Functional activity and white matter microstructure reveal the independent effects of age of acquisition and proficiency on second-language learning
Bibliographic record
Abstract
Two key factors govern how bilingual speakers neurally maintain two languages: the speakers' second language age of acquisition (AoA) and their subsequent proficiency. However, the relative roles of these two factors have been difficult to disentangle given that the two can be closely correlated, and most prior studies have examined the two factors in isolation. Here, we combine functional magnetic resonance imaging with diffusion tensor imaging to identify specific brain areas that are independently modulated by AoA and proficiency in second language speakers. First-language Mandarin Chinese speakers who are second language speakers of English were scanned as they performed a picture-word matching task in either language. In the same session we also acquired diffusion-weighted scans to assess white matter microstructure, along with behavioural measures of language proficiency prior to entering the scanner. Results reveal gray- and white-matter networks involving both the left and right hemisphere that independently vary as a function of a second-language speaker's AoA and proficiency, focused on the superior temporal gyrus, middle and inferior frontal gyrus, parahippocampal gyrus, and the basal ganglia. These results indicate that proficiency and AoA explain separate functional and structural networks in the bilingual brain, which we interpret as suggesting distinct types of plasticity for age-dependent effects (i.e., AoA) versus experience and/or predisposition (i.e., proficiency).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".