Electrophysiology reflects the influence of discourse context on auditory semantic processing in bilinguals.
Bibliographic record
Abstract
Most language experiences take place at the level of multiple sentences. However, previous studies of second language (L2) comprehension have typically focused on lexical- and sentence-level processing. Our study addresses this gap by examining auditory discourse comprehension in 32 English/French bilinguals. We tested the prediction of the noisy channel model (Futrell & Gibson, 2017) that bilinguals will rely more on top-down, discourse-level cues in L2 because these are common across languages, as opposed to the language-specific associations of an often weaker L2. We further hypothesized that these effects could be influenced by individual differences, such that participants with lower L2 proficiency or working memory would have more difficulty building and maintaining discourse context. Specifically, we measured the N400 response, an index of automatic semantic processing. Participants heard three-sentence stories with prime and target words in the final sentence whose lexical association was manipulated, as was the congruence of the target with the preceding discourse. Overall, our results support the noisy channel model of language comprehension in a sample of highly proficient bilinguals. We observed larger N400 effects of discourse congruence than lexical association, and the difference between these 2 conditions was greater in the L2 than in the L1. Additionally, the effects of lexical association were limited to the L1 and predicted by individual differences in language dominance but not working memory. These findings suggest that bilinguals do indeed make greater use of top-down, supralinguistic information in their L2 compared with their L1. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".