The relationship between trait mindfulness and psychotic-like experiences in a brief AI-generated music listening context: the roles of presence, perceived interactivity, and emotional arousal
Chenghan Zhang, Hao Huang, Guotao Wu, Mengke Luo
Frontiers in Psychiatry July 28, 2026 DOI: 10.3389/fpsyt.2026.1859243 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Cross-sectional survey Peer reviewed |
|---|---|
| Sample size | 527 |
| Population | Chinese participants |
| Intervention | AI-generated music listening |
| Topics | Meditation |
| Key findings | Trait mindfulness was negatively associated with psychotic-like experiences, and this link appeared to operate through presence and perceived interactivity during brief AI-generated music listening. Arousal moderated the relationship, with higher arousal strengthening mindfulness's positive prediction of both presence and perceived interactivity. The authors argue these pathways offer a basis for designing mental health-oriented AI music products. |
Abstract
Background and
Objective: Within the interdisciplinary field of cyberpsychology and mental health, trait mindfulness has been associated with lower levels of subclinical anomalous symptoms, such as Psychotic-Like Experiences (PLEs), has gained increasing attention. However, in the context of daily digital human-computer interactions (e.g., listening to AI-generated music), the specific pathways through which Mindfulness operates (the involvement with Presence and Perceived Interactivity) and the boundary conditions of physiological arousal, remain to be clarified. This study aims to explore the direct predictive relationship between mindfulness and individuals' PLEs, and to investigate the multipath effect of brief AI-generated music listening context (with Presence and Perceived Interactivity), along with the moderating effect of Arousal.
Methods: With a cross-sectional survey design, self-reported multimodal data were collected from 527 Chinese participants. Structural equation modeling (SEM) was conducted using Mplus 8.3 to empirically test the main effects (path coefficients) of the theoretical hypotheses and the moderation model.
Results: Both the measurement and structural models demonstrated good fit. The path analysis results indicated that: (1) trait trait mindfulness was significantly and negatively associated with PLEs (p < 0.001); (2) regarding the main effect paths of brief AI-generated music listening context, mindfulness significantly and positively predict individuals' Presence and Perceived Interactivity, while both significantly and negatively predict PLEs; (3) Arousal played a significant moderating role in the relationship between mindfulness and brief AI-generated music listening context, exhibiting a synergistic enhancement effect. Higher levels of Arousal significantly amplified the positive prediction of Mindfulness on both Presence and Perceived Interactivity (p < 0.01).
Conclusions: With the help the SEM, this study maps out the underlying multipath network through which mindfulness is associated with lower PLEs within a brief AI-generated music listening context. The observed associations suggest that presence and perceived interactivity may function as pivotal correlational nodes relevant to mental health correlates, while these results also nuance classic cognitive load assumptions by indicating a potential synergistic association between trait mindfulness and emotional arousal. These results provide a solid empirical foundation and prospective insights, for the mental health-oriented design of AI music products, such as the immersive acoustic environment construction and dynamic, arousal-based interaction recommendations.