Error Correction and the Structure of Inter-Trial Fluctuations in a Redundant Movement Task.
Error Correction and the Structure of Inter-Trial Fluctuations in a Redundant Movement Task.
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DOI：
10.1371/journal.pcbi.1005118
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发表时间：
2016-09
影响因子：
4.3
通讯作者：
Cusumano JP
中科院分区：
文献类型：
--
作者：
John J;Dingwell JB;Cusumano JP
我们研究人类参与者在重复执行虚拟沙狐球任务期间表现出的试验间运动波动。着眼于技能表现，对先前开发的试间误差校正通用模型的理论分析用于预测目标等效流形（GEM）附近的变异性的时间和几何结构。该理论还预测，目标级误差通过总体身体目标灵敏度与内在身体级噪声呈线性关系，这是一个新的导出量，说明了任务绩效如何从 GEM 上的主动纠错和被动灵敏度特性的相互作用中产生。根据观察到的波动估计的线性模型，以及自举法在试验间动力学的动力学和相关特性估计中的新颖应用，用于通过实验确认所有预测，从而验证我们的模型。此外，我们表明，与“静态”变异性分析不同，我们的动态方法产生的结果与用于测量任务执行的坐标无关，并且这样做提供了一组新的任务坐标，这些坐标是误差调节过程本身所固有的。在重复执行精密运动任务的过程中，人类面临着来自运动系统本身的两个巨大挑战：维度和噪声。人类运动表现涉及生物力学、神经运动和感知自由度，远远超过理论上规定典型目标导向任务所需的自由度。与此同时，在多个观察尺度上，人体中都存在噪音。生物运动的这种高维和随机特征是任务执行过程中普遍观察到的变异性的基本来源。然而，越来越明显的是，这两个挑战不仅仅是需要克服的障碍，而且是理解人类如何在不断变化的环境下（例如由疲劳、受伤或衰老引起的环境下）保持运动表现的关键。在这项工作中，通过研究熟练的人类参与者玩虚拟沙狐球游戏，我们证明了在分析许多试验中观察到的运动变异性时采用动态视角的根本重要性。使用这种动态方法，我们不仅可以研究观察到的试验间变异性的几何形状，还可以从理论上描述和实验上表征它是如何在时间上产生和调节的。此外，我们的理论框架和基于模型的数据分析方法有助于统一以前仅基于稳定性、相关性、控制理论或任务流形的变异性分析方法。这种概念上的统一支持了这样一种观点，即运动变异性的这种看似不同的特征源于单一的、相对简单的运动调节的潜在神经生理学过程。
We study inter-trial movement fluctuations exhibited by human participants during the repeated execution of a virtual shuffleboard task. Focusing on skilled performance, theoretical analysis of a previously-developed general model of inter-trial error correction is used to predict the temporal and geometric structure of variability near a goal equivalent manifold (GEM). The theory also predicts that the goal-level error scales linearly with intrinsic body-level noise via the total body-goal sensitivity, a new derived quantity that illustrates how task performance arises from the interaction of active error correction and passive sensitivity properties along the GEM. Linear models estimated from observed fluctuations, together with a novel application of bootstrapping to the estimation of dynamical and correlation properties of the inter-trial dynamics, are used to experimentally confirm all predictions, thus validating our model. In addition, we show that, unlike “static” variability analyses, our dynamical approach yields results that are independent of the coordinates used to measure task execution and, in so doing, provides a new set of task coordinates that are intrinsic to the error-regulation process itself. During the repeated execution of precision movement tasks, humans face two formidable challenges from the motor system itself: dimensionality and noise. Human motor performance involves biomechanical, neuromotor, and perceptual degrees of freedom far in excess of those theoretically needed to prescribe typical goal-directed tasks. At the same time, noise is present in the human body across multiple scales of observation. This high-dimensional and stochastic character of biological movement is the fundamental source of variability ubiquitously observed during task execution. However, it is becoming clear that these two challenges are not merely impediments to be overcome, but rather hold a key to understanding how humans maintain motor performance under changing circumstances, such as those caused by fatigue, injury, or aging. In this work, by studying skilled human participants as they play a virtual shuffleboard game, we demonstrate the fundamental importance of adopting a dynamical perspective when analyzing the motor variability observed over many trials. Using this dynamical approach, we can not only study the geometry of observed inter-trial variability, but can also theoretically describe and experimentally characterize how it is temporally generated and regulated. Furthermore, our theoretical framework and model-based data analysis approach helps to unify previous variability analysis approaches based on stability, correlation, control theory, or task manifolds alone. This conceptual unification supports the idea that such seemingly disparate features of motor variability arise from a single, relatively simple underlying neurophysiological process of motor regulation.
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影响因子：
2.5
作者：
Dingwell, Jonathan B.;Smallwood, Rachel F.;Cusumano, Joseph P.
通讯作者：
Cusumano, Joseph P.
影响因子：
64.8
作者：
Harris, CM;Wolpert, DM
通讯作者：
Wolpert, DM
DOI：
10.1007/978-1-4939-1338-1_9
发表时间：
2014-01-01
期刊：
PROGRESS IN MOTOR CONTROL: SKILL LEARNING, PERFORMANCE, HEALTH, AND INJURY, VOL 826
影响因子：
--
作者：
Cusumano, Joseph P.;Mahoney, Joseph M.;Dingwell, Jonathan B.
通讯作者：
Dingwell, Jonathan B.
影响因子：
2
作者：
Cohen, R. G.;Sternad, D.
通讯作者：
Sternad, D.
影响因子：
2.4
作者：
Dingwell, Jonathan B.;Cusumano, Joseph P.
通讯作者：
Cusumano, Joseph P.