Learning and decision making are interactive processes, yet cognitive modelling of error-driven learning and decision making have largely evolved separately. Recently, evidence accumulation models (EAMs) of decision making and reinforcement learning (RL) models of error-driven learning have been combined into joint RL-EAMs that can in principle address these interactions. However, we show that the most commonly used combination, based on the diffusion decision model (DDM) for binary choice, consistently fails to capture crucial aspects of response times observed during reinforcement learning. We propose a new RL-EAM based on an advantage racing diffusion (ARD) framework for choices among two or more options that not only addresses this problem but captures stimulus difficulty, speed-accuracy trade-off, and stimulus-response-mapping reversal effects. The RL-ARD avoids fundamental limitations imposed by the DDM on addressing effects of absolute values of choices, as well as extensions beyond binary choice, and provides a computationally tractable basis for wider applications.
All data analysed in this study are available from https://osf.io/ygrve/
A new model of decision processing in instrumental learning tasksOpen Science Framework, ygrve.
- Birte U Forstmann
- Andrew Heathcote
- Andrew Heathcote
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Human subjects: Informed consent was obtained in all experiments prior to the experiment onset. The local ethics board of the University of Amsterdam, Department of Psychology approved the study, with reference numbers 2018-BC-9620 (experiment 1), 2019-BC-10672 (experiment 2), 2019-BC-10250 (experiment 3), and 2020-BC-12788 (experiment 4).
- Valentin Wyart, École normale supérieure, PSL University, INSERM, France
© 2021, Miletić et al.
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