Selective attention to the mouth of talking faces in monolinguals and bilinguals aged 5 months to 5 years.
Bibliographic record
Abstract
A talking face provides redundant cues on the mouth that might support language learning and highly salient social cues in the eyes. What drives children's looking toward the mouth versus eyes of a talking face? This study reports data from 292 children who viewed faces speaking English, French, and Russian. We investigated the impact of children's age (5 months to 5 years) and language background (monolingual English, monolingual French, bilingual English-French), and the speaker's language (dominant, nondominant, or nonnative) relative to children's native language(s). Data from 129 bilingual adults were also collected for comparison. Five-month-olds showed balanced attention to the eyes and mouth, but children up to 5 years tended to be most interested in the mouth. In contrast, adults were most interested in the eyes. We found little evidence for different patterns of attention for monolinguals versus bilinguals, or to a native versus a nonnative speaker. Using percentile scores, monolinguals with larger productive vocabularies looked more at the mouth, while bilinguals with larger comprehension vocabularies looked marginally less at the mouth, although both effects were small and not as robust with raw vocabulary scores. Children showed large but stable individual variability in their face scanning patterns across different speakers. Our results show that the way that children allocate their attention to talking faces continues to change from infancy through the preschool years and beyond. Future studies will need to go beyond looking at bilingualism, speaker language, and vocabulary size to understand what drives children's in-the-moment attention to talking faces. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".