Joint angle variability in 3D bimanual pointing: uncontrolled manifold analysis
The structure of joint angle variability and its changes with practice were investigated using the uncontrolled manifold (UCM) computational approach. Subjects performed fast and accurate bimanual pointing movements in 3D space, trying to match the tip of a pointer, held in the right hand, with the...
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Published in | Experimental brain research Vol. 163; no. 1; pp. 44 - 57 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Berlin
Springer
01.05.2005
Springer Nature B.V |
Subjects | |
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Abstract | The structure of joint angle variability and its changes with practice were investigated using the uncontrolled manifold (UCM) computational approach. Subjects performed fast and accurate bimanual pointing movements in 3D space, trying to match the tip of a pointer, held in the right hand, with the tip of one of three different targets, held in the left hand during a pre-test, several practice sessions and a post-test. The prediction of the UCM approach about the structuring of joint angle variance for selective stabilization of important task variables was tested with respect to selective stabilization of time series of the vectorial distance between the pointer and aimed target tips (bimanual control hypothesis) and with respect to selective stabilization of the endpoint trajectory of each arm (unimanual control hypothesis). The components of the total joint angle variance not affecting (V(COMP)) and affecting (V(UN)) the value of a selected task variable were computed for each 10% of the normalized movement time. The ratio of these two components R(V)=V(COMP)/V(UN) served as a quantitative index of selective stabilization. Both the bimanual and unimanual control hypotheses were supported, however the R(V) values for the bimanual hypothesis were significantly higher than those for the unimanual hypothesis applied to the left and right arm both prior to and after practice. This suggests that the CNS stabilizes the relative trajectory of one endpoint with respect to the other more than it stabilizes the trajectories of each of the endpoints in the external space. Practice-associated improvement in both movement speed and accuracy was accompanied by counter-intuitive lack of changes in R(V). Both V(COMP) and V(UN) variance components decreased such that their ratio remained constant prior to and after practice. We conclude that the UCM approach offers a unique and under-explored opportunity to track changes in the organization of multi-effector systems with practice and allows quantitative assessment of the degree of stabilization of selected performance variables. |
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AbstractList | The structure of joint angle variability and its changes with practice were investigated using the uncontrolled manifold (UCM) computational approach. Subjects performed fast and accurate bimanual pointing movements in 3D space, trying to match the tip of a pointer, held in the right hand, with the tip of one of three different targets, held in the left hand during a pre-test, several practice sessions and a post-test. The prediction of the UCM approach about the structuring of joint angle variance for selective stabilization of important task variables was tested with respect to selective stabilization of time series of the vectorial distance between the pointer and aimed target tips (bimanual control hypothesis) and with respect to selective stabilization of the endpoint trajectory of each arm (unimanual control hypothesis). The components of the total joint angle variance not affecting (V sub(COMP)) and affecting (V sub(UN)) the value of a selected task variable were computed for each 10% of the normalized movement time. The ratio of these two components R sub(V)=V sub(COMP)/V sub(UN) served as a quantitative index of selective stabilization. Both the bimanual and unimanual control hypotheses were supported, however the R sub(V) values for the bimanual hypothesis were significantly higher than those for the unimanual hypothesis applied to the left and right arm both prior to and after practice. This suggests that the CNS stabilizes the relative trajectory of one endpoint with respect to the other more than it stabilizes the trajectories of each of the endpoints in the external space. Practice-associated improvement in both movement speed and accuracy was accompanied by counter-intuitive lack of changes in R sub(V). Both V sub(COMP) and V sub(UN) variance components decreased such that their ratio remained constant prior to and after practice. We conclude that the UCM approach offers a unique and under-explored opportunity to track changes in the organization of multi-effector systems with practice and allows quantitative assessment of the degree of stabilization of selected performance variables. The structure of joint angle variability and its changes with practice were investigated using the uncontrolled manifold (UCM) computational approach. Subjects performed fast and accurate bimanual pointing movements in 3D space, trying to match the tip of a pointer, held in the right hand, with the tip of one of three different targets, held in the left hand during a pre-test, several practice sessions and a post-test. The prediction of the UCM approach about the structuring of joint angle variance for selective stabilization of important task variables was tested with respect to selective stabilization of time series of the vectorial distance between the pointer and aimed target tips (bimanual control hypothesis) and with respect to selective stabilization of the endpoint trajectory of each arm (unimanual control hypothesis). The components of the total joint angle variance not affecting (VCOMP) and affecting (VUN) the value of a selected task variable were computed for each 10% of the normalized movement time. The ratio of these two components RV=VCOMP/VUN served as a quantitative index of selective stabilization. Both the bimanual and unimanual control hypotheses were supported, however the RV values for the bimanual hypothesis were significantly higher than those for the unimanual hypothesis applied to the left and right arm both prior to and after practice. This suggests that the CNS stabilizes the relative trajectory of one endpoint with respect to the other more than it stabilizes the trajectories of each of the endpoints in the external space. Practice-associated improvement in both movement speed and accuracy was accompanied by counter-intuitive lack of changes in RV. Both VCOMP and VUN variance components decreased such that their ratio remained constant prior to and after practice. We conclude that the UCM approach offers a unique and under-explored opportunity to track changes in the organization of multi-effector systems with practice and allows quantitative assessment of the degree of stabilization of selected performance variables. The structure of joint angle variability and its changes with practice were investigated using the uncontrolled manifold (UCM) computational approach. Subjects performed fast and accurate bimanual pointing movements in 3D space, trying to match the tip of a pointer, held in the right hand, with the tip of one of three different targets, held in the left hand during a pre-test, several practice sessions and a post-test. The prediction of the UCM approach about the structuring of joint angle variance for selective stabilization of important task variables was tested with respect to selective stabilization of time series of the vectorial distance between the pointer and aimed target tips (bimanual control hypothesis) and with respect to selective stabilization of the endpoint trajectory of each arm (unimanual control hypothesis). The components of the total joint angle variance not affecting (V(COMP)) and affecting (V(UN)) the value of a selected task variable were computed for each 10% of the normalized movement time. The ratio of these two components R(V)=V(COMP)/V(UN) served as a quantitative index of selective stabilization. Both the bimanual and unimanual control hypotheses were supported, however the R(V) values for the bimanual hypothesis were significantly higher than those for the unimanual hypothesis applied to the left and right arm both prior to and after practice. This suggests that the CNS stabilizes the relative trajectory of one endpoint with respect to the other more than it stabilizes the trajectories of each of the endpoints in the external space. Practice-associated improvement in both movement speed and accuracy was accompanied by counter-intuitive lack of changes in R(V). Both V(COMP) and V(UN) variance components decreased such that their ratio remained constant prior to and after practice. We conclude that the UCM approach offers a unique and under-explored opportunity to track changes in the organization of multi-effector systems with practice and allows quantitative assessment of the degree of stabilization of selected performance variables. The structure of joint angle variability and its changes with practice were investigated using the uncontrolled manifold (UCM) computational approach. Subjects performed fast and accurate bimanual pointing movements in 3D space, trying to match the tip of a pointer, held in the right hand, with the tip of one of three different targets, held in the left hand during a pre-test, several practice sessions and a post-test. The prediction of the UCM approach about the structuring of joint angle variance for selective stabilization of important task variables was tested with respect to selective stabilization of time series of the vectorial distance between the pointer and aimed target tips (bimanual control hypothesis) and with respect to selective stabilization of the endpoint trajectory of each arm (unimanual control hypothesis). The components of the total joint angle variance not affecting (V(COMP)) and affecting (V(UN)) the value of a selected task variable were computed for each 10% of the normalized movement time. The ratio of these two components R(V)=V(COMP)/V(UN) served as a quantitative index of selective stabilization. Both the bimanual and unimanual control hypotheses were supported, however the R(V) values for the bimanual hypothesis were significantly higher than those for the unimanual hypothesis applied to the left and right arm both prior to and after practice. This suggests that the CNS stabilizes the relative trajectory of one endpoint with respect to the other more than it stabilizes the trajectories of each of the endpoints in the external space. Practice-associated improvement in both movement speed and accuracy was accompanied by counter-intuitive lack of changes in R(V). Both V(COMP) and V(UN) variance components decreased such that their ratio remained constant prior to and after practice. We conclude that the UCM approach offers a unique and under-explored opportunity to track changes in the organization of multi-effector systems with practice and allows quantitative assessment of the degree of stabilization of selected performance variables.The structure of joint angle variability and its changes with practice were investigated using the uncontrolled manifold (UCM) computational approach. Subjects performed fast and accurate bimanual pointing movements in 3D space, trying to match the tip of a pointer, held in the right hand, with the tip of one of three different targets, held in the left hand during a pre-test, several practice sessions and a post-test. The prediction of the UCM approach about the structuring of joint angle variance for selective stabilization of important task variables was tested with respect to selective stabilization of time series of the vectorial distance between the pointer and aimed target tips (bimanual control hypothesis) and with respect to selective stabilization of the endpoint trajectory of each arm (unimanual control hypothesis). The components of the total joint angle variance not affecting (V(COMP)) and affecting (V(UN)) the value of a selected task variable were computed for each 10% of the normalized movement time. The ratio of these two components R(V)=V(COMP)/V(UN) served as a quantitative index of selective stabilization. Both the bimanual and unimanual control hypotheses were supported, however the R(V) values for the bimanual hypothesis were significantly higher than those for the unimanual hypothesis applied to the left and right arm both prior to and after practice. This suggests that the CNS stabilizes the relative trajectory of one endpoint with respect to the other more than it stabilizes the trajectories of each of the endpoints in the external space. Practice-associated improvement in both movement speed and accuracy was accompanied by counter-intuitive lack of changes in R(V). Both V(COMP) and V(UN) variance components decreased such that their ratio remained constant prior to and after practice. We conclude that the UCM approach offers a unique and under-explored opportunity to track changes in the organization of multi-effector systems with practice and allows quantitative assessment of the degree of stabilization of selected performance variables. |
Author | Jaric, Slobodan Laczko, Jozsef Domkin, Dmitry Latash, Mark L. Djupsjöbacka, Mats |
Author_xml | – sequence: 1 givenname: Dmitry surname: Domkin fullname: Domkin, Dmitry – sequence: 2 givenname: Jozsef surname: Laczko fullname: Laczko, Jozsef – sequence: 3 givenname: Mats surname: Djupsjöbacka fullname: Djupsjöbacka, Mats – sequence: 4 givenname: Slobodan surname: Jaric fullname: Jaric, Slobodan – sequence: 5 givenname: Mark L. surname: Latash fullname: Latash, Mark L. |
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Snippet | The structure of joint angle variability and its changes with practice were investigated using the uncontrolled manifold (UCM) computational approach. Subjects... |
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SubjectTerms | Adult Arm - physiology Biological Biological and medical sciences Biomechanical Phenomena - methods Biomechanical Phenomena - standards Biomechanics methods standards Elbow Joint - physiology Female Finger Joint - physiology Fundamental and applied biological sciences. Psychology Human Humans Hypotheses Joints - physiology Learning Learning. Memory Models, Biological Motor control and motor pathways. Reflexes. Control centers of vegetative functions. Vestibular system and equilibration Movement - physiology Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Reproducibility of Results Shoulder Joint - physiology Variables Vertebrates: nervous system and sense organs Volition - physiology Wrist Joint - physiology |
Title | Joint angle variability in 3D bimanual pointing: uncontrolled manifold analysis |
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