Second Language Acquisition of Gender Agreement in Explicit and Implicit Training Conditions: An Event‐Related Potential Study
Bibliographic record
Abstract
This study employed an artificial language learning paradigm together with a combined behavioral/event-related potential (ERP) approach to examine the neurocognition of the processing of gender agreement, an aspect of inflectional morphology that is problematic in adult second language (L2) learning. Subjects learned to speak and comprehend an artificial language under either explicit (classroomlike) or implicit (immersionlike) training conditions. In each group, both noun-article and noun-adjective gender agreement processing were examined behaviorally and with ERPs at both low and higher levels of proficiency. Results showed that the two groups learned the language to similar levels of proficiency but showed somewhat different ERP patterns. At low proficiency, both types of agreement violations (adjective, article) yielded N400s, but only for the group with implicit training. Additionally, noun-adjective agreement elicited a late N400 in the explicit group at low proficiency. At higher levels of proficiency, noun-adjective agreement violations elicited N400s for both the explicit and implicit groups, whereas noun-article agreement violations elicited P600s for both groups. The results suggest that interactions among linguistic structure, proficiency level, and type of training need to be considered when examining the development of aspects of inflectional morphology in L2 acquisition.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".