Learning two languages from birth shapes pre-attentive processing of vowel categories: Electrophysiological correlates of vowel discrimination in monolinguals and simultaneous bilinguals
Bibliographic record
Abstract
Using event-related brain potentials (ERPs), we measured pre-attentive processing involved in native vowel perception as reflected by the mismatch negativity (MMN) in monolingual and simultaneous bilingual (SB) users of Canadian English and Canadian French in response to various pairings of four vowels: English /u/, French /u/, French /y/, and a control /y/. The monolingual listeners exhibited a discrimination pattern that was shaped by their native language experience. The SB listeners, on the other hand, exhibited a MMN pattern that was distinct from both monolingual listener groups, suggesting that the SB pre-attentive system is tuned to access sub-phonemic detail with respect to both input languages, including detail that is not readily accessed by either of their monolingual peers. Additionally, simultaneous bilinguals exhibited sensitivity to language context generated by the standard vowel in the MMN paradigm. The automatic access to fine phonetic detail may aid SB listeners to rapidly adjust their perception to the variable listening conditions that they frequently encounter.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".