VOLUME FIFTY NINE THE PSYCHOLOGY OF LEARNING AND MOTIVATION Series Editor BRIAN H. ROSS Beckman Institute and Department of Psychology University of Illinois, Urbana, Illinois VOLUME FIFTY NINE THE PSYCHOLOGY OF LEARNING AND MOTIVATION Edited by BRIAN H. ROSS Beckman Institute and Department of Psychology University of Illinois, Urbana, Illinois AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier Academic Press is an imprint of Elsevier 225 Wyman Street, Waltham, MA 02451, USA 525 B Street, Suite 1800, San Diego, CA 92101-4495, USA Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands The Boulevard, Langford Lane, Kidlington, Oxford, OX5 1GB, UK 32 Jamestown Road, London NW1 7BY, UK Copyright © 2013 Elsevier Inc. All rights reserved. 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Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made ISBN: 978-0-12-407187-2 ISSN: 0079-7421 For information on all Academic Press publications visit our website at store.elsevier.com Printed and bound in USA 13 14 15 10 9 8 7 6 5 4 3 2 1 CONTENTS Contributors ix 1. Toward a Unified Theory of Reasoning 1 P. N. Johnson-Laird, Sangeet S. Khemlani 1. Introduction 2 2. What Is Reasoning? 4 3. Models of Possibilities 6 4. Icons and Symbols 9 5. The Principle of Truth 11 6. Models as Counterexamples 13 7. Modulation and the Use of Knowledge 16 8. Induction and Abduction 20 9. Probabilities: Extensional and Intensional 23 10. Mental Simulations and Informal Programs 27 11. Toward a Unified Theory 33 12. Conclusions 37 Acknowledgments 37 References 38 2. The Self-Organization of Human Interaction 43 Rick Dale, Riccardo Fusaroli, Nicholas D. Duran, Daniel C. Richardson 1. Introduction: The “Centipede’s Dilemma” of Interaction Research 44 2. An Example Theoretical Debate and the Need for Integration 46 3. Self-Organization and Human Interaction 49 4. Cognitive Dynamics under Social Constraints 57 5. Coordination, Complementarity, and Interactive Performance 68 6. Conclusion: Time for More Models 77 Acknowledgments 84 References 84 3. Conceptual Composition: The Role of Relational Competition in the Comprehension of Modifier-Noun Phrases and Noun–Noun Compounds 97 Christina L. Gagné, Thomas L. Spalding 1. Introduction 98 2. Modifier-Noun Phrases and Compounds as Expressions of Combined Concepts 100 v vi Contents 3. Theoretical Framework: A Three-Stage Theory of Conceptual Combination 101 4. Evidence of the Modifier’s Role in Relation Suggestion 104 5. The Nature of Relations and the Nature of Relational Competition 108 6. The Role of Relation Competition in the Processing of Compounds that Lack an Underlying Relation 115 7. Evaluation of Relational Interpretations 119 8. Elaboration of Combined Concepts Following Relation Selection 121 9. Summary 124 10. Concluding Remarks 126 References 127 4. List-Method Directed Forgetting in Cognitive and Clinical Research: A Theoretical and Methodological Review 131 Lili Sahakyan, Peter F. Delaney, Nathaniel L. Foster, Branden Abushanab 1. Introduction 133 2. List-Method DF: Design and Measurement 134 3. Our Framework of List-Method DF 138 4. Forgetting is a Strategic Decision 140 5. Context Change as an Explanation for DF Impairment 148 6. Areas of Disagreement Across Studies 165 7. Strategy Change Explains DF Benefits 167 8. Implications for Clinical Populations 172 9. Concluding Thoughts 181 Acknowledgments 182 References 182 5. Recollection is Fast and Easy: Pupillometric Studies of Face Memory 191 Stephen D. Goldinger, Megan H. Papesh 1. Introduction 192 2. Recognition Memory 192 3. Models of Memory 193 4. Estimating Recollection and Familiarity 198 5. Pupillometry 203 6. Psychophysiological Correlates of Memory for Faces 210 7. General Conclusions 215 References 215 Contents vii 6. A Mechanistic Approach to Individual Differences in Spatial Learning, Memory, and Navigation 223 Amy L. Shelton, Steven A. Marchette, Andrew J. Furman 1. Introduction 224 2. What Does It Mean to Measure Spatial Learning and Navigational Ability? 225 3. Dual Systems for Spatial Learning in Rodents 229 4. Place and Response Learning in Humans 232 5. The Place/Response Framework for Individual Differences 239 6. Connections to Other Sources of Variability 246 7. Competition or Interaction of Systems 250 8. Conclusions 252 References 255 7. When Do the Effects of Distractors Provide a Measure of Distractibility? 261 Alejandro Lleras, Simona Buetti, J. Toby Mordkoff 1. Introduction 262 2. When Do “Distractors” Cause Distraction? 264 3. A Brief Case Study on Distraction 291 4. A Theory of Attention and Distractibility 300 5. Conclusions 307 References 310 Index 317 Contents of Previous Volumes 331 This page intentionally left blank CONTRIBUTORS Branden Abushanab Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC, USA Simona Buetti Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, USA Rick Dale Cognitive and Information Sciences, University of California Merced, Merced, CA, USA Peter F. Delaney Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC, USA Nicholas D. Duran Cognitive and Information Sciences, University of California Merced, Merced, CA, USA; Division of Psychology and Language Sciences, University College London, London, UK Nathaniel L. Foster Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC, USA Andrew J. Furman Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA Riccardo Fusaroli Cognitive and Information Sciences, University of California Merced, Merced, CA, USA; Interacting Minds Center and Center for Semiotics, Aarhus University, Aarhus, Denmark Christina L. Gagné Department of Psychology, University of Alberta, Edmonton, AB, Canada Stephen D. Goldinger Department of Psychology, Arizona State University, Tempe, AZ, USA P. N . Johnson-Laird Department of Psychology, Princeton University, Princeton, NJ, USA; Department of Psychology, New York University, New York, NY, USA Sangeet S. Khemlani Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Washington, DC, USA Alejandro Lleras Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, USA ix x Contributors Steven A. Marchette Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA J. To by Mordkoff Department of Psychology, University of Iowa, Iowa City, IA, USA Megan H. Papesh Department of Psychology, Louisiana State University, Baton Rouge, LA, USA Daniel C. Richardson Division of Psychology and Language Sciences, University College London, London, UK Lili Sahakyan Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC, USA Amy L. Shelton Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA Thomas L. Spalding Department of Psychology, University of Alberta, Edmonton, AB, Canada CHAPTER ONE Toward a Unif ied Theory of Reasoning P. N. Johnson-Laird*,†,1, Sangeet S. Khemlani‡ *Department of Psychology, Princeton University, Princeton, NJ, USA †Department of Psychology, New York University, New York, NY, USA ‡Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Washington, DC, USA 1Corresponding author: E-mail: [email protected] Contents 1. Introduction 2 2. What Is Reasoning? 4 3. Models of Possibilities 6 4. Icons and Symbols 9 5. The Principle of Truth 11 6. Models as Counterexamples 13 7. Modulation and the Use of Knowledge 16 8. Induction and Abduction 20 9. Probabilities: Extensional and Intensional 23 10. Mental Simulations and Informal Programs 27 11. Toward a Unified Theory 33 12. Conclusions 37 Acknowledgments 37 References 38 Abstract This article describes a theory that uses mental models to integrate deductive, induc- tive, and probabilistic reasoning. It spells out the main principles of the theory and illustrates them with examples from various domains. It shows how models underlie inductions, explanations, estimates of probabilities, and informal algorithms. In all these cases, a central principle is that the mind represents each sort of possibility in a separate mental model and infers whatever holds in the resulting set of models. Finally, the article reviews what has been accomplished in implementing the theory in a single large-scale computer program, mReasoner. Psychology of Learning and Motivation, Volume 59 © 2013 Elsevier Inc. ISSN 0079-7421, http://dx.doi.org/10.1016/B978-0-12-407187-2.00001-0