关键词: 社会学习, 计算模型, 神经影像, 强化学习模型, 贝叶斯模型

Abstract:

Social learning refers to the belief updates of others’ personal attributes and intentions as well as social norms under various circumstances during social interactions, which helps to optimize social decision-making and maintain positive 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. 
In the framework of reinforcement learning models, an active agent adaptively adjusts his behaviors according to the feedback in social interactions to achieve a certain goal, with  positive feedback will increasing the possibility of the previous behavior and negative feedback weakening it. Accordingly, social learning mainly engages the computation of subjective expectation and prediction error. Consistent with the findings in nonsocial learning, these computations involve brain regions associated with reward and punishment processing (e.g., the ventral striatum and ventromedial prefrontal cortex). Notably, in social situations, brain regions associated with social cognition (e.g., the dorsomedial prefrontal cortex and the temporal-parietal junctions) are also involved due to the inference of the traits and intentions of others.
Although reinforcement learning models provide powerful explanations for social learning processes, they did not account for the representation of social uncertainty. Instead, the Bayesian models assume that the social learning process follows the Bayesian information updating, and the perceived uncertainty is represented in the posterior distribution of psychological variables. Therefore, the Bayesian models can depict the representation of uncertainty. People represent their prior beliefs about others and calculate the deviation between actual feedback and prior beliefs, which is similar to the representation of subjective expectations and expected errors respectively in reinforcement learning style. In addition, representation of uncertainty and information integration are involved, engaging brain regions associated with reward and punishment processing, social cognition, 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, rather, it is in a many-to-many-pattern, that is, a single cognitive process involves multiple brain regions, and a specific brain region can be involved in multiple calculations. Therefore, 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