Bibliographic record
Abstract
Abstract Four patterns describe how bilingualism affects the functional connectivity of the brain. First, a general observation across most of the studies I surveyed was that bilinguals tended to have higher functional connectivity when compared to monolinguals. Second, increased connectivity with the salience network, a set of regions including the anterior cingulate cortex, the bilateral insula, and subcortical regions is often associated with language training or language diversity where proactive attention to content is paramount. Third, to the degree that individuals have greater exposure or mastery of a second language, (greater proficiency and an earlier or simultaneous age of acquisition) and can rely more on reactive control, studies often show greater bilateral connectivity between the inferior frontal gyri. This is also sometimes associated with decreased activation of frontal regions implying distributed load and greater neural efficiency. The distributed neural pattern in young adulthood may also explain how bilingual older adults are able to sustain their cognition at levels of neuropathology most monolinguals cannot endure. Fourth, in studies that examined anticorrelations between task and rest networks, bilinguals tended to have more distinct (e.g., modular organization), and more strongly anticorrelated task-positive and default-mode networks, and this was often correlated with cognitive control.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".