A Bayesian perspective on observers' inference of group norms.

Inferring group norms is crucial for adapting behaviors in novel situations, but its underlying basis and computational account remain unclear. This study manipulated the prevalence of norm-consistent behaviors (i.e., straight-line movements) to examine whether and how norm inference is influenced by observed group behavior, exploring its consistency with Bayesian updating, robustness, and independence. The results revealed no significant difference in prior probabilities regarding the existence of group norms across conditions, but posterior probabilities increased with the prevalence of norm-consistent behaviors. Furthermore, the Bayesian inference model outputs positively predicted participants' judgments, indicating that norm inference aligned with Bayesian updating. Even in the presence of deviant behaviors, norm inference remained consistent with Bayesian principles, demonstrating its robustness. Finally, the study revealed that individuals could infer group norms from observed behaviors, independent of desire inferences. These findings enhance our understanding of how individuals navigate group norms in novel situations.