When sentence meaning biases another language: an eye-tracking investigation of cross-language activation during second language reading
Bibliographic record
Abstract
Abstract Bilingual adults use semantic context to manage cross-language activation while reading. An open question is how lexical, contextual and individual differences simultaneously constrain this process. We used eye-tracking to investigate how 83 French–English bilinguals read L2-English sentences containing interlingual homographs ( chat ) and control words ( pact ). Between subjects, sentences biased target language or non-target language meanings (English = conversation; French = feline). Both conditions contained unbiased control sentences. We examined the impact of word- and participant-level factors (cross-language frequency and L2 age of acquisition/AoA and reading entropy, respectively). There were three key results: (1) L2 readers showed global homograph interference in late-stage reading (total reading times) when English sentence contexts biased non-target French homograph meanings; (2) interference increased as homographs’ non-target language frequency increased and L2 AoA decreased; (3) increased reading entropy globally facilitated early-stage reading (gaze durations) in the non-target language bias condition. Thus, cross-language activation during L2 reading is constrained by multiple factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".