Thermography as a Physiological Measure of Sexual Arousal in Both Men and Women
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it