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Learning the English past tense is characterized by a U-shaped learning function for the irregular verbs. Existing cognitive models often rely on a sudden increase in vocabulary, a high token-frequency of regular verbs, and complicated schemes of feedback in order to model this phenomenon. All these assumptions are at odds with empirical data. In this paper a hybrid ACT-R model is presented that shows U-shaped learning without direct feedback, changes in vocabulary, or unrealistically high rates of regular verbs. The model is capable of learning the default rule, even if regular forms are infrequent. It can also help explore the question of why there is a distinction between regular and irregular verbs in the first place, by examining the costs and benefits of both types of verbs. (C) 2002 Elsevier Science B.V. All rights reserved. |
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