The Influence of Language Proficiency on Lexical Semantic Processing in Native and Late Learners of English
Bibliographic record
Abstract
We investigated the influence of English proficiency on ERPs elicited by lexical semantic violations in English sentences, in both native English speakers and native Spanish speakers who learned English in adulthood. All participants were administered a standardized test of English proficiency, and data were analyzed using linear mixed effects (LME) modeling. Relative to native learners, late learners showed reduced amplitude and delayed onset of the N400 component associated with reading semantic violations. As well, after the N400 late learners showed reduced anterior negative scalp potentials and increased posterior potentials. In both native and late learners, N400 amplitudes to semantically appropriate words were larger for people with lower English proficiency. N400 amplitudes to semantic violations, however, were not influenced by proficiency. Although both N400 onset latency and the late ERP effects differed between L1 and L2 learners, neither correlated with proficiency. Different approaches to dealing with the high degree of correlation between proficiency and native/late learner group status are discussed in the context of LME modeling. The results thus indicate that proficiency can modulate ERP effects in both L1 and L2 learners, and for some measures (in this case, N400 amplitude), L1-L2 differences may be entirely accounted for by proficiency. On the other hand, not all effects of L2 learning can be attributed to proficiency. Rather, the differences in N400 onset and the post-N400 violation effects appear to reflect fundamental differences in L1-L2 processing.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".