Comparison of the effect of arousing and relaxing music during imagery training for power and fine motor skill sport tasks \n
Bibliographic record
Abstract
BACKGROUND: Imagery is a psychological training used by professional athletes and music is believed could influence the efficacy of imagery. \nAIM: This study examined the effects of arousing and relaxing music during an imagery intervention on performance of power and fine-motor skill tasks. \nMETHOD: Twenty competitive elite shooters and weightlifters were assigned at random to one of two interventions: Unfamiliar relaxing or arousing music with imagery. This produced four conditions: Fine motor task (pistol shooting) imagery with either relaxing (matched) or arousing (mismatched) music and power task (weightlifting) imagery with either relaxing (mismatched) or arousing (matched) music. A pretest-intervention posttest design was used with two simulation competitions: 10m air-pistol shooting performance, and a standard weightlifting event - Clean and Jerk. Participants completed 12 sessions of imagery over four weeks before the posttest was conducted. \nRESULTS: MANOVA analysis for pistol shooting showed that the differences across type of music used with imagery \nwere significant on the gain-score for competition performance F(1,16)=8.85, p<.05, ç2=.36, with a significantly larger increase in performance for relaxing music than arousing music. In addition, the self-confidence gain score was significant F(1,16)=12.57, p<.05, ç2=.44. As for the weightlifters, MANOVA analysis results showed that the differences across types of music used with imagery were significant in terms of gain-scores for competition \nperformance F(1,16)=12.27, p<.05, ç2=.43, with significantly larger increases in performance with relaxing music than arousing music. The self-confidence gain score was also significant F(1,16)=10.09, p<.05, ç2=.39. \nDISCUSSION: Contrary to findings when music is played before or during the actual tasks, in this study relaxing \nmusic facilitated imagery of both fine-motor and power tasks, suggesting that relaxation plays a role in imagery of \nsports skills.
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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 | 0.002 |
| 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) | 0.003 | 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".