自动化情绪调节对抑郁症状的干预及作用机制&#x0002A;

李亚琴; 袁加锦

doi:10.16719/j.cnki.1671-6981.20250618

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心理科学 ›› 2025, Vol. 48 ›› Issue (6) : 1516-1526. DOI: 10.16719/j.cnki.1671-6981.20250618

理论与史

自动化情绪调节对抑郁症状的干预及作用机制^*

李亚琴, 袁加锦^**

作者信息 +

Intervention Effects of Automatic Emotion Regulation on Depressive Symptoms and Mechanisms

Li Yaqin, Yuan Jiajin

Author information +

文章历史 +

摘要

抑郁的核心认知特征之一是认知努力匮乏和情绪调节困难。自动化情绪调节相较于有意情绪调节不消耗或消耗较少的认知资源,因此被应用于抑郁症状的干预。然而,其干预效果尚无定论。本文整合以往研究发现,自动化情绪调节在干预抑郁症状方面具有一定成效,且相关认知过程(如注意、记忆)及眶额叶皮层、杏仁核、丘脑、前扣带回、纹状体和梭状回等区域的神经活动可能是潜在机制。个体差异性和自动化情绪调节类型也可能影响干预效果。未来应重点探讨自动化情绪调节在抑郁干预中的作用机制,考虑个体差异性的影响,考察其持续性和泛化效应以提高干预的生态效度,并进行策略优化。上述工作将有助于了解自动化情绪调节干预抑郁症状的全貌,促进其精准干预。

Abstract

One of the core cognitive features of depression is a lack of cognitive effort and difficulty in emotion regulation. Given that voluntary emotion regulation consumes significant cognitive resources, it may be less effective in downregulating depressive symptoms. Considering that automatic emotion regulation (AER) can reduce negative emotional experiences while consuming little or no cognitive resources, researchers have employed this approach as an intervention for depression. However, its effectiveness remains inconclusive. Therefore, this study systematically reviews previous research to explore the intervention effects of automatic emotion regulation and to examine its underlying mechanisms. Specifically, it first summarizes the intervention effects of automatic emotion regulation on depressive symptoms, explores the underlying mechanisms involved in these effects, and proposes new perspectives based on the findings.
This study suggests that automatic emotion regulation is an effective intervention for depressive symptoms. Moreover, cognitive processes such as attention and memory, as well as neural activities in brain regions including the orbitofrontal cortex, amygdala, and anterior cingulate gyrus, might serve as key neural and psychological mechanisms.
Future research should further investigate the roles of these regions in automatic emotion regulation for depressive symptoms. It is also important to consider individual differences that may affect intervention outcomes, particularly factors such as age, gender, and the severity of depressive symptoms. A substantial body of research suggests that these variables may influence intervention effectiveness. However, the specific ways in which age, gender, and the severity of depressive symptoms affect intervention outcomes remain unclear and require further exploration.
In addition, future research should focus on the sustainability and generalization of automatic emotion regulation. The sustainability of AER refers to its capacity to exert lasting regulatory effects on negative emotions over time. The generalization effect of AER refers to the extension of regulation from specified to unspecified situations. Although the sustainability and generalization effects of automatic emotion regulation have been demonstrated in healthy populations, they have not been thoroughly examined in individuals with depression. Future studies should address this gap, including among patients with comorbid anxiety and depression.
Nonetheless, numerous studies exploring automatic emotion regulation for depression have been conducted in laboratory settings, which limits their ecological validity. Thus, it is crucial to improve the ecological validity of automatic emotion regulation for depressive intervention. For instance, depressive individuals with impaired error monitoring have more difficulty coping with errors during uncertain situations, which potentially exacerbates their depressive symptoms. Therefore, understanding how depressive individuals cope with errors and regulate emotions in uncertain contexts is particularly important. However, no studies to date have examined error-processing characteristics of depressed individuals in uncertain situations and assessed intervention effectiveness. Future studies should address this gap. In addition, since automatic emotion regulation can serve as a potential intervention for depressive symptoms, it is important to examine its effects on depressive individuals in uncertain situations to enhance ecological validity. Furthermore, some studies have suggested that the integration of multiple psychotherapies may be more helpful for the intervention and treatment of depression. Thus, combining automatic emotion regulation with cognitive behavioral therapy or acceptance and commitment therapy may be a more effective intervention for depression.
Finally, future studies should compare the intervention effects of various types of automatic emotion regulation strategies. Specifically, previous studies have primarily compared automatic emotion regulation with voluntary emotion regulation and control conditions, without examining differences among various AER strategies. In this regard, future studies should explore this, such as comparing strategies based on implementation intention with those based on goal priming. On the other hand, it is also necessary to compare the regulatory efficacy of different strategies in a specific form of automatic emotion regulation, such as comparing the effect of cognitive reappraisal and attentional distraction in the form of goal priming. These will contribute to a comprehensive understanding of automatic emotion regulation for depression interventions.

导出引用

李亚琴, 袁加锦. 自动化情绪调节对抑郁症状的干预及作用机制^*[J]. 心理科学. 2025, 48(6): 1516-1526 https://doi.org/10.16719/j.cnki.1671-6981.20250618

Li Yaqin, Yuan Jiajin. Intervention Effects of Automatic Emotion Regulation on Depressive Symptoms and Mechanisms[J]. Journal of Psychological Science. 2025, 48(6): 1516-1526 https://doi.org/10.16719/j.cnki.1671-6981.20250618

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