关键词: 社会认知, 社交焦虑, 认知计算, 认知神经机制, 脑成像

Abstract:

Social anxiety disorder (SAD) is among the most common anxiety disorders, marked by overwhelming fear and avoidance of social behaviors and social scenarios, and debilitates patients’ lives and work. Previous studies have provided ample evidence of dysregulated social cognition in social anxiety, such as negative cognitive biases, demonstrating a negative processing of social information. However, the factors driving the dysregulated social cognition remain unclear, impeding the elucidation of the underlying computational neural mechanisms of social anxiety symptoms and guiding personalized interventions. Within the Bayesian framework, the current project proposed that the negative cognitive biases phenomenon may stem from negative prior expectations. By integrating psychophysics experiments, electroencephalography (EEG), functional magnetic resonance imaging (fMRI), computational modeling, and machine learning, we will systematically investigate prior expectations' characteristics, formation, and dynamic modulation.
The key innovation of this project lies in three major contributions. First, this study will be the first to propose and quantitatively examine the impact of prior expectations on dysregulated social cognition. Previous socially anxious research has primarily focused on behavioral manifestations and their associations with social information processing, yet largely overlooked the role of prior expectations in shaping social cognitive distortions. By identifying the static features and dynamic formation process of prior expectations in dysregulated social cognition, our study expands existing cognitive-behavioral models of social anxiety, providing a more comprehensive framework for understanding its underlying mechanisms.
Second, our project aims to construct a mechanistic framework of social anxiety that systematically links behavioral manifestations to cognitive mechanisms, and further to neural mechanisms. By integrating behavioral experiments, neuroimaging, and computational modeling as methodological tools, we are able to map distorted cognitive components onto their specific neural underpinnings. This integrative approach provides robust empirical evidence, thereby advancing the theoretical understanding of social anxiety and offering a foundation for future research and intervention development.
Third, this project extends the investigation to the translational level by evaluating the potential of neural decoding feedback as an intervention for social anxiety. By leveraging real-time neural data to modulate maladaptive social-cognitive expectations, we aim to assess the feasibility of neurofeedback-based treatments in social anxiety, providing a potential pathway for developing novel, data-driven therapeutic strategies.
In summary, this project not only advances the theoretical understanding of social anxiety but also explores its translational potential. By extending the traditional cognitive-behavioral model to incorporate prior expectations and constructing a comprehensive behavioral-cognitive-neural framework, it systematically maps the progressive linkage from behavioral manifestations to cognitive processes and neural underpinnings—offering a novel perspective for studying anxiety-related disorders.
Importantly, this project goes beyond theoretical contributions by identifying specific intervention targets derived from our computational framework and assessing their clinical applicability through neurofeedback. By leveraging real-time neural decoding to modulate maladaptive prior expectations, we aim to evaluate the efficacy of a novel, data-driven intervention approach. This translational effort holds promise for the development of precision-targeted treatments that can significantly enhance therapeutic outcomes for individuals with SAD.
By elucidating the mechanisms underlying dysregulated social cognition through an integrative, multi-level approach, this project lays the foundation for a paradigm shift in both research and clinical practice. We encourage the broader adoption of computational psychiatry methods, redefine the understanding of dysregulated social cognition in social anxiety, and bridge the gap between mechanistic theory and personalized intervention. Ultimately, this work paves the way toward a new era of individualized, mechanism-informed mental health care empowered by technological innovation and theoretical precision.

Key words: social cognition, social anxiety, cognitive modeling, cognitive neural mechanisms, brain imaging