Sex differences in visuospatial mental rotation persist under 3D VR conditions
Bibliographic record
Abstract
The classic Shepard and Metzler (1971) mental rotation task showed a male advantage for visuospatial mental rotation of block structure images. This finding has been replicated numerous times and despite the ubiquity of the effect, some researchers question the ecological validity of the pen and paper task. One study rendered semi-immersive VR stereoscopic block structure images which eliminated this sex difference (Parsons et al., 2004). They generated 3D images with the ImmersaDesk stereo-goggle system and found no sex difference in ability to manually rotate a virtual object to the same spatial orientation as the previously seen target object. Here, we sought to re-examine potential sex differences in mental rotation ability using a novel VR adaptation of the original Shepard and Metzler mental rotation task with more capable and modern VR equipment (HTC VIVE). Our hypothesis was that using VR to generate 3D depth information in the block structures for mental rotation would yield sufficient additional structural information for the female observers to better complete the visual mental rotation task and thereby reduce the previously observed male advantage. Twenty-three female and 23 male participants indicated which two of four spatially rotated 3D blocks were the same as the target block. They were given unlimited time to complete the of stimulus consisting of 20 trials. We measured proportion correct as a function of sex of participant. Despite the VR set-up, we found a large male advantage, greater than is typically reported. These results stand in contrast to Parson’s et al (2004) VR study which found no sex differences. However, because that study allowed participants to manually rotate the virtual images it likely assessed visuomotor spatial ability rather than pure visual spatial ability with mental rotation. Thus, the male advantage in pure mental rotation ability appears to persist even when presented in VR.
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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 | 0.000 | 0.003 |
| 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) | 0.006 | 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".