Assessment of Distraction From Erotic Stimuli by Nonerotic Interference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it