Combined Imagery/Physical Practice Yields Comparable Benefits to Physical Practice in Snare Drum Performance
Bibliographic record
Abstract
This study explored the effectiveness of combining motor imagery with physical practice in enhancing snare drum performance among trained percussionists. Motor imagery has promoted learning in related contexts such as sport but has yet to be applied in music training. Twenty-eight percussion majors were assigned to either a physical practice group or a combined imagery/physical practice group. Participants performed a novel snare drum excerpt while motion capture measured upper-limb movements prior to and following training. Temporal errors were also computed by comparing note onsets to the ideal timing specified by a metronome. Results revealed that temporal errors were lower in post- vs. pre-training performances, irrespective of group. In both groups, post-test performances were characterized by a higher average position of the mallets above the playing surface and greater hand velocity vs. pre-training performances. Notably, the combined imagery/physical practice group reported less perceived effort associated with training which coincided with an increase in training adherence likelihood. These findings highlight the potential of integrating motor imagery into music education to optimize practice efficiency, particularly when time constraints limit physical rehearsal opportunities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".