Visual Error Augmentation enhances learning in three dimensions

Ian Sharp*, Felix C. Huang, James L. Patton

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Recent human motor learning and neurorehabilitation experiments have identified the benefits of assisting the learning process by artificially enhancing the errors one might experience. A yet untested question is just how far the nervous system will trust such treatments, especially in transformations with very large sensorimotor discrepancies. Our study asked 10 healthy subjects to perform targeted reaching in a virtual reality environment, where the transformation of the hand position matrix was a complete reversal - rotated 180 degrees about an arbitrary axis (hence 2 of the 3 coordinates are reversed). Our data show that after 500 practice trials, subject who received 2× Error Augmentation (EA) were able to reach their desired target 0.4 seconds more quickly and with a Maximum Perpendicular Trajectory deviation of 0.9 cm less, when compared to the control group. Furthermore, the manner in which subjects practiced was influenced by the error augmentation, resulting in more continuous motions for this group. These data further support that this type of enhancement, as well as possibly other distorted reality methods, may promote more complete adaptation/learning when compared to regular training.

Original languageEnglish (US)
Title of host publication2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Pages5915-5918
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina
Duration: Aug 31 2010Sep 4 2010

Other

Other2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
CountryArgentina
CityBuenos Aires
Period8/31/109/4/10

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Health Informatics

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