scholarly journals On the encoding capacity of human motor adaptation

2021 ◽  
Author(s):  
Seung-Yeon Kim ◽  
Jae-Woon Kwon ◽  
Jin-Min Kim ◽  
Frank Chong-Woo Park ◽  
Sang-Hoon Yeo
Keyword(s):  
Motor Learning ◽  
Force Field ◽  
Information Content ◽  
Motor Adaptation ◽  
Movement Speed ◽  
Limiting Factor ◽  
Information Encoding ◽  
Motor Primitives ◽  
Human Motor ◽  
Human Motor Adaptation

Primitive-based models of motor learning suggest that adaptation occurs by tuning the responses of motor primitives. Based on this idea, we consider motor learning as an information encoding procedure, that is, a procedure of encoding a motor skill into primitives. The capacity of encoding is determined by the number of recruited primitives, which depends on how many primitives are "visited" by the movement, and this leads to a rather counter-intuitive prediction that faster movement, where a larger number of motor primitives are involved, allows learning more complicated motor skills. Here we provide a set of experimental results that support this hypothesis. First, we show that learning occurs only with movement, i.e., only with non-zero encoding capacity. When participants were asked to counteract a rotating force applied to a robotic handle, they were unable to do so when maintaining a static posture but were able to adapt when making small circular movements. Our second experiment further investigated how adaptation is affected by movement speed. When adapting to a simple (low-information-content) force field, fast (high-capacity) movement did not have an advantage over slow (low-capacity) movement. However, for a complex (high-information-content) force field, the fast movement showed a significant advantage over slow movement. Our final experiment confirmed that the observed benefit of high-speed movement is only weakly affected by mechanical factors. Taken together, our results suggest that the encoding capacity is a genuine limiting factor of human motor adaptation.

scholarly journals Remembering forward: Neural correlates of memory and prediction in human motor adaptation

NeuroImage ◽  
2012 ◽  
Vol 59 (1) ◽  
pp. 582-600 ◽  
Author(s):  
Robert A. Scheidt ◽  
Janice L. Zimbelman ◽  
Nicole M.G. Salowitz ◽  
Aaron J. Suminski ◽  
Kristine M. Mosier ◽  
...  
Keyword(s):  
Motor Adaptation ◽  
Neural Correlates ◽  
Human Motor ◽  
Human Motor Adaptation

scholarly journals Linear Hypergeneralization of Learned Dynamics Across Movement Speeds Reveals Anisotropic, Gain-Encoding Primitives for Motor Adaptation

2011 ◽  
Vol 105 (1) ◽  
pp. 45-59 ◽  
Author(s):  
Wilsaan M. Joiner ◽  
Obafunso Ajayi ◽  
Gary C. Sing ◽  
Maurice A. Smith
Keyword(s):  
High Speed ◽  
Time Course ◽  
Motor Adaptation ◽  
Motor Output ◽  
Movement Speed ◽  
Close Match ◽  
Motor Primitives ◽  
Motion State ◽  
Point To Point ◽  
Motor Actions

The ability to generalize learned motor actions to new contexts is a key feature of the motor system. For example, the ability to ride a bicycle or swing a racket is often first developed at lower speeds and later applied to faster velocities. A number of previous studies have examined the generalization of motor adaptation across movement directions and found that the learned adaptation decays in a pattern consistent with the existence of motor primitives that display narrow Gaussian tuning. However, few studies have examined the generalization of motor adaptation across movement speeds. Following adaptation to linear velocity-dependent dynamics during point-to-point reaching arm movements at one speed, we tested the ability of subjects to transfer this adaptation to short-duration higher-speed movements aimed at the same target. We found near-perfect linear extrapolation of the trained adaptation with respect to both the magnitude and the time course of the velocity profiles associated with the high-speed movements: a 69% increase in movement speed corresponded to a 74% extrapolation of the trained adaptation. The close match between the increase in movement speed and the corresponding increase in adaptation beyond what was trained indicates linear hypergeneralization. Computational modeling shows that this pattern of linear hypergeneralization across movement speeds is not compatible with previous models of adaptation in which motor primitives display isotropic Gaussian tuning of motor output around their preferred velocities. Instead, we show that this generalization pattern indicates that the primitives involved in the adaptation to viscous dynamics display anisotropic tuning in velocity space and encode the gain between motor output and motion state rather than motor output itself.

scholarly journals A Very Fast Time Scale of Human Motor Adaptation: Within Movement Adjustments of Internal Representations during Reaching

eNeuro ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. ENEURO.0149-19.2019 ◽  
Author(s):  
Frédéric Crevecoeur ◽  
Jean-Louis Thonnard ◽  
Philippe Lefèvre
Keyword(s):  
Time Scale ◽  
Motor Adaptation ◽  
Fast Time ◽  
Internal Representations ◽  
Fast Time Scale ◽  
Human Motor ◽  
Human Motor Adaptation

scholarly journals Human motor adaptation in whole body motion

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Jan Babič ◽  
Erhan Oztop ◽  
Mitsuo Kawato
Keyword(s):  
Motor Adaptation ◽  
Body Motion ◽  
Whole Body ◽  
Human Motor ◽  
Human Motor Adaptation

scholarly journals No effects of cerebellar transcranial direct current stimulation on force field and visuomotor reach adaptation in young and healthy subjects

2019 ◽  
Vol 121 (6) ◽  
pp. 2112-2125 ◽  
Author(s):  
A. Mamlins ◽  
T. Hulst ◽  
O. Donchin ◽  
D. Timmann ◽  
J. Claassen
Keyword(s):  
Motor Learning ◽  
Force Field ◽  
Direct Current ◽  
Transcranial Direct Current Stimulation ◽  
Healthy Subjects ◽  
Motor Adaptation ◽  
Visuomotor Rotation ◽  
Direct Current Stimulation ◽  
Cerebellar Tdcs ◽  
Adaptation Task

Previous studies have shown that cerebellar transcranial direct current stimulation (tDCS) leads to faster adaptation of arm reaching movements to visuomotor rotation and force field perturbations in healthy subjects. The first aim of the present study was to confirm a stimulation-dependent effect on motor adaptation. Second, we investigated whether tDCS effects differ depending on onset, that is, before or at the beginning of the adaptation phase. A total of 120 healthy and right-handed subjects (60 women, mean age 23.2 ± SD 2.7 yr, range 18–31 yr) were tested. Subjects moved a cursor with a manipulandum to one of eight targets presented on a vertically orientated screen. Three baseline blocks were followed by one adaptation block and three washout blocks. Sixty subjects did a force field adaptation task (FF), and 60 subjects did a visuomotor adaptation task (VM). Equal numbers of subjects received anodal, cathodal, or sham cerebellar tDCS beginning either in the third baseline block or at the start of the adaptation block. In FF and VM, tDCS and the onset of tDCS did not show a significant effect on motor adaptation (all P values >0.05). We were unable to support previous findings of modulatory cerebellar tDCS effects in reaching adaptation tasks in healthy subjects. Prior to possible application in patients with cerebellar disease, future experiments are needed to determine which tDCS and task parameters lead to robust tDCS effects. NEW & NOTEWORTHY Transcranial direct current stimulation (tDCS) is a promising tool to improve motor learning. We investigated whether cerebellar tDCS improves motor learning in force field and visuomotor tasks in healthy subjects and what influence the onset of stimulation has. We did not find stimulation effects of tDCS or an effect of onset of stimulation. A reevaluation of cerebellar tDCS in healthy subjects and at the end of the clinical potential in cerebellar patients is demanded.

scholarly journals Feedback adaptation to unpredictable force fields in 250ms

2019 ◽  
Author(s):  
Frédéric Crevecoeur ◽  
James Mathew ◽  
Marie Bastin ◽  
Philippe Lefevre
Keyword(s):  
Nervous System ◽  
Motor Learning ◽  
Force Field ◽  
Recent Work ◽  
Muscle Activity ◽  
Force Fields ◽  
Control Strategies ◽  
Motor Adaptation ◽  
Classical Studies ◽  
Learning And Adaptation

AbstractMotor learning and adaptation are important functions of the nervous system. Classical studies have characterized how humans adapt to changes in the environment during tasks such as reaching, and have documented improvements in behavior across movements. Yet little is known about how quickly the nervous system adapts to such disturbances. In particular, recent work has suggested that adaptation could be sufficiently fast to alter the control strategies of an ongoing movement. To further address the possibility that learning occurred within a single movement, we designed a series of human reaching experiments to extract in muscles recordings the latency of feedback adaptation. Our results confirmed that participants adapted their feedback responses to unanticipated force fields applied randomly. In addition, our analyses revealed that the feedback response was specifically and finely tuned to the ongoing perturbation not only across trials with the same force field, but also across different kinds of force fields. Finally, changes in muscle activity consistent with feedback adaptation occurred in about 250ms following reach onset. We submit this estimate as the latency of motor adaptation in the nervous system.

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 Reward and punishment enhance motor adaptation in stroke

2017 ◽  
Author(s):  
Graziella Quattrocchi ◽  
Richard Greenwood ◽  
John C Rothwell ◽  
Joseph M Galea ◽  
Sven Bestmann
Keyword(s):  
Motor Learning ◽  
Force Field ◽  
Motor Adaptation ◽  
Similar Degree ◽  
Stroke Patients ◽  
Neutral Group ◽  
Rehabilitation Outcomes ◽  
First Time ◽  
Adaptation Phase ◽  
Reward And Punishment

ABSTRACTThe effects of motor learning, such as motor adaptation, in stroke rehabilitation are often transient, thus mandating approaches that enhance the amount of learning and retention. Previously, we showed in young individuals that reward-and punishment-feedback have dissociable effects on motor adaptation, with punishment improving adaptation and reward enhancing retention. If these findings were able to generalise to stroke patients, they would provide a way to optimize motor learning in these patients. Therefore, we tested this in 45 chronic stroke patients allocated in three groups. Patients performed reaching movements with their paretic arm with a robotic manipulandum. After training (day 1), day 2 involved adapting to a novel force-field. During this adaptation phase, patients received performance-based feedback according to the group they were allocated: reward, punishment or no feedback (neutral). On day 3, patients readapted to the force-field but all groups now received neutral feedback. All patients adapted, with reward and punishment groups displaying greater adaptation and readaptation than the neutral group, irrespective of demographic, cognitive or functional differences. Remarkably, the reward and punishment groups adapted to similar degree as healthy controls. Finally, the reward group showed greater retention. This study provides, for the first time, evidence that reward and punishment can enhance motor adaptation in stroke patients. Further research on reinforcement-based motor learning regimes is warranted to translate these promising results into clinical practice and improve motor rehabilitation outcomes in stroke patients.

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