Bilingual Language Control in Perception versus Action: MEG Reveals Comprehension Control Mechanisms in Anterior Cingulate Cortex and Domain-General Control of Production in Dorsolateral Prefrontal Cortex
Bibliographic record
Abstract
For multilingual individuals, adaptive goal-directed behavior as enabled by cognitive control includes the management of two or more languages. This work used magnetoencephalography (MEG) to investigate the degree of neural overlap between language control and domain-general cognitive control both in action and perception. Highly proficient Arabic-English bilingual individuals participated in maximally parallel language-switching tasks in production and comprehension as well as in analogous tasks in which, instead of the used language, the semantic category of the comprehended/produced word changed. Our results indicated a clear dissociation of language control mechanisms in production versus comprehension. Language-switching in production recruited dorsolateral prefrontal regions bilaterally and, importantly, these regions were similarly recruited by category-switching. Conversely, effects of language-switching in comprehension were observed in the anterior cingulate cortex and were not shared by category-switching. These results suggest that bilingual individuals rely on adaptive language control strategies and that the neural involvement during language-switching could be extensively influenced by whether the switch is active (e.g., in production) or passive (e.g., in comprehension). In addition, these results support that humans require high-level cognitive control to switch languages in production, but the comprehension of language switches recruits a distinct neural circuitry. The use of MEG enabled us to obtain the first characterization of the spatiotemporal profile of these effects, establishing that switching processes begin ∼ 400 ms after stimulus presentation. SIGNIFICANCE STATEMENT: This research addresses the neural mechanisms underlying multilingual individuals' ability to successfully manage two or more languages, critically targeting whether language control is uniform across linguistic domains (production and comprehension) and whether it is a subdomain of general cognitive control. The results showed that language production and comprehension rely on different networks: whereas language control in production recruited domain-general networks, the brain bases of switching during comprehension seemed language specific. Therefore, the crucial assumption of the bilingual advantage hypothesis, that there is a close relationship between language control and general cognitive control, seems to only hold during production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".