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Eye movement sequences-or scanpaths-vary depending on the stimulus characteristics and the task (Foulsham &amp; Underwood Journal of Vision, 8(2), 6:1-17, 2008; Land, Mennie, &amp; Rusted, Perception, 28, 1311-1328, 1999). Common methods for comparing scanpaths, however, are limited in their ability to capture both the spatial and temporal properties of which a scanpath consists. Here, we validated a new method for scanpath comparison based on geometric vectors, which compares scanpaths over multiple dimensions while retaining positional and sequential information (Jarodzka, Holmqvist, &amp; Nyström, Symposium on Eye-Tracking Research and Applications (pp. 211-218), 2010). "MultiMatch" was tested in two experiments and pitted against ScanMatch (Cristino, Mathôt, Theeuwes, &amp; Gilchrist, Behavior Research Methods, 42, 692-700, 2010), the most comprehensive adaptation of the popular Levenshtein method. In Experiment 1, we used synthetic data, demonstrating the greater sensitivity of MultiMatch to variations in spatial position. In Experiment 2, real eye movement recordings were taken from participants viewing sequences of dots, designed to elicit scanpath pairs with commonalities known to be problematic for algorithms (e.g., when one scanpath is shifted in locus or when fixations fall on either side of an AOI boundary). The results illustrate the advantages of a multidimensional approach, revealing how two scanpaths differ. For instance, if one scanpath is the reverse copy of another, the difference is in the direction but not the positions of fixations; or if a scanpath is scaled down, the difference is in the length of the saccadic vectors but not in the overall shape. As well as having enormous potential for any task in which consistency in eye movements is important (e.g., learning), MultiMatch is particularly relevant for "eye movements to nothing" in mental imagery and embodiment-of-cognition research, where satisfactory scanpath comparison algorithms are lacking.

It depends on how you look at it: Scanpath comparison in multiple dimensions with MultiMatch, a vector-based approach