Cognitive control for language switching in bilinguals: A quantitative meta-analysis of functional neuroimaging studies
Bibliographic record
Abstract
In a quantitative meta-analysis, using the activation likelihood estimation method, we examined the neural regions involved in bilingual cognitive control, particularly when engaging in switching between languages. The purpose of this study was to evaluate the bilingual cognitive control model originally proposed as a qualitative analysis of functional neuroimaging studies. After reviewing 128 peer-reviewed articles, 10 neuroimaging studies met our inclusion criteria and in each study, bilinguals switched between languages in response to cues. We isolated regions involved in voluntary language switching, by including reported contrasts between the switching conditions and high-level baseline conditions involving similar tasks but requiring the use of only one language. Eight brain regions showed significant and reliable activation: left inferior frontal gyrus, left middle temporal gyrus, left middle frontal gyrus, right precentral gyrus, right superior temporal gyrus, midline pre-supplementary motor area, and bilateral caudate nuclei. This quantitative result is consistent with bilingual aphasia studies that report switching deficits associated with lesions to the caudate nuclei or prefrontal cortex. It also extends the previously reported qualitative model. We discuss the implications of the findings for accounts of bilingual cognitive control.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.019 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".