Computational Modeling in Cognition Principles and Practice 1st Edition by Stephan Lewandowsky , Simon Farrell ISBN 1412970768 978-1412970761

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Product details: 

ISBN 10: 1412970768 

ISBN 13: 978-1412970761

Author: Stephan Lewandowsky , Simon Farrell

An accessible introduction to the principles of computational and mathematical modeling in psychology and cognitive science

This practical and readable work provides students and researchers, who are new to cognitive modeling, with the background and core knowledge they need to interpret published reports, and develop and apply models of their own. The book is structured to help readers understand the logic of individual component techniques and their relationships to each other.

Table of contents: 

Chapter 1: Introduction
1.1 Models and Theories in Science
1.2 Why Quantitative Modeling?
1.3 Quantitative Modeling in Cognition
1.3.1 Models and Data
1.3.2 From Ideas to Models
1.3.3 Summary
1.4 The Ideas Underlying Modeling and Its Distinct Applications
1.4.1 Elements of Models
1.4.2 Data Description
1.4.3 Process Characterization
1.4.4 Process Explanation
1.4.5 Classes of Models
1.5 What Can We Expect from Models?
1.5.1 Classification of Phenomena
1.5.2 Emergence of Understanding
1.5.3 Exploration of Implications
1.6 Potential Problems
1.6.1 Scope and Testability
1.6.2 Identification and Truth
Chapter 2: From Words to Models: Building a Toolkit
2.1 Working Memory
2.1.1 The Phonological Loop
2.1.2 Theoretical Accounts of the Word Length Effect
2.2 The Phonological Loop: 144 Models of Working Memory
2.2.1 Decay Process
2.2.2 Recall Process
2.2.3 Rehearsal
2.3 Building a Simulation
2.3.1 MATLAB
2.3.2 The Target Methodology
2.3.3 Setting Up the Simulation
2.3.4 Some Unanticipated Results
2.3.5 Variability in Decay
2.4 What Can We Learn from These Simulations?
2.4.1 The Phonological Loop Model Instantiated
2.4.2 Testability and Falsifiability
2.4.3 Revisiting Model Design: Foundational Decisions
2.5 The Basic Toolkit
2.5.1 Parameters
2.5.2 Discrepancy Function
2.5.3 Parameter Estimation and Model-Fitting Techniques
2.6 Models and Data: Sufficiency and Explanation
2.6.1 Sufficiency of a Model
2.6.2 Verisimilitude versus Truth
2.6.3 The Nature of Explanations
Chapter 3: Basic Parameter Estimation Techniques
3.1 Fitting Models to Data: Parameter Estimation
3.1.1 Visualizing Modeling
3.1.2 An Example
3.1.3 Inside the Box: Parameter Estimation Techniques
3.2 Considering the Data: What Level of Analysis?
3.2.1 Implications of Averaging
3.2.2 Fitting Individual Participants
3.2.3 Fitting Subgroups of Data
3.2.4 Fitting Aggregate Data
3.2.5 Having Your Cake and Eating It: Multilevel Modeling
3.2.6 Recommendations
Chapter 4: Maximum Likelihood Estimation
4.1 Basics of Probabilities
4.1.1 Defining Probability
4.1.2 Properties of Probabilities
4.1.3 Probability Functions
4.2 What Is a Likelihood?
4.2.1 Inverse Probability and Likelihood
4.3 Defining a Probability Function
4.3.1 Probability Functions Specified by the Psychological Model
4.3.2 Probability Functions Via Data Models
4.3.3 Two Types of Probability Functions
4.3.4 Extending the Data Model
4.3.5 Extension to Multiple Data Points and Multiple Parameters
4.4 Finding the Maximum Likelihood
4.5 Maximum Likelihood Estimation for Multiple Participants
4.5.1 Estimation for Individual Participants
4.5.2 Estimating a Single Set of Parameters
4.6 Properties of Maximum Likelihood Estimators
Chapter 5: Parameter Uncertainty and Model Comparison
5.1 Error on Maximum Likelihood Estimates
5.1.1 Standard Errors Across Participants
5.1.2 Curvature of the Likelihood Surface
5.1.3 Bootstrapping
5.1.4 What Do Confidence Limits Tell Us?
5.2 Introduction to Model Selection
5.3 The Likelihood Ratio Test
5.4 Information Criteria and Model Comparison
5.4.1 Kullback-Leibler Distance and Akaike’s Information Criterion
5.4.2 Bayesian Information Criterion
5.4.3 Model Comparison with the AIC and BIC
5.4.4 An Example of Model Comparison Using AIC and BIC
5.4.5 Choosing Between AIC and BIC
5.5 Conclusion
Chapter 6: Not Everything That Fits Is Gold: Interpreting the Modeling
6.1 Psychological Data and the Very Bad Good Fit
6.1.1 Overfitting
6.1.2 Generalizability versus Goodness of Fit
6.2 Parameter Identifiability and Model Testability
6.2.1 Identifiability
6.2.2 Testability
6.2.3 Putting It All Together: Deciding the Identifiability and Testability of a Model
6.2.4 Models That Are Identifiable but Not Testable
6.3 Drawing Lessons and Conclusions from Modeling
6.3.1 Explorations of a Model: Effects of Parameters
6.3.2 Demonstrations of Sufficiency
6.3.3 Demonstrations of Necessity
6.3.4 Summary
Chapter 7: Drawing It All Together: Two Examples
7.1 WITNESS: Simulating Eyewitness Identification
7.1.1 WITNESS: Architecture
7.1.2 WITNESS and Verbal Overshadowing
7.1.3 WITNESS in MATLAB
7.1.4 WITNESS Simulation Results
7.2 Exemplar versus Boundary Models: Choosing Between Candidates
7.2.1 Models of Categorization
7.2.2 A Probabilistic Feedback Experiment
7.2.3 MATLAB Code for ML Parameter Estimation for GCM, DEM, and GRT
7.2.4 Fitting the Models to Data
7.2.5 What Have We Learned About Categorization?
7.3 Conclusion
Chapter 8: Modeling in a Broader Context
8.1 Bayesian Theories of Cognition
8.2 Neural Networks
8.2.1 Basic Architecture and Operation
8.2.2 Hebbian Models
8.2.3 Backpropagation Models
8.2.4 Summary
8.3 Neuroscientific Modeling
8.3.1 The Allure of Neuroscience
8.3.2 The True Promise of Neuroscience
8.4 Cognitive Architectures
8.4.1 Cognitive Architectures: Convergence to a Standard
8.4.2 Cognitive Architectures: Successes, Problems, and Solutions
8.4.3 Marrying Architectures and Neuroscience
8.4.4 Architectures and Models
8.5 Conclusion
8.5.1 Memory
8.5.2 Language
8.5.3 Perception and Action
8.5.4 Choice and Decision Making
8.5.5 Identification and Categorization

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