Why Does Speech Sometimes Sound Like Song? Exploring the Role of Music-Related Priors in the “Speech-to-Song Illusion”
Bibliographic record
Abstract
The speech-to-song illusion is a perceptual effect emerging at the interplay of two cognitive domains, music and language. It arises upon repetitions of a spoken phrase that shifts to being perceived as song, and varies in the likelihood, ease, and vividness of its occurrence among individuals. A prevailing explanation of the illusion suggests that listeners’ attention shifts to rhythm and melody of the phrase once their involvement with the linguistic meaning subsides. The present study tested this mechanism by manipulating meaning plausibility and structural complexity of French and English phrases and by obtaining measures of attentional and working memory capacity from 80 French and English listeners who were exposed to repetitions of sentences in their native language. The results show that the transformation was facilitated in listeners with fewer cognitive resources and in less plausible, more complex phrases, which is at odds with the previously proposed mechanism underpinning the speech-to-song illusion. The illusion-promoting effect of musical training was visible only in simple but not in complex phrases. We propose a new account of the perceptual transformation from speech to song as a cognitive effect arising from the accumulation of music-related priors in a linguistically ambiguous context of massed repetitions.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".