Sensory Contributions to Piano Learning for Novices
Bibliographic record
Abstract
Previous research has examined the sensory contributions involved in expert piano playing, however, limited research has examined the role of sensory information in novice piano learning. This study investigated the relative contributions of auditory and visual feedback in piano acquisition among novice participants. Novice piano learners (n = 10, age = 21.1, 5 males, 5 females) played sequences in 3 sensory-training conditions (Audiovisual, Visual Only, Audio Only). On Day 1 participants performed a pre-test, an acquisition period, and an immediate retention test. On Day 2 participants performed a 24-hour delayed retention test. In the pre-test, participants were presented with three 7-note sequences at 120 bpm and were instructed to reproduce the sequences. In the acquisition phase, participants practiced the three sequences at both 60 and 120 bpm, with each sequence randomized to one of the three sensory-training conditions. During the acquisition phase, participants were given visual feedback of their accuracy (% of correct trials) and their timing performance (i.e., the inter onset interval IOI) as a percent difference between performed and presented sequence. The immediate and delayed retention were the same as the pre-test. Analysis of the IOI revealed that participants were significantly better in the Visual compared to the Audiovisual and Auditory sensory-training conditions in both immediate and delayed retention tests. This data suggests that training in Visual Only conditions leads to greater timing performance than Audio Only and Audiovisual training conditions, reflecting better learning. Thus, providing visual cues early in novice piano learning may be beneficial.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".