An Invitation to Cognitive Science provides a point of entry into the vast realm of cognitive science by treating in depth examples of issues and theories from many subfields. The first three volumes of the series cover Language, Visual Cognition, and Thinking. Volume 4, Methods, Models, and Conceptual Issues, expands the series in new directions. The chapters span many areas of cognitive science--including artificial intelligence, neural network models, animal cognition, signal detection theory, computational models, reaction-time methods, and cognitive neuroscience. The volume also offers introductions to several general methods and theoretical approaches for analyzing the mind, and shows how some of these approaches are applied in the development of quantitative models. Rather than general and inevitably superficial surveys of areas, the contributors present "case studies"--detailed accounts of one or two achievements within an area. The goal is to tell a good story, challenging the reader to embark on an intellectual adventure.
1.1 Introduction
1.1.1 Behavior Suggestive of a Cognitive Map
1.1.2 Maps and Navigational Computation
1.1.3 Representational (Symbol-Processing) and Nonrepresentational (Subsymbolic) Theories of Mind
1.1.4 Symbol-Processing Systems
1.1.5 Neural Nets
1.1.6 Difficulty of Reconciling Symbol Processing with Our Current Understanding of Neurobiology
1.1.7 Mental Representations
1.1.8 Summary
1.2 Breaking the Question Down
1.2.1 Can Insects Determine and Remember Angles?
1.2.1.1 The Dance of the Honeybee Symbolizes an Angle
1.2.1.2 Bees Record Landmark Angles
1.2.1.3 Bees Record the Compass Directions of Landmarks
1.2.2 Can Insects Determine and Remember Distances?
1.2.2.1 The Honeybee's Dance Symbolizes Distance
1.2.2.2 Ants Know the Distance Home
1.2.2.3 Locusts and Bees Compute Distance by Triangulation
1.2.3 Can Insects Do Dead Reckoning?
1.2.4 How Do Insects Hold a Course?
1.2.4.1 The Sun Compass Mechanism
1.2.4.2 Clock and Ephemeris: Two Brain-World Isomorphisms
1.2.5 How Do Insects Recognize Landmarks?
1.2.5.1 Terrain Matching
1.2.5.2 View Matching
1.2.6 Do Insects Have an Integrated Map?
1.2.6.1 Bees Sometimes Compute Novel Courses
1.2.6.2 Novel Terrain-Based Course Holding
1.2.6.3 Homing from Release Sites
1.3 Concluding Observations
Suggestions for Further Reading
Problems and Questions for Further Thought
References
About the Author
The Mental Representation of Time: Uncovering a Biological Clock
Editors' Introduction
2.1 Introduction
2.1.1 History of Animal Timing
2.1.2 Importance of Animal Timing
2.2 Distinctness of the Clock
2.2.1 Independence of Two Measures of Behavior
2.2.2 Other Evidence for Distinctness
2.2.3 Importance of Distinctness
2.3 Other Properties of the Clock
2.3.1 The Clock Can Be Stopped Temporarily
2.3.2 The Same Clock Times Light and Sound
2.3.3 The Clock Is Path-Independent
2.3.4 The Clock Times Multiple Intervals by Varying the End Point
2.3.5 The Clock Times Selectively
2.3.6 The Clock Has a Linear Scale
2.3.7 The Clock Depends on an Internal Pacemaker
2.3.8 The Clock Is Precise
2.4 Use of Animals to Study Cognition: Strengths and Weaknesses
2.5 What Have We Learned?
2.5.1 Substance
2.5.2 Method
Suggestions for Further Reading
Problems
Questions for Further Thought
References
About the Author
The Evolution of Cognition: Questions We Will Never Answer
Editors' Introduction
3.1 An Outline of the Argument
3.1.1 An Example from Biology
3.1.2 The Application to Human Cognition
3.2 Traits in Evolution
3.3 History, Form, and Function
3.3.1 Evolutionary Description
3.3.2 Functional Changes
3.3.3 Evolutionary Constraints
3.4 Problems of Reconstruction
3.4.1 Reconstruction of Relationships
3.4.2 Reconstruction of Function and Changes
3.5 Specific Problems in the Evolution of Human Cognition
3.5.1 Human Relations and Ancestors
3.5.2 Ancestors
3.5.3 Homology and Analogy
3.5.3.1 Linguistic Ability
3.6 Function and Selection
3.7 A Final Note to the Reader
Suggestions for Further Reading
Questions for Further Thought
References
About the Author
Consciousness and the Mind: Contributions from Philosophy, Neuroscience, and Psychology
Editors' Introduction
4.1 What Is Consciousness?
4.1.1 Cartesian Dualism
4.1.2 Parallelism
4.1.3 Epiphenomenalism
4.1.4 Constructive Naturalism: An Alternative to Dualism and Materialism
4.2 The Natural Method
4.2.1 Neural Correlates of Subjective Experience: Animal Experiments
4.2.2 Splitting Auditory Attention
4.2.3 Complex Learning without Consciously Accessible Memories
4.3 Four Claims about Consciousness
4.4 Experiential Sensitivity and Informational Sensitivity
4.5 Conscious Inessentialism and the Epiphenomenalist Suspicion
4.6 The Default Assumption against Epiphenomenalism
4.7 An Experiment in Epiphenomenalism
4.8 Further Evidence for a Function for Consciousness
4.8.1 The Role of Consciousness in Skilled Performance
4.8.2 Brain Damage and Defects of Consciousness
4.8.3 The Case against Epiphenomenalism
4.9 Conclusion
Suggestions for Further Reading
Questions for Further Thought
References
About the Authors
Cognitive Algorithms: Questions of Representation and Computation in Building a Theory
Editors' Introduction
5.1 Algorithms, Architectures, and Representations
5.2 A Case Study in Constraint Satisfaction
5.3 Huffman/Clowes Line Labeling
5.4 Varieties of Algorithms
5.5 Waltz/Mackworth Constraint Propagation Algorithm
5.6 Enlarging the Set of Junction Labels
5.7 Parallelizing the Algorithm
5.8 How Many Global Interpretations?
5.9 NP-completeness and Its Implications
5.10 Concluding Observations
Suggestions for Further Reading
Problems and Questions for Further Thought
References
About the Author
A Gentle Introduction to Soar: An Architecture for Human Cognition
Editors' Introduction
6.1 Introduction
6.2 The Idea of Architecture
6.3 What Cognitive Behaviors Have in Common
6.4 Behavior as Movement through Problem Spaces
6.5 Tying the Content to the Architecture
6.6 Memory, Perception, Action, and Cognition
6.7 Detecting a Lack of Knowledge
6.8 Learning
6.9 Putting It All Together: A Soar Model of Joe Rookie
6.10 Stepping Back: The Soar Architecture in Review
6.11 From Architecture to Unified Theories of Cognition
Suggestions for Further Reading
Problems
Questions for Further Thought
References
About the Authors
Learning Arithmetic with a Neural Network: Seven Times Seven Is About Fifty
Editors' Introduction
7.1 Arithmetic Learning
7.2 Neural Networks
7.3 Data Representation
7.4 Arithmetic and Associative Interference
7.5 Doing Mathematics
7.6 Putting the Pieces Together
7.7 A Neural Network Model for Multiplication
7.8 Flexibility: Doing More Than You Learned
Suggestions for Further Reading
Problems
References
About the Author
Models for Reading Letters and Words
Editors' Introduction
8.1 Introduction
8.2 Pattern Recognition
8.2.1 Domains of Pattern Recognition
8.2.2 Computational Models
8.3 Approaches to Pattern Recognition
8.3.1 Template Matching
8.3.2 Feature Analysis
8.4 Letter Recognition
8.4.1 Fuzzy Letters and Continuous Rating Judgments
8.4.2 Discrete Model
8.4.3 Continuous Model
8.4.4 Experimental Test
8.5 Multifactor Experiments
8.6 Models of Recognition
8.6.1 Template Model
8.6.2 Discrete Feature Model
8.6.2.1 Elaborating the Presumed Operations
8.6.2.2 Free Parameters and Their Estimation
8.6.3 Fuzzy Logical Model of Perception
8.6.3.1 Benchmark Measures of Goodness of Fit
8.7 Context Effects in Pattern Recognition
8.7.1 Test of the FLMP
8.7.2 Sentence Context in Word Recognition
8.8 Artificial Neural Network Models
8.8.1 Connectionist Model of Perception
8.8.2 Interactive Activation Model
8.8.3 IAM with Input Noise and Best-One-Wins Decision Rule
8.9 Justification of Computational Modeling
8.9.1 Difficulties in Psychological Inquiry
8.9.2 Implications for Psychological Inquiry
8.10 Metatheoretical Issues and the Computational Approach
8.10.1 Identifiability Issue
8.10.2 Optimality of Pattern Recognition
Note
Suggestions for Further Reading
Problems and Questions for Further Thought
References
About the Author
Inferring Mental Operations from Reaction-Time Data: How We Compare Objects
Editors' Introduction
9.1 Introduction
9.1.1 A Three-Attribute Stimulus Set
9.1.2 Major Issues in Comparing Multiattribute Objects
9.1.2.1 Holistic versus Feature Comparison
9.1.2.2 Sequential versus Parallel Tests
9.1.3 Some Typical Data
9.1.3.1 Data from Geometric Patterns
9.1.3.2. Data from Letter Strings
9.1.4 Plan of the Chapter
9.1.5 Theories, Models, and Data
9.2 Reaction Time to Judge "Different"
9.2.1 Sequential Tests: Defining Properties
9.2.2 Sequential Tests: Prediction of the Number of Tests
9.2.2.1 Effect of Number of Mismatching Features on Number of Tests
9.2.2.2 Effect of Number of Relevant Features on Number of Tests
9.2.2.3 A General Statement of the Two Effects on Number of Tests for "Different" Responses
9.2.3 Sequential Tests: Relation between the Number of Tests and Mean Reaction-Time
9.2.3.1 The Contribution of Residual Operations to Reaction Time
9.2.3.2 Implications of Four Constraints on Test Durations
9.2.4 Sequential Tests: Application to Letter-String Data
9.2.4.1 The Fully Constrained Model
9.2.4.2. Relaxing Constraint 1: Allowing Variable Test Durations
9.2.4.3 Relaxing Constraint 2: Allowing Unequal Residual Durations for "Same" and "Different" Respon...
9.2.4.4. Relaxing Constraint 3: Allowing Unequal Durations of Matches and Mismatches
9.2.4.5. Relaxing Constraint 4: Allowing Unequal Mean Test-Durations for Different Attributes
9.2.4.6 Implications of a Nonballistic Response Process
9.2.4.7 Status of the Squential-Test Model
9.2.5 Parallel Tests: Defining Properties
9.2.5.1 Statistical Facilitation and the Effects of Process Variability
9.2.6 Parallel Tests: Effect of Number of Relevant Features on Mean Reaction-Time
9.2.7 Parallel Tests: Effect of Number of Mismatching Features on Mean Reaction-Time
9.2.7.1 Parallel Variant 1: Equal Fixed Test-Durations
9.2.7.2 Parallel Variant 2: Unequal Mean Test-Durations with Limited Variability
9.2.7.3 Parallel Variant 3: Variable Test-Durations with Unconstrained Means
9.2.7.4 Parallel Variant 4: Variable Test-Durations with Equal Means and Identical Distributions
9.2.7.5 Status of the Parallel- Test Model
9.2.8 Sequential versus Parallel Tests: Inferences Based on Differential Mismatch-Durations
9.2.9 Sequential versus Parallel Tests: Conclusions from "Different" Responses
9.3 Reaction Time to Judge "Same"
9.3.1 Difficulties for Sequential Tests
9.3.2 Parallel Tests Revisited
9.3.2.1 Parallel Variant 1: Equal Fixed Test-Durations
9.3.2.2 Parallel Variant 4: Variable Test-Durations with Equal Means and Identical Distributions
9.3.2.3 Parallel Variant 2: Unequal Mean Test-Durations with Limited Variability
9.3.2.4 Parallel Variant 3: Variable Test-Durations with Unconstrained Means
9.4 Two-Process Mechanisms and Holistic Stimulus-Comparison
9.4.1 Separate Mechanisms for "Same" and "Different" Responses, and Their Temporal Arrangement
9.4.2 The Nature of the Sameness-Detection Process
9.5 Concluding Remarks
Appendix 1: Error Rates and the Interpretation of Reaction-Time Data
Appendix 2: Donders' Subtraction Method and Modern Variants
Glossary
Suggestions for Further Reading
Questions For Further Thought
Notes
References
Models of Visual Search: Finding a Face in the Crowd
Editors' Introduction
10.1 Models and Phenomena
10.1.1 Phenomena
10.1.2 What Is a Model?
10.1.3 A Simple Model of Visual Search
10.2 A Quantitative Model for Visual Search
10.2.1 The Linear Model
10.2.2 Model Parameters and Prediction Error
10.2.3 The Error Surface and Error Minimization
10.2.4 Interpretation of Model Parameters
10.3 Attention and Preattention in Visual Search
10.3.1 Feature Conjunction Searches
10.3.2 Feature Searches
10.3.3 Feature Integration Theory
10.3.4 Ambiguities and Alternative Models
10.3.5 Selective Search
10.3.5.1 Illustrations
10.3.5.2 Predictions for Unbalanced Displays
10.3.5.3 Unbalanced Display Experiments
10.3.6 Selective Search Models
10.3.6.1 Consistent and Inconsistent Selective Search
10.3.6.2 Examples of Evaluating Models Quantitatively
10.3.6.3 Goodness-of-Fit Measures
10.3.7 Guided Search Model
10.3.7.1 Model Mechanisms
10.3.7.2 Predicting Variability and Errors
10.3.7.3 Simulation Parameters
10.3.7.4 Guided Search Predictions
10.3.7.5 Selective Search Models and Triple-Feature Conjunctions
10.3.7.6 Evaluation of Simulation Models
10.3.8 Summary
10.4 Representations in Modeling
10.4.1 Representations in Visual Search
10.4.2 Representation and Process in Other Domains
10.4.3 Some Examples
10.4.3.1 Ordered Sequences
10.4.3.2 Representing Items and Groups
10.4.4 Representation and Process
10.5 Models as Tools for Theory Development
Suggestions for Further Reading
Problems and Questions for Further Thought
References
About the Author
Skill Acquisition and Plans for Actions: Learning to Write with Your Other Hand
Editors' Introduction
11.1 Exercising Handwriting to Introduce Our Topic
11.1.1 The Puzzle of Shape Similarity
11.1.2 Improving Handwriting with Practice
11.1.3 The Goal of This Chapter
11.2 Plans and Planning
11.2.1 Nature of Plans
11.3 Plans in Motor Behavior: Motor Programs
11.3.1 A Movement-Specific Conception of Motor Programs
11.3.2 Generalized Motor Programs
11.3.3 Effector-Independent, Generalized Motor Programs
11.3.4 Evidence for Effector Independence in Handwriting: Shape Similarity
11.3.4.1 A Problem Interpreting Shape Similarity
11.3.4.2 Other Types of Evidence
11.4 Hierarchical Representation of Plans
11.4.1 From Reaching to Playing Shortstop: Development of Hierarchical Plans
11.4.2 Hierarchy in the Motor Programs for Handwriting
11.4.3 Strokes in Handwriting and the Analysis of Tangential Velocity
11.4.4 Hierarchical Structure of Motor Programs: Why Does it Matter?
11.5 Studying Motor Programs: Two Approaches
11.5.1 Planning
11.5.2 Transfer of Learning
11.6 Learning to Write with the Left Hand
11.6.1 Learning to Write with the Left Hand: The Number 1 Team
11.6.2 Learning to Write with the Left Hand: Two Moves to Mate
11.6.3 Learning to Write with the Left Hand: Three to Dine
11.7 Preparations: Identifying Generic Strokes, Characterizing Learning Curves, Methods and Design
11.7.1 Identifying Generic Strokes: G-Strokes
11.7.2 Power Law Learning Functions
11.7.3 A Final Design Issue
11.7.4 Notes on Method
11.7.5 Quantifying Writing Skill
11.7.5.1 Measuring Stroke Angle and Curvature
11.7.5.2 Using Variability to Track Consistency
11.7.5.3 A Composite Measure of Writing Fluency
11.8 Results: How One Righty Learned to Write Lefty
11.8.1 Overall Improvements with Practice
11.8.2 Changes in Fluency When the Words Being Written Change
11.8.2.1 From Word Set 1 to Word Set 2: Same Letters, New Words
11.8.2.2 From Word Set 2 to Word Set 3: Same Strokes, New Letters
11.8.3 Results for the Component Measures
11.8.3.1 The Problem of the Speed-Accuracy Trade-Off
11.8.3.2 Mean Stroke Duration
11.8.3.3 Mean Peak Velocity
11.8.3.4 Stroke Shape Variability
11.8.3.5 Stroke Duration Variability
11.8.4 The Development of Hierarchical Control at the Stroke Level
11.9 What Does It All Mean: Looking for the Writing on the Wall
11.9.1 Conclusions about Hand Independence and Context Specificity of the Hierarchical Representatio...
11.9.2 Summary
11.9.3 Looking beyond the Data
11.9.3.1 More Evidence about the Stroke Level
11.9.3.2 What Might Happen with Lots More Practice?
11.10 Comparing Your Results to Ours
Appendix A: Neurophysiological Levels of Motor Control
Appendix B: Could Extended Training Produce Hand-Dependent Representations at the Letter and/or Word...
Suggestions for Further Reading
Questions for Further Thought
Notes
References
About the Authors
Drawing Conclusions from Data: Statistical Methods for Coping with Uncertainty
Editors' Introduction
12.1 A Memory Experiment
12.2 Summarizing the Memory Experiment
12.3 Statistics and Cognitive Research
12.4 Statistics and the Researcher
12.5 Variability
12.6 Populations and Samples
12.7 Sampling Distributions and Confidence Intervals
12.8 Testing Theories
12.9 Decision Rules and Types of Error
12.10 Null Hypothesis Testing
12.11 Testing the Memory Experiment
12.12 The Analysis of Variance and Interactions
12.13 Counted Data and Chi-Square Tests
12.14 Association and Correlation
Suggestions for Further Reading
Problems and Questions for Further Thought
References
About the Author
Separating Discrimination and Decision in Detection, Recognition, and Matters of Life and...
Editors' Introduction
13.1 Introduction
13.1.1 Detection, Recognition, and Diagnostic Tasks
13.1.2 The Tasks' Two Component Processes: Discrimination and Decision
13.1.3 Diagnosing Breast Cancer by Mammography: A Case Study
13.1.3.1 Reading a Mammogram
13.1.3.2 Decomposing Discrimination and Decision Processes
13.1.4 Scope of This Chapter
13.2 Theory for Separating the Two Processes
13.2.1 Two-by-Two Table
13.2.1.1 Change in Discrimination Acuity
13.2.1.2 Change in the Decision Criterion
13.2.1.3 Separation of Two Processes
13.2.2 Statistical Decision and Signal Detection Theories
13.2.2.1 Assumptions About an Observation
13.2.2.2 Distributions of Observations
13.2.2.3 The Need for a Decision Criterion
13.2.2.4 Decision Criterion Measured by the Likelihood Ratio
13.2.2.5 Optimal Decision Criterion
13.2.2.6 A Traditional Measure of Acuity
13.3 The Relative Operating Characteristic
13.3.1 Obtaining an Empirical ROC
13.3.2 A Measure of the Decision Criterion
13.3.3 A Measure of Discrimination Acuity
13.3.4 Empirical Estimates of the Two Measures
13.4 Illustrations of Decomposition of Discrimination and Decision
13.4.1 Signal Detection during a Vigil
13.4.2 Recognition Memory
13.4.3 Polygraph Lie Detection
13.4.4 Information Retrieval
13.4.5 Weather Forecasting
13.5 Computational Example of Decomposition: A Dice Game
13.5.1 Distributions of Observations
13.5.2 The Optimal Decision Criterion for the Symmetrical Game
13.5.3 The Optimal Decision Criterion in General
13.5.4 The Likelihood Ratio
13.5.5 The Dice Game's ROC
13.5.6 The Game's Generality
13.6 Improving Discrimination Acuity by Combining Observations
13.7 Enhancing the Interpretation of Mammograms
13.7.1 Improving Discrimination Acuity
13.7.1.1 Determining Candidate Perceptual Features
13.7.1.2 Reducing the Set of Features and Designing the Reading Aid
13.7.1.3 Determining the Final List of Features and Their Weights
13.7.1.4 The Merging Aid
13.7.1.5 Experimental Test of the Effectiveness of the Aids
13.7.1.6 Clinical Significance of the Observed Enhancement
13.7.2 Optimizing the Decision Criterion
13.7.2.1 The Expected Value Optimum
13.7.2.2 The Optimal Criterion Defined by a Particular False Positive Proportion
13.7.2.3 Societal Factors in Setting a Criterion
13.7.3 Adapting the Enhancements to Medical Practice
13.8 Detecting Cracks in Airplane Wings: A Second Practical Example
13.8.1 Discrimination Acuity and the Decision Criterion
13.8.2 Positive Predictive Value
13.8.3 Data on the State of the Art in Materials Testing
13.8.4 Diffusion of the Concept of Decomposing Diagnostic Tasks
13.9 Some History
Suggestions for Further Reading
Problems
References
About the Author
Discovering Mental Processing Stages: The Method of Additive Factors
Editors' Introduction
14.1 Introduction
14.1.1 The Search for Modules
14.1.2 The Language of Factorial Experiments
14.1.3 Stages, Selective Modifiability, and Invariant Factor Effects
14.1.4 Plan of the Chapter
14.2 Additive and Interacting Factors
14.2.1 Examples from Naming a Digit
14.2.2 Examples from Searching Memory
14.3 Effects of Factors on a Shopping Trip: A Process with Observable Stages
14.3.1 Jim and Alice's Story
14.3.2 The Stages of Jim's Trip and the Factors That Influence Them
14.3.3 Details of the Effects of Factors on Stages of the Trip
14.3.4 Trip Duration Data: Additive Factors
14.3.5 Trip Duration Data: Interacting Factors
14.3.6 Conclusions from Jim and Alice's Story
14.4 Stage Models and the Effects of Factors on Mental Operations
14.4.1 Stages: The Modules of a Sequential Process
14.4.2 The Subtraction Method of Frans Cornelis Donders (1818-1889)
14.4.3 How Plausible Is It for Mental Processes to Be Sequential?
14.4.4 An Example: Stages in a Choice Reaction
14.4.4.1 Separate Stages for Stimulus Identification and Response Selection
14.4.4.2 Why Are Choices Slowed by Signal Uncertainty?
14.4.5 The Method of Additive Factors
14.5 Some Applications of the Additive-Factor Method
14.5.1 What Else Do We Lose When We Lose Sleep?
14.5.2 How We Prepare to Choose
14.5.3 Retrieving Item versus Context Information from Memory
14.5.4 When We Improve with Practice, What Improves?
14.5.5 Transfer of Information between the Cerebral Hemispheres
14.5.6 Task Switching and the Frontal Lobes: Inference about Stages from Localized Brain Damage
14.5.7 Stages in Speech Production: Real-Time Evidence of a Stage-Specific Effect
14.5.8 Rotation and Magnification of Mental Images
14.5.8.1 Combining Two Image Transformations
14.5.8.2 Mental Rotation in Detail: Application of the Subtraction Method
14.5.9 Doing Two Things at Once: Why Are We Slower?
14.5.9.1 The Overlapping-Tasks Paradigm
14.5.9.2 The Bottleneck/Deferred-Processing Model
14.5.9.3 Some Implications of the Model
14.5.9.4 Tests of Three Implications of the Model
14.5.10 How Do Repetition and Familiarity Speed Word Recognition?
14.5.11 Do Readers Recognize One Word at a Time?
14.5.11.1 Equivalent Substages
14.5.12 Are Characters Encoded in Parallel, Sequentially, or Both?
14.5.13 What Do We Search for When We Search Memory?
14.5.14 How Do We Benefit from Seeing Ahead When We Search a Display?
14.6 The Additive-Factor Method: Concluding Remarks
14.6.1 Processing Stages and Brain Structures
14.6.2 Perspectives from Other Module-Finding Methods
14.6.2.1 Separate Measures
14.6.2.2 Composite Measures
14.6.3 What is "Additive-Factors Logic"?
14.6.4 Extending the Method beyond the Mean
14.6.5 Nonstage Architectures That Produce Additive Effects
14.6.5.1 Alternate Pathways
14.6.5.2 Overlapping Processes
14.6.6 Some Strengths and Limitations of the Method
14.6.7 Design Matters
15.6.7.1 Multiple Factors
14.6.7.2 Multiple Levels
14.6.8 Statistical Issues
14.6.9 The Importance of Ronald A. Fisher (1890-1962)
Suggestions for Further Reading
Notes
References
Brainwaves and Mental Processes: Electrical Evidence of Attention, Perception, and Intent...
Editors' Introduction
15.1 Event-Related Brain Potentials
15.2 The Electrophysiology of Attention
15.2.1 Attention and Information Processing
15.2.2 ERPs during Visual Perception
15.2.3 Selective Attention to Location and Color
15.2.4 ERP Signature of Visual-Spatial Selective Attention
15.2.5 ERP Signature of Attentional Selection Based on Color
15.2.6 The Temporal Organization of Attentional Selection
15.2.7 Conclusions
15.3 The Architecture of Cognition
15.3.1 Mental Chronometry
15.3.2 Transmission of Partial Information
15.3.2.1 Lateralized Readiness Potential
15.3.2.2 Choice-Reaction Go/Nogo Procedure
15.3.3 Experiment 1: Detecting Response Preparation Based on Partial Information
15.3.3.1 LRP on Nogo Trials
15.3.3.2 Alternative Hypotheses
15.3.4 Experiment 2: Further Examination of the LRP on Nogo Trials
15.3.4.1 Dissociating Stimulus and Response Side
15.3.4.2 Effects of the Go/Nogo Discrimination on the LRP
15.3.5 Inferring the Cognitive Architecture of a Simple Task
15.3.5.1 Alternative Cognitive Architectures
15.3.5.2 Cognitive Psychophysiology and Mental Chronometry
15.4 A Brief Tour of Cognitive Psychophysiology
15.4.1 Overview of ERPs
15.4.1.1 The Exogenous-Endogenous Continuum
15.4.1.2 Two Endogenous Components: P300 and N400
15.4.2 Other Measures
15.4.2.1 Cognitive Energetics: Autonomic Measures
15.4.2.2 Event-Related Magnetic Fields
15.4.2.3 Blood Flow and Metabolism: New Brain-Imaging Technologies
15.4.3 Role of Cognitive-Psychophysiological Measures
15.5 The Body as a Window on the Mind
15.5.1 Locating the Sources of ERPs
15.5.2 The Epiphenomenon Problem
15.5.3 A Two-Way Street
Suggestions for Further Reading
Questions for Further Thought
Notes
References
About the Author
Author Index
Subject Index
Saul Sternberg is Professor of Psychology at the University of Pennsylvania.
