How Does Our Motor System Determine Its Learning Rate?

• Published in: PloS one Vol. 7; no. 11; p. e49373
• Main Author: van Beers, Robert J.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 12.11.2012; Public Library of Science (PLoS)
• Subjects: Adaptation; Adolescent; Algorithms; Biology; Brain; Computer Science; Decision theory; Error correction; Feedback; Female; Humans; Kalman filter; Kalman filters; Learning; Learning - physiology; Learning curves; Male; Mathematics; Medicine; Models, Biological; Motor Activity - physiology; Motor skill learning; Nervous system; Noise; Optimization; Predictions; Reliability aspects; State estimation; Studies; Time Factors; Time series; Visual perception; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0049373

Motor learning is driven by movement errors. The speed of learning can be quantified by the learning rate, which is the proportion of an error that is corrected for in the planning of the next movement. Previous studies have shown that the learning rate depends on the reliability of the error signal and on the uncertainty of the motor system's own state. These dependences are in agreement with the predictions of the Kalman filter, which is a state estimator that can be used to determine the optimal learning rate for each movement such that the expected movement error is minimized. Here we test whether not only the average behaviour is optimal, as the previous studies showed, but if the learning rate is chosen optimally in every individual movement. Subjects made repeated movements to visual targets with their unseen hand. They received visual feedback about their endpoint error immediately after each movement. The reliability of these error-signals was varied across three conditions. The results are inconsistent with the predictions of the Kalman filter because correction for large errors in the beginning of a series of movements to a fixed target was not as fast as predicted and the learning rates for the extent and the direction of the movements did not differ in the way predicted by the Kalman filter. Instead, a simpler model that uses the same learning rate for all movements with the same error-signal reliability can explain the data. We conclude that our brain does not apply state estimation to determine the optimal planning correction for every individual movement, but it employs a simpler strategy of using a fixed learning rate for all movements with the same level of error-signal reliability.