Proceedings of the Annual Meeting of the Cognitive Science Society

Learning from feedback is a fundamental mechanism through which individuals make decisions under uncertainty. An important open question is how people integrate prior information with repeated feedback to improve their decisions. To address this, we conducted an experiment in which we manipulated whether participants had structural knowledge about the decision environment: half of the participants were told the number of distinct outcomes each option could produce, whereas the other half received no such information and had to infer the outcome structure from feedback alone. Structural knowledge changed behavior under partial feedback, bringing the proportion of risky choice closer to 0.5. We propose a Bayesian model that accounts for participants' initial beliefs about outcome probabilities before receiving any feedback. By capturing choice and confidence patterns across different decision environments, the model demonstrates how structural knowledge shapes learning by altering prior beliefs, thereby linking prior information to experience-based risky choice.