Pursuing Artful Movement Science in Music Performance: Single Subject Motor Analysis With Two Elite Pianists
Bibliographic record
Abstract
Piano performance motor learning research requires more "artful" methodologies if it is to meaningfully address music performance as a corporeal art. To date, research has been sparse and it has typically constrained multiple performance variables in order to isolate specific phenomena. This approach has denied the fundamental ethos of music performance which, for elite performers, is an act of interpretation, not mere reproduction. Piano performances are intentionally manipulated for artistic expression. We documented motor movements in the complex task of performance of the first six measures of Chopin's "Revolutionary" Etude by two anthropometrically different elite pianists. We then discussed their motor strategy selections as influenced by anthropometry and the composer's musical directives. To quantify the joint angles of the trunk, shoulders, elbows, and wrists, we used a VICON 3 D motion capture system and biomechanical modeling. A Kistler force plate (1 N, Swiss) quantified center of gravity (COG) shifts. Changes in COG and trunk angles had considerable influence on the distal segments of the upper limbs. The shorter pianist used an anticipatory strategy, employing larger shifts in COG and trunk angles to produce dynamic stability as compensation for a smaller stature. Both pianists took advantage of low inertial left shoulder internal rotation and adduction to accommodate large leaps in the music. For the right arm, motor strategizing was confounded by rests in the music. These two cases illustrated, in principle, that expert pianists' individualized motor behaviors can be explained as compensatory efforts to accommodate both musical goals and anthropometric constraints. Motor learning among piano students can benefit from systematic attention to motor strategies that consider both of these factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".