Conscious Regulation of Sexual Arousal in Men
Bibliographic record
Abstract
The goals of this study were to examine the effectiveness of emotional reappraisal in regulating male sexual arousal and to investigate a set of variables theoretically linked to sexual arousal regulation success. Participants first completed a series of online sexuality questionnaires. Subsequently, they were assessed for their success in regulating sexual arousal in the laboratory. Results showed that the ability to regulate emotion may cross emotional domains; those men best able to regulate sexual arousal were also the most skilled at regulating their level of amusement to humorous stimuli. Participants, on average, were somewhat able to regulate their physiological and cognitive sexual arousal, although there was a wide range of regulation success. Whereas some men were very adept at regulating their sexual arousal, others became more sexually aroused while trying to regulate. Age, sexual experience, and sexual compulsivity were unrelated to sexual arousal regulation. Conversely, sexual excitation, inhibition, and desire correlated with sexual arousal regulation success. Increased sexual excitation and desire were associated with poorer regulatory performance, whereas a propensity for sexual inhibition due to fear of performance consequences was related to regulatory success.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".