Cognitive Psychology In And Out Of The Laboratory - Edition 6 PDF

COGNITIVE PSYCHOLOGY IN AND OUT OF THE LABORATORY Edition 6 To my kids, Tim and Kimmie, for giving meaning and purpose to my life; to my dogs, Tandy, Bussey, Eskie, Flit, Tackle, Lizzy, Tryker, and Nimo, for leavening that life with lightheartedness; and to my fabulous Carleton students, past, present, and future, for consistently keeping me on my toes. COGNITIVE PSYCHOLOGY IN AND OUT OF THE LABORATORY Edition 6 Kathleen M. Galotti Carleton College FOR INFORMATION: SAGE Publications, Inc. 2455 Teller Road Thousand Oaks, California 91320 E-mail: [email protected] SAGE Publications Ltd. 1 Oliver’s Yard 55 City Road London EC1Y 1SP United Kingdom SAGE Publications India Pvt. Ltd. B 1/I 1 Mohan Cooperative Industrial Area Mathura Road, New Delhi 110 044 India SAGE Publications Asia-Pacific Pte. Ltd. 3 Church Street #10-04 Samsung Hub Singapore 049483 Copyright © 2018 by SAGE Publications, Inc. 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 and retrieval system, without permission in writing from the publisher. Acknowledgment: SAGE Publishing thanks Robert Neffson for his kind permission to reproduce his art on the cover and in the interior of this book. Printed in the United States of America Library of Congress Cataloging-in-Publication Data Names: Galotti, Kathleen M., 1957- author. Title: Cognitive psychology in and out of the laboratory / Kathleen M. Galotti, Carleton College. Description: 6th edition. | Thousand Oaks, CA : SAGE, 2018. | Includes bibliographical references and index. Identifiers: LCCN 2017011208 | ISBN 9781506351568 (hardcover : alk. paper) Subjects: LCSH: Cognitive psychology. | Cognition. Classification: LCC BF201.G35 2018 | DDC 153—dc23 LC record available at https://lccn.loc.gov/2017011208 This book is printed on acid-free paper. Editorial Director: Reid Hester Acquisitions Editor: Abbie Rickard Senior Editorial Development Manager: Eve Oettinger Content Development Editor: Morgan Shannon Editorial Assistant: Jennifer Cline Production Editor: Jane Haenel Copy Editor: D. J. Peck Typesetter: C&M Digitals (P) Ltd. Proofreader: Tricia Currie-Knight Interior and Cover Designer: Janet Kiesel Marketing Manager: Katherine Hepburn BRIEF CONTENTS Preface About the Author Chapter 1 Cognitive Psychology: History, Methods, and Paradigms Chapter 2 The Brain: An Overview of Structure and Function Chapter 3 Perception: Recognizing Patterns and Objects Chapter 4 Attention: Deploying Cognitive Resources Chapter 5 Working Memory: Forming and Using New Memory Traces Chapter 6 Retrieving Memories From Long-Term Storage Chapter 7 The Reconstructive Nature of Memory Chapter 8 Knowledge Representation: Storing and Organizing Information in Long-Term Memory Chapter 9 Visual Imagery and Spatial Cognition Chapter 10 Language Chapter 11 Thinking and Problem Solving Chapter 12 Reasoning and Decision Making Chapter 13 Cognitive Development Through Adolescence Chapter 14 Individual Differences in Cognition Chapter 15 Cognition in Cross-Cultural Perspective Glossary References Author Index Subject Index DETAILED CONTENTS Preface About the Author Chapter 1 Cognitive Psychology: History, Methods, and Paradigms Setting the Stage Influences on the Study of Cognition Structuralism Functionalism Behaviorism Gestalt Psychology The Study of Individual Differences The “Cognitive Revolution” and the Birth of Cognitive Science General Points Research Methods in Cognitive Psychology Experiments and Quasi-Experiments Naturalistic Observation Controlled Observation and Clinical Interviews Introspection Investigations of Neural Underpinnings General Points Paradigms of Cognitive Psychology The Information-Processing Approach The Connectionist Approach The Evolutionary Approach The Ecological Approach and Embodied Cognition Cognitive Neuroscience General Points Summary Review Questions Key Terms SAGE edge Chapter 2 The Brain: An Overview of Structure and Function Setting the Stage Structure of the Brain The Hindbrain and Midbrain The Forebrain Localization of Function Faculty Psychology and Phrenology Studies of Aphasia and Other Mapping Techniques Lateralization of Function Studies of Split-Brained Patients Brain-Imaging Techniques CAT (CT) Scans Magnetic Resonance Imaging (MRI) Positron Emission Tomography (PET) Functional Magnetic Resonance Imaging (fMRI) Other Brain-Recording Techniques Electroencephalography (EEG) Event-Related Potential (ERP) Transcranial Magnetic Stimulation (TMS) Training the Brain Summary Review Questions Key Terms SAGE edge Chapter 3 Perception: Recognizing Patterns and Objects Setting the Stage Gestalt Approaches to Perception Bottom-Up Processes Template Matching Featural Analysis Prototype Matching Top-Down Processes Perceptual Learning The Word Superiority Effect A Connectionist Model of Word Perception Face Perception Direct Perception Disruptions of Perception: Visual Agnosias Summary Review Questions Key Terms SAGE edge Chapter 4 Attention: Deploying Cognitive Resources Setting the Stage Selective Attention Bottleneck Theories Spotlight Approaches Schema Theory Inattentional Blindness Divided Attention Dual-Task Performance The Attention Hypothesis of Automatization Automaticity and the Effects of Practice The Stroop Task Automatic Versus Attentional (Controlled) Processing Feature Integration Theory Attentional Capture Controlling Attention Neural Underpinnings of Attention Networks of Visual Attention Event-Related Potentials and Selective Attention Divided Attention Outside the Laboratory: Cell Phone Use While Driving Summary Review Questions Key Terms SAGE edge Chapter 5 Working Memory: Forming and Using New Memory Traces Setting the Stage Traditional Approaches to the Study of Memory Sensory Memory Iconic Memory Echoic Memory Short-Term Memory Capacity and Coding Retention Duration and Forgetting Retrieval of Information Working Memory Executive Functioning Neurological Studies of Memory Processes Summary Review Questions Key Terms SAGE edge Chapter 6 Retrieving Memories From Long-Term Storage Setting the Stage Aspects of Long-Term Memory Capacity Coding Retention Duration and Forgetting Retrieval of Information The Use of Mnemonics Other Retrieval Principles The Testing Effect Subdivisions of Long-Term Memory Semantic Versus Episodic Memory Implicit Versus Explicit Memory Declarative Versus Procedural Memory The Levels-of-Processing View Amnesia Anterograde Amnesia Retrograde Amnesia Summary Review Questions Key Terms SAGE edge Chapter 7 The Reconstructive Nature of Memory Setting the Stage Narrative Memory Autobiographical Memory Flashbulb Memories Eyewitness Memory The Recovered/False Memory Debate Memory Consolidation and Reconsolidation Summary Review Questions Key Terms SAGE edge Chapter 8 Knowledge Representation: Storing and Organizing Information in Long-Term Memory Setting the Stage Organizing Knowledge Network Models Adaptive Control of Thought (ACT) Models Connectionist Models Forming Concepts and Categorizing New Instances The Classical View of Concepts and Categorization The Prototype View of Concepts and Categorization The Exemplar View of Concepts and Categorization The Schemata/Scripts View of Concepts and Categorization The Knowledge-Based View of Concepts and Categorization Summary Review Questions Key Terms SAGE edge Chapter 9 Visual Imagery and Spatial Cognition Setting the Stage Codes in Long-Term Memory The Dual-Coding Hypothesis The Relational–Organizational Hypothesis Empirical Investigations of Imagery Mental Rotation of Images Scanning Images The Nature of Mental Imagery Principles of Visual Imagery Implicit Encoding Perceptual Equivalence Spatial Equivalence Transformational Equivalence Structural Equivalence Critiques of Mental Imagery Research and Theory Tacit Knowledge and Demand Characteristics The Picture Metaphor Propositional Theory Neuropsychological Findings Spatial Cognition Summary Review Questions Key Terms SAGE edge Chapter 10 Language Setting the Stage Defining Language The Structure of Language Phonology Syntax Semantics Pragmatics Language Comprehension and Production Speech Perception Speech Errors in Production Sentence Comprehension Comprehending Text Passages Story Grammars Gricean Maxims of Conversation Language and Cognition The Modularity Hypothesis The Whorfian Hypothesis Neuropsychological Views and Evidence Bilingualism Summary Review Questions Key Terms SAGE edge Chapter 11 Thinking and Problem Solving Setting the Stage Defining Terms Classic Problems and General Methods of Solution Generate-and-Test Technique Means–Ends Analysis Working Backward Backtracking Reasoning by Analogy Blocks to Problem Solving Mental Set Using Incomplete or Incorrect Representations Lack of Problem-Specific Knowledge or Expertise The Problem Space Hypothesis Expert Systems Finding Creative Solutions Unconscious Processing and Incubation Everyday Mechanisms Critical Thinking Summary Review Questions Key Terms SAGE edge Chapter 12 Reasoning and Decision Making Setting the Stage Reasoning Deductive Reasoning Propositional Reasoning Syllogistic Reasoning Inductive Reasoning Analogical Reasoning Hypothesis Testing Everyday Reasoning Decision Making Setting Goals Gathering Information Structuring the Decision Making a Final Choice Evaluating Cognitive Illusions in Decision Making Availability Representativeness Framing Effects Anchoring Sunk Cost Effects Illusory Correlation Hindsight Bias Confirmation Bias Overconfidence Utility Models of Decision Making Expected Utility Theory Multi-Attribute Utility Theory Descriptive Models of Decision Making Image Theory Recognition-Primed Decision Making Neuropsychological Evidence on Reasoning and Decision Making Summary Review Questions Key Terms SAGE edge Chapter 13 Cognitive Development Through Adolescence Setting the Stage Stage and Nonstage Theories of Cognitive Development Piagetian Theory General Principles Stages of Development The Sensorimotor Stage The Preoperational Stage The Concrete Operations Stage The Formal Operations Stage Reactions to Piaget’s Theory Non-Piagetian Approaches to Cognitive Development Perceptual Development During Infancy Toddlers’ Acquisition of Syntax Preschoolers’ Use of Memorial Strategies The Development of Reasoning Abilities During Middle and Late Childhood Some Post-Piagetian Answers to the Question “What Develops?” Neurological Maturation Working Memory Capacity and Processing Speed Attention and Perceptual Encoding The Knowledge Base and Knowledge Structures Strategies Metacognition Summary Review Questions Key Terms SAGE edge Chapter 14 Individual Differences in Cognition Setting the Stage Individual Differences in Cognition Ability Differences Cognitive Styles Learning Styles Expert/Novice Differences The Effects of Aging on Cognition Gender Differences in Cognition Gender Differences in Skills and Abilities Verbal Abilities Visuospatial Abilities Quantitative and Reasoning Abilities Gender Differences in Learning and Cognitive Styles Motivation for Cognitive Tasks Connected Learning Summary Review Questions Key Terms SAGE edge Chapter 15 Cognition in Cross-Cultural Perspective Setting the Stage Defining “Culture” Examples of Studies of Cross-Cultural Cognition Cross-Cultural Studies of Perception Picture Perception Visual Illusions Cross-Cultural Studies of Memory Free Recall Visuospatial Memory Cross-Cultural Studies of Categorization Cross-Cultural Studies of Reasoning Cross-Cultural Studies of Counting Effects of Schooling and Literacy Situated Cognition in Everyday Settings Summary Review Questions Key Terms SAGE edge Glossary References Author Index Subject Index PREFACE When I wrote the first edition of this book about 25 years ago, I had yet to become a mother and had just been tenured at Carleton College. I was still excited to get paid for doing a job that I loved enough to do for free. I still feel that way about what I do for a living—there is nothing better than teaching, and there are no better students than the Carleton kids I’ve grown so fond of. Many of them have influenced this edition and previous editions—in the examples I use to illustrate concepts, in their own independent projects that extend our understanding of those concepts, and in their feedback to me on previous editions. (They particularly enjoy finding my mistakes.) Still, much has changed since 1992. I’ve birthed one son (now a college graduate and married) and adopted an infant daughter from Vietnam (she’s now 15 years old and has started driving!). The students and campus have changed as well—we’ve all become much more adept with and dependent on technology, for example. And the field of cognitive psychology has changed a lot, placing much more emphasis on both neuroscience and situated cognition as well as making advances in the basic research that informs our understanding of how people acquire and use information. These changes certainly merit periodic revisions of the book, and voilà!—we have the sixth edition. Undergraduate students studying psychology have different reactions to the field of cognitive psychology. Some find it exciting and elegant, covering topics essential to understanding the human mind. Cognitive psychology, after all, raises questions about how the mind works—how we perceive people, events, and things; how and what we remember; how we mentally organize information; how we call on our mental resources to make important decisions. Other students find the field of cognitive psychology technical and “geeky”—filled with complicated models of phenomena far removed from everyday life. My goal throughout the writing of all editions of this book has been to bridge that gap —to try to reach out to students who are in the latter camp to show them what this field offers to be excited about. I think much of the problem is due to the disconnection of laboratory phenomena from everyday life. Too often, cognition texts focus exclusively on the laboratory research without showing students how that work bears on important real-world issues of consequence. I hope when students finish reading this book, they see why cognitive psychologists are so passionate about their topic and their research. A textbook author can choose either to be comprehensive and strive for encyclopedic coverage or to be selective and omit many worthwhile topics and studies. I hope I’ve struck a balance between these extremes but must confess I prefer the latter. This reflects my own teaching goals; I like to supplement textbook chapters with primary literature from journals. I have tried to keep chapters relatively short in the hope that instructors will supplement the text with other readings. My firm belief is that the best courses are those in which instructors are enthusiastic about the material; the relative brevity of the text is intended to encourage instructors to supplement and customize it with added coverage on topics they find especially interesting. My further hope is to encourage instructors and students alike to consider cognitive phenomena as having contexts that both foster and constrain their occurrence. Universals assumed or generalized from the laboratory do not always translate to every person in every situation. Too often, topics in cognitive psychology are presented as absolute unchanging aspects of everyone’s experience. Recent work in developmental psychology, cross-cultural psychology, and individual differences strongly suggests that this presentation is, at best, oversimplification and, at worst, fiction. I hope newer work in cognitive psychology can retain its rigor and elegance but can frame questions and issues more inclusively, reflecting a recognition of the ways people and situations differ as well as share similarities. ORGANIZATION OF THIS BOOK Cognitive Psychology In and Out of the Laboratory is intended for a one-semester or one-term course for students who have already completed an introductory psychology course. We begin with a chapter that surveys the field and describes its research methods and paradigms. A chapter reviewing the structure and function of the brain comes next. These two introductory chapters are followed by chapters covering topics that would generally be regarded as core aspects of cognition: perception, attention, and memory. The emphasis in these chapters is on reviewing both the “classic” studies that define the field and the newer approaches that challenge long-standing assumptions. Next come chapters on knowledge representation and organization. These chapters center on questions of how we mentally represent and store the vast amounts of information we acquire throughout our lives. The next few chapters, covering topics in “higher-order” cognition, include discussions of language, problem solving, reasoning, and decision making. It is in the last three chapters where this book departs most from a “prototypical” cognitive psychology textbook. Chapter 13 gives an overview of the development of cognition from infancy through adolescence. The last two chapters, on individual differences and cross-cultural approaches, include material not often covered in cognitive psychology courses. I feel strongly that these topics belong in a thorough examination of cognitive phenomena. Although traditional cognitive psychologists don’t always consider these issues in their work, I believe they ought to and, in the future, will. Almost all important material is integrated into the text rather than pulled out into boxes, asides, or extras that students might skip. This choice reflects my own experience as a student as well as feedback from my students who say they find boxed material distracting and often treat it as optional. I hope that omitting these extras reinforces the message to students that their learning and mastery will be best enhanced through their own careful reading and note taking rather than more superficial approaches such as highlighting and skimming. NEW TO THIS EDITION In the fifth edition, there was much streamlining, with sections and chapters combined to improve the organization and to shorten the text. In this edition, we have expanded slightly, dividing the former chapter on long-term memory into two different chapters. Throughout the book, discussion of recent work has been incorporated. To take just a few examples, there is now exposition of brain training programs in Chapter 2 as well as coverage of face perception in Chapter 3, mindfulness meditation in Chapter 4, and consolidation in memory in Chapter 7. Newer research is incorporated throughout all the chapters. DIGITAL RESOURCES FOR STUDENTS AND INSTRUCTORS edge.sagepub.com/galotticogpscyh6e SAGE edge for Instructors supports teaching by making it easy to integrate quality content and create a rich learning environment for students. Test banks provide a diverse range of pre-written options as well as the opportunity to edit any question and/or insert personalized questions to effectively assess students’ progress and understanding. Sample course syllabi for semester and quarter courses provide suggested models for structuring one’s course. Editable, chapter-specific PowerPoint® slides offer complete flexibility for creating a multimedia presentation for the course. EXCLUSIVE! Access to full-text SAGE journal articles that have been carefully selected to support and expand on the concepts presented in each chapter to encourage students to think critically. Multimedia includes videos that appeal to students with different learning styles. Lecture notes summarize key concepts by chapter to ease preparation for lectures and class discussions. SAGE edge for Students provides a personalized approach to help students accomplish their coursework goals in an easy-to-use learning environment. Mobile-friendly eFlashcards strengthen understanding of key terms and concepts. Mobile-friendly practice quizzes allow for independent assessment by students of their mastery of course material. Learning objectives reinforce the most important material. Web Exercises and meaningful web links facilitate student use of internet resources, further exploration of topics, and responses to critical thinking questions. EXCLUSIVE! Access to full-text SAGE journal articles that have been carefully selected to support and expand on the concepts presented in each chapter. Multimedia includes videos that appeal to students with different learning styles. ACKNOWLEDGMENTS The actual writing of the first edition of this book was a 5-year project. However, the groundwork for the book evolved over 15 years, stretching back to my own undergraduate and graduate education. I was fortunate to have benefited from the rigorous and dynamic teaching of Blythe Clinchy at Wellesley College and of Jonathan Baron, John Sabini, and Henry and Lila Gleitman at the University of Pennsylvania. My education and thinking about cognitive and developmental issues continued to profit from interactions with colleagues at Carleton College. Colleagues in Carleton’s Cognitive Science Program—especially Roy Elveton, Susan Singer, and Jason Decker—as well as colleagues from other disciplines, including Deanna Haunsperger, Steven Kennedy, Marion Cass, Martha Paas, and Steven Kozberg, have sharpened my pedagogical philosophy and helped me to maintain a sense of humor and balance about the craziness that periodically invades Carleton. One of the real joys of working at Carleton has been the privilege of teaching some incredibly talented, motivated, and energetic students. Students in my Cognitive Processes courses over the past 34 years have been kind enough to give me feedback on which chapters worked well and which ones didn’t, and I thank them for their candor. Other current and former Carleton students have helped me with the mundane but necessary tasks of checking references and writing for permissions throughout all of the editions; they include April Anderson, Stephanie Aubry, Julie Greene, Andy Hebrank, Simin Ho, Allison Logeman, Matt Maas, Diane Mistele, Kitty Nolan, Emily Snyder, Scott Staupe, Jennifer Tourjé, Valerie Umscheid, Elizabeth White, and James Whitney. My former administrative assistants, Marianne Elofson, Ruby Hagberg, and Lorie Tuma, all helped with previous editions and just generally made the workplace much more inviting than it otherwise would have been. Pamela Gaggioli has now taken on that role and continues to be an invaluable assistant in every aspect of the work I do. Several current and former students posed for some of the photographs in this edition, including Zoe Cohen, Zack Delpier, Chris Leppink-Shands, Hope Altenbaumer Molaizay, Zach Montes, Jonathan Rowe, Anna Smith, Laura Soter, Jane Tandler, and Jessa Youso. Because my students have contributed so much to my thinking and professional development, it is special to me to be able to make them a tangible part of the book! Other friends, neighbors, and colleagues who “modeled” for various photographs include Audrey and Susannah Battiste, Jason and Micah Decker, and Julia Kallestad. My own children (Timothy Komatsu and Kimmie Galotti) and my daughter in-law (Julia Mandsager Komatsu) are also depicted in one or more photos (sometimes to their chagrin). Carleton College has supported this book through various sabbaticals and faculty development grants. Then dean of the college, Roy Elveton, enthusiastically endorsed and funded this endeavor from the start. A dean can really make a difference in a faculty member’s professional development, and Roy often went above and beyond the call of duty for me and several of my talented colleagues at Carleton during his brief administrative tenure. His belief in my ability to write this book is something I will always be grateful for. As an emeritus colleague in our Cognitive Science Program and the Department of Philosophy, Roy remains a most trusted mentor and inspirer of our program. Steve Poskanzer, current president, and Bev Nagel, current dean of the college, have made the milieu at Carleton an inviting and vibrant one in which to work. I owe a special debt to Vicki Knight, editor of the first and third editions. Her wise counsel, sharp sense of humor, love of animals, and excellent taste in restaurants made our collaboration a very engaging one. I never would have been able to finish the first edition without her encouragement, and without the first edition there would not have been any subsequent ones! For the fourth edition, Michele Sordi took the reins, passing the job along to Reid Hester for the fifth edition. Reid once again served as my editor for this sixth edition and once again offered sage (!) advice throughout the revision process. He was ably assisted by Eve Oettinger and Abbie Rickard. The cover designer, Janet Kiesel, worked with my plea to use another Robert Neffson painting, integrating text and elements beautifully. D. J. Peck has been a delightful copyeditor to work with—eagle-eyed and with an impressive insistence on consistency. I am delighted to be lucky enough to work once again with Jane Haenel, the production editor, as she is so on top of myriad details and so sane in her approach to the whole process. When I heard she would be the production editor for this edition, my heart was filled with joy! Reviewers of past editions of the book, who also made important contributions, include for the first edition Sharon Armstrong, Central College (Pella, IA); Terry Au, University of California, Los Angeles; Ira Fischler, University of Florida; John H. Flowers, University of Nebraska–Lincoln; Margery Lucas, Wellesley College; Robert Seibel; Steven M. Smith, Texas A&M University; and Margaret Thomas, University of Central Florida; and for the second edition Brenda J. Byers, Arkansas State University; Robert Campbell, Clemson University; L. Mark Carrier, Florida State University; David G. Elmes, Washington and Lee University; Ira Fischler, University of Florida; John H. Flowers, University of Nebraska–Lincoln; Nancy Franklin, State University of New York at Stony Brook; Peter Graf, University of British Columbia; Morton A. Heller, Winston–Salem State University; Lorna Jarvis, Hope College– Peale Science Center; Douglas Johnson, Colgate University; James Juola, University of Kansas; Richard Metzger, University of Tennessee; John Pani, University of Louisville; Aimee M. Surprenant, Purdue University; Joseph Thompson, Washington and Lee University; and Lori R. Van Wallendael, University of North Carolina. For the third edition, I received many very constructive and helpful suggestions and insights for strengthening the book from Lisa Abrams, University of Florida; Nancy Alvarado, California State Polytechnic University, Pomona; Jeffrey Anastasi, Arizona State University; Krystine Batcho, Le Moyne College; Stephanie Buchert, Kent State University; Walt Chromiak, Dickinson College; John Flowers, University of Nebraska–Lincoln; Allen Keniston, University of Wisconsin–Eau Claire; Kristy Nielson, Marquette University; Evelyn Schaefer, University of Winnipeg; Elizabeth Spievak, Hanover College; Mark Stewart, Willamette University; Brian Sundermeier, University of Minnesota–Minneapolis; and Lori Van Wallendael, University of North Carolina at Charlotte. Fourth edition reviewers included Sue Astley, Cornell College; Robert Boughner, Rogers State University; Laura Bowman, Central Connecticut State University; Myra Fernandes, University of Waterloo; Allen Keniston, University of Wisconsin; James MacDougall, Eckard College; Chuck Robertson, North Georgia College & State University; Linda Rueckert, Northeastern Illinois University; Dennis Shaffer, The Ohio State University; Alycia Silman, Wake Forest University; Ami Spears, Mercer University; and Frank Yeatman, Stonehill College. Fifth edition reviewers included Michael Dodd, University of Nebraska–Lincoln; Stephen Dopkins, The George Washington University; Kendall J. Eskine, Loyola University New Orleans; Rhiannon E. Hart, Rochester Institute of Technology; Conor T. McLennan, Cleveland State University; Rolf Nelson, Wheaton College; and Ruth Tincoff, Bucknell University. Once again, for the sixth edition I received a really helpful and substantive set of reviews, this time from Kristen Weede Alexander, California State University, Sacramento; Mary A. Dolan, California State University, San Bernardino; Marissa Greif-Hackett, Florida Atlantic University; Ben Hinnant, Catholic University of America; Elaine M. Justice, Old Dominion University; Darcy B. Mitchell, Colby- Sawyer College; Monica Riordan, Chatham University; Joni N. Saby, Temple University; and Harriet S. Waters, Stony Brook University. I would also like to thank artist Robert Neffson for allowing us to use another one of his wonderful paintings for the cover and interior design art. It’s beautiful! ABOUT THE AUTHOR Kathleen M. Galotti holds a B.A. in psychology and economics from Wellesley College as well as an M.A. and Ph.D. in psychology and an M.S.E. in computer and information sciences from the University of Pennsylvania. At Carleton College, she holds an endowed chair as the W. H. Laird Professor of Cognitive Science and serves as the director of that interdisciplinary program, which she helped to establish in 1989. She also is a former chair of the Department of Psychology. She teaches courses in cognitive and developmental psychology and cognitive science and has also taught courses in statistics and introductory psychology. Dr. Galotti’s research centers on the development of reasoning and decision-making skills from the preschool period through adulthood and on the styles with which adolescents and adults plan for the future, make important life commitments, and learn new information. Her research has been funded through the National Science Foundation, the Spencer Foundation, and the National Institutes of Health. She is the author of Making Decisions That Matter: How People Face Important Life Choices (Lawrence Erlbaum, 2002) as well as the second edition of the textbook Cognitive Development: Infancy Through Adolescence (Sage, 2017). She has also authored or co-authored dozens of articles in peer-reviewed journals. Dr. Galotti is the parent of two children, Timothy and Kimberlynn, and spends much of her time enjoying their exuberance, energy, and all of their many activities and performances. In her spare time, she raises and trains Bernese Mountain dogs and a Cavalier King Charles spaniel and shows them in competition in licensed obedience, rally, and agility trials. She is an approved obedience and rally judge for the American Kennel Club. 1 COGNITIVE PSYCHOLOGY : HISTORY, METHODS, AND PARADIGMS CHAPTER OUTLINE Setting the Stage Influences on the Study of Cognition Structuralism Functionalism Behaviorism Gestalt Psychology The Study of Individual Differences The “Cognitive Revolution” and the Birth of Cognitive Science General Points Research Methods in Cognitive Psychology Experiments and Quasi-Experiments Naturalistic Observation Controlled Observation and Clinical Interviews Introspection Investigations of Neural Underpinnings General Points Paradigms of Cognitive Psychology The Information-Processing Approach The Connectionist Approach The Evolutionary Approach The Ecological Approach and Embodied Cognition Cognitive Neuroscience General Points Setting the Stage This book is about cognitive psychology—that branch of psychology concerned with how people acquire, store, transform, use, and communicate information (Neisser, 1967). Put differently, cognitive psychology deals with our mental life: what goes on inside our heads when we perceive, attend, remember, think, categorize, reason, decide, and so forth. To get a better feel for the domain of cognitive psychology, let’s consider an example of cognitive activity: You’re walking along a dark, unfamiliar city street. It’s raining and foggy, and you are cold and a bit apprehensive. As you walk past a small alley, you catch some movement out of the corner of your eye. You turn to look down the alley and start to make out a shape coming toward you. As the shape draws nearer, you are able to make out more and more features, and you suddenly realize that it’s... What cognitive processes are going on in this admittedly melodramatic example? In general, this example illustrates the initial acquisition and processing of information. In particular, the cognitive processes depicted include attention, mentally focusing on some stimulus (the mysterious shape); perception, interpreting sensory information to yield meaningful information; and pattern recognition, classifying a stimulus into a known category. In recognizing the shape as something familiar, you no doubt called on memory, the storage facilities and retrieval processes of cognition. All this processing occurred rapidly, probably within a few seconds or less. Most of the cognitive processing in this example appears so effortless and automatic that we usually take it for granted. Here’s another example: You’re in a crowded public place such as a shopping mall during the holiday season. Throngs of people push past you, and you’re hot and tired. You head for a nearby bench, aiming to combine some rest with some people watching. As you make your way, a young woman about your age jostles up against you. You both offer polite apologies (“Oh, excuse me!” “Sorry!”), glancing at each other as you do. She immediately exclaims, “Oh, it’s you! How are you? I never thought I’d run into anyone I know here—can you believe it?” You immediately paste a friendly but vague smile on your face to cover your frantic mental search. Who is this woman? She looks familiar, but why? Is she a former classmate? Did you and she attend camp together? Is she saying anything that you can use as a clue to place her? This example illustrates your use of memory processes, including recognition (you see the woman as familiar) and recall (you try to determine where you know her from). Other cognitive processes are involved here too, although they play a lesser role. For instance, you perceive the entity talking to you as a person, specifically a woman, more specifically a vaguely familiar woman. You pay attention to her. You may be using various strategies or techniques of reasoning and problem solving to try to figure out who she is. Your success or failure at this task may also depend on your mental organization of the knowledge you have accumulated in your lifetime —your knowledge representation. To communicate with her, you use language as well as nonverbal cues or signals. Eventually, you’ll need to use decision making to determine how to deal with the situation: Will you admit your forgetfulness, or will you try to cover it up? Photo 1.1: An ordinary activity, such as reading a map, involves a great deal of cognitive processing. As these two examples demonstrate, our everyday lives involve a great deal of cognition. Furthermore, this everyday cognition is complex, often involving several cognitive processes. We tend to remain unaware of this complexity, however, because much of our cognitive processing occurs so often, so rapidly, and with so little effort that we might not even know it is taking place. In both of the preceding examples, several cognitive processes were occurring either simultaneously or very closely in time. In fact, it is nearly impossible to specify, in either of these examples, exactly how many cognitive processes occurred or in what sequence. This uncertainty typifies everyday situations: So much is going on so quickly that we can’t even be sure of what information is being received or used. How, then, can cognition be studied with any precision? This kind of problem is one all scientists face: how to study a naturally occurring phenomenon with sufficient experimental rigor to draw firm conclusions. The answer, for many, is to try to isolate the phenomenon and bring it (or some stripped-down version of it) into the laboratory. With this approach, the challenge is to decide what is essential and what is inessential about the phenomenon under study. For example, in studying how memory works, psychologists have often used experiments in which people are presented with lists of words or nonsense syllables. The experimenters then control or systematically vary variables such as the complexity, length, frequency, meaningfulness, relatedness, and rate of presentation of items on the list along with the state of alertness, expertise, practice, and interest of the research participants. The experimenters assume that factors that increase or decrease performance in the laboratory will also increase or decrease performance under less controlled conditions. Furthermore, the researchers assume that although in everyday life people do not encounter material to be remembered in this manner, the processes of memory work in essentially the same ways in laboratory experiments as in everyday life. So if increasing the number of items to be remembered decreases memory performance in a laboratory, then we can expect that needing to remember more information is more difficult than remembering less in an everyday situation. The key challenge for all scientists, however, is to make sure the laboratory tasks they develop preserve the essential workings of the processes under study. The most rigorously controlled experiment is of, at best, limited value if the phenomenon being studied does not occur or occurs in significantly different ways outside the laboratory. Unfortunately, there is no simple or guaranteed way to ensure that laboratory tasks model everyday tasks. Therefore, students and other “consumers” of science must take a critical stance when considering how experimental situations apply to everyday ones. Throughout this book, we will look at how laboratory models do or don’t accurately describe, explain, and predict cognitive processing in real life. We will also consider how situational and personal factors, such as people’s level of development, personality variables, degree of expertise, gender, and cultural background, affect cognitive processing. Before we discuss specific cognitive processes, however, an overview of the field of cognitive psychology will provide a useful framework within which to consider specific topics, experiments, and findings in the field. We will first examine the historical roots of cognitive psychology to see how the field has developed. Next, we will look at traditional and common research methods used in cognitive psychology. Finally, we will consider four paradigms, or schools of thought, that represent the current streams of thought in the field. INFLUENCES ON THE STUDY OF COGNITION A complete treatise on how modern cognitive psychology has evolved over the course of human history could fill several volumes and would obviously be beyond our scope. Worth noting, however, is that several ideas about certain mental abilities date back to at least the Greek philosophers Aristotle and Plato (Murray, 1988). Both of these philosophers wrote extensively on the nature of memory. Plato, for instance, likened storing something in memory to writing on a wax tablet. In other writings, he compared the mind to an aviary in which many birds are flying and compared memory retrieval to trying to catch a specific bird: Sometimes you can, but other times you can grab only a nearby bird. Similarly, when I try to recall the name of the girl who sat behind me in third grade, I have trouble latching on to exactly the right one (was it Joan? Joanne? Anne?), but my choices are probably pretty close. Other historians of psychology trace the field’s roots to the philosophers of the 17th to 19th centuries, including John Locke, David Hume, John Stuart Mill, René Descartes, George Berkeley, and Immanuel Kant. These philosophers also debated the nature of mind and knowledge, with Locke, Hume, Berkeley, and Mill following Aristotle and a more empiricist position and Descartes and Kant aligning with Plato and a nativist position. Briefly, empiricism rests on the tenet that knowledge comes from an individual’s own experience—that is, from the empirical information that people collect from their senses and experiences. Empiricists recognize individual differences in genetics but emphasize human nature’s malleable, or changeable, aspects. Empiricists believe people are the way they are, and have the capabilities they have, largely because of previous learning. One mechanism by which such learning is thought to take place is through the mental association of two ideas. Locke (1690/1964) argued that two distinct ideas or experiences, having nothing to do with each other, could become joined in the mind simply because they happened to occur or be presented to the individual at the same time. Empiricists accordingly believe the environment plays a powerful role in determining one’s intellectual (and other) abilities. Nativism, by contrast, emphasizes the role of constitutional factors—of native ability —over the role of learning in the acquisition of abilities and tendencies. Nativists attribute differences in individuals’ abilities less to differences in learning than to differences in original, biologically endowed capacities and abilities. Nativism is an important idea in cognitive psychology, as we will see. Nativists often suggest that some cognitive functions come built in as part of our legacy as humans. “Hard-wired” functions such as working memory, for example, are attributed to innate structures of the human mind that are present in at least rudimentary form at birth and are not learned, formed, or created as a result of experience. Interestingly, only during the last 120 years have central cognitive issues, such as the nature of the mind and the nature of information in the mind, been seen as amenable to scientific psychological investigation. Indeed, until the 1870s, no one really thought to ask whether actual data could help to resolve any of these questions. When people began doing so, experimental psychology was born. However, the nativist–empiricist debate is still a controversial one in the 21st century (Pinker, 2002). We will look next at the different schools of experimental psychology that laid the foundations for cognitive psychology today. Structuralism Many students are surprised to find out that psychology as a formal discipline has been around for little more than a century. Historians often date the “founding” of the field of psychology back to 1879, when Wilhelm Wundt converted a laboratory into the first institute for research in experimental psychology (Fancher, 1979). Wundt wanted to establish a “science of mind” to discover the laws and principles that explained our immediate conscious experience. In particular, Wundt wanted to identify the simplest essential units of the mind. In essence, he wanted to create a table of “mental elements,” much like a chemist’s periodic chart. Once the set of elements was identified, Wundt believed, psychologists could determine how these units combine to produce complex mental phenomena. Wundt (1904) foresaw an entire field devoted to the study of how systematically varying stimuli would affect or produce different mental states; he described this field in a volume titled Principles of Physiological Psychology. Wundt and his students carried out hundreds of studies, many involving a technique of investigation called introspection. Although this term today connotes “soul searching,” Wundt’s technique was much more focused. It consisted of presenting highly trained observers (usually graduate students) with various stimuli and asking them to describe their conscious experiences. Wundt assumed that the raw materials of consciousness were sensory and thus “below” the level of meaning. In particular, Wundt thought any conscious thought or idea resulted from a combination of sensations that could be defined in terms of exactly four properties: mode (e.g., visual, auditory, tactile, olfactory), quality (e.g., color, shape, texture), intensity, and duration. Wundt’s goal was to “cut through the learned categories and concepts that define our everyday experience of the world” (Fancher, 1979, p. 140). Wundt believed strongly that with proper training people could detect and report the workings of their own minds. A student of Wundt, Edward B. Titchener, applied the term structuralism to his own endeavors as well as to Wundt’s (Hillner, 1984). The term was meant to convey Wundt’s focus on what the elemental components of the mind are rather than on the question of why the mind works as it does. The method of introspection, unfortunately, proved to be problematic, as we will see shortly. Nonetheless, modern cognitive psychologists owe Wundt more than a historical debt. A pioneer in the study of many cognitive phenomena, he was the first to approach cognitive questions scientifically and the first to design experiments to test cognitive theories. Functionalism While Wundt was working in Leipzig, Germany, an American named William James was working to establish the new discipline of psychology in the United States. In many ways, Wundt and James were opposites. A prolific researcher who personally carried out or supervised hundreds of rigorous experiments, Wundt was not known for his interpersonal style. James (the brother of the writer Henry James), in contrast, carried out little original research but wrote eloquently about psychological findings and their relevance to everyday life (Fancher, 1979). His textbook The Principles of Psychology (James, 1890/1983) is still highly regarded and widely cited today. James regarded psychology’s mission to be the explanation of our experience. Like Wundt, James was interested in conscious experience. Unlike Wundt, however, James was not interested in the elementary units of consciousness. Instead, he asked why the mind works the way it does. He assumed that the way the mind works has a great deal to do with its function—the purposes of its various operations. Hence, the term functionalism was applied to his approach. James’s writings, which introduced psychological questions to American academics, still offer food for thought to students and teachers of psychology, perhaps because they so directly address everyday life. Consider one of the best-known chapters in his textbook on “habit.” James (1890/1983) saw habit as the “flywheel of society” (Vol. 1, p. 125), a mechanism basic to keeping our behavior within bounds. He saw habits as inevitable and powerful and drew from this a practical conclusion: Every smallest stroke of virtue or of vice leaves its ever so little scar. The drunken Rip Van Winkle, in Jefferson’s play, excuses himself for every fresh dereliction by saying, “I won’t count this time!” Well! He may not count it, and a kind Heaven may not count it; but it is being counted none the less. Down among his nerve-cells and fibres the molecules are counting it, registering and storing it up to be used against him when the next temptation comes. (p. 131) James’s point, of course, is that people should take great care to avoid bad habits and establish good ones. He offered advice about how to do so, urging people to never allow an exception when trying to establish a good habit, to seize opportunities to act on resolutions, and to engage in a “little gratuitous effort” every day to keep the “faculty of effort” alive (James, 1890/1983, Vol. 1, p. 130). Other American psychologists shared James’s assumptions and approaches. Fellow functionalists such as John Dewey and Edward L. Thorndike, for example, shared James’s conviction that the most important thing the mind did was to let the individual adapt to her or his environment. Functionalists drew heavily on Darwinian evolutionary theory and tried to extend biological conceptions of adaptation to psychological phenomena (Hillner, 1984). Structuralists and functionalists differed in their methods as well as their focus. The structuralists were convinced that the proper setting for experimental psychology was the laboratory, where experimental stimuli could be stripped of their everyday meanings to determine the true nature of mind. The functionalists disagreed sharply with this approach, attempting instead to study mental phenomena in real-life situations. Their basic belief was that psychologists should study whole organisms doing whole real-life tasks. Behaviorism You probably learned the terms classical conditioning and instrumental conditioning in your introductory psychology class. The Russian psychologist Ivan Pavlov used the first term, and psychologists such as Edward Thorndike used the second term, to explain psychological phenomena strictly in terms of observable stimuli and responses. In the United States, a school of psychology known as behaviorism took root during the 1930s and dominated academic psychology until well into the 1960s. Many regard it as a branch of functionalism (Amsel, 1989). One of the general doctrines of behaviorism is that references to unobservable subjective mental states (such as consciousness), as well as to unobservable subjective processes (such as expecting, believing, understanding, remembering, hoping for, deciding, and perceiving), are to be banished from psychology proper, which behaviorists took to be the scientific study of behavior. Behaviorists rejected such techniques of study as introspection, which they found in principle to be untestable. In an article published in 1913, John Watson most directly described his view of what psychology is and isn’t: Psychology as the behaviorist views it is a purely objective natural science. Its theoretical goal is the prediction and control of behavior. Introspection forms no essential part of its methods, nor is the scientific value of its data dependent upon the readiness with which they lend themselves to interpretation in terms of consciousness. The behaviorist, in his efforts to get a unitary scheme of animal response, recognizes no dividing line between man and brute. The behavior of man, with all of its refinement and complexity, forms only a part of the behaviorist’s total scheme of investigation. (p. 158) Why did behaviorists so disdain the technique of introspection? Their disdain was mainly because of its obviously subjective nature and its inability to resolve disagreements about theory. Suppose two observers are presented with the same stimulus, and one reports an experience of “greenness” and the other an experience of “green-yellowness.” Which one is correct? Is one misrepresenting or misinterpreting his or her experience? If no physiological cause (e.g., color blindness) explains the different reports, then the scientist is left with an unresolvable dispute. Titchener restricted his research participants to graduate students trained to introspect “properly” (advising those who couldn’t learn to do this to find another career). This, however, created more problems than it solved. The reasoning was circular. How do we know that a particular sensation is a true building block of cognition? Because trained observers report it to be so. How do we know the observers are trained? Because they consistently report that certain sensations and not others are the true elements of consciousness. Watson, in fact, regarded all “mental” phenomena as reducible to behavioral and physiological responses. Such things as “images” and “thoughts,” he believed, resulted from low-level activity of glands or small muscles. In his first textbook, Watson cited evidence showing that when people report they are “thinking,” muscles in the tongue and larynx are actually moving slightly. Thought, for Watson, simply amounted to perception of these muscle movements (Fancher, 1979). Watson’s contribution to cognitive psychology—banishing all “mental language” from use—was largely negative insofar as he believed the scientific study of mental phenomena was simply not possible. Watson and his followers did, however, encourage psychologists to think in terms of measures and research methods that moved beyond subjective introspection, thereby challenging later psychologists to develop more rigorous and more testable hypotheses and theories as well as stricter research protocols. B. F. Skinner (1963/1984), psychology’s best-known behaviorist, took a different tack with regard to mental events and the issue of mental representations. Skinner argued that such “mentalistic” entities as images, sensations, and thoughts should not be excluded simply because they are difficult to study. Skinner believed in the existence of images, thoughts, and the like and agreed they were proper objects of study, but he objected to treating mental events and activities as fundamentally different from behavioral events and activities. In particular, he objected to hypothesizing the existence of mental representations (internal depictions of information), which he took to be internal copies of external stimuli. Skinner believed images and thoughts were likely to be no more or less than verbal labels for bodily processes. But even if mental events were real and separate entities, Skinner believed, they were triggered by external environmental stimuli and gave rise to behaviors. Therefore, he held, a simple functional analysis of the relationship between the stimuli and behaviors would avoid the well-known problems of studying mental events (Hergenhahn, 1986). Other behaviorists were more accepting of the idea of mental representations. Edward Tolman, for example, believed that even rats have goals and expectations. As he explained it, a rat learning to run a maze must have the goal of attaining food and must acquire an internal representation—some cognitive map or other means of depicting information “in the head”—to locate the food at the maze’s end. Tolman’s work centered on demonstrating that animals had both expectations and internal representations that guided their behavior. Gestalt Psychology The school of Gestalt psychology began in 1911 in Frankfurt, Germany, in a meeting of three psychologists: Max Wertheimer, Kurt Koffka, and Wolfgang Köhler (Murray, 1988). As the name Gestalt (a German word that loosely translates to “configuration” or “shape”) suggests, these psychologists’ central assumption was that psychological phenomena could not be reduced to simple elements but rather needed to be analyzed and studied in their entirety. Gestalt psychologists, who studied mainly perception and problem solving, believed an observer did not construct a coherent perception from simple, elementary sensory aspects of an experience but instead apprehended the total structure of an experience as a whole. As a concrete example, consider Figure 1.1. Notice that (A), (B), and (C) contain the same elements—namely, eight equal line segments. However, most people experience the three arrays quite differently, seeing (A) as four pairs of line segments, (B) as eight line segments haphazardly arranged, and (C) as a circle or, more precisely, an octagon made up of eight line segments. The arrangement of lines—that is, the relationships among the elements as a whole—plays an important role in determining our experience. The Gestalt psychologists thus rejected structuralism, functionalism, and behaviorism as offering incomplete accounts of psychological and, in particular, cognitive experiences. They chose to study people’s subjective experience of stimuli and to focus on how people use or impose structure and order on their experiences. They believed that the mind imposes its own structure and organization on stimuli and, in particular, organizes perceptions into wholes rather than discrete parts. These wholes tend to simplify stimuli. Thus, when we hear a melody, we experience not a collection of individual sounds but rather larger and more organized units— melodic lines. Figure 1.1: Examples of Gestalt figures. Although (A), (B), and (C) all contain eight equal lines, most people experience them differently, seeing (A) as four pairs of lines, (B) as eight unrelated lines, and (C) as a circle made up of eight line segments. The Study of Individual Differences Yet another strand of the history of psychology is important to mention here, even though no particular “school” is associated with it: the investigations into individual differences in human cognitive abilities by Sir Francis Galton and his followers. Galton, a half-cousin of Charles Darwin, inherited a substantial sum of money during his early 20s that afforded him the time and resources to pursue his interests. A child prodigy himself (he read and wrote by the age of 2½ years), Galton trained in medicine and mathematics at Cambridge University in England. Like many of his fellow students (and many of today’s college students), Galton felt a great deal of academic pressure and competitiveness and “was constantly preoccupied with his standing relative to his fellow students” (Fancher, 1979, p. 257). This strong preoccupation (which may have contributed to a breakdown he suffered at Cambridge) developed into a lifelong interest in measuring intellectual ability. Galton’s interest in intellectual differences among people stemmed in part from his reading of his cousin Darwin’s writings on evolution. Darwin believed animals (including humans) evolved through a process he called natural selection, by which certain inherited traits are perpetuated because individuals possessing those traits are more likely to survive and reproduce. Galton wondered whether intellectual talents could also be inherited. Galton noticed “intelligence,” “smartness,” or “eminence” seemed to run in families; that is, smart parents appeared to produce smart children. Of course, this could be explained in terms of either genetics or environment (e.g., intelligent parents may have greater resources to spend on their children’s education and/or greater interest or motivation to do so). Thus, Galton’s question of how large a role genetics plays in intelligence was difficult to answer. To address it, Galton put his mathematical training to use in analyzing data (usually family trees of “eminent” men) and, later, inventing statistical tests, some of which are still used today. Galton (1883/1907) studied a variety of cognitive abilities, in each case focusing on ways of measuring the ability and then noting its variation among different individuals. Among the abilities he studied (in both laboratory and “naturalistic” settings) was mental imagery. He developed a questionnaire instructing respondents to “think of some definite object—suppose it is your breakfast-table as you sat down this morning—and consider carefully the picture that rises before your mind’s eye” (p. 58). He then asked a few questions. Is the image dim or clear? Are all of the objects in the image well defined? Does part of the image seem to be better defined? Are the colors of the objects in the image distinct and natural? Galton was surprised to discover much variability in this capacity: Some respondents reported almost no imagery; others experienced images so vividly they could hardly tell they were images. Galton left a large legacy to psychology and to cognitive psychology in particular. His invention of tests and questionnaires to assess mental abilities inspired later cognitive psychologists to develop similar measures. His statistical analyses, later refined by other statisticians, allowed hypotheses to be rigorously tested. His work on mental imagery is still cited by current investigators. Most broadly, Galton’s work challenged psychologists, both those who believed genetic influences are crucially important and those who were strongly opposed to the idea, to think about the nature of mental—that is, cognitive—abilities and capacities. The “Cognitive Revolution” and the Birth of Cognitive Science Despite the early attempts to define and study mental life, psychology, especially American psychology, came to embrace the behaviorist tradition during the first five decades of the 1900s. A number of historical trends, both within and outside academia, came together in the years during and following World War II to produce what many psychologists think of as a “revolution” in the field of cognitive psychology. This cognitive revolution, a new series of psychological investigations, was mainly a rejection of the behaviorist assumption that mental events and states were beyond the realm of scientific study or that mental representations did not exist. In particular, the “revolutionaries” came to believe no complete explanation of a person’s functioning could exist that did not refer to the person’s mental representations of the world. This directly challenged the fundamental tenet of radical behaviorism that concepts such as mental representation were not needed to explain behavior. One of the first of these historical trends was a product of the war itself: the establishment of the field of human factors engineering. During the war, military personnel needed to be trained to operate complicated pieces of equipment. Engineers quickly found they needed to design equipment (such as instrument operating panels, radar screens, and communication devices) to suit the capacities of the people operating it. Lachman, Lachman, and Butterfield (1979) offered an anecdote about why such problems were important to solve: One type of plane often crashed while landing. It turned out that the lever that the pilot had to use for braking was near the lever that retracted the landing gear. During landing, the pilot could not take his eyes off the runway: He had to work by touch alone. Sometimes pilots retracted their landing gear instead of putting on their brakes; they touched the ground with the belly of the plane at top speed. The best way to keep them from crashing was not to exhort them to be careful; they were already highly motivated to avoid crashing and getting killed. Improving training procedures was also an inefficient approach; pilots with many safe landings behind them committed this error as well as rookie pilots. The most reasonable approach was to redesign the craft’s controls so that completely different arm movements were required for braking and for retracting the landing gear. (p. 57) Psychologists and engineers thus developed the concept of the man–machine system, now more accurately referred to as the person–machine system: the idea that machinery operated by a person must be designed to interact with the operator’s physical, cognitive, and motivational capacities and limitations. Psychologists during World War II also borrowed concepts, terminology, and analogies from communications engineering. Engineers concerned with the design of such things as telephones and telegraph systems talked about the exchange of information through various “channels” (such as telegraph wires and telephone lines). Different kinds of channels differ in how much information they can transmit per unit of time and how accurately. Humans were quickly seen to be a particular kind of communication channel, sharing properties with better-known inanimate communications channels. Thus, people came to be described as limited-capacity processors of information. What is a limited-capacity processor? As the name suggests, it means that people can do only so many things at once. When I’m typing, I find it difficult (actually, impossible) to simultaneously keep up my end of a conversation, read an editorial, or follow a television news broadcast. Similarly, when I concentrate on balancing my checkbook, I can’t also recite multiplication tables or remember all the teachers I’ve had from kindergarten onward. Although I can do some tasks at the same time (I can fold the laundry while I watch television), the number and kinds of things I can do at the same time are limited. A classic article focusing on capacity limitations was authored by George Miller in 1956. This article, titled “The Magical Number Seven, Plus or Minus Two,” observed that (a) the number of unrelated things we can perceive distinctly without counting, (b) the number of unrelated things on a list we can immediately remember, and (c) the number of stimuli we can make absolute discriminations among are, for most normal adults, between five and nine. Miller’s work exemplified how the limits of people’s cognitive capacities could be measured and tested. At about the same time, developments in the field of linguistics, the study of language, made clear that people routinely process enormously complex information. Work by linguist Noam Chomsky revolutionized the field of linguistics, and both linguists and psychologists began to see the central importance of studying how people acquire, understand, and produce language. In addition, Chomsky’s (1957, 1959, 1965) early work showed that behaviorism cannot adequately explain language. Consider the question of how language is acquired. A behaviorist might explain language acquisition as the result of parents’ reinforcing a child’s grammatical utterances and punishing (or at least not reinforcing) ungrammatical utterances. However, both linguists and psychologists soon realized such an account must be wrong. For one thing, psychologists and linguists who observed young children with their parents found that parents typically respond to the content rather than to the form of the children’s language utterances (Brown & Hanlon, 1970). For another, even when parents (or teachers) explicitly tried to correct children’s grammar, they could not. Children seemed simply not to “hear” the problems, as is evident in the following dialogue (McNeill, 1966, p. 69): Child: Nobody don’t like me. Mother: No, say, “Nobody likes me.” [eight repetitions of this dialogue] Mother: No, now listen carefully; say, “Nobody likes me.” Child: Oh! Nobody don’t likes me. (Clearly, this mother was more focused on the child’s linguistic development than emotional development!) Chomsky’s work thus posed a fundamental challenge to psychologists: Here were humans, already shown to be limited-capacity processors, quickly acquiring what seemed to be an enormously complicated body of knowledge—language—and using it easily. How could this be? Reversing engineers’ arguments that machines must be designed to fit people’s capabilities, many linguists tried to describe structures complex enough to process language. Chomsky (1957, 1965) argued that underlying people’s language abilities is an implicit system of rules, collectively known as a generative grammar. These rules allow speakers to construct, and listeners to understand, sentences that are “legal” in the language. For example, “Did you eat all the oat bran cereal?” is a legal, well-formed sentence, but “Bran the did all oat eat you cereal?” is not. Our generative grammar, a mentally represented system of rules, tells us so because it can produce (generate) the first sentence but not the second. Chomsky (1957, 1965) did not believe all the rules of a language are consciously accessible to speakers of that language. Instead, he believed the rules operate implicitly: We don’t necessarily know exactly what all the rules are, but we use them rather easily to produce understandable sentences and to avoid producing gobbledygook. Another strand of the cognitive revolution came from developments in neuroscience, the study of the brain-based underpinnings of psychological and behavioral functions. A major debate in the neuroscience community had been going on for centuries, all the way back to Descartes, over the issue of localization of function. To say a function is “localized” in a particular region is, roughly, to claim that the neural structures supporting that function reside in a specific brain area. In a major work published in 1929, a very influential neuroscientist, Karl Lashley, claimed there was no reason to believe that major functions (such as language and memory) are localized (H. Gardner, 1985). However, research during the late 1940s and 1950s accumulated to challenge that view. Work by Donald Hebb (1949) suggested that some kinds of functions, such as visual perceptions, were constructed over time by the building of cell assemblies— connections of sets of cells in the brain. During the 1950s and 1960s, Nobel Prize– winning neurophysiologists David Hubel and Torsten Wiesel discovered that specific cells in the visual cortex of cats were in fact specialized to respond to specific kinds of stimuli (such as orientation of lines and particular shapes). Equally important, Hubel and Wiesel (1959) demonstrated the importance of early experience on nervous system development. Kittens that were experimentally restricted to an environment with only horizontal lines would fail to develop the ability to perceive vertical lines. This work suggested that at least some functions are localized in the brain (H. Gardner, 1985). There is yet one more thread to the cognitive revolution, also dating from about World War II: the development of computers and artificially intelligent systems. In 1936, a mathematician named Alan Turing wrote an article describing “universal machines,” mathematical entities that are simple in nature but capable in principle of solving logical or mathematical problems. This article ultimately led to what some psychologists and computer scientists call the computer metaphor: the comparison of people’s cognitive activities to an operating computer. Just as computers need to be fed data, people need to acquire information. Both computers and people often store information and therefore must have structures and processes that allow such storage. People and computers often need to recode information—that is, to change the way it is recorded or presented. People and computers must also manipulate information in other ways—transform it, for example, by rearranging it, adding to or subtracting from it, deducing from it, and so on. Computer scientists working on the problem of artificial intelligence study how to program computers to solve the same kinds of problems humans can and to try to determine whether computers can use the same methods that people apparently use to solve such problems. During the 1970s, researchers in different fields started to notice they were investigating common questions: the nature of mind and of cognition; how information is acquired, processed, stored, and transmitted; and how knowledge is represented. Scholars from fields such as cognitive psychology, computer science, philosophy, linguistics, neuroscience, and anthropology, recognizing their mutual interests, came together to found an interdisciplinary field known as cognitive science. H. Gardner (1985) even gave this field a birth date—September 11, 1956— when several founders of the field attended a symposium on information theory at the Massachusetts Institute of Technology. H. Gardner (1985) pointed out that the field of cognitive science rests on certain common assumptions. Most important among these is the assumption that cognition must be analyzed at what is called the level of representation. This means cognitive scientists agree that cognitive theories incorporate such constructs as symbols, rules, images, and ideas—in Gardner’s words, “the stuff... found between input and output” (p. 38). Thus, cognitive scientists focus on representations of information rather than on how nerve cells in the brain work or on historical or cultural influences. General Points Each school of psychology described so far has left a visible legacy to modern cognitive psychology. Structuralists asked the question, what are the elementary units and processes of the mind? Functionalists reminded psychologists to focus on the larger purposes and contexts that cognitive processes serve. Behaviorists challenged psychologists to develop testable hypotheses and to avoid unresolvable debates. Gestalt psychologists pointed out that an understanding of individual units would not automatically lead to an understanding of whole processes and systems. Galton demonstrated that individuals can differ in their cognitive processing. Developments in engineering, computer science, linguistics, and neuroscience have uncovered processes by which information can be efficiently represented, stored, and transformed, providing analogies and metaphors for cognitive psychologists to use in constructing and testing models of cognition. As we take up particular topics, we will see more of how cognitive psychology’s different roots have shaped the field. Keep in mind that cognitive psychology shares in the discoveries made in other fields, just as other fields share in the discoveries made by cognitive psychology. This sharing and borrowing of research methods, terminology, and analyses gives many investigators a sense of common purpose. It also all but requires cognitive psychologists to keep abreast of new developments in fields related to cognition. RESEARCH METHODS IN COGNITIVE PSYCHOLOGY Throughout this book, we will review different empirical studies of cognition. Before we plunge into those studies, however, we will look at some of the different kinds of studies that cognitive psychologists conduct. The following descriptions do not exhaust all the studies a cognitive psychologist could conduct but should acquaint you with the major methodological approaches to cognitive psychology. Experiments and Quasi-Experiments The most frequently adopted approach to cognitive investigations is the psychological experiment. A true experiment is one in which the experimenter manipulates one or more independent variables (the experimental conditions) and observes how the recorded measures (dependent variables) change as a result. A major distinction between experiments and observational methods (which we will examine in just a bit) is the investigator’s degree of experimental control. Having experimental control means the experimenter can assign participants to different experimental conditions so as to minimize preexisting differences between them. Ideally, the experimenter can control all variables that might affect the performance of research participants other than the variables on which the study is focusing. For example, an experiment in cognitive psychology might proceed as follows. An experimenter recruits a number of people for a study of memory, randomly assigns them to one of two groups, and presents each group with exactly the same stimuli, using exactly the same procedures and settings and varying only the instructions (the independent variable) for the two groups of participants. The experimenter then observes the overall performance of the participants on a later memory test (the dependent variable). This example illustrates a between-subjects design, where different experimental participants are assigned to different experimental conditions and the researcher looks for differences in performance between the two groups. In contrast, a within- subjects design exposes the same experimental participants to more than one condition. For example, participants might perform several memory tasks but receive a different set of instructions for each task. The investigator then compares the performance of the participants in the first condition with the performance of the same participants in another condition. Some independent variables preclude random assignment (i.e., having the experimenter assign a research participant to a particular condition in an experiment). For example, experimenters cannot reassign participants to a different gender, ethnicity, age, or educational background. Studies that appear in other ways to be experiments but that have one or more of these factors as independent variables (or fail to be true experiments in other ways) are called quasi-experiments (D. T. Campbell & Stanley, 1963). Scientists value experiments and quasi-experiments because they enable researchers to isolate causal factors and make better-supported claims about causality than is possible using observational methods alone. However, many experiments fail to fully capture real-world phenomena in the experimental task or research design. The laboratory setting or the artificiality or formality of the task may prevent research participants from behaving normally, for example. Furthermore, the kinds of tasks amenable to experimental study might not be those most important or most common in everyday life. As a result, experimenters sometimes risk studying phenomena that relate only weakly to people’s real-world experience. Naturalistic Observation As the name suggests, naturalistic observation consists of an observer watching people in familiar everyday contexts going about their cognitive business. For example, an investigator might watch as people try to figure out how to work a new smartphone. Ideally, the observer remains as unobtrusive as possible so as to disrupt or alter the behaviors being observed as little as possible. In this example, the investigator might stand nearby and surreptitiously note what people who use the smartphone do and say. Being unobtrusive is much harder than it might sound. The observer needs to make sure the people being observed are comfortable and do not feel as though they are “under a microscope.” At the same time, the observer wants to avoid causing the people being observed to “perform” for the observer. In any case, the observer can hardly fully assess his or her own effects on the observation. After all, how can one know what people would have done had they not been observed? Photo 1.2: Recording people engaged in everyday behaviors in typical settings uses the naturalistic observation method of investigation. Photo by Kathleen Galotti Observational studies have the advantage that the things studied occur in the real world and not just in an experimental laboratory. Psychologists call this property ecological validity. Furthermore, the observer has a chance to see just how cognitive processes work in natural settings: how flexible they are, how they are affected by environmental changes, and how rich and complex actual behavior is. Naturalistic observation is relatively easy to do, doesn’t typically require a lot of resources to carry out, and doesn’t require other people to formally volunteer for study. The disadvantage of naturalistic observation is a lack of experimental control. The observer has no means of isolating the causes of different behaviors or reactions. All the observer can do is collect observations and try to infer relationships among them. However plausible different hypotheses may seem, the observer has no way to verify them. Some psychologists believe that naturalistic observation is most appropriately used to identify problems, issues, or phenomena of interest to then be investigated with other research methods. A second problem, which all scientists face, is that an observer’s recordings are only as good as her or his initial plan for what is important to record. The settings and people the observer chooses to observe, the behaviors and reactions she or he chooses to record, the manner of recording, and the duration and frequency of observation all influence the results and conclusions the observer can later draw. Moreover, whatever biases the observer brings to the study (and, as we will see in Chapter 12, all of us are subject to a large number of biases) limit and possibly distort the recordings made. Controlled Observation and Clinical Interviews As the term controlled observation suggests, this method gives researchers some degree of influence over the setting in which observations are conducted. Investigators using this research method try to standardize the setting for all participants, in many cases manipulating specific conditions to see how participants will be affected. In the smartphone example, for instance, the investigator might arrange for the smartphone to display different instructions to different people. The study would still be observational (because the researcher would not control who used the machine or when), but the researcher would be trying to channel the observed behavior in certain ways. In clinical interviews, the investigator tries to channel the process even more. The investigator begins by asking each participant a series of open-ended questions. The interviewer might ask the participant to think about a problem and describe his or her approaches to it. With the clinical interview method, however, instead of allowing the participant to respond freely, the interviewer follows up with another set of questions. Depending on the participant’s responses, the interviewer may pursue one or another of many possible lines of questioning, trying to follow the participant’s own thinking and experience while focusing on specific issues or questions. Introspection We have already seen one special kind of observation dating back to the laboratory of Wundt. In the technique of introspection, the observer observes his or her own mental processes. For example, participants might be asked to solve complicated arithmetic problems without paper or pencil and to “think aloud” as they do so. Introspection has all the benefits and drawbacks of other observational studies plus a few more. One additional benefit is that observing one’s own reactions and behavior may give one better insight into an experience and the factors that influenced it, thereby yielding a richer, more complete picture than an outsider could observe. But observing yourself is a double-edged sword. Although perhaps a better observer in some ways than an outsider, you may also be more biased in regard to your own cognition. People observing their own mental processes may be more concerned with their level of performance and may be motivated to subtly and unconsciously distort their observations. They may try to make their mental processes appear more organized, logical, thorough, and so forth than they actually are, and they may be unwilling to admit when their cognitive processes seem flawed or random. Moreover, with some cognitive tasks (especially demanding ones), observers may have few resources left with which to observe and record while they work on the task. Investigations of Neural Underpinnings Much work in cognitive neuroscience involves examining people’s brains. Before the second half of the 20th century, this kind of examination could be conducted only during an autopsy after a patient died. However, since the 1970s, various techniques of brain imaging, the construction of pictures of the anatomy and functioning of intact brains, have been developed. We will discuss many of these techniques in Chapter 2. General Points This brief outline of different research designs barely scratches the surface of all the important things we could look at. There are a few general points to note, however. First, cognitive psychologists use a variety of approaches to study cognitive phenomena. In part, these approaches reflect philosophical differences among psychologists over what is important to study and how trade-offs should be made between certain drawbacks and benefits. In part, they reflect the intellectual framework or paradigms (examples to be discussed very shortly) within which researchers work. They may also reflect how amenable different areas of cognition are to different research approaches. Second, no research design is perfect. Each has certain potential benefits and limitations that researchers must weigh in designing studies. Students, professors, and other researchers must also carefully think, both critically and appreciatively, about how the research design answers the research question posed. I hope you’ll keep these thoughts in mind as you discover in the rest of this book examples of the wide variety of research studies that cognitive psychologists have carried out. PARADIGMS OF COGNITIVE PSYCHOLOGY Having looked at cognitive psychology’s historical roots and research methods, we can now focus on modern cognitive psychology. In this section, we will examine the four major paradigms that cognitive psychologists use in planning and executing their research. First of all, what is a paradigm? The word has several related meanings, but you can think of it as a body of knowledge structured according to what its proponents consider important and what they do not. Paradigms include the assumptions investigators make in studying a phenomenon. Paradigms also specify what kinds of experimental methods and measures are appropriate for an investigation. Thus, paradigms are intellectual frameworks that guide investigators in studying and understanding phenomena. In learning about each paradigm, ask yourself the following questions. What assumptions underlie the paradigm? What questions or issues does the paradigm emphasize? What analogies (such as the analogy between the computer and the mind) does the paradigm use? What research methods and measures does the paradigm favor? The Information-Processing Approach The information-processing approach dominated cognitive psychology during the 1960s and 1970s and remains influential today (Atkinson & Shiffrin, 1968). As its name implies, the information-processing approach draws an analogy between human cognition and computerized processing of information. Central to the information-processing approach is the idea that cognition can be thought of as information (what we see, hear, read about, and think about) passing through a system (us or, more specifically, our minds). Researchers following an information-processing approach often assume that information is processed (received, stored, recoded, transformed, retrieved, and transmitted) in stages and that it is stored in specific places while being processed. One goal within this framework, then, is to determine what these stages and storage places are and how they work. Other assumptions underlie the information-processing approach as well. One is that people’s cognitive abilities can be thought of as “systems” of interrelated capacities. We know different individuals have different cognitive capacities—different attention spans, memory capacities, and language skills, to name a few. Information- processing theorists try to find the relationships between these capacities to explain how individuals go about performing specific cognitive tasks. In accordance with the computer metaphor, information-processing theorists assume that people, like computers, are general-purpose symbol manipulators. In other words, people, like computers, can perform astonishing cognitive feats by applying only a few mental operations to symbols (such as letters, numbers, propositions, and scenes). Information is then stored symbolically, and the way it is coded and stored greatly affects how easy it is to use it later (as when we want to recall information or manipulate it in some way). A general-purpose information-processing system is shown in Figure 1.2. Note the various memory stores where information is held for possible later use and the different processes that operate on the information at different points or that transfer it from store to store. Certain processes, such as detection and recognition, are used at the beginning of information processing; others, such as recoding and retrieval, have to do with memory storage; still others, such as reasoning and concept formation, have to do with putting information together in new ways. In this model, boxes represent stores and arrows represent processes (leading some to refer to information-processing models as “boxes-and-arrows” models of cognition). Altogether, information-processing models are depicted best by something computer scientists call flowcharts, which illustrate the sequential flow of information through a system. Figure 1.2: A typical information-processing model. The information-processing tradition is rooted in structuralism in that its followers attempt to identify the basic capacities and processes we use in cognition. The computer metaphor used in this approach also shows indebtedness to the fields of engineering and communications. Psychologists working in the information- processing tradition are interested in relating individual and developmental differences to differences in basic capacities and processes. Typically, information- processing psychologists use experimental and quasi-experimental techniques in their investigations. The Connectionist Approach Early in the 1980s, researchers from a variety of disciplines began to explore alternatives to the information-processing approach that could explain cognition. The framework they established is known as connectionism (sometimes also called parallel-distributed processing, or PDP). Its name is derived from models depicting cognition as a network of connections among simple (and usually numerous) processing units (McClelland, 1988). Because these units are sometimes compared to neurons, the cells that transmit electrical impulses and underlie all sensation and muscle movement, connectionist models are sometimes called neural networks (technically speaking, there are distinctions between connectionist and neural network models, but we will not review them here). Each unit is connected to other units in a large network. Each unit has some level of activation at any particular moment in time. The exact level of activation depends on the input to that unit from both the environment and the other units to which it is connected. Connections between two units have weights, which can be positive or negative. A positively weighted connection causes one unit to excite, or raise the level of activation of, units to which it is connected; a negatively weighted connection has the opposite effect, inhibiting or lowering the activation of connected units. Figure 1.3 depicts a (very partial) connectionist representation of the dogs that showed up to my training class the other night. To reduce complexity, it shows only positively weighted connections. To “unpack” this figure, look at the node in the center circle labeled “A.” This node doesn’t have particular meaning by itself, just as, for example, any individual neuron in your body doesn’t have any one particular function. But if node A were to become activated, that activation would spread to all the other nodes with which it is connected—the “Kathie” node in the “Owner” group, the “Nimo” node in the “Name” group, the “Bernese Mountain Dog” node in the “Breed” group, the “Dog” node in the “Sex” group, and the “Chicken” node in the “Favorite Treat Flavor” group of nodes. The “representation” of Nimo in this network is the simultaneous activation of these nodes. One major difference between the information-processing and connectionist approaches is the manner in which cognitive processes are assumed to occur. In information-processing models, cognition is typically assumed to occur serially—that is, in discrete stages (first one process occurs, which feeds information into the next process, which feeds information into the next process, etc.). In contrast, most (but not all) connectionist models assume that cognitive processes occur in parallel, many at the same time. The connectionist framework allows for a wide variety of models, which can vary in the number of units hypothesized, number and pattern of connections among units, and connection of units to the environment. All connectionist models share the assumption, however, that there is no need to hypothesize a central processor that directs the flow of information from one process or storage area to another. Instead, different patterns of activation account for the various cognitive processes (Dawson, 1998). Knowledge is not stored in various storehouses (such as the boxes depicted in Figure 1.2) but rather is stored within connections between units. Learning occurs when new connective patterns are established that change the weights of connections between units. Figure 1.3: A depiction of a connectionist model. Feldman and Ballard (1982), in an early description of connectionism, argued that this approach is more consistent with the way the brain functions than an information-processing approach. The brain, they argued, is made up of many neurons connected to one another in various complex ways. The authors asserted that the fundamental premise of connectionism is that individual neurons do not transmit large amounts of symbolic information. Instead they compute by being appropriately connected to large numbers of similar units. This is in sharp contrast to the conventional computer model of intelligence prevalent in computer science and cognitive psychology. (p. 208) Rumelhart (1989) put the issue more simply: “Connectionism seeks to replace the computer metaphor of the information-processing framework with a brain metaphor” (p. 134). Like the information-processing approach, connectionism draws from structuralism an interest in the elements of cognitive functioning. However, whereas information processors look to computer science, connectionists look to cognitive neuropsychology (the study of people with damaged or otherwise unusual brain structures) and cognitive neuroscience for information to help them construct their theories and models. Information-processing accounts of cognition try to provide explanations at a more abstract symbolic level than do connectionist accounts. Connectionist models are more concerned with the “subsymbolic” level: how cognitive processes actually could be carried out by a brain. Connectionism, being much newer than information processing, is just beginning to map out explanations for individual and developmental differences. Most connectionist work seeks to replicate the findings of experimental and quasi-experimental research using computer programs based on a neural network model. The Evolutionary Approach Some of our most remarkable cognitive abilities and achievements are ones we typically take for granted. Two that come immediately to mind are the ability to perceive three-dimensional objects correctly and the ability to understand and produce language. These abilities may seem rather trivial and mundane—after all, a 3-year-old can do quite a bit of both. However, researchers in the field of artificial intelligence quickly found that it is not easy to program computers to carry out even rudimentary versions of these tasks (Winston, 1992). So why can young children do these tasks? In fact, how can a wide range of people, even people who don’t seem particularly gifted intellectually, carry them out with seemingly little effort? Some psychologists s

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