Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing.

ABSTRACT: Previous studies suggest that factual learning, that is, learning from obtained outcomes, is biased, such that participants preferentially take into account positive, as compared to negative, prediction errors. However, whether or not the prediction error valence also affects counterfactual learning, that is, learning from forgone outcomes, is unknown. To address this question, we analysed the performance of two groups of participants on reinforcement learning tasks using a computational model that was adapted to test if prediction error valence influences learning. We carried out two experiments: in the factual learning experiment, participants learned from partial feedback (i.e., the outcome of the chosen option only); in the counterfactual learning experiment, participants learned from

SUBMITTER: Palminteri S 

PROVIDER: S-EPMC5568446 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature