Individual Differences in Perception of the Speech-to-Song Illusion are Linked to Musical Aptitude but not Musical Experience
Bibliographic record
Abstract
In the speech-to-song illusion certain spoken phrases are perceived as sung after repetition. Prior work has established that perception of the illusion is not limited to specialist listeners, such as individuals with musical training, but is perceived widely across the general population. However, little is known about whether there are stable individual differences in perception of the illusion, and if so, what factors underlie this variability. Here we assessed the strength of the song illusion across individuals using phrases that tend to be perceived as song when repeated, as well as phrases that continue to be perceived as speech when repeated, measuring the strength of the illusion as the rating difference between these two stimulus categories after repetition. Illusion strength varied widely, with differences in perception of the two categories ranging from 0% to 80% of the rating scale. Although variability in illusion strength was unrelated to degree of musical training, participants who perceived the illusion more strongly were proficient in several musical skills, including beat perception, tonality perception, and selective attention to pitch. This finding supports theoretical models of the speech-to-song illusion in which experience of the illusion is based on detection of musical characteristics latent in spoken phrases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".