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Computational models of visual attention have become popular over the pastdecade, we believe primarily for two reasons: First, models make testablepredictions that can be explored by experimentalists as well as theoreticians,second, models have practical and technological applications of interest to theapplied science and engineering communities. In this chapter, we take acritical look at recent attention modeling efforts. We focus on {\emcomputational models of attention} as defined by Tsotsos \& Rothenstein\shortcite{Tsotsos_Rothenstein11}: Models which can process any visual stimulus(typically, an image or video clip), which can possibly also be given some taskdefinition, and which make predictions that can be compared to human or animalbehavioral or physiological responses elicited by the same stimulus and task.Thus, we here place less emphasis on abstract models, phenomenological models,purely data-driven fitting or extrapolation models, or models specificallydesigned for a single task or for a restricted class of stimuli. Fortheoretical models, we refer the reader to a number of previous reviews thataddress attention theories and models more generally\cite{Itti_Koch01nrn,Paletta_etal05,Frintrop_etal10,Rothenstein_Tsotsos08,Gottlieb_Balan10,Toet11,Borji_Itti12pami}.

Computational models: Bottom-up and top-down aspects