Introduction to Modeling Cognitive Processes
by Verguts
ISBN: 9780262045360 | Copyright 2022
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| Contents (pg. v) | |
| Preface and Acknowledgments (pg. ix) | |
| 1. What Is Cognitive Modeling? (pg. 1) | |
| The Use of Models (pg. 1) | |
| Time Scales of Modeling (pg. 5) | |
| Striving for a Goal (pg. 6) | |
| Optimization (pg. 8) | |
| TensorFlow (pg. 13) | |
| Minimizing Energy or Getting Groceries (pg. 14) | |
| 2. Decision Making (pg. 17) | |
| Minimization in Activation Space (pg. 17) | |
| A Minimal Energy Model (pg. 21) | |
| Cooperative and Competitive Interactions in Visual Word Recognition (pg. 25) | |
| The Hopfield Model (pg. 27) | |
| Harmony Theory (pg. 30) | |
| Solving Puzzles with the Hopfield Model (pg. 31) | |
| Human Memory and the Hopfield Model (pg. 32) | |
| The Diffusion Model (pg. 33) | |
| The Diffusion Model in Psychology (pg. 35) | |
| 3. Hebbian Learning (pg. 37) | |
| The Hebbian Learning Rule (pg. 37) | |
| Biology of the Hebbian Learning Rule (pg. 40) | |
| Hebbian Learning in Matrix Notation (pg. 41) | |
| Memory Storage in the Hopfield Model (pg. 44) | |
| Hebbian Learning in Models of Human Memory (pg. 48) | |
| 4. The Delta Rule (pg. 53) | |
| The Delta Rule in Two-Layer Networks (pg. 53) | |
| The Geometry of the Delta Rule (pg. 58) | |
| The Delta Rule in Cognitive Science (pg. 61) | |
| The Rise, Fall, and Return of the Delta Rule (pg. 66) | |
| 5. Multilayer Networks (pg. 69) | |
| Geometric Intuition of the Multilayer Model (pg. 69) | |
| Generalizing the Delta Rule: Backpropagation (pg. 72) | |
| Some Drawbacks of Backpropagation (pg. 74) | |
| Varieties of Backpropagation (pg. 76) | |
| Networks and Statistical Models (pg. 82) | |
| Multilayer Networks in Cognitive Science: The Case of Semantic Cognition (pg. 83) | |
| Criticisms of Neural Networks (pg. 85) | |
| 6. Estimating Parameters in Computational Models (pg. 89) | |
| Parameter Space Exploration (pg. 89) | |
| Parameter Estimation by Error Minimization (pg. 91) | |
| Parameter Estimation by the Maximum Likelihood Method (pg. 92) | |
| Applications (pg. 99) | |
| 7. Testing and Comparing Computational Models (pg. 107) | |
| Model Testing (pg. 108) | |
| Model Testing across Modalities (pg. 114) | |
| Model Comparison (pg. 116) | |
| Applications of Model Comparison (pg. 120) | |
| 8. Reinforcement Learning: The Gradient Ascent Approach (pg. 123) | |
| Gradient Ascent Reinforcement Learning in a Two-Layer Model (pg. 124) | |
| An N-Armed Bandit (pg. 126) | |
| A General Algorithm (pg. 127) | |
| Backpropagating RL Errors (pg. 129) | |
| Three- and Four-Term RL Algorithms: Attention for Learning (pg. 130) | |
| 9. Reinforcement Learning: The Markov Decision Process Approach (pg. 133) | |
| The MDP Formalism (pg. 134) | |
| Finding an Optimal Policy (pg. 138) | |
| Value Estimation (pg. 138) | |
| Policy Updating (pg. 143) | |
| Policy Iteration (pg. 143) | |
| Exploration and Exploitation in Reinforcement Learning (pg. 143) | |
| Applications (pg. 145) | |
| Combining Gradient-Ascent and MDP Approaches (pg. 149) | |
| Reinforcement Learning for Human Cognition? (pg. 151) | |
| Open AI Gym (pg. 152) | |
| 10. Unsupervised Learning (pg. 153) | |
| Unsupervised Hebbian Learning (pg. 153) | |
| Competitive Learning (pg. 156) | |
| Kohonen Learning (pg. 158) | |
| Auto-Encoders (pg. 161) | |
| Boltzmann Machines (pg. 162) | |
| Restricted Boltzmann Machines (pg. 166) | |
| 11. Bayesian Models (pg. 173) | |
| Bayesian Statistics (pg. 173) | |
| The Rational Approach (pg. 179) | |
| Bayesian Models of Cognition (pg. 182) | |
| 12. Interacting Organisms (pg. 191) | |
| Social Decision Making (pg. 192) | |
| Combining Information (pg. 193) | |
| Game Theory (pg. 193) | |
| Cultural Transmission and the Evolution of Languages (pg. 198) | |
| To Conclude (pg. 201) | |
| Conventions and Notation (pg. 203) | |
| Glossary (pg. 205) | |
| Hints and Solutions to Select Exercises (pg. 207) | |
| Notes (pg. 217) | |
| Chapter 1 (pg. 217) | |
| Chapter 2 (pg. 217) | |
| Chapter 3 (pg. 217) | |
| Chapter 4 (pg. 217) | |
| Chapter 5 (pg. 217) | |
| Chapter 6 (pg. 218) | |
| Chapter 7 (pg. 218) | |
| Chapter 9 (pg. 218) | |
| Chapter 10 (pg. 218) | |
| Chapter 12 (pg. 218) | |
| Hints and Solutions to Select Exercises (pg. 218) | |
| References (pg. 219) | |
| Index (pg. 243) | |
Tom Verguts
Tom Verguts is Professor in the Department of Experimental Psychology at Ghent University, Belgium.
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