Computational modeling of cognition and behavior

By:

Farrell, Simon

Contributor(s):

Lewandowsky, Stephan [Co-author]

Publication details: Cambridge University Press, 2018. UK:Edition: 2nd edDescription: xvii, 461p. hb.; 23cmISBN:

9781107109995

Subject(s):

DDC classification:

153.015118 FAR

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 behavioral 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.

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Books	IIT Gandhinagar General Stacks	General	153.015118 FAR (Browse shelf(Opens below))	1	Available		031070

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Previous	153 HAR Self awareness	153 SCH Mind and the brain: neuroplasticity and the power of mental force	153 WIL Cognition: the thinking animal	153.015118 FAR Computational modeling of cognition and behavior	153.1 GLU Learning and memory: from brain to behavior	153.15 CLO Principles of learning and behavior	153.15 DEH How we learn: the new science of education and the brain	Next

Includes References and Index

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 behavioral 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.

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