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.
Additional Information© 2013 The Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Accepted: October 4, 2013; Published: December 18, 2013. We thank Tim Behrens and Matthew Rushworth for helpful discussions and comments on the manuscript. This research was supported by the NSF (SES-0851408, SES-0926544, and SES-0850840), NIH (R01 AA018736 and R21 AG038866), the Betty and Gordon Moore Foundation, the Lipper Foundation, and the Wellcome Trust (to E.D.B.).
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