Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences.

ABSTRACT: In economics and perceptual decision-making contextual effects are well documented, where decision weights are adjusted as a function of the distribution of stimuli. Yet, in reinforcement learning literature whether and how contextual information pertaining to decision states is integrated in learning algorithms has received comparably little attention. Here, we investigate reinforcement learning behavior and its computational substrates in a task where we orthogonally manipulate outcome valence and magnitude, resulting in systematic variations in state-values. Model comparison indicates that subjects' behavior is best accounted for by an algorithm which includes both reference point-dependence and range-adaptation-two crucial features of state-dependent valuation. In addition, we find tha

SUBMITTER: Bavard S 

PROVIDER: S-EPMC6206161 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature