  Computational models of visual attention have become popular over the past
decade, we believe primarily for two reasons: First, models make testable
predictions that can be explored by experimentalists as well as theoreticians,
second, models have practical and technological applications of interest to the
applied science and engineering communities. In this chapter, we take a
critical look at recent attention modeling efforts. We focus on {\em
computational 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 task
definition, and which make predictions that can be compared to human or animal
behavioral 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 specifically
designed for a single task or for a restricted class of stimuli. For
theoretical models, we refer the reader to a number of previous reviews that
address attention theories and models more generally
\cite{Itti_Koch01nrn,Paletta_etal05,Frintrop_etal10,Rothenstein_Tsotsos08,Gottlieb_Balan10,Toet11,Borji_Itti12pami}.
