The Oxford Handbook of Computational and Mathematical Psychology

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.

Dr. Jerome R. Busemeyer is Provost Professor of Psychology at Indiana University.

Dr. Zheng Wang is an Associate Professor at the Ohio State University and directs the Communication and Psychophysiology Lab.

Dr. James T. Townsend is Distinguished Rudy Professor of Psychology at Indiana University.

Dr. Ami Eidels is a senior lecturer in Cognitive Psychology at the School of Psychology, University of Newcastle, Australia.

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