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Computational Modeling in Cognition Stephan Lewandowsky

Computational Modeling in Cognition von Stephan Lewandowsky

Computational Modeling in Cognition Stephan Lewandowsky

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Zusammenfassung

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

Computational Modeling in Cognition Zusammenfassung

Computational Modeling in Cognition: Principles and Practice 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 Bewertungen

[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

Inhaltsverzeichnis

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

Zusätzliche Informationen

GOR013338468
9781412970761
1412970768
Computational Modeling in Cognition: Principles and Practice Stephan Lewandowsky
Stephan Lewandowsky
Gebraucht - Wie Neu
Broschiert
SAGE Publications Inc
20110125
376
N/A
Die Abbildung des Buches dient nur Illustrationszwecken, die tatsächliche Bindung, das Cover und die Auflage können sich davon unterscheiden.
Das Buch wurde gelesen, ist aber in gutem Zustand. Alle Seiten sind intakt, der Einband ist unversehrt. Leichte Gebrauchsspuren am Buchrücken. Das Buch wurde gelesen, sieht jedoch noch wie neu aus. Der Bucheinband weist keine sichtbaren Gebrauchsspuren auf. Gegebenenfalls ist auch ein Schutzumschlag verfügbar. Keine fehlenden oder beschädigten Seiten, keine Risse, eventuell minimale Knicke, keine unterstrichenen oder markierten Textstellen, keine beschrifteten Ränder.