A Quantitative Analysis of Athletes’ Voluntary Use of Slow Motion, Real Time, and Fast Motion Images
Bibliographic record
Abstract
The current study examined athletes’ reported intentional use of slow-motion, real-time, and fast-motion images. Athletes (N = 604; 298 males and 306 females; Mage = 21.73 years, SD = 4.54) completed the Image Speed Questionnaire, an instrument created to assess the frequency with which athletes reported employing the three image speeds. Despite the applied sport psychology guideline of imaging only at real time speed, athletes reported employing all three image speeds to varying degrees depending on the function of imagery being employed and the stage of learning of the athlete. Gender and competitive level were found not to influence athletes’ reported voluntary image speed use. Athletes reported employing slow-motion images most often when learning or developing a skill or strategy. Real-time images were consistently used most often by athletes regardless of imagery function or stage of learning, and fast-motion images were used most often when imaging skills or strategies that had been mastered. Findings are discussed within the context of the stages of learning (Fitts & Posner, 1967 Fitts, P. M. and Posner, M. I. 1967. Human performance, Monterey, CA: Brooks/Cole. [Google Scholar]) and the PETTLEP (Physical; Environmental; Task; Timing; Learning; Emotional, and Perspective) approach to motor imagery (Holmes & Collins, 2001 Holmes, P. S. and Collins, D. J. 2001. The PETTLEP approach to motor imagery: A functional equivalence model for sport psychologists. Journal of Applied Sport Psychology, 13: 60–83. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). Implications for imagery practitioners and future directions for image speed research are also offered.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".