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 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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".