Eh. To me it looks like the authors extends "lines-as-edges" explanation of why we can understand line drawings to the claim that the visual cortex works almost only on edge detection, which is obviously bunk and so easily refuted by him. Then later he shows a shillouette image we can understand even though the edges themselves are not understandable, and somehow this is supposed to be evidence against lines as edges. No, it just means that we have other methods of understanding images: shillouette, color, texture etc.

In my opinion these kinds of arguments trying to decompose the brains functioning into a couple of distinct techniques become quite obviously pointless once you look at the activation patterns of neural networks. Just looking at the features that neurons detect throughout the different layers of an inage classifying neural network tells you more than these kinds of papers ever will.

You see that edges, shillouettes, circles, circles with holes, textures, shinyness patterns, grid patterns etc. up to complex patterns and then real things like heads or arms are detected.

There is some manifold of all the images a being is somehow likely to see on this world and it has a complicated structure. You can extract the major features of the geomatry of this manifold and you come across the usual patterns. At simple complexity you find things like edges and textures, at higher complexity things like eyes or appendages. You try to find these features in an image and hope one of them works well. Maybe edges work, maybe shillouettes work or maybe both or neither.

Look I know ANNs and NNs are quite different, but the experimental evidence with NNs shows that what I described above, a mix of feature detectors that just approximate the structures of the data to deeper and deeper detail and are just all somehow applied to see what works, is much more plausible than some constructed algorithm a neuroscientist or philosopher would write down.