The impact of sexual arousal on elements of sexual decision making: Sexual self-restraint, motivational state, and self-control
Bibliographic record
Abstract
Sexual arousal has been shown to have an impact on risk-taking and intentions to engage in risky sexual behaviour (e.g., Ariely & Loewenstein, 2006; Shuper & Fisher, 2008; Skakoon-Sparling, Cramer, & Shuper, 2016); however, the mechanisms underlying this effect are not well understood. To further investigate the effects of sexual arousal on sexual health decision-making, the current study was designed to examine the associations among self-control, sexual self-restraint, and motivational state, as well as the impact of sexual arousal on these factors. Forty-nine female and 26 male participants viewed either sexually arousing (experimental condition) or control video clips and responded to inventories designed to measure their self-control, sexual self-restraint, and meta-motivational state balance (within the Rules domain of Reversal Theory). A moderate positive correlation was found across all participants between self-control and self-restraint. Participants in the sexual arousal condition scored significantly lower on measures of self-control and sexual self-restraint; no effect was found for the meta-motivational state measure used. The results of this study suggest that sexual arousal either functions to deplete individuals' internal reserves of self-control or that it creates conditions that make it difficult to access the cognitive capacity to engage in self-control. This effect, combined with the correspondingly low score on our measure of sexual self-restraint, suggest that this may be an avenue through which sexual arousal negatively impacts sexual health decision-making.
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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 | 0.000 | 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".