Context Estimation for Sensorimotor Control

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Abstract
Human motor behavior is remarkably accurate and appropriate even though the properties of our own bodies as well as the objects we interact with vary over time. To adjust appropriately, the motor system has to estimate the context, that is the properties of objects in the world and the prevailing environmental conditions. Here we show that to determine the current context the CNS uses information from both prior knowledge of how the context might evolve over time and from the comparison of predicted and actual sensory feedback. We show that these two sources of information may be modeled within the CNS and combined to derive an accurate estimate of the context which adjusts motor command selection. This provides a novel probabilistic framework for sensorimotor control.