Enhanced processing of vocal melodies in childhood.
Bibliographic record
Abstract
Music cognition is typically studied with instrumental stimuli. Adults remember melodies better, however, when they are presented in a biologically significant timbre (i.e., the human voice) than in various instrumental timbres (Weiss, Trehub, & Schellenberg, 2012). We examined the impact of vocal timbre on children's processing of melodies. In Study 1, 9- to 11-year-olds listened to 16 unfamiliar folk melodies (4 each of voice, piano, banjo, or marimba). They subsequently listened to the same melodies and 16 timbre-matched foils, and judged whether each melody was old or new. Vocal melodies were recognized better than instrumental melodies, which did not differ from one another, and the vocal advantage was consistent across age. In Study 2, 5- to 6-year-olds and 7- to 8-year-olds were tested with a simplified design that included only vocal and piano melodies. Both age groups successfully differentiated old from new melodies, but memory was more accurate for the older group. The older children recognized vocal melodies better than piano melodies, whereas the younger children tended to label vocal melodies as old whether they were old or new. The results provide the first evidence of differential processing of vocal and instrumental melodies in childhood.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".