Advances in our understanding of bilingual brain organization: A look back and a view forward
Bibliographic record
Abstract
Multilingual and bilingual environments provide natural settings to study the implications of acquiring and developing competence in more than one language. Models of language processing have often focused on monolingual contexts, but researchers who live in countries where bilingualism and multilingualism are the norm, have an opportunity to extend these ideas; the work discussed here focuses on two such linguistic contexts, South Africa and Canada. In 1992, Klein and Doctor took inspiration from English-Afrikaans bilinguals to extend models of monolingual processing to bilingual individuals. Since then, Klein and colleagues have taken advantage of the unique language environment of Quebec, Canada, and the substantial possibilities arising from the burgeoning field of functional neuroimaging to explore how two languages exist in a single cognitive system and what that tells us about neural representations. More recently there has been a burgeoning of research in this field, examining also the implications of bilingual language processing for cognition more generally. Our paper reviews the progress made in the field, from the original findings of Klein and Doctor to key findings and advances that have taken place since then. • Methodological and theoretical advances since Klein and Doctor's (1992) chapter. • Implications of bilingualism for brain organization. • Implications of bilingualism for 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.021 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".