## 5 Discussion
### Comparison of Manual Selection and Eye Movements  
Overall, manually generated attention maps were most similar to CNN, much more so than eye movements. Importantly, this cannot be ascribed to a larger size of the drawing maps because for the Dice score analysis, all maps were reduced to 5% of the image area. In this section, we will discuss three potential explanations: the role of context, the specificmethod of manual selection and the information contents reflected in different types of attention maps.  
First, the role of context was highlighted in a previous study that seems to be at odds with our findings. [PERSON] et al. [9] had attributed the dissimilarity between their manually generated attention maps and CNN to differences in considering relevant scene context. Such context was used by their CNN but neglected by humans. Based on these findings, we had hypothesized eye movements to produce higher human-CNN similarity (at least for gaze-pointing) because eye movements are guided by scene context [20; 21; 22]. However, this hypothesis was not supported by our data and, at least for objects, the opposite was found. To account for this discrepancy, it should be noted that coarse representations of scene gist can be established without eye movements [12] and even the earliest eye movements already target task-relevant areas [19]. Thus, our prediction might have been somewhat naive: just because scene context guides eye movements to relevant areas, this does not necessarily mean that people look at this context.  
A second reason might be our specific method of manual selection. Participants could freely select image areas by drawing polygons around them, while [PERSON] et al. [9] had participants order small, predefined image segments by relevance. In consequence, our participants included considerable amounts of context in their drawings. This is also reflected by the large size of manual selections when considering their total area. However, we also observed considerable variation and thus arbitrariness in individual participants' drawings for indoor scenes and landscapes (see Figure 4, fourth column). This finding aligns with previous research [47; 58], suggesting that a high variance in manual selections is not unusual.  
A third reason for the higher similarity between drawings and Grad-CAM could be that different types of attention maps provide fundamentally different information. One might argue that the higher human-CNN similarity for drawings is an obvious result because eye movements reflect the path to a classification decision while drawing reflects a high-level end result--similar to Grad-CAM, which relies on the last convolutional layer. In line with this argument, neither drawing nor Grad-CAM were prone to task-irrelevant biases (e.g., central fixation bias, attentional capture by salient distractors). However, given that even the earliest eye movements are highly task-specific [19], our results did not change in a control analysis using attention maps from only the first fixation. Still, our attention maps, aggregated across all participants, reveal that eye movements were more dispersed than drawings (see Figure 4, sixth to eighth column). Thus, drawing might be a closer match to XAI methods, which rely on high-level information from the last convolutional layer. This would make it even more surprising why eye movements are commonly considered the gold standard for comparing CNN to human attention.