期刊
NEURON
卷 80, 期 6, 页码 1558-1571出版社
CELL PRESS
DOI: 10.1016/j.neuron.2013.10.024
关键词
-
资金
- NSF [SES-0851408, SES-0926544, SES-0850840]
- NIH [R01 AA018736, R21 AG038866]
- Betty and Gordon Moore Foundation
- Lipper Foundation
- Wellcome Trust
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据