## 3 Methods
### Apparatus and Stimuli
#### 3.3.2 Images  
Images were taken from the Places365 dataset [62], which provides a wide variety of images from 365 different classes. We chose this dataset because it offers a wide variety of different scene types (i.e., objects, indoor scenes, landscapes) compared to the heavily object-focused ImageNet dataset [63]. The complexity of its natural scenes was important to us for three reasons. First, we needed a dataset that allowed us to investigate the distinction between images that rely on diagnostic objects versus object-to-object-relations versus global properties. Second, we wanted to increase the likelihood of being able to obtain eye movements at all, which would have been even more uncertain if we had used simpler images. Third, Places365 provides images at a much higher and consistent spatial resolution than other datasets like ImageNet, and in this way enabled us to present images in a format that is large enough to observe subtle differences in fixation locations.  
We used the whole dataset for training the CNN but only a subset of 102 images for the human experiment. Suitable images were selected, center-cropped and resized to a square format of 1024 \(\times\) 1024 pixels. These images were then presented at the center of the screen in front of a white background. We manually went through the images to ensure that the relevant areas still were in full view and replaced images where this was not the case. In total, 102 images were selected; 60 of them for the main experiment and 42 for practice.  
For the main experiment, we selected 20 images for each of our three image types. Within each image type, we used two categories and thus each category consisted of 10 exemplars (see Figure 2 for examples). To make categorization more challenging, these two categories were highly similar in terms of their scene statistics and global properties (e.g., openness, temperature). Other than that, we aimed for a high variability in the images for exploratory purposes. Accordingly, images differed in whether they included salient, category-irrelevant objects that might attract eye movements and had no fixed ratio of the area covered by potentially category-defining versus less relevant contents.  
For the image type _objects_, all scenes included one clearly discernible object of the respective category (i.e., lighthouse, windmill) and thus categories were unambiguously defined by local object information. While the objects were embedded in natural contexts, these contexts could be more or less informative. For instance, only some of our lighthouses were presented in front of a coastline. Some images also contained other, irrelevant objects (e.g., ships or cars). For _indoor scenes_, the images presented an arrangement of objects in a room with a specific function (i.e., office, dining room). All images included chairs and tables and three offices also included a person. For _landscapes_, nature scenes with large, relatively uniform areas were used (i.e., desert, wheat field). Some landscapes also contained salient objects (e.g., agricultural machinery, houses).  
Besides these images, the following additional stimulus screens were used in the experiment. First, at the start of the experiment participants saw a screen on which they had to input their demographic data (i.e., age and gender). Second, before each block, instruction screens summarized the respective task. However, the main instruction was provided in a video before the experiment. Third, in the first practice block, verbal category labels (instead of images) were presented centrally on a white background in black font (Tahoma, 30 pt). Finally, in the practice blocks, a feedback screen informed participants about the correctness of their response. In case of an error, a schematic image of the Cedrus pad was shown that linked the category labels to the respective keys to remind participants of the correct key assignment. All materials were provided in German language.