Disgust versus Lust: Exploring the Interactions of Disgust and Fear with Sexual Arousal in Women
Bibliographic record
Abstract
Sexual arousal is a motivational state that moves humans toward situations that inherently pose a risk of disease transmission. Disgust is an emotion that adaptively moves humans away from such situations. Incongruent is the fact that sexual activity is elementary to human fitness yet involves strong disgust elicitors. Using an experimental paradigm, we investigated how these two states interact. Women (final N=76) were assigned to one of four conditions: rate disgust stimuli then watch a pornographic clip; watch a pornographic clip then rate disgust stimuli; rate fear stimuli then watch a pornographic clip; or watch a pornographic clip then rate fear stimuli. Women's genital sexual arousal was measured with vaginal photoplethysmography and their disgust and fear reactions were measured via self-report. We did not find that baseline disgust propensity predicted sexual arousal in women who were exposed to neutral stimuli before erotic content. In the Erotic-before-Disgust condition we did not find that sexual arousal straightforwardly predicted decreased image disgust ratings. However, we did find some evidence that sexual arousal increased self-reported disgust in women with high trait disgust and sexual arousal decreased self-reported disgust in women with low trait disgust. Women who were exposed to disgusting images before erotic content showed significantly less sexual arousal than women in the control condition or women exposed to fear-inducing images before erotic content. In the Disgust-before-Erotic condition the degree of self-reported disgust was negatively correlated with genital sexual arousal. Hence, in the conflict between the ultimate goals of reproduction and disease avoidance, cues of the presence of pathogens significantly reduce the motivation to engage in mating behaviors that, by their nature, entail a risk of pathogen transmission.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".