Uncertainty-guided learning with scaled prediction errors in the basal ganglia.

ABSTRACT: To accurately predict rewards associated with states or actions, the variability of observations has to be taken into account. In particular, when the observations are noisy, the individual rewards should have less influence on tracking of average reward, and the estimate of the mean reward should be updated to a smaller extent after each observation. However, it is not known how the magnitude of the observation noise might be tracked and used to control prediction updates in the brain reward system. Here, we introduce a new model that uses simple, tractable learning rules that track the mean and standard deviation of reward, and leverages prediction errors scaled by uncertainty as the central feedback signal. We show that the new model has an advantage over conventional reinforcement lear

SUBMITTER: Moller M 

PROVIDER: S-EPMC9182698 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature