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

Edoardo Pinzuti1,2, Oliver Tüscher1,2,3,4, André Ferreira Castro5

  • 1Leibniz Institute for Resilience Research, Mainz, Germany.

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AI-powered visual feedback enhances cognitive reappraisal for emotion regulation. This novel approach uses generative AI to create supportive images, significantly reducing negative affect compared to traditional methods.

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AI-generated visual feedbackaffective computingaffective responsesaversive imagescognitive emotion regulation (CER) taskcognitive reappraisaldiffusion modelsemotion regulation

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Area of Science:

  • Cognitive psychology
  • Affective computing
  • Human-computer interaction

Background:

  • Cognitive reappraisal is a core emotion regulation strategy, but its effectiveness is limited by its abstract and verbal nature, especially for individuals with trauma or depression.
  • Current reappraisal techniques are cognitively demanding and may not be suitable for all populations.

Purpose of the Study:

  • To introduce and evaluate a novel, visually-based augmentation of cognitive reappraisal using generative AI.
  • To explore the potential of text-to-image diffusion models to support emotion regulation.

Main Methods:

  • A within-subjects experiment (N=20) using a modified cognitive emotion regulation task.
  • Participants used spoken reappraisals on negative images, with or without AI-generated visual feedback from stable diffusion models.
  • Evaluated the impact of AI-assisted reappraisal on negative affect reduction.

Main Results:

  • AI-assisted reappraisal significantly reduced negative affect compared to non-AI reappraisal and control conditions.
  • Sentiment alignment between reappraisals and generated images correlated with greater affective relief.
  • Demonstrated the feasibility of using generative visual support to enhance regulatory efficacy.

Conclusions:

  • Generative AI can be effectively integrated into cognitive reappraisal for emotion regulation.
  • Visually-based AI feedback offers a promising avenue for therapeutic technology design in mental health.
  • Multimodal coherence between user input and AI output enhances emotional regulation outcomes.