Chapter 1. Bilingualism, executive control, and eye movement measures of reading
Bibliographic record
Abstract
This chapter selectively reviews the literature on bilingual language processing, with a special focus on the link to executive control, eye movements during reading, and differences between two different groups that are often lumped together: bilinguals (i.e., individuals who know two languages) and multilinguals (i.e., individuals who know more than two languages). To this end, we first discuss ideas about the cognitive demands associated with knowing more than a single language. We then review how eye movement reading research has clarified two important consequences of knowledge and use of more than one language: (1) cross-language activation and its relation to executive function and (2) weakened local (i.e., word-level) and global (i.e., text-level) aspects of reading performance. Finally, we review what is currently known about the bilingual vs. multilingual distinction, and present a re-analysis of previously published data (Whitford & Titone, 2016) exploring the effects of bilingual vs. multilingual status on natural reading in both younger and older adults. Although preliminary, these findings, along with the growing literature reviewed here from other domains, illustrate the importance of taking the bilingualism/multilingualism distinction into account when trying to understand the cognitive implications of knowing more than one language.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".