The Active Infant's Developing Role in Musical Interactions: Insights From an Online Parent Questionnaire
Bibliographic record
Abstract
Musical interactions between caregivers and their infants typically rely on a limited repertoire of live vocal songs and recorded music. Research suggests that these well-known songs are especially effective at eliciting engaged behaviors from infants in controlled settings, but how infants respond to familiar music with their caregivers in their everyday environment remains unclear. The current study used an online questionnaire to quantify how often and why caregivers present certain songs and musical recordings to their infants. Using a cross-sectional approach, we explored infants' changing behavioral profiles to music from birth to 24 months. Caregivers additionally reported on their feelings of affective attachment toward their infants. Results reveal that caregivers sing and play recorded music for younger and older infants at comparably high rates. In turn, infants actively respond to their favorite songs and recordings by demonstrating positive emotions, movements, and attentive listening. Caregivers mainly consider their infants' musical preferences when building their shared musical repertoire at home. Both caregivers' engagement in musical activities with their children and infants' enthusiastic responsiveness to singing predicted stronger dyadic attachment bonding. Caregivers and infants jointly contribute to building musical relationships, and these musical relationships may be intertwined with their emerging social-emotional bonds.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".