Disgust responses to virtual erotica: Analysis of their interaction with sexual anxiety and immersive tendencies
Bibliographic record
Abstract
Previous research suggests that experiencing disgust in sexual contexts may negatively impact sexual satisfaction and functioning. However, little is known about the mechanisms (e.g., sexual anxiety) that influence or underlie sex-related disgust. Additionally, immersive tendencies may play a role in shaping emotional experiences when presented with sexual cues. Recent technological advancements, especially in virtual reality (VR), offer a promising avenue to explore emotions in simulated intimate and sexual interactions. This study aimed to examine the influence of sexual anxiety and immersive tendencies on reported levels of disgust when exposed to virtual erotica. A sample of 59 participants (≥ 18 years) completed self-report questionnaires of sexual anxiety and immersive tendencies. Levels of disgust were assessed during exposure to synthetic virtual characters engaging in erotic behaviours of increasing intensity across six scenarios, ranging from flirting to nudity, masturbation, and orgasm. Linear mixed models were performed on observed data. Higher levels of sexual anxiety were significantly associated with increased disgust throughout the immersive experience (β = 0.48), while greater immersive tendencies were significantly linked to lower disgust ratings (β = −0.66). Additionally, disgust ratings significantly increased with the intensity of the virtual sexual stimuli (β = 0.48). Virtual erotica shows promise as a tool to investigate sex-related disgust and its related mechanisms, such as sexual anxiety and immersive tendencies.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".