a neural model of learning to write

[Stephen Grossberg]

The following article can be accessed at

http://www.cns.bu.edu/Profiles/Grossberg

Paper copies can also be gotten by writing Mr. Robin Amos, Department of
Cognitive and Neural Systems, Boston University, 677 Beacon Street, Boston,
MA 02215 or amos at cns.bu.edu.

Grossberg S. and Paine R. W. (2000). A neural model of corticocerebellar
interactions during attentive imitation and predictive learning of
sequential handwriting movement.

Special Issue of Neural Networks on "The global Brain: Imaging and Neural
Modeling", in press. A  preliminary version is available as Boston
University Technical Report, CAS/CNS TR-2000-009. The paper is available in
PDF format GrossbergPaine2000.pdf, or in Gzipped postscript format
GrossbergPaine2000.ps.gz.

ABSTRACT

Much sensory-motor behavior develops through imitation, as during the
learning of  handwriting by children.  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, but a practiced movement can be
made smoothly, with a continuous, often bell-shaped, velocity profile. How
does learning of complex movements transform reactive imitation into
predictive, automatic performance?  A neural model is developed which
suggests how parietal and motor cortical mechanisms, such as difference
vector encoding, interact with adaptively-timed, predictive cerebellar
learning during movement imitation and predictive performance. To initiate
movement, visual attention shifts along the shape to be imitated and
generates vector movement using motor cortical cells.  During such an
imitative movement, cerebellar Purkinje cells with a spectrum of delayed
response profiles sample and learn the changing directional information
and, in turn, send that learned information back to the cortex and
eventually to the muscle synergies involved.  If the imitative movement
deviates from an attentional focus around a shape to be imitated, the
visual system shifts attention, and may make an eye movement, back to the
shape, thereby providing corrective directional information to the arm
movement system.  This imitative movement cycle repeats until the
corticocerebellar system can accurately drive the movement based on memory
alone.  A cortical working memory buffer transiently stores the cerebellar
output and releases it at a variable rate, allowing speed scaling of
learned movements which is limited by the rate of cerebellar memory
readout.  Movements can be learned at variable speeds if the density of the
spectrum of delayed cellular responses in the cerebellum varies with speed.
Learning at slower speeds facilitates learning at faster speeds. Size can
be varied after learning while keeping the movement duration constant
(isochrony).  Context-effects arise from the overlap of cerebellar memory
outputs.  The model is used to simulate key
psychophysical and neural data about learning to make curved movements,
including a decrease in writing time as learning progresses; generation of
unimodal, bell-shaped velocity profiles for each movement synergy; size and
speed scaling with preservation of the letter shape and the shapes of the
velocity profiles; an inverse relation between curvature and
tangential velocity; and a Two-Thirds Power Law relation between angular
velocity and curvature.