Assessment of Distraction From Erotic Stimuli by Nonerotic Interference
Bibliographic record
Abstract
Distraction from erotic cues during sexual encounters is a major contributor to sexual difficulties in men and women. Being able to assess distraction in studies of sexual arousal will help clarify underlying contributions to sexual problems. The current study aimed to identify the most accurate assessment of distraction from erotic cues in healthy men (n = 29) and women (n = 38). Participants were assigned to a no distraction, low distraction, or high distraction condition. Distraction was induced using an auditory distraction task presented during the viewing of an erotic video. Attention to erotic cues was assessed using three methods: a written quiz, a visual quiz, and a self-reported distraction measure. Genital and psychological sexual responses were also measured. Self-reported distraction and written quiz scores most accurately represented the level of distraction present, while self-reported distraction also corresponded with a decrease in genital arousal. Findings support the usefulness of self-report measures in conjunction with a brief quiz on the erotic material as the most accurate and sensitive ways to simply measure experimentally-induced distraction. Insight into distraction assessment techniques will enable evaluation of naturally occurring distraction in patients suffering from sexual problems.
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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 | 0.000 | 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".