Proceedings of the Annual Meeting of the Cognitive Science Society

This paper presents a connectionist model of motor learning in which performance becomes more and more efficient by "chunking" output sequences, organizing small action components into increasingly large structures. The model consists cf two sequential networks: one that maps a stationary representation of an intention to a sequence of action specifications or action plans, and one that maps an action plan to a sequence of action components. As the network is trained to produce output sequences faster and faster, the units that represent the action plans gradually discover representational formats that can encode larger and larger chunks of subsequences.The model also shows digraph frequency effects similar to that observed in typewriting, and it generates capture errors similar to that observed in human actions.Organization of