Photographs of real human figures: Item types and persistent sex differences in mental rotation
Bibliographic record
Abstract
The goal of the current study was to provide a better understanding of the role of image familiarity, embodied cognition, and cognitive strategies on sex differences in performance when rotating blocks and photographs of real human bodies. Two new Mental Rotation Tests (MRTs) were created: one using photographs of real human models positioned as closely as possible to computer drawn figures from the human figures MRT used in Doyle and Voyer's 2013 study, and one using analogous block figures. It was hypothesised that, when compared to the analogous block figures, the real human figures would lead to improved accuracy among both men and women, a reduced magnitude of sex differences in accuracy, and a reduced effect of occlusion on women's performance when compared to analogous block figures. The three-way interaction between test, sex, and occlusion reported in Doyle and Voyer's 2013 study was not replicated in the current study. However, women's scores on the real human figures improved significantly more than men's scores on the real human figures test compared to gender differences in improvement on the block figures test. This finding points to a greater strategy shift among women than men when rotating human figures.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".