Effects of focus of attention depend on golfers' skill
Bibliographic record
Abstract
In this study, we examined the influence of internal and external attention instructions on the performance of a pitch shot by golfers who were either highly skilled (mean handicap = 4) or low skilled (mean handicap = 26). Ten golfers in each skill group used a 9-iron to pitch a ball as close as possible to an orange pylon, which was located at distances of 10, 15, 20 or 25 m from the golfer. Focus of attention was manipulated within participants (counterbalanced across golfers). Under internal focus of attention instructions, the participants were told to concentrate on the form of the golf swing and to adjust the force of their swing depending on the distance of the shot. For the external focus of attention conditions, the participants were told to concentrate on hitting the ball as close to the target pylon as possible. The most intriguing finding was an interaction of skill with focus of attention instructions for variability in performance. Similar to the findings of Wulf and colleagues, the highly skilled golfers performed better with external attention instructions than with internal focus instructions. In contrast, the low-skill golfers performed better with the internal than with the external focus of attention instructions. These findings are discussed relative to theoretical issues in motor learning and practical issues for golf instruction.
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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 | 0.015 |
| 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) | 0.003 | 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".