Moving toward a neuroplasticity view of bilingualism, executive control, and aging
Bibliographic record
Abstract
ABSTRACT Normal aging is an inevitable race between increasing knowledge and decreasing cognitive capacity. Crucial to understanding and promoting successful aging is determining which of these factors dominates for particular neurocognitive functions. Here, we focus on the human capacity for language, for which healthy older adults are simultaneously advantaged and disadvantaged. In recent years, a more hopeful view of cognitive aging has emerged from work suggesting that age-related declines in executive control functions are buffered by life-long bilingualism. In this paper, we selectively review what is currently known and unknown about bilingualism, executive control, and aging. Our ultimate goal is to advance the views that these issues should be reframed as a specific instance of neuroplasticity more generally and, in particular, that researchers should embrace the individual variability among bilinguals by adopting experimental and statistical approaches that respect the complexity of the questions addressed. In what follows, we set out the theoretical assumptions and empirical support of the bilingual advantages perspective, review what we know about language, cognitive control, and aging generally, and then highlight several of the relatively few studies that have investigated bilingual language processing in older adults, either on their own or in comparison with monolingual older adults. We conclude with several recommendations for how the field ought to proceed to achieve a more multifactorial view of bilingualism that emphasizes the notion of neuroplasticity over that of simple bilingual versus monolingual group comparisons.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".