The impact of a momentary language switch on bilingual reading: Intense at the switch but merciful downstream for L2 but not L1 readers
Bibliographic record
Abstract
We investigated whether cross-language activation is sensitive to shifting language demands and language experience during first and second language (i.e., L1, L2) reading. Experiment 1 consisted of L1 French – L2 English bilinguals reading in the L2, and Experiment 2 consisted of L1 English - L2 French bilinguals reading in the L1. Both groups read English sentences with target words serving as indices of cross-language activation: cross-language homographs, cognates, and matched language-unique control words. Critically, we manipulated whether English sentences contained a momentary language switch into French before downstream target words. This allowed us to assess the consequences of shifting language demands, both in the moment, and residually following a switch as a function of language experience. Switches into French were associated with a reading cost at the switch site for both L2 and L1 readers. However, downstream cross-language activation was larger following a switch only for L1 readers. These results suggest that cross-language activation is jointly sensitive to momentary shifts in language demands and language experience, likely reflecting different control demands faced by L2 vs. L1 readers, consistent with models of bilingual processing that ascribe a primary role for 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".