The Relationship of Lyrics and Tunes in the Processing of Unfamiliar Songs: A Functional Magnetic Resonance Adaptation Study
Bibliographic record
Abstract
The cognitive relationship between lyrics and tunes in song is currently under debate, with some researchers arguing that lyrics and tunes are represented as separate components, while others suggest that they are processed in integration. The present study addressed this issue by means of a functional magnetic resonance adaptation paradigm during passive listening to unfamiliar songs. The repetition and variation of lyrics and/or tunes in blocks of six songs was crossed in a 2 x 2 factorial design to induce selective adaptation for each component. Reductions of the hemodynamic response were observed along the superior temporal sulcus and gyrus (STS/STG) bilaterally. Within these regions, the left mid-STS showed an interaction of the adaptation effects for lyrics and tunes, suggesting an integrated processing of the two components at prelexical, phonemic processing levels. The degree of integration decayed toward more anterior regions of the left STS, where the lack of such an interaction and the stronger adaptation for lyrics than for tunes was suggestive of an independent processing of lyrics, perhaps resulting from the processing of meaning. Finally, evidence for an integrated representation of lyrics and tunes was found in the left dorsal precentral gyrus (PrCG), possibly relating to the build-up of a vocal code for singing in which musical and linguistic features of song are fused. Overall, these results demonstrate that lyrics and tunes are processed at varying degrees of integration (and separation) through the consecutive processing levels allocated along the posterior-anterior axis of the left STS and the left PrCG.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".