Computational Modeling in Cognition

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

Principles and Practice. Computational Modeling in Cognition provides a clear introduction to the principles of using computational and mathematical models in psychology and cognitive science.

Klappentext

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.

Inhalt

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