Table_1_Non-linear Amplification of Variability Through Interaction Across Scales Supports Greater Accuracy in Manual Aiming: Evidence From a Multifractal Analysis With Comparisons to Linear Surrogates in the Fitts Task.DOCX

Movement coordination depends on directing our limbs to the right place and in the right time. Movement science can study this central requirement in the Fitts task that asks participants to touch each of two targets in alternation, as accurately and as fast as they can. The Fitts task is an experimental attempt to focus on how the movement system balances its attention to speed and to accuracy. This balance in the Fitts task exhibits a hierarchical organization according to which finer details (e.g., kinematics of single sweeps from one target to the other) change with relatively broader constraints of task parameters (e.g., distance between targets and width of targets). The present work seeks to test the hypothesis that this hierarchical organization of movement coordination reflects a multifractal tensegrity in which non-linear interactions across scale support stability. We collected movement series data during a easy variant of the Fitts task to apply just such a multifractal analysis with surrogate comparison to allow clearer test of non-linear interactions across scale. Furthermore, we test the role of visual feedback both in potential and in fact, i.e., by manipulating both whether experimenters instructed participants that they might potentially have to close their eyes during the task and whether participants actually closed their eyes halfway through the task. We predict that (1) non-linear interactions across scales in hand movement series will produce variability that will actually stabilize aiming in the Fitts task, reducing standard deviation of target contacts; (2) non-linear interactions across scales in head sway will stabilize aiming following the actual closing eyes; and (3) non-linear interactions across scales in head sway and in hand movements will interact to support stabilizing effects of expectation about closing eyes. In sum, this work attempts to make the case that the multifractal-tensegrity hypothesis supports more accurate aiming behavior in the Fitts task.

动作协调依赖于将我们的肢体导向正确的位置并在正确的时间进行。运动科学可以研究这一核心需求，即 Fitts 任务，要求参与者交替触摸两个目标，尽可能准确和迅速。Fitts 任务是一种实验尝试，旨在聚焦于运动系统如何平衡对速度和准确性的关注。Fitts 任务中的这种平衡呈现出一种层次组织结构，其中更细致的细节（例如，从一个目标到另一个目标的单一挥扫的动力学）随着相对较宽泛的任务参数约束（例如，目标之间的距离和目标宽度）而变化。本研究旨在检验以下假设：运动协调的这种层次组织反映了一种多分形张紧结构，其中跨尺度的非线性相互作用支持稳定性。我们在 Fitts 任务的简单变体中收集了运动系列数据，以便进行此类多分形分析，并与替代比较相结合，以便更清晰地测试跨尺度的非线性相互作用。此外，我们还测试了视觉反馈在潜在和实际中的作用，即通过操纵实验者是否指示参与者可能在任务中闭眼，以及参与者是否在实际任务过程中闭眼。我们预测：(1)手部运动系列中的跨尺度非线性相互作用将产生变异，实际上将稳定 Fitts 任务中的瞄准，降低目标接触的标准差；(2)头部摇摆中的跨尺度非线性相互作用将在实际闭眼后稳定瞄准；(3)头部摇摆和手部运动中的跨尺度非线性相互作用将相互作用，以支持关于闭眼的预期稳定效应。总之，本研究旨在证明多分形张紧结构假设支持 Fitts 任务中更准确的瞄准行为。