HeatMatch: Orientation-Aware Visualization and Comparison of Very Dense Saccade Patterns

Similarity analysis of saccade patterns across conditions or subjects is valuable in vision research, but remains challenging for prolonged free viewing of static stimuli, such as the aesthetic perception of complex artworks, where saccade sequences are densely sampled and individual scanpaths diverge substantially. We propose mapping saccades onto continuous orientation fields via local estimates of density, mean orientation, and angular coherence. By specifying researcher-chosen Orientations of Interest (OOIs), the method yields heatmap visualizations that highlight where gaze flow aligns with compositional structure and symmetry. These heatmaps provide compact, interpretable depictions of dominant saccadic organization and enable visual analytics of very large saccade sets. We further present HeatMatch, a robust, scalable, and segmentation-free similarity framework computed directly from the field statistics. In an art-perception eye-tracking study with long viewing times, we show that HeatMatch recovers genre-, stimulus-, and subject-level regularities and reveals stable individual viewing signatures that generalize across different paintings.