Maternal Vocal Interactions with Infants: Reciprocal Visual Influences
Bibliographic record
Abstract
Abstract The present study examined the influence of infant visual cues on maternal vocal and facial expressiveness while speaking or singing and the influence of maternal visual cues on infant attention. Experiment 1 asked whether mothers exhibit more vocal emotion when speaking and singing to infants in or out of view. Adults judged which of each pair of audio excerpts (in view, out of view) sounded more emotional. Face‐to‐face vocalizations were judged more emotional than vocalizations to infants out of view. Moreover, mothers smiled considerably more while singing than while speaking to infants. Experiment 2 examined the influence of video feedback from infants on maternal speech and singing. Maternal vocalizations in the context of video feedback were judged to be less emotional than those in face‐to‐face contexts but more emotional than those in out‐of‐view contexts. Experiment 3 compared six‐month‐old infants’ attention to maternal speech and singing with audio‐only versions or with silent video‐only versions. Infants exhibited comparable attention to audio‐only versions of speech and singing but greater attention to video‐only versions of singing. The present investigation is unique in documenting the contribution of infant visual feedback to maternal vocal emotion in contexts that control for infants’ presence, visibility, and proximity.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".