| Abstract: | Spreading activation, in the form of computer models and cognitive theories, has recently been undergoing a resurgence of interest in the cognitive science and AI communities. Two different types of cognitive models have been proposed to explain the activation spreading results. One approach, that of marker-passing, concentrates on the spreading of symbolic information through an associative knowledge representation. The other technique, including the work in local connectionism, has focused on the passage of numeric information through a network. In this article, it is shown that these two techniques can be merged. The implementation of a mechanism in which a local-connectionist-like model is integrated with a symbolic marker-passer is described and shows that the combined system is more powerful than either of the separate models alone. Finally, some early steps toward a hybrid model in which a distributed network is used to learn the microfeatures is described. |
