Learning Shared Visual Exploration Primitives for Cross-Subject Mental Disorder Diagnosis from Eye Movements

Cross-subject mental disorder diagnosis from eye movements requires learning disease-discriminative representations that generalize beyond the subjects observed during training. This is challenging because eye-movement behaviors exhibit substantial inter-subject variability, such that the same disorder may manifest through heterogeneous visual exploration strategies. Existing methods may collapse individual-specific pathological manifestations into a common representation, limiting generalization to unseen subjects. Motivated by shared structural patterns in eye movements across individuals, we propose to represent eye movements using shared, fine-grained, latent visual exploration primitives and their personalized compositions. Thus, we develop a shared Visual Exploration Primitive-Aware (VEPA) model for cross-subject mental disorder diagnosis. By jointly modeling shared primitives and personalized compositions, VEPA learns robust, disease-discriminative representations that generalize to unseen subjects. Specifically, our model first employs a Visual Primitive Discovery module (VPD) to model diverse visual exploration primitives as Gaussian distributions, whose correspondence with eye-movement patterns is characterized through post-hoc visualization. Then a Personalized Primitive Composition module (PPC) is designed to efficiently encode primitive compositions via differentiable Gumbel-Softmax routing and adaptive layer selection, with contrastive learning boosting classification. Experiments on two clinical eye-tracking datasets for depression and schizophrenia diagnosis demonstrate that our model outperforms existing methods, achieving a 2.3% relative accuracy improvement.