Second Language Immersion Experience Could Help the Brain Response to Second Language Reading for Native Chinese Speakers
Bibliographic record
Abstract
Native language background exerts constraints on the individual's brain automatic response while learning a second language. It remains unclear, however, whether second language immersion experience could help the brain overcome such constraints and meet the requirements of a second language. This study compared native Chinese speakers with English-as-a-second-language immersion experience (immersive English learners), native Chinese speakers without English-as-a-second-language immersion experience (nonimmersive English learners), and native English speakers with an ERP cross-modal MMN paradigm. The results found that English-as-a-second-language immersion could benefit speech perception for native Chinese speakers. In addition, both immersive English learners and native English speakers showed enhanced cross-modal MMN, indicating that second language immersion could help native Chinese speakers successfully integrate English letter-sound like native English speakers. The present study further revealed that English listening and speaking exposure in an immersive environment is important in English letter-sound integration for immersive English learners.
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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 | 0.001 |
| 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) | 0.003 | 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".