Bibliographic record
Abstract
Interlocutor context affects proficient bilinguals’ spoken language processing. For instance, bilinguals in a visual-auditory lexical decision task are able to predict the context-appropriate language based on the visual cues of interlocutor context (e.g., Molnar et al., 2015). Because it has been also demonstrated that bilinguals, as compared to monolinguals, process talker-voice information more efficiently (Levi, 2017), in the current study we addressed the question whether bilinguals are able to predict context-appropriate language based on voice information alone. First, in a same-different task, English monolingual and bilingual participants were familiarized with the voices of 4 female speakers who either spoke English (shared language across the monolingual and bilingual participants) or Farsi (unknown to both monolingual and bilingual participants). Then, in a lexical decision task, the participants heard the same 4 voices again, but all of the voices spoke in English this time. We predicted that if the participants established a voice-language link in the first part of the task, then their response times should decrease when they hear an “English-voice” (as opposed to a “Farsi-voice”) uttering an English word in the lexical decision task. Accordingly, our preliminary results suggest that the bilinguals’ performance is facilitated by the established voice-language link.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".