Exploring the female advantage in fine visuomotor control
Bibliographic record
Abstract
Females enjoy an advantage on tasks requiring fine visuomotor control. Our work using the grooved pegboard (GPB) supports these findings, but why do females exhibit this advantage. Peters argued this advantage arose because females have smaller fingers. Indeed he found when peg placing times were corrected for finger size the female advantage disappeared. In this study we reasoned that if finger size is important, the female advantage should be sensitive to the size of the pegs: this advantage should be reduced when using larger pegs. We also explored an alternate explanation: the demands of the task for fine visuomotor control. This factor was examined by comparing two subtasks in the GPB, the place task involving picking up the pegs from a receptacle and placing them in the holes and the replace task which involved removing the pegs from the board and replacing them in the receptacle. Our work has shown that the place task requires greater visuomotor control. Accordingly if the female advantage is sensitive to these demands it should be larger in the more demanding place task. Our study involving 32 females and 30 males revealed that the female advantage was significantly larger for the small than the large pegs, but did not change as a function of task. These findings support the importance of finger size as an explanation for the female advantage. The implications of these findings for understanding gender differences in visuomotor tasks will be examined.Acknowledgments: NSERC (EAR & PJB)
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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 | 0.001 |
| 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) | 0.010 | 0.001 |
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".