Reconstruction of a nonface object (melon) using tensorfaces (rank 8). (ai) Original melon image. (ii) Reconstructed melon using tensorfaces derived from human face sample set. (iii) Ideal reconstruction of melon, which should be null for a specialized face processing system. (b) Reconstruction error for melon compared to other stimuli. Human faces' response shows the mean and standard deviation for 128 faces in the sample set. Other response values are for a single stimulus image. (c) Average response magnitudes of tensorfaces to face and nonface stimuli, which are very similar. This similarity leads tensorface populations to create spurious reconstructions of nonface objects. Shows mean responses of 100 tensorfaces of rank 8 to 512 faces and 512 nonface objects. (d) To prevent spurious reconstructions of nonface stimuli, the face identification stage (tensorfaces) requires a nonlinearity. Two possible organizations for such nonlinearity are (i) sequential nonlinearity, with nonlinear face detector stage preceding linear face identification stage in separate neurons, and (ii) parallel nonlinearity with nonlinear spatial interactions present within receptive fields of single face cells. In this case, face detection and face identification occur concurrently within single face cells.
This site uses cookies. By continuing to use our website, you are agreeing to our privacy policy. No content on this site may be used to train artificial intelligence systems without permission in writing from the MIT Press.