Measurement of Sexual Interests with Pupillary Responses: A Meta-Analysis
Bibliographic record
Abstract
Objective measures of sexual interest are important for research on human sexuality. There has been a resurgence in research examining pupil dilation as a potential index of sexual orientation. We carried out a meta-analytic review of studies published between 1965 and 2020 (Mdn year = 2016) measuring pupil responses to visual stimuli of adult men and women to assess sexual interest. Separate meta-analyses were performed for six sexual orientation categories. In the final analysis, 15 studies were included for heterosexual men (N = 550), 5 studies for gay men (N = 65), 4 studies for bisexual men (N = 124), 13 studies for heterosexual women (N = 403), and 3 studies for lesbian women (N = 132). Only heterosexual and gay men demonstrated discrimination in pupillary responses that was clearly in line with their sexual orientation, with greater pupil dilation to female and male stimuli, respectively. Bisexual men showed greater pupil dilation to male stimuli. Although heterosexual women exhibited larger pupils to male stimuli compared to female stimuli, the magnitude of the effect was small and non-significant. Finally, lesbian women displayed greater pupil dilation to male stimuli. Three methodological moderators were identified-the sexual explicitness of stimulus materials, the measurement technique of pupillary response, and inclusion of self-report measures of sexual interest. These meta-analyses are based on a limited number of studies and are therefore preliminary. However, the results suggest that pupillary measurement of sexual interest is promising for men and that standardization is essential to gain a better understanding of the validity of this measurement technique for sexual interest.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.043 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".