The Impact of Limb Velocity Variability on Mallet Accuracy in Marimba Performance
Bibliographic record
Abstract
The present study examined spatial accuracy of mallet endpoints in a marimba performance context. Trained percussionists performed two- (i.e., Experiment 1) and four-mallet (i.e., Experiment 2) excerpts in three tempo conditions including slow, intermediate, and fast. Motion capture was utilized to gather data of upper-limb and mallet movements, as well as to compute velocities of the upper-limb joints. Mallet spatial accuracy was assessed by comparing mallet endpoints to a visual target positioned on the marimba. It was hypothesized that mallet spatial accuracy would be reduced as tempo condition increased, with effects on joint kinematics potentially revealing sensorimotor mechanisms underlying optimal sound production in marimba. Across both experiments, mallet accuracy was reduced as tempo condition increased. Interestingly, velocity variability in the elbows, wrists, and hands increased as mallet accuracy decreased. Such a pattern of effects suggested that sound production in marimba is suboptimal at fast relative to slow tempi. In addition, the velocity variability effects highlight the impact of motor planning mechanisms on sound production. Overall, the results shed new light on sensorimotor control in percussion which can be leveraged to enhance the training of percussionists.
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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 | 0.005 |
| 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) | 0.002 | 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".