## 3 Methods
### Data Analysis  
#### 3.6.1 Attention Map Generation  
To generate human attention maps, the data (i.e., fixations or polygons) were summed over all 25 participants prior to map generation. We compared two types of attention maps between humans and CNN: binary masks of a fixed size and gradual density maps. To define our binary masks, the same basic approach was used for all four types of attention maps. That is, we used the gradual density maps as a basis and set a cut-off that only kept those 5% of the area visible that received the most weight, while the rest was hidden. In this way, all attention maps had the same size, while only their shape and position varied. The threshold of 5% was chosen for two reasons. First, previous work has shown that areas as small as 5% best differentiate between human and CNN attention, whereas for larger areas of about 20%, maps get much less distinguishable [8]. Second, for object images (i.e., lighthouses, windmills), human eye movements were usually restricted to a rather small area, despite being summed over all participants, with area sizes ranging from 5.3 to 17.6% and an average of 11.0%. Thus, using a higher threshold than 5% would have required uncovering areas for some images that were never selected by any participant. However, to better understand how broadly participants actually spread their attention across the image in different task and image conditions, we additionally compared the sizes and variability of maps that uncovered all areas ever attended. The following sections will describe the specifics of area definition for different types of attention maps. **Eye movement attention maps**. To generate our eye movement attention maps, we excluded all fixations outside the image, the first fixation (i.e., the one that started during drift correction), fixations with durations of less than 180 ms [64] and all fixations from trials with response times that deviated more than 2.5 SD from the average response time of 2106 ms (i.e., longer than 7629 ms). For the remaining data, the following procedure was used to generate fixation maps (for similar approaches see [8,53]). Around each fixation, we considered an area of 2 degrees of visual angle (58 pixels), which corresponds to foveal vision. Within this area, we applied a Gaussian kernel that caused the weight to decrease from the center to the outside. This kernel was scaled so that the weight was 1 at the center of the fixation and 1/58 at a distance of 58 pixels from the center (i.e., linear distance-weighting). Furthermore, each kernel was multiplicatively weighted by the fixation duration measured in milliseconds. The final attention map was generated by adding up the weighted kernels of all fixations. The resulting map was then normalized to the interval [0, 1] to simplify visualization. Note that we decided not to normalize the data for individual participants. That is, the areas only depended on fixation duration, meaning that participants with more or longer fixations had higher impact. This is because we saw no theoretical reason to discount the fixations of participants who scanned the image more thoroughly. However, we also performed exploratory analyses using a normalized procedure (i.e., each participant contributing the same weight) but found that this did not have any noteworthy impacts on the results. **Drawing attention maps**. When defining the attended areas based on human drawings, we noticed some missing data due to technical problems (i.e., polygon coordinates were not recorded). This happened 20 times in total and thus removed 1.3% of the data from the analysis. Other than that, we did not exclude any drawings, regardless of how many polygon points participants defined or how long it took them. To obtain the attention maps, we started with a matrix of the same resolution as the image with all values set to 0. If an area was inside the drawn polygon for a participant, the value in the matrix was increased by 1. Adding up all polygons then produced our attention map.