Singing in the Brain: Insights from Cognitive Neuropsychology
Bibliographic record
Abstract
Singing abilities are rarely examined despite the fact that their study represents one of the richest sources of information regarding how music is processed in the brain. In particular, the analysis of singing performance in brain-damaged patients provides key information regarding the autonomy of music processing relative to language processing. Here, we review the relevant literature, mostly on the perception and memory of text and tunes in songs, and we illustrate how lyrics can be distinguished from melody in singing, in the case of brain damage. We report a new case, G.D., who has a severe speech disorder,marked by phonemic errors and stuttering, without a concomitant musical production disorder. G.D. was found to produce as few intelligible words in speaking as in singing familiar songs. Singing “la, la, la” was intact and hence could not account for the speech deficit observed in singing. The results indicate that verbal production, be it sung or spoken, is mediated by the same (impaired) language output system and that this speech route is distinct from the (spared) melodic route. In sum, we provide here further evidence that the autonomy of music and language processing extends to production tasks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".