Proceedings of the Annual Meeting of the Cognitive Science Society

We propose a novel framework for the analysis of learning al-gorithms that allows us to say when such algorithms can andcannot generalize certain patterns from training data to testdata. In particular we focus on situations where the rule thatmust be learned concerns two components of a stimulus beingidentical. We call such a basis for discrimination an identity-based rule. Identity-based rules have proven to be difficult orimpossible for certain types of learning algorithms to acquirefrom limited datasets. This is in contrast to human behaviouron similar tasks. Here we provide a framework for rigorouslyestablishing which learning algorithms will fail at generalizingidentity-based rules to novel stimuli. We use this frameworkto show that such algorithms are unable to generalize identity-based rules to novel inputs unless trained on virtually all possi-ble inputs. We demonstrate these results computationally witha multilayer feedforward neural network.