Thermography as a Physiological Measure of Sexual Arousal in Both Men and Women
Bibliographic record
Abstract
INTRODUCTION: Current physiological measures of sexual arousal are intrusive, hard to compare between genders, and quantitatively problematic. AIM: To investigate thermal imaging technology as a means of solving these problems. METHODS: Twenty-eight healthy men and 30 healthy women viewed a neutral film clip, after which they were randomly assigned to view one of three other video conditions: (i) neutral (N = 19); (ii) humor (N = 19); and (iii) sexually explicit (N = 20). MAIN OUTCOME MEASURES: Genital and thigh temperatures were continuously recorded using a TSA ImagIR camera. Subjective measures of sexual arousal, humor, and relaxation were assessed using Likert-style questions prior to showing the baseline video and following each film. RESULTS: Statistical (Tukey HSD) post-hoc comparisons (P < 0.05) demonstrated that both men and women viewing the sexually arousing video had significantly greater genital temperature (mean = 33.89 degrees C, SD = 1.00) than those in the humor (mean = 32.09 degrees C, SD = 0.93) or neutral (mean = 32.13 degrees C, SD = 1.24) conditions. Men and women in the erotic condition did not differ from each other in time to peak genital temperature (men mean = 664.6 seconds, SD = 164.99; women mean = 743 seconds, SD = 137.87). Furthermore, genital temperature was significantly and highly correlated with subjective ratings of sexual arousal (range r = 0.51-0.68, P < 0.001). There were no significant differences in thigh temperature between groups. CONCLUSION: Thermal imaging is a promising technology for the assessment of physiological sexual arousal in both men and women.
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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) | 0.003 | 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".