Optimizing song retention through the spacing effect
Bibliographic record
Abstract
The spacing effect refers to the improvement in memory retention for materials learned in a series of sessions, as opposed to massing learning in a single session. It has been extensively studied in the domain of verbal learning using word lists. Less evidence is available for connected discourse or tasks requiring the complex coordination of verbal and other domains. In particular, the effect of spacing on the retention of words and music in song has yet to be determined. In this study, university students were taught an unaccompanied two-verse song based on traditional materials to a criterion of 95% correct memory for sung words. Subsequent training sessions were either massed or spaced by two days or one week and tested at a retention interval of three weeks. Performances were evaluated for number of correct and incorrect syllables, number of correctly and incorrectly pitched notes, degree notes were off-pitch, and number of hesitations while singing. The data revealed strong evidence for a spacing effect for song between the massed and spaced conditions at a retention interval of three weeks, and evidence of no difference between the two spaced conditions. These findings suggest that the ongoing cues offered by surface features in the song are strong enough to enable verbatim recall across spaced conditions, as long as the spacing interval reaches a critical threshold.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 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".