Phrase parsing in a second language as indexed by the closure positive shift: The impact of language experience and acoustic cue salience
Bibliographic record
Abstract
Despite the importance of prosodic processing in utterance parsing, a majority of studies investigating boundary localization in a second language focus on word segmentation. The goal of the present study was to investigate the parsing of phrase boundaries in first and second languages from different prosodic typologies (stress-timed vs. syllable-timed). Fifty English-French bilingual adults who varied in native language (French or English) and second language proficiency listened to English and French utterances with different prosodic structures while event-related brain potentials were recorded. The utterances were built around target words presented either in phrase-final position (bearing phrase-final lengthening) or in penultimate position. Each participant listened to both English and French stimuli, providing data in their native language (used as reference) and their second language. Target words in phrase-final position elicited closure positive shifts across listeners in both languages, regardless of the language-specific acoustic cues associated with phrase-final lengthening (shorter phrase-final lengthening in English compared to French). Interestingly, directional effects were observed, where learning to parse English as a second language in a native-like manner seemed to require a higher proficiency level than learning to parse French as a second language. This pattern of results supports the idea that L2 listeners need to learn to recognize L2-specific phrase-final lengthening regardless of the apparent similarity across languages and that some language combinations might present greater challenges than others.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".