Pilot study of an anxiety induction method applying generative artificial intelligence and mixed reality

Worry is theorized to be a predominantly verbal, abstract thinking style that suppresses vivid mental imagery and blunts affect, thus serving avoidance. In order to test a new way to circumvent this avoidance, we examined whether personalized, AI-generated depictions of worry scenarios can elicit anxiety. These images were presented in a mixed reality worry gallery, arranged spatially and embedded in the physical environment using a mixed reality head-mounted display. Applying a within-subject repeated-measures design, we assessed subjective state anxiety and affect, as well as physiological responses (heart rate and skin conductance level) of 34 healthy adults (70.6% female, 24.47 years on average). Additional measures included representation accuracy and realism of worry depictions. Multivariate analyses of variance revealed higher, but only medium-sized anxiety ratings for AI-generated worry versus neutral images. Negative affect increased and positive affect decreased after worry-image presentation. Higher perceived realism and alignment were associated with greater anxiety. Physiological indices did not show significant increases. Our results suggest that AI-generated worry imagery can induce subjective anxiety and modulate state affect in mixed reality settings. Further work should improve stimulus reliability, include clinical samples, and benchmark mixed reality against other presentation modalities such as imaginal worry exposure to clarify equivalence and incremental value.

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Publication Details

Journal
Journal of Anxiety Disorders
Published
2026-03-14
DOI
https://doi.org/10.1016/j.janxdis.2026.103150
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
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article

Pilot study of an anxiety induction method applying generative artificial intelligence and mixed reality

Matthew McGinity, Florian Schier, Jürgen Hoyer, Daniel Badeja et al.
Journal of Anxiety Disorders
Emotion and Mood Recognition
article

Pilot study of an anxiety induction method applying generative artificial intelligence and mixed reality

Matthew McGinity, Florian Schier, Jürgen Hoyer, Daniel Badeja, Hans-Christian Straka
article en

Abstract

Worry is theorized to be a predominantly verbal, abstract thinking style that suppresses vivid mental imagery and blunts affect, thus serving avoidance. In order to test a new way to circumvent this avoidance, we examined whether personalized, AI-generated depictions of worry scenarios can elicit anxiety. These images were presented in a mixed reality worry gallery, arranged spatially and embedded in the physical environment using a mixed reality head-mounted display. Applying a within-subject repeated-measures design, we assessed subjective state anxiety and affect, as well as physiological responses (heart rate and skin conductance level) of 34 healthy adults (70.6% female, 24.47 years on average). Additional measures included representation accuracy and realism of worry depictions. Multivariate analyses of variance revealed higher, but only medium-sized anxiety ratings for AI-generated worry versus neutral images. Negative affect increased and positive affect decreased after worry-image presentation. Higher perceived realism and alignment were associated with greater anxiety. Physiological indices did not show significant increases. Our results suggest that AI-generated worry imagery can induce subjective anxiety and modulate state affect in mixed reality settings. Further work should improve stimulus reliability, include clinical samples, and benchmark mixed reality against other presentation modalities such as imaginal worry exposure to clarify equivalence and incremental value.

Journal of Anxiety DisordersVol. 119
Technische Universität Dresden (DE), Immersion (United States) (US)
Reduced inequalities
Openalex Percentile: Top 27%
Emotion and Mood Recognition
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