Introduction to Modeling Cognitive Processes
- Length: 264 pages
- Edition: 1
- Language: English
- Publisher: The MIT Press
- Publication Date: 2022-02-01
- ISBN-10: 0262045362
- ISBN-13: 9780262045360
- Sales Rank: #355005 (See Top 100 Books)
An introduction to computational modeling for cognitive neuroscientists, covering both foundational work and recent developments.
Cognitive neuroscientists need sophisticated conceptual tools to make sense of their field’s proliferation of novel theories, methods, and data. Computational modeling is such a tool, enabling researchers to turn theories into precise formulations. This book offers a mathematically gentle and theoretically unified introduction to modeling cognitive processes. Theoretical exercises of varying degrees of difficulty throughout help readers develop their modeling skills.
After a general introduction to cognitive modeling and optimization, the book covers models of decision making; supervised learning algorithms, including Hebbian learning, delta rule, and backpropagation; the statistical model analysis methods of model parameter estimation and model evaluation; the three recent cognitive modeling approaches of reinforcement learning, unsupervised learning, and Bayesian models; and models of social interaction. All mathematical concepts are introduced gradually, with no background in advanced topics required. Hints and solutions for exercises and a glossary follow the main text. All code in the book is Python, with the Spyder editor in the Anaconda environment. A GitHub repository with Python files enables readers to access the computer code used and start programming themselves. The book is suitable as an introduction to modeling cognitive processes for students across a range of disciplines and as a reference for researchers interested in a broad overview.
Cover Title Page Copyright Page Contents Preface and Acknowledgments 1. What Is Cognitive Modeling? The Use of Models Time Scales of Modeling Striving for a Goal Optimization TensorFlow Minimizing Energy or Getting Groceries 2. Decision Making Minimization in Activation Space A Minimal Energy Model Cooperative and Competitive Interactions in Visual Word Recognition The Hopfield Model Harmony Theory Solving Puzzles with the Hopfield Model Human Memory and the Hopfield Model The Diffusion Model The Diffusion Model in Psychology 3. Hebbian Learning The Hebbian Learning Rule Biology of the Hebbian Learning Rule Hebbian Learning in Matrix Notation Memory Storage in the Hopfield Model Hebbian Learning in Models of Human Memory 4. The Delta Rule The Delta Rule in Two-Layer Networks The Geometry of the Delta Rule The Delta Rule in Cognitive Science The Rise, Fall, and Return of the Delta Rule 5. Multilayer Networks Geometric Intuition of the Multilayer Model Generalizing the Delta Rule: Backpropagation Some Drawbacks of Backpropagation Varieties of Backpropagation Networks and Statistical Models Multilayer Networks in Cognitive Science: The Case of Semantic Cognition Criticisms of Neural Networks 6. Estimating Parameters in Computational Models Parameter Space Exploration Parameter Estimation by Error Minimization Parameter Estimation by the Maximum Likelihood Method Applications 7. Testing and Comparing Computational Models Model Testing Model Testing across Modalities Model Comparison Applications of Model Comparison 8. Reinforcement Learning: The Gradient Ascent Approach Gradient Ascent Reinforcement Learning in a Two-Layer Model An N-Armed Bandit A General Algorithm Backpropagating RL Errors Three- and Four-Term RL Algorithms: Attention for Learning 9. Reinforcement Learning: The Markov Decision Process Approach The MDP Formalism Finding an Optimal Policy Value Estimation Policy Updating Policy Iteration Exploration and Exploitation in Reinforcement Learning Applications Combining Gradient-Ascent and MDP Approaches Reinforcement Learning for Human Cognition? Open AI Gym 10. Unsupervised Learning Unsupervised Hebbian Learning Competitive Learning Kohonen Learning Auto-Encoders Boltzmann Machines Restricted Boltzmann Machines 11. Bayesian Models Bayesian Statistics The Rational Approach Bayesian Models of Cognition 12. Interacting Organisms Social Decision Making Combining Information Game Theory Cultural Transmission and the Evolution of Languages To Conclude Conventions and Notation Glossary Hints and Solutions to Select Exercises References Index
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