Choosing and learning: outcome valence differentially affects learning from free versus forced choices

Abstract

Positivity bias refers to learning more from positive than negative events. This learning asymmetry could either reflect a preference for positive events in general, or be the upshot of a more general, and perhaps, ubiquitous, \"choice-confirmation\" bias, whereby agents preferentially integrate information that confirms their previous decision. We systematically compared these two theories with 3 experiments mixing free- and forced-choice conditions, featuring factual and counterfactual learning and varying action requirements across \"go\" and \"no-go\" trials. Computational analyses of learning rates showed clear and robust evidence in favour of the \"choice-confirmation\" theory: participants amplified positive prediction errors in free-choice conditions while being valence-neutral on forced-choice conditions. We suggest that a choice-confirmation bias is adaptive to the extent that it reinforces actions that are most likely to meet an individuals needs, i.e. freely chosen actions. In contrast, outcomes from unchosen actions are more likely to be treated impartially, i.e. to be assigned no special value in self-determined decisions.