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 psycho- linguistic processes of semantic representation, morphological generalization, and recovery from generalizations in the acquisition of reversive prefixes such as un- and dis-. These results attest to the utility of self-organizing neural networks in the study of language acquisition.