Substituting some unassisted practice with robotic guidance: Assessing the feasibility of auditory-cued mixed practice for music-based interventions
Bibliographic record
Abstract
BACKGROUND: There is equivocal evidence regarding the effectiveness of robotic guidance on the (re)learning of voluntary motor skills. Robotic guidance can improve the performance of continuous/ tracking skills, although being seldom more effective than unassisted practice alone. However, most of the previous studies employed robotic guidance on all intervention trials. Recently, we showed that mixing robotic guidance with unassisted practice (i.e., mixed practice) can significantly improve the learning of a golf putting task. Yet, these mixed practice studies involved self-paced movements in a standing posture, thus less applicable to rehabilitation contexts. OBJECTIVE: The current study aimed to investigate the influence of mixed practice on the timing accuracy of an upper-limb, rhythmic, sequential task. The goal was to assess the feasibility of integrating mixed practice with music-based interventions. METHODS: Two groups of participants performed circle-drawing sequences in synchrony with rhythmic auditory signals. They completed a pre-test and an acquisition phase, followed by immediate retention and transfer tests. One group received robotic guidance on 50% of the acquisition trials (i.e., mixed practice), whereas another group always practiced unassisted. The pre-test, retention, and transfer tests were performed unassisted. RESULTS: Both groups significantly improved their timing accuracy and precision between the pre-test and the retention test. CONCLUSION: This study provides further evidence that mixed practice can facilitate the (re)learning of voluntary actions, especially with the type of externally paced upper-limb movements employed in music-based interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".