The dynamical law behind eye movements: distinguishing between L\'evy and intermittent strategies

Abstract

Foraging is a complex spatio-temporal process which is often described with stochastic models. Two particular ones, L\'evy walks (LWs) and intermittent search (IS), became popular in this context. Researchers from the two communities, each advocating for either L\'evy or intermittent approach, independently analyzed foraging patterns and reported agreement between empirical data and the model they used. We resolve this L\'evy-intermittent dichotomy for eye-gaze trajectories collected in a series of experiments designed to stimulate free foraging for visual information. By combining analytical results, statistical quantifiers, and basic machine learning techniques, we devise a method to score the performance of the models when they are used to approximate an individual gaze trajectory. Our analysis indicates that the intermittent search model consistently yields higher scores and thus approximates the majority of the eye-gaze trajectories better.