The Effect of Visual Information on Young Children’s Perceptual Sensitivity to Musical Beat Alignment
Bibliographic record
Abstract
Recent work examined five-year-old children’s perceptual sensitivity to musical beat alignment. In this work, children watched pairs of videos of puppets drumming to music with simple or complex metre, where one puppet’s drumming sounds (and movements) were synchronized with the beat of the music and the other drummed with incorrect tempo or phase. The videos were used to maintain children’s interest in the task. Five-year-olds were better able to detect beat misalignments in simple than complex metre music. However, adults can perform poorly when attempting to detect misalignment of sound and movement in audiovisual tasks, so it is possible that the moving stimuli actually hindered children’s performance. Here we compared children’s sensitivity to beat misalignment in conditions with dynamic visual movement versus still (static) visual images. Eighty-four five-year-old children performed either the same task as described above or a task that employed identical auditory stimuli accompanied by a motionless picture of the puppet with the drum. There was a significant main effect of metre type, replicating the finding that five-year-olds are better able to detect beat misalignment in simple metre music. There was no main effect of visual condition. These results suggest that, given identical auditory information, children’s ability to judge beat misalignment in this task is not affected by the presence or absence of dynamic visual stimuli. We conclude that at five years of age, children can tell if drumming is aligned to the musical beat when the music has simple metric structure.
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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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".