Literature DB >> 21748334

Quickly 'learning' to move optimally.

Eli Brenner1, Jeroen B J Smeets.   

Abstract

People take account of the variability in their movements in a near-optimal manner in various visuo-motor tasks. Is knowledge of one's variability needed for such near-optimal performance, or could it arise from responding to one's success in previous attempts in some simple manner? We asked subjects to move a pen back and forth across a tablet to make a cursor move as quickly as possible between two targets. The cursor had to stop within the targets. Task difficulty was varied between blocks. Part of the variation in difficulty was explicit (three target sizes) whereas the rest had to be discovered during the movements (two mappings between the movements of pen and cursor). In all cases, subjects sped up after stopping within a target and slowed down after failing to do so. We interpret this as evidence that explicit knowledge of one's variability is not necessary for performing close to optimally.

Entities:  

Mesh:

Year:  2011        PMID: 21748334      PMCID: PMC3140948          DOI: 10.1007/s00221-011-2786-9

Source DB:  PubMed          Journal:  Exp Brain Res        ISSN: 0014-4819            Impact factor:   1.972


  20 in total

1.  Optimal feedback control as a theory of motor coordination.

Authors:  Emanuel Todorov; Michael I Jordan
Journal:  Nat Neurosci       Date:  2002-11       Impact factor: 24.884

2.  Bayesian integration in sensorimotor learning.

Authors:  Konrad P Körding; Daniel M Wolpert
Journal:  Nature       Date:  2004-01-15       Impact factor: 49.962

3.  Fast corrections of movements with a computer mouse.

Authors:  Eli Brenner; Jeroen B J Smeets
Journal:  Spat Vis       Date:  2003

4.  Sources of signal-dependent noise during isometric force production.

Authors:  Kelvin E Jones; Antonia F Hamilton; Daniel M Wolpert
Journal:  J Neurophysiol       Date:  2002-09       Impact factor: 2.714

5.  The information capacity of the human motor system in controlling the amplitude of movement.

Authors:  P M FITTS
Journal:  J Exp Psychol       Date:  1954-06

6.  Effects of biomechanical and task constraints on the organization of movement in precision aiming.

Authors:  Laure Fernandez; Reinoud J Bootsma
Journal:  Exp Brain Res       Date:  2004-07-14       Impact factor: 1.972

7.  Bayesian decision theory in sensorimotor control.

Authors:  Konrad P Körding; Daniel M Wolpert
Journal:  Trends Cogn Sci       Date:  2006-06-27       Impact factor: 20.229

8.  Motor learning is optimally tuned to the properties of motor noise.

Authors:  Robert J van Beers
Journal:  Neuron       Date:  2009-08-13       Impact factor: 17.173

9.  Signal-dependent noise determines motor planning.

Authors:  C M Harris; D M Wolpert
Journal:  Nature       Date:  1998-08-20       Impact factor: 49.962

10.  Near optimal combination of sensory and motor uncertainty in time during a naturalistic perception-action task.

Authors:  A Aldo Faisal; Daniel M Wolpert
Journal:  J Neurophysiol       Date:  2008-12-24       Impact factor: 2.714

View more
  10 in total

1.  How the required precision influences the way we intercept a moving object.

Authors:  Eli Brenner; Rouwen Cañal-Bruland; Robert J van Beers
Journal:  Exp Brain Res       Date:  2013-07-16       Impact factor: 1.972

2.  Advancing Anterior Cruciate Ligament Injury Prevention Using Real-Time Biofeedback for Amplified Sensorimotor Integration.

Authors:  Scott Bonnette; Christopher A DiCesare; Jed A Diekfuss; Dustin R Grooms; Ryan P MacPherson; Michael A Riley; Gregory D Myer
Journal:  J Athl Train       Date:  2019-08-22       Impact factor: 2.860

3.  Optimizing the control of high ID movements: rethinking the obvious.

Authors:  Jason Boyle; Deanna Kennedy; Charles H Shea
Journal:  Exp Brain Res       Date:  2012-09-22       Impact factor: 1.972

4.  Tongue Part Movement Trajectories for /r/ Using Ultrasound.

Authors:  Sarah Dugan; Sarah R Li; Jack Masterson; Hannah Woeste; Neeraja Mahalingam; Caroline Spencer; T Douglas Mast; Michael A Riley; Suzanne E Boyce
Journal:  Perspect ASHA Spec Interest Groups       Date:  2019-12

5.  Ebbinghaus figures that deceive the eye do not necessarily deceive the hand.

Authors:  Hester Knol; Raoul Huys; Jean-Christophe Sarrazin; Andreas Spiegler; Viktor K Jirsa
Journal:  Sci Rep       Date:  2017-06-08       Impact factor: 4.379

6.  Reward abundance interferes with error-based learning in a visuomotor adaptation task.

Authors:  Katinka van der Kooij; Leonie Oostwoud Wijdenes; Tessa Rigterink; Krista E Overvliet; Joeren B J Smeets
Journal:  PLoS One       Date:  2018-03-07       Impact factor: 3.240

7.  Competition Rather Than Observation and Cooperation Facilitates Optimal Motor Planning.

Authors:  Mamoru Tanae; Keiji Ota; Ken Takiyama
Journal:  Front Sports Act Living       Date:  2021-02-26

8.  Having several options does not increase the time it takes to make a movement to an adequate end point.

Authors:  Eli Brenner; Jeroen B J Smeets
Journal:  Exp Brain Res       Date:  2022-05-12       Impact factor: 2.064

9.  The influences of target size and recent experience on the vigour of adjustments to ongoing movements.

Authors:  Eli Brenner; Hidde Hardon; Ryan Moesman; Emily M Crowe; Jeroen B J Smeets
Journal:  Exp Brain Res       Date:  2022-02-19       Impact factor: 2.064

10.  A new method for tracking of motor skill learning through practical application of Fitts' law.

Authors:  Jim Ashworth-Beaumont; Alexander Nowicky
Journal:  J Mot Behav       Date:  2013-04-14       Impact factor: 1.328

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.