The Behavioral and Neural Mechanisms Underlying the Tracking of Expertise

Evaluating the abilities of others is fundamental for successful economic and social behavior. We investigated the computational and neurobiological basis of ability tracking by designing an fMRI task that required participants to use and update estimates of both people and algorithms’ expertise through observation of their predictions. Behaviorally, we find a model-based algorithm characterized subject predictions better than several alternative models. Notably, when the agent’s prediction was concordant rather than discordant with the subject’s own likely prediction, participants credited people more than algorithms for correct predictions and penalized them less for incorrect predictions. Neurally, many components of the mentalizing network—medial prefrontal cortex, anterior cingulate gyrus, temporoparietal junction, and precuneus—represented or updated expertise beliefs about both people and algorithms. Moreover, activity in lateral orbitofrontal and medial prefrontal cortex reflected behavioral differences in learning about people and algorithms. These findings provide basic insights into the neural basis of social learning. •We use our own likely actions and others’ explicit accuracy to track their expertise•We do this differently when observing humans compared to algorithms•Expertise beliefs and updates are tracked by brain regions linked to mentalizing•lOFC and mPFC mediate differences in expertise learning for humans and algorithms People learn about others’ expertise by considering their own likely actions and others’ accuracy but do this differently for humans and nonhuman agents. Boorman et al. show that key expertise learning signals are encoded by brain regions linked to theory of mind and contingent learning.