Abstract

In the past twenty years the connectionist approach to language development and learning has emerged as an alternative to traditional linguistic theories. This article introduces the connectionist paradigm by describing basic operating principles of neural network models as well as different network architectures. The application of neural network models to explanations for linguistic problems is illustrated by reviewing a number of models for different aspects of language development, from speech sound acquisition to the development of syntax. Two main benefits of the connectionist approach are highlighted: implemented models offer a high degree of specificity for a particular theory, and the explicit integration of a learning process into theory building allows for detailed investigation of the effect of the linguistic environment on a child. Issues regarding learnability or the need to assume innate and domain specific knowledge thus become an empirical question that can be answered by evaluating a model's performance.