Task complexity interacts with state-space uncertainty in the arbitration between model-based and model-free learning.

ABSTRACT: It has previously been shown that the relative reliability of model-based and model-free reinforcement-learning (RL) systems plays a role in the allocation of behavioral control between them. However, the role of task complexity in the arbitration between these two strategies remains largely unknown. Here, using a combination of novel task design, computational modelling, and model-based fMRI analysis, we examined the role of task complexity alongside state-space uncertainty in the arbitration process. Participants tended to increase model-based RL control in response to increasing task complexity. However, they resorted to model-free RL when both uncertainty and task complexity were high, suggesting that these two variables interact during the arbitration process. Computational fMRI revea

SUBMITTER: Kim D 

PROVIDER: S-EPMC6915739 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature