2025/11/11 by Marius Mercier, Olivier Morin, Hugo Mercier +1 · 1 voice
Decision Sciences · Neuroscience · Psychology · #Child and Animal Learning Development #Decision-Making and Behavioral Economics #Memory Processes and Influences
paper · doi:10.31234/osf.io/rt8ux_v1
openalex created_date 2025/11/11 · openalex publication_date 2025/11/11 · openalex updated_date 2026/08/01
One of the main challenges of social cognition is inferring the competence of others, which often occurs in contexts of limited information. Recent researchs suggest that people can successfully infer the knowledgeability of others from past accuracy, but the computational principles underlying these judgments are unknown. We test whether people can both infer and search for information about others' competence in a near-optimal way, consistent with rational Bayesian reasoning. In Studies 1 and 2, 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. Replicating and extending past results, we observe that participants very accurately predict performance from limited information. Computational modelling shows that participants' inferences are better described by Bayesian processes than by plausible heuristics, suggesting that participants rationally integrate new information with their prior expectations about others' competence. Study 3 shows that participants can select which information would be most diagnostic for inferring an individual's competence, again in a manner consistent with Bayesian rationality. Overall, our results suggest that people approximate a rational Bayesian model both when searching for and when integrating information about others' competence.