抑郁风险人群的情绪语义加工异常：基于情绪概念网络的探索

Abnormal Emotional Semantic Processing in Populations at Risk for Depression: An Exploration Based on the Emotional Concept Network

摘要: 近期的理论研究指出，概念知识通过影响情绪类别的认知结构，调节情绪面孔感知和情绪自我报告体验。这一发现揭示了情绪语义网络在情绪感知与体验中的重要作用。然而，情绪语义网络不仅与日常情绪处理密切相关，也可能在精神健康领域具有深远意义，尤其是在理解抑郁症状的认知机制方面。本研究招募抑郁风险组(n = 33)和控制组(n = 41)，采用情绪语义相似性判断任务，结合抑郁症状评估，探究抑郁症状与情绪语义网络特性的关系。研究发现，在构建的情绪语义网络中，抑郁风险组相对于控制组在情绪语义网络中具有更高的聚类系数、全局效率和局部效率，表明其情绪网络结构更加紧密、信息传递更加高效。这揭示了抑郁风险组在情绪语义加工中对负性情绪的高度敏感性和关联性，可能导致情绪调节的困难。上述发现为理解抑郁的认知机制提供了新视角，并为开发基于情绪网络的精准干预策略提供了启示。

Abstract: Recent theoretical research indicates that conceptual knowledge regulates the perception of emotional faces and the self-reported experience of emotions by influencing the cognitive structure of emotional categories. This finding reveals the significant role of the emotional semantic network in emotional perception and experience. Furthermore, the emotional semantic network is not only closely related to everyday emotional processing but may also have profound implications in the field of mental health, particularly in understanding the cognitive mechanisms of depressive symptoms. This study recruited a depression risk group (n = 33) and a control group (n = 41), employing an emotional semantic similarity judgment task combined with an assessment of depressive symptoms to explore the relationship between depressive symptoms and the characteristics of the emotional semantic network. The research found that within the constructed emotional semantic network, the depression risk group exhibited higher clustering coefficients, global efficiency, and local efficiency compared to the control group, indicating a tighter emotional network structure and more efficient information transmission. This reveals the heightened sensitivity and relevance of the depression risk group to negative emotions in emotional semantic processing, which may lead to difficulties in emotional regulation. The aforementioned findings provide a new perspective for understanding the cognitive mechanisms of depression and offer insights for developing precise intervention strategies based on emotional networks.