Proceedings of the Annual Meeting of the Cognitive Science Society

A technique is described whereby the output of ACME, a localist constraint satisfaction model of analogical mapping (Holyoak & Thagard, 1989) is used to constrain the distributed representations of domain objects developed by Hinton's (1986) multilayer model of prepositional learning. In a series of computational experiments, the ability of Hinton's network to transfer knowledge from a source domain to a target domain is systematically examined by first training the model on the full set of propositions representing a source domain together with a subset of propositions representing an isomorphic target domain, and then testing the network on the untrained target propositions. Without additional constraints, basic gradient descent can recover only a negligible proportion of the untrained propositions. Comparison of simulation results using various combinations of the distributed mapping technique and weight decay, indicate that general purpose network optimization techniques may go some ways towards improving the transfer performance of distributed network models. However, performance can be improved substantially more when optimization techniques are combined with the distributed representation mapping technique.