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Optimal task-dependent changes of bimanual feedback control and adaptation.

Diedrichsen J - Curr. Biol. (2007)

Bottom Line: Adaptation, the influence of a perturbation onto the next movement, also depended on task goals.In the two-cursor condition, only the perturbed hand adapted to a force perturbation [2], whereas in the one-cursor condition, both hands adapted.These findings demonstrate that the central nervous system changes bimanual feedback control and adaptation optimally according to the current task requirements.

View Article: PubMed Central - PubMed

Affiliation: Wolfson Centre for Cognitive Neuroscience, School of Psychology, University of Wales, Bangor, Gwynedd LL57 2AS, United Kingdom. j.diedrichsen@bangor.ac.uk

ABSTRACT
The control and adaptation of bimanual movements is often considered to be a function of a fixed set of mechanisms [1, 2]. Here, I show that both feedback control and adaptation change optimally with task goals. Participants reached with two hands to two separate spatial targets (two-cursor condition) or used the same bimanual movements to move a cursor presented at the spatial average location of the two hands to a single target (one-cursor condition). A force field was randomly applied to one of the hands. In the two-cursor condition, online corrections occurred only on the perturbed hand, whereas the other movement was controlled independently. In the one-cursor condition, online correction could be detected on both hands as early as 190 ms after the start. These changes can be shown to be optimal in respect to a simple task-dependent cost function [3]. Adaptation, the influence of a perturbation onto the next movement, also depended on task goals. In the two-cursor condition, only the perturbed hand adapted to a force perturbation [2], whereas in the one-cursor condition, both hands adapted. These findings demonstrate that the central nervous system changes bimanual feedback control and adaptation optimally according to the current task requirements.

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Endpoint Correlation in Unperturbed Movements(A) Predicted endpoint correlation in the x direction in the two- (blue) and one- (red) cursor condition, with the same simulation parameters as in Figure 1.(B) Predicted time course of the correlation between movement directions.(C) Endpoint correlation of one representative participant in experiment 1.(D) Time course of correlation, averaged across all participants of experiment 1, with the shaded area indicating the SEM.
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fig3: Endpoint Correlation in Unperturbed Movements(A) Predicted endpoint correlation in the x direction in the two- (blue) and one- (red) cursor condition, with the same simulation parameters as in Figure 1.(B) Predicted time course of the correlation between movement directions.(C) Endpoint correlation of one representative participant in experiment 1.(D) Time course of correlation, averaged across all participants of experiment 1, with the shaded area indicating the SEM.

Mentions: When adding signal-dependent noise separately to the movement of each hand, the optimal control policy for the one-cursor condition predicts that the endpoints of the two hands should become negatively correlated. The effect arises because of bilateral corrections of motor noise, and should gradually arise over the course of the movement (Figures 3A and 3B). Congruent with this prediction, the movement endpoints on unperturbed trials were more negatively correlated in the one-cursor than in the two-cursor condition, both with (−0.81 versus −0.22, t(9) = 7.283, p < 0.001) and without (−0.44 versus −0.18, t(9) = 3.881, p = 0.004) visual feedback. Furthermore, the effect arose after the predicted time course (Figures 3C and 3D). Thus, participants corrected only for task-relevant error [3, 5], whereas negative covariation of the hands accumulated.


Optimal task-dependent changes of bimanual feedback control and adaptation.

Diedrichsen J - Curr. Biol. (2007)

Endpoint Correlation in Unperturbed Movements(A) Predicted endpoint correlation in the x direction in the two- (blue) and one- (red) cursor condition, with the same simulation parameters as in Figure 1.(B) Predicted time course of the correlation between movement directions.(C) Endpoint correlation of one representative participant in experiment 1.(D) Time course of correlation, averaged across all participants of experiment 1, with the shaded area indicating the SEM.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2230536&req=5

fig3: Endpoint Correlation in Unperturbed Movements(A) Predicted endpoint correlation in the x direction in the two- (blue) and one- (red) cursor condition, with the same simulation parameters as in Figure 1.(B) Predicted time course of the correlation between movement directions.(C) Endpoint correlation of one representative participant in experiment 1.(D) Time course of correlation, averaged across all participants of experiment 1, with the shaded area indicating the SEM.
Mentions: When adding signal-dependent noise separately to the movement of each hand, the optimal control policy for the one-cursor condition predicts that the endpoints of the two hands should become negatively correlated. The effect arises because of bilateral corrections of motor noise, and should gradually arise over the course of the movement (Figures 3A and 3B). Congruent with this prediction, the movement endpoints on unperturbed trials were more negatively correlated in the one-cursor than in the two-cursor condition, both with (−0.81 versus −0.22, t(9) = 7.283, p < 0.001) and without (−0.44 versus −0.18, t(9) = 3.881, p = 0.004) visual feedback. Furthermore, the effect arose after the predicted time course (Figures 3C and 3D). Thus, participants corrected only for task-relevant error [3, 5], whereas negative covariation of the hands accumulated.

Bottom Line: Adaptation, the influence of a perturbation onto the next movement, also depended on task goals.In the two-cursor condition, only the perturbed hand adapted to a force perturbation [2], whereas in the one-cursor condition, both hands adapted.These findings demonstrate that the central nervous system changes bimanual feedback control and adaptation optimally according to the current task requirements.

View Article: PubMed Central - PubMed

Affiliation: Wolfson Centre for Cognitive Neuroscience, School of Psychology, University of Wales, Bangor, Gwynedd LL57 2AS, United Kingdom. j.diedrichsen@bangor.ac.uk

ABSTRACT
The control and adaptation of bimanual movements is often considered to be a function of a fixed set of mechanisms [1, 2]. Here, I show that both feedback control and adaptation change optimally with task goals. Participants reached with two hands to two separate spatial targets (two-cursor condition) or used the same bimanual movements to move a cursor presented at the spatial average location of the two hands to a single target (one-cursor condition). A force field was randomly applied to one of the hands. In the two-cursor condition, online corrections occurred only on the perturbed hand, whereas the other movement was controlled independently. In the one-cursor condition, online correction could be detected on both hands as early as 190 ms after the start. These changes can be shown to be optimal in respect to a simple task-dependent cost function [3]. Adaptation, the influence of a perturbation onto the next movement, also depended on task goals. In the two-cursor condition, only the perturbed hand adapted to a force perturbation [2], whereas in the one-cursor condition, both hands adapted. These findings demonstrate that the central nervous system changes bimanual feedback control and adaptation optimally according to the current task requirements.

Show MeSH