Influence of Positive and Negative Outcome Images on the Putting Success of Skilled Amateur Golfers
Bibliographic record
Abstract
Putting comprises 43 ± 2% of golf strokes (Pelz, 2000); therefore putting well, consistently is important to success in golf. Between-groups research with unskilled golfers has shown the performance benefits of positive over negative outcome imagery (e.g. Short, Bruggeman, Engel, Marback, Wang, Willadsen, et al., 2002; Taylor & Shaw, 2002). Can rehearsal of positive and negative images influence the putting success of individual skilled golfers? PURPOSE: To examine the impact of positive and negative images on the putting success of individual skilled golfers using an alternating treatment, single-case research design. METHODS: Participants were three amateur golfers (20, 22 & 52 yrs) meeting the inclusion criteria of (1) a Nova Scotia Golf Association handicap of ≤ 5, and (2) average visual and kinaesthetic imagery ability scores ≥ 5 on the revised Movement Imagery Questionnaire (Hall & Martin, 1997). Golfers completed 50 7-foot putts per day for 4 days, rehearsing a target positive or negative image prior to each putt. Participants were given positive or negative imagery instructions according to an alternating treatment BCBC single-case research design. Following each putt, golfers reported the image they had actually rehearsed prior to the putt (positive, negative, neither) which was recorded along with the outcome of the putt. RESULTS: Participants demonstrated 70–80% compliance to positive imagery instructions (M = 76.0%) and 62–76% compliance to negative imagery instructions (M = 69.7%). Regardless of the experimental condition in which the images occurred, data for each of the 3 participants showed that fewer attempts were required to achieve success when putts were preceded by positive images (M = 1.6) and more attempts were required to achieve success when putts were preceded by negative images (M = 4.8). No clear trend was seen for putts preceded by neutral images. CONCLUSIONS: The benefit of positive over negative pre-putt images can emerge on a case by case basis for skilled golfers who demonstrate adequate imagery ability. Researchers examining the impact of positive and negative imagery should expect approximately 20–35% of rehearsed images to be other than the instructed target image. For imagery direction research, this suggests that analyzing performance data on the basis of experimental condition will under-estimate the true impact of imagery direction on performance; data should be analyzed on the basis of individual performance trials preceded by positive, negative, or neutral imagery content.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".