It was just my imagination: Combined imagery/physical practice yields comparable benefits as physical practice in percussion performance
Bibliographic record
Abstract
No studies have compared physical vs. combined imagery/physical practice in music training. Motion capture measured hand and stick movements of 28 percussionists assigned to a physical (P) or combined imagery/physical (IP) group. Both groups practiced an excerpt on an electronic drum to a metronome. During acquisition, the P group performed the excerpt on all 40 trials. The IP group physically performed (20 trials) and imagined performing the except (20 trials). Pre-test and post-test trials were obtained before and after acquisition. Participants also completed a survey measuring affect and motivation to continue training. Temporal errors were computed by subtracting midi note onset data from tempo-defined note onsets for pre- and post-test trials. Temporal errors and kinematics only differed in relation to time and not group. Temporal errors improved from pre- (51.4 ms) to post-test (37.9 ms). Both sticks initiated strokes from higher average positions in post- (left: 12.3 cm; right: 13.4 cm) vs. pre-test (left: 11 cm; right: 6.2 cm). Hand velocity was greater in post- (left: 16.7 cm/s; right: 15.8 cm/s) vs. pre-test (left: 13.9 cm/s; right: 13.8 cm/s). The IP group (3.8) reported less perceived effort during training vs. the P group (5.3) and greater interest in continuing imagery/physical training over the short-term (5) and indefinitely (5.2) vs. the P group continuing physical training (short term = 3.6; indefinitely = 3.5). Combined IP practice yields comparable rhythmic accuracy to P practice while potentially enhancing training adherence via reduced perceived effort.
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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 | 0.002 |
| 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) | 0.005 | 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".