Does Trait Sexual Desire Predict Subjective Sexual Response to Erotic Stimuli? Effects of Participant Gender, Stimulus Gender, and Relationship Status Among Cisgender Heterosexual Women and Men
Bibliographic record
Abstract
Understanding how trait sexual desire (TSD) relates to subjective sexual arousal (SSA) is essential for clarifying how enduring sexual motivation influences momentary arousal responses, especially considering individual differences, gender-specific patterns, and relational contexts. This study examined how trait sexual desire relates to SSA across TSD dimensions, considering gender and relationship status. We tested 323 cisgender participants, 139 gynephilic men and 184 androphilic women, who anonymously assessed their TSD levels and rated their SSA while individually viewing erotic and non-erotic stimuli depicting both sexes. Results showed higher levels of TSD in men than in women. However, these differences were moderated by relationship status and were maintained only between men and women in a stable relationship. In addition, the association between TSD and SSA was gender-specific in men-stronger for sexual response to preferred sexual stimuli-and gender-nonspecific in women. Relationship status influenced TSD-SSA associations, especially in dyadic TSD toward a partner, highlighting the role of sociocontextual factors in the association between trait sexual desire and arousal among cisgender heterosexual individuals. The findings support the association between TSD and SSA, moderated by individual differences such as gender and sociocontextual factors like relationship status. We conclude that TSD affects SSA, and that TSD appears to be dynamic and context-dependent in nature.
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 | 0.000 | 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) | 0.002 | 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".