Gender-Specificity of Women's and Men's Self-Reported Attention to Sexual Stimuli
Bibliographic record
Abstract
Men's sexual arousal is largely dependent on the actor's gender in a sexual stimulus (gender-specific), whereas for women, particularly androphilic women, arousal is less dependent on gender (gender-nonspecific). According to information-processing models of sexual response, sexual arousal requires that attention be directed toward sexual cues. We evaluated whether men's and women's self-reported attention to sexual stimuli of men or women were consistent with genital responses and self-reported arousal. We presented gynephilic men (n = 21) and women (n = 22) and androphilic men (n = 16) and women (n = 33) with audiovisual stimuli depicting men or women engaged in sexual activities. Genital responses were continuously recorded and, following each stimulus, participants reported the amount of attention paid to the video and feelings of sexual arousal. Self-reported attention was gender-specific for men and gender-nonspecific for women, and generally mirrored genital responses and self-reported arousal. Gender-specificity of genital responses significantly predicted gender-specificity of self-reported arousal; however, for men only, this effect was significantly mediated by gender-specificity of self-reported attention. Gender differences in gender-specificity of sexual arousal may be partially accounted for by differences in gender-specificity of self-reported attention, although attention may play a greater role in men's sexual arousal than women's.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".