"I can't move in this": Clothing influences movement efficiency
Bibliographic record
Abstract
Seminal research has shown that wearing revealing clothing may increase women's body-related self-conscious emotions, which consumes cognitive resources and in turn diminishes mental performance (Fredrickson et al., 1998, J Pers Soc Psychol). Here, we manipulated women's clothing to determine whether increased body-related awareness also impacts motor performance. Women were randomly assigned to wear a tight vs. loose black athletic outfit (n = 40/group). Participants executed rapid aiming movements to a target presented with an overlapping penalty circle. Participants gained points when the target was contacted and lost points when the penalty circle was contacted. Task success required selecting an optimal endpoint based on participants' own movement variability and the rewards/penalties associated with the regions of the aiming environment. The groups did not differ in endpoint selection and movement variability (ps > .57), indicating that both groups accomplished the movement task goals. However, the tight-clothing group performed with significantly greater movement time variability (p = .001) and only the loose-clothing group significantly decreased their movement time across blocks of trials (p = 0.028). Thus, the loose-clothing group demonstrated more efficient action execution. These findings suggest that the clothing worn can impact motor performance – a result consistent with research demonstrating that a focus on the body hinders the performance of skilled motor behaviour. These findings may have implications for best practices in physical activity contexts.Acknowledgments: SSHRC
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".