Computational mechanisms of distributed value representations and mixed learning strategies.

ABSTRACT: Learning appropriate representations of the reward environment is challenging in the real world where there are many options, each with multiple attributes or features. Despite existence of alternative solutions for this challenge, neural mechanisms underlying emergence and adoption of value representations and learning strategies remain unknown. To address this, we measure learning and choice during a multi-dimensional probabilistic learning task in humans and trained recurrent neural networks (RNNs) to capture our experimental observations. We find that human participants estimate stimulus-outcome associations by learning and combining estimates of reward probabilities associated with the informative feature followed by those of informative conjunctions. Through analyzing representations

SUBMITTER: Farashahi S 

PROVIDER: S-EPMC8664930 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature