Using the Dual Control Model to Investigate the Relationship Between Mood, Genital, and Self-Reported Sexual Arousal in Men and Women
Bibliographic record
Abstract
Recent findings suggest that there is considerable interindividual variability in how mood affects sexual arousal and that the dual control model may be helpful in explaining this variation. The current research investigated whether mood interacted with sexual excitation and inhibition proneness to predict subjective and genital arousal. In this study, 33 participants (18 men; 15 women), ages 18 to 45, attended three laboratory sessions where they completed questionnaires assessing preexisting mood and propensity for sexual excitation and inhibition, then watched a series of neutral and sexually explicit films. Subjective sexual arousal was continuously indicated during each film, while genital temperature was measured using thermographic imaging. Sexual excitation and inhibition interacted with various mood scores to significantly predict both subjective and genital arousal in men and women. Several gender differences were found. For example, vigor scores interacted with sexual excitation proneness to significantly predict genital but not subjective arousal in women, while the same interaction significantly predicted subjective but not genital arousal in men. The findings supported the hypothesis that the dual control model is an important framework in understanding how mood influences both subjective and genital sexual arousal.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".