The Piano Keyboard as Task Constraint: Timing Patterns of Pianists’ Scales Persist Across Instruments
Bibliographic record
Abstract
Variation in one form or another is an inevitable aspect of human motor performance as the body negotiates the degrees of freedom problem while also adapting to ever-changing task constraints. The constraints to action model suggests that movement patterns arise from within a framework of environmental, task, and personal constraints. Like athletes, musicians adapt to a wide variety of constraints such as the presence and effect of spectators; acoustics in different performing spaces; humidity affecting tuning; and interpersonal interactions characterizing chamber and ensemble music. A crucial constraint particular to piano performance is adapting to the unique attributes of a wide variety of keyboard instruments. Pianists often refer to the distinct “feel” of a particular instrument: its responsiveness and sensitivity; key resistance; and the evenness and predictability of the instrument. Movement control both within and across pianos is essential for optimal performance, and in that sense, each instrument presents a type of task constraint. In this study, seven pianists performed 10 bimanual, two-octave, C major scales on 3 different piano keyboards to facilitate comparison of performance characteristics across instruments. Pianists performed 4 keystrokes per second, paced by a metronome set at 60 BPM. No timing differences were observed among keyboards as consistent patterns emerged, specifically anticipatory adjustments prior to thumb strokes. These results suggest that pianists are able to produce performances of similar musical structure across different instruments.
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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 | 0.000 | 0.005 |
| 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 | 0.000 |
| 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".