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The hare or the tortoise? Modeling optimal speed-accuracy tradeoff settings
van Ravenzwaaij, D.
Publication date
2012
Link to publication
Citation for published version (APA):
van Ravenzwaaij, D. (2012). The hare or the tortoise? Modeling optimal speed-accuracy
tradeoff settings.
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Contents
Acknowledgments v Contents vi 1 Introduction 1 1.1 Chapter Outline . . . 2I
Theory
5
2 An Integrated Perspective on the Relation Between Response Speed and Intelligence 7 2.1 The Diffusion Model . . . 92.2 Key Phenomena in Intelligence Research Captured by Drift Rate . . . 11
2.3 Advantages and Limitations of a Diffusion Model Approach to the Study of Intelligence . . . 24
2.4 Concluding Comments . . . 26
3 Optimal Decision Making in Neural Inhibition Models 29 3.1 The DDM . . . 31
3.2 The LCA . . . 33
3.3 LCA Phase Plane Analysis . . . 34
3.4 LCA Discrepancy 1: Negative Activations . . . 36
3.5 LCA Discrepancy 2: Changing Boundaries . . . 38
3.6 LCA Simulation 1: Performance . . . 39
3.7 LCA Simulation 2: Parameters . . . 42
3.8 Interim Conclusion . . . 44
3.9 The Feedforward Inhibition Model . . . 44
3.10 FFI Discrepancy: Negative Activations . . . 45
3.11 FFI Simulation 1: Performance . . . 47
3.12 FFI Simulation 2: Parameters . . . 48
3.13 A Possible Solution to the Problem of Truncation: Baseline Activation . . 50
3.14 Conclusion . . . 51 4 Do the Dynamics of Prior Information Depend on Task Context? An
Analysis of Optimal Performance and an Empirical Test 55 vi
Contents
4.1 The Drift Diffusion Model (DDM) . . . 57
4.2 Bias in the DDM . . . 58
4.3 Bias in Theory . . . 59
4.4 Bias in Practice . . . 66
4.5 Conclusion . . . 71
5 How to Use the Diffusion Model: Parameter Recovery of Three Meth-ods: EZ, Fast–dm, and DMAT 75 5.1 Ratcliff’s Diffusion Model . . . 77
5.2 Methods for Measuring Parameters of the Diffusion Model . . . 78
5.3 Simulation 1: Fitting Data Generated by the Diffusion Model . . . 80
5.4 Simulation 2: Fitting Data Generated by Other Models . . . 85
5.5 Discussion . . . 92
II Application
97
6 A Diffusion Model Decomposition of the Effects of Alcohol on Per-ceptual Decision Making 99 6.1 The Diffusion Model . . . 1016.2 Method . . . 103
6.3 Results . . . 104
6.4 Concluding Comments . . . 109
7 Does the Name–Race Implicit Association Test Measure Racial Prej-udice? 111 7.1 Experiment . . . 113
7.2 Method . . . 113
7.3 Results . . . 114
7.4 Interim Conclusion . . . 116
7.5 The Ratcliff Diffusion Model . . . 116
7.6 Discussion . . . 119
8 Cognitive Model Decomposition of the BART: Assessment and Ap-plication 121 8.1 The BART Models . . . 124
8.2 Bayesian Parameter Estimation . . . 125
8.3 Parameter Recovery Simulations . . . 127
8.4 Experiment . . . 131 8.5 Concluding Comments . . . 139 9 Conclusion 143 9.1 Summary of Results . . . 143 9.2 Model Validity . . . 146 9.3 Concluding Remarks . . . 148
IIIAppendices
151
A Appendix to Chapter 3: “Optimal Decision Making in Neural
Inhi-bition Models” 153
Contents
A.1 Higher Input LCA Simulation 1 . . . 153
A.2 Matched Accuracy LCA Simulation 1 . . . 153
A.3 Full DDM Estimates LCA Simulation 2 . . . 153
A.4 Matched Accuracy FFI Simulation 1 . . . 154
A.5 Full DDM Estimates FFI Simulation 2 . . . 155
A.6 LCA Truncation with Starting Points Above Zero . . . 155
B Appendix to Chapter 4: “Do the Dynamics of Prior Information De-pend on Task Context? An Analysis of Optimality and an Empirical Test” 161 B.1 Derivation of Equation 4.6 . . . 161
C Appendix to Chapter 7: “Does the Name–Race Implicit Association Test Measure Racial Prejudice?” 163 C.1 Supplementary Analyses . . . 163
C.2 Delta Plots . . . 164
C.3 Diffusion Model Estimates . . . 165
C.4 Study 2 . . . 167
D Appendix to Chapter 8: “Cognitive Model Decomposition of the BART: Assessment and Application” 171 D.1 Additional Parameter Recovery Simulations . . . 171
D.2 WinBUGS Code of the BART Model . . . 178
References 181
Samenvatting 197
