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Cognitive Modeling

Cognitive Modeling

April 2009 | 224 pages | SAGE Publications, Inc

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.

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
About the Authors

Very informative, in-depth coverage of the research topic.
Unfortunately, due to the high level of maths required (which is inevitable for this subject), the text is beyond the scope of many of our psychology students. I recommend this book to master students who will use some of these methods for their master's thesis.

Dr Daniel Oberfeld
Department of Psychology, Johannes Gutenberg-University Mainz
July 6, 2015

I think the book is a very good introduction in modeling in the first chapters. But, I am concerned about current methods in a introductory course, I think students should take at least a reference since the beginning.
Currently the lecturer of Cognitive Neuroscience, gives several papers with EEG and fMRI methods seeking models of cognition at least in half of the lectures. Therefore, in my opinion, would be suit better a book which takes also some aspects currently working, such as Connectivity Analysis and Dynamical Causal Modeling (since 2003).
Let me to recommend to extend the figure 6.5, that could be the route to explain in a short way about Bayesian Inference and Dynamical Causal Modeling. Even more, I would suggest (if it is possible) add a small chapter explaining Structural Equation Modeling, Psycho-Physiological Interaction and Dynamical Causal Modeling, among others ways, as a way to compute effective models working in the brain.

Mr Carlos Mugruza Vassallo
Psychology , Dundee University
April 28, 2010
Key features
  • Outstanding AAA authorship will lend authority to the book. As confirmed by reviewers, the authors are considered stars within the fields of mathematical and cognitive psychology, with clear strengths as researchers, writers, presenters, and teachers.
  • A student-friendly tone with concrete examples will make this much more accessible than the current intimidating and mathematically sophisticated books on the market. Each of the core chapters will introduce a basic problem, illustrate the concept with three examples, and end with a summary of general principles.
  • An accompanying web site with relevant and relevant and downloadable programs and data sets will allow students to practice the concepts they are learning from the book using such software packages as SAS SPSS, Matlab, and Mathematica.
  • A focus on how to do cognitive modeling, with selected models rather than a catalog of all current models, equips students with broadly applicable tools and methods for understanding cognitive models, in general. In the Introduction the authors will explain their rationale for the models they've selected.
  • Broad applicability makes this book suitable for students and researchers across the various domains of cognitive sciences, e.g., perception, learning, decision-making , inference, etc.

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