Temporal dynamics of late second language acquisition: evidence from event-related brain potentials
Bibliographic record
Abstract
The ways in which age of acquisition (AoA) may affect (morpho)syntax in second language acquisition (SLA) are discussed. We suggest that event-related brain potentials (ERPs) provide an appropriate online measure to test some such effects. ERP findings of the past decade are reviewed with a focus on recent and ongoing research. It is concluded that, in contrast to previous suggestions, there is little evidence for a strict critical period in the domain of late acquired second language (L2) morphosyntax. As illustrated by data from our lab and others, proficiency rather than AoA seems to predict brain activity patterns in L2 processing, including native-like activity at very high levels of proficiency. Further, a strict distinction between linguistic structures that late L2 learners can vs. cannot learn to process in a native-like manner (Clahsen and Felser, 2006a; 2006b) may not be warranted. Instead, morphosyntactic real-time processing in general seems to undergo dramatic, but systematic, changes with increasing proficiency levels. We describe the general dynamics of these changes (and the corresponding ERP components) and discuss how ERP research can advance our current understanding of SLA in general.
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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) | 0.001 | 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".