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Abstract
Humans can adapt their motor behaviors to deal with ongoing changes. To achieve this, the nervous system needs to estimate central variables for our movement based on past knowledge and new feedback, both of which are uncertain. In the Bayesian framework, rates of adaptation characterize how noisy feedback is in comparison to the uncertainty of the state estimate. The predictions of Bayesian models are intuitive: the nervous system should adapt slower when sensory feedback is more noisy and faster when its state estimate is more uncertain. Here we want to quantitatively understand how uncertainty in these two factors affects motor adaptation. In a hand reaching experiment we measured trial-by-trial adaptation to a randomly changing visual perturbation to characterize the way the nervous system handles uncertainty in state estimation and feedback. We found both qualitative predictions of Bayesian models confirmed. Our study provides evidence that the nervous system represents and uses uncertainty in state estimate and feedback during motor adaptation.
Highlights
To successfully perform sensorimotor tasks the nervous system needs to estimate the state of both the body and the environment; almost all real life estimation problems are plagued with uncertainty (von Helmholtz, 1863/1954)
As qualitatively predicted by Bayesian models, adaptation is significantly faster with less feedback uncertainty and with more state estimation uncertainty. These results suggest that the nervous system represents feedback uncertainty and state estimation uncertainty and uses knowledge of uncertainty during motor adaptation
The subject moved the hand to a target and the feedback about hand position was only shown briefly at the end of each movement. This visual feedback was perturbed spatially and subjects made corrections in the opposite direction of the perturbation during the trial. The size of this adaptation was calculated as a function of the uncertainty of the visual feedback (Experiment 1) or as a function of state estimation uncertainty (Experiment 2)
Summary
To successfully perform sensorimotor tasks the nervous system needs to estimate the state of both the body and the environment; almost all real life estimation problems are plagued with uncertainty (von Helmholtz, 1863/1954). The brain predicts changes of the state using knowledge of motor commands (efference copy) and the physics of the environment (McIntyre et al, 2001) possibly using an internal model of the task (Wolpert et al, 1995) This so-called forward prediction can be combined with sensory feedback to improve the accuracy of state estimation. This prediction is affected by the state of the body, which evolves in partially unpredictable ways and on many different time scales: neuromuscular noise contaminates muscle activity, muscles fatigue and recover frequently, and the body undergoes long-term changes such as diseases and development (Körding et al, 2007). Understanding the interaction between feedback uncertainty and state estimation uncertainty during sensorimotor tasks is one of the central problems in neural control of movement
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