Ambiguity and confirmation bias in reward learning

We tend to interpret feedback in ways that confirm our pre-existing beliefs. This confirmation bias is often treated as irrational, but may have adaptive foundations. In this project, we propose a new Bayesian computational model of confirmation bias and a novel experimental paradigm to study its impact on learning. When faced with an ambiguous outcome, confirmation bias may constitute an inductive bias that speeds up learning, analogous to missing data imputation. We test this theory using a reward learning task in which participants are only provided partial information about outcomes, allowing more leeway for subjective interpretation. We find that our Bayesian model better explains the dynamics of behavior and stated beliefs compared to more traditional learning models, supporting an adaptive basis for confirmation biased learning from repeated feedback. Moreover, participants higher in trait optimism have more positive beliefs about ambiguous outcomes.