Bibliographic record
Abstract
Athletes have been shown to have greater movement imagery abilities than non-athletes. However, since these differences were observed using questionnaires where participants subjectively judged the vividness of performing imagined movements, it is possible that responses could be biased by other factors such as social desirability. One possible solution is to use an objective test, such as the Test of Ability in Movement Imagery (TAMI; Madan, C. R., & Singhal, A. (2013). Introducing TAMI: An objective test of ability in movement imagery. Journal of Motor Behavior, 45, 153-166.). Unfortunately, young adults perform relatively well on the TAMI, leaving little room for statistical sensitivity in observing higher scores. Here we propose an alternate scoring method for the TAMI that resolves this limitation by weighing items according to their difficulty. We apply this scoring method to existing data and show that this improves the TAMI's selectivity to measuring ability in movement imagery, rather than related imagery processes. Thus, we have successfully improved the TAMI to be more suited for use with athletic populations.
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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 | 0.002 | 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) | 0.000 | 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".