## 2 Theoretical Background  
### Visual Scene Processing of Humans and CNN  
#### 2.1.1 How Do Humans Process Scenes? Humans can infer the basic-level category of scenes at a glance. The representation of this so-called _gist_ refers to the overall meaning of a scene [12]: people instantly extract both low-level features (e.g., spatial frequencies, color) and high-level semantic information (e.g., birthday party). Gist perception relies on two complementary information sources: global scene statistics and diagnostic objects. On the one hand, there are regularities in the structure and color patterns of scenes from different categories, which enable a fast and reliable categorization [13]. These physical scene statistics generate subjectively perceivable global properties (e.g., openness, temperature or dynamics) that are vital for categorization [14]. For instance, deserts are high in openness and temperature but low in dynamics, while waterfalls are low in openness and temperature but high in dynamics. Accordingly, people are less likely to confuse deserts and waterfalls than deserts and wheat fields. On the other hand, diagnostic objects provide important information and a single object can be enough to categorize a scene [15]. For instance, people are able to infer that a scene is an office merely based on the presence of a computer screen. Taken together, scene statistics and objects allow people to quickly extract the gist of a scene. Subsequently, this gist representation helps people decide where to move their eyes for more thorough analysis. Eye movements are needed as people can only see sharply within the small area of foveal vision and thus have to sequentially fixate relevant parts of the image [16; 17]. Such _fixations land on informative areas_ which typically contain objects, while people rarely fixate uniform background areas like the sky or desert sand [18]. Ample evidence suggests that it is the meaningfulness (i.e., semantic relevance) of image areas that controls where people look, even when the areas are not physically salient [19]. How do people know whether an area is meaningful without already having looked at it? This can largely be attributed to _scene context_: based on learned knowledge about statistical regularities, people can predict where meaningful objects are likely to be found [20]. Similar to initial gist processing, the contextual guidance of eye movements relies on the two complementary sources of global scene statistics and object-to-object relations. First, physical scene statistics and the resulting global properties determine where people move their eyes [21]. This is because objects are systematically organized along horizontal layers, so that people can expect airplanes to appear in the sky and pedestrians on the ground. A second important source of contextual guidance are object-to-object relations [22] because particular objects systematically co-occur in the real world. On the one hand, some objects serve as anchors for others [23]. For instance, when looking for a laptop, people may initially fixate a table rather than a window sill. On the other hand, subsequent fixations land on semantically related objects even when this relation is not hierarchical [24]. For instance, after fixating a plate, people are more likely to fixate a fork than a Chandelier. The relative importance of global properties and object-to-object relations may depend on the _type of scene_[15]: global properties are particularly informative for outdoor scenes that differ in spatial layout, while objects are more informative for indoor scenes. Systematic transitions between semantically related objects may lead to a higher predictability of eye movements in indoor scenes. Conversely, eye movements are less deterministic for landscapes, which encourage exploration [25]. These findings indicate that it is worthwhile to consider the type of image when comparing human and CNN attention. Although human scene viewing is highly efficient, eye movements do not depend only on task-relevant image features.