The effect of static versus dynamic stimuli on visual processing of sexual cues in androphilic women and gynephilic men
Bibliographic record
Abstract
Models of sexual response posit that attentional processing of sexual cues is requisite for sexual responding. Despite hypothesized similarities in the underlying processes resulting in sexual response, gender differences in sexual arousal patterns are abundant. One such gender difference relates to the stimulus features (e.g. gender cues, sexual activity cues) that elicit a response in men and women. In this study, we examined how stimulus modality (static visual images versus dynamic audiovisual films) and stimulus features (gender, sexual activity and nonsexual contextual cues) influences attentional (i.e. gaze) and elaborative (i.e. self-reported attraction (SRA), self-reported arousal) processing of sexual stimuli. Men's initial and controlled attention was consistently gender-specific (i.e. greater attention towards female targets), and this was not influenced by stimulus modality or the presence of sexual activity cues. By contrast, gender-specificity of women's attention patterns differed as a function of attentional stage, stimulus modality and the features within the stimulus. Degree of specificity was positively predictive of SRA in both genders; however, it was not significantly predictive of self-reported arousal. These findings are discussed in the context of gendered processing of visual sexual information, including a discussion of implications for research designs.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".