Attentional Avoidance of Sexual Cues in Psychogenic Erectile Dysfunction: An Eye‐Tracking and Machine Learning Study

BACKGROUND: Psychogenic erectile dysfunction (pED) is a prevalent male erectile dysfunction without organic causes, and difficulties in erection attainment and post-penetration maintenance often co-occur. Although neuroimaging studies have implicated abnormalities in attentional control networks, direct behavioral evidence of how pED patients with this comorbid pattern process sexual cues is lacking. OBJECTIVES: To provide direct behavioral evidence and characterize attentional allocation to sexual cues in pED patients with the comorbid pattern versus healthy men, we conducted a study using eye-tracking and machine learning. MATERIALS AND METHODS: Thirty-seven men with pED exhibiting the comorbid pattern and 38 age-matched healthy controls completed free viewing of standardized sexual images varying in explicitness, whereas eye movements were recorded. Ninety gaze features were derived across six attentional metrics and five regions of interest. A two-stage feature-selection procedure identified a compact subset of discriminative features, which were used to train a classifier with cross-validation. Standardized self-report questionnaires assessing sexual function, sexual inhibition, anxiety, and depression were also administered. RESULTS: Eight eye-tracking features distinguished pED from controls with 94.8% accuracy under leave-one-out cross-validation. Relative to controls, men with pED showed reduced initial and sustained attention to sexually salient regions and increased attention to neutral regions, consistent with attentional avoidance. Questionnaire scores revealed lower sexual arousal and higher sexual inhibition, anxiety, and depression in pED, with gaze features indicative of attentional avoidance correlating with reduced arousal and elevated affective symptoms. DISCUSSION AND CONCLUSION: This is, to our knowledge, the first study to combine eye-tracking and machine learning to investigate pED, providing direct behavioral evidence for attentional avoidance of sexual cues. These findings provide behavioral evidence of altered attentional processing in pED patients with the comorbid pattern and underscore the potential of integrating eye-tracking with machine learning to support research in sexual dysfunction and sexual psychophysiology.