Manuello J, Ciceri T, Longatelli V, Maronati C, Biffi E, Cavallo A, Casartelli L
Move'N'Brains Lab, Department of Psychology, University of Turin, Turin, Italy.
NeuroImaging Lab, Scientific Institute IRCCS E. Medea, Bosisio Parini (LC), Italy.
Behav Res Methods. 2025 Apr 9;57(5):140. doi: 10.3758/s13428-025-02635-0.
Traditional average-based metrics have long been considered the gold standard in behavioral and brain research. However, recent advances emphasize the importance of examining the dispersion around the mean to uncover the nuances of individual differences and challenge simplistic assumptions. Thus, the study of variability is becoming increasingly central across a wide range of domains. Here, we tackle the composite architecture of motor variability by proposing a new geometric method to model it. Three independent gait datasets are used to: i) develop the method (Dataset 1), ii) evaluate its performance when transitioning from optoelectronic cameras to inertial measurement units (Dataset 2), and iii) generalize it in an experimental design with cognitive manipulations (Dataset 3). The method is based on the Procrustes transformation and multidimensional scaling. This geometric approach allows us to define the individual space of variability (i.e., the amount of bidimensional space covered by each individual's trial-by-trial data). In turn, it provides robust evidence to identify each individual unique and specific motor signature (motor fingerprint). Our approach represents a fundamental shift from previous research: It is not the value of kinematic parameters per se that defines an individual's motor signature, but rather the distinct way in which each individual varies these parameters, i.e., the dispersion of the distribution of their kinematic data. This novel perspective provides a single-subject-weighted proxy for motor signature, based on the characteristic dispersion of each individual's data. The potential applications of this new method in research and clinical settings represent a fascinating future challenge.
长期以来，传统的基于平均值的指标一直被视为行为和大脑研究的黄金标准。然而，最近的进展强调了研究均值周围离散度以揭示个体差异细微之处并挑战简单化假设的重要性。因此，变异性研究在广泛的领域中变得越来越核心。在此，我们通过提出一种新的几何方法来对其进行建模，以解决运动变异性的复合架构问题。使用三个独立的步态数据集来：i）开发该方法（数据集1），ii）评估从光电相机过渡到惯性测量单元时其性能（数据集2），以及iii）在具有认知操作的实验设计中对其进行推广（数据集3）。该方法基于普罗克汝斯忒斯变换和多维缩放。这种几何方法使我们能够定义变异性的个体空间（即每个个体逐次试验数据所覆盖的二维空间量）。反过来，它为识别每个个体独特且特定的运动特征（运动指纹）提供了有力证据。我们的方法代表了与先前研究的根本转变：定义个体运动特征的并非运动学参数本身的值，而是每个个体改变这些参数的独特方式，即其运动学数据分布的离散度。基于每个个体数据的特征离散度，这种新颖的观点为运动特征提供了一个单受试者加权代理。这种新方法在研究和临床环境中的潜在应用代表了一个引人入胜的未来挑战。
