No abstract
Summary Background The motor system has the remarkable ability to not only 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 environment 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, finding 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 of 20-fold. Conclusions Understanding the mechanisms that determine the rate of motor adaptation may lead to the principled design of improved procedures for motor training and rehabilitation. Regimens designed to control environmental consistency and repetition during training may yield faster, more robust motor learning.
To reduce the risk of slip, grip force (GF) control includes a safety margin above the force level ordinarily sufficient for the expected load force (LF) dynamics. The current view is that this safety margin is based on the expected LF dynamics, amounting to a static safety factor like that often used in engineering design. More efficient control could be achieved, however, if the motor system reduces the safety margin when LF variability is low and increases it when this variability is high. Here we show that this is indeed the case by demonstrating that the human motor system sizes the GF safety margin in proportion to an internal estimate of LF variability to maintain a fixed statistical confidence against slip. In contrast to current models of GF control that neglect the variability of LF dynamics, we demonstrate that GF is threefold more sensitive to the SD than the expected value of LF dynamics, in line with the maintenance of a 3-sigma confidence level. We then show that a computational model of GF control that includes a variability-driven safety margin predicts highly asymmetric GF adaptation between increases versus decreases in load. We find clear experimental evidence for this asymmetry and show that it explains previously reported differences in how rapidly GFs and manipulatory forces adapt. This model further predicts bizarre nonmonotonic shapes for GF learning curves, which are faithfully borne out in our experimental data. Our findings establish a new role for environmental variability in the control of action.
1The human motor system can rapidly adapt its motor output in response to errors, reducing errors in 2 subsequent movements and thereby improving performance. It remains unclear, however, exactly what 3 mechanism supports this learning. It has been proposed that the implicit adaptation of motor commands 4 in response to errors occurs through updating an internal forward model which predicts the consequences 5 of motor commands. This model can then be used to select appropriate motor commands that will lead 6 to a desired outcome. Alternatively, however, it has been suggested that implicit adaptation might occur 7 by using errors to directly update an underlying policy (often referred to as an inverse model). There is 8currently little evidence to distinguish between these two possibilities. Here, we exploit the fact that these 9 two different learning architectures make opposing predictions about how people should behave under 10 mirror--reversed visual feedback, and find that peoples' behavior is consistent with direct policy learning, 11but not with forward--model--based learning. 12 13
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