## 5 Discussion
### Implications for Practical Application and Perpectives for Future Research  
The present findings have several implications for practical application in the field of deep learning. First, a specific practical implication is that our findings may assist researchers in choosing suitable tasks for their comparisons between the attention maps of humans and CNN. In this regard, our results suggest that the choice of an attention elicitation task should depend on contextual constraints. When relevant image areas can be located unambiguously (e.g., for images that focus on particular objects), manual selection is an easy way of producing consistent maps that are free from task-irrelevant viewing biases. However, it is less suitable when the relevance of image areas is up to human preferences and when they might not even be aware of their information needs (e.g., for images that focus on large-scale scenes). In this case, eye tracking may be a better methodological choice as scene guidance makes the resulting maps less arbitrary.  
When taking a broader perspective on practical application, a second implication of our findings becomes evident: different human tasks may have different effects when being used to enhance CNN classifications. Several studies suggest that CNN performance can be improved by harmonizing them with human attention maps. This has been shown for attention maps generated from eye movements [8; 51] and manual selections [11; 76]. However, to the best of our knowledge, no previous study has compared both approaches. The present findings suggest that the choice of tasks to generate human attention maps may lead to systematic differences, which may further depend on image characteristics. Thus, future studies should investigate whether and under what conditions it is better to use eye movements or manual selections to enhance CNN performance.  
This question extends to other deep learning architectures besides CNN. For instance, Transformer-based architectures follow a different learning approach, which also affects their similarity to human attention [11; 55]. Accordingly, [PERSON] et al. [55] found that the similarity to human attention was lower for a Vison Transformer (CaiT) than for two CNN-based architectures and a Swin Transformer (but note that these results were strongly affected by the XAI method used). Moreover, [PERSON] et al. [11] reported that a VisionTransformer learned a fundamentally different strategy than humans but at the same time experienced the greatest benefit from being harmonized with human attention maps. An important question for future work is whether these benefits depend on the tasks and images used to elicit human attention maps.  
From a psychological perspective, future research should strive for a better understanding of how humans select the areas they consider relevant. For instance, how do attention maps depend on individual differences? This is especially relevant for applications where CNN should not be similar to just any human but to humans with particular characteristics (e.g., domain experts). After all, high similarity might also mean that both humans and CNN are mistaken about the actual relevance of image areas (cf. [45]). To avoid the pitfalls of inferring the suitability of CNN attention maps from their similarity to humans, external criteria are needed, such as whether they are interpretable and support human task performance [68; 77; 78].