  Identifying oculomotor behaviors relevant for eye-tracking applications is a
critical but often challenging task. Aiming to automatically learn and extract
knowledge from existing eye-tracking data, we develop a novel method that
creates rich representations of oculomotor scanpaths to facilitate the learning
of downstream tasks. The proposed stimulus-agnostic Oculomotor Behavior
Framework (OBF) model learns human oculomotor behaviors from unsupervised and
semi-supervised tasks, including reconstruction, predictive coding, fixation
identification, and contrastive learning tasks. The resultant pre-trained OBF
model can be used in a variety of applications. Our pre-trained model
outperforms baseline approaches and traditional scanpath methods in autism
spectrum disorder and viewed-stimulus classification tasks. Ablation
experiments further show our proposed method could achieve even better results
with larger model sizes and more diverse eye-tracking training datasets,
supporting the model's potential for future eye-tracking applications. Open
source code: http://github.com/BeibinLi/OBF.
