| Abstract: | This article provides an overview of a dynamical systems approach to visual word recognition. In this approach, the dynamics of word recognition are characterized in terms of a connectionist network model. According to this model, seeing a word results in changes in the pattern of activation over the nodes in the lexical network such that, over time, the network moves into an attractor state representing the orthographic, phonological, and semantic properties of that word. At a slower timescale, a learning process modifies the strengths of the connections among the nodes in a way that attunes the network to the statistical regularities in its environment. This view of word identification accommodates a wide body of empirical results, a representative sampling of which is discussed here. Finally, the article closes with a discussion of some of the theoretical issues that should be addressed as the dynamical approach continues to develop. |
