Optimal control predicts human performance on objects with internal degrees of freedom.

On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Arne J Nagengast, Daniel A Braun, Daniel M Wolpert
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/1539334b039744d9ae3f10b8a1055567
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:1539334b039744d9ae3f10b8a1055567
record_format	dspace
spelling	oai:doaj.org-article:1539334b039744d9ae3f10b8a10555672021-11-25T05:42:19ZOptimal control predicts human performance on objects with internal degrees of freedom.1553-734X1553-735810.1371/journal.pcbi.1000419https://doaj.org/article/1539334b039744d9ae3f10b8a10555672009-06-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19557193/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.Arne J NagengastDaniel A BraunDaniel M WolpertPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 6, p e1000419 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Arne J Nagengast Daniel A Braun Daniel M Wolpert Optimal control predicts human performance on objects with internal degrees of freedom.
description	On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.
format	article
author	Arne J Nagengast Daniel A Braun Daniel M Wolpert
author_facet	Arne J Nagengast Daniel A Braun Daniel M Wolpert
author_sort	Arne J Nagengast
title	Optimal control predicts human performance on objects with internal degrees of freedom.
title_short	Optimal control predicts human performance on objects with internal degrees of freedom.
title_full	Optimal control predicts human performance on objects with internal degrees of freedom.
title_fullStr	Optimal control predicts human performance on objects with internal degrees of freedom.
title_full_unstemmed	Optimal control predicts human performance on objects with internal degrees of freedom.
title_sort	optimal control predicts human performance on objects with internal degrees of freedom.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/1539334b039744d9ae3f10b8a1055567
work_keys_str_mv	AT arnejnagengast optimalcontrolpredictshumanperformanceonobjectswithinternaldegreesoffreedom AT danielabraun optimalcontrolpredictshumanperformanceonobjectswithinternaldegreesoffreedom AT danielmwolpert optimalcontrolpredictshumanperformanceonobjectswithinternaldegreesoffreedom
_version_	1718414502010028032

Optimal control predicts human performance on objects with internal degrees of freedom.

Ejemplares similares