This book presents an integrated framework for developing and testing computational models in psychology and related disciplines. Researchers and students are given the knowledge and tools to interpret models published in their area, as well as to develop, fit, and test their own models.

An accessible introduction to the principles of computational and mathematical modeling in psychology and cognitive science This practical and readable work provides students and researchers, who are new to cognitive modeling, with the background and core knowledge they need to interpret published reports, and develop and apply models of their own. The book is structured to help readers understand the logic of individual component techniques and their relationships to each other.

A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.

A cutting-edge reference source for the interdisciplinary field of computational cognitive modeling.

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.

A comprehensive introduction to the computational modeling of human cognition.

Cognitive psychologists have found the production systems class of computer simulation models to be one of the most direct ways to cast complex theories of human intelligence. There have been many scattered studies on production systems since they were first proposed as computational models of human problem-solving behavior by Allen Newell some twenty years ago, but this is the first book to focus exclusively on these important models of human cognition, collecting and giving many of the best examples of current research. In the first chapter, Robert Neches, Pat Langley, and David Klahr provide an overview of the fundamental issues involved in using production systems as a medium for theorizing about cognitive processes, emphasizing their theoretical power. The remaining chapters take up learning by doing and learning by understanding, discrimination learning, learning through incremental refinement, learning by chunking, procedural earning, and learning by composition. A model of cognitive development called BAIRN is described, and a final chapter reviews John Anderson's ACT theory and discusses how it can be used in intelligent tutoring systems, including one that teaches LISP programming skills. In addition to the editors, the contributors are Yuichiro Anzai (Hokkaido University, Japan), Paul Rosenbloom (Stanford) and Allen Newell (Carnegie-Mellon), Stellan Ohlsson (University of Pittsburgh), Clayton Lewis (University of Colorado, Boulder), Iain Wallace and Kevin Bluff (Deakon University, Australia), and John Anderson (Carnegie-Mellon). David Klahr is Professor and Head of the Department of Psychology at Carnegie-Mellon University. Pat Langley is Associate Professor, Department ofInformation and Computer Science, University of California, Irvine, and Robert Neches is Research Computer Scientist at University of Southern California Information Sciences Institute. "Production System Models of Learning and Development" is included in the series Computational Models of Cognition and Perception, edited by Jerome A. Feldman, Patrick J. Hayes, and David E.Rumelhart. A Bradford Book.