Introduction to Modeling Cognitive Processes

by Verguts

ISBN: 9780262369695 | 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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