Proceedings of the Annual Meeting of the Cognitive Science Society

Inferring others' competence is a challenge of social cognition, often occurring in contexts of limited information. Recent research suggests that people can infer the competence of others through Bayesian inference, but it is unclear whether these rational principles generalize to naturalistic settings of knowledge attribution. Using trivia questionnaires, we test whether people can infer others' competence and search for informative evidence in a way consistent with a rational Bayesian model. In Study 1, participants were presented with an individual's performance on a trivia question and predicted the individual's ability to answer other trivia questions from the same theme. Participants accurately predicted performance from limited information. Study 2 shows that participants can select which information would be most diagnostic for inferring an individual's competence. Computational modelling shows that participants' inferences, both when searching for and when integrating information about others' competence, are better described by Bayesian processes than by plausible heuristics.