Error fields: personalized robotic movement training that augments one’s more likely mistakes
Abstract Control of movement is learned and uses error feedback during practice to predict actions for the next movement. We previously showed that augmenting error can enhance learning, but while such findings are encouraging, the methods need to be refined to accommodate a person’s individual reac...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-87331-x |
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| Summary: | Abstract Control of movement is learned and uses error feedback during practice to predict actions for the next movement. We previously showed that augmenting error can enhance learning, but while such findings are encouraging, the methods need to be refined to accommodate a person’s individual reactions to error. The current study evaluates error fields (EF) method, where the interactive robot tempers its augmentation when the error is less likely. 22 healthy participants were asked to learn moving with a visual transformation, and we enhanced the training with error fields. We found that training with error fields led to greatest reduction in error. EF training reduced error 264% more than controls who practiced without error fields, but subjects learned more slowly than our previous error magnification technique. These robotic training enhancements should be further explored in combination to optimally leverage error statistics to teach people how to move better. This study reports results from a clinical trial registered on ClinicalTrials.gov with ID: NCT02720341. |
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| ISSN: | 2045-2322 |