Discerning the functional networks behind processing of music and speech through human vocalizations
Bibliographic record
Abstract
ABSTRACT Musicality refers to specific biological traits that allow us to perceive, generate and enjoy music. These abilities can be studied at different organizational levels (e.g., behavioural, physiological, evolutionary), and all of them reflect that music and speech processing are two different cognitive domains. Previous research has shown evidence of this functional divergence in auditory cortical regions in the superior temporal gyrus (such as the planum polare ), showing increased activity upon listening to music, as compared to other complex acoustic signals. Here, we examine brain activity underlying vocal music and speech perception, while we compare musicians and non-musicians. We designed a stimulation paradigm using the same voice to produce spoken sentences, hummed melodies, and sung sentences; the same sentences were used in speech and song categories, and the same melodies were used in the musical categories (song and hum). Participants listened to this paradigm while we acquired functional magnetic resonance images (fMRI). Different analyses demonstrated greater involvement of specific auditory and motor regions during music perception, as compared to speech vocalizations. This music sensitive network includes bilateral activation of the planum polare and temporale , as well as a group of regions lateralized to the right hemisphere that included the supplementary motor area, premotor cortex and the inferior frontal gyrus. Our results show that the simple act of listening to music generates stronger activation of motor regions, possibly preparing us to move following the beat. Vocal musical listening, with and without lyrics, is also accompanied by a higher modulation of specific secondary auditory cortices such as the planum polare , confirming its crucial role in music processing independently of previous musical training. This study provides more evidence showing that music perception enhances audio-sensorimotor activity, crucial for clinical approaches exploring music based therapies to improve communicative and motor skills.
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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 | 0.000 |
| 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".