Literature DB >> 26746312

Emotion and reward are dissociable from error during motor learning.

Sara B Festini1,2, Stephanie D Preston3, Patricia A Reuter-Lorenz3, Rachael D Seidler3,4.   

Abstract

Although emotion is known to reciprocally interact with cognitive and motor performance, contemporary theories of motor learning do not specifically consider how dynamic variations in a learner's affective state may influence motor performance during motor learning. Using a prism adaptation paradigm, we assessed emotion during motor learning on a trial-by-trial basis. We designed two dart-throwing experiments to dissociate motor performance and reward outcomes by giving participants maximum points for accurate throws and reduced points for throws that hit zones away from the target (i.e., "accidental points"). Experiment 1 dissociated motor performance from emotional responses and found that affective ratings tracked points earned more closely than error magnitude. Further, both reward and error uniquely contributed to motor learning, as indexed by the change in error from one trial to the next. Experiment 2 manipulated accidental point locations vertically, whereas prism displacement remained horizontal. Results demonstrated that reward could bias motor performance even when concurrent sensorimotor adaptation was taking place in a perpendicular direction. Thus, these experiments demonstrate that affective states were dissociable from error magnitude during motor learning and that affect more closely tracked points earned. Our findings further implicate reward as another factor, other than error, that contributes to motor learning, suggesting the importance of incorporating affective states into models of motor learning.

Entities:  

Keywords:  Emotion; Error-based learning; Motor learning; Reward-based learning; Sensorimotor adaptation

Mesh:

Year:  2016        PMID: 26746312     DOI: 10.1007/s00221-015-4542-z

Source DB:  PubMed          Journal:  Exp Brain Res        ISSN: 0014-4819            Impact factor:   1.972


  28 in total

1.  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

2.  How each movement changes the next: an experimental and theoretical study of fast adaptive priors in reaching.

Authors:  Timothy Verstynen; Philip N Sabes
Journal:  J Neurosci       Date:  2011-07-06       Impact factor: 6.167

Review 3.  Human sensorimotor learning: adaptation, skill, and beyond.

Authors:  John W Krakauer; Pietro Mazzoni
Journal:  Curr Opin Neurobiol       Date:  2011-07-20       Impact factor: 6.627

4.  Use-dependent and error-based learning of motor behaviors.

Authors:  Jörn Diedrichsen; Olivier White; Darren Newman; Níall Lally
Journal:  J Neurosci       Date:  2010-04-14       Impact factor: 6.167

5.  Reward improves long-term retention of a motor memory through induction of offline memory gains.

Authors:  Mitsunari Abe; Heidi Schambra; Eric M Wassermann; Dave Luckenbaugh; Nicolas Schweighofer; Leonardo G Cohen
Journal:  Curr Biol       Date:  2011-03-17       Impact factor: 10.834

6.  Differential effect of reward and punishment on procedural learning.

Authors:  Tobias Wächter; Ovidiu V Lungu; Tao Liu; Daniel T Willingham; James Ashe
Journal:  J Neurosci       Date:  2009-01-14       Impact factor: 6.167

7.  Changes in performance monitoring during sensorimotor adaptation.

Authors:  Joaquin A Anguera; Rachael D Seidler; William J Gehring
Journal:  J Neurophysiol       Date:  2009-07-15       Impact factor: 2.714

8.  Decision making, movement planning and statistical decision theory.

Authors:  Julia Trommershäuser; Laurence T Maloney; Michael S Landy
Journal:  Trends Cogn Sci       Date:  2008-07-07       Impact factor: 20.229

9.  Functional dissociation in frontal and striatal areas for processing of positive and negative reward information.

Authors:  Xun Liu; David K Powell; Hongbin Wang; Brian T Gold; Christine R Corbly; Jane E Joseph
Journal:  J Neurosci       Date:  2007-04-25       Impact factor: 6.167

10.  Negative affect reduces performance in implicit sequence learning.

Authors:  Junchen Shang; Qiufang Fu; Zoltan Dienes; Can Shao; Xiaolan Fu
Journal:  PLoS One       Date:  2013-01-22       Impact factor: 3.240

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  1 in total

1.  Reward abundance interferes with error-based learning in a visuomotor adaptation task.

Authors:  Katinka van der Kooij; Leonie Oostwoud Wijdenes; Tessa Rigterink; Krista E Overvliet; Joeren B J Smeets
Journal:  PLoS One       Date:  2018-03-07       Impact factor: 3.240

  1 in total

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