Effects of altered sensory feedback on piano performance errors: An exploratory study
Bibliographic record
Abstract
Music performance is an intensive sensorimotor task that involves the generation of mental representations of musical information that are actively accessed, maintained, and manipulated according to the demands of the performance. Internal representations and external information interact through feedback and feedforward processes that adjust the musician’s motor behavior to optimize a musical performance. This study aimed to examine the relationship between altered sensory feedback and performance errors. Seventeen experienced pianists aged between 33 and 54 years performed Hanon Exercise N°1 from memory under four different conditions: (1) normal (normal sensory feedback); (2) closed fallboard (altered haptic and auditory feedback); (3) blindfolded (altered visual feedback); and (4) combined (blindfolded and closed fallboard; altered haptic, auditory, and visual feedback). Performance errors were quantified based on a video analysis of the performances. Results indicated that compared with normal performance, participants made significantly more note errors in the blindfolded condition and more bar-adding errors per trial in the closed fallboard condition. The comparison between the normal condition and the three altered sensory feedback conditions revealed the impact of altering sensory feedback in musical performance. These findings are discussed in the context of music learning.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".