Generality of the Memory Advantage for Vocal Melodies
Bibliographic record
Abstract
Children and adults, with or without music training, exhibit better memory for vocal melodies (without lyrics) than for instrumental melodies (Weiss, Schellenberg, Trehub, & Dawber, 2015; Weiss, Trehub, & Schellenberg, 2012; Weiss, Trehub, Schellenberg, & Habashi, 2016; Weiss, Vanzella, Schellenberg, & Trehub, 2015). In the present study, we compared adults’ memory for vocal and instrumental melodies, as before, but with two additional singers, one female (same pitch level as the original female) and one male (7 semitones lower). In an exposure phase, 90 participants (M = 4.1 years training, SD = 3.9) rated their liking of 24 melodies—6 each in voice, piano, banjo, and marimba. After a short break, they heard the same melodies plus 24 timbre-matched foils (6 per timbre) and rated their recognition of each melody. Recognition was better for vocal melodies than for melodies in every other timbre, replicating previous findings. Importantly, the memory advantage was comparable across voices, despite the fact that liking ratings for vocal melodies differed by singer. Our results provide support for the notion that the vocal advantage in memory for melodies is independent of the idiosyncrasies of specific singers or of vocal attractiveness, arising instead from enhanced processing of a biologically significant timbre.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".