Literature DB >> 25355957

Reward feedback accelerates motor learning.

Ali A Nikooyan1, Alaa A Ahmed2.   

Abstract

Recent findings have demonstrated that reward feedback alone can drive motor learning. However, it is not yet clear whether reward feedback alone can lead to learning when a perturbation is introduced abruptly, or how a reward gradient can modulate learning. In this study, we provide reward feedback that decays continuously with increasing error. We asked whether it is possible to learn an abrupt visuomotor rotation by reward alone, and if the learning process could be modulated by combining reward and sensory feedback and/or by using different reward landscapes. We designed a novel visuomotor learning protocol during which subjects experienced an abruptly introduced rotational perturbation. Subjects received either visual feedback or reward feedback, or a combination of the two. Two different reward landscapes, where the reward decayed either linearly or cubically with distance from the target, were tested. Results demonstrate that it is possible to learn from reward feedback alone and that the combination of reward and sensory feedback accelerates learning. An analysis of the underlying mechanisms reveals that although reward feedback alone does not allow for sensorimotor remapping, it can nonetheless lead to broad generalization, highlighting a dissociation between remapping and generalization. Also, the combination of reward and sensory feedback accelerates learning without compromising sensorimotor remapping. These findings suggest that the use of reward feedback is a promising approach to either supplement or substitute sensory feedback in the development of improved neurorehabilitation techniques. More generally, they point to an important role played by reward in the motor learning process.
Copyright © 2015 the American Physiological Society.

Entities:  

Keywords:  decision-making; dopaminergic; reinforcement learning; sensorimotor mapping; temporal-difference model

Mesh:

Year:  2014        PMID: 25355957     DOI: 10.1152/jn.00032.2014

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


  53 in total

1.  The cerebellum does more than sensory prediction error-based learning in sensorimotor adaptation tasks.

Authors:  Peter A Butcher; Richard B Ivry; Sheng-Han Kuo; David Rydz; John W Krakauer; Jordan A Taylor
Journal:  J Neurophysiol       Date:  2017-06-21       Impact factor: 2.714

2.  Characteristics of Implicit Sensorimotor Adaptation Revealed by Task-irrelevant Clamped Feedback.

Authors:  J Ryan Morehead; Jordan A Taylor; Darius E Parvin; Richard B Ivry
Journal:  J Cogn Neurosci       Date:  2017-02-14       Impact factor: 3.225

3.  Financial incentives enhance adaptation to a sensorimotor transformation.

Authors:  Kathrin Gajda; Sandra Sülzenbrück; Herbert Heuer
Journal:  Exp Brain Res       Date:  2016-06-06       Impact factor: 1.972

4.  Explicit and Implicit Processes Constitute the Fast and Slow Processes of Sensorimotor Learning.

Authors:  Samuel D McDougle; Krista M Bond; Jordan A Taylor
Journal:  J Neurosci       Date:  2015-07-01       Impact factor: 6.167

5.  Selective reward affects the rate of saccade adaptation.

Authors:  Yoshiko Kojima; Robijanto Soetedjo
Journal:  Neuroscience       Date:  2017-05-10       Impact factor: 3.590

6.  Decomposition of a sensory prediction error signal for visuomotor adaptation.

Authors:  Peter A Butcher; Jordan A Taylor
Journal:  J Exp Psychol Hum Percept Perform       Date:  2017-05-15       Impact factor: 3.332

7.  Robot Reinforcement and Error-Based Movement Learning in Infants With and Without Cerebral Palsy.

Authors:  Thubi H A Kolobe; Andrew H Fagg
Journal:  Phys Ther       Date:  2019-06-01

8.  Error-driven learning in statistical summary perception.

Authors:  Judith E Fan; Nicholas B Turk-Browne; Jordan A Taylor
Journal:  J Exp Psychol Hum Percept Perform       Date:  2015-09-21       Impact factor: 3.332

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

Authors:  Guy Avraham; Raz Leib; Assaf Pressman; Lucia S Simo; Amir Karniel; Lior Shmuelof; Ferdinando A Mussa-Ivaldi; Ilana Nisky
Journal:  eNeuro       Date:  2017-12-26

10.  Comparing the effects of positive and negative feedback in information-integration category learning.

Authors:  Michael Freedberg; Brian Glass; J Vincent Filoteo; Eliot Hazeltine; W Todd Maddox
Journal:  Mem Cognit       Date:  2017-01
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