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Repeating the same task 'not the best way to learn a new move'

June 24, 2011 - Washington

A new study at the Harvard School of Engineering and Applied Sciences (SEAS) has contradicted common assumption about how the body learns to make accurate movements.

Instead, it suggested that simple task repetition might not be the most efficient way for the brain to learn a new motor skill, but adjusting the task to different environments.

In essence, when people make an imperfect movement during practice, their brains learn less about what they plan to do than about what they actually do.

With that in mind, the researchers led by Maurice Smith propose a new approach to neurological rehabilitation: one that continually adjusts the goals of practice movements so that systematic differences (errors) between these movements and the intended motion can be reduced.

In order to perform any movement accurately-whether that means reaching for a glass of juice without knocking it over, or swimming across a pool without sinking-the brain has to learn exactly which muscles to activate, and in what manner.

The muscle activation required for a given movement depends on the environment.

"Individuals learn to accommodate varying physical dynamics, making errors when encountering new situations, but quickly improving with practice. The brain builds internal models of these dynamics, producing patterns of muscle activation that account for external conditions," explained Smith, an Assistant Professor of Biomedical Engineering at SEAS.

Yet, for people who have suffered neurological damage, such as victims of stroke, the simplest of actions can be difficult to relearn.

"For a simple reaching task, we found that when we adjusted the target position from one trial to the next, so that adaptations could build up around the intended movement, our subjects learned 50 percent faster than when they just practiced the intended movement," said lead author Nicolas Gonzalez Castro, a graduate student in Smith's Neuromotor Control Lab

The findings reveal that the brain is "wired" to maximize stability, a concept that has been essential in the development of algorithms for machine learning, but perhaps underplayed in scientists' previous understanding of human learning.

The study has been published in PLoS Computational Biology.


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