Department of Psychology and Neuroscience Program, Rhodes College, Memphis, TN, USA.
Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
Sci Rep. 2021 Apr 29;11(1):9245. doi: 10.1038/s41598-021-88688-5.
When a person makes a movement, a motor error is typically observed that then drives motor planning corrections on subsequent movements. This error correction, quantified as a trial-by-trial adaptation rate, provides insight into how the nervous system is operating, particularly regarding how much confidence a person places in different sources of information such as sensory feedback or motor command reproducibility. Traditional analysis has required carefully controlled laboratory conditions such as the application of perturbations or error clamping, limiting the usefulness of motor analysis in clinical and everyday environments. Here we focus on error adaptation during unperturbed and naturalistic movements. With increasing motor noise, we show that the conventional estimation of trial-by-trial adaptation increases, a counterintuitive finding that is the consequence of systematic bias in the estimate due to noise masking the learner's intention. We present an analytic solution relying on stochastic signal processing to reduce this effect of noise, producing an estimate of motor adaptation with reduced bias. The result is an improved estimate of trial-by-trial adaptation in a human learner compared to conventional methods. We demonstrate the effectiveness of the new method in analyzing simulated and empirical movement data under different noise conditions.
当一个人进行运动时，通常会观察到运动误差，然后驱动后续运动的运动规划校正。这种误差校正，以逐次试验适应率来量化，可以深入了解神经系统的运作方式，特别是关于一个人对不同信息源（如感觉反馈或运动指令可重复性）的信任程度。传统的分析需要精心控制实验室条件，例如施加扰动或误差钳制，这限制了运动分析在临床和日常环境中的有用性。在这里，我们关注未受干扰和自然运动过程中的误差适应。随着运动噪声的增加，我们发现逐次试验适应的传统估计会增加，这是一个违反直觉的发现，这是由于噪声掩盖了学习者的意图，导致估计中的系统偏差的结果。我们提出了一种基于随机信号处理的分析解决方案，以减少这种噪声的影响，从而产生具有较小偏差的运动适应估计。与传统方法相比，这会得到一个在人类学习者中逐次试验适应的改进估计。我们展示了在不同噪声条件下分析模拟和经验运动数据时，新方法的有效性。
