Sexual Self-Regulation and Cognitive Absorption as Factors of Sexual Response Toward Virtual Characters
Bibliographic record
Abstract
The eye movements and penile responses of 20 male participants were recorded while they were immersed with virtual sexual stimuli. These participants were divided into two groups according to their capacity to focus their attention in immersion (high and low focus). In order to understand sexual self-regulation better, we subjected participants to three experimental conditions: (a) immersion with a preferred sexual stimulus, without sexual inhibition; (b) immersion with a preferred sexual stimulus, with sexual inhibition; and (c) immersion with a neutral stimulus. A significant difference was observed between the effects of each condition on erectile response and scanpath. The groups differed on self-regulation of their erectile responses and on their scanpath patterns. High focus participants had more difficulties than low focus participants with inhibiting their sexual responses and displayed less scattered eye movement trajectories over the critical areas of the virtual sexual stimuli. Results are interpreted in terms of sexual self-regulation and cognitive absorption in virtual immersion. In addition, the use of validated virtual sexual stimuli is presented as a methodological improvement over static and moving pictures, since it paves the way for the study of the role of social interaction in an ecologically valid and well-controlled way.
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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) | 0.002 | 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".