The Source of Enhanced Cognitive Control in Bilinguals: Evidence From Bimodal-Bilinguals
Bibliographic record
Abstract
Bilinguals often outperform monolinguals on nonverbal tasks that require resolving conflict from competing alternatives.The regular need to select a target language is argued to enhance executive control.We investigated whether this enhancement stems from a general effect of bilingualism (the representation of two languages) or from a modality constraint that forces language selection.Bimodal bilinguals can, but do not always, sign and speak at the same time.Their two languages involve distinct motor and perceptual systems, leading to weaker demands on language control.We compared the performance of 15 monolinguals, 15 bimodal bilinguals, and 15 unimodal bilinguals on a set of flanker tasks.There were no group differences in accuracy, but unimodal bilinguals were faster than the other groups; bimodal bilinguals did not differ from monolinguals.These results trace the bilingual advantage in cognitive control to the unimodal bilingual's experience controlling two languages in the same modality.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".