Individual differences predict ERP signatures of second language learning of novel grammatical rules
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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 it