Literature DB >> 29379875

State-Based Delay Representation and Its Transfer from a Game of Pong to Reaching and Tracking.

Guy Avraham1,2, Raz Leib1,2, Assaf Pressman1,2,3, Lucia S Simo4, Amir Karniel1,2, Lior Shmuelof2,5,6, Ferdinando A Mussa-Ivaldi3,4,7, Ilana Nisky1,2.   

Abstract

To accurately estimate the state of the body, the nervous system needs to account for delays between signals from different sensory modalities. To investigate how such delays may be represented in the sensorimotor system, we asked human participants to play a virtual pong game in which the movement of the virtual paddle was delayed with respect to their hand movement. We tested the representation of this new mapping between the hand and the delayed paddle by examining transfer of adaptation to blind reaching and blind tracking tasks. These blind tasks enabled to capture the representation in feedforward mechanisms of movement control. A Time Representation of the delay is an estimation of the actual time lag between hand and paddle movements. A State Representation is a representation of delay using current state variables: the distance between the paddle and the ball originating from the delay may be considered as a spatial shift; the low sensitivity in the response of the paddle may be interpreted as a minifying gain; and the lag may be attributed to a mechanical resistance that influences paddle's movement. We found that the effects of prolonged exposure to the delayed feedback transferred to blind reaching and tracking tasks and caused participants to exhibit hypermetric movements. These results, together with simulations of our representation models, suggest that delay is not represented based on time, but rather as a spatial gain change in visuomotor mapping.

Entities:  

Keywords:  Delay; reaching; representation; tracking; transfer

Mesh:

Year:  2017        PMID: 29379875      PMCID: PMC5788056          DOI: 10.1523/ENEURO.0179-17.2017

Source DB:  PubMed          Journal:  eNeuro        ISSN: 2373-2822


  94 in total

1.  Independent learning of internal models for kinematic and dynamic control of reaching.

Authors:  J W Krakauer; M F Ghilardi; C Ghez
Journal:  Nat Neurosci       Date:  1999-11       Impact factor: 24.884

Review 2.  Internal models for motor control and trajectory planning.

Authors:  M Kawato
Journal:  Curr Opin Neurobiol       Date:  1999-12       Impact factor: 6.627

3.  Brain-computer interface technology: a review of the first international meeting.

Authors:  J R Wolpaw; N Birbaumer; W J Heetderks; D J McFarland; P H Peckham; G Schalk; E Donchin; L A Quatrano; C J Robinson; T M Vaughan
Journal:  IEEE Trans Rehabil Eng       Date:  2000-06

4.  Is interlimb transfer of force-field adaptation a cognitive response to the sudden introduction of load?

Authors:  Nicole Malfait; David J Ostry
Journal:  J Neurosci       Date:  2004-09-15       Impact factor: 6.167

Review 5.  Computational mechanisms of sensorimotor control.

Authors:  David W Franklin; Daniel M Wolpert
Journal:  Neuron       Date:  2011-11-03       Impact factor: 17.173

6.  Reach adaptation: what determines whether we learn an internal model of the tool or adapt the model of our arm?

Authors:  JoAnn Kluzik; Jörn Diedrichsen; Reza Shadmehr; Amy J Bastian
Journal:  J Neurophysiol       Date:  2008-07-02       Impact factor: 2.714

7.  Internal models in the cerebellum.

Authors:  D M Wolpert; R C Miall; M Kawato
Journal:  Trends Cogn Sci       Date:  1998-09-01       Impact factor: 20.229

8.  Target size matters: target errors contribute to the generalization of implicit visuomotor learning.

Authors:  Maayan Reichenthal; Guy Avraham; Amir Karniel; Lior Shmuelof
Journal:  J Neurophysiol       Date:  2016-04-27       Impact factor: 2.714

9.  The coordination of arm movements: an experimentally confirmed mathematical model.

Authors:  T Flash; N Hogan
Journal:  J Neurosci       Date:  1985-07       Impact factor: 6.167

10.  Acquisition and generalization of visuomotor transformations by nonhuman primates.

Authors:  Rony Paz; Chen Nathan; Thomas Boraud; Hagai Bergman; Eilon Vaadia
Journal:  Exp Brain Res       Date:  2004-10-05       Impact factor: 1.972

View more
  6 in total

1.  Exposure to Auditory Feedback Delay while Speaking Induces Perceptual Habituation but does not Mitigate the Disruptive Effect of Delay on Speech Auditory-motor Learning.

Authors:  Douglas M Shiller; Takashi Mitsuya; Ludo Max
Journal:  Neuroscience       Date:  2020-07-30       Impact factor: 3.590

2.  Energy exchanges at contact events guide sensorimotor integration.

Authors:  Ali Farshchian; Alessandra Sciutti; Assaf Pressman; Ilana Nisky; Ferdinando A Mussa-Ivaldi
Journal:  Elife       Date:  2018-05-29       Impact factor: 8.140

3.  Neglect-Like Effects on Drawing Symmetry Induced by Adaptation to a Laterally Asymmetric Visuomotor Delay.

Authors:  Chen Avraham; Guy Avraham; Ferdinando A Mussa-Ivaldi; Ilana Nisky
Journal:  Front Hum Neurosci       Date:  2018-08-28       Impact factor: 3.169

4.  Better grip force control by attending to the controlled object: Evidence for direct force estimation from visual motion.

Authors:  Shinya Takamuku; Hiroaki Gomi
Journal:  Sci Rep       Date:  2019-09-11       Impact factor: 4.379

5.  Explicit Control of Step Timing During Split-Belt Walking Reveals Interdependent Recalibration of Movements in Space and Time.

Authors:  Marcela Gonzalez-Rubio; Nicolas F Velasquez; Gelsy Torres-Oviedo
Journal:  Front Hum Neurosci       Date:  2019-07-03       Impact factor: 3.169

6.  Embodied virtual reality for the study of real-world motor learning.

Authors:  Shlomi Haar; Guhan Sundar; A Aldo Faisal
Journal:  PLoS One       Date:  2021-01-27       Impact factor: 3.240

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.