Motor skills of basketball players transfer to dart, but not their perceptual component
Bibliographic record
Abstract
The Quiet Eye (QE) is defined as the final fixation prior to the motor response (Vickers, 1996) and is associated with expertise and performance. However, the extent to which this specific perceptual skill is tranferable to similar tasks is unknown. The aim of this study was to examine the transferability of motor and perceptual skills from basketball to darts. 13 skilled and 13 less skilled basketball players participated and performed 15 basketball free throws and 15 dart throws in counterbalanced order. Throwing accuracy (motor skill) and QE duration (perceptual skill) were measured for both tasks. Skilled basketball players significantly outperformed the less skilled in basketball free throw accuracy, t (24) = 9.21, p d = 3.61, and dart throwing accuracy, t (24) = 2.96, p d = 1.16. Interestingly the unexpected superior performance in dart throwing can be explained by the deviation on the x-axis, t (24) = 2.23, p = .03, d = 0.87, but not on the y-axis, t (24) = 1.42, p = .17, d = 0.56, TP = .30. The QE duration showed no significant skill level differences, neither for the basketball task, t (24) = 1.53, p = .07, d = .60, nor for the dart throwing task, t (24) = 0.02, p = .98, d = .01, TP = .05. These results suggest some transfer of motor skills, arguably due to the alignment of the motor components, since skilled players outperformed less skilled in both tasks. However, these differences are not easily discussed by transferred perceptual skills captured by QE.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".