## 4 Results
### Qualitative Observations  
When visually inspecting the human attention maps and their overlaps with Grad-CAM, a number of noteworthy differences became apparent. Examples for some of the points are provided in Figure 4. As the comparison between attention maps was highly dependent on image type, we will structure the following section accordingly. For objects, eye movements reflected several phenomena known from the scene viewing literature. For instance, fixations did not only target the category-defining object but also other salient objects (e.g., boats in lighthouse images). Occasionally, Grad-CAM fell victim to the same biases. This typically happened when the context was atypical (e.g., for a lighthouse in an urban area, Grad-CAM focused on a car) but not when it was typical (e.g., for lighthouses by the sea, Grad-CAM did not look at boats). Thus, the resulting low overlap often stemmed from problems in gaze, not Grad-CAM. Accordingly, Grad-CAM  
Figure 4: Example image overlays with attention maps to illustrate our qualitative observations. overlapped more strongly with drawings, which did not suffer from these biases. However, Grad-CAM also had a problem with object images. First, it tended to look at the lower part of the lighthouse or windmill. In contrast, human eye movements and drawings preferably targeted the upper, more diagnostic part. Second, Grad-CAM even missed the object entirely when the context was highly atypical (e.g., lighthouse in the snow). Finally, human drawings also showed a typical pattern for objects: they did not include much context but focused on the object, sometimes aiming to precisely draw its outlines (e.g., individual rotor leaves of windmills). Thus, participants mainly varied in how carefully they specified the object boundaries but largely agreed in selecting only the object. For _indoor scenes_, we observed similar distraction in eye movements. For instance, fixations always landed on people when they were present in offices, while Grad-CAM only looked at a human face once. However, participants also included people in their drawings, suggesting that they actually considered them relevant for categorization. For drawings, participants generally used different strategies. Some selected an individual object (e.g., computer screen), some selected anchor objects (e.g., desk area) and some included almost the entire room. However, this also depended on the specific image category: including the entire scene was more common for offices than dining rooms, where people usually selected the table and the objects on it. For _landscapes_, the similarity between eye movements and Grad-CAM was lowest. On the one hand, a few factors were conducive to similarity. For instance, both eye movements and Grad-CAM were sensitive to physically salient features (e.g., object boundaries, horizon), while rarely looking at noninformative areas (e.g., sand). Moreover, both tended to look at objects (e.g., agricultural machinery). On the other hand, the factors that reduced similarity played a larger role. For instance, fixations were biased toward the center even when it was noninformative, while Grad-CAM showed no signs of center bias. Moreover, Grad-CAM usually highlighted one area, while eye movements were widely dispersed across the scene (with the peaks of density maps appearing as blobs in the binary masks). For drawing, there seemed to be two major strategies, with some participants selecting most of the category-defining area (e.g., all desert sand) and some selecting an arbitrary part (e.g., a small patch of sand). However, many participants adopted strategies in between. Similar to indoor scenes, strategies differed between the two categories, with more variation for deserts than wheat fields. Concerning the _comparison between human tasks_, we were quite surprised that the two types of eye movement maps rarely differed from each other, or only for individual images.