\chapter[Introduction]{Introduction}
Natural scenes are made up of highly detailed stimuli. Only effective filtering mechanisms allow us to move and survive in the environment and to make sense of it \cite{AuffarthRobots}. One important filtering mechanism is the shift of gaze to different locations. Visual acuity is highest at the fovea, which spans around one or two degrees of visual angle, and rapidly abates with eccentricity. During saccades, a mechanism called \textit{saccadic suppression} (also \textit{saccadic masking}) blocks the influx of visual information during saccades \cite{Burr}. Due to this mechanism, we cannot see the movements of our own eyes and we do not notice motion blur. 

Eye movements are not random. Rather they are influenced by high level processing, such as experience and task goals \cite{Yarbus} and low level processing, such as luminance contrast, texture contrast, spatial correlations, information content, among others (see \cite{PriviteraStark} for an overview). Influences of stimulus properties are emphasized in various models of visual attention. 

Among the first and still influential models is Treisman's \textit{feature integration theory} \citeyear{Treisman}. According to this account, several primary visual features are processed independently the representations of which, stored in feature maps, are later integrated in a saliency map that attributes saliency to locations. This principle of feature-based attention was later stated to underlie gaze direction. Treisman et al. found the phenomenon of \textit{pop-out}, where an object that differs strongly globally from its environment attracts immediate attention. 

Koch and Ullman \citeyear{KochUllman} proposed the saliency-based model of visual attention. From a multi-featured input a scalar saliency map is computed, where a winner-take-all mechanism and inhibition of return provide the locations for consecutive attentional shifts in order of their conspicuity. According to this model of human bottom-up vision, from each feature channel - such as luminance, color, and orientation - representations of local contrast are computed and summed up to the saliency map. 

In an eye-tracking study, \cite{ReinagelZador} gave evidence for the contribution of saliency to fixation point selection, by showing increased luminance contrast at fixation points compared to non-fixated points (controls). This was later confirmed by others (e.g. \cite{Einhaeuser2003,Parkhurst2002}). They further showed that fixated regions have a high variance (low correlation). They suggested that these mechanisms may serve to increase information for visual processing. This effect cannot be reduced to centricity of fixated points, because it also shows in test images of artificial stimuli comprising the regions of highest contrasts. 

Although rhesus macaque are one of the most heavily relied on models of human cognition in scientific experiments, visual attention in macaque has been studied, but there have been relatively few studies that compare visual attention in monkeys and humans. 

Lierhaus \cite{Lierhaus} investigated gaze movements of monkeys in a free viewing condition. They were shown natural stimuli that were chosen for their interest to monkeys. He found luminance contrast increased by twenty percent at fixation points, significantly higher that in humans as per \cite{ReinagelZador}. He also found a reduced spatial correlation with monkeys as compared to humans. His conclusion was firstly that low-level features are more important in monkeys and secondly that luminance contrast could not account for all of the effects of saliency in monkeys gaze direction and suggested texture contrast and top-down cues should also be causal. 

Contrarily however, Wolfgang Einh\"auser and colleagues \citeyear{WolfgangDiss,Einhaeuser2006}, who did an eye tracking experiment with monkeys and humans during free viewing of images, found that luminance contrast, texture contrast and ik-saliency are related to overt attention to similar degrees in humans and monkeys. They further argued that humans show a higher inter-individual consistency regarding which locations they fixate than monkeys. Moderate local image modifications (increased or decreased luminance or texture at some points) affect monkeys stronger than they affect humans. They concluded that humans were guided rather by high-level processes compared to monkeys, who were more driven by low-level features. 

The object of this study are eye movements in monkeys and humans and their \textit{bottom-up} determinants as they can be calculated in the images. For this purpose we showed images to humans that were shown in a previous experiment to monkeys (described in \cite{Lierhaus}) and compared the eye movements of both species. 