Luis Nicolas Gonzalez Castro1, Alkis M Hadjiosif2, Matthew A Hemphill2, Maurice A Smith3. 1. Neuromotor Control Laboratory, Harvard School of Engineering and Applied Sciences, Cambridge, MA 02138, USA; Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02138, USA; Harvard Medical School, Boston, MA 02115, USA. 2. Neuromotor Control Laboratory, Harvard School of Engineering and Applied Sciences, Cambridge, MA 02138, USA. 3. Neuromotor Control Laboratory, Harvard School of Engineering and Applied Sciences, Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA. Electronic address: mas@seas.harvard.edu.
Abstract
BACKGROUND: The motor system has the remarkable ability not only to learn but also to learn how fast it should learn. However, the mechanisms behind this ability are not well understood. Previous studies have posited that the rate of adaptation in a given environment is determined by Bayesian sensorimotor integration based on the amount of variability in the state of the environment. However, experimental results have failed to support several predictions of this theory. RESULTS: We show that the rate at which the motor system adapts to changes in the environment is primarily determined not by the degree to which environmental change occurs but by the degree to which the changes that do occur persist from one movement to the next, i.e., the consistency of the environment. We demonstrate a striking double dissociation whereby feedback response strength is predicted by environmental variability rather than consistency, whereas adaptation rate is predicted by environmental consistency rather than variability. We proceed to elucidate the role of stimulus repetition in speeding up adaptation and find that repetition can greatly potentiate the effect of consistency, although unlike consistency, repetition alone does not increase adaptation rate. By leveraging this understanding, we demonstrate that the rate of motor adaptation can be modulated over a range that encompasses a 20-fold increase from lowest to highest. CONCLUSIONS: Understanding the mechanisms that determine the rate of motor adaptation could lead to the principled design of improved procedures for motor training and rehabilitation. Regimens designed to control environmental consistency and repetition during training might yield faster, more robust motor learning.
BACKGROUND: The motor system has the remarkable ability not only to learn but also to learn how fast it should learn. However, the mechanisms behind this ability are not well understood. Previous studies have posited that the rate of adaptation in a given environment is determined by Bayesian sensorimotor integration based on the amount of variability in the state of the environment. However, experimental results have failed to support several predictions of this theory. RESULTS: We show that the rate at which the motor system adapts to changes in the environment is primarily determined not by the degree to which environmental change occurs but by the degree to which the changes that do occur persist from one movement to the next, i.e., the consistency of the environment. We demonstrate a striking double dissociation whereby feedback response strength is predicted by environmental variability rather than consistency, whereas adaptation rate is predicted by environmental consistency rather than variability. We proceed to elucidate the role of stimulus repetition in speeding up adaptation and find that repetition can greatly potentiate the effect of consistency, although unlike consistency, repetition alone does not increase adaptation rate. By leveraging this understanding, we demonstrate that the rate of motor adaptation can be modulated over a range that encompasses a 20-fold increase from lowest to highest. CONCLUSIONS: Understanding the mechanisms that determine the rate of motor adaptation could lead to the principled design of improved procedures for motor training and rehabilitation. Regimens designed to control environmental consistency and repetition during training might yield faster, more robust motor learning.