Embodiment of music expression through muscle activity in expert pianists: A case study
Bibliographic record
Abstract
From an embodied cognition approach, pianists' gestures might be affected by their expressive intentions while helping shape these intentions during performance.Studies investigating pianists' embodiment of music expression have mainly focused on kinematic analysis.The objective of this case study was to evaluate changes in upper-body muscle activity (a key aspect of gesture) and performance features in relation to pianists' expressive intentions.Two expert pianists (P1, P2) played six excerpts on an instrumented piano under a normal condition and a control condition (performance of the score as objectively as possible).Muscle activity of twelve upper-body muscles was recorded using electromyography.P1 and P2 showed opposed neuromuscular strategies.P1 increased muscle activity in the normal condition, while no clear trend was reported for P2.Both participants modulated performance features in a similar trend.Our results suggest that pianists might embody their expressive intentions through muscle activity.However, the presence and the extent of this embodiment process might depend on their playing approach and experience, on the specific musical context, and on structural and semantic expression features.These findings provide empirical support to the embodied music cognition perspective and are also relevant for literature on musicians' risk factors of injuries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".