Isolating the effect of beat salience on RAS outcomes
Bibliographic record
Abstract
Abstract Rhythmic auditory stimulation (RAS) is an intervention for gait-disordered populations that involves synchronizing footsteps to regular auditory cues. Previous research has shown that high-groove music (music that induces the desire to move or dance to it) improves gait relative to low-groove music, but how this effect occurs is unclear. Greater beat salience in high-groove music may improve gait because salient beats are easier to synchronize with. Here, we manipulated beat salience by embedding metronome tones to emphasize beat onsets in both high- and low-groove music. We expected that, if beat salience drives gait improvements to high-groove music, then embedding metronome in low-groove music would elicit similar gait improvements (e.g. increased stride velocity). Here, we quantified gait synchronization in terms of period-matching (overall step rate to the cue pace) and phase-matching (individual step onsets to beat onsets). We tested a sample of healthy younger and older adults, with auditory cues matched to 10% faster than baseline. Low-groove music with embedded metronome, compared to without, elicited better period-matching; there were no differences between metronome conditions in high-groove music. These findings suggest gait improvements to high-groove music could be due to its high beat salience. On the other hand, embedded metronome did not improve phase-matching accuracy, but high-groove music did. This suggests that beat salience may not improve gait via easing step-to-beat synchronization, but rather through an overall increase in movement vigor.
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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 | 0.000 | 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) | 0.002 | 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".