## 1 Introduction  
Human-technology cooperation could greatly benefit from recent advances in deep learning. In the context of image classification, convolutional neural networks (CNN) can match or even surpass human abilities [1]. For instance, they can support humans in tasks such as medical image analysis [2], the recognition of facial expressions [3] or the understanding of complex scenes [4]. However, CNN act like a black box, while successful human-technology cooperation requires transparency [5; 6]. The transparency of CNN can be enhanced by methods of explainable artificial intelligence (XAI). These XAI methods generate attention maps, highlighting the image areas that contributed to the CNN model's classification decision. Such transparency is crucial as humans lose trust in a model if it relies on image areas that do not make sense to them [7]. Accordingly, it has rightfully been claimed that similarity to human attention is a relevant dimension for evaluating CNN and XAI methods. But how similar or different are the areas attended by humans and CNN? Comparisons of XAI outputs to human attention maps indicate that CNN often base their decisions on different image areas than humans (e.g., [8; 9; 10; 11]). However, it is not always clear why. More precisely, most studies selectively focused on technological influences on human-CNN similarity such as the specific CNN or XAI method. In contrast, influences on human attention have not been in focus such as the task procedures used to elicit human attention maps (e.g., eye tracking vs. manual selection) or the images to be classified (e.g., single objects vs. complex scenes). While a striking diversity in these factors can be observed across studies, the effects of this diversity are poorly understood. The purpose of the present study was to specify how human-CNN similarity is influenced by two human-related factors (i.e., tasks and images). Understanding these influences is important for practical and theoretical reasons. From a practical perspective, it can assist researchers in choosing particular human tasks and images for their human-CNN comparisons. It enables them to estimate whether their choice is likely to be inconsequential or whether it is likely to have profound effects on their results. This also allows them to decide whether they can safely base this choice on convenience and practical feasibility (e.g., relying on manual selection instead of eye movements, as the latter cannot be assessed in online studies). Conversely, in some cases, it might be important to choose a particular task or image type in order to obtain interpretable results. Similarly, understanding these dependencies allows researchers to estimate whether their results are likely to generalize to other tasks and images. From a theoretical perspective, knowledge about the influence of human-related factors can foster a more thorough understanding of the similarities and differences between human and artificial image classification strategies. It allows us to specify the boundary conditions under which humans and CNN may use strategies that are highly similar or fundamentally different. Ultimately, this knowledge may contribute to the development of better deep learning models. To investigate the human-related influences on human-CNN similarity, the present study makes the following contributions:  
* We review the previous literature investigating the similarity between human and CNN attention. In this review, we specifically focus on the influence of tasks and images, concluding that the current knowledge about these two influences is insufficient;
* We conduct a human experiment in which participants have to select the areas most relevant for the classification of different types of images using different elicitation tasks;
* We compare the resulting human attention maps to those generated by a standard CNN model and XAI method that are most common in this area of research.