Using cognitive general imagery to improve soccer strategies
Bibliographic record
Abstract
Abstract Athletes use imagery for both cognitive and motivational functions (Paivio 1985) The cognitive function involves the rehearsal of skills (cognitive specific) and strategies of play (cognitive general). To date most of the imagery research has been concerned with skill rehearsal (cognitive specific), and there have been no controlled studies investigating the effects of cognitive general imagery on the learning and performance of game plans or strategies of play. The purpose of this study was to determine the effectiveness of a cognitive general imagery intervention on three distinct soccer strategies in a young elite female soccer team. Participants were 13 competitive female soccer players with a mean age of 12.54 years. Imagery scores were determined via the Sport Imagery Questionnaire (SIQ; Hall, Mack, Paivio, & Hausenblas, 1998) prior to, during, and after the intervention. A staggered multiple baseline design across behaviors was used to evaluate the effect of imagery on three distinct soccer strategies (defending a direct free kick, taking a direct free kick, and defending a corner kick) which were introduced at weeks 2, 4 and 6. Results indicated that cognitive general and cognitive specific imagery use as well as motivational general‐arousal imagery use significantly increased from baseline to post intervention. Based on the present study's findings, the execution of soccer strategies was not significantly enhanced with the implementation of a cognitive general intervention. Additional research should be conducted in order to reach clearer conclusions that will have implications for young athletes and their learning strategies.
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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 | 0.000 |
| 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) | 0.002 | 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".