Visuomotor Learning Enhanced by Augmenting Instantaneous Trajectory Error Feedback during Reaching (original) (raw)
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Visual Error Augmentation for Enhancing Motor Learning and Rehabilitative Relearning
9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005., 2005
We developed a real-time controller for a 2 degree-of-freedom robotic system using xPC Target. This system was used to investigate how different methods of performance error feedback can lead to faster and more complete motor learning in individuals asked to compensate for a novel visuo-motor transformation (a 30 degree rotation). Four groups of normal human subjects were asked to reach with their unseen arm to visual targets surrounding a central starting location. A cursor tracking hand motion was provided during each reach. For one group of subjects, deviations from the "ideal" compensatory hand movement (i.e. trajectory errors) were amplified with a gain of 2 whereas another group was provided visual feedback with a gain of 3.1. Yet another group was provided cursor feedback wherein the cursor was rotated by an additional (constant) offset angle. We compared the rates at which the hand paths converged to the steady-state trajectories. Our results demonstrate that error-augmentation can improve the rate and extent of motor learning of visuomotor rotations in healthy subjects. We also tested this method on straightening the movements of stroke subjects, and our early results suggest that error amplification can facilitate neurorehabilitation strategies in brain injuries such as stroke.
A real-time haptic/graphic demonstration of how error augmentation can enhance learning
2005
We developed a real-time controller for a 2 degree-of-freedom robotic system using xPC Target. Th is system was used to investigate how different methods of performance error feedback can lead to faster and more complete motor learning in individuals asked to compensate for a novel visuo-motor transformation (a 30 degree rotation). Four groups of human subjects were asked to reach with their unseen arm to visual targets surrounding a central starting location. A cursor tracking hand motion was provided during each reach. For one group of subjects, deviations from the "ideal" compensatory hand movement (i.e. trajectory errors) were amplified with a gain of 2 whereas another group was provided visual feedback with a gain of 3.1. Yet another group was provided cursor feedback wherein the cursor was rotated by an additional (constant) offset angle. We compared the rates at which the hand paths converged to the steady -state trajectories. Our results demonstrate that error-augmentation can improve the rate and extent of motor learning of visuomotor rotations in healthy subjects. Furthermore, our results suggest that both error amplification and offsetaugmentation may facilitate neuro-rehabilitation strategies that restore function in brain injuries such as stroke.
PLoS ONE, 2013
An important issue in motor learning/adaptation research is how the brain accepts the error information necessary for maintaining and improving task performance in a changing environment. The present study focuses on the effect of timing of error feedback. Previous research has demonstrated that adaptation to displacement of the visual field by prisms in a manual reaching task is significantly slowed by delayed visual feedback of the endpoint, suggesting that error feedback is most effective when given at the end of a movement. To further elucidate the brain mechanism by which error information is accepted in visuomotor adaptation, we tested whether error acceptance is linked to the end of a given task or to the end of an executed movement. We conducted a behavioral experiment using a virtual shooting task in which subjects controlled their wrist movements to meet a target with a cursor as accurately as possible. We manipulated the timing of visual feedback of the impact position so that it occurred either ahead of or behind the true time of impact. In another condition, the impact timing was explicitly indicated by an additional cue. The magnitude of the aftereffect significantly varied depending on the timing of feedback (p , 0.05, Friedman's Test). Interestingly, two distinct peaks of aftereffect were observed around movement-end and around task-end, irrespective of the existence of the timing cue. However, the peak around task-end was sharper when the timing cue was given. Our results demonstrate that the brain efficiently accepts error information at both movement-end and task-end, suggesting that two different learning mechanisms may underlie visuomotor transformation.
PLoS ONE, 2009
Computational models of motor control have often explained the straightness of horizontal planar reaching movements as a consequence of optimal control. Departure from rectilinearity is thus regarded as sub-optimal. Here we examine if subjects may instead select to make curved trajectories following adaptation to force fields and visuomotor rotations. Separate subjects adapted to force fields with or without visual feedback of their hand trajectory and were retested after 24 hours. Following adaptation, comparable accuracies were achieved in two ways: with visual feedback, adapted trajectories in force fields were straight whereas without it, they remained curved. The results suggest that trajectory shape is not always straight, but is also influenced by the calibration of available feedback signals for the state estimation required by the task. In a follow-up experiment, where additional subjects learned a visuomotor rotation immediately after force field, the trajectories learned in force fields (straight or curved) were transferred when directions of the perturbations were similar but not when directions were opposing. This demonstrates a strong bias by prior experience to keep using a recently acquired control policy that continues to produce successful performance inspite of differences in tasks and feedback conditions. On relearning of force fields on the second day, facilitation by intervening visuomotor rotations occurred only when required motor adjustments and calibration of feedback signals were similar in both tasks. These results suggest that both the available feedback signals and prior history of learning influence the choice and maintenance of control policy during adaptations.
Improvement in Hand Trajectory of Reaching Movements by Error-Augmentation
The purpose of this study was to investigate whether adaptive responses to error-augmentation force fields, would decrease the trajectory errors in hand-reaching movements in multiple directions in healthy individuals. The study was conducted, as a randomized controlled trial, in 41 healthy subjects. The study group trained on a 3D robotic system, applying error-augmenting forces on the hand during the execution of tasks. The control group carried out the same protocol in null-field conditions. A mixed-model ANOVA was implemented to investigate the interaction between groups and time, and changes in outcome measures within groups. The findings were that there was a significant interaction effect for group  time in terms of the magnitude of movement errors across game-sets. The trajectory error of the study group significantly decreased from 0.035 AE 0.013 m at baseline to 0.029 AE 0.011 m at a follow-up, which amounted to a 14.8% improvement. The degree of movement errors were not significantly changed within a game-set. We conclude that practicing hand-reaching movement in multiple random directions, using the error-augmentation technique, decreases the deviation of the hand trajectory from a straight line. However, this type of training prevents the generalizability of adaptation between consecutive reaching movements. Further studies should investigate the feasibility of this training method for rehabilitation of post-stroke individuals. Keywords Adaptation · Brain model · Error-augmentation · Force-field · Hand reaching · Hand trajectory
Real-time error detection but not error correction drives automatic visuomotor adaptation
Experimental Brain Research, 2010
We investigated the role of visual feedback of task performance in visuomotor adaptation. Participants produced novel two degrees of freedom movements (elbow flexion-extension, forearm pronation-supination) to move a cursor towards visual targets. Following trials with no rotation, participants were exposed to a 60°visuomotor rotation, before returning to the non-rotated condition. A colour cue on each trial permitted identification of the rotated/nonrotated contexts. Participants could not see their arm but received continuous and concurrent visual feedback (CF) of a cursor representing limb position or post-trial visual feedback (PF) representing the movement trajectory. Separate groups of participants who received CF were instructed that online modifications of their movements either were, or were not, permissible as a means of improving performance. Feedforward-mediated performance improvements occurred for both CF and PF groups in the rotated environment. Furthermore, for CF participants this adaptation occurred regardless of whether feedback modifications of motor commands were permissible. Upon re-exposure to the nonrotated environment participants in the CF, but not PF, groups exhibited post-training aftereffects, manifested as greater angular deviations from a straight initial trajectory, with respect to the pre-rotation trials. Accordingly, the nature of the performance improvements that occurred was dependent upon the timing of the visual feedback of task performance. Continuous visual feedback of task performance during task execution appears critical in realising automatic visuomotor adaptation through a recalibration of the visuomotor mapping that transforms visual inputs into appropriate motor commands.
Visuomotor Learning Generalizes Around the Intended Movement
Human motor learning is useful if it generalizes beyond the trained task. Here, we introduce a new idea about how human visuomotor learning generalizes. We show that learned reaching movements generalize around where a person intends to move (i.e., aiming direction) as opposed to where they actually move. We used a visual rotation paradigm that allowed us to disentangle whether generalization is centered on where people aim to move, where they actually move, or where visual feedback indicates they moved. Participants reached to a visual target with their arm occluded from view. The cursor feedback was rotated relative to the position of their unseen hand to induce learning. Participants verbally reported their aiming direction, reached, and then were shown the outcome. We periodically introduced single catch trials with no feedback to measure learning. Results showed that learning was maximal at the participants' aiming location, and not at the actual hand position or where the cursor was displayed. This demonstrates that visuomotor learning generalizes around where we intend to move rather than where we actually move, and thus introduces a new role for cognitive processes beyond simply reducing movement error.
Neuroscience Letters, 2013
Separating visual and proprioceptive information in terms of workspace locations during reaching movement has been shown to disturb transfer of visuomotor adaptation across the arms. Here, we investigated whether separating visual and motor workspaces would also disturb generalization of visuomotor adaptation across movement conditions within the same arm. Subjects were divided into four experimental groups (plus three control groups). The first two groups adapted to a visual rotation under a "dissociation" condition in which the targets for reaching movement were
eNeuro
When a visually guided reaching movement is unexpectedly perturbed, it is implicitly corrected in two ways: immediately after the perturbation by feedback control (online correction) and in the next movement by adjusting feedforward motor commands (offline correction or motor adaptation). Although recent studies have revealed a close relationship between feedback and feedforward controls, the nature of this relationship is not yet fully understood. Here, we show that both implicit online and offline movement corrections utilize the same visuomotor map for feedforward movement control that transforms the spatial location of visual objects into appropriate motor commands. First, we artificially distorted the visuomotor map by applying opposite visual rotations to the cursor representing the hand position while human participants reached for two different targets. This procedure implicitly altered the visuomotor map so that changes in the movement direction to the target location were ...
Learning a new visuomotor transformation: error correction and generalization
Cognitive Brain Research, 1995
The use of an aiming tool requires learning a new transformation between visual and proprioceptive information and motor command. We have examined this question by quantifying the kinematics of the movement during the transitory phase of adaptation to a rotational bias (60" counterclockwise, then clockwise) added to a standard mouse-cursor device in the plane of the screen. Control-aiming movements were almost linear with a bell-shaped velocity profile. The bias induced an equivalent initial directional error which was usually corrected within 20 trials. The learning trajectories were combinations of spirals and fast or slow straight movements. The posture of the hand was slightly (less than 10") modified by the bias. These features suggest three corrective processes: on-line continuous correction based on evaluation of the relative cursor-to-target position, discrete correction based on assessment of the discrepancy angle between the cursor-to-target direction and the effective cursor direction, and memorization of trial-to-trial correction. These results are interpreted in the light of neurophysiological data and neural net modeling, which suggest that the visuomotor transformation performed by cortical areas for reaching is effected by projecting the visual information on a reference frame that rotates with the arm. The initial directional error reappeared when the direction of the target was changed and increased with degree of change. The limited generalization suggests that bias correction is stored in relation to the coding of the target direction and that movement towards a new direction is computed as a projection of the previously learned bias on the new visual direction.