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Computational Modeling of Cognition and Behavior

$140.00 (P)

  • Date Published: March 2018
  • availability: Available
  • format: Hardback
  • isbn: 9781107109995

$ 140.00 (P)
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About the Authors
  • Computational modeling is now ubiquitous in psychology, and researchers who are not modelers may find it increasingly difficult to follow the theoretical developments in their field. This book presents an integrated framework for the development and application of models in psychology and related disciplines. Researchers and students are given the knowledge and tools to interpret models published in their area, as well as to develop, fit, and test their own models. Both the development of models and key features of any model are covered, as are the applications of models in a variety of domains across the behavioural sciences. A number of chapters are devoted to fitting models using maximum likelihood and Bayesian estimation, including fitting hierarchical and mixture models. Model comparison is described as a core philosophy of scientific inference, and the use of models to understand theories and advance scientific discourse is explained.

    • Covers both basic and advanced topics to appeal to students and researchers alike
    • Provides a framework for using models in a variety of domains across psychology and related disciplines
    • Describes the application of models by walking through code written in R, a popular and free statistical programming language
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    Reviews & endorsements

    ‘I shudder to think about the time I could have saved had this book been available earlier. This educational masterpiece presents classic insights, modern methods, concrete examples, and expert advice; it should be required reading for anybody who seeks to understand human cognition and behavior.' Eric-Jan Wagenmakers, Psychological Methods, University of Amsterdam

    ‘This timely book is a must-read for every aspiring student of cognitive modeling. It provides a comprehensive and in-depth coverage of the conceptual and practical foundations of computational cognition, for the beginner and the experienced reader alike. The art of applying all major modeling frameworks, including Bayesian, frequentist, and neural networks, is explained in a most lucid and accessible manner.' Jay Myung, Ohio State University

    ‘An extraordinary achievement: the authors guide the reader from simple ideas about the nature of science to detailed, but lucidly explained, computer models of human behaviour. Associated statistical methods are comprehensively discussed. A pleasure to read.' Philip T. Smith, University of Reading

    'Farrell and Lewandowsky have succeeded in their ambition of spanning introductory to cutting-edge material. This book, and a willingness to dive in and learn by doing the exercises provided, is all that undergraduate and graduate students, and even established researchers, need to become a cognitive modeller.' Andrew Heathcote, University of Tasmania, Australia

    'Whether you are just setting out on your journey into computational modelling or whether you need to update your skills to incorporate newer and more coherent current practices, Farrell and Lewandowsky's book is likely to earn its place on your bookshelf.' Tom Hartley, Quarterly Journal of Experimental Psychology

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    Product details

    • Date Published: March 2018
    • format: Hardback
    • isbn: 9781107109995
    • length: 482 pages
    • dimensions: 254 x 183 x 25 mm
    • weight: 1.1kg
    • contains: 114 b/w illus. 15 tables
    • availability: Available
  • Table of Contents

    Preface
    Part I. Introduction to Modeling:
    1. Introduction
    2. From words to models: building a toolkit
    Part II. Parameter Estimation:
    3. Basic parameter estimation techniques
    4. Maximum likelihood parameter estimation
    5. Combining information from multiple participants
    6. Bayesian parameter estimation: basic concepts
    7. Bayesian parameter estimation: Monte Carlo methods
    8. Bayesian parameter estimation: the JAGS language
    9. Multilevel or hierarchical modeling
    Part III. Model Comparison:
    10. Model comparison
    11. Bayesian model comparison using Bayes factors
    Part IV. Models in Psychology:
    12. Using models in psychology
    13. Neural network models
    14. Models of choice response time
    15. Models in neuroscience
    Appendix A: Greek symbols
    Appendix B: mathematical terminology
    References
    Index.

  • Authors

    Simon Farrell, University of Western Australia, Perth
    Simon Farrell is professor and an Australian Research Council Future Fellow in the School of Psychology at the University of Western Australia. He uses computational modeling and experiments to understand memory, judgement, choice, and the role of memory in decision-making. He is the co-author of Computational Modeling in Cognition: Principles and Practice (2011) and has published numerous papers on the application of models to psychological data. Simon was Associate Editor of the Journal of Memory and Language (2009–11) and the Quarterly Journal of Experimental Psychology (2011–16). In 2009 Farrell was awarded the Bertelson Award by the European Society for Cognitive Psychology for his outstanding early career contribution to European Cognitive Psychology.

    Stephan Lewandowsky, University of Bristol
    Stephan Lewandowsky is a cognitive scientist in the School of Experimental Psychology at the University of Bristol. He was awarded a Discovery Outstanding Researcher Award from the Australian Research Council in 2011 and received a Wolfson Research Fellowship from the Royal Society upon moving to Bristol in 2013. He was appointed a Fellow of the Academy of Social Sciences in 2017. His research examines people's memory and decision-making, with an emphasis on how people update information in memory. He has published over 150 scholarly articles and books, including numerous papers on how people respond to corrections of misinformation and what determines people's acceptance of scientific findings.

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