Proceedings of the Annual Meeting of the Cognitive Science Society

This paper presents a connectionist architecture for deriving unknown role fillers in relational expressions. First, a restricted solution to the binding problem is presented which ensures systematicity in principle, and allows for sufficient compositionality so as to enable instantiation of shared variables in conjunctive expressions where the same object may fill a variety of roles in a variety of relations. Next, a more detailed architecture is explicated (an extension of McClelland's 1981 "Interactive Activation Competition" architecture) which allows for systematicity in practice while providing a training procedure for relations. Finally, results of the learning procedure for the Family Tree data set (Hinton, 1990) are used to demonstrate robust generalization in this domain.