AN ARTIFICIAL NEURAL-NETWORK ANALOG OF LEARNING IN AUTISM

An artificial neural network is simulated that shares formal qualitati ve similarities with the selective attention and generalization defici ts seen in people with autism. The model is based on neuropathological studies which suggest that affected individuals have either too few o r too many neuronal connections in various regions of the brain. In si mulations where the model was taught to discriminate children with aut ism from children with mental retardation, having too few simulated ne uronal connections led to relatively inferior discrimination of the tw o groups in a training set and, consequently, relatively inferior gene ralization of the discrimination to a novel test set. Tao many connect ions produced excellent discrimination but inferior generalization bec ause of overemphasis on details unique to the training set. It is conc luded that within the context of the current model, the neuropathologi cal observations that have been described in the literature are suffic ient to explain some of the unique pattern recognition and discriminat ion learning abilities seen in some people with autism as well as thei r problems with generalization and concept acquisition. The model gene rates testable hypotheses that have implications for understanding the pathogenesis, treatment, and phenomenology of autism.