Effects of Kindermusik training on infants’ rhythmic enculturation
Bibliographic record
Abstract
Phillips-Silver and Trainor (2005) demonstrated a link between movement and the metrical interpretation of rhythm patterns in 7-month-old infants. Infants bounced on every second beat of a rhythmic pattern with no auditory accents later preferred to listen to an accented version of the pattern with accents every second beat (duple or march meter), whereas infants bounced on every third beat of the same rhythmic pattern preferred to listen to a version with accents every third beat (triple or waltz meter). The present study compared infants participating in Kindermusik classes with infants not participating in music classes. In Kindermusik classes infants receive enriched experience moving to music. Following Western musical norms, the majority of the music samples in the classes are in duple meter. During the preference test, Kindermusik infants listened longer overall, indicating heightened interest in musical rhythms. Both groups listened longer to the accented version that matched how they had been bounced, but only the Kindermusik group showed a stronger preference in the case of duple bouncing than in the case of triple bouncing. We conclude that musical classes for infants can accelerate the development of culture-specific metrical perception.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".