Examining the consistency in bilingualism and white matter research: A meta-analysis
Bibliographic record
Abstract
This study aimed to systematically investigate the relationship between bilingualism, age, L2 onset age of acquisition (AoA), and white matter integrity (operationalized as fractional anisotropy, FA), addressing inconsistencies in the literature. We conducted a meta-analysis of 23 studies and used meta-regression models to assess the influence of age and L2AoA on effect sizes in studies comparing monolinguals and bilinguals. Even though the overall between-group effect size across the whole brain was unreliable, bilingualism was associated with increased white matter integrity in specific tracts and in groups with a limited range of age and L2AoA. Age had a small, negative effect on white matter integrity, with differences between monolinguals and bilinguals more pronounced in younger adults, consistent with a view of an initial increase in white matter integrity, followed by remodeling for efficiency over time. In contrast, later L2AoA was associated with greater white matter integrity in bilinguals than monolinguals, again consistent with the remodeling for efficiency model. Our findings highlight the importance of considering age and L2AoA when examining the neural basis of bilingualism on white matter in the brain and how bilingualism contributes to structural changes that stave off cognitive decline in older age.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.027 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".