The association of multilingualism with diverse language families and cognition among adults with and without education in India.
Bibliographic record
Abstract
OBJECTIVE: Early-life socioeconomic factors, such as education, closely associated with the opportunity to become multilingual (ML), are important determinants of late-life cognition. To study the cognitive advantage of multilingualism, it is critical to disentangle whether cognitive benefit is driven by multilingualism or education. With rich linguistic diversity across all socioeconomic gradients, India provides an excellent setting to examine the role of multilingualism on cognition among individuals with and without education. METHOD: Using data from the Longitudinal Aging Study in India-Diagnostic Assessment of Dementia, we evaluated the association of multilingualism by language similarity (i.e., speaking languages from the same or different language families) and education with cognition. Longitudinal Aging Study in India-Diagnostic Assessment of Dementia is a nationally representative sample of older Indian adults aged 60 and over, speaking 40 different languages and dialects (N = 4,088, 54% without formal schooling). Multilingual participants were categorized whether they spoke ≥2 languages within the same (classified as ML1) or different (classified as ML2) language families. Participants completed a comprehensive cognitive assessment assessing the domains of executive functioning, language, memory, and visuospatial ability. RESULTS: Education stratified regression models adjusted for relevant covariates in the full sample and in a propensity-score matched sample. Among those with education, multilingualism was associated with better cognitive functioning across all domains regardless of language family (all p's < .05). Among those without education, only ML1 (not ML2) was associated with better executive functioning (B = 0.17 [0.07, 0.27]) compared to monolinguals. CONCLUSIONS: These findings add to the growing literature on cognitive advantage of multilingualism, disentangling them from education and suggesting differential effects by language similarity. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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".