scholarly journals Motor learning in reaching tasks leads to homogenization of task space error distribution

2021 ◽  
Author(s):  
Puneet Singh ◽  
Oishee Ghosal ◽  
Aditya Murthy ◽  
Ashitava Ghodal
Keyword(s):  
Motor Learning ◽  
Force Field ◽  
Error Distribution ◽  
Task Space ◽  
Field Perturbation ◽  
Directional Dependence ◽  
Reaching Tasks ◽  
Space Error ◽  
Human Arm ◽  
Point To Point

A human arm, up to the wrist, is often modelled as a redundant 7 degree-of-freedom serial robot. Despite its inherent nonlinearity, we can perform point-to-point reaching tasks reasonably fast and with reasonable accuracy in the presence of external disturbances and noise. In this work, we take a closer look at the task space error during point-to-point reaching tasks and learning during an external force-field perturbation. From experiments and quantitative data, we confirm a directional dependence of the peak task space error with certain directions showing larger errors than others at the start of a force-field perturbation, and the larger errors are reduced with repeated trials implying learning. The analysis of the experimental data further shows that a) the distribution of the peak error is made more uniform across directions with trials and the error magnitude and distribution approaches the value when no perturbation is applied, b) the redundancy present in the human arm is used more in the direction of the larger error, and c) homogenization of the error distribution is not seen when the reaching task is performed with the non-dominant hand. The results support the hypothesis that not only magnitude of task space error, but the directional dependence is reduced during motor learning and the workspace is homogenized possibly to increase the control efficiency and accuracy in point-to-point reaching tasks. The results also imply that redundancy in the arm is used to homogenize the workspace, and additionally since the bio-mechanically similar dominant and non-dominant arms show different behaviours, the homogenizing is actively done in the central nervous system.

scholarly journals Exploration of joint redundancy but not task space variability facilitates supervised motor learning

2016 ◽  
Vol 113 (50) ◽  
pp. 14414-14419 ◽  
Author(s):  
Puneet Singh ◽  
Sumitash Jana ◽  
Ashitava Ghosal ◽  
Aditya Murthy
Keyword(s):  
Motor Learning ◽  
Force Field ◽  
Visuomotor Adaptation ◽  
Dominant Hand ◽  
Task Space ◽  
Motor Variability ◽  
3D Space ◽  
Human Arm ◽  
Force Field Adaptation ◽  
Redundant Component

The number of joints and muscles in a human arm is more than what is required for reaching to a desired point in 3D space. Although previous studies have emphasized how such redundancy and the associated flexibility may play an important role in path planning, control of noise, and optimization of motion, whether and how redundancy might promote motor learning has not been investigated. In this work, we quantify redundancy space and investigate its significance and effect on motor learning. We propose that a larger redundancy space leads to faster learning across subjects. We observed this pattern in subjects learning novel kinematics (visuomotor adaptation) and dynamics (force-field adaptation). Interestingly, we also observed differences in the redundancy space between the dominant hand and nondominant hand that explained differences in the learning of dynamics. Taken together, these results provide support for the hypothesis that redundancy aids in motor learning and that the redundant component of motor variability is not noise.

scholarly journals Separate representations of dynamics in rhythmic and discrete movements: evidence from motor learning

2011 ◽  
Vol 105 (4) ◽  
pp. 1722-1731 ◽  
Author(s):  
Ian S. Howard ◽  
James N. Ingram ◽  
Daniel M. Wolpert
Keyword(s):  
Motor Learning ◽  
Force Field ◽  
Motor System ◽  
Neural Mechanisms ◽  
Control Mechanisms ◽  
Discrete Movements ◽  
Field Perturbation ◽  
Movement Path ◽  
Neural Representations ◽  
Separate Control

Rhythmic and discrete arm movements occur ubiquitously in everyday life, and there is a debate as to whether these two classes of movements arise from the same or different underlying neural mechanisms. Here we examine interference in a motor-learning paradigm to test whether rhythmic and discrete movements employ at least partially separate neural representations. Subjects were required to make circular movements of their right hand while they were exposed to a velocity-dependent force field that perturbed the circularity of the movement path. The direction of the force-field perturbation reversed at the end of each block of 20 revolutions. When subjects made only rhythmic or only discrete circular movements, interference was observed when switching between the two opposing force fields. However, when subjects alternated between blocks of rhythmic and discrete movements, such that each was uniquely associated with one of the perturbation directions, interference was significantly reduced. Only in this case did subjects learn to corepresent the two opposing perturbations, suggesting that different neural resources were employed for the two movement types. Our results provide further evidence that rhythmic and discrete movements employ at least partially separate control mechanisms in the motor system.

scholarly journals Effects of Human Arm Impedance on Dynamics Learning and Generalization

2009 ◽  
Vol 101 (6) ◽  
pp. 3158-3168 ◽  
Author(s):  
Mohammad Darainy ◽  
Andrew A. G. Mattar ◽  
David J. Ostry
Keyword(s):  
Motor Learning ◽  
Force Field ◽  
Strong Correlation ◽  
Transfer Of Learning ◽  
Kinematic Error ◽  
Robotic Device ◽  
Human Arm ◽  
Applied Forces ◽  
Static Conditions ◽  
Dynamic Perturbation

Previous studies have demonstrated anisotropic patterns of hand impedance under static conditions and during movement. Here we show that the pattern of kinematic error observed in studies of dynamics learning is associated with this anisotropic impedance pattern. We also show that the magnitude of kinematic error associated with this anisotropy dictates the amount of motor learning and, consequently, the extent to which dynamics learning generalizes. Subjects were trained to reach to visual targets while holding a robotic device that applied forces during movement. On infrequent trials, the load was removed and the resulting kinematic error was measured. We found a strong correlation between the pattern of kinematic error and the anisotropic pattern of hand stiffness. In a second experiment subjects were trained under force-field conditions to move in two directions: one in which the dynamic perturbation was in the direction of maximum arm impedance and the associated kinematic error was low and another in which the perturbation was in the direction of low impedance where kinematic error was high. Generalization of learning was assessed in a reference direction that lay intermediate to the two training directions. We found that transfer of learning was greater when training occurred in the direction associated with the larger kinematic error. This suggests that the anisotropic patterns of impedance and kinematic error determine the magnitude of dynamics learning and the extent to which it generalizes.

scholarly journals Cerebellar motor learning: are environment dynamics more important than error size?

2013 ◽  
Vol 110 (2) ◽  
pp. 322-333 ◽  
Author(s):  
Tricia L. Gibo ◽  
Sarah E. Criscimagna-Hemminger ◽  
Allison M. Okamura ◽  
Amy J. Bastian
Keyword(s):  
Motor Learning ◽  
Force Field ◽  
Complex Dynamics ◽  
External Perturbation ◽  
Joint Space ◽  
Relative Effect ◽  
Small Error ◽  
Cerebellar Damage ◽  
Movement Dynamics ◽  
Compensatory Strategy

Cerebellar damage impairs the control of complex dynamics during reaching movements. It also impairs learning of predictable dynamic perturbations through an error-based process. Prior work suggests that there are distinct neural mechanisms involved in error-based learning that depend on the size of error experienced. This is based, in part, on the observation that people with cerebellar degeneration may have an intact ability to learn from small errors. Here we studied the relative effect of specific dynamic perturbations and error size on motor learning of a reaching movement in patients with cerebellar damage. We also studied generalization of learning within different coordinate systems (hand vs. joint space). Contrary to our expectation, we found that error size did not alter cerebellar patients' ability to learn the force field. Instead, the direction of the force field affected patients' ability to learn, regardless of whether the force perturbations were introduced gradually (small error) or abruptly (large error). Patients performed best in fields that helped them compensate for movement dynamics associated with reaching. However, they showed much more limited generalization patterns than control subjects, indicating that patients rely on a different learning mechanism. We suggest that patients typically use a compensatory strategy to counteract movement dynamics. They may learn to relax this compensatory strategy when the external perturbation is favorable to counteracting their movement dynamics, and improve reaching performance. Altogether, these findings show that dynamics affect learning in cerebellar patients more than error size.

scholarly journals High variability impairs motor learning regardless of whether it affects task performance

2018 ◽  
Vol 119 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Marco Cardis ◽  
Maura Casadio ◽  
Rajiv Ranganathan
Keyword(s):  
Motor Learning ◽  
Task Performance ◽  
Null Space ◽  
Motor Task ◽  
Task Space ◽  
Movement Variability ◽  
Motor Variability ◽  
Different Dimensions ◽  
Left And Right

Motor variability plays an important role in motor learning, although the exact mechanisms of how variability affects learning are not well understood. Recent evidence suggests that motor variability may have different effects on learning in redundant tasks, depending on whether it is present in the task space (where it affects task performance) or in the null space (where it has no effect on task performance). We examined the effect of directly introducing null and task space variability using a manipulandum during the learning of a motor task. Participants learned a bimanual shuffleboard task for 2 days, where their goal was to slide a virtual puck as close as possible toward a target. Critically, the distance traveled by the puck was determined by the sum of the left- and right-hand velocities, which meant that there was redundancy in the task. Participants were divided into five groups, based on both the dimension in which the variability was introduced and the amount of variability that was introduced during training. Results showed that although all groups were able to reduce error with practice, learning was affected more by the amount of variability introduced rather than the dimension in which variability was introduced. Specifically, groups with higher movement variability during practice showed larger errors at the end of practice compared with groups that had low variability during learning. These results suggest that although introducing variability can increase exploration of new solutions, this may adversely affect the ability to retain the learned solution.NEW & NOTEWORTHY We examined the role of introducing variability during motor learning in a redundant task. The presence of redundancy allows variability to be introduced in different dimensions: the task space (where it affects task performance) or the null space (where it does not affect task performance). We found that introducing variability affected learning adversely, but the amount of variability was more critical than the dimension in which variability was introduced.

scholarly journals Plastic Changes in Hand Proprioception Following Force-Field Motor Learning

2010 ◽  
Vol 104 (3) ◽  
pp. 1213-1215 ◽  
Author(s):  
Daniel J. Goble ◽  
Joaquin A. Anguera
Keyword(s):  
Motor Learning ◽  
Force Field ◽  
Recent Work ◽  
Recent Article ◽  
Sensory Feedback ◽  
Motor Output ◽  
Feedback Processing ◽  
Plastic Changes ◽  
Proprioceptive Function ◽  
New Evidence

Motor neurophysiologists are placing greater emphasis on sensory feedback processing than ever before. In line with this shift, a recent article by Ostry and colleagues provided timely new evidence that force-field motor learning influences not only motor output, but also proprioceptive sense. In this Neuro Forum, the merits and limitations of Ostry and colleagues are explored in the context of recent work on proprioceptive function, including several recent studies from this journal.

scholarly journals Motor learning and its sensory effects: time course of perceptual change and its presence with gradual introduction of load

2013 ◽  
Vol 109 (3) ◽  
pp. 782-791 ◽  
Author(s):  
Andrew A. G. Mattar ◽  
Mohammad Darainy ◽  
David J. Ostry
Keyword(s):  
Motor Learning ◽  
Force Field ◽  
Motor Performance ◽  
Time Course ◽  
Sensory Perception ◽  
Learning Task ◽  
Perceptual Change ◽  
Sensory Effects ◽  
Gradual Introduction ◽  
Lateral Displacements

A complex interplay has been demonstrated between motor and sensory systems. We showed recently that motor learning leads to changes in the sensed position of the limb (Ostry DJ, Darainy M, Mattar AA, Wong J, Gribble PL. J Neurosci 30: 5384–5393, 2010). Here, we document further the links between motor learning and changes in somatosensory perception. To study motor learning, we used a force field paradigm in which subjects learn to compensate for forces applied to the hand by a robotic device. We used a task in which subjects judge lateral displacements of the hand to study somatosensory perception. In a first experiment, we divided the motor learning task into incremental phases and tracked sensory perception throughout. We found that changes in perception occurred at a slower rate than changes in motor performance. A second experiment tested whether awareness of the motor learning process is necessary for perceptual change. In this experiment, subjects were exposed to a force field that grew gradually in strength. We found that the shift in sensory perception occurred even when awareness of motor learning was reduced. These experiments argue for a link between motor learning and changes in somatosensory perception, and they are consistent with the idea that motor learning drives sensory change.

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