Cross-modal Synchrony Between Music and Visual Motion Modulates Vection, Urge to Move, and Comfort in VR
Bibliographic record
Abstract
Sensorimotor entrainment, the spontaneous alignment of movement with external rhythms, plays a key role in how we experience music and motion. In this study, we introduce a novel method combining musical stimuli, vection, and visually induced motion sickness (VIMS) in virtual reality (VR) to investigate how cross-modal rhythmic stimulation shapes self-motion perception and comfort.In a within‐subjects design with 30 participants, we manipulated auditory conditions (Music versus Silence) and VR conditions (Realistic, Static, Isochronous, and Non-isochronous) during eight‐second trials. Participants rated their urge to move, perceived self‐motion, and comfort. Additionally, head motion data were analyzed for movement quantity, variability, spectral power, and intertrial phase coherence at the musical beat frequency. Results showed that music significantly increased the urge to move, particularly when paired with rhythmic visual motion. Both Isochronous and Non-isochronous visual motion reliably induced vection with no additional effect of musical beat alignment on illusion strength. Crucially, music reduced discomfort induced by rhythmic visual motion by over 20%, an effect that may have been further amplified by cross-modal synchrony.By leveraging sensorimotor entrainment elicited by music and rhythmic visual motion, this study provides new insights into how cross-modal rhythms shape perceptual and affective responses. By highlighting the potential of music to enhance engagement and reduce motion sickness, our findings pave the way for more immersive, comfortable, and musically enriched virtual experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".