Effects of formant proximity and language experience on subcortical neural encoding of vowels in adulthood
Bibliographic record
Abstract
Formant frequency convergence (or “focalization”) and linguistic experience interact to shape the perception of vowels in adulthood. Here, we provide evidence of the effects of formant proximities and language experience at subcortical levels of the auditory pathway. Using a passive oddball/reversed oddball paradigm, the frequency-following response (FFR) in the auditory brainstem was elicited in sixteen healthy monolingual English speakers by a less-focal/English prototypic /u/ and a more-focal/French prototypic /u/. We examined the FFR as a function of stimulus type (English vs. French prototype) and condition (Standard vs. Deviant). A cross-correlation analysis revealed higher overall similarity between the FFR and evoking stimulus for the English prototype than the French prototype, suggesting an effect of language experience. Yet, deviants exhibited higher correlational values than standards. This effect was largely driven by the French prototype, suggesting an influence of focalization. A subsequent Fourier analysis revealed an effect of stimulus type at the f0 peak, whereas only a condition effect was observed at the first harmonic peak. The latter effect was largely driven by the French prototype. Altogether, these findings suggest that subcortical encoding of vowels in adulthood is influenced by a complex interplay between formant proximities and long-term linguistic experience.
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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".