Genetic bases of language control in bilinguals: Evidence from an <scp>EEG</scp> study
Bibliographic record
Abstract
Previous studies have debated whether the ability for bilinguals to mentally control their languages is a consequence of their experiences switching between languages or whether it is a specific, yet highly-adaptive, cognitive ability. The current study investigates how variations in the language-related gene FOXP2 and executive function-related genes COMT, BDNF, and Kibra/WWC1 affect bilingual language control during two phases of speech production, namely the language schema phase (i.e., the selection of one language or another) and lexical response phase (i.e., utterance of the target). Chinese-English bilinguals (N = 119) participated in a picture-naming task involving cued language switches. Statistical analyses showed that both genes significantly influenced language control on neural coding and behavioral performance. Specifically, FOXP2 rs1456031 showed a wide-ranging effect on language control, including RTs, F(2, 113) = 4.00, FDR p = .036, and neural coding across three-time phases (N2a: F(2, 113) = 4.96, FDR p = .014; N2b: F(2, 113) = 4.30, FDR p = .028, LPC: F(2, 113) = 2.82, FDR p = .060), while the COMT rs4818 (ts >2.69, FDR ps < .05), BDNF rs6265 (Fs >5.31, FDR ps < .05), and Kibra/WWC1 rs17070145 (ts > -3.29, FDR ps < .05) polymorphisms influenced two-time phases (N2a and N2b). Time-resolved correlation analyses revealed that the relationship between neural coding and cognitive performance is modulated by genetic variations in all four genes. In all, these findings suggest that bilingual language control is shaped by an individual's experience switching between languages and their inherent genome.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".