Athletes' perceive self-control of feedforward self-modeling improves competitive performance and self-regulation
Bibliographic record
Abstract
Feedforward self-modeling (FF-SM), often an edited video displaying the self performing a task beyond one's present ability (Dowrick, 1999), has been under-researched as a tool to enhance competitive sporting performance. Ste-Marie, Rymal, Vertes, and Martini (2011) showed that experimenter controlled scheduling of a FF-SM video enhanced competitive beam performance. In this research, we allowed athletes to self-control the viewing of a FF-SM video. Nine trampolinists(M = 5, F= 4; M = 12. 7, SD= 1.6)were provided a FF-SM video of their trampoline routine and given the opportunity to control their video viewings at their leisure at 3 consecutive competitions. Through the use of 3 semi-structured interviews,we explored why the trampolinists chose to view their videos in competition and the self-reported outcomes of their viewings. Eight trampolinists used the FF-SM video throughout the 3 competitions. Coding their data revealed that the trampolinists most commonly reported using their video for the skill function of observation (i.e., to assist with motor execution). Although the self-reported outcomes included improved motor execution; they also identified changes to self-regulatory processes (e.g., higher levels of self-efficacy; greater use of task strategies and adaptive inferences). The athletes' positive reports point to the benefits of the use of FF-SM videos in competition settings. Discussion will focus on the practical implications of the research findings.Acknowledgments: Supported by SSHRC
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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 | 0.003 |
| 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".