ISSN 1671-3710
CN 11-4766/R

Advances in Psychological Science ›› 2021, Vol. 29 ›› Issue (4): 677-696.doi: 10.3724/SP.J.1042.2021.00677

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The computational and neural substrates underlying social learning

LI Suiqing, CHEN Xinling, ZHAI Yuzhu, ZHANG Yijie, ZHANG Zhixing, FENG Chunliang   

  1. Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education; School of Psychology, South China Normal University; Center for Studies of Psychological Application, South China Normal University; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
  • Received:2020-08-10 Online:2021-04-15 Published:2021-02-23

Abstract: Social learning refers to the belief updates of others’ personal attributes and intentions as well as social norms under certain circumstances during social interactions. Due to its critical role in human decisions and social interactions, the past years have witnessed a growing body of studies that examine computational and neural basis of social learning combining computational models and human brain imaging techniques. The current literature indicates that human social learning can be well captured by reinforcement learning model and Bayesian model, based on which four computational subcomponents have been consistently identified for social learning, including subjective expectation, prediction error, uncertainty, and information integration. These computational processes have frequently engaged the involvement of brain systems associated with reward and punishment processing (e.g. ventral striatum and ventromedial prefrontal cortex), social cognition (e.g. dorsomedial prefrontal cortex and temporo-parietal junction), and cognitive control (e.g. dorsolateral prefrontal cortex). However, it should be noted that there is no one-to-one mapping between computational processes and brain regions, suggesting that multivoxel pattern analysis and brain network analysis should be utilized in future studies to reveal how different computational processes are implemented in large-scale networks according to systems neuroscience. Moreover, future studies should try to increase the ecological validity by creating real social interactions between people and by leveraging novel neuroimaging approaches (e.g. hyperscanning techniques). Finally, more efforts are needed to unravel the neural and computational signatures of implicit social learning.

Key words: social learning, computational modeling, neuroimaging, reinforcement learning model, Bayesian model

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