Proceedings of the Annual Meeting of the Cognitive Science Society

challenging problem for connectionist models is the representation of varying-length sequences, e.g., the sequence of phonemes that compose a word. One representation that has been proposed involves encoding each sequence element with respect to its local context; this is known as a Wickelfeature representation. Handcrafted Wickelfeature representations suffer from a number of limitations, as pointed out by Pinker and Prince (1988). However, these limitations can be avoided if the representation is constructed with a priori knowledge of the set of possible sequences. This paper proposes a specialized connectionist network architecture and learning algorithm for the discovery of faithful Wickelfeature representations — ones that do not lose critical information about the sequence to be encoded. The architecture is applied to a simplified version of Rumclhart and McCleiland's (1986) verb past-tense model.