Literature DB >> 35225229

Competition between parallel sensorimotor learning systems.

Scott T Albert1,2, Jihoon Jang1,3, Shanaathanan Modchalingam4, Bernard Marius 't Hart4, Denise Henriques4, Gonzalo Lerner5, Valeria Della-Maggiore5, Adrian M Haith6, John W Krakauer6,7,8, Reza Shadmehr1.   

Abstract

Sensorimotor learning is supported by at least two parallel systems: a strategic process that benefits from explicit knowledge and an implicit process that adapts subconsciously. How do these systems interact? Does one system's contributions suppress the other, or do they operate independently? Here, we illustrate that during reaching, implicit and explicit systems both learn from visual target errors. This shared error leads to competition such that an increase in the explicit system's response siphons away resources that are needed for implicit adaptation, thus reducing its learning. As a result, steady-state implicit learning can vary across experimental conditions, due to changes in strategy. Furthermore, strategies can mask changes in implicit learning properties, such as its error sensitivity. These ideas, however, become more complex in conditions where subjects adapt using multiple visual landmarks, a situation which introduces learning from sensory prediction errors in addition to target errors. These two types of implicit errors can oppose each other, leading to another type of competition. Thus, during sensorimotor adaptation, implicit and explicit learning systems compete for a common resource: error.
© 2022, Albert et al.

Entities:  

Keywords:  explicit learning; human; implicit learning; interference; motor learning; neuroscience; savings

Mesh:

Year:  2022        PMID: 35225229      PMCID: PMC9068222          DOI: 10.7554/eLife.65361

Source DB:  PubMed          Journal:  Elife        ISSN: 2050-084X            Impact factor:   8.713


  93 in total

Review 1.  Internal models of limb dynamics and the encoding of limb state.

Authors:  Eun Jung Hwang; Reza Shadmehr
Journal:  J Neural Eng       Date:  2005-08-31       Impact factor: 5.379

2.  An implicit plan overrides an explicit strategy during visuomotor adaptation.

Authors:  Pietro Mazzoni; John W Krakauer
Journal:  J Neurosci       Date:  2006-04-05       Impact factor: 6.167

3.  Cerebellar Control of Reach Kinematics for Endpoint Precision.

Authors:  Matthew I Becker; Abigail L Person
Journal:  Neuron       Date:  2019-06-04       Impact factor: 17.173

4.  Savings in locomotor adaptation explained by changes in learning parameters following initial adaptation.

Authors:  Firas Mawase; Lior Shmuelof; Simona Bar-Haim; Amir Karniel
Journal:  J Neurophysiol       Date:  2014-01-15       Impact factor: 2.714

5.  Task Errors Drive Memories That Improve Sensorimotor Adaptation.

Authors:  Li-Ann Leow; Welber Marinovic; Aymar de Rugy; Timothy J Carroll
Journal:  J Neurosci       Date:  2020-02-06       Impact factor: 6.167

6.  Savings for visuomotor adaptation require prior history of error, not prior repetition of successful actions.

Authors:  Li-Ann Leow; Aymar de Rugy; Welber Marinovic; Stephan Riek; Timothy J Carroll
Journal:  J Neurophysiol       Date:  2016-07-13       Impact factor: 2.714

7.  Implicit adaptation compensates for erratic explicit strategy in human motor learning.

Authors:  Yohsuke R Miyamoto; Shengxin Wang; Maurice A Smith
Journal:  Nat Neurosci       Date:  2020-02-28       Impact factor: 24.884

8.  Explicit and implicit contributions to learning in a sensorimotor adaptation task.

Authors:  Jordan A Taylor; John W Krakauer; Richard B Ivry
Journal:  J Neurosci       Date:  2014-02-19       Impact factor: 6.167

9.  Teaching the cerebellum about reward.

Authors:  Javier F Medina
Journal:  Nat Neurosci       Date:  2019-06       Impact factor: 28.771

10.  Individual differences in proprioception predict the extent of implicit sensorimotor adaptation.

Authors:  Jonathan S Tsay; Hyosub E Kim; Darius E Parvin; Alissa R Stover; Richard B Ivry
Journal:  J Neurophysiol       Date:  2021-03-03       Impact factor: 2.974

View more
  5 in total

1.  Motor learning without movement.

Authors:  Olivia A Kim; Alexander D Forrence; Samuel D McDougle
Journal:  Proc Natl Acad Sci U S A       Date:  2022-07-19       Impact factor: 12.779

2.  Competition between parallel sensorimotor learning systems.

Authors:  Scott T Albert; Jihoon Jang; Shanaathanan Modchalingam; Bernard Marius 't Hart; Denise Henriques; Gonzalo Lerner; Valeria Della-Maggiore; Adrian M Haith; John W Krakauer; Reza Shadmehr
Journal:  Elife       Date:  2022-02-28       Impact factor: 8.713

Review 3.  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

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.  Interactions between sensory prediction error and task error during implicit motor learning.

Authors:  Jonathan S Tsay; Adrian M Haith; Richard B Ivry; Hyosub E Kim
Journal:  PLoS Comput Biol       Date:  2022-03-23       Impact factor: 4.779

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.