Cross-task adaptation effects of bilingual language control on cognitive control: a dual-brain eeg examination of simultaneous production and comprehension
Bibliographic record
Abstract
For bilinguals, speaking and listening are assisted by complex control processes including conflict monitoring and inhibition. However, the extent to which these processes adapt to linguistic and situational needs has been examined separately for language production and comprehension. In the present study, we use a dual-EEG to record the carry-over effects of language control on general cognitive control in three language contexts (single-first language [L1], single-second language [L2], and mixed). Chinese learners of English were placed in dyads in which one participant was asked to name pictures while the other listened. Interleaved after each naming/listening trial were flanker trials. The results from picture naming and listening revealed higher delta and theta synchronization in the single-L2 and mixed contexts compared with the single-L1 context and higher theta synchronization in the mixed context compared with the single-L2 and single-L1 contexts. The results from the interleaved flanker trials demonstrated that inhibition was adaptively generalized in the single-L2 and mixed contexts. Altogether, the findings support the natural adaptation of language control to cognitive control and underscore the importance of linguistic context. We argue that these adaptive patterns have the potential to affect corresponding control processes across language and cognitive control tasks.
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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 | 0.000 | 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".