Bayesian Modelling Captures Inter-Individual Differences In Social Belief Computations In The Putamen And Insula

Computational models of social learning and decision-making provide mechanistic tools to investigate the neural mechanisms that are involved in understanding other people. While most studies employ explicit instructions to learn from social cues, everyday life is characterized by the spontaneous use of such signals (e.g., the gaze of others) to infer on internal states such as intentions. To investigate the neural mechanisms of the impact of gaze cues on learning and decision-making, we acquired behavioural and fMRI data from 50 participants performing a probabilistic task, in which cards with varying winning probabilities had to be chosen. In addition, the task included a computer-generated face that gazed towards one of these cards providing implicit advice. Participants' individual belief trajectories were inferred using a hierarchical Gaussian filter (HGF) and used as predictors in a linear model of neuronal activation. During learning, social prediction errors were correlated with activity in inferior frontal gyrus and insula. During decision-making, the belief about the accuracy of the social cue was correlated with activity in inferior temporal gyrus, putamen and pallidum while the putamen and insula showed activity as a function of individual differences in weighting the social cue during decision-making. Our findings demonstrate that model-based fMRI can give insight into the behavioural and neural aspects of spontaneous social cue integration in learning and decision-making. They provide evidence for a mechanistic involvement of specific components of the basal ganglia in subserving these processes. (C) 2020 The Authors. Published by Elsevier Ltd.