Reading Russian–English homographs in sentence contexts: Evidence from ERPs
Bibliographic record
Abstract
The current study investigated whether Russian–English bilinguals activate knowledge of Russian when reading English sentences. Russian and English share only a few letters, but there are some interlingual homographs (e.g., POT, which means “mouth” in Russian). Critical sentences were written such that the Russian meaning of the homographs fit the context. Sentences presented to participants contained either the English translation of the Russian meaning of a homograph, an interlingual homograph, or a control word (e.g., TO SEE TOM'S THROAT, THE DOCTOR ASKED TOM TO OPEN HIS MOUTH / POT / NET WIDELY ). Bilinguals showed a reduction in the N400 component of the event-related potential (ERP) signal for interlingual homographs compared to control words, whereas the N400 of monolingual English speakers was of a similar magnitude in the two conditions. The finding provides evidence that bilinguals automatically activate representations in both of their languages when reading in one language, even when the combination of a language-specific script and the preceding language context indicates that the other language is not relevant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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".