Abstract
Recent landmark studies show that the brain is equipped to learn not just average expected outcomes, but entire distributions of expected outcomes. Yet the role of such distributional learning in shaping human decision-making remains to be determined. To study this question, we designed two tasks where participants experienced different outcome distributions, provided their estimates of each, and reported their preferences among them. In one task, which facilitated distributional learning, participants' preferences significantly diverged from their own estimates, consistent with predictions of Prospect Theory. Conversely, in a task that hindered distributional learning, the divergence of preferences from estimates was eliminated. Computational modelling showed how distributional learning may be responsible for disassociating preferences from estimations by enabling the application of a utility function to different potential outcomes. Our findings offer a new understanding of when and how preferences deviate from normative decision-making, a fundamental question in the study of human rationality.