Prosody–syntax integration in a second language: Contrasting event-related potentials from German and Chinese learners of English using linear mixed effect models
Bibliographic record
Abstract
The role of prosodic information in sentence processing is not usually addressed in second language (L2) instruction, and neurocognitive studies on prosody–syntax interactions are rare. Here we compare event-related potentials (ERP) of Chinese and German learners of English L2 to those of native English speakers and show how first language (L1) background and L2 proficiency influence the online processing of prosody-induced garden-path effects. Unlike most previous ERP studies, we use linear mixed effect models to analyse L2 proficiency as a continuous (rather than categorical) variable. Our results show that both L1 background and language proficiency shape the integration of prosodic and syntactic cues, and that, importantly, even English native speakers’ ERPs were influenced by their English proficiency level. Lastly, this article also addresses why coverage of prosody in L2 classroom instruction may be beneficial.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".