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Computational Modeling in Cognition: Principles and Practice [Paperback]

Stephan Lewandowsky , Simon Farrell
5.0 out of 5 stars  See all reviews (1 customer review)
RRP: £41.99
Price: £37.79 & FREE Delivery in the UK. Details
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Book Description

25 Jan 2011 1412970768 978-1412970761
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.

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Product details

  • Paperback: 376 pages
  • Publisher: SAGE Publications, Inc (25 Jan 2011)
  • Language: English
  • ISBN-10: 1412970768
  • ISBN-13: 978-1412970761
  • Product Dimensions: 2.3 x 15 x 22.3 cm
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Bestsellers Rank: 386,166 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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"[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 2011-12-05)

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5.0 out of 5 stars Recommended 25 Oct 2014
Format:Paperback|Verified Purchase
Great book, took me from first principles to being able to model. Sometimes the code could be unpacked a little more so that there weren't nested calls, but highly recommended
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Most Helpful Customer Reviews on (beta) 5.0 out of 5 stars  2 reviews
4 of 4 people found the following review helpful
5.0 out of 5 stars Fantastic introduction to computational modeling. 7 Feb 2013
By Nathaniel Phillips - Published on
This is a very welcome introductory text that fills a gaping gap in the literature. In the past 10 years, computational modeling has moved from a niche area of psychology (and other fields) into the main stream. This book covers most of the essentials, from the philosophy of computational modeling, to maximum-likelihood estimation, to model comparison. Throughout the book, the authors show the reader how to balance theoretical integration and model parsimony with statistical fit. Indeed, even if you never go through the steps of creating and testing models, this lesson will serve you well in all areas of scientific reasoning.

Armed with this book, a free copy of the statistics programming language R (and of course some data), you'll be off and running in no time. I highly recommend this book for motivated undergraduate students and all graduate students studying computational cognitive modeling.

One more thing - the authors are great, if you have questions while working through the book I'm sure they would be happy to answer them.
1 of 1 people found the following review helpful
5.0 out of 5 stars Marvelous Modeling Introduction 3 Oct 2013
By J. Whitlow - Published on
Format:Paperback|Verified Purchase
This is one of the best books I've read on computational modeling in psychology. Lewandowsky and Farrell show how to do the computations, using MATLAB code, and they present everything in a clear, accessible, thoughtful, and witty manner. I was hooked with the picture of the orbits of planets as seen from earth to make the point that, without a theoretical model (in this case of planetary motion), the data can be incredibly complex, and they continue with the same level of interesting and well-informed exposition throughout. Reading this is like seeing something clearly for the first time.
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