Bilinguals benefit from semantic context while perceiving speech in noise in both of their languages: Electrophysiological evidence from the N400 ERP
Bibliographic record
Abstract
Abstract Although bilinguals benefit from semantic context while perceiving speech-in-noise in their native language (L1), the extent to which bilinguals benefit from semantic context in their second language (L2) is unclear. Here, 57 highly proficient English–French/French–English bilinguals, who varied in L2 age of acquisition, performed a speech-perception-in-noise task in both languages while event-related brain potentials were recorded. Participants listened to and repeated the final word of sentences high or low in semantic constraint, in quiet and with a multi-talker babble mask. Overall, our findings indicate that bilinguals do benefit from semantic context while perceiving speech-in-noise in both their languages. Simultaneous bilinguals showed evidence of processing semantic context similarly to monolinguals. Early sequential bilinguals recruited additional neural resources, suggesting more effective use of semantic context in L2, compared to late bilinguals. Semantic context use was not associated with bilingual language experience or working memory.
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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) | 0.003 | 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".