Proceedings of the Annual Meeting of the Cognitive Science Society

We develop an interactive model of human mem- ory called the Hebbian Recurrent Network ( HRN ) which integrates work in the mathematical modeling of memory with that in error correcting connection- ist networks. It incorporates the Matrix Model (Pike, 1984) into the Simple Recurrent Network (SRN, El- man, 1989). The result is an architecture which has the desirable memory characteristics of the matrix model such as low interference and massive general- ization, but which is able to learn appropriate en- codings for items, decision criteria and the control functions of memory which have traditionally been chosen a priori in the mathematical memory litera- ture. Simulations demonstrate that the HRN is well suited to a recognition task inspired by typical mem- ory peiradigms. In comparison to the SRN , the HRN is able to learn longer lists, and is not degraded sig- nificantly by increasing the vocabulary size.