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Full Description
This Oxford Handbook offers a comprehensive and authoritative review of important developments in computational and mathematical psychology. With chapters written by leading scientists across a variety of subdisciplines, it examines the field's influence on related research areas such as cognitive psychology, developmental psychology, clinical psychology, and neuroscience. The Handbook emphasizes examples and applications of the latest research, and will appeal to readers possessing various levels of modeling experience.
The Oxford Handbook of Computational and mathematical Psychology covers the key developments in elementary cognitive mechanisms (signal detection, information processing, reinforcement learning), basic cognitive skills (perceptual judgment, categorization, episodic memory), higher-level cognition (Bayesian cognition, decision making, semantic memory, shape perception), modeling tools (Bayesian estimation and other new model comparison methods), and emerging new directions in computation and mathematical psychology (neurocognitive modeling, applications to clinical psychology, quantum cognition).
The Handbook would make an ideal graduate-level textbook for courses in computational and mathematical psychology. Readers ranging from advanced undergraduates to experienced faculty members and researchers in virtually any area of psychology--including cognitive science and related social and behavioral sciences such as consumer behavior and communication--will find the text useful.
Contents
Preface ; 1. Introduction ; Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels ; Part I. Elementary Cognitive Mechanisms ; 2. Multidimensional Signal Detection Theory ; F. Gregory Ashby and Fabian A. Soto ; 3. Modeling Simple Decisions and Applications Using a Diffusion Model ; Roger Ratcliff and Philip Smith ; 4. Features of Response Times: Identification of Cognitive Mechanisms through Mathematical Modeling ; Daniel Algom, Ami Eidels, Robert X. D. Hawkins, Brett Jefferson, and James T. Townsend ; 5. Computational Reinforcement Learning ; Todd M. Gureckis and Bradley C. Love ; Part II. Basic Cognitive Skills ; 6. Why Is Accurately Labeling Simple Magnitudes So Hard? A Past, Present, and Future Look at Simple Perceptual Judgment ; Chris Donkin, Babette Rae, Andrew Heathcote, and Scott D. Brown ; 7. An Exemplar-Based Random-Walk Model of Categorization and Recognition ; Robert M. Nosofsky and Thomas J. Palmeri ; 8. Models of Episodic Memory ; Amy H. Criss and Marc W. Howard ; Part III. Higher Level Cognition ; 9. Structure and Flexibility in Bayesian Models of Cognition ; Joseph L. Austerweil, Samuel J. Gershman, and Thomas L. Griffiths ; 10. Models of Decision Making under Risk and Uncertainty ; Timothy J. Pleskac, Adele Diederich, and Thomas S. Wallsten ; 11. Models of Semantic Memory ; Michael N. Jones, Jon Willits, and Simon Dennis ; 12. Shape Perception ; Tadamasa Sawada, Yunfeng Li, and Zygmunt Pizlo ; Part IV. New Directions ; 13. Bayesian Estimation in Hierarchical Models ; John K. Kruschke and Wolf Vanpaemel ; 14. Model Comparison and the Principle of Parsimony ; Joachim Vandekerckhove, Dora Matzke, and Eric-Jan Wagenmakers ; 15. Neurocognitive Modeling of Perceptual Decision Making ; Thomas J. Palmeri, Jeffrey D. Schall, and Gordon D. Logan ; 16. Mathematical and Computational Modeling in Clinical Psychology ; Richard W. J. Neufeld ; 17. Quantum Models of Cognition and Decision ; Jerome R. Busemeyer, Zheng Wang, and Emmanuel Pothos ; Index
