Neural coding of spectrotemporal modulations in the auditory cortex supports speech and music categorization
Bibliographic record
Abstract
Auditory processing is typically described as hierarchical, culminating in neural representation of abstract categories. However, it remains unclear whether category-selective responses in auditory cortex require representational mechanisms beyond the coding of acoustic features, or whether the acoustic representations already available in the auditory cortex are sufficient to account for categorization. Here, we test whether cortical coding of spectrotemporal modulation (STM) features is sufficient to support speech–music categorization by combining human intracranial recordings with continuous behavioral judgments of a naturalistic soundtrack in which speech and music occur both separately and simultaneously. We show that temporal and spectral modulation patterns largely characterize speech and music, respectively, and that cortical auditory regions robustly track these features over time, with distinct oscillatory frequency bands preferentially encoding temporal and spectral modulations. Critically, cortical representations of STMs predicted perceptual categorical judgments gathered in an independent sample. Finally, speech- and music-related STM representations showed stronger tracking of category-specific acoustical features in left versus right cortical auditory regions, respectively. These findings indicate that the efficient neural coding of acoustical features provides a sufficient basis for the categorical distinction between speech and music, and that the temporal and spectral components of this representation are implemented through distinct oscillatory mechanisms.
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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 | 0.000 |
| 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".