Who knows what? Bayesian competence inference guides knowledge attribution and information search.

Area of Science:

• Cognitive Psychology
• Social Cognition
• Decision Science

Background:

• Inferring others' competence is crucial for social interaction, often under information scarcity.
• Previous research on Bayesian inference of competence used artificial stimuli, limiting generalizability to real-world scenarios.

Purpose of the Study:

• To investigate if people infer competence and seek evidence rationally in naturalistic settings.
• To determine if human inference aligns with Bayesian principles when using prior knowledge.

Main Methods:

• Participants predicted individuals' competence based on trivia performance.
• Computational modeling analyzed inference strategies against Bayesian models and heuristics.
• Study 3 assessed participants' selection of diagnostic information for competence assessment.

Main Results:

• Participants accurately predicted competence from limited information, replicating prior findings.
• Bayesian processes better explained participants' inferences than heuristics.
• Participants effectively identified the most informative evidence for competence evaluation.

Conclusions:

• Human social cognition approximates rational Bayesian principles in inferring competence.
• People integrate new information with prior expectations about competence effectively.
• Rationality extends to both information seeking and integration in social judgments.