Computational modeling of cognition and behavior / Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
- Author
- Farrell, Simon, 1976-
- Published
- Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2018.
- Copyright Date
- ©2018
- Physical Description
- xxii, 461 pages ; 26 cm
- Additional Creators
- Lewandowsky, Stephan
- Contents
- Machine generated contents note: pt. I Introduction to Modeling -- 1.Introduction -- 1.1.Models and Theories in Science -- 1.2.Quantitative Modeling in Cognition -- 1.2.1.Models and Data -- 1.2.2.Data Description -- 1.2.3.Cognitive Process Models -- 1.3.Potential Problems: Scope and Falsifiability -- 1.4.Modeling as a "Cognitive Aid" for the Scientist -- 1.5.In Vivo -- 2.From Words to Models -- 2.1.Response Times in Speeded-Choice Tasks -- 2.2.Building a Simulation -- 2.2.1.Getting Started: R and RStudio -- 2.2.2.The Random-Walk Model -- 2.2.3.Intuition vs. Computation: Exploring the Predictions of a Random Walk -- 2.2.4.Trial-to-Trial Variability in the Random-Walk Model -- 2.2.5.A Family of Possible Sequential-Sampling Models -- 2.3.The Basic Toolkit -- 2.3.1.Parameters -- 2.3.2.Connecting Model and Data -- 2.4.In Vivo -- pt. II Parameter Estimation -- 3.Basic Parameter Estimation Techniques -- 3.1.Discrepancy Function -- 3.1.1.Root Mean Squared Deviation (RMSD) -- 3.1.2.Chi-Squared(χ2) -- 3.2.Fitting Models to Data: Parameter Estimation Techniques -- 3.3.Least-Squares Estimation in a Familiar Context -- 3.3.1.Visualizing Modeling -- 3.3.2.Estimating Regression Parameters -- 3.4.Inside the Box: Parameter Estimation Techniques -- 3.4.1.Simplex -- 3.4.2.Simulated Annealing -- 3.4.3.Relative Merits of Parameter Estimation Techniques -- 3.5.Variability in Parameter Estimates -- 3.5.1.Bootstrapping -- 3.6.In Vivo -- 4.Maximum Likelihood Parameter 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? -- 4.3.Defining a Probability Distribution -- 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.Properties of Maximum Likelihood Estimators -- 4.6.In Vivo -- 5.Combining Information from Multiple Participants -- 5.1.It Matters How You Combine Data from Multiple Units -- 5.2.Implications of Averaging -- 5.3.Fitting Aggregate Data -- 5.4.Fitting Individual Participants -- 5.5.Fitting Subgroups of Data and Individual Differences -- 5.5.1.Mixture Modeling -- 5.5.2.K-Means Clustering -- 5.5.3.Modeling Individual Differences -- 5.6.In Vivo -- 6.Bayesian Parameter Estimation -- 6.1.What Is Bayesian Inference? -- 6.1.1.From Conditional Probabilities to Bayes Theorem -- 6.1.2.Marginalizing Probabilities -- 6.2.Analytic Methods for Obtaining Posteriors -- 6.2.1.The Likelihood Function -- 6.2.2.The Prior Distribution -- 6.2.3.The Evidence or Marginal Likelihood -- 6.2.4.The Posterior Distribution -- 6.2.5.Estimating the Bias of a Coin -- 6.2.6.Summary -- 6.3.Determining the Prior Distributions of Parameters -- 6.3.1.Non-Informative Priors -- 6.3.2.Reference Priors -- 6.4.In Vivo -- 7.Bayesian Parameter Estimation -- 7.1.Markov Chain Monte Carlo Methods -- 7.1.1.The Metropolis-Hastings Algorithm for MCMC -- 7.1.2.Estimating Multiple Parameters -- 7.2.Problems Associated with MCMC Sampling -- 7.2.1.Convergence of MCMC Chains -- 7.2.2.Autocorrelation in MCMC Chains -- 7.2.3.Outlook -- 7.3.Approximate Bayesian Computation: A Likelihood-Free Method -- 7.3.1.Likelihoods That Cannot be Computed -- 7.3.2.From Simulations to Estimates of the Posterior -- 7.3.3.An Example: ABC in Action -- 7.4.In Vivo -- 8.Bayesian Parameter Estimation -- 8.1.Gibbs Sampling -- 8.1.1.A Bivariate Example of Gibbs Sampling -- 8.1.2.Gibbs vs. Metropolis-Hastings Sampling -- 8.1.3.Gibbs Sampling of Multivariate Spaces -- 8.2.JAGS: An Introduction -- 8.2.1.Installing JAGS -- 8.2.2.Scripting for JAGS -- 8.3.JAGS: Revisiting Some Known Models and Pushing Their Boundaries -- 8.3.1.Bayesian Modeling of Signal-Detection Theory -- 8.3.2.A Bayesian Approach to Multinomial Tree Models: The High-Threshold Model -- 8.3.3.A Bayesian Approach to Multinomial Tree Models -- 8.3.4.Summary -- 8.4.In Vivo -- 9.Multilevel or Hierarchical Modeling -- 9.1.Conceptualizing Hierarchical Modeling -- 9.2.Bayesian Hierarchical Modeling -- 9.2.1.Graphical Models -- 9.2.2.Hierarchical Modeling of Signal-Detection Performance -- 9.2.3.Hierarchical Modeling of Forgetting -- 9.2.4.Hierarchical Modeling of Inter-Temporal Preferences -- 9.2.5.Summary -- 9.3.Hierarchical Maximum Likelihood Modeling -- 9.3.1.Hierarchical Maximum Likelihood Model of Signal Detection -- 9.4.Recommendations -- 9.5.In Vivo -- pt. III Model Comparison -- 10.Model Comparison -- 10.1.Psychological Data and the Very Bad Good Fit -- 10.1.1.Model Complexity and Over-Fitting -- 10.2.Model Comparison -- 10.3.The Likelihood Ratio Test -- 10.4.Akaike's Information Criterion -- 10.5.Other Methods for Calculating Complexity and Comparing Models -- 10.5.1.Cross-Validation -- 10.5.2.Minimum Description Length -- 10.5.3.Normalized Maximum Likelihood -- 10.6.Parameter Identifiability and Model Testability -- 10.6.1.Identifiability -- 10.6.2.Testability -- 10.7.Conclusions -- 10.8.In Vivo -- 11.Bayesian Model Comparison Using Bayes Factors -- 11.1.Marginal Likelihoods and Bayes Factors -- 11.2.Methods for Obtaining the Marginal Likelihood -- 11.2.1.Numerical Integration -- 11.2.2.Simple Monte Carlo Integration and Importance Sampling -- 11.2.3.The Savage-Dickey Ratio -- 11.2.4.Transdimensional Markov Chain Monte Carlo -- 11.2.5.Laplace Approximation -- 11.2.6.Bayesian Information Criterion -- 11.3.Bayes Factors for Hierarchical Models -- 11.4.The Importance of Priors -- 11.5.Conclusions -- 11.6.In Vivo -- pt. IV Models in Psychology -- 12.Using Models in Psychology -- 12.1.Broad Overview of the Steps in Modeling -- 12.2.Drawing Conclusions from Models -- 12.2.1.Model Exploration -- 12.2.2.Analyzing the Model -- 12.2.3.Learning from Parameter Estimates -- 12.2.4.Sufficiency of a Model -- 12.2.5.Model Necessity -- 12.2.6.Verisimilitude vs. Truth -- 12.3.Models as Tools for Communication and Shared Understanding -- 12.4.Good Practices to Enhance Understanding and Reproducibility -- 12.4.1.Use Plain Text Wherever Possible -- 12.4.2.Use Sensible Variable and Function Names -- 12.4.3.Use the Debugger -- 12.4.4.Commenting -- 12.4.5.Version Control -- 12.4.6.Sharing Code and Reproducibility -- 12.4.7.Notebooks and Other Tools -- 12.4.8.Enhancing Reproducibility and Runnability -- 12.5.Summary -- 12.6.In Vivo -- 13.Neural Network Models -- 13.1.Hebbian Models -- 13.1.1.The Hebbian Associator -- 13.1.2.Hebbian Models as Matrix Algebra -- 13.1.3.Describing Networks Using Matrix Algebra -- 13.1.4.The Auto-Associator -- 13.1.5.Limitations of Hebbian Models -- 13.2.Backpropagation -- 13.2.1.Learning and the Backpropagation of Error -- 13.2.2.Applications and Criticisms of Backpropagation in Psychology -- 13.3.Final Comments on Neural Networks -- 13.4.In Vivo -- 14.Models of Choice Response Time -- 14.1.Ratcliff's Diffusion Model -- 14.1.1.Fitting the Diffusion Model -- 14.1.2.Interpreting the Diffusion Model -- 14.1.3.Falsifiability of the Diffusion Model -- 14.2.Ballistic Accumulator Models -- 14.2.1.Linear Ballistic Accumulator -- 14.2.2.Fitting the LBA -- 14.3.Summary -- 14.4.Current Issues and Outlook -- 14.5.In Vivo -- 15.Models in Neuroscience -- 15.1.Methods for Relating Neural and Behavioral Data -- 15.2.Reinforcement Learning Models -- 15.2.1.Theories of Reinforcement Learning -- 15.2.2.Neuroscience of Reinforcement Learning -- 15.3.Neural Correlates of Decision-Making -- 15.3.1.Rise-to-Threshold Models of Saccadic Decision-Making -- 15.3.2.Relating Model Parameters to the BOLD Response -- 15.3.3.Accounting for Response Time Variability -- 15.3.4.Using Spike Trains as Model Input -- 15.3.5.Jointly Fitting Behavioral and Neural Data -- 15.4.Conclusions -- 15.5.In Vivo.
- Subject(s)
- ISBN
- 9781107109995 hardcover
110710999X hardcover
9781107525610 paperback
1107525616 paperback - Bibliography Note
- Includes bibliographical references and index.
View MARC record | catkey: 22603899