Early social communication through music: State of the art and future perspectives
Bibliographic record
Abstract
A growing body of research shows that the universal capacity for music perception and production emerges early in development. Possibly building on this predisposition, caregivers around the world often communicate with infants using songs or speech entailing song-like characteristics. This suggests that music might be one of the earliest developing and most accessible forms of interpersonal communication, providing a platform for studying early communicative behavior. However, little research has examined music in truly communicative contexts. The current work aims to facilitate the development of experimental approaches that rely on dynamic and naturalistic social interactions. We first review two longstanding lines of research that examine musical interactions by focusing either on the caregiver or the infant. These include defining the acoustic and non-acoustic features that characterize infant-directed (ID) music, as well as behavioral and neurophysiological research examining infants' processing of musical timing and pitch. Next, we review recent studies looking at early musical interactions holistically. This research focuses on how caregivers and infants interact using music to achieve co-regulation, mutual engagement, and increase affiliation and prosocial behavior. We conclude by discussing methodological, technological, and analytical advances that might empower a comprehensive study of musical communication in early childhood.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".