We explore the role of eye movements in a chase detection task. Unlike the previous studies, which focused on overall performance as indicated by response speed and chase detection accuracy, we decompose the search process into gaze events such as smooth eye movements and use a data-driven approach to separately describe these gaze events. We measured eye movements of four human subjects engaged in a chase detection task displayed on a computer screen. The subjects were asked to detect two chasing rings among twelve other randomly moving rings. Using principal component analysis and support vector machines, we looked at the template and classification images that describe various stages of the detection process. We showed that the subjects mostly search for pairs of rings that move one after another in the same direction with a distance of 3.5–3.8 degrees. To find such pairs, the subjects first looked for regions with a high ring density and then pursued the rings in this region. Most of these groups consisted of two rings. Three subjects preferred to pursue the pair as a single object, while the remaining subject pursued the group by alternating the gaze between the two individual rings. In the discussion, we argue that subjects do not compare the movement of the pursued pair to a singular preformed template that describes a chasing motion. Rather, subjects bring certain hypotheses about what motion may qualify as chase and then, through feedback, they learn to look for a motion pattern that maximizes their performance.
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