Throwing Accuracy during Prism Adaptation: Male Advantage for Throwing Accuracy is Independent of Prism Adaptation Rate
Bibliographic record
Abstract
Previous studies have found that men are more accurate at throwing an object at a target than are women, independent of experience. However, these studies' results are based on average scores from multiple trials. As such, it is unknown whether the male advantage results from superior throwing accuracy or from a superior ability to calibrate subsequent throws. This study examined whether men can calibrate repeated throws more quickly and accurately than women, 25 men and 30 women were required to throw velcro-covered balls at a carpet-covered target, both with and without 10-diopter prism lenses. Participants had multiple trials in both conditions. Analyses examined whether there was a sex difference in the rate of adaptation to the prism lenses (as indicated by calibration of subsequent throws), instead of simply averaging all throwing accuracy scores and looking for an overall sex difference. Men threw the balls significantly more accurately than women, both with and without the prism lenses. However, there was no significant sex difference found on the rate of prism adaptation, as measured by improvement across the trials, i.e., calibration. Although men were more accurate at throwing balls overall, there was no sex difference in calibration of subsequent throws in adapting to the prism lenses, therefore indicating that the male advantage in throwing accuracy does not result from superior ability to calibrate subsequent throws but rather from superior throwing accuracy overall.
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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 | 0.000 | 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 | 0.001 |
| 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".