RT Journal Article
SR Electronic
T1 Valence biases factual and counterfactual learning in opposite directions
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 090654
DO 10.1101/090654
A1 Palminteri, Stefano
A1 Lefebvre, Germain
A1 Kilford, Emma J.
A1 Blakemore, Sarah-Jayne
YR 2017
UL http://biorxiv.org/content/early/2017/01/17/090654.abstract
AB 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 cohorts of participants on reinforcement learning tasks using a computational model that was adapted to test if prediction error valance influences learning. Concerning factual learning, we replicated previous findings of a valence-induced bias, whereby participants learned preferentially from positive, relative to negative, prediction errors. In contrast, for counterfactual learning, we found the opposite valence-induced bias: negative prediction errors were preferentially taken into account relative to positive ones. When considering valence-induced bias in the context of both factual and counterfactual learning, it appears that people tend to preferentially take into account information that confirms their current choice