We present a neural network model of learning and processing the English past tense that is based on the notion that experience-dependent cortical development is a core aspect of cognitive development. During learning the model adds and removes units and connections to develop a task-specific final architecture. The model provides an integrated…

We present a neurocomputational model with self-organizing maps that accounts for the emergence of taxonomic responding and fast mapping in early word learning, as well as a rapid increase in the rate of acquisition of words observed in late infancy. The quality and efficiency of generalization of word-object associations is directly related to…