Reward Bases: A simple mechanism for adaptive acquisition of multiple reward types.

ABSTRACT: Animals can adapt their preferences for different types of reward according to physiological state, such as hunger or thirst. To explain this ability, we employ a simple multi-objective reinforcement learning model that learns multiple values according to different reward dimensions such as food or water. We show that by weighting these learned values according to the current needs, behaviour may be flexibly adapted to present preferences. This model predicts that individual dopamine neurons should encode the errors associated with some reward dimensions more than with others. To provide a preliminary test of this prediction, we reanalysed a small dataset obtained from a single primate in an experiment which to our knowledge is the only published study where the responses of dopamine neuro

SUBMITTER: Millidge B 

PROVIDER: S-EPMC11614280 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature