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 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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".