Correcting for natural visuo-proprioceptive matching errors based on reward as opposed to error feedback does not lead to higher retention

Irene A. Kuling, Anouk J. de Brouwer, Jeroen B.J. Smeets (Corresponding author), J. Randall Flanagan

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
4 Downloads (Pure)

Abstract

When asked to move their unseen hand-to-visual targets, people exhibit idiosyncratic but reliable visuo-proprioceptive matching errors. Unsurprisingly, vision and proprioception quickly align when these errors are made apparent by providing visual feedback of the position of the hand. However, retention of this learning is limited, such that the original matching errors soon reappear when visual feedback is removed. Several recent motor learning studies have shown that reward feedback can improve retention relative to error feedback. Here, using a visuo-proprioceptive position-matching task, we examined whether binary reward feedback can be effectively exploited to reduce matching errors and, if so, whether this learning leads to improved retention relative to learning based on error feedback. The results show that participants were able to adjust the visuo-proprioceptive mapping with reward feedback, but that the level of retention was similar to that observed when the adjustment was accomplished with error feedback. Therefore, similar to error feedback, reward feedback allows for temporary recalibration, but does not support long-lasting retention of this recalibration.

Original languageEnglish
Pages (from-to)735-741
Number of pages7
JournalExperimental Brain Research
Volume237
Issue number3
DOIs
Publication statusPublished - 4 Mar 2019
Externally publishedYes

Keywords

  • Error feedback
  • Position sense
  • Reward-based learning
  • Sensory matching errors

Fingerprint Dive into the research topics of 'Correcting for natural visuo-proprioceptive matching errors based on reward as opposed to error feedback does not lead to higher retention'. Together they form a unique fingerprint.

Cite this