I get by with a little help from my partner: Comparing the effectiveness of paired vs. individual practice for motor learning in music
Bibliographic record
Abstract
Practicing motor skills with a partner (dyad practice) has been shown to be an effective and efficient method of learning, which may provide additional psychosocial benefits compared to individual practice. Here we studied dyad practice in a musical context. Musicians (N=73) learned a novel excerpt on the marimba individually or in dyads. Participants had to terminate mallet movements near visual targets on the marimba bars. During acquisition, the individual group performed the excerpt 50 times. In the dyad groups, partners alternated turns performing and observing after each block. In the dyad-full practice group, each partner performed (and observed) 50 trials. In the dyad-half practice group, each partner performed (and observed) 25 trials. Pre- and post-tests were completed before and after acquisition followed by a 24hr retention test. Accuracy was calculated by subtracting mallet endpoint positions from the target locations. Emotional responses to training (e.g., enjoyment) and training motivation were obtained via survey. At retention, left mallet accuracy was greater in the dyad-full vs. dyad-half group (p
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".