Beat Perception and Production in Musicians and Dancers
Bibliographic record
Abstract
The ability to perceive and produce a beat is believed to be universal in humans, but individual ability varies. The current study examined four factors that may influence beat perception and production capacity: 1) expertise: music or dance, 2) training style: percussive or nonpercussive, 3) stimulus modality: auditory or visual, and 4) movement type: finger-tap or whole-body bounce. Experiment 1 examined how expertise and training style influenced beat perception and production performance using an auditory beat perception task and a finger-tapping beat production task. Experiment 2 used a similar sample with an audiovisual variant of the beat perception task, and a standing knee-bend (bounce) beat production task to assess whole-body movement. The data showed that: 1) musicians were more accurate in a finger-tapping beat synchronization task compared to dancers and controls, 2) training style did not significantly influence beat perception and production, 3) visual beat information did not benefit any group, and 4) beat synchronization in a full-body movement task was comparable for musicians and dancers; both groups outperformed controls. The current study suggests that the type of task and measured response interacts with expertise, and that expertise effects may be masked by selection of nonoptimal response types.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".