  Object detection and recognition are fundamental functions underlying the
success of species. Because the appearance of an object exhibits a large
variability, the brain has to group these different stimuli under the same
object identity, a process of generalization. Does the process of
generalization follow some general principles or is it an ad-hoc
"bag-of-tricks"? The Universal Law of Generalization provided evidence that
generalization follows similar properties across a variety of species and
tasks. Here we test the hypothesis that the internal representations underlying
generalization reflect the natural properties of object detection and
recognition in our environment rather than the specifics of the system solving
these problems. By training a deep-neural-network with images of "clear" and
"camouflaged" animals, we found that with a proper choice of category
prototypes, the generalization functions are monotone decreasing, similar to
the generalization functions of biological systems. Our findings support the
hypothesis of the study.
