Decoding Temporal Structure in Music and Speech Relies on Shared Brain Resources but Elicits Different Fine-Scale Spatial Patterns
Bibliographic record
Abstract
Music and speech are complex sound streams with hierarchical rules of temporal organization that become elaborated over time. Here, we use functional magnetic resonance imaging to measure brain activity patterns in 20 right-handed nonmusicians as they listened to natural and temporally reordered musical and speech stimuli matched for familiarity, emotion, and valence. Heart rate variability and mean respiration rates were simultaneously measured and were found not to differ between musical and speech stimuli. Although the same manipulation of temporal structure elicited brain activation level differences of similar magnitude for both music and speech stimuli, multivariate classification analysis revealed distinct spatial patterns of brain responses in the 2 domains. Distributed neuronal populations that included the inferior frontal cortex, the posterior and anterior superior and middle temporal gyri, and the auditory brainstem classified temporal structure manipulations in music and speech with significant levels of accuracy. While agreeing with previous findings that music and speech processing share neural substrates, this work shows that temporal structure in the 2 domains is encoded differently, highlighting a fundamental dissimilarity in how the same neural resources are deployed.
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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 | 0.002 |
| 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) | 0.001 | 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".