Neural Correlates of Speech Segregation Based on Formant Frequencies of Adjacent Vowels
Bibliographic record
Abstract
Abstract The neural substrates by which speech sounds are perceptually segregated into distinct streams are poorly understood. Here, we recorded high-density scalp event-related potentials (ERPs) while participants were presented with a cyclic pattern of three vowel sounds (/ee/-/ae/-/ee/). Each trial consisted of an adaptation sequence, which could have either a small, intermediate, or large difference in first formant (Δf1) as well as a test sequence, in which Δf1 was always intermediate. For the adaptation sequence, participants tended to hear two streams (“streaming”) when Δf1 was intermediate or large compared to when it was small. For the test sequence, in which Δf1 was always intermediate, the pattern was usually reversed, with participants hearing a single stream with increasing Δf1 in the adaptation sequences. During the adaptation sequence, Δf1-related brain activity was found between 100–250 ms after the /ae/ vowel over fronto-central and left temporal areas, consistent with generation in auditory cortex. For the test sequence, prior stimulus modulated ERP amplitude between 20–150 ms over left fronto-central scalp region. Our results demonstrate that the proximity of formants between adjacent vowels is an important factor in the perceptual organization of speech, and reveal a widely distributed neural network supporting perceptual grouping of speech sounds.
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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".