The song, not the singer: Infants prefer to listen to familiar songs, regardless of singer identity
Bibliographic record
Abstract
Parent's infant-directed vocalizations are highly dynamic and emotive compared to their adult-directed counterparts, and correspondingly, more effectively capture infants' attention. Infant-directed singing is a specific type of vocalization that is common throughout the world. Parents tend to sing a small handful of songs in a stereotyped way, and a number of recent studies have highlighted the significance of familiar songs in young children's social behaviors and evaluations. To date, no studies have examined whether infants' responses to familiar versus unfamiliar songs are modulated by singer identity (i.e., whether the singer is their own parent). In the present study, we investigated 9- to 12-month-old infants' (N = 29) behavioral and electrodermal responses to relatively familiar and unfamiliar songs sung by either their own mother or another infant's mother. Familiar songs recruited more attention and rhythmic movement, and lower electrodermal levels relative to unfamiliar songs. Moreover, these responses were robust regardless of whether the singer was their mother or a stranger, even when the stranger's rendition differed greatly from their mothers' in mean fundamental frequency and tempo. Results indicate that infants' interest in familiar songs is not limited to idiosyncratic characteristics of their parents' song renditions, and points to the potential for song as an effective early signifier of group membership.
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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 | 0.000 | 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) | 0.003 | 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".