- Children usually spill if trying to drink from a full
cup, but adults rarely do. How we learn to almost automatically complete
complex movements -- like how to lift a cup and tip it so the liquid is
right at the edge when we're ready to drink -- is one of our brain's
mysterious
abilities.
-
- Now, by conducting experiments with robots and humans,
scientists at Johns Hopkins have solved part of this mystery and created
a new computer model that accurately reflects how the brain uses experience
to improve motor control.
-
- "Now we have a much better idea of how the brain
uses information from a variety of sources to create a model of the world
around us, and how errors modify that model and change subsequent
movements,"
says Reza Shadmehr, Ph.D., associate professor of biomedical engineering
at The Johns Hopkins University School of Medicine. "We don't just
know how to control objects around us, we have to learn how."
-
- The researchers' work is described in the November issue
of PLoS Biology, a new peer-reviewed journal launched by the Public Library
of Science.
-
- In the researchers' experiments, volunteers grasped the
end of a robot arm that precisely tracked their attempts to overcome
resistance
to reach a target, a stopping point 10 centimeters (about four inches)
away. While real-life resistance might be a paperweight or a full mug,
in these experiments the researchers programmed forces that would hinder
movement of the robot arm in predictable ways. To reach the target in the
allotted time (half a second), volunteers had to learn to balance those
forces.
-
- To provide the spatial information necessary for the
brain to create a model, or map, of forces expected in the
"world"
of the experiment, subjects started from one of three positions -- left,
center or right. For different groups of subjects, starting positions were
separated by as little as half a centimeter (less than a quarter inch)
up to 12 centimeters (about four and three-quarter inches).
-
- In initial trials without resistance, subjects moved
the robot arm in a straight line toward the target from each of the
starting
positions. In the next set of trials, subjects had to overcome resistance
when beginning from the left and right starting positions, but not from
the center.
-
- At first, the resistance pushed subjects' movements
aside.
With practice, most groups of subjects were able to reach the target in
a more-or-less straight line again, indicating they had learned to account
for the forces applied to the robot arm.
-
- However, if the starting positions were too close
together,
the brain failed to draw appropriate conclusions about where to expect
forces, even though visual cues reinforced whether the subject was starting
from the left, middle or right, the researchers report.
-
- "When the starting positions were just half a
centimeter
apart, the brain couldn't create an accurate picture of the forces -- even
with practice -- and improve movement," says Shadmehr. "When
the starting positions were farther apart, however, subjects more easily
adjusted to the resistance and generalized their experiences to anticipate
forces likely outside of the tested space."
-
- With information from these experiments, the scientists
developed a new computer model of how the brain uses experience to create
an impression of the world to apply to similar but new situations. The
new computer model matches observations from this and all previous
experiments,
and Shadmehr says it's the first to show that the brain multiplies, rather
than adds, electrical signals from nerve cells that convey the arm's
position
and velocity.
-
- "We know the brain transforms sensory cues -- the
arm's position and velocity, among other things -- into motor
commands,"
says Shadmehr. "Our model suggests that it does so by multiplying
signals of position and velocity to create what we call a gain field --
a system that allows the brain to predict appropriate movement for a wide
range of new but similar movements."
-
- In subsequent experiments with volunteers, the
researchers
proved correct two predictions based on the computer model: how people
generalize experience in the tests to other starting positions and under
what circumstances people most effectively learn to balance the
resistance.
-
- Authors on the paper are Shadmehr, graduate student Eun
Jung Hwang and postdoctoral fellows Opher Donchin and Maurice Smith, all
of Johns Hopkins. Funding for the study was provided by the National
Institute
of Neurological Diseases and Stroke, and by postdoctoral fellowships from
the National Institutes of Health and the Johns Hopkins Department of
Biomedical
Engineering.
-
- Editor's Note: The original news release can be found
- http://www.hopkinsmedicine.org/Press_releases/2003/11_25_03.html
-
- http://www.sciencedaily.com/releases/2003/11/031126065027.htm
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