A Neurocomputational Account of Taxonomic Responding and Fast Mapping in Early Word Learning

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 the quality of prelexical, categorical representations in the model. We show how synaptogenesis supports coherent generalization of word-object associations and show that later synaptic pruning minimizes metabolic costs without being detrimental to word learning. The role played by joint-attentional activities is identified in the model, both at the level of selecting efficient cross-modal synapses and at the behavioral level, by accelerating and refining overall vocabulary acquisition. The model can account for the qualitative shift in the way infants use words, from an associative to a referential-like use, for the pattern of overextension errors in production and comprehension observed during early childhood and typicality effects observed in lexical development. Interesting by-products of the model include a potential explanation of the shift from prototype to exemplar-based effects reported for adult category formation, an account of mispronunciation effects in early lexical development, and extendability to include accounts of individual differences in lexical development and specific disorders such as Williams syndrome. The model demonstrates how an established constraint on lexical learning, which has often been regarded as domain-specific. can emerge from domain-general learning principles that are simultaneously biologically, psychologically, and socially plausible.