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Attentive Learning of Sequential Handwriting Movements: A Neural Network Model

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Sequence Learning

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1828))

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Abstract

Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children (Burns, 1962; Freeman, 1914; Iacoboni et al., 1999). Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an inverse relation between curvature and speed. How are such complex movements learned through attentive imitation? Novel movements may be made as a series of distinct segments that may be quite irregular both in space and time, but a practiced movement can be made smoothly, with a continuous, often bell-shaped, velocity profile. How does learning of sequential movements transform reactive imitation into predictive, automatic performance?

Supported in part by the Defense Advanced Research Projects Agency and the Office of Naval Research (DARPA/ONR N00014-95-1-0409), and by the National Science Foundation (NSF IRI-97-20333).

Supported in part by the Defense Advanced Research Projects Agency and the Office of Naval Research (DARPA/ONR N00014-95-1-0409, ONR N00014-92-J-1309), and by the National Institutes of Health (NIH 1-R29-DC02952-01).

Acknowledgments: The authors wish to thank Robin Amos and Diana Meyers for their valuable assistance in the preparation of the manuscript and graphics.

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  1. Department of Cognitive and Neural Systems, Center for Adaptive Systems, Boston University, 677 Beacon Street, Boston, MA, 02215, USA

    Stephen Grossberg & Rainer W. Paine

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  1. CECS Department, University of Missouri-Columbia, 201 Engineering Building West, Columbia, MO, 65211-2060, USA

    Ron Sun

  2. NEC Research Institute, 4 Independence Way, Princeton, NJ, 08540, USA

    C. Lee Giles

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Grossberg, S., Paine, R.W. (2000). Attentive Learning of Sequential Handwriting Movements: A Neural Network Model. In: Sun, R., Giles, C.L. (eds) Sequence Learning. Lecture Notes in Computer Science(), vol 1828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44565-X_16

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