Literature DB >> 34169838

De novo learning versus adaptation of continuous control in a manual tracking task.

Christopher S Yang1, Noah J Cowan2, Adrian M Haith3.   

Abstract

How do people learn to perform tasks that require continuous adjustments of motor output, like riding a bicycle? People rely heavily on cognitive strategies when learning discrete movement tasks, but such time-consuming strategies are infeasible in continuous control tasks that demand rapid responses to ongoing sensory feedback. To understand how people can learn to perform such tasks without the benefit of cognitive strategies, we imposed a rotation/mirror reversal of visual feedback while participants performed a continuous tracking task. We analyzed behavior using a system identification approach, which revealed two qualitatively different components of learning: adaptation of a baseline controller and formation of a new, task-specific continuous controller. These components exhibited different signatures in the frequency domain and were differentially engaged under the rotation/mirror reversal. Our results demonstrate that people can rapidly build a new continuous controller de novo and can simultaneously deploy this process with adaptation of an existing controller.
© 2021, Yang et al.

Entities:  

Keywords:  adaptation; continuous control; human; motor learning; neuroscience

Year:  2021        PMID: 34169838      PMCID: PMC8266385          DOI: 10.7554/eLife.62578

Source DB:  PubMed          Journal:  Elife        ISSN: 2050-084X            Impact factor:   8.140


  75 in total

1.  Conditions for interference versus facilitation during sequential sensorimotor adaptation.

Authors:  O Bock; S Schneider; J Bloomberg
Journal:  Exp Brain Res       Date:  2001-06       Impact factor: 1.972

2.  Prism adaptation and aftereffect: specifying the properties of a procedural memory system.

Authors:  J Fernández-Ruiz; R Díaz
Journal:  Learn Mem       Date:  1999 Jan-Feb       Impact factor: 2.460

3.  Multisensory fusion: simultaneous re-weighting of vision and touch for the control of human posture.

Authors:  Kelvin S Oie; Tim Kiemel; John J Jeka
Journal:  Brain Res Cogn Brain Res       Date:  2002-06

4.  Closed-loop stabilization of the Jamming Avoidance Response reveals its locally unstable and globally nonlinear dynamics.

Authors:  Manu S Madhav; Sarah A Stamper; Eric S Fortune; Noah J Cowan
Journal:  J Exp Biol       Date:  2013-08-30       Impact factor: 3.312

Review 5.  A neural substrate of prediction and reward.

Authors:  W Schultz; P Dayan; P R Montague
Journal:  Science       Date:  1997-03-14       Impact factor: 47.728

6.  The influence of movement preparation time on the expression of visuomotor learning and savings.

Authors:  Adrian M Haith; David M Huberdeau; John W Krakauer
Journal:  J Neurosci       Date:  2015-04-01       Impact factor: 6.167

7.  Sinusoidal visuomotor tracking: intermittent servo-control or coupled oscillations?

Authors:  D M Russell; D Sternad
Journal:  J Mot Behav       Date:  2001-12       Impact factor: 1.328

8.  Sensitivity derivatives for flexible sensorimotor learning.

Authors:  M N Abdelghani; T P Lillicrap; D B Tweed
Journal:  Neural Comput       Date:  2008-08       Impact factor: 2.026

9.  Adaptation reveals independent control networks for human walking.

Authors:  Julia T Choi; Amy J Bastian
Journal:  Nat Neurosci       Date:  2007-07-01       Impact factor: 24.884

10.  Dissociable cognitive strategies for sensorimotor learning.

Authors:  Samuel D McDougle; Jordan A Taylor
Journal:  Nat Commun       Date:  2019-01-03       Impact factor: 14.919

View more
  5 in total

1.  Long-Term Motor Learning in the "Wild" With High Volume Video Game Data.

Authors:  Jennifer B Listman; Jonathan S Tsay; Hyosub E Kim; Wayne E Mackey; David J Heeger
Journal:  Front Hum Neurosci       Date:  2021-12-20       Impact factor: 3.169

Review 2.  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

3.  Understanding implicit and explicit sensorimotor learning through neural dynamics.

Authors:  Xueqian Deng; Mengzhan Liufu; Jingyue Xu; Chen Yang; Zina Li; Juan Chen
Journal:  Front Comput Neurosci       Date:  2022-08-03       Impact factor: 3.387

4.  Understanding implicit sensorimotor adaptation as a process of proprioceptive re-alignment.

Authors:  Jonathan S Tsay; Hyosub Kim; Adrian M Haith; Richard B Ivry
Journal:  Elife       Date:  2022-08-15       Impact factor: 8.713

5.  Playing the piano with a robotic third thumb: assessing constraints of human augmentation.

Authors:  Ali Shafti; Shlomi Haar; Renato Mio; Pierre Guilleminot; A Aldo Faisal
Journal:  Sci Rep       Date:  2021-11-01       Impact factor: 4.379

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.