Previous investigations of the neural code for complex object shape have focused on two-dimensional pattern representation. This may be the primary mode for object vision given its simplicity and direct relation to the retinal image. In contrast, three-dimensional shape representation requires higher-dimensional coding derived from extensive computation. We found evidence for an explicit neural code for complex three-dimensional object shape. We used an evolutionary stimulus strategy and linear/nonlinear response models to characterize three-dimensional shape responses in macaque monkey inferotemporal cortex (IT). We found widespread tuning for three-dimensional spatial configurations of surface fragments characterized by their three-dimensional orientations and joint principal curvatures. Configural representation of three-dimensional shape could provide specific knowledge of object structure to support guidance of complex physical interactions and evaluation of object functionality and utility.
A primary goal in the study of object vision is to decipher the neural code for complex object shape. At the retinal level, object shape is represented isomorphically (that is, replicated point for point) across a two-dimensional map comprising approximately 106 pixels. This isomorphic representation is far too unwieldy and unstable (as a result of continual changes in object position and orientation) to be useful for object perception. The ventral pathway of visual cortex1, 2 must transform the isomorphic image into a compact, stable neural code that efficiently captures the shape information needed for identification and other aspects of object vision.