## 3 Methods
### CNN and XAI  
#### 3.5.1 CNN Model  
The CNN used in the present study was a ResNet-152 [35]. This architecture was chosen because it is the one most commonly used in previous investigations of human-CNN similarity (e.g., [8; 9; 10; 47; 51; 52; 54]), allowing for meaningful comparisons to previous research. The ResNet-152 consists of 152 consecutive layers, which are connected in a special structure. The basic idea is to iteratively apply convolutional filters to perform feature extraction and thus increase the information density in every layer. This is mostly done using bottleneck blocks, which consist of three convolutional layers. The input and output of each block are connected via so-called residual connections. These residual connections are used to address the vanishing gradient problem, which can disrupt the training process for CNN that consist of many layers.  
The input consists of 224 \(\times\) 224 \(\times\) 3 tensors (i.e., three-dimensional matrices), representing the pixel values in the RGB image. Through the application of the convolutional layers, the image is successively transformed into a collection of feature maps, which are matrices of activation values. The output of the convolutional part is 2048 feature maps  
Figure 3: Procedure of the main experiment.  
of size 14 \(\times\) 14, which are condensed to 2048 single scalar activation values via average pooling. On these values, a fully connected layer with linear weights is applied. This so-called classification head outputs a scalar score for each of the 365 classes. Finally, a softmax function is used, which assigns a value between 0 and 100 to each class based on the respective score. This allows for an interpretation of the output as a percentage of the CNN's certainty that the image belongs to a particular class. The overall structure has approximately 58 \(\times\) 10\({}^{6}\) free parameters (i.e., weights), which allows for a relatively fast training compared to more sophisticated CNN.  
The network was trained with all images of the dataset (i.e., all 365 classes) for 10 epochs with the goal of minimizing the classification error and it achieved a Top5-accuracy of 85%. The training followed the standard procedure described by [PERSON] et al. [35]: The weights are initialized with random values. Subsequently, images with known class values are fed into the net and the difference between the actual and the desired output is backpropagated to the weights so that the weights are changed according to a cost function. This optimization process for the weights is repeated until overall classification performance converges. The problem of overfitting the CNN to the training images is addressed by validating the performance on test images that are not used in the training process. The model source code can be found in our OSF repository.  
To apply this ResNet structure to our 60 selected images with a resolution of 1024 \(\times\) 1024 pixels, these images were downsampled to 224 \(\times\) 224 in order to match the dimension of the input layer. Note that, for the training process, a total of 1.825 million nonsquare images with varying resolution from the Places365 dataset were used. To make these images compatible with the input layer, transformations such as resizing and random-cropping were applied, as described by [PERSON] et al. [35].