Moving to the Beat and Singing are Linked in Humans
Bibliographic record
Abstract
The abilities to sing and to move to the beat of a rhythmic auditory stimulus emerge early during development, and both engage perceptual, motor, and sensorimotor processes. These similarities between singing and synchronization to a beat may be rooted in biology. Patel (2008) has suggested that motor synchronization to auditory rhythms may have emerged during evolution as a byproduct of selection for vocal learning ("vocal learning and synchronization hypothesis"). This view predicts a strong link between vocal performance and synchronization skills in humans. Here, we tested this prediction by asking occasional singers to tap along with auditory pulse trains and to imitate familiar melodies. Both vocal imitation and synchronization skills were measured in terms of accuracy and precision or consistency. Accurate and precise singers tapped more in the vicinity of the pacing stimuli (i.e., they were more accurate) than less accurate and less precise singers. Moreover, accurate singers were more consistent when tapping to the beat. These differences cannot be ascribed to basic motor skills or to motivational factors. Individual differences in terms of singing proficiency and synchronization skills may reflect the variability of a shared sensorimotor translation mechanism.
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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".