Originally published October 10 2005
Researchers emphasize the role of environment in motor training
by Mike Adams, the Health Ranger, NaturalNews Editor
Publishing their findings in Nature Neuroscience, Kurt Thoroughman, Washington University assistant professor of biomedical engineering, and Jordan Taylor, Washington University doctoral student in biomedical engineering, found in their research of motor training that the complexity of the learning environment was more important than previous theories have suggested.
Kurt Thoroughman, Ph.D., Washington University assistant professor of biomedical engineering, and Jordan Taylor, Washington University doctoral student in biomedical engineering, tested a dozen volunteers who played a video game that involved a robotic arm.
Thoroughman and Taylor found that the subjects learned different levels of the game in just 20 minutes of training over different environmental difficulties.
Human subjects made reaching movements while holding a robotic arm with perturbing forces that changed directions at the same rate, twice as fast, or four times as fast as the direction of movement, therefore exposing subjects to environments of increasing complexity across movement space.
Subjects learned all three environments and learned the low and medium complexity environments equally well.
Specifically, subjects lessened their movement-by-movement adaptation and narrowed the spatial extent of generalization to match the environmental complexity, showing that people can rapidly reshape the transformation of sense into motor prediction to best learn a new movement task.
"The big picture is that in a single sitting people changed their expectations of the complexity of the world, in that a single movement's experience could be generalized very broadly or else generalized very narrowly.
We've shown for the first time that the learning process itself is flexible.
The researchers published their findings in the Sept. 28, 2005 issue of Nature Neuroscience.
Thoroughman and Taylor then modeled this adaptation using a neural network.
They found that, to mimic human behavior, the modeled neuronal tuning of movement space needed to narrow and reduce gain with increased environmental complexity.
According to Thoroughman, prominent theories of neural computation have hypothesized that neuronal tuning of space, which determines generalization, should remained fixed during learning so that a combination of neuronal outputs can underlie adaptation simply and flexibly.
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