## 2 Theoretical Background
### Comparing Human and CNN Attention Maps
#### 2.2.2 Influence of Tasks
Such procedures provide a binary value (0/1) for each participant and image and gradual variations in attention maps only emerge from the aggregation of several participants. Conversely, another method immediately provides gradual relevance estimates for each participant [47]: participants viewed blurred images and had to _deblur relevant areas_ by repetitive rubbing with their mouse cursor. This enabled a slight deblurring of areas that were only coarsely searched and a complete deblurring of areas that, upon closer inspection, turned out to be actually relevant. For one, this procedure provides aclose analogy to eye movements, which also combine a quick ambient \"where\" processing of the layout and a subsequent focal \"what\" analysis of object details [59]. Finally, one study elicited human attention maps by asking participants to _order predefined image segments_ according to their relevance for classification [9]. Another version of the same principle is to click on relevant image areas with the mouse to make them visible for a partner [11]. An important dimension that differentiates between these manual selection tasks is the likelihood of including scene context. First, including context can be encouraged by means of deblurring, where context processing is an integral part of the elicitation procedure [47]. Second, including context can be up to participants when they are free to select whatever areas they want [45]. Third, it can be discouraged when segment ordering by relevance pushes participants to start with all parts belonging to the category-defining object [9; 11]. Finally, it can be prevented entirely when only the image parts inside an object segmentation mask are available for selection [55; 58]. Aside from the specific implementation, _manual selection per se_ comes with characteristic advantages and disadvantages. These are complementary to those of eye tracking: manual selection is a highly conscious, intentional activity and thus may not adequately reflect the information humans actually use for categorization. On the one hand, they might conceive of their task as selecting the image areas that define an object instead of the image context they actually need [9]. However, the opposite effect is also possible: humans may select more than they actually need for categorization, especially when the category is defined by large proportions of the image (e.g., for landscapes). Thus, they may select all areas of equal importance, even when in fact they need much less information. Taken together, humans might not be well aware of their inner processes of categorization and thus may not even know what information they are attending to. **Comparing tasks**. Despite the striking diversity of tasks used in previous studies, their impacts on human-CNN similarity have not been systematically investigated. A few studies assessed the impacts of task characteristics on human attention but did not relate these results to CNN attention. First, [PERSON] et al. [51] compared two eye tracking procedures: free viewing versus explaining why an image matches its label. Free viewing led to distributed eye movements that often targeted irrelevant objects, while explanation induced a focus on key features. Second, [PERSON] et al. [47] used manual deblurring and varied the information available to participants (e.g., full image, model prediction). A medium amount of information produced attention maps that could most easily be interpreted by new participants. No previous studies have assessed how task variations affect the similarity between humans and CNN; thus, we can only speculate about that. Figure 1 presents a continuum of intentionality on which different elicitation tasks can be placed. Eye movement tasks are located at the low intentionality end of the continuum whereas manual selection tasks are located at the high end. Presumably, tasks at the low end reflect attentional processes more directly but also produce more false positive information.