Centro de Investigación y Transferencia en Acústica (CINTRA), CONICET, Universidad Tecnológica Nacional Facultad Regional Córdoba, Córdoba, Argentina.
Facultad de Ingeniería, Universidad Nacional de Entre Ríos, Oro Verde, Entre Ríos, Argentina.
Cogn Process. 2022 May;23(2):285-298. doi: 10.1007/s10339-021-01068-9. Epub 2022 Jan 4.
Active Perception perspectives claim that action is closely related to perception. An empirical approach that supports these theories is the minimalist, in which participants perform a task using an interface that provides minimal information. Their exploratory movements are crucial to generating a meaningful sequence of information. Previous studies analyzed sensorimotor trajectories describing qualitative strategies and linear quantification of participants' movement performance, but that approach struggles to capture the behavior of non-stationary data. In the present study, we applied the recurrence plot (RP) and recurrence quantification analysis (RQA) to study the structure of sensorimotor trajectories developed by participants trying to discriminate between two invisible geometric shapes (Triangle or Rectangle). The exploratory movements were made using a computer mouse and sonification-mediated feedback was provided, which depended exclusively on whether the pointer was inside or outside the shape. We applied RP and RQA to the sensorimotor trajectories, with the aim of studying their fine structure characteristics, focusing on their repetitive patterns. Recurrence analysis proved to be useful for quantifying differences in dynamic behavior that emerge when participants explore invisible virtual geometric shapes. The differences obtained in RQA-based measures associated with the vertical structures allowed to postulate the existence of particular exploration strategies for each figure. It was also possible to determine that the complexity of the dynamics changed according to the shape. We discuss these results in light of antecedents in haptic and visual perceptual exploration.
主动感知观点认为,动作与感知密切相关。支持这些理论的一种经验方法是最小主义方法,参与者使用提供最少信息的界面来执行任务。他们的探索性运动对于生成有意义的信息序列至关重要。以前的研究分析了描述参与者运动表现的定性策略和线性量化的运动传感器轨迹,但这种方法难以捕捉非平稳数据的行为。在本研究中,我们应用递归图(RP)和递归量化分析(RQA)来研究参与者试图区分两个不可见几何形状(三角形或矩形)时开发的运动传感器轨迹的结构。探索性运动是使用计算机鼠标进行的,并提供了声音介导的反馈,该反馈仅取决于指针是否在形状内或形状外。我们将 RP 和 RQA 应用于运动传感器轨迹,目的是研究它们的精细结构特征,重点是它们的重复模式。递归分析被证明可用于量化参与者探索不可见虚拟几何形状时出现的动态行为差异。与垂直结构相关的基于 RQA 的测量值中获得的差异允许假设每个图形存在特定的探索策略。还可以确定根据形状,动态的复杂性发生了变化。我们根据触觉和视觉感知探索的先前研究来讨论这些结果。