Social learning with a grain of salt

Abstract

Humans are remarkably effective social learners, with several recent studies formalizing this capacity using computational models. However, previous research has often been limited to tasks where observer and demonstrator share the same reward function. In contrast, humans can learn from others who have different preferences, skills, or goals. To study social learning under individual differences, we introduce the socially correlated bandit, where participants have personalized rewards, which are correlated with but not identical to those of others. Social information can still be useful, but not when used verbatim. We present a model of Social Generalization that integrates individual and social information into the generalization process, but assumes social information to be noisier and thus less informative. This model out-competes previous models, with it being the dominant strategy in evolutionary simulations. Our findings expand on previous models of social learning, showing humans can integrate social information more flexibly than previously assumed.

Humans flexibly integrate social information despite interindividual differences in reward

“…As the baseline asocial learning model is nested in all social models, we determined bounds for the social models to minimize model mimicry, and thus improve recovery. This serves to make the modelling more stringent compared to previous work 62 . The bounds were determined based on the social mechanisms of the respective models.…”