Computer vision systems are, on most counts, poor performers, when compared to their biological counterparts. The reason for this may be that computer vision is handicapped by an unreasonable assumption regarding what it means to see, which became prevalent as the notions of intrinsic images and of representation by reconstruction took over the field in the late 1970’s. Learning from biological vision may help us to overcome this handicap.

There is a long and distinguished tradition in philosophy and psychology according to which the mind’s fundamental, foundational connection to the world is made by connecting perceptually to features of objects. On this picture, which we’ll call feature prioritarianism, minds like ours first make contact with the colors, shapes, and sizes of distal items, and then, only on the basis of the representations so obtained, build up representations of the objects that bear these features. The feature priority view maintains, then, (...) that our perception/knowledge of objects asymmetrically depends on our perception/knowledge of simple features. This paper has two aims. First, we will present evidence, drawn from a variety of perceptual effects, that feature prioritarianism cannot be true, since there are cases that speak against the priority of feature representations in perceptual processing. Instead, we claim that the evidence supports an alternative —-and more complex—- no-priority view. Second, we will offer a framework for a no-priority view that both captures the cases we cite and provides a more sensible architecture in which to understand a variety of productive projects in perceptual science, and show how the framework cross-cuts some recent discussions in philosophy of perception. (shrink)