Oscillatory Brain Activity in Response to Familiar and Unfamiliar Languages in Monolingual and Bilingual Young Adults
Bibliographic record
Abstract
This study investigated how bilingual experience modulates neural oscillations during spoken language processing as a function of language familiarity. Two groups of English-speaking young adults-monolinguals and simultaneous bilinguals-actively listened to long passages (∼30 s) in either a familiar (English) or unfamiliar (Hebrew) language while their EEG activity was recorded. To characterize the cognitive processes underpinning spoken language processing, we analyzed spectral power across a broad frequency spectrum (1-80 Hz), spanning delta to gamma bands, and conducted time-frequency analyses to capture neural dynamics during sustained listening. Results revealed that bilingual experience was associated with differential oscillatory patterns, particularly in alpha and theta frequencies. Bilinguals exhibited stronger alpha suppression when processing the unfamiliar language, reflecting experience-dependent engagement of attentional and control-related processes. Additionally, greater home exposure to non-English languages was linked to higher theta power during familiar language processing, consistent with enhanced attention and increased working memory load. Reduced beta power in response to the unfamiliar language suggested limited maintenance of stable representational states and prediction during novel input processing. However, no significant effects were observed in gamma or delta power, nor in the time-frequency analyses. Altogether, these findings suggest that bilingual experience shapes brain responses to spoken language primarily through attention- and memory-related processes. Furthermore, our results underscore the importance of approaching bilingualism as a continuous variable beyond binary categories.
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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 | 0.000 |
| 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) | 0.002 | 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".