Learning How to Prepare Athletes for Peak Performance: Use of Mental Imagery Training as a Psychological Strategy to Enhancing Motor Learning, Retention and Transfer of Sport Rifle Marksmanship
Bibliographic record
Abstract
This article is intended to (a) compare the effects of mental imagery and physical practice only on the learning and transfer of an open motor skill; (b) identify the mental imagery modality (visual, kinesthetic, or temporal) which is most efficient for sport rifle marksmanship; and (c) determine the relationship between movement image vividness and motor performance. Seventy students from the United States Military Academy, West Point, New York, participated in this study. They used their dominant hand to shoot (live-fire shooting) rotating targets. This study comprised four principal phases, namely the pretest, treatment, posttest (retention) and transfer. The results demonstrated that the retention performance obtained by each group using mental imagery combined with physical practice was equivalent to that produced by physical practice only. Furthermore, each group using visual or kinesthetic mental imagery combined with physical practice showed significantly superior performance than physical practice only during the transfer. These results may be explained by three functional evidences, namely behavioural, central and peripheral (Feltz & Landers, 2007; Holmes & Collins, 2001).
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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 | 0.001 |
| 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) | 0.001 | 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".