Influence of Viscous Loads on Motor Planning

Thoroughman, Kurt A.; Wang, Wei; Tomov, Dimitre

doi:10.1152/jn.01126.2006

Cited by 8 publications

(4 citation statements)

References 28 publications

(34 reference statements)

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“…The minimum variance is equal to the minimum weighted sum of motor commands when signal-dependent noise is assumed. This notion was supported by the simulations of Thoroughman et al (2007), in which large overcompensation was predicted by both minimum torque change and minimum variance models in the force-field task. Because these models all minimize the sum of motor commands, the typical OFC problem that has motor costs is closely related to these two models.…”

Section: Discussionmentioning

confidence: 72%

Motor Adaptation as a Process of Reoptimization

Izawa¹,

Rane²,

Donchin³

et al. 2008

J. Neurosci.

305

258

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Adaptation is sometimes viewed as a process in which the nervous system learns to predict and cancel effects of a novel environment, returning movements to near baseline (unperturbed) conditions. An alternate view is that cancellation is not the goal of adaptation. Rather, the goal is to maximize performance in that environment. If performance criteria are well defined, theory allows one to predict the reoptimized trajectory. For example, if velocity-dependent forces perturb the hand perpendicular to the direction of a reaching movement, the best reach plan is not a straight line but a curved path that appears to overcompensate for the forces. If this environment is stochastic (changing from trial to trial), the reoptimized plan should take into account this uncertainty, removing the overcompensation. If the stochastic environment is zero-mean, peak velocities should increase to allow for more time to approach the target. Finally, if one is reaching through a via-point, the optimum plan in a zero-mean deterministic environment is a smooth movement but in a zero-mean stochastic environment is a segmented movement. We observed all of these tendencies in how people adapt to novel environments. Therefore, motor control in a novel environment is not a process of perturbation cancellation. Rather, the process resembles reoptimization: through practice in the novel environment, we learn internal models that predict sensory consequences of motor commands. Through reward-based optimization, we use the internal model to search for a better movement plan to minimize implicit motor costs and maximize rewards.

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Section: Discussionmentioning

confidence: 72%

Motor Adaptation as a Process of Reoptimization

Izawa¹,

Rane²,

Donchin³

et al. 2008

J. Neurosci.

305

258

View full text Add to dashboard Cite

show abstract

“…When the same perturbation is applied repetitively, these models generally predict an exponential learning curve toward complete adaptation, which has been observed in various motor adaptation studies. With block learning, subjects can produce forces that very well cancel force perturbations such as a velocity-dependent force field (Shadmehr and Mussa-Ivaldi 1994;Thoroughman et al 2007), position-dependent force field (Tong et al 2002), and altered inertial property of the limb (Krakauer et al 1999). They can also produce movements that cancel a visual perturbation such as a visuomotor rotation (Ghahramani and Wolpert 1997;Pine et al 1996).…”

Section: Discussionmentioning

confidence: 99%

The Nervous System Uses Nonspecific Motor Learning in Response to Random Perturbations of Varying Nature

Wei

Wert

Körding

2010

Journal of Neurophysiology

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We constantly make small errors during movement and use them to adapt our future movements. Movement experiments often probe this error-driven learning by perturbing movements and analyzing the after-effects. Past studies have applied perturbations of varying nature such as visual disturbances, position- or velocity-dependent forces and modified inertia properties of the limb. However, little is known about how the specific nature of a perturbation influences subsequent movements. For a single perturbation trial, the nature of a perturbation may be highly uncertain to the nervous system, given that it receives only noisy information. One hypothesis is that the nervous system can use this rough estimate to partially correct for the perturbation on the next trial. Alternatively, the nervous system could ignore uncertain information about the nature of the perturbation and resort to a nonspecific adaptation. To study how the brain estimates and responds to incomplete sensory information, we test these two hypotheses using a trial-by-trial adaptation experiment. On each trial, the nature of the perturbation was chosen from six distinct types, including a visuomotor rotation and different force fields. We observed that corrective forces aiming to oppose the perturbation in the following trial were independent of the nature of the perturbation. Our results suggest that the nervous system uses a nonspecific strategy when it has high uncertainty about the nature of perturbations during trial-by-trial learning.

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“…Previous studies using reach adaptation in static environments have also shown that energy is not always the most important factor that drives the movement [ 26 – 29 ]. Kistemaker and co-workers [ 27 , 28 ] showed that subjects, after they successfully learnt to reach through a force field along an energetic optimal path, do not choose this path in trials where they were free to choose how to move through the force field.…”

Section: Discussionmentioning

confidence: 99%

Stability of Phase Relationships While Coordinating Arm Reaches with Whole Body Motion

2015

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The human movement repertoire is characterized by the smooth coordination of several body parts, including arm movements and whole body motion. The neural control of this coordination is quite complex because the various body parts have their own kinematic and dynamic properties. Behavioral inferences about the neural solution to the coordination problem could be obtained by examining the emerging phase relationship and its stability. Here, we studied the phase relationships that characterize the coordination of arm-reaching movements with passively-induced whole-body motion. Participants were laterally translated using a vestibular chair that oscillated at a fixed frequency of 0.83 Hz. They were instructed to reach between two targets that were aligned either parallel or orthogonal to the whole body motion. During the first cycles of body motion, a metronome entrained either an in-phase or an anti-phase relationship between hand and body motion, which was released at later cycles to test phase stability. Results suggest that inertial forces play an important role when coordinating reaches with cyclic whole-body motion. For parallel reaches, we found a stable in-phase and an unstable anti-phase relationship. When the latter was imposed, it readily transitioned or drifted back toward an in-phase relationship at cycles without metronomic entrainment. For orthogonal reaches, we did not find a clear difference in stability between in-phase and anti-phase relationships. Computer simulations further show that cost models that minimize energy expenditure (i.e. net torques) or endpoint variance of the reach cannot fully explain the observed coordination patterns. We discuss how predictive control and impedance control processes could be considered important mechanisms underlying the rhythmic coordination of arm reaches and body motion.

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Influence of Viscous Loads on Motor Planning

Cited by 8 publications

References 28 publications

Motor Adaptation as a Process of Reoptimization

Motor Adaptation as a Process of Reoptimization

The Nervous System Uses Nonspecific Motor Learning in Response to Random Perturbations of Varying Nature

Stability of Phase Relationships While Coordinating Arm Reaches with Whole Body Motion

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