As long as two or three geons in their specified relations can be extracted from the image, entry-level classification will almost always be successful despite drastic variations in the object's silhouette, specific contours, and occlusion of large regions of the object. Other experiments showed that, indeed, the invariant properties of edges are far more important in object classification than metric properties, properties that do not remain invariant under rotation in depth, such as degree of curvature or aspect ratio. The dorsal pathway is assumed to contribute to old-new recognition memory judgments, which provides an account of why such judgments show none of the invariances characteristic of object classification. Whereas an arrangement of geons appears to suffice for object recognition, a similarity space that preserves the metrics implicit in the original spatial filter activations may suffice for face recognition. We are presently initiating fMRI studies on a theory of perceptual and cognitive pleasure.
People generally seek out, with every visual fixation, new but interpretable information.
Lorincz A. Inferior temporal neurons show greater sensitivity to nonaccidental than metric differences. Biederman I. Localizing the cortical region mediating visual awareness of object identity. Biederman I, Kalocsai P. Neurocomputational bases of object and face recognition. Hummel JE, Biederman I. Dynamic binding in a neural network for shape recognition. A Theory of Human Image Understanding.