Proceedings of the Annual Meeting of the Cognitive Science Society

Fluent human sequential behavior, such as that observed in speech production, ischaracterized by a high degree of parallelbm, fiizzy boundaries, and insensitivity toperturbations, hi this paper, I consider a theoretical treatment of sequential behaviorwhich is based on data from speech production. A networi^ is discussed which tsessentially a sequential machine built out of connectionist components. The networkrelies on distributed representations and a hi(^ degree of parallelism at the level of thecomponent processing units. These properties lead to parallelism at the level at whichwhole output vectors arise, and constraints must be imposed to make the performanceof the network more sequential. The sequential tr^ectories that are realized by thenetwork have dynamic properties that are analogous to those observed in networkswith point attractors (Hopfield, 1982): learned tn^ectories generalize, and attractorssuch as limit cycles can arise.