On the One Hand or on the Other: Trade-Off in Timing Precision in Bimanual Musical Scale Playing
Bibliographic record
Abstract
Music performance requires simultaneously producing challenging movement sequences with the left and right hand. A key question in bimanual motor control research is whether bimanual movements are produced by combining unimanual controllers or through a dedicated bimanual controller. Here, 34 expert pianists performed musical scale playing movements with the left or right hand alone and with both hands simultaneously. We found that for the left hand, scale playing was more variable when playing with both hands simultaneously rather than with one hand at a time, but for the right hand, performance was identical. This indicates that when task constraints are high, musicians prioritize timing accuracy in the right hand at the cost of detriment of performance in the left hand. We also found that individual differences in timing substantially overlap between the unimanual and bimanual condition, suggesting control policies are similar but not identical when playing with two hands or one. In the bimanual condition, the left-hand keystrokes tended to occur before right-hand ones, and more so when the hands were further apart. Performance of the two hands was furthermore coupled so that they tended to be early and late together, especially in the beginning and end of each scale. This suggests that experts are able to achieve tightly coupled timing of scale playing movements between the hands. Taken together, these findings show evidence for partially overlapping and partially separate controllers for bimanual and unimanual movements in piano playing.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".