Bibliographic record
Abstract
Distributed learning is a powerful tool for optimizing retention of verbal materials. We examined the effect of distributing learning on long-term memory for a melody and found strong evidence of better recall in the spaced conditions. In the current study, music students were taught a four-phrase melody in learning sessions that were massed, spaced at 2 days, or spaced at 1 week. Three weeks later, they were tested for recall. Performances were evaluated for note omissions, number of incorrect notes and intervals, and number of correct notes and intervals. Results indicated strong evidence for a spacing effect for melody learning between the massed and spaced conditions at a retention interval of 3 weeks, and no evidence of difference between the two spaced conditions. Unlike most spacing studies, memory did not improve with longer spacing between learning episodes. These results suggest that memory for a melody may rely primarily on structural constraints within the material itself. Once these constraints are understood and associated with the cue, the performance unspools. Results have implications for best practices in melodic learning and for the role of constraining cues in the retrieval of structured non-verbal material.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".