Eye Movement Measures of Within-Language and Cross-Language Activation During Reading in Monolingual and Bilingual Children and Adults: A Focus on Neighborhood Density Effects
Bibliographic record
Abstract
We used eye movement measures of first-language (L1) and second-language (L2) paragraph reading to investigate how the activation of multiple lexical candidates, both within and across languages, influences visual word recognition in four different age and language groups: (1) monolingual children; (2) monolingual young adults; (3) bilingual children; and (4) bilingual young adults. More specifically, we focused on within-language and cross-language orthographic neighborhood density effects, while controlling for the potentially confounding effects of orthographic neighborhood frequency. We found facilitatory within-language orthographic neighborhood density effects (i.e., words were easier to process when they had many vs. few orthographic neighbors, evidenced by shorter fixation durations) across the L1 and L2, with larger effects in children vs. adults (especially the bilingual ones) during L1 reading. Similarly, we found facilitatory cross-language neighborhood density effects across the L1 and L2, with no modulatory influence of age or language group. Taken together, our findings suggest that word recognition benefits from the simultaneous activation of visually similar word forms during naturalistic reading, with some evidence of larger effects in children and particularly those whose words may have differentially lower baseline activation levels and/or weaker links between word-related information due to divided language exposure: bilinguals.
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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) | 0.002 | 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".