Finding phrases: On the role of co-verbal facial information in learning word order in infancy
Bibliographic record
Abstract
The input contains perceptually available cues, which might allow young infants to discover abstract properties of the target language. Thus, word frequency and prosodic prominence correlate systematically with basic word order in natural languages. Prelexical infants are sensitive to these frequency-based and prosodic cues, and use them to parse new input into phrases that follow the order characteristic of their native languages. Importantly, young infants readily integrate auditory and visual facial information while processing language. Here, we ask whether co-verbal visual information provided by talking faces also helps prelexical infants learn the word order of their native language in addition to word frequency and prosodic prominence. We created two structurally ambiguous artificial languages containing head nods produced by an animated avatar, aligned or misaligned with the frequency-based and prosodic information. During 4 minutes, two groups of 4- and 8-month-old infants were familiarized with the artificial language containing aligned auditory and visual cues, while two further groups were exposed to the misaligned language. Using a modified Headturn Preference Procedure, we tested infants' preference for test items exhibiting the word order of the native language, French, vs. the opposite word order. At 4 months, infants had no preference, suggesting that 4-month-olds were not able to integrate the three available cues, or had not yet built a representation of word order. By contrast, 8-month-olds showed no preference when auditory and visual cues were aligned and a preference for the native word order when visual cues were misaligned. These results imply that infants at this age start to integrate the co-verbal visual and auditory cues.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; both teacher heads agree on what is shown here.
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".