Distributional Learning is Associated with Modified Risk Preferences

Recent studies suggest that the human brain is equipped to learn not only expected outcomes, but entire distributions of possible outcomes. However, the role of this distributional learning in shaping decision-making remains unclear. To investigate this question, we designed two tasks where participants experienced different outcome distributions, estimated their properties, and reported their preferences. In a simplified observation task, participants were able to learn and report the outcome distributions they experienced. Notably, their risk preferences in this task resembled classic patterns from behavioral economics—patterns typically observed when information is provided by description rather than learned through experience. By contrast, in a more ecological choice task, distributional learning was constrained, and these preference patterns were absent. This led us to suggest that distributional learning may play a causal role in preference formation. Computational modeling supported this interpretation: Preferences were best explained by a model in which distributional learning enables the application of a utility function across possible outcomes. These findings suggest distributional learning may have a critical influence on preference formation, offering new insight into the computational foundations of human decision-making.