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Computational Modeling in Cognition Stephan Lewandowsky

Computational Modeling in Cognition By Stephan Lewandowsky

Computational Modeling in Cognition by Stephan Lewandowsky

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Summary

A clear introduction to the principles of using computational and mathematical models in psychology and cognitive science.

Computational Modeling in Cognition Summary

Computational Modeling in Cognition: Principles and Practice by Stephan Lewandowsky

An accessible introduction to the principles of computational and mathematical modeling in psychology and cognitive science

This practical and readable work provides students and researchers, who are new to cognitive modeling, with the background and core knowledge they need to interpret published reports, and develop and apply models of their own. The book is structured to help readers understand the logic of individual component techniques and their relationships to each other.

Computational Modeling in Cognition Reviews

[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

Table of Contents

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

Additional information

NPB9781412970761
9781412970761
1412970768
Computational Modeling in Cognition: Principles and Practice by Stephan Lewandowsky
Stephan Lewandowsky
New
Paperback
SAGE Publications Inc
20110125
376
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
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Customer Reviews - Computational Modeling in Cognition