Individual differences predict ERP signatures of second language learning of novel grammatical rules
Bibliographic record
Abstract
We investigated the extent to which second-language (L2) learning is influenced by the similarity of grammatical features in one's first language (L1). We used event-related potentials to identify neural signatures of a novel grammatical rule – grammatical gender – in L1 English speakers. Of interest was whether individual differences in L2 proficiency and age of acquisition (AoA) influenced these effects. L2 and native speakers of French read French sentences that were grammatically correct, or contained either a grammatical gender or word order violation. Proficiency and AoA predicted Left Anterior Negativity amplitude, with structure violations driving the proficiency effect and gender violations driving the AoA effect. Proficiency, group, and AoA predicted P600 amplitude for gender violations but not structure violations. Different effects of grammatical gender and structure violations indicate that L2 speakers engage novel grammatical processes differently from L1 speakers and that this varies appreciably based on both AoA and proficiency.
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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 | 0.003 |
| 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 | 0.000 |
| 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".