Generalization, Representation, and Recovery in a Self-Organizing Feature-Map Model of Language Acquisition

This study explores the self-organizing neural network as a model of lexical and morphological acquisition. We examined issues of generalization, representation, and recovery in a multiple feature-map model. Our results indicate that self-organization and Hebbian learning are two important computational principles that can account for the psycholinguistic processes of semantic representation, morphological generalization, and recovery from generalizations in the acquisition of reversive prefixes such as unand dis-. These results attest to the utility of self-organizing neural networks in the study of language acquisition.