3 Influence of initial fixation position However, current computational models for the prediction of fixation locations are not exclusively based on bottom-up features. Although the original meaning of saliency refers to the bottom-up features of an image, newer computational models that include other features are also termed saliency models by their authors (Judd, Ehinger, Durand, & Torralba, 2009; Bylinskii et al., 2015). Therefore, we will refer to all stationary models that aim at the prediction of fixation locations as saliency models. Recent models incorporate top-down processes like the task demands (Navalpakkam & Itti, 2005) and other higher- level image features like face processing (Cerf, Harel, Einh¨ auser, & Koch, 2008). Moreover, systematic tendencies such as the central fixation bias (Tatler, 2007) are included in the computation of saliency maps. As a result, current models integrate multiple features from all three categories of processes into a coherent computational framework (Cerf et al., 2008; Judd et al., 2009). All saliency models need to predict the density of the eye’s fixation locations (so-called first-order statistics). Thus, the evaluation of saliency models is primarily based on the assumption of statistically independent fixations without reference to previous fixations (i.e., the scanpath). Compared to static saliency models, dynamic models try to capture some additional aspects of the scanpath. Dynamical principles for saccade planning are inhibitory tagging (Klein, 1988; Itti et al., 1998; Bays & Husain, 2012; Le Meur & Liu, 2015), saccadic momentum (Smith & Henderson, 2009, 2011; Wilming, Harst, Schmidt, & K¨ onig, 2013) and facilitation of return (Smith & Henderson, 2009, 2011; Luke, Schmidt, & Henderson, 2013) Inhibitory tagging is motivated by the effect of inhibition of return, a neural mechanism that inhibits the processing at recently attended locations (Posner & Cohen, 1984; Posner, Rafal, Choate, & Vaughan, 1985; Klein, 2000) and is often interpreted as a foraging