Discerning the functional networks behind processing of music and speech through human vocalizations
Bibliographic record
Abstract
A fundamental question regarding music processing is its degree of independence from speech processing, in terms of their underlying neuroanatomy and influence of cognitive traits and abilities. Although a straight answer to that question is still lacking, a large number of studies have described where in the brain and in which contexts (tasks, stimuli, populations) this independence is, or is not, observed. We examined the independence between music and speech processing using functional magnetic resonance imagining and a stimulation paradigm with different human vocal sounds produced by the same voice. The stimuli were grouped as Speech (spoken sentences), Hum (hummed melodies), and Song (sung sentences); the sentences used in Speech and Song categories were the same, as well as the melodies used in the two musical categories. Each category had a scrambled counterpart which allowed us to render speech and melody unintelligible, while preserving global amplitude and frequency characteristics. Finally, we included a group of musicians to evaluate the influence of musical expertise. Similar global patterns of cortical activity were related to all sound categories compared to baseline, but important differences were evident. Regions more sensitive to musical sounds were located bilaterally in the anterior and posterior superior temporal gyrus (planum polare and temporale), the right supplementary and premotor areas, and the inferior frontal gyrus. However, only temporal areas and supplementary motor cortex remained music-selective after subtracting brain activity related to the scrambled stimuli. Speech-selective regions mainly affected by intelligibility of the stimuli were observed on the left pars opecularis and the anterior portion of the medial temporal gyrus. We did not find differences between musicians and non-musicians Our results confirmed music-selective cortical regions in associative cortices, independent of previous musical training.
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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 | 0.001 | 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) | 0.001 | 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".