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Computational Modeling in Cognition

Computational Modeling in Cognition
Principles and Practice

January 2011 | 376 pages | SAGE Publications, Inc
Computational Modelling in Psychology 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 permits readers with a background in cognition, but without any modeling expertise.

The authors present the content step-by-step by moving from the basic concepts of modeling to issues and application. The book is structured to make clear the logic of individual component techniques and how they relate to each other. The authors focus on the logic of models and the types of arguments that can be made from them, as well as providing detailed practical knowledge about parameter-estimation techniques and model selection and so on. Readability is emphasized throughout to make the necessary mathematics and programming less daunting for beginners. The book's supporting web page provides additional information and programming code.

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

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."

Koen Lamberts
University of Warwick

This book covers the most essential topics for cognitive modeling.
It does so at a level that a) students can still understand, yet b) the skills/knowledge provided are actually applicable for research.
MATLAB/pseudocode is provided, which is helpful for a start.
Also, the chapters are well written.
Overall, a very good read for students and researcher who want to get into cognitive modeling.

Professor Rene Huster
Psychology Dept, University of Oslo
November 30, 2015

This book offers a great introduction and explanation of advanced statistical methods to research cognition. Along the way it also gives an excellent account of several key statistics constructs that must be understood by all behavioral and social scientists and students.

Dr Rodolfo Leyva
Sociology, Middlesex University
November 19, 2015

One of the best books on computational modelling I know!

Professor Markus Knauff
Psychology , University of Giessen
November 8, 2011

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