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

Stefano Palminteri, Germain Lefebvre, Emma J Kilford and Sarah-Jayne Blakemore

PLOS Computational Biology, 2017, vol. 13, issue 8, 1-22

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 complete feedback information (i.e., the outcomes of both the chosen and unchosen option were displayed). In the factual learning experiment, 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.Author summary: While the investigation of decision-making biases has a long history in economics and psychology, learning biases have been much less systematically investigated. This is surprising as most of the choices we deal with in everyday life are recurrent, thus allowing learning to occur and therefore influencing future decision-making. Combining behavioural testing and computational modeling, here we show that the valence of an outcome biases both factual and counterfactual learning. When considering factual and counterfactual learning together, it appears that people tend to preferentially take into account information that confirms their current choice. Increasing our understanding of learning biases will enable the refinement of existing models of value-based decision-making.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005684

DOI: 10.1371/journal.pcbi.1005684

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