Is imagery enough? Do the effects of imagery practice provide substantial results to replace physical practice
Bibliographic record
Abstract
Authors: Shelby P. Rodden-Aubut Objectives: It has been proven that there is a possibility to improve the strength of a muscle through the use of imagery. The aim of this study is to determine whether or not, if the increase in strength is possible, the use of imagery can provide significant increases in a skill related task – in this case, kicking. The purpose of the experiment is to determine if imagery alone can be sufficient in the acquisition and or improvement of a soccer kick for accuracy. Design: A quantitative comparison to explore the link between imagery practice and kicking practice on skill maintenance, acquisition, and improvement. Method: A comparison of participant scores from control, physical practice, and imagery practice interventions to determine improvement from baseline kicking scores to final kicking scores. Baseline scores and final scores were compared between groups, and the differences between baseline and final scores were compared within each group. Results: The study demonstrated that imagery practice can provide significant increases in the performance of a skill related task when compared against the use of physical practice and no practice. The imagery practice group was the sole group to experience a significant increase at the .05 level. Conclusions: With the results a window has been opened into the uses of imagery for the benefit of skill improvement, and the possibility to apply imagery to the field of physiotherapy and rehabilitation to maintain and improve skill capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".