Computational Modeling of Cognition and Behavior

This book presents an integrated framework for developing and testing computational models in psychology and related disciplines.

Simon Farrell (Author), Stephan Lewandowsky (Author)

9781107525610, Cambridge University Press

Paperback / softback, published 22 February 2018

482 pages, 114 b/w illus. 15 tables
24.6 x 17.3 x 2.3 cm, 0.97 kg

'Whether you are just setting out on your journey into computational modelling or whether you need to update your skills to incorporate newer and more coherent current practices, Farrell and Lewandowsky's book is likely to earn its place on your bookshelf.' Tom Hartley, Quarterly Journal of Experimental Psychology

Computational modeling is now ubiquitous in psychology, and researchers who are not modelers may find it increasingly difficult to follow the theoretical developments in their field. This book presents an integrated framework for the development and application of 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. Both the development of models and key features of any model are covered, as are the applications of models in a variety of domains across the behavioural sciences. A number of chapters are devoted to fitting models using maximum likelihood and Bayesian estimation, including fitting hierarchical and mixture models. Model comparison is described as a core philosophy of scientific inference, and the use of models to understand theories and advance scientific discourse is explained.

Preface
Part I. Introduction to Modeling: 1. Introduction
2. From words to models: building a toolkit
Part II. Parameter Estimation: 3. Basic parameter estimation techniques
4. Maximum likelihood parameter estimation
5. Combining information from multiple participants
6. Bayesian parameter estimation: basic concepts
7. Bayesian parameter estimation: Monte Carlo methods
8. Bayesian parameter estimation: the JAGS language
9. Multilevel or hierarchical modeling
Part III. Model Comparison: 10. Model comparison
11. Bayesian model comparison using Bayes factors
Part IV. Models in Psychology: 12. Using models in psychology
13. Neural network models
14. Models of choice response time
15. Models in neuroscience
Appendix A: Greek symbols
Appendix B: mathematical terminology
References
Index.

Subject Areas: Neurosciences [PSAN], Cognition & cognitive psychology [JMR], Analytical & Jungian psychology [JMAJ], Psychology [JM], Sociology [JHB]