Effects of motor imagery training on service performance in novice tennis players: the role of imagery ability
Bibliographic record
Abstract
The aim of this study was to examine how imagery ability could affect service improvement, following pre-performance motor imagery (MI) intervention, in young novice tennis players. Participants were divided into 3 groups with regard to their MI ability scores (Poor imager, Good imager and Control groups) obtained on the Movement Imagery Questionnaire for Children. During a pre-test, participants performed 10 services. The pre-performance MI practice was included during physical practice for 24 sessions. Each session consisted of 20 services, which were first imagined and then physically performed. Participants of the Poor and Good imager groups were required to use external visual MI, while those of the control group were given a countdown task. Participants performed an intermediate-test, after a first block of 12 practice sessions, and a post-test one-week after the last block of practice session, which were identical to the pre-test. The results of this study showed that MI improved service performance (i.e., percentage of success, speed and efficiency), and that this improvement was faster in the Good imager than in the Poor imager group. More specifically, The Poor imager group required more MI interventions to achieve equivalent performance to Good imager group. The impact of MI practice on service performance, for novice tennis players, is discussed.
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 | 0.000 | 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".