Marker‐passing over Microfeatures: Towards a Hybrid Symbolic/Connectionist Model
James Hendler · Cognitive Science · 1989
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.