Electrophysiological Approaches to L1 Attrition
Bibliographic record
Abstract
This chapter provides an overview of the first few event-related potential (ERP) studies on L1 attrition, discussing their results and future directions. After briefly introducing the technique of ERPs in psycholinguistics, it shows that ERP studies are particularly suited to advance attrition research due to their power to track even subtle changes in cognitive processing in considerable detail. The ERP data available provide initial physiological evidence that L1 attrition in migrants’ brains occurs at lexical and morpho-syntactic levels of processing, modulated by the degree of exposure to the two languages. In extreme cases, L2-dominant attriters may perceive a grammatical sentence in their L1 as ungrammatical, if it violates the L2 grammar. Where ERP data patterns seem inconsistent across studies from different labs, the potential underlying reasons are discussed, briefly touching upon how L1 attrition may positively influence one’s L2, due to greater L1 inhibition and therefore less interference.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".