From Finger Taps to Footsteps: Gait as a Model for Investigating and Training Rhythmic Abilities
Bibliographic record
Abstract
Synchronization of movements to auditory rhythmic cues, such as music or metronomes, often occurs spontaneously. Nonetheless, important interindividual differences exist in auditory-motor synchronization (AMS). Effects of rhythm on movements are partly modulated by rhythmic abilities, which include beat perception, motor production, and sensorimotor integration. These rhythmic abilities are often assessed using finger-tapping tasks, which can be performed in highly controlled environments and are easy to implement. In this article, we present limitations associated with finger-tapping tasks and propose gait as an alternative model for investigating and training rhythmic abilities. We focus on three key elements that differentiate gait from tapping and are critical in assessing AMS: the need to coordinate multiple effectors, emergent timing associated with continuous actions, and movement automaticity. Interestingly, cued-gait interventions (i.e., walking to rhythmic auditory cues for several weeks) have shown positive effects on all aspects of rhythmic abilities, while tapping interventions (e.g., playing tablet-based serious games) might lead to more limited transfer. In sum, gait offers a functionally rich behavioral model that can capture the complexity and ecological validity necessary to study and train AMS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".