Demonstrates how innate representational capabilities for serial and temporal structure of language could arise from a common neural architecture, distinct from that required for the representation of abstract structure, and provides a predictive testable model of the initial computational state of the language learner. (Author/VWL)

Describes a connectionist model accounting for newborn infants' ability to finely discriminate almost all human speech contrasts and the fact that their phonemic category boundaries are identical, even for phonemes outside their target language. The model posits an innately guided learning in which an artificial neural network is stored in a…

Examines Nickels's claim that interactive models of lexical access in speech production cannot account for naming data from a group of anomic patients. This paper reiterates that the behavior of connectionist models is not easily predictable without running the appropriate simulations, and discusses the role of frequency in lexical access in…