Journal ArticleDOI
The central nervous system stabilizes unstable dynamics by learning optimal impedance.
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The results show that humans learn to stabilize unstable dynamics using the skilful and energy-efficient strategy of selective control of impedance geometry.Abstract:
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 dynamics1,2,3,4,5,6, that is, a neural representation of the relation between motor command and movement5,7. In these studies interaction with the physical environment was stable, but many common tasks are intrinsically unstable8,9. For example, keeping a screwdriver in the slot of a screw is unstable because excessive force parallel to the slot can cause the screwdriver to slip and because misdirected force can cause loss of contact between the screwdriver and the screw. Stability may be dependent on the control of mechanical impedance in the human arm because mechanical impedance can generate forces which resist destabilizing motion. Here we examined arm movements in an unstable dynamic environment created by a robotic interface. Our results show that humans learn to stabilize unstable dynamics using the skilful and energy-efficient strategy of selective control of impedance geometry.read more
Citations
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Journal ArticleDOI
Optimality principles in sensorimotor control
TL;DR: This work has redefined optimality in terms of feedback control laws, and focused on the mechanisms that generate behavior online, allowing researchers to fit previously unrelated concepts and observations into what may become a unified theoretical framework for interpreting motor function.
Journal ArticleDOI
Principles of sensorimotor learning.
TL;DR: Here a review of recent research in human motor learning with an emphasis on the computational mechanisms that are involved is reviewed.
Journal ArticleDOI
Optimal feedback control and the neural basis of volitional motor control
TL;DR: Optimal feedback control theory might provide the important link across these levels of the motor system and help to unravel how the primary motor cortex and other regions of the brain plan and control movement.
Journal ArticleDOI
Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation
Wei He,Yiting Dong,Changyin Sun +2 more
TL;DR: In this article, an adaptive impedance controller for a robotic manipulator with input saturation was developed by employing neural networks. But the adaptive impedance control was not considered in the tracking control design, and the input saturation is handled by designing an auxiliary system.
Journal ArticleDOI
Role of Cocontraction in Arm Movement Accuracy
TL;DR: The goal of this study was to test for a possible relationship between cocontraction and movement accuracy in multi-joint limb movements and observed an inverse relationship between target size and cocontracted activity: as target size was reduced, cocontractions activity increased.
References
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Journal ArticleDOI
Adaptive representation of dynamics during learning of a motor task
TL;DR: The investigation of how the CNS learns to control movements in different dynamical conditions, and how this learned behavior is represented, suggests that the elements of the adaptive process represent dynamics of a motor task in terms of the intrinsic coordinate system of the sensors and actuators.
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Internal models for motor control and trajectory planning
TL;DR: The 'minimum variance model' is another major recent advance in the computational theory of motor control, strongly suggesting that both kinematic and dynamic internal models are utilized in movement planning and control.
Journal ArticleDOI
Internal models in the cerebellum
TL;DR: This review will focus on the possibility that the cerebellum contains an internal model or models of the motor apparatus, and the necessity of such a model and the evidence, based on the ocular following response, that inverse models are found within the Cerebellar circuitry.
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Motor-output variability: a theory for the accuracy of rapid motor acts.
TL;DR: A theory of motor-output variability that accounts for the relationship among the movement amplitude, movement time, the mass to be moved, and the resulting movement error is presented.
Journal ArticleDOI
Episodic-like memory during cache recovery by scrub jays
TL;DR: It is shown that scrub jays remember ‘when’ food items are stored by allowing them to recover perishable ‘wax worms’ (wax-moth larvae) and non-perishable peanuts which they had previously cached in visuospatially distinct sites.