Cognitive modeling
著者
書誌事項
Cognitive modeling
Sage, c2010
- : pbk
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注記
Includes bibliographical references (p. 195-200) and index
内容説明・目次
内容説明
Cognitive Modeling is the first book to provide students with an easy-to understand introduction to the basic methods used to build and test cognitive models. Authors Jerome R. Busemeyer and Adele Diederich answer many of the questions that researchers face when beginning work on cognitive models, such as the following: What makes a cognitive model different from conceptual or statistical models? How do you develop such a model? How can you derive qualitatively different predictions between two cognitive models? Focusing on a few key representations, the authors introduce a basic problem in each chapter, illustrate the concept with three examples, and end with a summary of general principles, making this book by far the most accessible cognitive modeling book on the market.
Key Features
Emphasizes modeling by presenting the tools needed to build a cognitive model, rather than simply reviewing existing models of cognition
Provides tutorial presentations of psychological, mathematical, statistical, and computational methods used in all areas of cognitive modeling
Includes detailed examples applied to real cognitive models published in the literature in a variety of areas, including recognition, categorization, decision making, and learning
Stresses the importance of designing the right conditions for evaluating models
Addresses the issues of individual differences in cognitive modeling head-on
Cognitive Modeling is ideal for students and researchers across the various domains of cognitive sciences, including perception, learning, decision making, and inference. It is intended for use in upper-level undergraduate and graduate courses such as Cognition/Cognitive Modeling, Cognitive Science, Cognitive Psychology, Quantitative Methods, and Mathematical Modeling in Psychology.
目次
Preface
Acknowledgments
1. Introduction to Cognitive Modeling
2. Qualitative Model Comparisons
3. Nonlinear Parameter Estimation
4. Application to Choice and Response Time Measures
5. Quantitative Model Comparisons
6. Hierarchical Modeling
References
Index
About the Authors
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