Stabilizing Joint Angle Velocity Contributes to Motor Learning in Percussion
Bibliographic record
Abstract
This study quantified movement changes in the upper-limb joints during motor learning in percussion performance. Eleven percussionists practiced a novel excerpt on 3 consecutive training days, followed 1 week later by a retention assessment. Motion capture technology quantified the orientation and angular velocity of the shoulders, elbows, and wrists. To manipulate task difficulty, and thus the behavioral demands on upper-limb movements, participants learned the test excerpt in fast and slow tempo conditions. Given previous studies examining motor performance in percussion, it was hypothesized that variability in elbow and wrist angular velocity would be altered by performance tempo but would stabilize when comparing training vs. retention performances. The results showed greater abduction of the left shoulder in the slow vs. fast training condition. Variability in the velocity of right elbow and left wrist movements was lower at retention vs. training. Judges’ ratings of the performances revealed improved quality and rhythmic accuracy at retention vs. training. The findings may suggest that angular velocity mechanistically reflects motor learning in percussion performance. Such findings may be applied towards enhancing percussion training.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".