University of Groningen
Optimal bounds, bounded optimality
Böhm, Udo
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Publication date: 2018
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Böhm, U. (2018). Optimal bounds, bounded optimality: Models of impatience in decision-making. University of Groningen.
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Optimal Bounds, Bounded Optimality
Models of Impatience in Decision-Making
This research was supported by BCN and the Netherlands Organisation for Scientific Research (406-12-125).
ISBN printed version: 978-94-034-0495-0 ISBN digital version: 978-94-034-0496-7
This publication is typeset in LATEXusing the Memoir class
Printed by Ipskamp Printing B.V., Enschede Cover by Viktor Beekman, viktorbeekman.nl Copyright c 2018 by Udo B¨ohm
Optimal Bounds, Bounded Optimality
Models of Impatience in Decision-Making
PhD thesis
to obtain the degree of PhD at the University of Gronigen
on the authority of the Rector Magnificus Prof. E. Sterken
and in accordance with the decision by the College of Deans. This thesis will be defended in public on
Thursday 5 April 2018 at 11.00 hours
by
Udo Böhm
born on 10 March 1988Supervisor
Prof. E.-J. Wagenmakers
Co-supervisors
Dr. D. Matzke Dr. L. van Maanen
Assessment committee
Prof. R.R. Meijer
Prof. H.L.J. van der Maas Prof. F. Tuerlinckx
Contents
Contents
1 Introduction 1
1.1 Chapter Outline . . . 4
2 Of Monkeys and Men: Impatience in Perceptual Decision-Making 9 2.1 Introduction . . . 10
2.2 Why a Dynamic Component? . . . 12
2.3 Summary and Future Directions . . . 20
3 On the Relationship Between Reward Rate and Dynamic De-cision Criteria 25 3.1 Introduction . . . 26
3.2 Theoretical Analysis . . . 29
3.3 Experimental Study . . . 40
3.4 Discussion . . . 55
4 Trial-by-Trial Fluctuations in CNV Amplitude Reflect Anticip-atory Adjustment of Response Caution 59 4.1 Introduction . . . 60
4.2 Materials and Methods . . . 62
4.3 Results . . . 67
4.4 Discussion . . . 77
4.5 Conclusions . . . 80
5 Using Bayesian Regression to Test Hypotheses About Relation-ships Between Parameters and Covariates in Cognitive Models 81 5.1 Introduction . . . 82
5.2 Regression Framework for Relating Cognitive Model Parameters to Covariates . . . 85
5.3 Simulation Study . . . 95
Contents
6 Estimating Between-Trial Variability Parameters of the Drift Diffusion Model: Expert Advice and Recommendations 109
6.1 Introduction . . . 110
6.2 Structure of the Collaboration Project . . . 113
6.3 Individual Contributions - Bayesian Estimation . . . 115
6.4 Individual Contributions - Maximum-Likelihood Estimation . . . . 131
6.5 Individual Contributions - χ2 Minimisation . . . 138
6.6 Summary . . . 142
6.7 Discussion . . . 146
7 On the Importance of Avoiding Shortcuts in Applying Cognit-ive Models to Hierarchical Data 151 7.1 Introduction . . . 152
7.2 Statistical Background . . . 155
7.3 Practical Ramifications . . . 160
7.4 Discussion . . . 172
8 Discussion and Conclusions 177 8.1 Summary and Conclusions . . . 177
8.2 Discussion and Future Directions . . . 180 A Appendix to Chapter 3: ‘On the Relationship Between Reward
Rate and Dynamic Decision Criteria’ 183 B Appendix to Chapter 4: ‘Trial-by-Trial Fluctuations in CNV
Amplitude Reflect Anticipatory Adjustment of Response
Cau-tion’ 187
C Appendix to Chapter 5: ‘Using Bayesian Regression to Test Hypotheses About Relationships Between Parameters and
Co-variates in Cognitive Models’ 195
Bibliography 201
Nederlandse Samenvatting 227
Acknowledgements 233
Publications 235
