Proceedings of the Annual Meeting of the Cognitive Science Society

One argument in favor of current PDP models has been that the availability of "hidden units" allows the system to create an internal representation of the input domain, and to use this representation in producing output weights. The "microfeatures" learned by sets of hidden units, it has been argued, provide an alternative to symbols for certain reasoning tasks. In this paper we try to further this argument by demonstrating several results that indicate that such representations are formed. We show that by using a spreading activation model over the weights learned by networks trained via backpropagation, we can model certain cognitive effects. In particular, we show some results in the areas of modeling phoneme confusions and handling word-sense disambiguation, and some preliminary results demonstrating that priming effects can be modeled by this activation spreading approach.