A Normative Account of Confirmation Bias During Reinforcement Learning.

ABSTRACT: Reinforcement learning involves updating estimates of the value of states and actions on the basis of experience. Previous work has shown that in humans, reinforcement learning exhibits a confirmatory bias: when the value of a chosen option is being updated, estimates are revised more radically following positive than negative reward prediction errors, but the converse is observed when updating the unchosen option value estimate. Here, we simulate performance on a multi-arm bandit task to examine the consequences of a confirmatory bias for reward harvesting. We report a paradoxical finding: that confirmatory biases allow the agent to maximize reward relative to an unbiased updating rule. This principle holds over a wide range of experimental settings and is most influential when decisions

SUBMITTER: Lefebvre G 

PROVIDER: S-EPMC7612695 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature