Contextual Distinctiveness Affects the Memory Advantage for Vocal Melodies
Bibliographic record
Abstract
Memory is affected by stimulus salience. For example, vocal melodies are remembered better than instrumental melodies, presumably because of their status as biologically significant signals. We asked whether the memorability of inherently salient vocal melodies is affected by local factors such as contextual distinctiveness. In Experiments 1A and 1B, three conditions differed in the prevalence of vocal renditions (sung to la la) relative to piano renditions– 25%, 50%, or 75%. After asingle exposure to 24 unfamiliar folk melodies, listeners rated their confidence that each of 48 melodies (half heard previously) was old or new. In Experiment 2, contextual distinctiveness was manipulated by blocking melodies (half vocal, half piano) by timbre during exposure with mixed timbres at test, or timbres mixed at exposure and blocked at test. In Experiments 1A and 1B, the memory advantage for vocal melodies was largest when the melody set was 25% vocal, smaller but still evident when 50% vocal, and absent when 75% vocal, even with three different vocalists. In Experiment 2, both conditions yielded a similar voice advantage. The results replicated the recognition advantage for vocal melodies and revealed that contextual distinctiveness involving the prevalence of vocal melodies influenced this advantage but blocking by timbre did not.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".