Bayesian Cognitive Modeling
A Practical Course

Using a practical, hands-on approach, this book will teach anyone how to carry out Bayesian analyses and interpret the results.

Michael D. Lee (Author), Eric-Jan Wagenmakers (Author)

9781107603578, Cambridge University Press

Paperback / softback, published 3 April 2014

280 pages
24.1 x 18.3 x 1.8 cm, 0.55 kg

'This is a very powerful exposition of how Bayesian methods, and WinBUGS in particular, can be used to deal with cognitive models that are apparently intractable. When we produced WinBUGS, we had no idea it could be used like this - it's amazing and gratifying to see these applications.' David Spiegelhalter, Winton Professor for the Public Understanding of Risk, Statistical Laboratory, Centre for Mathematical Sciences, Cambridge

Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.

Part I. Getting Started: 1. The basics of Bayesian analysis
2. Getting started with WinBUGS
Part II. Parameter Estimation: 3. Inferences with binomials
4. Inferences with Gaussians
5. Some examples of data analysis
6. Latent mixture models
Part III. Model Selection: 7. Bayesian model comparison
8. Comparing Gaussian means
9. Comparing binomial rates
Part IV. Case Studies: 10. Memory retention
11. Signal detection theory
12. Psychophysical functions
13. Extrasensory perception
14. Multinomial processing trees
15. The SIMPLE model of memory
16. The BART model of risk taking
17. The GCM model of categorization
18. Heuristic decision-making
19. Number concept development.

Subject Areas: Artificial intelligence [UYQ], Cognition & cognitive psychology [JMR], Experimental psychology [JML], Psychology [JM]