Musical groove shapes children's free dancing
Bibliographic record
Abstract
The drive to move to music is evident across a variety of contexts, from the simple urge to tap our toe to a song on the radio, to massive crowds dancing in time at a rock concert. Though seemingly effortless, beat synchronization is difficult to master and children are often poor beat synchronizers. Nevertheless, auditory-motor integration is fundamental for many daily processes, such as speech. A topic that has been relatively understudied concerns how stimulus properties affect young children's movement in responses to auditory stimuli. In the present study, we examined how musical groove (adult-rated desire to move) affected 3.0- to 6.9-year-old children's free dancing in the comfort of their home (n = 78). In the high groove conditions, children danced more and with more energy compared to the low groove conditions. Moreover, in the high groove condition, children's movement tempos corresponded better with the tempos of the music. Results point to early childhood sensitivity to the musical features that motivate adults to move to music. High groove music may therefore prove especially effective at facilitating early auditory-motor integration. A video abstract of this article can be viewed at https://youtu.be/vli0-6N12Ts.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".