Table of Contents

Table of Contents

Preface

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 Distinction 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
3.1.2.1. Representation in the Top Level
3.1.2.2. Representation in the Bottom Level
3.1.2.3. Action Decision Making
3.1.3. Learning
3.1.3.1. Learning in the Bottom Level
3.1.3.2. 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
3.2.2.1. Overall Algorithm
3.2.2.2. Representation in the Top Level
3.2.2.3. Representation in the Bottom Level
3.2.2.4. Representation of Hierarchies
3.2.3. Learning
3.2.3.1. Learning in the Bottom Level
3.2.3.2. 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
3.3.2.1. Rule Extraction and Refinement
3.3.2.2. Independent Rule Learning
3.3.2.3. 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
3.3.7.1. Learning about "Knife"
3.3.7.2. Learning about "Knife" within CLARION
3.3.7.3. 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
Appendix
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
4.2.2.1. Primary Drives
4.2.2.2. Secondary Drives
4.2.2.3. Approach versus Avoidance Drives
4.2.2.4. Drive Strength
4.2.3. Goals
4.2.4. Modules and Their Functions
4.2.4.1. Initialization Module
4.2.4.2. Preprocessing Module
4.2.4.3. Drive Core Module
4.2.4.4. Deficit Change Module
4.3. The Metacognitive Subsystem
4.3.1. Essential Considerations
4.3.2. Modules and Their Functions
4.3.2.1. Goal Module
4.3.2.2. Reinforcement Module
4.3.2.3. Processing Mode Module
4.3.2.4. Input/output Filtering Modules
4.3.2.5. Reasoning/learning Selection Modules
4.3.2.6. Monitoring Buffer
4.3.2.7. 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
5.2.3.1. Simulation Setup
5.2.3.2. 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
5.5.1.1. Uncertain Information
5.5.1.2. Incomplete Information
5.5.1.3. Similarity
5.5.1.4. Inheritance
5.5.1.5. Cancellation of Inheritance
5.5.1.6. Mixed Rules and Similarities
5.5.2. Reasoning with Heuristics
5.5.2.1. Representativeness Heuristic
5.5.2.2. Availability Heuristic
5.5.2.3. Probability Matching
5.5.3. Inductive Reasoning
5.5.3.1. Similarity between the Premise and the Conclusion
5.5.3.2. Multiple Premises
5.5.3.3. 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
6.4.2.1. Principles and Justifications
6.4.2.2. Explaining Personality within CLARION
6.4.3. Simulations of Personality
6.4.3.1. Simulation 1
6.4.3.2. Simulation 2
6.4.3.3. Simulation 3
6.4.4. Discussion
6.5. Accounting for Human Moral Judgment
6.5.1. Background
6.5.2. Human Data
6.5.2.1. Effects of Personal Physical Force
6.5.2.2. Effects of Intention
6.5.2.3. Effects of Cognitive Load
6.5.3. Two Contrasting Views
6.5.3.1. Details of Model 1
6.5.3.2. 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
7.2.3.1. Effects of Social and Environmental Factors
7.2.3.2. 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
7.3.2.1. Effects of Social and Environmental Factors
7.3.2.2. Effects of Cognitive Factors
7.3.2.3. 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
7.4.2.1. Simulation I: Matching Human Data
7.4.2.2. Simulation II: Extending Simulation Temporally
7.4.2.3. Simulation III: Varying Cognitive Parameters
7.4.2.4. 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

References