人际互动中社会学习的计算神经机制

人类在社会互动中通过他人的行为对他人特质、意图及特定情境下的社会规范进行学习, 是优化决策、维护积极社会互动的重要条件。近年来, 越来越多的研究通过结合计算模型与神经影像技术对社会学习的认知计算机制及其神经基础进行了深入考察。已有研究发现, 人类的社会学习过程能够较好地被强化学习模型与贝叶斯模型刻画, 主要涉及的认知计算过程包括主观期望、预期误差和不确定性的表征以及信息整合的过程。大脑对这些计算过程的执行主要涉及奖惩加工相关脑区(如腹侧纹状体与腹内侧前额叶)、社会认知加工相关脑区(如背内侧前额叶和颞顶联合区)及认知控制相关脑区(如背外侧前额叶)。需要指出的是, 计算过程与大脑区域之间并不是一一映射的关系, 提示未来研究可借助多变量分析与脑网络分析等技术从系统神经科学的角度来考察大尺度脑网络如何执行不同计算过程。此外, 将来研究应注重生态效度, 利用超扫描技术考察真实互动下的社会学习过程, 并更多地关注内隐社会学习的计算与神经机制。

The computational and neural substrates underlying social learning

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.