Computational Modeling in Cognition : Principles and Practice
by Stephan LewandowskyBuy New
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Summary
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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