The Differential Role of Executive Functions in the Cognitive Control of Language Switching
Bibliographic record
Abstract
Studies of bilingual speech production suggest that different executive functions (EFs) contribute to the cognitive control of language production. However, no study has simultaneously examined the relationship between different EFs and language control during online speech production. The current study examined individual differences in three EFs (working memory updating, inhibitory control, and task-set switching) and their relationship with performance in a trilingual language-switching task for a group of forty-seven native English (L1) speakers learning French (L2) and Spanish (L3). Analyses indicate complex interactions between EFs and language switching: better inhibitory control was related to smaller L1 switch costs, whereas better working memory was related to larger L1 switch costs. Working memory was also related to larger L2 switch costs, but only when switching from L1. These results support theories of cognitive control that implicate both global and local control mechanisms, and suggest unique contributions of each EF to both global and local cognitive control during language switching. Finally, we discuss the implications for theories of multilingual language control.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".