Intrinsic rewards explain context-sensitive valuation in reinforcement learning.

ABSTRACT: When observing the outcome of a choice, people are sensitive to the choice's context, such that the experienced value of an option depends on the alternatives: getting $1 when the possibilities were 0 or 1 feels much better than when the possibilities were 1 or 10. Context-sensitive valuation has been documented within reinforcement learning (RL) tasks, in which values are learned from experience through trial and error. Range adaptation, wherein options are rescaled according to the range of values yielded by available options, has been proposed to account for this phenomenon. However, we propose that other mechanisms-reflecting a different theoretical viewpoint-may also explain this phenomenon. Specifically, we theorize that internally defined goals play a crucial role in shaping the sub

SUBMITTER: Molinaro G 

PROVIDER: S-EPMC10374061 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature