Event-related Potential Measures of Visual Word Processing in Monolingual and Bilingual Children and Adults: A Focus on Word Frequency Effects
Bibliographic record
Abstract
How does language background influence the neural correlates of visual word recognition in children? To address this question, we used an ERP lexical decision task to examine first-language (L1) and second-language (L2) visual word processing in monolingual and bilingual school-aged children and young adults (n = 123). In particular, we focused on the effects of word frequency (an index of lexical accessibility) on RTs and the N400 ERP component. Behaviorally, we found larger L1 versus L2 word frequency effects among bilingual children, driven by faster and more accurate responses to higher-frequency words (no other language or age group differences were observed). Neurophysiologically, we found larger L1 word frequency effects in bilinguals versus monolinguals (across both age groups), reflected in more negative ERP amplitudes to lower-frequency words. However, the bilingual groups processed L1 and L2 words similarly, despite lower levels of subjective and objective L2 proficiency. Taken together, our findings suggest that divided L1 experience (but not L2 experience) influences the neural correlates of visual word recognition across childhood and adulthood.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".