Computational Modeling of Cognition and Behavior - Farrell, Simon

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Résumé :
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....

Biographie:
Simon Farrell is professor and an Australian Research Council Future Fellow in the School of Psychology at the University of Western Australia. He uses computational modeling and experiments to understand memory, judgement, choice, and the role of memory in decision-making. He is the co-author of Computational Modeling in Cognition: Principles and Practice (2011) and has published numerous papers on the application of models to psychological data. Simon was Associate Editor of the Journal of Memory and Language (2009?11) and the Quarterly Journal of Experimental Psychology (2011?16). In 2009 Farrell was awarded the Bertelson Award by the European Society for Cognitive Psychology for his outstanding early career contribution to European Cognitive Psychology.

Sommaire:
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.