@article{ref1,
title="Bayesian integration and non-linear feedback control in a full-body motor task",
journal="PLoS computational biology",
year="2009",
author="Stevenson, Ian H. and Fernandes, Hugo L. and Vilares, Iris and Wei, Kunlin and Körding, Konrad P.",
volume="5",
number="12",
pages="e1000629-e1000629",
abstract="A large number of experiments have asked to what degree human reaching movements can be understood as being close to optimal in a statistical sense. However, little is known about whether these principles are relevant for other classes of movements. Here we analyzed movement in a task that is similar to surfing or snowboarding. Human subjects stand on a force plate that measures their center of pressure. This center of pressure affects the acceleration of a cursor that is displayed in a noisy fashion (as a cloud of dots) on a projection screen while the subject is incentivized to keep the cursor close to a fixed position. We find that salient aspects of observed behavior are well-described by optimal control models where a Bayesian estimation model (Kalman filter) is combined with an optimal controller (either a Linear-Quadratic-Regulator or Bang-bang controller). We find evidence that subjects integrate information over time taking into account uncertainty. However, behavior in this continuous steering task appears to be a highly non-linear function of the visual feedback. While the nervous system appears to implement Bayes-like mechanisms for a full-body, dynamic task, it may additionally take into account the specific costs and constraints of the task.<p /> <p>Language: en</p>",
language="en",
issn="1553-734X",
doi="10.1371/journal.pcbi.1000629",
url="http://dx.doi.org/10.1371/journal.pcbi.1000629"
}