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To manipulate objects or to use tools we must compensate for any forces arising from interaction with the physical environment. Recent studies indicate that this compensation is achieved by learning an internal model of the dynamics, that is, a neural representation of the relation between motor command and movement. In these studies interaction with the(More)
Human arm viscoelasticity is important in stabilizing posture, movement, and in interacting with objects. Viscoelastic spatial characteristics are usually indexed by the size, shape, and orientation of a hand stiffness ellipse. It is well known that arm posture is a dominant factor in determining the properties of the stiffness ellipse. However, it is still(More)
Studies have shown that humans cannot simultaneously learn opposing force fields or opposing visuomotor rotations, even when provided with arbitrary contextual information, probably because of interference in their working memory. In contrast, we found that subjects can adapt to two opposing force fields when provided with contextual cues and can(More)
Rhythmic movements, such as walking, chewing or scratching, are phylogenetically old motor behaviors found in many organisms, ranging from insects to primates. In contrast, discrete movements, such as reaching, grasping or kicking, are behaviors that have reached sophistication primarily in younger species, particularly primates. Neurophysiological and(More)
Current methods for measuring stiffness during human arm movements are either limited to one-joint motions, or lead to systematic errors. The technique presented here enables a simple, accurate and unbiased measurement of endpoint stiffness during multi-joint movements. Using a computer-controlled mechanical interface, the hand is displaced relative to a(More)
Quantitative examinations of internal representations for arm trajectory planning: minimum commanded torque change model. A number of invariant features of multijoint planar reaching movements have been observed in measured hand trajectories. These features include roughly straight hand paths and bell-shaped speed profiles where the trajectory curvatures(More)
In the field of motor control, two hypotheses have been controversial: whether the brain acquires internal models that generate accurate motor commands, or whether the brain avoids this by using the viscoelasticity of musculoskeletal system. Recent observations on relatively low stiffness during trained movements support the existence of internal models.(More)
We propose a new model of motor learning to explain the exceptional dexterity and rapid adaptation to change, which characterize human motor control. It is based on the brain simultaneously optimizing stability, accuracy and efficiency. Formulated as a V-shaped learning function, it stipulates precisely how feedforward commands to individual muscles are(More)
Stiffness properties of the musculo-skeletal system can be controlled by regulating muscle activation and neural feedback gain. To understand the regulation of multijoint stiffness, we examined the relationship between human arm joint stiffness and muscle activation during static force control in the horizontal plane by means of surface electromyographic(More)
There is an infinity of impedance parameter values, and thus different co-contraction levels, that can produce similar movement kinematics from which the CNS must select one. Although signal-dependent noise (SDN) predicts larger motor-command variability during higher co-contraction, the relationship between impedance and task performance is not(More)