The dynamic influence of language switching contexts on domain-general cognitive control: An EEG study
Bibliographic record
Abstract
In everyday conversation, bilingual individuals switch between languages not only in reaction to monolinguals with different language profiles but also voluntarily and naturally. However, whether and how various switching contexts dynamically modulate the domain-general control were still illusive. Using a cross-task paradigm which flanker task was interleaved with language switching task trial-by-trial, the present study manipulated forced, voluntary and natural switching contexts. A group of unbalanced Chinese-English bilinguals performed a flanker task in the three switching contexts. The results showed that the cross-domain interaction on the P3 effect revealed an atypical flanker effect in forced switching contexts only, and P3 amplitude of incongruent trials in forced switching contexts was smaller than both natural and voluntary switching contexts. Furthermore, the robust brain-brain and brain-behavior relationships between language control and domain-general control were significantly emerged in the forced switching context only. Altogether, our findings support the dynamic adaptation of language control to cognitive control and highlight the importance of switching contexts.
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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) | 0.001 | 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".