Literature DB >> 22815501

The influence of visual motion on motor learning.

Zachary Danziger1, Ferdinando A Mussa-Ivaldi.   

Abstract

How does visual perception shape the way we coordinate movements? Recent studies suggest that the brain organizes movements based on minimizing reaching errors in the presence of motor and sensory noise. We present an alternative hypothesis in which movement trajectories also result from acquired knowledge about the geometrical properties of the object that the brain is controlling. To test this hypothesis, we asked human subjects to control a simulated kinematic linkage by continuous finger motion, a completely novel experience. This paradigm removed all biases arising from influences of limb dynamics and past experience. Subjects were exposed to two different types of visual feedback; some saw the entire simulated linkage and others saw only the moving extremity. Consistent with our hypothesis, subjects learned to move the simulated linkage along geodesic lines corresponding to the geometrical structure of the observed motion. Thus, optimizing final accuracy is not the unique determinant of trajectory formation.

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Year:  2012        PMID: 22815501      PMCID: PMC3437549          DOI: 10.1523/JNEUROSCI.5528-11.2012

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  25 in total

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Authors:  Robert A Scheidt; Claude Ghez
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8.  Trajectory adaptation to a nonlinear visuomotor transformation: evidence of motion planning in visually perceived space.

Authors:  J R Flanagan; A K Rao
Journal:  J Neurophysiol       Date:  1995-11       Impact factor: 2.714

9.  Rapid adaptation to Coriolis force perturbations of arm trajectory.

Authors:  J R Lackner; P Dizio
Journal:  J Neurophysiol       Date:  1994-07       Impact factor: 2.714

10.  Kinematic features of unrestrained vertical arm movements.

Authors:  C G Atkeson; J M Hollerbach
Journal:  J Neurosci       Date:  1985-09       Impact factor: 6.167

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  9 in total

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2.  Upper Body-Based Power Wheelchair Control Interface for Individuals With Tetraplegia.

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3.  Remapping residual coordination for controlling assistive devices and recovering motor functions.

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4.  White matter microstructure changes induced by motor skill learning utilizing a body machine interface.

Authors:  Xue Wang; Maura Casadio; Kenneth A Weber; Ferdinando A Mussa-Ivaldi; Todd B Parrish
Journal:  Neuroimage       Date:  2013-11-09       Impact factor: 6.556

5.  Effects of continuous visual feedback during sitting balance training in chronic stroke survivors.

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Review 6.  Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks.

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7.  Analysis of accuracy in pointing with redundant hand-held tools: a geometric approach to the uncontrolled manifold method.

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8.  Sensory Agreement Guides Kinetic Energy Optimization of Arm Movements during Object Manipulation.

Authors:  Ali Farshchiansadegh; Alejandro Melendez-Calderon; Rajiv Ranganathan; Todd D Murphey; Ferdinando A Mussa-Ivaldi
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9.  Visual guidance can help with the use of a robotic exoskeleton during human walking.

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  9 in total

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