Lexical competition during second-language listening: Sentence context, but not proficiency, constrains interference from the native lexicon.
Bibliographic record
Abstract
A spoken language eye-tracking methodology was used to evaluate the effects of sentence context and proficiency on parallel language activation during spoken language comprehension. Nonnative speakers with varying proficiency levels viewed visual displays while listening to French sentences (e.g., Marie va décrire la poule [Marie will describe the chicken]). Displays depicted several objects including the final noun target (chicken) and an interlingual near-homophone (e.g., pool) whose name in English is phonologically similar to the French target (poule). Listeners' eye movements reflected temporary consideration of the interlingual competitor when hearing the target noun, demonstrating cross-language lexical competition. However, competitor fixations were dramatically reduced when prior sentence information was incompatible with the competitor (e.g., Marie va nourrir... [Marie will feed...]). In contrast, interlingual competition from English did not vary according to participants' rated proficiency in French, even though proficiency reliably predicted other aspects of processing behavior, suggesting higher proficiency in the active language does not provide a significant independent source of control over interlingual competition. The results provide new insights into the nature of parallel language activation in naturalistic sentential contexts.
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 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 | 0.003 |
| 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 | 0.001 |
| 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".