Distributed representations of semantic knowledge in the brain.

Category-specific language impairments have been postulated to require the existence of an explicit category organization within semantic memory. However, it may be possible to demonstrate analytically that this is not necessary. We hypothesize that category-specific organization can emerge from perceptual, functional, and associative feature information about objects that is maintained in order to process language. In this paper, we conduct several experiments to test the computational validity of this hypothesis. Physical objects were encoded in terms of semantic features, based on basic perceptual and motor modalities and higher level knowledge of function, for use in artificial neural networks. Mathematical methods were used to analyse the encodings and the neural networks. The results demonstrate the emergence of semantic categories in the networks, although such information was not preprogrammed. We conclude that category-specific language organization can emerge from the inherent nature of semantic features themselves, and does not require special internal categorical organization of semantic memory.