Slides (PDF, 1 Mb)
Many modern theories of computational neuroscience view the brain --- or, at least, some parts of it --- as a probabilistic inference mechanism. Vision, in particular, entails converting visual stimuli into probabilistic neural encodings of scene interpretations, including both low-level and high-level features. Probabilistic reasoning seems to be essential to building highly-automatic computer vision systems as well.
Although no one *really* knows what's going on in the brain, I argue that such theories provide insights into the visual arts (including photography), and suggest how we might one day be able to build a computational neuroscience theory of art (or, at least, some aspects of art). Given such a model (or simplified versions of it, some of which are available today), we could use these models to build algorithms to help enable and improve artistic applications and photography.