Department of Psychology and Cognitive Science, University of Trento, corso Bettini 31, 38068, Rovereto, TN, Italy.
Faculty of Psychology, Vita-Salute San Raffaele University, Milan, Italy.
Behav Res Methods. 2017 Dec;49(6):2012-2030. doi: 10.3758/s13428-016-0839-5.
Mouse tracker methodology has recently been advocated to explore the motor components of the cognitive dynamics involved in experimental tasks like categorization, decision-making, and language comprehension. This methodology relies on the analysis of computer-mouse trajectories, by evaluating whether they significantly differ in terms of direction, amplitude, and location when a given experimental factor is manipulated. In this kind of study, a descriptive geometric approach is usually adopted in the analysis of raw trajectories, where they are summarized with several measures, such as maximum-deviation and area under the curve. However, using raw trajectories to extract spatial descriptors of the movements is problematic due to the noisy and irregular nature of empirical movement paths. Moreover, other significant components of the movement, such as motor pauses, are disregarded. To overcome these drawbacks, we present a novel approach (EMOT) to analyze computer-mouse trajectories that quantifies movement features in terms of entropy while modeling trajectories as composed by fast movements and motor pauses. A dedicated entropy decomposition analysis is additionally developed for the model parameters estimation. Two real case studies from categorization tasks are finally used to test and evaluate the characteristics of the new approach.
鼠标追踪方法最近被提倡用于探索分类、决策和语言理解等实验任务中涉及的认知动态的运动成分。这种方法依赖于对计算机鼠标轨迹的分析，通过评估在操纵给定的实验因素时，它们在方向、幅度和位置上是否有显著差异。在这种研究中，通常采用描述性的几何方法来分析原始轨迹，其中使用几个度量标准对其进行总结，例如最大偏差和曲线下面积。然而，由于经验运动路径的噪声和不规则性质，使用原始轨迹来提取运动的空间描述符是有问题的。此外，运动的其他重要组成部分，如运动暂停，被忽略了。为了克服这些缺点，我们提出了一种分析计算机鼠标轨迹的新方法（EMOT），该方法根据熵来量化运动特征，同时将轨迹建模为由快速运动和运动暂停组成。还为模型参数估计开发了专门的熵分解分析。最后，使用两个来自分类任务的实际案例研究来测试和评估新方法的特征。
