Tiny dancers: Effects of musical familiarity and tempo on children’s free dancing
Bibliographic record
Abstract
Around the world, musical engagement frequently involves movement. Most adults easily clap or sway to a wide range of tempos, even without formal musical training. The link between movement and music emerges early –young infants move more rhythmically to music than speech, but do not reliably align their movements to the beat. Laboratory work encouraging specific motor patterns (e.g., drumming, tapping) demonstrates that toddlers and young children’s movements are affected by music in a rudimentary way, such that they move faster to faster rhythms (tempo flexibility). In the present study, we developed and implemented a novel home recording method to investigate how musical familiarity and tempo affect children’s naturalistic free-dance movements. Caregivers made home recordings of their children’s responses to an experimenter-created playlist (N = 83, age range = 1.25 to 3.91 years, M age = 2.39 years, SD = 0.74 years; 41 girls, 42 boys; 75% of household incomes > $90 000 CAD). Children listened to 1-min excerpts of their favorite music and unfamiliar, genre-matched music, each played at 90, 120, and 150 bpm (pitch constant; order randomized). Children moved faster to faster music and demonstrated tempo flexibility for both favorite and unfamiliar music. Favorite music encouraged more smiling across tempo conditions than unfamiliar music, as well as more dancing in the slowest tempo condition. Results demonstrate that young children’s self-selected movements are affected by musical tempo and familiarity. We also demonstrate the usefulness of a naturalistic home recording method for assessing early auditory-motor integration.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".