Robustness of metrics used for scanpath comparison

Abstract

In every quantitative eye tracking research study, researchers need to compare eye movements between subjects or conditions. For both static and dynamic tasks, there is a variety of metrics that could serve this purpose. It is important to explore the robustness of the metrics with respect to artificial noise. For dynamic tasks, where eye movement data is represented as scanpaths, there are currently no studies regarding the robustness of the metrics. In this study, we explored properties of five metrics (Levenshtein distance, correlation distance, Fréchet distance, mean and median distance) used for comparison of scanpaths. We systematically added noise by applying three transformations to the scanpaths: translation, rotation, and scaling. For each metric, we computed baseline similarity for two random scanpaths and explored the metrics' sensitivity. Our results allow other researchers to convert results between studies.