Does Self-Modeling Affect Imagery Ability or Vividness?
Bibliographic record
Abstract
Research has shown imagery interventions to be important tools for learning new skills, as well as enhancing competitive performance. Moreover, imagery vividness and ability are two factors shown to contribute to their effectiveness. Therefore, learning ways to increase one's imagery vividness and ability is important. Hence, the present research examined the effects of an external stimulus (i.e., a self-modeling video) on one's imagery vividness and ability. A self-modeling video is an edited video showing the desired target behaviors; in this case it was a competitive dive. Two imagery measures (VMIQ and MIQ-R) were used to capture whether the self-modeling video would influence competitive divers' imagery vividness and ability. Seven competitive divers were administered both imagery measures at pre-test and post-test. After pre-test scores were taken, the participants' individual self-modeling videos were shown on three occasions before each competition and once at each competition. The results for the VMIQ indicated that imagery vividness when imaging the self was significantly better than when imaging others, F(1,6) = 7.44, p < 0.05, ?2 = .54. Of more importance is that the participants' imagery vividness increased after the self-modeling video had been administered for imaging one self but not for imaging others, although this only approached significance, F(1,6) = 3.70, p = .107, ?2 = .38. No significant results, however, were found for imagery ability. These findings suggest that there is potential for a self-modeling video to positively influence an athletes' imagery vividness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".