Department of Human Sciences, Institute for Sport Science, Technische Universität Darmstadt, Darmstadt, Germany.
Intelligent Autonomous Systems Group, Department of Computer Science, Technische Universität Darmstadt, Darmstadt, Germany.
PLoS One. 2021 Apr 23;16(4):e0249518. doi: 10.1371/journal.pone.0249518. eCollection 2021.
The purpose of this paper is to examine, whether and under which conditions humans are able to predict the putting distance of a robotic device. Based on the "flash-lag effect" (FLE) it was expected that the prediction errors increase with increasing putting velocity. Furthermore, we hypothesized that the predictions are more accurate and more confident if human observers operate under full vision (F-RCHB) compared to either temporal occlusion (I-RCHB) or spatial occlusion (invisible ball, F-RHC, or club, F-B). In two experiments, 48 video sequences of putt movements performed by a BioRob robot arm were presented to thirty-nine students (age: 24.49±3.20 years). In the experiments, video sequences included six putting distances (1.5, 2.0, 2.5, 3.0, 3.5, and 4.0 m; experiment 1) under full versus incomplete vision (F-RCHB versus I-RCHB) and three putting distances (2. 0, 3.0, and 4.0 m; experiment 2) under the four visual conditions (F-RCHB, I-RCHB, F-RCH, and F-B). After the presentation of each video sequence, the participants estimated the putting distance on a scale from 0 to 6 m and provided their confidence of prediction on a 5-point scale. Both experiments show comparable results for the respective dependent variables (error and confidence measures). The participants consistently overestimated the putting distance under the full vision conditions; however, the experiments did not show a pattern that was consistent with the FLE. Under the temporal occlusion condition, a prediction was not possible; rather a random estimation pattern was found around the centre of the prediction scale (3 m). Spatial occlusion did not affect errors and confidence of prediction. The experiments indicate that temporal constraints seem to be more critical than spatial constraints. The FLE may not apply to distance prediction compared to location estimation.
本文旨在探讨人类是否能够预测机器人设备的投掷距离,如果可以,其条件又是什么。基于“闪光-滞后效应”(FLE),我们预计随着投掷速度的增加,预测误差会增加。此外,我们假设如果人类观察者在全视野下(F-RCHB)操作,而不是在时间遮挡下(I-RCHB)或空间遮挡下(不可见球,F-RHC 或球杆,F-B),预测会更准确,更有信心。在两项实验中,向 39 名学生(年龄:24.49±3.20 岁)呈现了由 BioRob 机械臂执行的 48 个推杆动作的视频序列。在实验中,视频序列包括全视野与不完全视野(F-RCHB 与 I-RCHB)下的六个投掷距离(1.5、2.0、2.5、3.0、3.5 和 4.0 m;实验 1)和四个视觉条件(F-RCHB、I-RCHB、F-RCH 和 F-B)下的三个投掷距离(2.0、3.0 和 4.0 m;实验 2)。在呈现每个视频序列后,参与者在 0 到 6 m 的比例尺上估计投掷距离,并在 5 点量表上提供预测置信度。两个实验对于各自的因变量(误差和置信度测量)都显示出可比的结果。参与者在全视野条件下一致高估了投掷距离;然而,实验并没有显示出与 FLE 一致的模式。在时间遮挡条件下,无法进行预测;而是在预测尺度的中心周围发现了随机的估计模式(3 m)。空间遮挡对预测误差和置信度没有影响。实验表明,时间限制似乎比空间限制更为关键。与位置估计相比,FLE 可能不适用于距离预测。