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Computational modeling of cognition and behavior

By: Farrell, Simon.
Contributor(s): Lewandowsky, Stephan [Co author].
Material type: materialTypeLabelBookPublisher: London Cambridge University Press 2018Description: xxii, 461p. With index.ISBN: 9781107525610.Subject(s): Mathematics | Psychology - mathematical models | Cognition - mathematical models | Computational modelingDDC classification: 153.015118 Summary: 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. https://www.cambridge.org/core/books/computational-modeling-of-cognition-and-behavior/A4A90098E7CB9A58E5D030F408639D04#fndtn-information
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Slot 149 (0 Floor, West Wing) Non-fiction 153.015118 F2C6 (Browse shelf) Available 198960

Table of content
Part I - Introduction to Modeling

1 - Introduction

2 - From Words to Models

Part II - Parameter Estimation

3 - Basic Parameter Estimation Techniques

4 - Maximum Likelihood Parameter Estimation

5 - Combining Information from Multiple Participants

6 - Bayesian Parameter Estimation

7 - Bayesian Parameter Estimation

8 - Bayesian Parameter Estimation

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

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.

https://www.cambridge.org/core/books/computational-modeling-of-cognition-and-behavior/A4A90098E7CB9A58E5D030F408639D04#fndtn-information

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