参与式社会互动中脑间同步的自我-他人重叠机制

Abstract

Inter-brain synchronization (IBS) is a phenomenon in which individuals' neural activities tend to synchronize because of mutual influence during social interactions. This cross-species phenomenon is an inherent characteristic of mammals, including humans, and is closely linked to information transmission within groups. Research on attachment theory has shown that individuals display varying degrees of synchronization when interacting with companions at different levels of interpersonal closeness. Consequently, researchers have posited that the significance of IBS lies in the connections built between individuals.With advancements in hyper-scanning technology, researchers have increasingly advocated for the use of second-person neuroscience to explore IBS. Researchers have reached a consensus on the underlying mechanisms of IBS, attributing its foundation to shared representations. Building upon this, mutual prediction theory has been proposed, positing that IBS arises from individuals successfully predicting the behavior of their peers. This process is associated with the degree to which others are incorporated into one's self-representation (i.e., self-other overlap). However, limited research has focused on the relationship between IBS and self-other overlap. Thus, this study reviews recent second-person neuroscience research and suggests that self-other overlap serves as a latent mechanism for IBS. By incorporating others into their self-representation, individuals can enhance their accuracy in predicting other's minds and behaviors, leading to the formation of IBS. Furthermore, the medial prefrontal cortex plays a crucial role in this process.By elucidating the self-other overlap mechanism underlying IBS, this study's findings contribute to the current understanding of how group thoughts and emotions are transmitted, providing new insights for diagnosing and treating specific groups. Future research can explore the self-other overlap mechanism of IBS through approaches such as computational modeling, focusing on how self-other overlap influences the transmission of group emotions and corrects external group biases.