A failure of the interocular suppression paradigm to assess sexual preferences in two studies.
Bibliographic record
Abstract
Several methods have been developed to assess sexual preferences in men and women. Direct instruments (e.g., plethysmography, questionnaires) are the most widely used, but they have notable shortcomings, including a lack of response specificity in certain groups and a risk of false responding. Indirect instruments (e.g., reaction time), where preferences are assessed unobtrusively, may overcome these limitations and could therefore be used to measure sexual preferences more effectively. One promising instrument, published by Jiang et al. (2006), used an ocular suppression paradigm that exposed participants to sexual images while simultaneously masking them from conscious perception. Jiang et al. (2006) found that these "invisible" images attracted visual attention when they matched the participants' sexual preferences for nude male or female images and, in the case of heterosexual men, repelled attention when they did not match the participants' preferences. Here, we attempt to replicate these findings over two studies. In the first experiment, using a stereoscopic apparatus with 22 men and 25 women and a validation test (time spent rating the same nude images), we found no attentional attraction to or repulsion of invisible images even though the rating times for the same images were related to participants' sexual orientation. In the second experiment, with 32 men, we replaced the stereoscopic apparatus with a virtual reality headset, offering better control over stimulus delivery. Again, the invisible images produced no attentional attraction or repulsion. Our results suggest that the interocular suppression paradigm is not an effective method for assessing sexual preferences. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".