Music as a scaffold for listening to speech: Better neural phase-locking to song than speech
Bibliographic record
Abstract
Neural activity synchronizes with the rhythmic input of many environmental signals, but the capacity of neural activity to entrain to the slow rhythms of speech is particularly important for successful communication. Compared to speech, song has greater rhythmic regularity, a more stable fundamental frequency, discrete pitch movements, and a metrical structure, this may provide a temporal framework that helps listeners neurally track information better than the rhythmically irregular rhythms of speech. The current study used EEG to examine whether entrainment to the syllable rate of linguistic utterances, as indexed by cerebro-acoustic phase coherence, was greater when listeners heard sung than spoken sentences. We assessed listeners phase-locking in both easy (no time compression) and hard (50% time-compression) utterance conditions. Adults phase-locked equally well to speech and song in the easy listening condition. However, in the time-compressed condition, phase-locking was greater for sung than spoken utterances in the theta band (3.67-5 Hz). Thus, the musical temporal and spectral characteristics of song related to better phase-locking to the slow phrasal and syllable information (4-7 Hz) in the speech stream. These results highlight the possibility of using song as a tool for improving speech processing in individuals with language processing deficits, such as dyslexia.
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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) | 0.003 | 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".