Does language context impact the neural correlates of executive control in monolingual and multilingual young adults?
Bibliographic record
Abstract
Some previous studies have shown that creating a language context in which words from both languages are interspersed into a flanker task improves executive control performance for bilinguals, but these studies have produced inconsistent results. The studies have used different versions of the task and not included monolinguals, limiting generalization. Here, English-Chinese multilinguals and English monolinguals performed a flanker task while EEG was recorded. There were three language context blocks - English, Chinese, or both - and participants were instructed to ignore the interspersed words. Multilinguals displayed faster flanker RTs and earlier P2 and N2 waveforms than monolinguals. There was also a significant correlation between the P2/N2 latency and reaction times, connecting these waveforms to behavior. Finally, P2 amplitude differed between groups in the mixed context, and language context impacted P3 amplitude for monolinguals but not multilinguals. These results are interpreted in terms of language context effects on monolingual executive function processing and possible difference in bilingual experience between current participants and those in previous studies.
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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 | 0.001 | 0.001 |
| 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".