A Self-Organizing Connectionist Model of Early Word Production

In this paper we present DevLex-II, a self-organizing neural network model of early word production. It consists of three self-organizing feature maps (a semantic layer, a phonological layer and a phonemic layer) that are connected via associative links trained by Hebbian learning. We use this model to simulate the early stages of lexical acquisition in children. The simulating results indicate a number of important effects in determining the timing and function of children’s word production, such as word frequency and word length effects. In addition, results from lesioned models indicate developmental plasticity in the network’s recovery from damage. Plasticity occurs at early stages, and changes with time in a non-monotonic and nonlinear fashion. These simulated patterns are due to the nonlinear dynamic properties of the network and match up with data from empirical studies of children.