Literature DB >> 33146063

Back to reality: differences in learning strategy in a simplified virtual and a real throwing task.

Zhaoran Zhang1, Dagmar Sternad2.   

Abstract

Virtual environments have been widely used in motor neuroscience and rehabilitation, as they afford tight control of sensorimotor conditions and readily afford visual and haptic manipulations. However, typically, studies have only examined performance in the virtual testbeds, without asking how the simplified and controlled movement in the virtual environment compares to behavior in the real world. To test whether performance in the virtual environment was a valid representation of corresponding behavior in the real world, this study compared throwing in a virtual set-up with realistic throwing, where the task parameters were precisely matched. Even though the virtual task only required a horizontal single-joint arm movement, similar to many simplified movement assays in motor neuroscience, throwing accuracy and precision were significantly worse than in the real task that involved all degrees of freedom of the arm; only after 3 practice days did success rate and error reach similar levels. To gain more insight into the structure of the learning process, movement variability was decomposed into deterministic and stochastic contributions. Using the tolerance-noise-covariation decomposition method, distinct stages of learning were revealed: While tolerance was optimized first in both environments, it was higher in the virtual environment, suggesting that more familiarization and exploration was needed in the virtual task. Covariation and noise showed more contributions in the real task, indicating that subjects reached the stage of fine-tuning of variability only in the real task. These results showed that while the tasks were precisely matched, the simplified movements in the virtual environment required more time to become successful. These findings resonate with the reported problems in transfer of therapeutic benefits from virtual to real environments and alert that the use of virtual environments in research and rehabilitation needs more caution.NEW & NOTEWORTHY This study compared human performance of the same throwing task in a real and a matched virtual environment. With 3 days' practice, subjects improved significantly faster in the real task, even though the arm and hand movements were more complex. Decomposing variability revealed that performance in the virtual environment, despite its simplified hand movements, required more exploration. Additionally, due to fewer constraints in the real task, subjects could modify the geometry of the solution manifold, by shifting the release position, and thereby simplify the task.

Entities:  

Keywords:  noise; skill learning; throwing; variability; virtual environment

Mesh:

Year:  2020        PMID: 33146063      PMCID: PMC8087380          DOI: 10.1152/jn.00197.2020

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  56 in total

1.  Nondominant arm advantages in load compensation during rapid elbow joint movements.

Authors:  Leia B Bagesteiro; Robert L Sainburg
Journal:  J Neurophysiol       Date:  2003-05-07       Impact factor: 2.714

2.  Decomposition of variability in the execution of goal-oriented tasks: three components of skill improvement.

Authors:  Hermann Müller; Dagmar Sternad
Journal:  J Exp Psychol Hum Percept Perform       Date:  2004-02       Impact factor: 3.332

3.  Virtual reality in stroke rehabilitation: a meta-analysis and implications for clinicians.

Authors:  Gustavo Saposnik; Mindy Levin
Journal:  Stroke       Date:  2011-04-07       Impact factor: 7.914

4.  Motor learning from virtual reality to natural environments in individuals with Duchenne muscular dystrophy.

Authors:  Virgínia Helena Quadrado; Talita Dias da Silva; Francis Meire Favero; James Tonks; Thais Massetti; Carlos Bandeira de Mello Monteiro
Journal:  Disabil Rehabil Assist Technol       Date:  2017-11-10

5.  On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex.

Authors:  A P Georgopoulos; J F Kalaska; R Caminiti; J T Massey
Journal:  J Neurosci       Date:  1982-11       Impact factor: 6.167

6.  It's Not (Only) the Mean that Matters: Variability, Noise and Exploration in Skill Learning.

Authors:  Dagmar Sternad
Journal:  Curr Opin Behav Sci       Date:  2018-03-01

7.  Variability in motor learning: relocating, channeling and reducing noise.

Authors:  R G Cohen; D Sternad
Journal:  Exp Brain Res       Date:  2008-10-25       Impact factor: 1.972

8.  Interacting adaptive processes with different timescales underlie short-term motor learning.

Authors:  Maurice A Smith; Ali Ghazizadeh; Reza Shadmehr
Journal:  PLoS Biol       Date:  2006-05-23       Impact factor: 8.029

9.  Directionality in distribution and temporal structure of variability in skill acquisition.

Authors:  Masaki O Abe; Dagmar Sternad
Journal:  Front Hum Neurosci       Date:  2013-06-06       Impact factor: 3.169

10.  The Statistical Determinants of the Speed of Motor Learning.

Authors:  Kang He; You Liang; Farnaz Abdollahi; Moria Fisher Bittmann; Konrad Kording; Kunlin Wei
Journal:  PLoS Comput Biol       Date:  2016-09-08       Impact factor: 4.475

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  6 in total

1.  Highlights from the 30th Annual Meeting of the Society for the Neural Control of Movement.

Authors:  Marta Russo; Nofar Ozeri-Engelhard; Kathleen Hupfeld; Caroline Nettekoven; Simon Thibault; Ehsan Sedaghat-Nejad; Daniela Buchwald; David Xing; Omid Zobeiri; Konstantina Kilteni; Scott T Albert; Giacomo Ariani
Journal:  J Neurophysiol       Date:  2021-08-18       Impact factor: 2.974

2.  Interception of virtual throws reveals predictive skills based on the visual processing of throwing kinematics.

Authors:  Antonella Maselli; Paolo De Pasquale; Francesco Lacquaniti; Andrea d'Avella
Journal:  iScience       Date:  2022-09-24

3.  Towards functional robotic training: motor learning of dynamic tasks is enhanced by haptic rendering but hampered by arm weight support.

Authors:  Özhan Özen; Karin A Buetler; Laura Marchal-Crespo
Journal:  J Neuroeng Rehabil       Date:  2022-02-13       Impact factor: 4.262

Review 4.  Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks.

Authors:  Koenraad Vandevoorde; Lukas Vollenkemper; Constanze Schwan; Martin Kohlhase; Wolfram Schenck
Journal:  Sensors (Basel)       Date:  2022-03-23       Impact factor: 3.576

5.  Motor control beyond reach-how humans hit a target with a whip.

Authors:  Aleksei Krotov; Marta Russo; Moses Nah; Neville Hogan; Dagmar Sternad
Journal:  R Soc Open Sci       Date:  2022-10-05       Impact factor: 3.653

6.  A Hessian-based decomposition characterizes how performance in complex motor skills depends on individual strategy and variability.

Authors:  Paolo Tommasino; Antonella Maselli; Domenico Campolo; Francesco Lacquaniti; Andrea d'Avella
Journal:  PLoS One       Date:  2021-06-30       Impact factor: 3.240

  6 in total

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