标题： 通过扩散模型和用户驱动的再评价进行视觉基础的情绪调节 显示英文标题

标题： Visually grounded emotion regulation via diffusion models and user-driven reappraisal

摘要： 认知重新评估是情绪调节的关键策略，涉及对情绪刺激的重新解释以改变情感反应。 尽管在临床和认知科学中起着核心作用，现实世界中的重新评估干预措施仍然具有认知负担，抽象且主要依赖语言。 这种对高级认知和语言过程的依赖通常在创伤或抑郁个体中受损，限制了标准方法的效果。 在此，我们提出了一种基于视觉的新颖认知重新评估增强方法，通过将大规模文本到图像扩散模型整合到情绪调节过程中。 具体而言，我们引入了一个系统，用户通过口头重新评估来重新解释情绪负面图像，这些重新评估被转换为支持性的、情绪一致的可视化图像，使用经过微调的IP适配器的稳定扩散模型。 这种生成性转换在保持与原始刺激结构相似性的同时，将用户的重新评估视觉化，使调节意图外化并加强。 为了测试这种方法，我们进行了一项被试内实验（N = 20），使用修改后的认知情绪调节（CER）任务。 参与者对国际情感图片系统（IAPS）中的厌恶图像进行重新评估或描述，有或没有AI生成的视觉反馈。 结果表明，与非AI和对照条件相比，AI辅助的重新评估显著降低了负面情绪。 进一步分析显示，参与者重新评估与生成图像之间的情感一致性与情绪缓解相关，表明多模态一致性增强了调节效果。 这些发现表明，生成性视觉输入可以支持认知重新评估，并在生成式AI、情感计算和治疗技术的交叉领域开辟了新的方向。 显示英文摘要

摘要： Cognitive reappraisal is a key strategy in emotion regulation, involving reinterpretation of emotionally charged stimuli to alter affective responses. Despite its central role in clinical and cognitive science, real-world reappraisal interventions remain cognitively demanding, abstract, and primarily verbal. This reliance on higher-order cognitive and linguistic processes is often impaired in individuals with trauma or depression, limiting the effectiveness of standard approaches. Here, we propose a novel, visually based augmentation of cognitive reappraisal by integrating large-scale text-to-image diffusion models into the emotional regulation process. Specifically, we introduce a system in which users reinterpret emotionally negative images via spoken reappraisals, which are transformed into supportive, emotionally congruent visualizations using stable diffusion models with a fine-tuned IP-adapter. This generative transformation visually instantiates users' reappraisals while maintaining structural similarity to the original stimuli, externalizing and reinforcing regulatory intent. To test this approach, we conducted a within-subject experiment (N = 20) using a modified cognitive emotion regulation (CER) task. Participants reappraised or described aversive images from the International Affective Picture System (IAPS), with or without AI-generated visual feedback. Results show that AI-assisted reappraisal significantly reduced negative affect compared to both non-AI and control conditions. Further analyses reveal that sentiment alignment between participant reappraisals and generated images correlates with affective relief, suggesting that multimodal coherence enhances regulatory efficacy. These findings demonstrate that generative visual input can support cogitive reappraisal and open new directions at the intersection of generative AI, affective computing, and therapeutic technology.