A computational account of optimizing social predictions reveals that adolescents are conservative learners in social contexts

Abstract

As adolescents transition to the complex world of adults, optimizing predictions about others’ preferences becomes vital for successful social interactions. Mounting evidence suggests that these social learning processes are affected by ongoing brain development across adolescence. A mechanistic understanding of how adolescents optimize social predictions and how these learning strategies are implemented in the brain is lacking. To fill this gap, we combined computational modeling with functional neuroimaging. In a novel social learning task, male and female human adolescents and adults predicted the preferences of peers and could update their predictions based on trial-by-trial feedback about the peers’ actual preferences. Participants also rated their own preferences for the task items and similar additional items. To describe how participants optimize their inferences over time, we pitted simple reinforcement learning models against more specific “combination” models, which describe inferences based on a combination of reinforcement learning from past feedback and participants’ own preferences. Formal model comparison revealed that, of the tested models, combination models best described how adults and adolescents update predictions of others. Parameter estimates of the best-fitting model differed between age groups, with adolescents showing more conservative updating. This developmental difference was accompanied by a shift in encoding predictions and the errors thereof within the medial prefrontal and fusiform cortices. In the adolescent group, encoding of own preferences and prediction errors scaled with parent-reported social traits, which provides additional external validity for our learning task and the winning computational model. Our findings thus help to specify adolescent-specific social learning processes.