Early selectivity for vocal and musical sounds: electrophysiological evidence from an adaptation paradigm
Bibliographic record
Abstract
There is growing interest in characterizing the neural basis of music perception and, in particular, assessing how similar, or not, it is to that of speech. To further explore this question, we employed an EEG adaptation paradigm in which we compared responses to short sounds belonging to the same category, either speech (pseudo-sentences) or music (piano or violin), depending on whether they were immediately preceded by a same- or different-category sound. We observed a larger reduction in the N100 component magnitude in response to musical sounds when they were preceded by music (either the same or different instrument) than by speech. In contrast, the N100 amplitude was not affected by the preceding stimulus category in the case of speech. For P200 component, we observed a diminution of amplitude when speech sounds were preceded speech, compared to music. No such decrease was found when we compared the responses to music sounds. These differences in the processing of speech and music are consistent with the proposal that some degree of category selectivity for these two classes of complex stimuli already occurs at early stages of auditory processing, possibly subserved by partly separated neuronal populations.
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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 | 0.001 |
| 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".