Integration of visual context in early and late bilingual language processing: evidence from eye-tracking
Bibliographic record
Abstract
Previous research on the processing of language embedded in a rich visual context has revealed the strong effect that a recently viewed action event has on language comprehension. It has been shown that listeners are more likely to view the target object of a recently performed event than look at the target object of a plausible future event during sentence utterance, regardless of the tense cue. In the current visual-world eye-tracking experiments, we tested the strength of the recently observed visual context with a group of English monolingual and two groups of English-French early and late bilingual speakers. By comparing these different groups, we examined whether bilingual speakers, as a consequence of greater cognitive flexibility when integrating visual context and language information, show early anticipatory eye-movements toward the target object. We further asked whether early and late bilinguals show differences in their processing. The findings of the three eye-tracking experiments revealed an overall preference for the recently seen event. However, as a result of the early provision of tense cue, this preference was quickly diminished in all three groups. Moreover, the bilingual groups showed an earlier decrease in reliance on the recently seen event compared to monolingual speakers and the early bilinguals showed anticipatory eye-movements toward the plausible future event target. Furthermore, a post-experimental memory test revealed that the bilingual groups recalled the future events marginally better than the recent events, whereas the reverse was found in the monolingual groups.
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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".