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
1984 ◽  
Vol 31 (1) ◽  
pp. 81-92 ◽  
Author(s):  
R. O. Dendy ◽  
D. Ter Haar

We show what corrections have to be made to the equations of ideal magneto-hydrodynamics when there is fast-time-scale turbulence present in a magnetized plasma. We show how the dispersion relations for the ideal Alfvén and magnetoacoustic MHD normal modes are modified when such turbulence is present. Finally, we discuss the relation of our work to that of other authors.


2011 ◽  
Vol 108 (6) ◽  
pp. 2569-2574 ◽  
Author(s):  
A. Delekate ◽  
M. Zagrebelsky ◽  
S. Kramer ◽  
M. E. Schwab ◽  
M. Korte

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 ◽  
...  

1994 ◽  
Vol 230 (1-2) ◽  
pp. 87-92 ◽  
Author(s):  
Masahide Terazima
Keyword(s):  

2005 ◽  
Vol 5 (4) ◽  
Author(s):  
Igor D. Chueshov ◽  
Björn Schmalfuß

AbstractThe averaging method has been used to study random or non-autonomous systems on a fast time scale. We apply this method to a random abstract evolution equation on a fast time scale whose long time behavior can be characterized by a random attractor or a random inertial manifold. The main purpose is to show that the long-time behavior of such a system can be described by a deterministic evolution equation with averaged coefficients. Our first result provides an averaging result on finite time intervals which we use to show that under a dissipativity assumption the attractors of the fast time scale systems are upper semicontinuous when the scaling parameter goes to zero. Our main result deals with a global averaging procedure. Under some spectral gap condition we show that inertial manifolds of the fast time scale system tend to an inertial manifold of the averaged system when the scaling parameter goes to zero. These general results can be applied to semilinear parabolic differential equations containing a scaled ergodic noise on a fast time scale which includes scaled almost periodic motions.


Author(s):  
Seung-Yeon Kim ◽  
Jae-Woon Kwon ◽  
Jin-Min Kim ◽  
Frank Chong-Woo Park ◽  
Sang-Hoon Yeo

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.


Perception ◽  
10.1068/p3314 ◽  
2002 ◽  
Vol 31 (4) ◽  
pp. 421-434 ◽  
Author(s):  
Jochen Triesch ◽  
Dana H Ballard ◽  
Robert A Jacobs

The integration of information from different sensors, cues, or modalities lies at the very heart of perception. We are studying adaptive phenomena in visual cue integration. To this end, we have designed a visual tracking task, where subjects track a target object among distractors and try to identify the target after an occlusion. Objects are defined by three different attributes (color, shape, size) which change randomly within a single trial. When the attributes differ in their reliability (two change frequently, one is stable), our results show that subjects dynamically adapt their processing. The results are consistent with the hypothesis that subjects rapidly re-weight the information provided by the different cues by emphasizing the information from the stable cue. This effect seems to be automatic, ie not requiring subjects' awareness of the differential reliabilities of the cues. The hypothesized re-weighting seems to take place in about 1 s. Our results suggest that cue integration can exhibit adaptive phenomena on a very fast time scale. We propose a probabilistic model with temporal dynamics that accounts for the observed effect.


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