Gender Similarities and Differences in Sexual Arousal, Desire, and Orgasmic Pleasure in the Laboratory
Bibliographic record
Abstract
Relatively little is known about gender differences in the orgasm experience. The objectives of this study were to compare men's and women's patterns of sexual arousal and desire before and after orgasm, and the predictors of their orgasmic pleasure. Using their typical technique, where masturbation enjoyment was similar to that experienced at home, 38 men and 38 women masturbated to orgasm in the laboratory. Physiological sexual arousal (genital temperature) and subjective sexual arousal and desire measurements were taken at baseline, after masturbation almost to orgasm, and immediately and 15 minutes after orgasm. In both genders, all measures increased significantly during masturbation, with a greater buildup leading to a more pleasurable orgasm. After orgasm, however, sexual arousal and desire decreased more quickly and consistently in men than in women, thereby replicating Masters and Johnson's (1966) observations. More men than women exhibited resolution of subjective sexual arousal and sexual satiation; their genital temperature also decreased more than women's but did not return to baseline. Women's orgasmic pleasure was related to a postorgasmic decrease in genital temperature but, unexpectedly, the maintenance of subjective sexual arousal and desire. Future studies should explore whether this pattern explains gender differences in the pursuit of additional orgasms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".