This problem is naturally not unique to our biological brains. In fact, despite the excellent functioning of CNNs for many visual tasks, this problem of neural network pareidolia is one that computer vision researchers are always trying to solve. As we noted, CNNs learn to classify images through learning an assortment of filters that pick up useful features, capable of breaking down the input image in a probabilistic manner. However, the features learned by these filters do not represent all the information present in a given image. The orientation of these features, with respect to one another, matters just as much! The presence of two eyes, lips, and a nose does not inherently constitute the essence of a face. Rather, it's the spatial arrangement of these elements within an image that makes the face in question:
