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Computational Modeling in Cognition: Principles and Practice

by Stephan Lewandowsky and Simon Farrell SAGE Publications, Inc
Pub Date:
Pbk 376 pages
AU$169.00 NZ$173.91
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Computational Modeling in Cognition introduces the principles of using computational models in psychology and provides a clear idea about how model construction, parameter estimation and model selection are carried out in practice. The book is written at a level that suits readers with a background in cognition, but without any modelling expertise.

Key Features:

•The book’s practical approach shows readers how model construction, parameter estimation, and model selection are carried out in real world settings.
•An easy-to-follow, step-by-step presentation moves from the basic concepts of modeling to modeling issues and applications.
•The logic of models and the types of arguments that can be made from them is a primary focus.
•Programming examples from MATLAB are used to illustrate core concepts.
•A focus on readability makes mathematics and programming less daunting for beginners.

COURSE USE: Suitable as an accessible introduction to the principles of computational and mathematical modeling in psychology and cognitive science.

Preface / 1. Introduction / 1.1 Models and Theories in Science / 1.2 Why Quantitative Modeling? / 1.3 Quantitative Modeling in Cognition / 1.4 The Ideas Underlying Modeling and Its Distinct Applications / 1.5 What Can We Expect From Models? / 1.6 Potential Problems / 2. From Words to Models: Building a Toolkit / 2.1 Working Memory / 2.2 The Phonological Loop: 144 Models of Working Memory / 2.3 Building a Simulation / 2.4 What Can We Learn From These Simulations? / 2.5 The Basic Toolkit / 2.6 Models and Data: Sufficiency and Explanation / 3. Basic Parameter Estimation Techniques / 3.1 Fitting Models to Data: Parameter Estimation / 3.2 Considering the Data: What Level of Analysis? / 4. Maximum Likelihood Estimation / 4.1 Basics of Probabilities / 4.2 What Is a Likelihood? / 4.3 Defining a Probability Function / 4.4 Finding the Maximum Likelihood / 4.5 Maximum Likelihood Estimation for Multiple Participants / 4.6 Properties of Maximum Likelihood Estimators / 5. Parameter Uncertainty and Model Comparison / 5.1 Error on Maximum Likelihood Estimates / 5.2 Introduction to Model Selection / 5.3 The Likelihood Ratio Test / 5.4 Information Criteria and Model Comparison / 5.5 Conclusion / 6. Not Everything That Fits Is Gold: Interpreting the Modeling / 6.1 Psychological Data and The Very Bad Good Fit / 6.2 Parameter Identifiability and Model Testability / 6.3 Drawing Lessons and Conclusions From Modeling / 7. Drawing It All Together: Two Examples / 7.1 WITNESS: Simulating Eyewitness Identification / 7.2 Exemplar Versus Boundary Models: Choosing Between Candidates / 7.3 Conclusion / 8. Modeling in a Broader Context / 8.1 Bayesian Theories of Cognition / 8.2 Neural Networks / 8.3 Neuroscientific Modeling / 8.4 Cognitive Architectures / 8.5 Conclusion / References / Author Index / Subject Index / About the Authors

"[T]his is an excellent introduction to computational modeling. It is written at exactly the right level for its intended readership, and it covers all the essentials very well. I can only encourage anyone with an interest in cognition to work with this book."
Stephan Lewandowsky is Professorial Fellow at the school of Psychology at the University of Western Australia. His research is on the effects of time on memory; dynamic models of short-term and
working memory; individual differences in categorisation.
Simon Farrell is a reader in the Department of Experimental Psychology at the University of Bristol, U.K. His research involves experiments on human beings guided by computational models.