Answering the connectionist challenge: A symbolic model of learning the past tenses of English verbs

Supporters of eliminative connectionism have argued for a pattern association based explanation of language learning and language processing They deny that explicit rules and symbolic representations play any role in language processing and cognition in general Their argument is based to a large extent on two articial neural network ANN models that are claimed to be able to learn the past tenses of English verbs Rumelhart and McClelland MacWhinney and Leinbach In this article we critically review Rumelhart and McClelland s as well as MacWhinney and Leinbach s ANNmodels and conclude that they do not succeed in the assigned task of learning the past tenses of English verbs In order to answer their challenge to the symbolic processing approach we present our Symbolic Pattern Associator SPA a general purpose pattern associator that can learn to associate arbitrary discrete patterns We carried out several experiments with the SPA using the same set of verbs that was used in MacWhinney and Leinbac