Bayes optimal integration of social and endogenous uncertainty in numerosity estimation

Abstract

One of the most prominent social influences on human decision making is conformity, which is even more prominent when the perceptual information is ambiguous. The Bayes optimal solution to this problem entails weighting the relative reliability of cognitive information and perceptual signals in constructing the percept from self-sourced/endogenous and social sources, respectively. The current study investigated whether humans integrate the statistics (i.e., mean and variance) of endogenous perceptual and social information in a Bayes optimal way while estimating numerosities. Our results demonstrated adjustment of initial estimations toward group means only when group estimations were more reliable (or "certain"), compared to participants' endogenous metric uncertainty. Our results support Bayes optimal social conformity while also pointing to an implicit form of metacognition.