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Bayesian Integration and Non-Linear Feedback Control in a Full-Body Motor Task
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 simila...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2789327/ https://www.ncbi.nlm.nih.gov/pubmed/20041205 http://dx.doi.org/10.1371/journal.pcbi.1000629 |
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author | Stevenson, Ian H. Fernandes, Hugo L. Vilares, Iris Wei, Kunlin Körding, Konrad P. |
author_facet | Stevenson, Ian H. Fernandes, Hugo L. Vilares, Iris Wei, Kunlin Körding, Konrad P. |
author_sort | Stevenson, Ian H. |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-2789327 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-27893272009-12-30 Bayesian Integration and Non-Linear Feedback Control in a Full-Body Motor Task Stevenson, Ian H. Fernandes, Hugo L. Vilares, Iris Wei, Kunlin Körding, Konrad P. PLoS Comput Biol Research Article 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. Public Library of Science 2009-12-24 /pmc/articles/PMC2789327/ /pubmed/20041205 http://dx.doi.org/10.1371/journal.pcbi.1000629 Text en Stevenson et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Stevenson, Ian H. Fernandes, Hugo L. Vilares, Iris Wei, Kunlin Körding, Konrad P. Bayesian Integration and Non-Linear Feedback Control in a Full-Body Motor Task |
title | Bayesian Integration and Non-Linear Feedback Control in a Full-Body Motor Task |
title_full | Bayesian Integration and Non-Linear Feedback Control in a Full-Body Motor Task |
title_fullStr | Bayesian Integration and Non-Linear Feedback Control in a Full-Body Motor Task |
title_full_unstemmed | Bayesian Integration and Non-Linear Feedback Control in a Full-Body Motor Task |
title_short | Bayesian Integration and Non-Linear Feedback Control in a Full-Body Motor Task |
title_sort | bayesian integration and non-linear feedback control in a full-body motor task |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2789327/ https://www.ncbi.nlm.nih.gov/pubmed/20041205 http://dx.doi.org/10.1371/journal.pcbi.1000629 |
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