Understanding the consequences of bilingualism for language processing and cognition
Bibliographic record
Abstract
Contemporary research on bilingualism has been framed by two major discoveries. In the realm of language processing, studies of comprehension and production show that bilinguals activate information about both languages when using one language alone. Parallel activation of the two languages has been demonstrated for highly proficient bilinguals as well as second language learners and appears to be present even when distinct properties of the languages themselves might be sufficient to bias attention towards the language in use. In the realm of cognitive processing, studies of executive function have demonstrated a bilingual advantage, with bilinguals outperforming their monolingual counterparts on tasks that require ignoring irrelevant information, task switching, and resolving conflict. Our claim is that these outcomes are related and have the overall effect of changing the way that both cognitive and linguistic processing are carried out for bilinguals. In this article we consider each of these domains of bilingual performance and consider the kinds of evidence needed to support this view. We argue that the tendency to consider bilingualism as a unitary phenomenon explained in terms of simple component processes has created a set of apparent controversies that masks the richness of the central finding in this work: the adult mind and brain are open to experience in ways that create profound consequences for both language and cognition.
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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 | 0.001 | 0.002 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".