecting vertical after a red cue, and for selecting horizontal after a

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green cue, that is the CO procedure. But when the reinforcers for correct choices of the different lines are themselves different, then this is the DO procedure. Does this difference in reinforcement method after the choice change the way the animals cope with the memory load? Undoubtedly, as memory based performance established under CO quickly drops to chance after only a few seconds worth of delay in this task. In contrast, memory performance established using DO remains at near perfect levels even at delay intervals at which subjects trained under CO has dropped to chance levels (Linwick et al., 1988; Peterson et al., 1987). This is an effect of great significance. And, it implies that activation of different cognitive processes is engaged under DO rather than under CO. Let us give you one last example of our research work with humans that grows out of the animal laboratory work we have been discussing—one that we believe has practical applications. Long-term excessive consumption of alcohol (and resulting thiaminedeficiency) can lead to brain damage and a disorder historically referred to as Korsakoff’s disease, but now more generally called simply alcohol related dementia. These patients are relatively intact cognitively but do suffer a specific problem. They have impaired short-term working memory—especially for faces and names. Oliver Sacks (1985) vividly describes just such a patient in his chapter, “The Lost Mariner”. This memory disability for recognizing faces and remembering the names that go with faces has the sad effect of socially isolating these individuals. Cognitive impairments in laboratory animals can be produced which are similar in nature to Korsakoff’s disease through the use of pyrithiamine, which lesions brain areas (that is, the mammilary bodies of the hippocampus) important in memory. Savage and Langlais (1995) discovered that our DO procedure seems to aid memory in these animal models of Korsakoff’s disease. That is, our DO procedure provides remediation for the diseased memory of these rats. This body of work by Savage won for her an APA (American Psychological Association) award for early career contributions in 2002. Once again, you must wonder whether there is anything here that has meaning for your human clients. And, again, we believe the answer is “yes”. We have tested use of the DO procedure to help Korsakoff patients to more readily learn to recognize faces and even learn the names that go with the faces. After all, learning to recognize a recently seen face or to name someone after seeing their face is a discriminative conditional symbolic choice task very much like those we have been discussing. Our work here is relatively new, but the results are very promising (Hochhalter et al., 2001). To test whether our newly discovered knowledge about the power of DO to improve learning and memory could be applied to these patients, we set up an artificial task that was similar to those we have previously described. First, we would show the patient a picture of one person’s face. Then we would hide the picture. After a variable delay, we would then show a page of pictures of two faces or a page with two names on it. The patient’s task was to report or point to the face or the name of the person they had seen a few seconds earlier. This seems easy, but it is quite difficult for Korsakoff patients.

20    J. Bruce Overmier and John M. Holden We rewarded the patients for correct choices with money, or tokens for coffee, or points—whatever was small but valuable to them. For one set of faces, all correct identifications received the same reward—the CO procedure. For another set of faces, the reward was unique to each particular face—the DO procedure. This within-subject comparison allowed us to see the effects of the different teaching procedures. Figure 1.9 shows the working memory for faces of normal age-matched control and of Korsakoff patients taught under CO and those same patients taught under DO. Clearly, normal age-matched individuals have no problem with their recognition memory under either condition. Equally clearly, Korsakoff patients taught with CO have a serious recognition memory impairment—with declines in memory showing up with delays of as little as five seconds. But those same patients taught with DO (bottom) show markedly improved recognition memory—not differing from normal individuals until after 25 seconds, but even at 25 seconds they are substantially improved. In summary, we think that we have shown that simple associative processes—like those of Pavlovian conditioning—can and do play important roles in choice behaviours and Figure 1.9 Data on the short-term working memory of normal older men and older men with korsakoff’s disease

Note: Diagnosed with alcohol-related dementia taught face recognition using the traditional common outcomes procedure or with the new differential outcomes procedure.

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decision tasks. These examples arose from a reconceptualization of traditional learning theories. But we did not abandon associative accounts of learning to derive these complex choice phenomena. Although the examples were mostly from simpler conditional discriminative choice tasks, there are data from colleagues that suggest the same can be found in college students learning difficult types of equations (Estevez, personal communication) and even word equivalences across languages (Mahoney, 1991). Our research examples are not unique. They were meant to open up readers to the message that contemporary basic science research with animals on fundamental associative mechanisms continues to produce results that are of potential interest to cognitive scientists and certainly important and helpful to practitioners. We can even expand to normal aging phenomena. Now it turns out, that as animals get old, they, like humans, experience difficulties with working memory in delayed discriminative choice tasks when trained by the traditional CO training procedures. That is, when old, the rats cannot remember correct choices for more than a few seconds of delay. However, Lisa Savage, whom the primary author had the good fortune to work with in his laboratory some years ago, has recently shown that use of the DO training procedure can help these old animals to perform the memory-based task as well as young animals (Savage et al., 1999). Moreover, basic research with laboratory animals can enable us to discover things not possible through research with humans. For example, Savage and Parsons (1997) uncovered data in a double dissociation that suggests that there are different neurochemistries for memories and for expectancies. It appears that in conditional discriminated choice tasks, retrospective memories of the cue or sample stimulus are encoded in through cholinergicdependent processes because muscarinic antagonist scopolamine disrupts memory-based choosing more in the CO procedure than in DO procedure. Meanwhile, the expectations of reinforcer outcomes appear to be encoded through glutamineric-dependent processes because dizocilpine (MK-801) disrupted memory based choice more in the DO procedure than in the CO procedure. This should suggest to cognitive scientists that retrospective memories and prospective expectancies have different neural substrates and, perhaps, different brain modules. And indeed, we are also using fMRI (functional magnetic resonance imaging) to see if we might localize these different modules for memories and expectancies. (See Mok, Thomas, Lungu & Overmier, 2009). Our simple associative conditioning has taken us far.

References Edwards, C. A., J. A. Jagielo, T. R. Zentall, and D. E. Hogan. 1982. ‘Delayed matching-to-sample by pigeons: Mediation by reinforcer-specific expectancies’, Journal of Experimental Psychology: Animal Behavior Processes, 8 (3): 244–59. Estevez, A. F., L. J. Fuentes, P. Mari-Beffa, C. Gonzalez, and D. Alvarez. 2001. ‘The differential outcome effect as a useful tool to improve conditional discrimination learning in children’, Learning & Motivation, 32 (1): 48–64.

22    J. Bruce Overmier and John M. Holden Estevez, A. F., L. J. Fuentes, J. B. Overmier, and C. Gonzalez. 2003. ‘Differential outcomes effect in children and adults with Down syndrome’, American Journal on Mental Retardation, 108 (2): 108–16. Hochhalter, A. K., W. A. Sweeney, B. L. Bakke, B. L., R. J. Holub, and J. B. Overmier. 2000. ‘Improving face recognition in alcohol dementia’, Clinical Gerontologist, 22: 3–18. ———. 2001. Using animal models to address the memory deficits of Wernicke–Korsakoff syndrome in M. E. Carroll and J. B Overmier (eds). Animal research and human health: Advancing human welfare through behavioral science (pp. 281–92). Washington DC, US: American Psychological Association, xviii, 386. Hull, C. L. 1951. Essentials of Behavior. New Haven, CT, US: Yale University Press. Joseph, B., J. B. Overmier, and T. I. Thompson. 1997. ‘Food and nonfood related differential outcomes in equivalence learning by adults with Prader–Willi syndrome’, American Journal of Mental Retardation, 4 (4): 374–86. Kruse, J. M., J. B. Overmier, W. A. Konz, and E. Rokke. 1983. ‘Pavlovian conditioned stimulus effects upon instrumental choice behavior are reinforcer specific’, Learning & Motivation, 14 (2): 165–81. Linwick, D., J. B. Overmier, G. B. Peterson, and M. Mertens. 1988. ‘The interactions of memories and expectancies, as mediators of choice behavior’, American Journal of Psychology, 101 (3): 313–34. Mahoney, J. L. 1991. ‘An expansion of expectancy theory: Reaction time as a test of relative expectancy strength and forward vs. backward associations’, Proceedings: Undergraduate Research Opportunities Program in Behavioral Sciences, 43–74. Technical Report from the Center for research in Learning, Perception & Cognition, University of Minnesota. Maki, P., J. B. Overmier, S. Delos, and A. Gutmann. 1995. ‘Expectancies as factors influencing conditional discrimination performance of children’, Psychological Record, 45 (1): 45–71. Miyashita, Y., S. Nakajima, and H. Imada. 2000. ‘Differential outcome effect in the horse’, Journal of Experimental Analysis of Behavior, 74 (2): 245–54. Mok, L. W., Thomas, K. M., Lungu, O. V., and Overmier, J. B. (2009). Neural correlates of cue-unique out-come expectations under differential outcomes training: An fMRI study. Brain Research, 1265, April 10, 111–27. Mowrer, O. H. 1947. ‘On the dual nature of learning—A reinterpretation of ‘conditioning’ and ‘problem solving’. Harvard Educational Review, 17: 102–48. Overmier, J. B., J. A. Bull, and M. A. Trapold. 1971. ‘Discriminative cue properties of different fears and their role in response selection in dogs’, Journal of Comparative & Physiological Psychology, 76 (3): 478–82. Peterson, G. B., D. Linwick, and J. B. Overmier. 1987. ‘On the comparative efficacy of memories and expectancies as cues for choice behavior in pigeons’, Learning & Motivation, 18 (1): 1–21. Sacks, O. 1985. The Man Who Mistook His Wife for a Hat. New York: Touchstone. Savage, L. M., and J. Parsons. 1997. ‘The effects of delay interval, intertrial interval, amnestic drugs, and differential outcomes on matching to position in rats’, Psychobiology, 25: 303–12. Savage, L. M. and P. J. LanglaisJ. 1995. ‘Differential outcomes attenuates spatial memory impairments on matching to position following pyrithiamine-induced thiamine deficiency in rats’, Psychobiology, 23 (4): 153–60. Savage, L. M., S. R. Pitkin, and J. M. Careri. 1999. ‘Memory enhancement in aged rats: The differential outcomes effect’, Developmental Psychobiology, 35 (4): 318–27. Sidman, M., B. Kirk, and M. Willson-Morris. 1985. ‘Six-member stimulus classes generated by conditionaldiscrimination procedures’, Journal of the Experimental Analysis of Behavior, 43 (1): 21–42. Spence, K. W. 1937. ‘The differential response in animals to stimuli varying within a single dimension’, Psychological Review, 44 (5): 430–44. ———. 1956. Behavior Theory and Conditioning, vii, p. 262. New Haven, CT, US: Yale University Press.

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Thorndike, E. L. 1911. Animal Intelligence. New York: Macmillan. Tolman, E. C. 1945. ‘A stimulus-expectancy need-cathexis psychology’, Science, 101 (2616): 160–66. Trapold, M. A. 1970. ‘Are expectancies based upon different positive reinforcer events discriminably different?’, Learning & Motivation, 1 (2): 129–40. Trapold, M. A. and J. B. Overmier. 1972. ‘The second learning process in instrumental learning’, in A. H. Black and W. F. Prokasy (eds), Classical Conditioning II: Current Research and Theory (427–52). New York: Appleton-Century-Crofts.

Chapter 2 Minimizing Cognitive Load in Map-based Navigation: The Role of Landmarks Kazuhiro Tamura, Bipin Indurkhya, Kazuko Shinohara, Barbara Tversky, and Cees van Leeuwen

Introduction

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eading maps can be tricky; in particular when this happens during driving. We would like to minimize any unnecessary effort that navigators experience in using maps to locate themselves in their environment or figure out a route to their destination. This motivates efforts to find the optimal way in which the information on display is represented. People’s spontaneous sketches of environment they have experienced primarily through navigation are oriented as if they imagined themselves entering the environment from the bottom of the page (for example, Tversky, 1981). Navigators typically prefer maps that are oriented “heads up” that is, when the “up” direction in the map, the top of the page, corresponds to the direction the person is facing (for example, Levine, 1982). A map oriented this way is called aligned and preference for such maps is called the alignment effect. Modern technology offers ways to present maps that are always aligned. This is typically done in on-board navigation systems based on Global Positioning. Such systems provide the current position indicated on the map; with the destination of the individual user known, the orientation of the display can therefore easily be adjusted to align the map. Maps that are placed on public display, however, will have to indicate possible destinations in several directions, making it impossible to present them in an aligned fashion. This raises a question, whether there are other ways to facilitate map usage and, if so, what is their relation with the alignment effect? Specifically, is there a natural way to annotate maps to decrease the cognitive effort required to mentally navigate, in particular, in misaligned conditions? For meaningless shapes, salient “landmarks” are known to facilitate mental rotation (Hochberg and Gellman, 1977). Landmarks are a natural element in spontaneous sketch

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maps and in mental representations of environments (for example, Denis, 1997; Taylor and Tversky, 1992a, 1992b; Tversky and Lee, 1998, 1999). Properly placed and designed, landmarks might provide cues for making mental navigation decisions that facilitate map use. The study of whether and when landmarks facilitate map reading has, besides practical, also more fundamental implications, as it allows us to investigate what type of mental processes are being used in navigation. Facilities to assist navigation may address either of two types of information processing. The first is referred to alternatively as mental rotation or reorientation (for example, Shepard and Metzler, 1971; Corballis and Nagourney, 1978; Eley, 1982, 1988; Evans and Pezdek, 1980; Aretz and Wickens, 1992) because it can be done in at least two ways: either by mentally rotating the map, or by reorienting one’s own direction within the map. These two possibilities correspond, respectively, to two major ways of experiencing an environment; from a survey or overview perspective or from a route or embedded perspective (for example, Taylor and Tversky, 1992; Tversky, 1996; Zacks, et al., 2002). The present project does not attempt to distinguish the conditions inducing people to adopt each transformation, though other research has made efforts to do so (for example, Bryant and Tversky, 1999; Zacks, et al., 2002). We will, however, be able to determine whether an overview or embedded perspective is chosen in our experiments. With regard to processes such as mental rotation and reorientation we may predict the following: if landmarks facilitate these processes, they will reduce the alignment effect; we would expect the difference in navigation efficiency between aligned and misaligned conditions to become smaller as a result of adding landmarks to the map. The second major process, making the correct navigation decision, is predominantly involved in making left–right judgements. These are notoriously difficult (for example, Farrell, 1979; Franklin and Tversky, 1990; Maki and Braine, 1985). Map reading requires navigators to follow a route on a map and indicate whether the next turn is a right or left turn. Landmarks may facilitate left–right decisions, because they confer salient asymmetries to configurations that provide cues for which way to turn. If landmarks facilitate left–right decisions, placing landmarks is likely to facilitate navigation in aligned as well as in misaligned conditions. Do landmarks facilitate realigning misaligned maps or making left–right navigation decision? To test the contrasting predictions for these two processes, our first experiment varied independently the number of landmarks placed on a map, and its alignment with respect to the destination. In case landmarks facilitate mental rotation and/or reorientation, we expect an interaction between these two factors. In case the effect is due to left–right orientation, we expect independence of the main effects of alignment and landmark conditions. If both effects play a role, we may expect a main effect in combination with an interaction. A second way in which this issue was addressed in our experiments was related to the question: what kind of landmarks are most effective? We examined this question in both

26    Kazuhiro Tamura et al. our experiments. We compared global and local landmarks. Global landmarks confer asymmetries on the entire trajectory. They are, therefore, assumed to facilitate the processes of mental rotation or reorientation. We can distinguish overview versus embedded perspectives by comparing trajectories that curve around a global landmark versus ones in which it is placed outside the trajectory. In the first case, the global landmark is always at the same, left- or right-hand side from an embedded perspective; in the second, the landmark is always on the same side of the trajectory in overview. Thus, we expect the first to facilitate an embedded, the second an overview perspective. Local landmarks are placed as markers at points in the trajectory where a left- or right-hand turn needs to be chosen. They may, therefore, facilitate left–right decisions.

Experiment 1 Method Participants Twenty-eight undergraduates or adults from various cities in the Tokyo area with normal or corrected-to-normal vision (14 male, 14 female; age ranging between 18–36 years). They were paid an hourly fee of 1000 yen for participation in the experiment. Participants in this experiment also took part in Experiment 2, which was performed first.

Stimuli and Design Maps were constructed using the Java two-dimensional graphic library created by Sun Microsystems. Each map was rendered as an 800 by 800 pixel image on an 18.1 inch Liquid Crystal Display of an AT compatible personal computer. Each map subtended about 7 degrees of visual angle. The maps were based on those of Levine et al. (1982); they consisted of a route, presented as a segmented black line, connecting a destination and a target. Examples of the maps appear in Figure 2.1. Half of the test maps were aligned and half were misaligned (meaning rotated by 180° with respect to the aligned orientation). We distinguished maps that belong to a fixed-order and a variable-order condition. In the first condition, local landmarks were always in the fixed order: {building, tower, house, and windmill}. Maps could appear in three different global landmark conditions. In one-third of the conditions, a global landmark was placed inside the street pattern. In this condition, the landmark was always in the same orientation from an embedded perspective (always to the left or always to the right-hand side in mental navigation). In another one-third of the conditions, a global landmark was placed outside the street pattern. In this condition, the landmark was always in the same orientation from an external or overview position (always to the east or always to the west, irrespective of whether this was on the left-hand or righthand side in mental navigation). In the third condition, no global landmark was added.

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Figure 2.1 Examples of map displays used in Experiment 1: The global landmark conditions (global–inside: A, global–outside: B, and without–global landmark: C)

Two types of local landmark conditions could occur: In two-landmark maps, local landmarks were placed only to mark the beginning and ending points. In four-landmark maps, landmarks were placed in addition near each individual turn. Left- and right-hand turns were balanced across the maps, yielding 2 (Local landmarks; two or four) × 3 (Global landmarks: inside, outside, and without) × 2 (Reflections) = 12 unique maps.

28    Kazuhiro Tamura et al. The second set of maps belong to the variable-order condition: they used the same landmarks as in the fixed-order condition but these have been rearranged in random order. Of these, there were 2 (Local landmarks; two or four) × 2 (Reflections: left or right) = 4 map conditions in which the order of the local landmarks was randomly determined at the time of presentation.

Procedure Each trial consists of a sequence of three displays, shown in Figure 2.2. The first display consists of one the maps selected from Figure 2.1. Participants studied the map for three seconds and were asked to remember the configuration during an approximately one second period, in which the screen remained blank. Figure 2.2 Example sequences of displays used in Experiment 1 (Aligned and Misaligned versions of a global landmark inside two local landmark conditions)

Note: Blue arrows in test displays indicate the current location and orientation in the initial segment of the preceding map display, from which navigators must respond whether the next turn on the route to their destination, shown in the upper-left corner of the test display, requires a right or left turn. Blue arrows in feedback displays indicate the correct response.

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Next, a test display appeared showing an initial segment of the map from which they were to navigate to their destination. The destination was indicated by a landmark (for example, “windmill” see the centre of Figure 2.2), which was indicated at the left side at the top of the test display. The test was a two-alternative forced choice; participants determined whether the first turn along the route was a left- or right-hand turn. Participants responded by pressing one of two arrow keys on the computer keyboard indicating left or right. After each response, the original map was displayed with the correct response superimposed on the participant’s response. For the fixed-order conditions, in all of the cases the initial segment of the test-display was the long part of the trajectory. In the random-order conditions, in half of the cases the initial part of the trajectory was the short segment. This was done for two reasons: first, in order to prevent solutions based on reasoning such as “If the first turn is on the right-hand side of the map display my answer must be ‘right’ when the test display is aligned and ‘left’ when it is misaligned”. Second: to enable a check on differences between short and long initial segments. These should not be large in size if the processes currently under investigation capture a substantial part of the variability in display difficulty. Each participant received the following trials, 2 (Aligned or Misaligned test display) × 3 (Global landmarks: Inside, Outside, and Without) × 2 (Local landmarks: Two or Four in same order arrangement) × 2 (Map reflections: Left or Right) = 24 trials with fixed-order local landmarks, which were repeated two times, and 2 (Aligned or Misaligned test display) × 2 (Local landmarks: Two or Four) × 2 (First segment of the test display: Long or Short) × 2 (Map reflections: Left or Right) = 16 different trials which were repeated three times. This yielded a total of 96 trials which were randomized during the experiment.

Results and Discussion Responses were analyzed for speed (1/RT in second) and accuracy (number of errors) for the fixed and random-order trials separately, after which a comparison between the relevant subsets of both conditions was made.

Fixed-order Trials The fixed-order trials were evaluated in a 2 × 3 × 2 factorial design Analysis of Variance (ANOVA) with the within-subjects factors: Alignment test display, global and local landmarks conditions. The effect of Alignment reached significance for both speed, F(1, 27) = 75.3, p