Computational Modeling in Cognition : Principles and Practice

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Format: Paperback
Pub. Date: 2010-11-29
Publisher(s): SAGE Publications, Inc
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Summary

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.

Table of Contents

Introduction
Models and theories in science
Why quantitative modeling?
Quantitative modeling in cognition
The ideas underlying modeling and its distinct applicatiosn
What can we expect from models?
Potential problems
From Words to Models: Building a Toolkit
Working memory
The phonological loop: 144 models of working memory
Building a simulation
What can we learn from these simulations?
The basic toolkit
Models and data: Sufficiency and explanation
Basic Parameter-Estimation Techniques
Fitting models to data: Parameter estimation
Considering the data: What level of analysis?
Maximum Likelihood Estimation
Basics of probabilities
What is a likelihood?
Defining a probability distribution
Finding the maximum likelihood
Maximum likelihood estimation for multiple participants
Properties of maximum likelihood estimators
Parameter Uncertainty
Error on maximum likelihood estimates
Introduction to model selection
The likelihood ratio test
Information criteria and model comparison
Conclusion
Not Everything That Fits is Gold: Interpreting the Modeling
Psychological data and the very bad good fit
Parameter identi ability and Model testability
Drawing lessons and conclusions from modeling
Drawing it all Together: Two Examples
WITNESS: Simulating eyewitness identification
Exemplar vs boundary models: Choosing between candidates
Conclusion
Modeling in a broader context
Bayesian theories of cognition
Neural networks
Neuroscientific modeling
Cognitive architectures
Conclusion
Memory
Language
Perception and Action
Choice and Decision-Making
Identification and Categorization
Table of Contents provided by Ingram. All Rights Reserved.

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