The Role of Attractiveness in Gendered Sexual Response Patterns
Bibliographic record
Abstract
Previously documented sexual response patterns of gender-specificity among gynephilic men and gender-nonspecificity among gynephilic women could be explained by women responding more strongly to non-gendered aspects of sexual stimuli. Cues of attractiveness are known determinants of sexual decision-making, yet have not been directly tested as determinants of sexual response. The current study investigated the role of attractiveness cues in explaining gender-based patterns of sexual response. Thirty-one gynephilic men and 60 androphilic women were presented slideshows of images depicting individual nude men and women that were pre-rated in a pilot study as either attractive or unattractive. The men and women were posed with legs spread and aroused genitals displayed prominently. Images were isolated against a white background and included minimal contextual information. Three sexual responses - genital arousal (via photoplethysmographs), self-reported arousal, and visual attention (via eye-tracking) - were recorded continuously. Across all three response modalities, men's and women's responses were stronger for the attractive versus unattractive images and for their preferred versus non-preferred gender. For men's arousal and women's self-reported arousal, the effect of attractiveness was stronger for their preferred versus non-preferred gender. Thus, both men and women demonstrated preference-specific patterns of sexual response. Gender cues had the strongest effect on men's visual attention, whereas attractiveness cues had the strongest effect on women's visual attention. Findings establish the importance of target attractiveness in arousal to sexual stimuli and add to mounting evidence that androphilic women's sexual responses are sensitive to gender, but may be more sensitive to non-gendered features of sexual stimuli.
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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".