The presence of a foreign accent introduces lexical integration difficulties during late semantic processing
Bibliographic record
Abstract
Previous research suggests that native listeners may be more tolerant to syntactic errors when they are produced in a foreign accent. However, studies investigating this topic within the semantic domain remain conflicting. The current study examined the effects of mispronunciations leading to semantic abnormality in foreign-accented speech. While their EEG was recorded, native speakers of Spanish listened to semantically correct and incorrect sentences produced by another native speaker and a native speaker of Chinese. The anomaly in the incorrect sentences was caused by a subtle mispronunciation (typical or atypical in Chinese-accented Spanish) during a critical word production. While initial-stage semantic processing yielded no accent-specific differences, late processing revealed a persistent N400-effect in the foreign-accent but not in the native-accent. These findings suggest that foreign-accented mispronunciations are more difficult to integrate than native-accented errors, regardless of their relative typicality. The distinction between syntactic and semantic processing of foreign-accented speech is discussed.
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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) | 0.001 | 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".