Introduction to Modeling Cognitive Processes
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.
Hardcover$50.00 X ISBN: 9780262045360 264 pp. | 7 in x 10 in 49
“Neurocognitive modeling spans many levels of analysis, from neurons to cognitive function. Verguts presents an exceptionally lucid overview of theoretical and methodological approaches in this field that will be an amazing resource for students at all levels.”
Michael J. Frank
Edgar L. Marston Professor and Director of the Carney Center for Computational Brain Science, Brown University; coauthor of Computational Cognitive Neuroscience
“Verguts's clear and accessible text provides a concise introduction to both classic and contemporary approaches to modeling cognition. A fantastic on-ramp for those interested in developing precise models of cognitive processing, learning, and development.”
James L. McClelland
Director, Center for Mind, Brain, Computation and Technology, Stanford University; author of Parallel Distributed Processing, Explorations in Parallel Distributed Processing, and Semantic Cognition: A Parallel Distributed Processing Approach