Combining Unassisted and Robot-Guided Practice Benefits Motor Learning for a Golf Putting Task
Bibliographic record
Abstract
Robotic guidance has been employed with limited effectiveness in neurologically intact and patient populations. For example, our lab has effectively used robotic guidance to acutely improve movement smoothness of a discrete trajectory without influencing movement endpoint distributions. The purpose of the current study was to investigate the efficacy of combining robotic guidance and unassisted trials in the learning of a golf putting task. Participants completed a pre-test, an acquisition phase, and an immediate and delayed (24-hour) post-test. During the pre-test, kinematic data from the putter was converted into highly accurate, consistent, and smooth trajectories delivered by a robot arm. During acquisition, three groups performed putts towards three different targets with robotic guidance on either 0%, 50%, or 100% of acquisition trials. Only the 50% guidance group statistically reduced both the ball endpoint distance and variability between the pre-test and the immediate or 24-hr post-test. The results of the 50% guidance group yielded seminal evidence that combining both unassisted and robotic guidance trials (i.e., mixed practice) could facilitate at least short-term motor learning for a golf putting task. Such work is relevant to incorporating robotic guidance in sport skills and other practical areas (e.g., rehabilitation).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".