The computer model, Shadmehr says, almost precisely duplicates the results of experiments that tested the ability of monkeys to visually track rapid flashes of light. Experiments using such rapid eye movements, or saccades, are a staple in studying how the brain controls movement.
Initially, the animal learner made large errors, but also stored the information about its mistakes in a memory bank so it could adapt and make more accurate predictions the next time around. Every time the learner repeated the task, it would sift through the prior knowledge in its memory banks and make a prediction on how to move, which in turn would also be memorized. While short term memory was periodically purged, repeated errors were transferred to a long term memory bank.
The computer learner was tasked with “looking” at a spot of light. Then all the lights were turned off. The spot of light was turned on again and the computer learner was again asked to look at that same spot. The learner’s speed and pattern in adapting its movements matched the experimental results of the monkeys almost perfectly. “We found that this Bayesian model can explain almost all of the phenomena we observe in regard to learning motor movements,” says Shadmehr.
Beyond possible use in helping stroke patients, the new tool might also be applied to better understand how we learn language, develop ideas and make memories. “How we learn to think operates under many of the same principles as how we learn to move,” Shadmehr says.
Source:Johns Hopkins Medical Institutions