Abstract
Efficient human motor control is characterized by an extensive use of joint impedance modulation, which is achieved by co-contracting antagonistic muscles in a way that is beneficial to the specific task. While there is much experimental evidence available that the nervous system employs such strategies, no generally-valid computational model of impedance control derived from first principles has been proposed so far. Here we develop a new impedance control model for antagonistic limb systems which is based on a minimization of uncertainties in the internal model predictions. In contrast to previously proposed models, our framework predicts a wide range of impedance control patterns, during stationary and adaptive tasks. This indicates that many well-known impedance control phenomena naturally emerge from the first principles of a stochastic optimization process that minimizes for internal model prediction uncertainties, along with energy and accuracy demands. The insights from this computational model could be used to interpret existing experimental impedance control data from the viewpoint of optimality or could even govern the design of future experiments based on principles of internal model uncertainty.
Highlights
Suppose you are holding an umbrella in a stable upright position on a rainy day
Dynamics learning further provides us with means to model prediction uncertainty based on experienced stochastic movement data; we provide evidence that, in conjunction with an appropriate antagonistic arm and realistic motor variability model, impedance control emerges from a stochastic optimization process that minimizes these prediction uncertainties of the learned internal model
Internal models play a key role in efficient human motor control [23] and it has been suggested that the motor system forms an internal forward dynamics model to compensate for delays, uncertainty of sensory feedback, and environmental changes in a predictive fashion [24,25]
Summary
Suppose you are holding an umbrella in a stable upright position on a rainy day. This is an effortless task, if suddenly a seemingly random wind gust perturbs the umbrella, you will typically stiffen up your arm trying to reduce the effects of the ‘‘unpredictable’’ perturbation. Coming back to our umbrella example: If over time you realize the wind keeps blowing from the same direction, you expectedly will become more certain about the wind’s destabilizing effect on your arm and you will gradually reduce the stiffness and you will possibly try to place the umbrella in a new stable position. This simple example shows intuitively how co-activation is linked to uncertainties that you may experience in your limb dynamics, and the main objective in this work is to develop a computational model that unifies the concepts of learning, uncertainty and optimality in order to understand impedance control in a principled fashion
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