Slow it down: The effect of image speed on novice golf putting performance
Bibliographic record
Abstract
The PETTLEP model provides practitioners with a set of guidelines when implementing imagery interventions and is grounded in the concept of functional equivalence or simulating the actual performance during imagery (Holmes & Collins, 2001). A central tenet of the model emphasizes the speed at which imagery is completed and it has been suggested that image speed may change based on the skill level of the athlete (Beilock & Gonso, 2008). Therefore, the purpose of the current study was to examine the effect of image speed (slow-motion, real-time, and fast-motion) on putting performance. Participants consisted of 56 novice golfers (Mage = 21.50, SD = 1.89) each completing three sets of putts at different image speeds, in counterbalanced order. Prior to every putt, participants completed a practice swing and self-timed the speed of their imagery. Putting performance was determined by calculating the distance from the hole with higher scores representing a more successful putt. The results of a repeated-measures ANOVA demonstrated that fast-motion imaging significantly hindered putting performance, F(3,53) = 60.90 , p
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".