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最小化误差测量中的误差:一种计算二维运动任务中误差的提议方法。

Minimizing error in measurement of error: a proposed method for calculation of error in a two-dimensional motor task.

作者信息

Kim J, Chung S, Tennant L K, Singer R N, Janelle C M

机构信息

Kyungpook National University, Korea.

出版信息

Percept Mot Skills. 2000 Feb;90(1):253-61. doi: 10.2466/pms.2000.90.1.253.

Abstract

Traditional one-dimensional error scores are still consistently used in research on motor learning to quantify two-dimensional error; however, the inherent differences in two-dimensional tasks render that application inappropriate and often misleading. Consequently, the purpose of this paper was to propose a novel method of presenting errors, which more precisely represents the accuracy, direction, and variability of error in two-dimensional settings. Although closely related to several alternatives for representing errors, the methodology used and the results obtained provide a more accurate procedure for pinpointing critical trends in what have been commonly referred to as AE (absolute error), VE (variable error), CE (constant error), and E (total variability). The proposed measurements of AVE (adjusted variable error), DE (directional error), TSE (total spread of error), and RE (radial error) provide composite error scores carrying a variety of information about performance on two-dimensional tasks. Formulas and examples are provided to facilitate computation and enhance understanding of the proposed scores.

摘要

在运动学习研究中,传统的一维误差分数仍被持续用于量化二维误差;然而,二维任务中的固有差异使得这种应用并不恰当,且常常具有误导性。因此,本文的目的是提出一种呈现误差的新方法,该方法能更精确地表示二维环境中误差的准确性、方向和变异性。尽管与几种表示误差的替代方法密切相关,但所使用的方法和获得的结果为精确找出通常被称为绝对误差(AE)、可变误差(VE)、恒定误差(CE)和总变异性(E)中的关键趋势提供了更准确的程序。所提出的调整可变误差(AVE)、方向误差(DE)、误差总散布(TSE)和径向误差(RE)测量方法提供了包含二维任务表现各种信息的综合误差分数。文中给出了公式和示例,以方便计算并增进对所提出分数的理解。

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