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Computational motor control: feedback and accuracy
Author(s) -
Guigon Emmanuel,
Baraduc Pierre,
Desmurget Michel
Publication year - 2008
Publication title -
european journal of neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.346
H-Index - 206
eISSN - 1460-9568
pISSN - 0953-816X
DOI - 10.1111/j.1460-9568.2008.06028.x
Subject(s) - motor control , feedback control , psychology , control (management) , computer science , neuroscience , cognitive science , control theory (sociology) , control engineering , artificial intelligence , engineering
Speed/accuracy trade‐off is a ubiquitous phenomenon in motor behaviour, which has been ascribed to the presence of signal‐dependent noise (SDN) in motor commands. Although this explanation can provide a quantitative account of many aspects of motor variability, including Fitts' law, the fact that this law is frequently violated, e.g. during the acquisition of new motor skills, remains unexplained. Here, we describe a principled approach to the influence of noise on motor behaviour, in which motor variability results from the interplay between sensory and motor execution noises in an optimal feedback‐controlled system. In this framework, we first show that Fitts' law arises due to signal‐dependent motor noise (SDN m ) when sensory (proprioceptive) noise is low, e.g. under visual feedback. Then we show that the terminal variability of non‐visually guided movement can be explained by the presence of signal‐dependent proprioceptive noise. Finally, we show that movement accuracy can be controlled by opposite changes in signal‐dependent sensory (SDN s ) and SDN m , a phenomenon that could be ascribed to muscular co‐contraction. As the model also explains kinematics, kinetics, muscular and neural characteristics of reaching movements, it provides a unified framework to address motor variability.