Sex differences in tests of mental rotation: Direct manipulation of strategies with eye-tracking.
Bibliographic record
Abstract
We conducted what is likely the first large-scale comprehensive eye tracking investigation of the cognitive processes involved in the psychometric mental rotation task with three experiments comparing the performance of men and women on tests of mental rotation with blocks and human figures as stimuli. In all 3 experiments, men achieved higher mean accuracy than women on both tests and all participants showed improved performance on the human figures compared with the blocks. Experiment 1 used a moving window paradigm to elicit a piecemeal processing strategy, whereas Experiment 2 utilized that approach to encourage a holistic processing strategy. In these 2 experiments the pattern of eye fixations suggested that differences in processing between blocks and human figures can be accounted for by the greater difficulty of rotating block compared with human figures. Results also produced little support for the hypothesis that men favor a holistic strategy whereas women favor a piecemeal approach. In addition, these experiments did not support the notion that using human figures as stimuli promotes a holistic strategy whereas block figures invoke a piecemeal strategy. As a follow up, in Experiment 3 we used a free viewing procedure and examined 4 possible explanations of sex differences in mental rotation predicting different patterns of eye tracking (cognitive processing style, leaping, ocular efficiency) or offline processing (working memory). Results provided partial support for variations of the cognitive processing style hypotheses. The implications for common explanations of sex differences in mental rotation are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".