Native and non-native parsing of adjective placement—an ERP study of Mandarin and English sentence processing
Bibliographic record
Abstract
The rules governing adjective-noun order vary crosslinguistically, and event-related potentials have shown that violations of these rules elicit biphasic responses in native speakers and advanced non-native learners. We built on prior findings by replicating an English experiment and running a new experiment in Mandarin. In the replication, we tested native English speakers with advanced Mandarin proficiency (n=20); for the Mandarin experiment, we tested the same English-Mandarin bilinguals along with Mandarin native speakers (n=32). Native speakers in both languages showed the expected biphasic effect. However, to our surprise, native speakers’ Mandarin results showed an additional effect at the first word of the correct adjective-noun order, and Mandarin non-native speakers showed only an early effect. To explore these results, we compared individual differences, showing that participants varied in response patterns. We interpret our results as demonstrating that even when languages share adjective placement rules, processing can be impacted by crosslinguistic differences.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".