Vowel Content Influences Relative Pitch Perception in Vocal Melodies
Bibliographic record
Abstract
Note-to-note changes in brightness are able to influence the perception of interval size. Changes that are congruent with pitch tend to expand interval size, whereas changes that are incongruent tend to contract. In the case of singing, brightness of notes can vary as a function of vowel content. In the present study, we investigated whether note-to-note changes in brightness arising from vowel content influence perception of relative pitch. In Experiment 1, three-note sequences were synthesized so that they varied with regard to the brightness of vowels from note to note. As expected, brightness influenced judgments of interval size. Changes in brightness that were congruent with changes in pitch led to an expansion of perceived interval size. A follow-up experiment confirmed that the results of Experiment 1 were not due to pitch distortions. In Experiment 2, the final note of three-note sequences was removed, and participants were asked to make speeded judgments of the pitch contour. An analysis of response times revealed that brightness of vowels influenced contour judgments. Changes in brightness that were congruent with changes in pitch led to faster response times than did incongruent changes. These findings show that the brightness of vowels yields an extra-pitch influence on the perception of relative pitch in song.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".