Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences.
Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences.
复制标题
DOI：
10.3389/fnhum.2014.00168
复制
发表时间：
2014
影响因子：
2.9
通讯作者：
Braun DA
中科院分区：
文献类型：
--
作者：
Peng Z;Genewein T;Braun DA
复杂性是智能行为的一个标志，它由规则模式和随机变化组成。为了定量评估人体运动的复杂性和随机性，我们设计了一个运动任务，在这个任务中，我们将受试者的运动轨迹转换成一串符号序列。在实验的第一部分中，参与者被要求进行自节奏运动以创建重复模式、复制预先指定的字母序列并生成随机运动。为了研究随机性的程度是否可以被操纵，在实验的第二部分，参与者被要求在追逐游戏的背景下进行不可预测的运动，在那里他们从在线贝叶斯预测器那里得到反馈，猜测他们的下一步行动。我们分析了代表受试者的运动轨迹的符号序列与五个常见的复杂性措施：可预测性，可压缩性，近似熵，Lempel-Ziv复杂性，以及有效的措施复杂性。我们发现，受试者的自创模式是最复杂的，其次是字母的绘画运动和自定节奏的随机运动。我们还发现，参与者可以根据环境和反馈改变他们行为的随机性。我们的研究结果表明，人类可以在不同的运动类型和背景下调整复杂性和规律性，这可以用运动轨迹产生的符号序列的信息理论测量来评估。
Complexity is a hallmark of intelligent behavior consisting both of regular patterns and random variation. To quantitatively assess the complexity and randomness of human motion, we designed a motor task in which we translated subjects' motion trajectories into strings of symbol sequences. In the first part of the experiment participants were asked to perform self-paced movements to create repetitive patterns, copy pre-specified letter sequences, and generate random movements. To investigate whether the degree of randomness can be manipulated, in the second part of the experiment participants were asked to perform unpredictable movements in the context of a pursuit game, where they received feedback from an online Bayesian predictor guessing their next move. We analyzed symbol sequences representing subjects' motion trajectories with five common complexity measures: predictability, compressibility, approximate entropy, Lempel-Ziv complexity, as well as effective measure complexity. We found that subjects' self-created patterns were the most complex, followed by drawing movements of letters and self-paced random motion. We also found that participants could change the randomness of their behavior depending on context and feedback. Our results suggest that humans can adjust both complexity and regularity in different movement types and contexts and that this can be assessed with information-theoretic measures of the symbolic sequences generated from movement trajectories.
登录
查看更多内容
影响因子：
8.8
作者：
Bruhn, J;Röpcke, H;Hoeft, A
通讯作者：
Hoeft, A
影响因子：
2.9
作者：
Genewein T;Braun DA
通讯作者：
Braun DA
影响因子：
1
作者：
Antos, A;Kontoyiannis, I
通讯作者：
Kontoyiannis, I
影响因子：
3.7
作者：
Braun DA;Waldert S;Aertsen A;Wolpert DM;Mehring C
通讯作者：
Mehring C
影响因子：
2.9
作者：
Debowski, Lukasz
通讯作者：
Debowski, Lukasz