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

2025-07-14

AI Summary

Conventional cognitive reappraisal relies heavily on higher-order linguistic and abstract reasoning, limiting its efficacy for individuals with trauma or depression. Method: We propose “visualized cognitive reappraisal”—a novel paradigm wherein users verbally reinterpret negative emotional images in colloquial language; a fine-tuned Stable Diffusion model augmented with IP-Adapter then generates visually faithful, emotionally positive, and affectively consistent reconstructions. This approach enhances embodiment and cross-modal alignment, particularly benefiting populations with impaired linguistic capacity. Results: In a pilot study (N=20), AI-assisted reappraisal significantly reduced negative affect; moreover, higher affective consistency between generated images and reappraisal intent predicted stronger emotion regulation. Our core contribution is the first deep integration of generative AI into a closed-loop cognitive reappraisal framework, enabling aligned, quantifiable transformation from linguistic reappraisal to embodied visual feedback.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Computer Vision: Diffusion Models for Vision
• Humans and AI: Emotional Intelligence

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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.

Problem

• Enhancing emotion regulation through visual reinterpretation
• Reducing cognitive demand in reappraisal interventions
• Improving affective relief with AI-generated visual feedback

Innovation

• Diffusion models transform spoken reappraisals into visuals
• Fine-tuned IP-adapter maintains structural stimulus similarity
• Multimodal coherence enhances emotion regulation efficacy