Computational modeling of cognition and behavior:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2018
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | XXII, 461 Seiten Illustrationen, Diagramme |
| ISBN: | 9781107109995 9781107525610 |
Internformat
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Datensatz im Suchindex
| _version_ | 1804178323773849600 |
|---|---|
| adam_text | Contents
List of Illustrations Pa8e xiii
List of Tables xviii
List of Contributo rs xix
Preface xxi
Parti Introduction to Modeling 1
1 Introduction 3
1.1 Models and Theories in Science 3
1.2 Quantitative Modeling in Cognition 6
1.2.1 Models and Data 6
1.2.2 Data Description 9
1.2.3 Cognitive Process Models 13
1.3 Potential Problems: Scope and Falsifiability 17
1.4 Modeling as a “Cognitive Aid” for the Scientist 20
1.5 In Vivo 22 2
2 From Words to Models 24
2.1 Response Times in Speeded-Choice Tasks 24
2.2 Building a Simulation 26
2.2.1 Getting Started: R and RStudio 26
2.2.2 The Random-Walk Model 27
2.2.3 Intuition vs. Computation: Exploring the Predictions of a
Random Walk 31
2.2.4 Trial-to-Trial Variability in the Random-Walk Model 33
2.2.5 A Family of Possible Sequential-Sampling Models 37
2.3 The Basic Toolkit 38
2.3.1 Parameters 38
2.3.2 Connecting Model and Data 40
2.4 In Vivo 40
vii
viii
Contents
^¡fcvVWy^::
Part II Parameter Estimation 45
3 Basic Parameter Estimation Techniques 47
3.1 Discrepancy Function 47
3.1.1 Root Mean Squared Deviation (RMSD) 48
3.1.2 Chi-Squared (x2) 49
3.2 Fitting Models to Data: Parameter Estimation Techniques 50
3.3 Least-Squares Estimation in a Familiar Context 50
3.3.1 Visualizing Modeling 51
3.3.2 Estimating Regression Parameters 53
3.4 Inside the Box: Parameter Estimation Techniques 57
3.4.1 Simplex 57
3.4.2 Simulated Annealing 61
3.4.3 Relative Merits of Parameter Estimation Techniques 64
3.5 Variability in Parameter Estimates 65
3.5.1 Bootstrapping 65
3.6 In Vivo 70
4 Maximum Likelihood Parameter Estimation 72
4.1 Basics of Probabilities 22
4.1.1 Defining Probability 22
4.1.2 Properties of Probabilities 23
4.1.3 Probability Functions 25
4.2 What Is a Likelihood? 80
4.3 Defining a Probability Distribution 85
4.3.1 Probability Functions Specified by the Psychological Model 86
4.3.2 Probability Functions via Data Models 86
4.3.3 Two Types of Probability Functions 91
4.3.4 Extending the Data Model 92
4.3.5 Extension to Multiple Data Points and Multiple Parameters 93
4.4 Finding the Maximum Likelihood 95
4.5 Properties of Maximum Likelihood Estimators 101
4.6 In Vivo 103
5 Combining Information from Multiple Participants 105
5.1 It Matters How You Combine Data from Multiple Units 105
5.2 Implications of Averaging 106
5.3 Fitting Aggregate Data 109
5.4 Fitting Individual Participants 111
5.5 Fitting Subgroups of Data and Individual Differences 115
5.5.1 Mixture Modeling 113
5.5.2 K-Means Clustering 118
5.5.3 Modeling Individual Differences 121
5.6 In Vivo 123
Contents ix
6 Bayesian Parameter Estimation 126
6.1 What Is Bayesian Inference? 126
6.1.1 From Conditional Probabilities to Bayes Theorem 126
6.1.2 Marginalizing Probabilities 129
6.2 Analytic Methods for Obtaining Posteriors 130
6.2.1 The Likelihood Function 130
6.2.2 The Prior Distribution 131
6.2.3 The Evidence or Marginal Likelihood 134
6.2.4 The Posterior Distribution 135
6.2.5 Estimating the Bias of a Coin 136
6.2.6 Summary 139
6.3 Determining the Prior Distributions of Parameters 139
6.3.1 Non-Informative Priors 139
6.3.2 Reference Priors 142
6.4 In Vivo 143
7 Bayesian Parameter Estimation 146
7.1 Markov Chain Monte Carlo Methods 146
7.1.1 The Metropolis-Hastings Algorithm for MCMC 147
7.1.2 Estimating Multiple Parameters 153
7.2 Problems Associated with MCMC Sampling 160
7.2.1 Convergence of MCMC Chains 161
7.2.2 Autocorrelation in MCMC Chains 162
7.2.3 Outlook 162
7.3 Approximate Bayesian Computation: A Likelihood-Free Method 163
7.3.1 Likelihoods That Cannot be Computed 163
7.3.2 From Simulations to Estimates of the Posterior 164
7.3.3 An Example: ABC in Action 166
7.4 In Vivo 170
8 Bayesian Parameter Estimation 172
8.1 Gibbs Sampling 172
8.1.1 A Bivariate Example of Gibbs Sampling 173
8.1.2 Gibbs vs. Metropolis-Hastings Sampling 176
8.1.3 Gibbs Sampling of Multivariate Spaces 176
8.2 JAGS: An Introduction 177
8.2.1 Installing JAGS 177
8.2.2 Scripting for JAGS 177
8.3 JAGS: Revisiting Some Known Models and Pushing Their Boundaries 182
8.3.1 Bayesian Modeling of Signal-Detection Theory 182
8.3.2 A Bayesian Approach to Multinomial Tree Models:
The High-Threshold Model 186
8.3.3 A Bayesian Approach to Multinomial Tree Models 190
8.3.4 Summary 198
8.4 In Vivo 198
x Contents
9 Multilevel or Hierarchical Modeling 203
9.1 Conceptualizing Hierarchical Modeling 203
9.2 Bayesian Hierarchical Modeling 204
9.2.1 Graphical Models 204
9.2.2 Hierarchical Modeling of Signal-Detection Performance 207
9.2.3 Hierarchical Modeling of Forgetting 211
9.2.4 Hierarchical Modeling of Inter-Temporal Preferences 218
9.2.5 Summary 226
9.3 Hierarchical Maximum Likelihood Modeling 228
9.3.1 Hierarchical Maximum Likelihood Model of Signal Detection 228
9.4 Recommendations 233
9.5 In Vivo 234
Partlll Model Comparison 239
10 Model Comparison 241
10.1 Psychological Data and the Very Bad Good Fit 241
10.1.1 Model Complexity and Over-Fitting 243
10.2 Model Comparison 248
10.3 The Likelihood Ratio Test 249
10.4 Akaike’s Information Criterion 256
10.5 Other Methods for Calculating Complexity and Comparing Models 261
10.5.1 Cross-Validation 262
10.5.2 Minimum Description Length 262
10.5.3 Normalized Maximum Likelihood 263
10.6 Parameter Identifiability and Model Testability 264
10.6.1 Identifiability 264
10.6.2 Testability 269
10.7 Conclusions 270
10.8 In Vivo 271
11 Bayesian Model Comparison Using Bayes Factors 273
11.1 Marginal Likelihoods and Bayes Factors 273
11.2 Methods for Obtaining the Marginal Likelihood 277
11.2.1 Numerical Integration 278
11.2.2 Simple Monte Carlo Integration and Importance Sampling 280
11.2.3 The Savage-Dickey Ratio 284
11.2.4 Transdimensional Markov Chain Monte Carlo 287
11.2.5 Laplace Approximation 294
11.2.6 Bayesian Information Criterion 297
11.3 Bayes Factors for Hierarchical Models 301
11.4 The Importance of Priors 303
11.5 Conclusions 306
11.6 In Vivo 306
Contents xi
**/-* ¿sv .v iv ** **. v.^-. .^a - :*,% ».-, rj£-=: v--.. .,v*,v : s:v.-•-i-v: iw^ r. • : --■ * e»-=V?./s.r?Vj*..-*rv»
Part IV Models in Psychology 309
12 Using Models in Psychology 311
12.1 Broad Overview of the Steps in Modeling 311
12.2 Drawing Conclusions from Models 312
12.2.1 Model Exploration 312
12.2.2 Analyzing the Model 314
12.2.3 Learning from Parameter Estimates 315
12.2.4 Sufficiency of a Model 316
12.2.5 Model Necessity 318
12.2.6 Verisimilitude vs. Truth 323
12.3 Models as Tools for Communication and Shared Understanding 324
12.4 Good Practices to Enhance Understanding and Reproducibility 326
12.4.1 Use Plain Text Wherever Possible 326
12.4.2 Use Sensible Variable and Function Names 327
12.4.3 Use the Debugger 327
12.4.4 Commenting 328
12.4.5 Version Control 328
12.4.6 Sharing Code and Reproducibility 329
12.4.7 Notebooks and Other Tools 330
12.4.8 Enhancing Reproducibility and Runnability 331
12.5 Summary 332
12.6 In Vivo 332
13 Neural Network Models 334
13.1 Hebbian Models 334
13.1.1 The Hebbian Associator 334
13.1.2 Hebbian Models as Matrix Algebra 339
13.1.3 Describing Networks Using Matrix Algebra 348
13.1.4 The Auto-Associator 349
13.1.5 Limitations of Hebbian Models 356
13.2 Backpropagation 356
13.2.1 Learning and the Backpropagation of Error 360
13.2.2 Applications and Criticisms of Backpropagation in Psychology 364
13.3 Final Comments on Neural Networks 365
13.4 In Vivo 366
14 Models of Choice Response Time 369
14.1 Ratcliff’s Diffusion Model 369
14.1.1 Fitting the Diffusion Model 371
14.1.2 Interpreting the Diffusion Model 383
14.1.3 Falsifiability of the Diffusion Model 385
14.2 Ballistic Accumulator Models 386
14.2.1 Linear Ballistic Accumulator 386
14.2.2 Fitting the LBA 388
xii Contents
143 Summary 391
14.4 Current Issues and Outlook 392
14.5 In Vivo 393
15 Models in Neuroscience 395
15.1 Methods for Relating Neural and Behavioral Data 397
15.2 Reinforcement Learning Models 398
15.2.1 Theories of Reinforcement Learning 398
15.2.2 Neuroscience of Reinforcement Learning 404
15.3 Neural Correlates of Decision-Making 410
15.3.1 Rise-to-Threshold Models of Saccadic Decision-Making 410
15.3.2 Relating Model Parameters to the BOLD Response 411
15.3.3 Accounting for Response Time Variability 413
15.3.4 Using Spike Trains as Model Input 415
15.3.5 Jointly Fitting Behavioral and Neural Data 417
15.4 Conclusions 420
15.5 In Vivo 421
Appendix A Greek Symbols 424
Appendix B Mathematical Terminology 425
References 427
Index 455
|
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| spelling | Farrell, Simon 1976- Verfasser (DE-588)1012639738 aut Computational modeling of cognition and behavior Simon Farrell (University of Western Australia, Perth), Stephan Lewandowsky (University of Bristol) Cambridge Cambridge University Press 2018 XXII, 461 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Cognition Mathematical models Psychology Mathematical models Psychologie (DE-588)4047704-6 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Kognition (DE-588)4031630-0 gnd rswk-swf Kognition (DE-588)4031630-0 s Mathematisches Modell (DE-588)4114528-8 s Psychologie (DE-588)4047704-6 s DE-604 Lewandowsky, Stephan 1958- Verfasser (DE-588)1155510704 aut Erscheint auch als Online-Ausgabe 10.1017/9781316272503 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030221412&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Farrell, Simon 1976- Lewandowsky, Stephan 1958- Computational modeling of cognition and behavior Cognition Mathematical models Psychology Mathematical models Psychologie (DE-588)4047704-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd Kognition (DE-588)4031630-0 gnd |
| subject_GND | (DE-588)4047704-6 (DE-588)4114528-8 (DE-588)4031630-0 |
| title | Computational modeling of cognition and behavior |
| title_auth | Computational modeling of cognition and behavior |
| title_exact_search | Computational modeling of cognition and behavior |
| title_full | Computational modeling of cognition and behavior Simon Farrell (University of Western Australia, Perth), Stephan Lewandowsky (University of Bristol) |
| title_fullStr | Computational modeling of cognition and behavior Simon Farrell (University of Western Australia, Perth), Stephan Lewandowsky (University of Bristol) |
| title_full_unstemmed | Computational modeling of cognition and behavior Simon Farrell (University of Western Australia, Perth), Stephan Lewandowsky (University of Bristol) |
| title_short | Computational modeling of cognition and behavior |
| title_sort | computational modeling of cognition and behavior |
| topic | Cognition Mathematical models Psychology Mathematical models Psychologie (DE-588)4047704-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd Kognition (DE-588)4031630-0 gnd |
| topic_facet | Cognition Mathematical models Psychology Mathematical models Psychologie Mathematisches Modell Kognition |
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