Feeling the Beat: Bouncing Synchronization to Vibrotactile Music in Hearing and Early Deaf People
Bibliographic record
Abstract
The ability to dance relies on the ability to synchronize movements to a perceived musical beat. Typically, beat synchronization is studied with auditory stimuli. However, in many typical social dancing situations, music can also be perceived as vibrations when objects that generate sounds also generate vibrations. This vibrotactile musical perception is of particular relevance for deaf people, who rely on non-auditory sensory information for dancing. In the present study, we investigated beat synchronization to vibrotactile electronic dance music in hearing and deaf people. We tested seven deaf and 14 hearing individuals on their ability to bounce in time with the tempo of vibrotactile stimuli (no sound) delivered through a vibrating platform. The corresponding auditory stimuli (no vibrations) were used in an additional condition in the hearing group. We collected movement data using a camera-based motion capture system and subjected it to a phase-locking analysis to assess synchronization quality. The vast majority of participants were able to precisely time their bounces to the vibrations, with no difference in performance between the two groups. In addition, we found higher performance for the auditory condition compared to the vibrotactile condition in the hearing group. Our results thus show that accurate tactile-motor synchronization in a dance-like context occurs regardless of auditory experience, though auditory-motor synchronization is of superior quality.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".