ERP responses to sexual cues among young women attracted to men
Bibliographic record
Abstract
Abstract Previous event‐related potential (ERP) studies reported larger N170, P3, and late positive potential (LPP) amplitudes to sexual than nonsexual stimuli. These ERPs may not be specifically sensitive to processing sexual cues, however, because the sexual stimuli included information beyond sexual cues (e.g., faces, bodies, social interaction) to a greater extent than comparison stimuli. We investigated ERPs to stimuli that focused on sexual and nonsexual body regions, in different states of readiness for activity, to elucidate neural responses involved in processing sexual cues. Forty cisgender, primarily white, undergraduate women who were attracted to men (Mage = 18.6, SD = 0.9) viewed images that varied by male body part (penis, arm) and activity state (rest, poised for activity). Participants viewed 40 images per category (flaccid penises, erect penises, outstretched arms, bent arms). Electroencephalography (EEG) was recorded using a 128‐channel net, time‐locked to the onset of each image. Using a whole‐head cluster‐mass approach, we found that the P3 was sensitive to sexual readiness—P3 amplitudes were larger to erect than flaccid penises, but not to bent than outstretched arms. The N170 and LPP components did not show evidence of similarly specific responses to sexual readiness, revealing potential dissociation of different neural processes commonly elicited in response to more complex sexual stimuli. An additional novel finding was that an anterior N270‐400 was sensitive to sexual readiness. Findings clarify the brain's rapid responses to sexual stimuli, setting the stage for future research aimed at better understanding the neurocognitive processes that contribute to the coordination of sexual arousal.
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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 | 0.001 |
| 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) | 0.004 | 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".