Robotic guidance with variability of practice can improve the learning of a golf putting task
Bibliographic record
Abstract
Although robotic guidance has yielded limited effectiveness in improving motor functions in neurologically intact and patient populations (e.g., Krishnan et al., 2012; Kummel et al., 2014), it has typically employed constant practice. Employing variability of practice principles, our lab has previously employed robotic guidance to acutely improve movement smoothness of a discrete trajectory (see Manson et al., 2014). The purpose of the current study was to investigate the impact of physical guidance involving variability of practice on the learning of a sequential movement, namely a golf putt. The current study employed a pre-test, a training phase, followed by an immediate and a 24-hr post-test. During the pre-test, the kinematic data from the club head was collected and converted into robotic coordinates to be executed using a robot arm, which is highly accurate, consistent, and smooth (see Manson et al., 2014). During training, three groups of novice participants performed putts towards 3 targets (i.e., 192, 213, & 234 cm amplitudes), benefiting from robot guidance on 0%, 50% or 100% of training trials. Only the group that trained with the robot 50% of the trials significantly reduced the endpoint distance and variability between the pre-test and the immediate and/or 24-hr post-test. This study demonstrates that-following a single acquisition session-the combination of unassisted and robot assisted practice represents the most optimal approach to facilitating short-term learning of a sequential movement. Such work could be relevant to improving putting performance and other sport skills in addition to other practical areas (e.g., rehabilitation).Acknowledgments: Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), Ontario Research Fund (ORF), University of Toronto
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".