Anatomy of the Mind

Dr. Ron Sun is Professor of Cognitive Sciences at Rensselaer Polytechnic Institute. A well-known cognitive scientist, Ron Sun explores the fundamental structures of the human mind. He aims for the synthesis of many intellectual ideas into a coherent model of the human mind. The goal is to come up with a cognitive architecture that captures a variety of psychological processes and provides unified explanations of a wide range of data and phenomena.

• Table of ContentsPrefaceChapter 1. What is A Cognitive Architecture?1.1. A Theory of the Mind and Beyond1.2. Why Computational Models/Theories?1.3. Questions about Computational Models/Theories1.4. Why a Computational Cognitive Architecture?1.5. Why CLARION?1.6. Why This Book?1.7. A few Fundamental Issues1.7.1. Ecological-Functional Perspective1.7.2. Modularity1.7.3. Multiplicity of Representation1.7.4. Dynamic Interaction1.8. Concluding RemarksChapter 2. Essential Structures of the Mind2.1. Essential Desiderata2.2. An Illustration of the Desiderata2.3. Justifying the Desiderata2.3.1. Implicit-Explicit Distinction and Synergistic Interaction2.3.2. Separation of the Implicit-Explicit and the Procedural-Declarative Distinction2.3.3. Bottom-up and Top-down Learning2.3.4. Motivational and Metacognitive Control2.4. Four Subsystems of CLARION2.4.1. Overview of the Subsystems2.4.2. The Action-Centered Subsystem2.4.3. The Non-Action-Centered Subsystem2.4.4. The Motivational Subsystem2.4.5. The Metacognitive Subsystem2.4.6. Parameters of the Subsystems2.5. Accounting for Synergy within the Subsystems of CLARION2.5.1. Accounting for Synergy within the ACS2.5.2. Accounting for Synergy within the NACS2.6. Concluding RemarksChapter 3. Subsystems, Modules, and Algorithms I: The Action-Centered and Non-Action-Centered Subsystems3.1. The Action-Centered Subsystem3.1.1. Background3.1.2. Representation3.1.2.1. Representation in the Top Level3.1.2.2. Representation in the Bottom Level3.1.2.3. Action Decision Making3.1.3. Learning3.1.3.1. Learning in the Bottom Level3.1.3.2. Learning in the Top Level3.1.4. Level Integration3.1.5. An Example3.2. The Non-Action-Centered Subsystem3.2.1. Background3.2.2. Representation3.2.2.1. Overall Algorithm3.2.2.2. Representation in the Top Level3.2.2.3. Representation in the Bottom Level3.2.2.4. Representation of Hierarchies3.2.3. Learning3.2.3.1. Learning in the Bottom Level3.2.3.2. Learning in the Top Level3.2.4. Memory retrieval3.2.5. An Example3.3. Knowledge Extraction, Assimilation, and Transfer3.3.1. Background3.3.2. Bottom-Up Learning in the ACS3.3.2.1. Rule Extraction and Refinement3.3.2.2. Independent Rule Learning3.3.2.3. Implications of Bottom-up Learning3.3.3. Top-down Learning and Assimilation in the ACS3.3.4. Transfer of Knowledge from the ACS to the NACS3.3.5. Knowledge Extraction in the NACS3.3.6. Transfer of Knowledge from the NACS to the ACS3.3.7. An Example3.3.7.1. Learning about "Knife"3.3.7.2. Learning about "Knife" within CLARION3.3.7.3. Learning More Complex Concepts in CLARION3.4. General Discussion3.4.1. More on the Two Levels3.4.2. More on the Two Learning Directions3.4.3. Controversies3.4.4. SummaryAppendixA.1. Response TimeA.1.1. Response Time of the ACSA.1.2. Response Time of the NACSA.2. Learning in MLP (Backpropagation) NetworksA.3. Learning in Auto-associative NetworksA.4. Representation of Conceptual HierarchiesChapter 4. Subsystems, Levels, and Algorithms II: The Motivational and Metacognitive Subsystems4.1. Introduction4.2. The Motivational Subsystem4.2.1. Essential Considerations4.2.2. Drives4.2.2.1. Primary Drives4.2.2.2. Secondary Drives4.2.2.3. Approach versus Avoidance Drives4.2.2.4. Drive Strength4.2.3. Goals4.2.4. Modules and Their Functions4.2.4.1. Initialization Module4.2.4.2. Preprocessing Module4.2.4.3. Drive Core Module4.2.4.4. Deficit Change Module4.3. The Metacognitive Subsystem4.3.1. Essential Considerations4.3.2. Modules and Their Functions4.3.2.1. Goal Module4.3.2.2. Reinforcement Module4.3.2.3. Processing Mode Module4.3.2.4. Input/output Filtering Modules4.3.2.5. Reasoning/learning Selection Modules4.3.2.6. Monitoring Buffer4.3.2.7. Other MCS Modules4.4. General Discussion4.4.1. Reactivity versus Motivational Control4.4.2. Scope of the MCS4.4.3. Need for the MCS4.4.4. Information Flows Involving the MS and the MCS4.4.5. Concluding RemarksAppendix: Additional Details of the MS and the MCSA.1. Change of Drive DeficitsA.2. Determining Avoidance versus Approach Drives, Goals, and BehaviorsA.3. Learning in the MSA.4. Learning in the MCSA.4.1. Learning Drive-Goal ConnectionsA.4.2. Learning New GoalsChapter 5. Simulating Procedural and Declarative Processes5.1. Modeling the Dynamic Process Control Task5.1.1. Background5.1.2. Task and Data5.1.3. Simulation Setup5.1.4. Simulation Results5.1.5. Discussion5.2. Modeling the Alphabetic Arithmetic Task5.2.1. Background5.2.2. Task and Data5.2.3. Top-down Simulation5.2.3.1. Simulation Setup5.2.3.2. Simulation Results5.2.4. Alternative Simulations5.2.5. Discussion5.3. Modeling the Categorical Inference Task5.3.1. Background5.3.2. Task and Data5.3.3. Simulation Setup5.3.4. Simulation Results5.3.5. Discussion5.4. Modeling Intuition in the Discovery Task5.4.1. Background5.4.2. Task and Data5.4.3. Simulation Setup5.4.4. Simulation Results5.4.5. Discussion5.5. Capturing Psychological "Laws"5.5.1. Uncertain Deductive Reasoning5.5.1.1. Uncertain Information5.5.1.2. Incomplete Information5.5.1.3. Similarity5.5.1.4. Inheritance5.5.1.5. Cancellation of Inheritance5.5.1.6. Mixed Rules and Similarities5.5.2. Reasoning with Heuristics5.5.2.1. Representativeness Heuristic5.5.2.2. Availability Heuristic5.5.2.3. Probability Matching5.5.3. Inductive Reasoning5.5.3.1. Similarity between the Premise and the Conclusion5.5.3.2. Multiple Premises5.5.3.3. Functional Attributes5.5.4. Other Psychological "Laws"5.5.5. Discussion of Psychological "Laws"5.6. General DiscussionChapter 6. Motivational and Metacognitive Simulations6.1. Modeling Metacognitive Judgment6.1.1. Background6.1.2. Task and Data6.1.3. Simulation Setup6.1.4. Simulation Results6.1.5. Discussion6.2. Modeling Metacognitive Inference6.2.1. Task and Data6.2.2. Simulation Setup6.2.3. Simulation Results6.2.4. Discussion6.3. Modeling Motivation-Cognition Interaction6.3.1. Background6.3.2. Task and Data6.3.3. Simulation Setup6.3.4. Simulation Results6.3.5. Discussion6.4. Modeling Human Personality6.4.1. Background6.4.2. Principles of Personality Within CLARION6.4.2.1. Principles and Justifications6.4.2.2. Explaining Personality within CLARION6.4.3. Simulations of Personality6.4.3.1. Simulation 16.4.3.2. Simulation 26.4.3.3. Simulation 36.4.4. Discussion6.5. Accounting for Human Moral Judgment6.5.1. Background6.5.2. Human Data6.5.2.1. Effects of Personal Physical Force6.5.2.2. Effects of Intention6.5.2.3. Effects of Cognitive Load6.5.3. Two Contrasting Views6.5.3.1. Details of Model 16.5.3.2. Details of Model 26.5.4. Discussion6.6. Accounting for Emotion6.6.1. Issues of Emotion6.6.2. Emotion and Motivation6.6.3. Emotion and the Implicit-Explicit Distinction6.6.4. Effects of Emotion6.6.5. Emotion Generation and Regulation6.6.6. Discussion6.7. General Discussion Chapter 7. Cognitive Social Simulation7.1. Introduction and Background7.2. Cognition and Survival7.2.1. Tribal Society Survival Task7.2.2. Simulation Setup7.2.3. Simulation Results and Analysis7.2.3.1. Effects of Social and Environmental Factors 7.2.3.2. Effects of Cognitive Factors7.2.4. Discussion7.3. Motivation and Survival7.3.1. Simulation Setup7.3.2. Simulation Results and Analysis7.3.2.1. Effects of Social and Environmental Factors7.3.2.2. Effects of Cognitive Factors7.3.2.3. Effects of Motivational Factors7.3.3. Discussion7.4. Organizational Decision Making7.4.1. Organizational Decision Task7.4.2. Simulations and Results7.4.2.1. Simulation I: Matching Human Data7.4.2.2. Simulation II: Extending Simulation Temporally7.4.2.3. Simulation III: Varying Cognitive Parameters7.4.2.4. Simulation IV: Introducing Individual Differences7.4.3. Discussion7.5. Academic Publishing7.5.1. Academic Science7.5.2. Simulation Setup7.5.3. Simulation Results and Analysis7.5.4. Discussion7.6. General Discussion7.6.1. Theoretical Issues in Cognitive Social Simulation7.6.2. Challenges7.6.3. Concluding RemarksChapter 8. Some Important Questions and Their Short Answers8.1. Theoretical Questions8.2. Computational Questions8.3. Biological ConnectionsChapter 9. General Discussions and Conclusions9.1. A Summary of the Cognitive Architecture9.2. A Discussion of the Methodologies9.3. Relations to Some Important Notions9.4. Relations to Some Existing Approaches9.5. Comparisons with Other Cognitive Architectures9.6. Future Directions9.6.1. Directions for Cognitive Social Simulation9.6.2. Other Directions for Cognitive Architectures9.6.3. Final Words on Future DirectionsReferences

"I know of no other book on human cognition that attempts to integrate and account for such a wide range of psychological phenomena from so many different research areas via a cognitive architecture. Remarkably, Clarion achieves this integration via a basic dual representation and a handful of accompanying dual constructs. Hence, I regard the book as an important contribution, not only to cognitive architectures, but to cognitive and psychological science ingeneral." --Cognitive Systems Research