In humans and in foveated animals visual acuity is highly concentrated at thecenter of gaze, so that choosing where to look next is an important example ofonline, rapid decision making. Computational neuroscientists have developedbiologically-inspired models of visual attention, termed saliency maps, whichsuccessfully predict where people fixate on average. Using point process theoryfor spatial statistics, we show that scanpaths contain, however, importantstatistical structure, such as spatial clustering on top of distributions ofgaze positions. Here we develop a dynamical model of saccadic selection thataccurately predicts the distribution of gaze positions as well as spatialclustering along individual scanpaths. Our model relies on, first, activationdynamics via spatially- limited (foveated) access to saliency information, and,second, a leaky memory process controlling the re-inspection of target regions.This theoretical framework models a form of context-dependent decision-making,linking neural dynamics of attention to behavioral gaze data.
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