Rhythmic speech perception predicts novice musical composition in English and French.
Bibliographic record
Abstract
Music and speech have long been thought to have common cognitive underpinnings, and recent work demonstrates that the music of expert composers reflects the speech rhythm of their native language [H. Ollen (2003), P. Daniele (2003)]. In the current study, monolingual English speaking music novices composed simple “English” and “French” tunes on a piano keyboard. The rhythms produced reflected speech rhythms perceived in English and French, respectively. Yet, the pattern was opposite that produced by expert English and French composers and opposite that predicted by the acoustic determinants of speech rhythm that specify English speech as more rhythmically varied than French. Surprise recognition tests 2 weeks later confirmed that the music-speech relationship remained over time. Participants then rated the rhythmic variability of French and English speech samples. We found that native English speakers perceived French as more variable than English despite the measured greater variability of English. Finally, we repeated these procedures with a sample of monolingual French speakers and found similar, but opposite effects. The results suggest that common cognitive underpinnings of music and speech rhythm are more widespread than previously thought, and that novice rhythm production in music is concordant with perceived speech rhythms.
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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 | 0.004 |
| 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) | 0.003 | 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".