Chapter 15. Cognitive mechanisms underlying performance differences between monolinguals and bilinguals
Bibliographic record
Abstract
Lifelong experience with multiple languages is believed to produce a number of executive function advantages including enhanced top-down control, improved attention, and greater working memory capacity. This bilingual advantage is generally believed to be the result of having multiple lexical representations in each language that compete for selection. More specifically, the control that is required to select the relevant from the irrelevant language in any given context is believed to require cognitive control, and practicing this control leads to enhanced executive functioning. However, the specific underlying mechanisms of language control, including inhibition, monitoring, attention, and disengagement, that lead to enhanced executive functioning are still largely unknown. This is partly due to the complex nature of both language and domain general executive functions, which are multi-faceted. Here, we highlight some possibilities for disentangling the underlying mechanisms of executive function contributing to performance differences between monolinguals and bilinguals, and suggest that disengagement of attention from previous information is an important mechanism to consider.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".