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 Augmentation of task-relevant variability enhances consolidation of motor learning

2021 ◽  
Author(s):  
Mattia Pagano ◽  
Gaia Stochino ◽  
Maura Casadio ◽  
Rajiv Ranganathan
Keyword(s):  
Task Performance ◽  
Memory Consolidation ◽  
Null Space ◽  
Motor Memory ◽  
Task Space ◽  
Movement Variability ◽  
Motor Variability ◽  
Coordination Strategy ◽  
Left And Right ◽  
Throwing Motion

Motor memories undergo a period of consolidation before they become resistant to the practice of another task. Although movement variability is important in motor memory consolidation, its role is not fully understood in redundant tasks where variability can exist along two orthogonal subspaces (the 'task space' and the 'null space') that have different effects on task performance. Here, we used haptic perturbations to augment variability in these different spaces and examined their effect on motor memory consolidation. Participants learned a shuffleboard task, where they held a bimanual manipulandum and made a discrete throwing motion to slide a virtual puck towards a target. The task was redundant because the distance travelled by the puck was determined by the sum of the left and right hand speeds at the time of release. After participants initially practiced the task, we used haptic perturbations to introduce motor variability in the task space or null space, and subsequently examined consolidation of the original task on the next day. We found that regardless of the amplitude, augmenting variability in the task space resulted in significantly better consolidation. This benefit of increasing task space variability was likely due to the fact that it did not disrupt the pre-existing coordination strategy. These results suggest that the effects of variability on motor memory consolidation depend on the interplay between the induced variability and the pre-existing coordination strategy.

scholarly journals Motor variability prior to learning is a poor predictor of the ability to adopt new movement solutions

2020 ◽  
Author(s):  
Rajiv Ranganathan ◽  
Marco Lin ◽  
Samuel Carey ◽  
Rakshith Lokesh ◽  
Mei-Hua Lee ◽  
...  
Keyword(s):  
Individual Differences ◽  
Motor Learning ◽  
Task Performance ◽  
Task Context ◽  
Motor Variability ◽  
Poor Predictor ◽  
Potential Marker ◽  
Baseline Task ◽  
Left And Right ◽  
Highly Correlated

AbstractMany contexts in motor learning require a learner to change from an existing movement solution to a novel movement solution to perform the same task. Recent evidence has pointed to motor variability prior to learning as a potential marker for predicting individual differences in motor learning. However, it is not known if this variability is predictive of the ability to adopt a new movement solution for the same task. Here, we examined this question in the context of a redundant precision task requiring control of motor variability. Fifty young adults learned a precision task that involved throwing a virtual puck toward a target using both hands. Because the speed of the puck depended on the sum of speeds of both hands, this task could be achieved using multiple solutions. Participants initially performed a baseline task where there was no constraint on the movement solution, and then performed a novel task where they were constrained to adopt a specific movement solution requiring asymmetric left and right hand speeds. Results showed that participants were able to learn the new solution, and this change was associated with changes in both the amount and structure of variability. However, individual differences in baseline motor variability were only weakly correlated with initial and final task performance when using the new solution, with greater variability being associated with higher errors. We also found a strong specificity component – initial variability when using the new solution was highly correlated with final task performance with the new solution, but once again, higher variability was associated with greater errors. These results suggest that motor variability is not necessarily indicative of flexibility and highlight the need to consider the task context in determining the relation between motor variability and learning.

Visual Attention and Coactivation of Response Decisions for Features from Different Dimensions

2002 ◽  
Vol 13 (4) ◽  
pp. 361-369 ◽  
Author(s):  
Uri Feintuch ◽  
Asher Cohen
Keyword(s):  
Visual Attention ◽  
Task Performance ◽  
Redundancy Gain ◽  
Attentional Focus ◽  
Action Model ◽  
Different Dimensions ◽  
Response Decisions ◽  
High Level ◽  
Dimensional Module

The role of visual attention in task performance has been extensively debated. On the basis of the dimensional-action model, we hypothesized that a major role of attention is to transfer response decisions from targets on which it is focused to high-level centers dealing with response execution. This hypothesis predicts that response decisions for two targets will interact only when attention is focused on both targets, and only when the response to the targets is defined by different dimensions. Three experiments, using the redundancy-gain paradigm, tested and confirmed this prediction. Experiment 1 showed that coactivation of two cross-dimensional targets occurred only when the targets were positioned in the same location, not when they were in separate locations. Experiment 2 manipulated the focus of attention and showed that coactivation can occur even for targets positioned in different locations if they are both within the attentional focus. Experiment 3 showed that this attention-induced coactivation does not occur for targets from the same dimensional module. These results suggest that a major role of attention is postperceptual and involves gating of selected responses to executive functions.

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 Impaired refinement of kinematic variability in Huntington disease mice on an automated home-cage forelimb motor task

2021 ◽  
Author(s):  
Cameron L. Woodard ◽  
Marja D. Sepers ◽  
Lynn A. Raymond
Keyword(s):  
Motor Learning ◽  
Mouse Models ◽  
Huntington Disease ◽  
Neurological Disorders ◽  
Home Cage ◽  
Neurodegenerative Disorder ◽  
Motor Task ◽  
Testing System ◽  
Neuronal Dysfunction ◽  
Movement Variability

AbstractThe effective development of novel therapies in mouse models of neurological disorders relies on behavioural assessments that provide accurate read-outs of neuronal dysfunction and/or degeneration. We designed an automated behavioural testing system (‘PiPaw’) which integrates an operant lever-pulling task directly into the mouse home-cage. This task is accessible to group-housed mice 24-hours per day, enabling high-throughput longitudinal analysis of forelimb motor learning. Moreover, this design eliminates the need for exposure to novel environments and minimizes experimenter interaction, significantly reducing two of the largest stressors associated with animal behaviour. Mice improved their performance of this task over one week of testing by reducing inter-trial variability of reward-related kinematic parameters (pull amplitude or peak velocity). In addition, mice displayed short-term improvements in reward rate, and a concomitant decrease in movement variability, over the course of brief (<10 minutes) bouts of task engagement. We used this system to assess motor learning in mouse models of the inherited neurodegenerative disorder, Huntington disease (HD). Despite having no baseline differences in task performance, Q175-FDN HD mice were unable to modulate the variability of their movements in order to increase reward on either short or long timescales. Task training was associated with a decrease in the amplitude of spontaneous excitatory activity recorded from striatal medium spiny neurons in the hemisphere contralateral to the trained forelimb in wildtype mice; however, no such changes were observed in Q175-FDN mice. This behavioural screening platform should prove useful for preclinical drug trials towards improved treatments in HD and other neurological disorders.Significance StatementIn order to develop effective therapies for neurological disorders such as Huntington disease (HD), it’s important to be able to accurately and reliably assess the behaviour of mouse models of these conditions. Moreover, these behavioural assessments should provide an accurate readout of underlying neuronal dysfunction and/or degeneration. In this paper, we employed an automated behavioural testing system to assess motor learning in mice within their home-cage. Using this system, we were able to study motor abnormalities in HD mice with an unprecedented level of detail, and identified a specific behavioural deficit associated with an underlying impairment in striatal neuronal plasticity. These results validate the usefulness of this system for assessing behaviour in mouse models of HD and other neurological disorders.

The Effects of Handedness and Dominance on Motor Task Performance

2018 ◽  
Vol 62 (1) ◽  
pp. 1237-1241 ◽  
Author(s):  
Salim A. Mouloua ◽  
Mustapha Mouloua ◽  
Daniel S. McConnell ◽  
P. A. Hancock
Keyword(s):  
Task Performance ◽  
Motor Task ◽  
Dominant Hand ◽  
Hand Dominance ◽  
Prior Training ◽  
Future Studies ◽  
Fitts Law ◽  
Left Handed ◽  
Left And Right ◽  
Practical Implications

Two studies were carried out to examine the effects of user handedness and hand dominance on a motor task using Fitts’ law. Study one was designed to validate our previous findings showing differences between left- and right-handed participants who completed a mouse-pointing task using Fitts’ law. Results showed that right-handed participants were significantly faster than their left-handed peers, thereby validating our previous findings. Study two examined the effect of handedness and hand dominance on motor task performance by requiring two groups of left- and right-handed participants perform the motor task using both their dominant and non-dominant hands. Results showed a significant interaction between handedness and hand dominance on task performance. Right-handed participants were again significantly faster than their left-handed peers when both groups were using their dominant hand. However, left-handed participants were significantly faster than their right-handed peers when both groups were using their non-dominant hand. These findings might be attributed to prior training with computer mice designs that do not account for user handedness. Both theoretical and practical implications, as well as directions for future studies are also discussed.

scholarly journals Promoting Motor Variability During Robotic Assistance Enhances Motor Learning of Dynamic Tasks

2021 ◽  
Vol 14 ◽  
Author(s):  
Özhan Özen ◽  
Karin A. Buetler ◽  
Laura Marchal-Crespo
Keyword(s):  
Motor Learning ◽  
Sense Of Agency ◽  
Control Group ◽  
Haptic Guidance ◽  
Movement Variability ◽  
Robotic Training ◽  
Motor Variability ◽  
End Effector ◽  
Dynamic Task ◽  
Somatosensory Information

Despite recent advances in robot-assisted training, the benefits of haptic guidance on motor (re)learning are still limited. While haptic guidance may increase task performance during training, it may also decrease participants' effort and interfere with the perception of the environment dynamics, hindering somatosensory information crucial for motor learning. Importantly, haptic guidance limits motor variability, a factor considered essential for learning. We propose that Model Predictive Controllers (MPC) might be good alternatives to haptic guidance since they minimize the assisting forces and promote motor variability during training. We conducted a study with 40 healthy participants to investigate the effectiveness of MPCs on learning a dynamic task. The task consisted of swinging a virtual pendulum to hit incoming targets with the pendulum ball. The environment was haptically rendered using a Delta robot. We designed two MPCs: the first MPC—end-effector MPC—applied the optimal assisting forces on the end-effector. A second MPC—ball MPC—applied its forces on the virtual pendulum ball to further reduce the assisting forces. The participants' performance during training and learning at short- and long-term retention tests were compared to a control group who trained without assistance, and a group that trained with conventional haptic guidance. We hypothesized that the end-effector MPC would promote motor variability and minimize the assisting forces during training, and thus, promote learning. Moreover, we hypothesized that the ball MPC would enhance the performance and motivation during training but limit the motor variability and sense of agency (i.e., the feeling of having control over their movements), and therefore, limit learning. We found that the MPCs reduce the assisting forces compared to haptic guidance. Training with the end-effector MPC increases the movement variability and does not hinder the pendulum swing variability during training, ultimately enhancing the learning of the task dynamics compared to the other groups. Finally, we observed that increases in the sense of agency seemed to be associated with learning when training with the end-effector MPC. In conclusion, training with MPCs enhances motor learning of tasks with complex dynamics and are promising strategies to improve robotic training outcomes in neurological patients.

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