Clustered eye movement similarity matrices

Abstract

Eye movements recorded for many study participants are difficult to interpret, in particular when the task is to identify similar scanning strategies over space, time, and participants. In this paper we describe an approach in which we first compare scanpaths, not only based on Jaccard (JD) and bounding box (BB) similarities, but also on more complex approaches like longest common subsequence (LCS), Frechet distance (FD), dynamic time warping (DTW), and edit distance (ED). The results of these algorithms generate a weighted comparison matrix while each entry encodes the pairwise participant scanpath comparison strength. To better identify participant groups of similar eye movement behavior we reorder this matrix by hierarchical clustering, optimal-leaf ordering, dimensionality reduction, or a spectral approach. The matrix visualization is linked to the original stimulus overplotted with visual attention maps and gaze plots on which typical interactions like temporal, spatial, or participant-based filtering can be applied.