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," said Reza Shadmehr, associate
professor of biomedical
engineering at the
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 4 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 4.75 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," Shadmehr said. "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 said 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," Shadmehr said. "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.
Related Web site
'PloS Biology' online
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