Abstract
Planar shape, i.e., the silhouette contour of a solid body, carries rich information important for object recognition, including both local (curvature) and global shape cues. While curvature-selective neurons have been identified in area V4 of primate, it remains unclear whether a) curvature is the best way to characterize the shape selectivity of these neurons and b) whether selectivity is limited to local shape. Here we employ a unique array of shape stimuli to dissociate tuning for local and global shape properties. These stimuli have been used previously to identify an intriguing congruence between the curvature statistics of natural shape and the population response of shape-selective V4 neurons. However, this evidence is indirect, as neural curvature selectivity was not analyzed at the single-neuron level. To address these limitations, we first assess how model neurons, trained on single-unit V4 responses, encode the curvatures of various shape stimuli. A mutual information analysis reveals that these neurons are tuned to extract information more efficiently from shapes with natural curvature distributions, indicating a tuning to the ecological statistics of curvature. Second, to more directly measure neuronal tuning for natural shape we recorded activity from area V4 of a juvenile Macaca nemestrina observing natural and synthetic shapes. Consistent with our model neuron analysis, we found that synthetic shapes with natural curvature distributions elicited stronger responses than synthetic shapes with more random distributions, despite having much lower entropy. Remarkably, we also found that natural shapes elicited stronger V4 responses than synthetic shapes with matching curvature statistics, indicating selectivity for non-local shape features. Together, our findings demonstrate for the first time that V4 neurons are tuned to the ecological statistics of both local and non-local object shape not explained by existing models of V4 shape selectivity.