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Computational Modeling Of Cognition And Behavior

Computational Modeling Of Cognition And Behavior - Lewandowsky, Stephan (university Of Bristol); Farrell, Simon (university Of Western Australia, Perth) - ISBN: 9781107525610
Prijs: € 50,80
Levertijd: 3 tot 4 werkdagen
Bindwijze: Boek
Genre: Functieleer
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This book presents an integrated framework for developing and testing computational models in psychology and related disciplines.


Titel: Computational Modeling Of Cognition And Behavior
auteur: Lewandowsky, Stephan (university Of Bristol); Farrell, Simon (university Of Western Australia, Perth)
Mediatype: Boek
Taal: Engels
Aantal pagina's: 482
Uitgever: Cambridge University Press
Plaats van publicatie: 03
NUR: Functieleer
Afmetingen: 245 x 175 x 22
Gewicht: 980 gr
ISBN/ISBN13: 9781107525610
Intern nummer: 39445177

Biografie (woord)

Stephan Lewandowsky is a cognitive scientist in the School of Experimental Psychology at the University of Bristol. He was awarded a Discovery Outstanding Researcher Award from the Australian Research Council in 2011 and received a Wolfson Research Fellowship from the Royal Society upon moving to Bristol in 2013. He was appointed a Fellow of the Academy of Social Sciences in 2017. His research examines people's memory and decision-making, with an emphasis on how people update information in memory. He has published over 150 scholarly articles and books, including numerous papers on how people respond to corrections of misinformation and what determines people's acceptance of scientific findings.


'Farrell and Lewandowsky have succeeded in their ambition of spanning introductory to cutting-edge material. This book, and a willingness to dive in and learn by doing the exercises provided, is all that undergraduate and graduate students, and even established researchers, need to become a cognitive modeller.' Andrew Heathcote, University of Tasmania, Australia


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.


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