Enhanced timing abilities in percussionists generalize to rhythms without a musical beat
Bibliographic record
Abstract
The ability to entrain movements to music is arguably universal, but it is unclear how specialized training may influence this. Previous research suggests that percussionists have superior temporal precision in perception and production tasks. Such superiority may be limited to temporal sequences that resemble real music or, alternatively, may generalize to musically implausible sequences. To test this, percussionists and nonpercussionists completed two tasks that used rhythmic sequences varying in musical plausibility. In the beat tapping task, participants tapped with the beat of a rhythmic sequence over 3 stages: finding the beat (as an initial sequence played), continuation of the beat (as a second sequence was introduced and played simultaneously), and switching to a second beat (the initial sequence finished, leaving only the second). The meters of the two sequences were either congruent or incongruent, as were their tempi (minimum inter-onset intervals). In the rhythm reproduction task, participants reproduced rhythms of four types, ranging from high to low musical plausibility: Metric simple rhythms induced a strong sense of the beat, metric complex rhythms induced a weaker sense of the beat, nonmetric rhythms had no beat, and jittered nonmetric rhythms also had no beat as well as low temporal predictability. For both tasks, percussionists performed more accurately than nonpercussionists. In addition, both groups were better with musically plausible than implausible conditions. Overall, the percussionists' superior abilities to entrain to, and reproduce, rhythms generalized to musically implausible sequences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".