NEUROCORRELATES BETWEEN THEORY-OF-MIND AND BILINGUALISM IN GRAY MATTER VOLUME OF YOUNG AND OLDER ADULTS
Bibliographic record
Abstract
Abstract The experience of being bilingual may accrue cognitive reserve against age-related declines in older adults. Early bilingualism, e.g., second language age-of-acquisition (L2AoA), has been shown to be associated with better theory-of-mind (ToM) performance in older adults (Yow et al., 2021). Here, we aim to understand the brain structural correlates associated with bilingualism and ToM performance in normal aging. Forty-six young (YA, aged 19-30, M=21.87) and 51 older adult bilinguals (OA, aged 54-77, M=63.61) completed 1) ToM assessments, where they viewed vignettes and answered questions about the protagonists’ mental states, 2) an anatomical MRI scan, 3) a demographic questionnaire including L2AoA and years of education, and 4) a general cognitive ability assessment by the Montreal Cognitive Assessment (MoCA) test. As expected, ANCOVA on ToM composite scores revealed a significant main effect of age – YA showed better ToM performance than OA, F(1,93)=9.48, p=.003, controlling for education and MoCA scores. Importantly, MRI data were preprocessed to obtain gray matter volume (GMV), proxy of neuronal density, of 430 brain regions. Partial least square correlation analysis identified one significant multivariate pattern linking individual differences in GMV with ToM score and L2AoA (48.7% covariance explained, p=.014). Regardless of age, larger GMV in several regions including prefrontal, frontal, medial temporal, and superior temporal cortices were associated with earlier L2AoA (p=.003) and higher ToM score (p=.004), indicating shared variance between ToM and L2AoA in brain morphology. Findings suggest that earlier bilingual acquisition might promote brain maturation that would preserve ToM ability well into later stages of life.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".