Examining Attentional Biases Elicited by Sexual Stimuli Using MouseView.js: An Online Paradigm to Mimic Eye Movements
Bibliographic record
Abstract
Attention is a key mechanism underlying many aspects of sexuality, with eye-tracking studies revealing that attention is both sustained by sexual stimuli and corresponds with sexual interest. Despite its utility, eye-tracking experiments typically require specialized equipment and are conducted in a laboratory setting. The overarching objective of this research was to assess the utility of a novel online method, MouseView.js, for assessing attentional processing of sexual stimuli outside of a laboratory context. MouseView.js is an open-source, web-based application where the display is blurred to mimic peripheral vision and an aperture is directed using a mouse cursor to fixate on regions of interest within the display. Using a discovery (Study 1, n = 239) and replication (Study 2, n = 483) design, we examined attentional biases to sexual stimuli among two diverse samples with respect to gender/sex and sexual orientation. Results revealed strong attentional biases toward processing sexual stimuli relative to nonsexual stimuli, as well as dwell times that correlated with self-report sexuality measures. Results mirror those observed for laboratory-based eye-tracking research, but using a freely available instrument that mirrors gaze tracking. MouseView.js offers important advantages to traditional eye-tracking methods, including the ability to recruit larger and more diverse samples, and minimizes volunteer biases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".