A new model of decision processing in instrumental learning tasks

Learning and decision-making are interactive processes, yet cognitive modeling 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 comb...

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Bibliographic Details
Published in:eLife 2021-01, Vol.10
Main Authors: Miletić, Steven, Boag, Russell J, Trutti, Anne C, Stevenson, Niek, Forstmann, Birte U, Heathcote, Andrew
Format: Article
Language:English
Subjects:
Adult
Behavior
Cognition & reasoning
Cognitive ability
computational modelling
Conditioning, Operant
Decision Making
evidence accumulation
Female
Humans
Male
Neuroscience
Observational learning
Reaction Time
Reinforcement
reinforcement learning
Reinforcement, Psychology
value-based decision making
Young Adult
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Summary:Learning and decision-making are interactive processes, yet cognitive modeling 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.
ISSN:2050-084X
2050-084X
DOI:10.7554/eLife.63055