Neural Signatures Of Musical And Linguistic Interactions During Natural Song Listening
Bibliographic record
Abstract
How are songs processed in the human brain? In song, tunes and lyrics are tightly bound in a music-language synergy to convey meaning and emotions beyond mere linguistic content, raising questions on how the two components are represented and integrated into a cohesive perceptual whole. Previous research pointed to areas of the human cortex sensitive to music, speech, and song, finding both shared and specialized sites. Yet, the interactions between tunes and lyrics processing when listening to songs remain poorly understood. To tackle this question, we probed neural predictive mechanisms specific to music and speech with electroencephalography. The encoding of melodic predictions was compared when listeners were presented with songs or the corresponding hummed (speech-free) melodies. Similarly, the encoding of phonemic predictions was studied in song and the corresponding spoken (melody-free) lyrics. We found that the concurrence of music and speech in songs alters how their predictive signals are generated and processed, altering their neural encoding.Furthermore, we found a trade-off in the neural encoding of melodic and phonemic expectations, with their balance depending both on who was listening (internal driver reflecting the listener's preference, e.g., musical training) and how the song is composed and performed (external driver reflecting the salience of lyrics and tunes). Altogether, our results indicate that song involves parallel prediction processes competitively interacting for the use of shared processing resources.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".