Expert-novice differences with implement design variations and their affect on success and consistency of movement in a discrete motor task
Bibliographic record
Abstract
Discrete motor tasks, such as golf putting, have been tested with various techniques to demonstrate differences between expertise level and implement design. Perception is inherently linked with the motor system in order to allow for the control of these simple movements. The purpose of this experiment was to examine this relationship in a golf putt by comparing kinematic and gaze tracking changes between two expertise levels and two putter types. Four novices (mean age: 26.3y, limited golfing experience) and 4 experts (average age: 25.3y, mean self-reported handicap: 8) were asked to complete 15 putts each with a mallet (Odyssey White Hot Pro 2-ball) putter and blade (Odyssey White Ice 2.0) putter with counterbalanced presentation at a distance of 1-metre. Data was collected with the HS-H6 Eye-Tracker (ASL, Bedford, MA) integrated with Visualeyez Motion Capture System (PTI, Burnaby, BC). The following dependent variables were analyzed with a 2-expertise level (novice/expert) by 2-putter type (blade/mallet) ANOVA: preparation time (s), total stroke time (s), and change in aim line (degrees) between ball address and contact. A significant 2-way interaction was found for preparation time with experts preparing longer than novices. Aim line changes were greater for the mallet putter. In regards to success, experts were 96.6% and 86.2% successful with the blade and mallet putter, respectively. Novices were 77.0% and 81.7% successful with both putters. Relationships between head, eye, and shoulder movement and ocular fixation on key areas will also be addressed. These results will be discussed in relation to previously completed work by Karlsen et al. and Hung. Acknowledgments: University of Alberta Endowment Fund for the Future (EFF) Advancement of Scholarship (SAS)
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".