Executive control modulates cross-language lexical activation during L2 reading: Evidence from eye movements.
Bibliographic record
Abstract
Models of bilingual reading such as Bilingual Interactive Activation Plus (Dijkstra & van Heuven, 2002) do not predict a central role for domain-general executive control during bilingual reading, in contrast with bilingual models from other domains, such as production (e.g., the Inhibitory Control Model; Green, 1998). We thus investigated whether individual differences among bilinguals in domain-general executive control modulate cross-language activation during L2 sentence reading, over and above other factors such as L2 proficiency. Fifty French-English bilinguals read L2-English sentences while their eye movements were recorded, and they subsequently completed a battery of executive control and L2 proficiency tasks. High- and low-constraint sentences contained interlingual homographs (chat = "casual conversation" in English, "a cat" in French), cognates (piano in English and French), or L2-specific control words. The results showed that greater executive control among bilinguals but not L2 proficiency reduced cross-language activation in terms of interlingual homograph interference. In contrast, increased L2 proficiency but not executive control reduced cross-language activation in terms of cognate facilitation. These results suggest that models of bilingual reading must incorporate mechanisms by which domain-general executive control can alter the earliest stages of bilingual lexical activation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".