The visual areas of the primate cerebral cortex provide distinct representations of the visual world, each with a distinct function and topographic representation. Neurons in primary visual cortex respond selectively to orientation and spatial frequency, whereas neurons in inferotemporal and lateral occipital areas respond selectively to complex objects. But the areas in between, in particular V2 and V4, have been more difficult to differentiate on functional grounds. Bottom-up receptive field mapping is ineffective because these neurons respond poorly to artificial stimuli, and top-down approaches that employ the selection of "interesting" stimuli suffer from the curse of dimensionality and the arbitrariness of the stimulus ensemble. I will describe an alternative approach, in which we use the statistics of natural texture images and computational principles of hierarchical coding to generate controlled, but naturalistic stimuli, and then use these images as targeted experimental stimuli in electrophysiological and fMRI experiments. Responses to such "naturalistic" stimuli reliably differentiate neurons in area V2 from those in V1, in both single-units recorded from macaque monkey, and in humans as measured using fMRI. In humans, responses to these stimuli, alongside responses to both simpler and more complex stimuli, suggest a simple functional account of the visual cortical cascade: Whereas V1 encodes basic spectral properties, V2, V3, and to some extent V4 represent the higher-order statistics of textures. Downstream areas capture the kinds of global structures that are unique to images of natural scenes and objects.
Meeting abstract presented at VSS 2014