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

2017 ◽  
Author(s):  
Marco Cardis ◽  
Maura Casadio ◽  
Rajiv Ranganathan
Keyword(s):  
Motor Learning ◽  
Task Performance ◽  
Null Space ◽  
Motor Task ◽  
High Variability ◽  
Task Space ◽  
Movement Variability ◽  
Motor Variability ◽  
Left And Right ◽  
And Task

AbstractMotor variability plays an important role in motor learning, although the exact mechanisms of how variability affects learning is 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). Here 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 towards 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 to groups that had low variability during learning. These results suggest that although introducing variability can increase exploration of new solutions, this may come at a cost of decreased stability of the learned solution.

scholarly journals Variable training but not sleep improves consolidation of motor adaptation

2018 ◽  
Author(s):  
Benjamin Thürer ◽  
Frederik D. Weber ◽  
Jan Born ◽  
Thorsten Stein
Keyword(s):  
Force Field ◽  
Motor Adaptation ◽  
Motor Memory ◽  
Alpha Power ◽  
Dominant Hand ◽  
Motor Tasks ◽  
Task Execution ◽  
Force Field Adaptation ◽  
Dependent Tasks ◽  
Adaptation Task

How motor memory consolidates still remains elusive. Motor tasks’ consolidation were shown to depend on periods of sleep, whereas pure non-hippocampal dependent tasks, like motor adaptation, might not. Some research suggests that the mode of training might affect the sleep dependency of motor adaptation tasks. Here we investigated whether sleep differentially impacts memory consolidation dependent on the variability during training. Healthy men were trained with their dominant, right hand on a force field adaptation task and re-tested after an 11-h consolidation period either involving overnight sleep (Sleep) or daytime wakefulness (Wake). Retesting also included a transfer to the non-dominant hand. Half of the subjects in each group adapted to different force field magnitudes with low inter-trial variability (Sleep-Blocked; Wake-Blocked), the other half with high variability (Sleep-Random; Wake-Random). EEG was recorded during task execution and overnight polysomnography. Motor adaptation was comparable between Wake and Sleep groups, although performance changes over sleep correlated with sleep spindles nesting in slow wave upstates. Higher training variability improved retest, including transfer learning, and these improvements correlated with higher alpha power in contralateral parietal areas. Enhanced consolidation after training might foster the ability to correct ongoing movements by responsive feedback rather than their pre-execution prediction.

scholarly journals Assessing explicit strategies in force field adaptation

2020 ◽  
Vol 123 (4) ◽  
pp. 1552-1565 ◽  
Author(s):  
Raphael Schween ◽  
Samuel D. McDougle ◽  
Mathias Hegele ◽  
Jordan A. Taylor
Keyword(s):  
Force Field ◽  
Force Production ◽  
Visuomotor Adaptation ◽  
Explicit Learning ◽  
Force Field Adaptation ◽  
Novel Methods ◽  
Adaptation Task

While the contribution of explicit learning has been increasingly studied in visuomotor adaptation, its contribution to force field adaptation has not been studied extensively. We employed two novel methods to assay explicit learning in a force field adaptation task and found that learners can voluntarily control aspects of compensatory force production and manually report it with their untrained limb. This supports the general viability of the contribution of explicit learning also in force field adaptation.

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 Identifying the neural representation of fast and slow states in force field adaptation via fMRI

2019 ◽  
Author(s):  
Andria J. Farrens ◽  
Fabrizio Sergi
Keyword(s):  
Motor Learning ◽  
Force Field ◽  
Motor Adaptation ◽  
Neural Representation ◽  
Control Processes ◽  
Behavioral Studies ◽  
Motor Learning And Control ◽  
Force Field Adaptation ◽  
The Brain ◽  
Slow Learning

AbstractNeurorehabilitation is centered on motor learning and control processes, however our understanding of how the brain learns to control movements is still limited. Motor adaptation is a rapid form of motor learning that is amenable to study in the laboratory setting. Behavioral studies of motor adaptation have coupled clever task design with computational modeling to study the control processes that underlie motor adaptation. These studies provide evidence of fast and slow learning states in the brain that combine to control neuromotor adaptation.Currently, the neural representation of these states remains unclear, especially for adaptation to changes in task dynamics, commonly studied using force fields imposed by a robotic device. Our group has developed the MR-Softwrist, a robot capable of executing dynamic adaptation tasks during functional magnetic resonance imaging (fMRI) that can be used to localize these networks in the brain.We simulated an fMRI experiment to determine if signal arising from a switching force field adaptation task can localize the neural representations of fast and slow learning states in the brain. Our results show that our task produces reliable behavioral estimates of fast and slow learning states, and distinctly measurable fMRI activations associated with each state under realistic levels of behavioral and measurement noise. Execution of this protocol with the MR-Softwrist will extend our knowledge of how the brain learns to control movement.

scholarly journals Explicit strategies in force field adaptation

2019 ◽  
Author(s):  
Raphael Schween ◽  
Samuel D. McDougle ◽  
Mathias Hegele ◽  
Jordan A. Taylor
Keyword(s):  
Force Field ◽  
Explicit Knowledge ◽  
Force Production ◽  
Visuomotor Adaptation ◽  
Viscous Force ◽  
Control Group ◽  
Explicit Learning ◽  
Visuomotor Rotation ◽  
Left Hand ◽  
Force Field Adaptation

AbstractIn recent years, it has become increasingly clear that a number of learning processes are at play in visuomotor adaptation tasks. In addition to the presumed formation of an internal model of the perturbation, learners can also develop explicit knowledge allowing them to select better actions in responding to a given perturbation. Advances in visuomotor rotation experiments have underscored the important role that such “explicit learning” plays in shaping adaptation to kinematic perturbations. Yet, in adaptation to dynamic perturbations, its contribution has been largely overlooked, potentially because compensation of a viscous force field, for instance, is difficult to assess by commonly-used verbalization-based approaches. We therefore sought to assess the contribution of explicit learning in learners adapting to a dynamic perturbation by two novel modifications of a force field experiment. First, via an elimination approach, we asked learners to abandon any cognitive strategy before selected force channel trials to expose consciously accessible parts of overall learning. Learners indeed reduced compensatory force compared to standard Catch channels. Second, via a manual reporting approach, we instructed a group of learners to mimic their right hand’s adaptation by moving with their naïve left hand. While a control group displayed negligible left-hand force compensation, the Mimic group reported forces that approximated right-hand adaptation but appeared to under-report the velocity component of the force field in favor of a more position-based component. We take these results to clearly demonstrate the contribution of explicit learning to force adaptation, underscoring its relevance to motor learning in general.New & NoteworthyWhile the role of explicit learning has recently been appreciated in visuomotor adaptation tasks, their contribution to force field adaptation has not been as widely acknowledged. To address this issue, we employed two novel methods to assay explicit learning in force field adaptation tasks and found that learners can voluntarily control aspects of force production and manually report them with their untrained limb. This suggests that an explicit component contributes to force field adaptation and may provide alternative explanations to behavioral phenomena commonly thought to reveal a complex organization of internal models in the brain.

scholarly journals Increase in weighting of vision vs. proprioception associated with force field adaptation

2019 ◽  
Author(s):  
Brandon M. Sexton ◽  
Yang Liu ◽  
Hannah J. Block
Keyword(s):  
Force Field ◽  
Position Sense ◽  
Multisensory Perception ◽  
Hand Position ◽  
Null Field ◽  
Before And After ◽  
Force Field Adaptation ◽  
Perceptual Changes ◽  
The Brain ◽  
Straight Ahead

AbstractHand position can be encoded by vision, via an image on the retina, and proprioception (position sense), via sensors in the joints and muscles. The brain is thought to weight and combine available sensory estimates to form an integrated multisensory estimate of hand position with which to guide movement. Force field adaptation, a form of cerebellum-dependent motor learning in which reaches are systematically adjusted to compensate for a somatosensory perturbation, is associated with both motor and proprioceptive changes. The cerebellum has connections with parietal regions thought to be involved in multisensory integration; however, it is unknown if force adaptation is associated with changes in multisensory perception. One possibility is that force adaptation affects all relevant sensory modalities similarly, such that the brain’s weighting of vision vs. proprioception is maintained. Alternatively, the somatosensory perturbation might be interpreted as proprioceptive unreliability, resulting in vision being up-weighted relative to proprioception. We assessed visuo-proprioceptive weighting with a perceptual estimation task before and after subjects performed straight-ahead reaches grasping a robotic manipulandum. Each subject performed one session with a clockwise or counter-clockwise velocity-dependent force field, and one session in a null field to control for perceptual changes not specific to force adaptation. Subjects increased their weight of vision vs. proprioception in the force field session relative to the null field session, regardless of force field direction, in the straight-ahead dimension (F1,44 = 5.13, p = 0.029). This suggests that force field adaptation is associated with an increase in the brain’s weighting of vision vs. proprioception.

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