4.7 Review

Neural mechanisms of social learning and decision-making

期刊

SCIENCE CHINA-LIFE SCIENCES
卷 64, 期 6, 页码 897-910

出版社

SCIENCE PRESS
DOI: 10.1007/s11427-020-1833-8

关键词

social cognition; decision-making; reinforcement learning; value; altruism

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资金

  1. National Natural Science Foundation of China [31371019, 31871140]
  2. Postdoctoral Fellowship of Peking-Tsinghua Center for Life Sciences

向作者/读者索取更多资源

The article examines how the inference about others is dynamically acquired during social learning and how prosocial behavior results from interactions between different brain regions. It emphasizes the importance of combining computational decision theory with the identification of neural mechanisms for guiding behavioral output in complex social environments.
One of the hallmarks of human society is the ubiquitous interactions among individuals. Indeed, a significant portion of human daily routine decision making is socially related. Normative economic theory, namely game theory, has prescribed the canonical decision strategy when rational social agents have full information about the decision environment. In reality, however, social decision is often influenced by the trait and state parameters of selves and others. Therefore, understanding the cognitive and neural processes of inferring the decision parameters is pivotal for social decision making. Recently, both correlational and causal non-invasive neuroimaging studies have started to reveal the critical neural computations underlying social learning and decision-making, and highlighted the unique roles of social brain structures such as temporal-parietal junction (TPJ) and dorsomedial prefrontal cortex (dmPFC). Here we review recent advances in social decision neuroscience and maintain the focus on how the inference about others is dynamically acquired during social learning, as well as how the prosocial (altruistic) behavior results from orchestrated interactions of different brain regions specified under the social utility framework. We conclude by emphasizing the importance of combining computational decision theory with the identification of neural mechanisms that represent, evaluate and integrate value related social information and generate decision variables guiding behavioral output in the complex social environment.

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