Spoken Word Segmentation in First and Second Language: When ERP and Behavioral Measures Diverge
Bibliographic record
Abstract
Previous studies of word segmentation in a second language have yielded equivocal results. This is not surprising given the differences in the bilingual experience and proficiency of the participants and the varied experimental designs that have been used. The present study tried to account for a number of relevant variables to determine if bilingual listeners are able to use native-like word segmentation strategies. Here, 61 French-English bilingual adults who varied in L1 (French or English) and language dominance took part in an audiovisual integration task while event-related brain potentials (ERPs) were recorded. Participants listened to sentences built around ambiguous syllable strings (which could be disambiguated based on different word segmentation patterns), during which an illustration was presented on screen. Participants were asked to determine if the illustration was related to the heard utterance or not. Each participant listened to both English and French utterances, providing segmentation patterns that included both their native language (used as reference) and their L2. Interestingly, different patterns of results were observed in the event-related potentials (online) and behavioral (offline) results, suggesting that L2 participants showed signs of being able to adapt their segmentation strategies to the specifics of the L2 (online ERP results), but that the extent of the adaptation varied as a function of listeners' language experience (offline behavioral results).
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".