The 24-h savings of adaptation to novel movement dynamics initially reflects the recall of previous performance

Humans rapidly adapt reaching movements in response to perturbations (e.g., manipulations of movement dynamics or visual feedback). Following a break, when reexposed to the same perturbation, subjects demonstrate savings, a faster learning rate compared with the time course of initial training. Although this has been well studied, there are open questions on the extent early savings reflects the rapid recall of previous performance. To address this question, we examined how the properties of initial training (duration and final adaptive state) influence initial single-trial adaptation to force-field perturbations when training sessions were separated by 24 h. There were two main groups that were distinct based on the presence or absence of a washout period at the end of day 1 (with washout vs. without washout). We also varied the training duration on day 1 (15, 30, 90, or 160 training trials), resulting in 8 subgroups of subjects. We show that single-trial adaptation on day 2 scaled with training duration, even for similar asymptotic levels of learning on day 1 of training. Interestingly, the temporal force profile following the first perturbation on day 2 matched that at the end of day 1 for the longest training duration group that did not complete the washout. This correspondence persisted but was significantly lower for shorter training durations and the washout subject groups. Collectively, the results suggest that the adaptation observed very early in reexposure results from the rapid recall of the previously learned motor recalibration but is highly dependent on the initial training duration and final adaptive state. NEW & NOTEWORTHY The extent initial readaptation reflects the recall of previous motor performance is largely unknown. We examined early single-trial force-field adaptation on the second day of training and distinguished initial retention from recall. We found that the single-trial adaptation following the 24-h break matched that at the end of the first day, but this recall was modified by the training duration and final level of learning on the first day of training.

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

Journal of Neurophysiology ◽

10.1152/jn.00734.2011 ◽

2013 ◽

Vol 109 (3) ◽

pp. 782-791 ◽

Cited By ~ 35

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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Manipulating Single-Trial Motor Performance in Chronic Stroke Patients by Closed-Loop Brain State Interaction

IEEE Transactions on Neural Systems and Rehabilitation Engineering ◽

10.1109/tnsre.2021.3108187 ◽

2021 ◽

Vol 29 ◽

pp. 1806-1816

Author(s):

Andreas Meinel ◽

Jan Sosulski ◽

Stephan Schraivogel ◽

Janine Reis ◽

Michael Tangermann

Keyword(s):

Motor Performance ◽

Closed Loop ◽

Chronic Stroke ◽

State Interaction ◽

Brain State ◽

Single Trial ◽

Stroke Patients

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Trial-by-Trial Adaptation of Movements during Mental Practice under Force Field

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/109497 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Muhammad Nabeel Anwar ◽

Salman Hameed Khan

Keyword(s):

Nervous System ◽

Force Field ◽

Motor Imagery ◽

Internal Model ◽

Human Movement ◽

Mental Practice ◽

Higher Learning ◽

Reaching Movements ◽

Adaptive Compensation ◽

Human Nervous System

Human nervous system tries to minimize the effect of any external perturbing force by bringing modifications in the internal model. These modifications affect the subsequent motor commands generated by the nervous system. Adaptive compensation along with the appropriate modifications of internal model helps in reducing human movement errors. In the current study, we studied how motor imagery influences trial-to-trial learning in a robot-based adaptation task. Two groups of subjects performed reaching movements with or without motor imagery in a velocity-dependent force field. The results show that reaching movements performed with motor imagery have relatively a more focused generalization pattern and a higher learning rate in training direction.

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Whole-body adaptation to novel dynamics does not transfer between effectors

Journal of Neurophysiology ◽

10.1152/jn.00628.2020 ◽

2021 ◽

Author(s):

Alison Pienciak-Siewert ◽

Alaa A Ahmed

Keyword(s):

Postural Control ◽

Arm Movement ◽

Movement Planning ◽

Reaching Movements ◽

Whole Body ◽

Postural Control System ◽

Movement Dynamics ◽

Gradual Introduction ◽

Postural Movement ◽

The Brain

How does the brain coordinate concurrent adaptation of arm movements and standing posture? From previous studies, the postural control system can use information about previously adapted arm movement dynamics to plan appropriate postural control; however, it is unclear whether postural control can be adapted and controlled independently of arm control. The present study addresses that question. Subjects practiced planar reaching movements while standing and grasping the handle of a robotic arm, which generated a force field to create novel perturbations. Subjects were divided into two groups, for which perturbations were introduced in either an abrupt or gradual manner. All subjects adapted to the perturbations while reaching with their dominant (right) arm, then switched to reaching with their non-dominant (left) arm. Previous studies of seated reaching movements showed that abrupt perturbation introduction led to transfer of learning between arms, but gradual introduction did not. Interestingly, in this study neither group showed evidence of transferring adapted control of arm or posture between arms. These results suggest primarily that adapted postural control cannot be transferred independently of arm control in this task paradigm. In other words, whole-body postural movement planning related to a concurrent arm task is dependent on information about arm dynamics. Finally, we found that subjects were able to adapt to the gradual perturbation while experiencing very small errors, suggesting that both error size and consistency play a role in driving motor adaptation.

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

10.1101/544189 ◽

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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The effect of sequence learning on sensorimotor adaptation

10.1101/2020.07.06.190397 ◽

2020 ◽

Author(s):

Yang Liu ◽

Hannah J. Block

Keyword(s):

Force Field ◽

Sequence Learning ◽

Target Position ◽

Random Order ◽

Neural Substrates ◽

Skill Learning ◽

Sensorimotor Learning ◽

Sensorimotor Adaptation ◽

Sequence Information ◽

Force Field Adaptation

AbstractMotor skill learning involves both sensorimotor adaptation (calibrating the response to task dynamics and kinematics), and sequence learning (executing the task elements in the correct order at the necessary speed). These processes typically occur together in natural behavior and share much in common, such as working memory demands, development, and possibly neural substrates. However, sensorimotor and sequence learning are usually studied in isolation in research settings, for example as force field adaptation or serial reaction time tasks (SRTT), respectively. It is therefore unclear whether having predictive sequence information during sensorimotor adaptation would facilitate performance, perhaps by improving sensorimotor planning, or if it would impair performance, perhaps by occupying neural resources needed for sensorimotor learning. Here we evaluated adaptation to a distance-dependent force field in two different SRTT contexts: In Experiment 1, 28 subjects reached between 4 targets in a sequenced or random order. In Experiment 2, 40 subjects reached to one target, but 3 force field directions were applied in a sequenced or random order. We did not observe any consistent influence of target position sequence on force field adaptation in Experiment 1. However, sequencing of force field directions facilitated sensorimotor adaptation and retention in Experiment 2. This is inconsistent with the idea that sensorimotor and sequence learning share neural resources in any mutually exclusive fashion. These findings indicate that under certain conditions, perhaps especially when the sequence is related to the sensorimotor perturbation itself as in Experiment 2, sequence learning may interact with sensorimotor learning in a facilitatory manner.

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Abstract TP92: Brain Activity Related To The Effects Of Augmented Feedback On Learning Movements In A Dynamic Environment In Healthy Subjects And Stroke Survivors

Stroke ◽

10.1161/str.44.suppl_1.atp92 ◽

2013 ◽

Vol 44 (suppl_1) ◽

Author(s):

Mukul Mukherjee ◽

Wen-Pin Chang ◽

Ka-Chun Siu ◽

Pierre Fayad ◽

Nicholas Stergiou

Keyword(s):

Older Adults ◽

Force Field ◽

Cognitive Processing ◽

Visual Feedback ◽

Brain Activity ◽

Dynamic Environments ◽

Reaching Movements ◽

Stroke Survivors ◽

Frequency Bands ◽

Dynamic Task

Augmented visual feedback has been shown to be effective for learning reaching movements in dynamic environments after a stroke. However, the mechanisms behind such changes are not known. In addition, how brain activity changes with age as we learn novel dynamic tasks is also not clear. The purpose of this study was to examine brain activity changes that are observed when healthy younger and older adults and stroke survivors learn reaching movements in dynamic environments using augmented visual feedback. Healthy young and older adults and chronic stroke survivors were randomly assigned to either a control or an experimental group. They all performed reaching movements with the Inmotion2 robotic system (Interactive Motion Tech Inc., MA) using the dominant/affected arm in a velocity-dependent force field. Controls received actual feedback of their movement, while experimental subjects received augmented visual feedback. Electroencephalogram recordings were analyzed to determine Event Related Desynchronization percent (ERD%). The theta, alpha, and beta frequency bands were examined during movement and pre-movement phases. With learning, the absolute power of the frequency bands increased from the baseline to the adaptation condition, which was then washed out when the force field was removed. With age, there was a reduction in ERD% in alpha and beta bands as the motor task was learned. Stroke subjects had a further reduction in the ERD% in comparison to the healthy older adults. In addition, augmented visual feedback led to a significant increase in the ERD% in comparison to controls during the planning and execution stages of the movement. Past studies have shown when novel dynamics are learned, ERD% reduces indicating increased cognitive processing and memory load. We found that with aging, the cognitive processing and memory required for performing the same dynamic task, increased. After a stroke, there was a further increase. However, the utilization of augmented visual feedback may reduce such requirements and lessen the load on higher centers. These results provide mechanistic support for employing augmented visual feedback for stroke rehabilitation specific to reaching movements in dynamic environments.

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Corrigendum: Target Uncertainty During Motor Decision-Making: The Time Course of Movement Variability Reveals the Effect of Different Sources of Uncertainty on the Control of Reaching Movements

Frontiers in Psychology ◽

10.3389/fpsyg.2019.00913 ◽

2019 ◽

Vol 10 ◽

Author(s):

Melanie Krüger ◽

Joachim Hermsdörfer

Keyword(s):

Decision Making ◽

Time Course ◽

Reaching Movements ◽

Movement Variability ◽

Target Uncertainty ◽

Sources Of Uncertainty ◽

Different Sources

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

Journal of Neurophysiology ◽

10.1152/jn.00745.2012 ◽

2013 ◽

Vol 110 (2) ◽

pp. 322-333 ◽

Cited By ~ 46

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.

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