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Error amplification improves performance by reducing motor noise

机译:误差放大通过降低电机噪声来提高性能

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Information about error via visual feedback presents important information to promote motor learning. Amplifying error information has shown to be beneficial but the mechanism(s) behind such benefits have not been elucidated. We used a virtual throwing task and model-based system identification to test the hypothesis that performance improvement could not only be attained by an increase in feedback gain, but also by a decrease in intrinsic motor noise. The experiment manipulated the visual error by amplification at three gain levels. Some groups received error amplification with added noise. The experimental results showed that error amplification improved task performance, consistent with previous studies. The system identification results, based on a previously validated learning model, showed that this improvement was brought about primarily by a reduction in the overall amount of intrinsic motor noise, supporting our hypothesis. However, contrary to our expectations, adding noise to the error amplification did not enhance this effect.
机译:通过视觉反馈获得的有关错误的信息为促进运动学习提供了重要信息。已经显示放大错误信息是有益的,但是尚未阐明这种益处背后的机制。我们使用了虚拟投掷任务和基于模型的系统识别来测试以下假设:性能提高不仅可以通过增加反馈增益来实现,而且可以通过减少固有电动机噪声来实现。实验通过放大三个增益水平来操纵视觉误差。一些小组的错误放大带来了更多的噪音。实验结果表明,错误放大可改善任务性能,与以前的研究一致。基于先前验证的学习模型的系统识别结果表明,这种改进主要是通过减少固有电动机噪声的总量来实现的,从而支持了我们的假设。但是,与我们的预期相反,在误差放大中添加噪声并没有增强这种效果。

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