Singing in the brain: Neural representation of music and voice as revealed by fMRI
Bibliographic record
Abstract
The ubiquity of music across cultures as a means of emotional expression, and its proposed evolutionary relation to speech, motivated researchers to attempt a characterization of its neural representation. Several neuroimaging studies have reported that specific regions in the anterior temporal lobe respond more strongly to music than to other auditory stimuli, including spoken voice. Nonetheless, because most studies have employed instrumental music, which has important acoustic distinctions from human voice, questions still exist as to the specificity of the observed "music-preferred" areas. Here, we sought to address this issue by testing 24 healthy young adults with fast, high-resolution fMRI, to record neural responses to a large and varied set of musical stimuli, which, critically, included a capella singing, as well as purely instrumental excerpts. Our results confirmed that music; vocal or instrumental, preferentially engaged regions in the superior STG, particularly in the anterior planum polare, bilaterally. In contrast, human voice, either spoken or sung, activated more strongly a large area along the superior temporal sulcus. Findings were consistent between univariate and multivariate analyses, as well as with the use of a "silent" sparse acquisition sequence that minimizes any potential influence of scanner noise on the resulting activations. Activity in music-preferred regions could not be accounted for by any basic acoustic parameter tested, suggesting these areas integrate, likely in a nonlinear fashion, a combination of acoustic attributes that, together, result in the perceived musicality of the stimuli, consistent with proposed hierarchical processing of complex auditory information within the temporal lobes.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".