Sex and sexual orientation differences in perceptual processing.
Bibliographic record
Abstract
Research has identified sex differences on a number of perceptual processing tasks where one sex outperforms the other. For instance, female participants outperform males in face processing ability, perceptual speed, and language fluency, while males outperform females on certain navigational tasks. Further, sexual orientation has been shown to have a cross-sex shift where gay men’s performance resembles that of heterosexual females in lateralized cognitive tasks such as mental rotation and face recognition . We sought to confirm sex differences on a battery of perceptual processing tasks and in addition, to examine cross-sex shifts for gay men and women. We predicted that gay men will show more female typical behaviour on tasks that have previously shown sex differences. Participants performed a battery of tasks including; mental rotation, perceptual speed and accuracy, the Rod and Frame Test, the Symbol Digit Modalities Test, face recognition, mechanical and verbal reasoning, and spatial navigation, all of which have previously shown a sex difference. Participants’ sexual orientation was assessed with the Kinsey scale as well as the Klein sexual orientation grid. Preliminary results suggest gay males perform in a heterosexual female typical manner on all tasks where heterosexual females outperform males including face recognition, verbal reasoning, symbol digits and perceptual speed and accuracy. Surprisingly, on perceptual tests where heterosexual males tend to outperform females including the Rod and Frame test and mental rotation, gay males perform in a heterosexual male fashion. These early findings are consistent with previous studies showing sex differences, but in addition we show that sexual orientation contributes to sex effects such that gay males consistently exhibit the dimorphic sex advantage. Meeting abstract presented at VSS 2012
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".