## 5 Discussion
### Differences between Similarity Metrics  
Why did the two similarity metrics produce different outcomes? When comparing the results obtained with the Dice scores and cross-correlations, the overall effects of task were consistent, whereas their dependence on image type was strikingly different. Only for the Dice score did task effects on human-CNN similarity vary with image type: Grad-CAM was more similar to drawing than to the eye movement tasks for objects but these task differences were largely diminished for indoor scenes and completely disappeared for landscapes. In contrast, when using cross-correlations, Grad-CAM was more similar to drawing than to eye movements regardless of image type.  
How can this divergence between the two metrics be explained? At first glance, one might assume that the cross-correlation results are an artefact of attention breadth. In contrast to the Dice scores, cross-correlations do not control for task differences in area size. The areas were large for Grad-CAM and drawing but small for the eye movement tasks. Specifically, the total eye movement areas never exceeded 37% even for indoor scenes and landscapes (see maximum values in Table 2). This means that the majority of each image remained completely unattended (see black parts of the density maps in Figure 4). Conversely, in the drawing task, such unattended areas only amounted to 3 and 6% for indoor scenes and landscapes, respectively. This makes drawing more similar to Grad-CAM, which also assigns at least some relevance to all image areas. This similarity in the mere coverage of image areas would also increase cross-correlations in a nonspecific manner. Thus, the explanation would be that cross-correlations increase with a broad focus of attention (or lack of specificity) and therefore remain high for drawing, while the eye movement tasks produced more specific attention maps (cf. [8]) that necessarily were less correlated with the nonspecific Grad-CAM. In the extreme case, our cross-correlation results would be a mere artefact of (non)specificity.  
However, this explanation is refuted by another observation: the cross-correlations between our three human tasks. While the total area sizes became more different between drawing and eye movements for indoor scenes and landscapes (as compared to objects), the cross-correlations between them _increased_ rather than decreased. Thus, differences in attention breadth do not automatically reduce cross-correlations.  
Therefore, instead of arguing which metric is more valid, we should consider that they answer different questions. On the one hand, the Dice scores can be used to compare which areas humans and CNN attend to most. In this case, image type has a major impact on the results. On the other hand, cross-correlations can be used to compare how humans and CNN generally deploy their attention across the entire image. In this case, image type does not seem to matter. That said, an advantage of cross-correlations is that they retain more variance. Thus, they are able to detect nuances instead of forcing the selection of specific areas even when the actual attention is widely spread for some image types.