But I think "feature detectors" are exactly what the earlier comment was referring to, e.g. a Gabor-wavelet-style decomposition of the retinal image. Deep learning systems have to learn those; we're born with them.
Well, that's one theory. But I think it will turn out to be a lot more complex than that. One thing that I haven't seen anyone pay much attention to is feature detectors in the time domain, which we clearly have. We notice movement as a fundamental feature. Our movement detectors can actually be triggered by static images [1]. One of the ways we distinguish dogs from cats (I believe) is by the way they move. It would be a very interesting experiment to use CGI to make a dog move like a cat and vice versa and see how those are perceived.