Anatomy of Mind: Exploring Psychological Mechanisms and Processes with the Clarion Cognitive Architecture

ISBN : 9780199794553

Ron Sun
464 Pages
156 x 235 mm
Pub date
Mar 2016
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This book aims to understand human cognition and psychology through a comprehensive computational theory of the human mind, namely, a computational "cognitive architecture" (or more specifically, the CLARION cognitive architecture). The goal of this work is to develop a unified framework for understanding the human mind, and within the unified framework, to develop process-based, mechanistic explanations of a large variety of psychological phenomena. Specifically, the book first describes the essential CLARION framework and its cognitive-psychological justifications, then its computational instantiations, and finally its applications to capturing, simulating, and explaining various psychological phenomena and empirical data. The book shows how the models and simulations shed light on psychological mechanisms and processes through the lens of a unified framework. In fields ranging from cognitive science, to psychology, to artificial intelligence, and even to philosophy, researchers, graduate and undergraduate students, and practitioners of various kinds may have interest in topics covered by this book. The book may also be suitable for seminars or courses, at graduate or undergraduate levels, on cognitive architectures or cognitive modeling (i.e. computational psychology).


Table of Contents

Chapter 1. What is A Cognitive Architecture?
1.1. A Theory of the Mind and Beyond
1.2. Why Computational Models/Theories?
1.3. Questions about Computational Models/Theories
1.4. Why a Computational Cognitive Architecture?
1.5. Why CLARION?
1.6. Why This Book?
1.7. A few Fundamental Issues
1.7.1. Ecological-Functional Perspective
1.7.2. Modularity
1.7.3. Multiplicity of Representation
1.7.4. Dynamic Interaction
1.8. Concluding Remarks

Chapter 2. Essential Structures of the Mind
2.1. Essential Desiderata
2.2. An Illustration of the Desiderata
2.3. Justifying the Desiderata
2.3.1. Implicit-Explicit Dichotomy and Synergistic Interaction
2.3.2. Separation of the Implicit-Explicit and the Procedural-Declarative Distinction
2.3.3. Bottom-up and Top-down Learning
2.3.4. Motivational and Metacognitive Control
2.4. Four Subsystems of CLARION
2.4.1. Overview of the Subsystems
2.4.2. The Action-Centered Subsystem
2.4.3. The Non-Action-Centered Subsystem
2.4.4. The Motivational Subsystem
2.4.5. The Metacognitive Subsystem
2.4.6. Parameters of the Subsystems
2.5. Accounting for Synergy within the Subsystems of CLARION
2.5.1. Accounting for Synergy within the ACS
2.5.2. Accounting for Synergy within the NACS
2.6. Concluding Remarks

Chapter 3. Subsystems, Modules, and Algorithms I: The Action-Centered and Non-Action-Centered Subsystems
3.1. The Action-Centered Subsystem
3.1.1. Background
3.1.2. Representation Representation in the Top Level Representation in the Bottom Level Action Decision Making
3.1.3. Learning Learning in the Bottom Level Learning in the Top Level
3.1.4. Level Integration
3.1.5. An Example
3.2. The Non-Action-Centered Subsystem
3.2.1. Background
3.2.2. Representation Overall Algorithm Representation in the Top Level Representation in the Bottom Level Representation of Hierarchies
3.2.3. Learning Learning in the Bottom Level Learning in the Top Level
3.2.4. Memory retrieval
3.2.5. An Example
3.3. Knowledge Extraction, Assimilation, and Transfer
3.3.1. Background
3.3.2. Bottom-Up Learning in the ACS Rule Extraction and Refinement Independent Rule Learning Implications of Bottom-up Learning
3.3.3. Top-down Learning and Assimilation in the ACS
3.3.4. Transfer of Knowledge from the ACS to the NACS
3.3.5. Knowledge Extraction in the NACS
3.3.6. Transfer of Knowledge from the NACS to the ACS
3.3.7. An Example Learning about Knife Learning about Knife within CLARION Learning More Complex Concepts in CLARION
3.4. General Discussion
3.4.1. More on the Two Levels
3.4.2. More on the Two Learning Directions
3.4.3. Controversies
3.4.4. Summary
A.1. Response Time
A.1.1. Response Time of the ACS
A.1.2. Response Time of the NACS
A.2. Learning in MLP (Backpropagation) Networks
A.3. Learning in Auto-associative Networks
A.4. Representation of Conceptual Hierarchies

Chapter 4. Subsystems, Levels, and Algorithms II: The Motivational and Metacognitive Subsystems
4.1. Introduction
4.2. The Motivational Subsystem
4.2.1. Essential Considerations
4.2.2. Drives Primary Drives Secondary Drives Approach versus Avoidance Drives Drive Strength
4.2.3. Goals
4.2.4. Modules and Their Functions Initialization Module Preprocessing Module Drive Core Module Deficit Change Module
4.3. The Metacognitive Subsystem
4.3.1. Essential Considerations
4.3.2. Modules and Their Functions Goal Module Reinforcement Module Processing Mode Module Input/output Filtering Modules Reasoning/learning Selection Modules Monitoring Buffer Other MCS Modules
4.4. General Discussion
4.4.1. Reactivity versus Motivational Control
4.4.2. Scope of the MCS
4.4.3. Need for the MCS
4.4.4. Information Flows Involving the MS and the MCS
4.4.5. Concluding Remarks
Appendix: Additional Details of the MS and the MCS
A.1. Change of Drive Deficits
A.2. Determining Avoidance versus Approach Drives, Goals, and Behaviors
A.3. Learning in the MS
A.4. Learning in the MCS
A.4.1. Learning Drive-Goal Connections
A.4.2. Learning New Goals

Chapter 5. Simulating Procedural and Declarative Processes
5.1. Modeling the Dynamic Process Control Task
5.1.1. Background
5.1.2. Task and Data
5.1.3. Simulation Setup
5.1.4. Simulation Results
5.1.5. Discussion
5.2. Modeling the Alphabetic Arithmetic Task
5.2.1. Background
5.2.2. Task and Data
5.2.3. Top-down Simulation Simulation Setup Simulation Results
5.2.4. Alternative Simulations
5.2.5. Discussion
5.3. Modeling the Categorical Inference Task
5.3.1. Background
5.3.2. Task and Data
5.3.3. Simulation Setup
5.3.4. Simulation Results
5.3.5. Discussion
5.4. Modeling Intuition in the Discovery Task
5.4.1. Background
5.4.2. Task and Data
5.4.3. Simulation Setup
5.4.4. Simulation Results
5.4.5. Discussion
5.5. Capturing Psychological Laws
5.5.1. Uncertain Deductive Reasoning Uncertain Information Incomplete Information Similarity Inheritance Cancellation of Inheritance Mixed Rules and Similarities
5.5.2. Reasoning with Heuristics Representativeness Heuristic Availability Heuristic Probability Matching
5.5.3. Inductive Reasoning Similarity between the Premise and the Conclusion Multiple Premises Functional Attributes
5.5.4. Other Psychological Laws
5.5.5. Discussion of Psychological Laws
5.6. General Discussion

Chapter 6. Motivational and Metacognitive Simulations
6.1. Modeling Metacognitive Judgment
6.1.1. Background
6.1.2. Task and Data
6.1.3. Simulation Setup
6.1.4. Simulation Results
6.1.5. Discussion
6.2. Modeling Metacognitive Inference
6.2.1. Task and Data
6.2.2. Simulation Setup
6.2.3. Simulation Results
6.2.4. Discussion
6.3. Modeling Motivation-Cognition Interaction
6.3.1. Background
6.3.2. Task and Data
6.3.3. Simulation Setup
6.3.4. Simulation Results
6.3.5. Discussion
6.4. Modeling Human Personality
6.4.1. Background
6.4.2. Principles of Personality Within CLARION Principles and Justifications Explaining Personality within CLARION
6.4.3. Simulations of Personality Simulation 1 Simulation 2 Simulation 3
6.4.4. Discussion
6.5. Accounting for Human Moral Judgment
6.5.1. Background
6.5.2. Human Data Effects of Personal Physical Force Effects of Intention Effects of Cognitive Load
6.5.3. Two Contrasting Views Details of Model 1 Details of Model 2
6.5.4. Discussion
6.6. Accounting for Emotion
6.6.1. Issues of Emotion
6.6.2. Emotion and Motivation
6.6.3. Emotion and the Implicit-Explicit Distinction
6.6.4. Effects of Emotion
6.6.5. Emotion Generation and Regulation
6.6.6. Discussion
6.7. General Discussion

Chapter 7. Cognitive Social Simulation
7.1. Introduction and Background
7.2. Cognition and Survival
7.2.1. Tribal Society Survival Task
7.2.2. Simulation Setup
7.2.3. Simulation Results and Analysis Effects of Social and Environmental Factors Effects of Cognitive Factors
7.2.4. Discussion
7.3. Motivation and Survival
7.3.1. Simulation Setup
7.3.2. Simulation Results and Analysis Effects of Social and Environmental Factors Effects of Cognitive Factors Effects of Motivational Factors
7.3.3. Discussion
7.4. Organizational Decision Making
7.4.1. Organizational Decision Task
7.4.2. Simulations and Results Simulation I: Matching Human Data Simulation II: Extending Simulation Temporally Simulation III: Varying Cognitive Parameters Simulation IV: Introducing Individual Differences
7.4.3. Discussion
7.5. Academic Publishing
7.5.1. Academic Science
7.5.2. Simulation Setup
7.5.3. Simulation Results and Analysis
7.5.4. Discussion
7.6. General Discussion
7.6.1. Theoretical Issues in Cognitive Social Simulation
7.6.2. Challenges
7.6.3. Concluding Remarks
Chapter 8. Some Important Questions and Their Short Answers
8.1. Theoretical Questions
8.2. Computational Questions
8.3. Biological Connections
Chapter 9. General Discussions and Conclusions
9.1. A Summary of the Cognitive Architecture
9.2. A Discussion of the Methodologies
9.3. Relations to Some Important Notions
9.4. Relations to Some Existing Approaches
9.5. Comparisons with Other Cognitive Architectures
9.6. Future Directions
9.6.1. Directions for Cognitive Social Simulation
9.6.2. Other Directions for Cognitive Architectures
9.6.3. Final Words on Future Directions

About the author: 

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.

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