Decision landscapes: visualizing mouse-tracking data.
Decision landscapes: visualizing mouse-tracking data.
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DOI：
10.1098/rsos.170482
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发表时间：
2017-11
影响因子：
3.5
通讯作者：
di Bernardo M
中科院分区：
文献类型：
--
作者：
Zgonnikov A;Aleni A;Piiroinen PT;O'Hora D;di Bernardo M
计算机化的范例使得能够收集关于人类行为的丰富数据，包括关于决策的运动执行的信息，例如通过跟踪鼠标光标轨迹。这些轨迹可以揭示有关正在进行的决策过程的新信息。随着老鼠跟踪研究的数量和复杂性的增加，需要更复杂的方法来分析决策轨迹。在这里，我们提出了一种新的计算方法来生成基于鼠标跟踪数据的决策景观可视化。决策景观是一种类似于能量势场的模型，它是从决策过程中鼠标移动的速度中数学推导出来的。可视化为三维表面，它提供了决策动态的全面概述。采用动态系统理论框架，我们开发了一种新的方法来生成决策景观的基础上任意数量的轨迹。该方法不仅生成决策景观的三维图示，而且通过一些可解释的参数来描述鼠标轨迹。这些参数与传统的措施相比，更详细地描述了决策的动态特性，并且可以在实验条件下进行比较，甚至可以在个体之间进行比较。决策景观可视化方法是一种新的工具，用于分析鼠标轨迹在决策执行过程中，它可以提供新的见解决策动态的个体差异。
Computerized paradigms have enabled gathering rich data on human behaviour, including information on motor execution of a decision, e.g. by tracking mouse cursor trajectories. These trajectories can reveal novel information about ongoing decision processes. As the number and complexity of mouse-tracking studies increase, more sophisticated methods are needed to analyse the decision trajectories. Here, we present a new computational approach to generating decision landscape visualizations based on mouse-tracking data. A decision landscape is an analogue of an energy potential field mathematically derived from the velocity of mouse movement during a decision. Visualized as a three-dimensional surface, it provides a comprehensive overview of decision dynamics. Employing the dynamical systems theory framework, we develop a new method for generating decision landscapes based on arbitrary number of trajectories. This approach not only generates three-dimensional illustration of decision landscapes, but also describes mouse trajectories by a number of interpretable parameters. These parameters characterize dynamics of decisions in more detail compared with conventional measures, and can be compared across experimental conditions, and even across individuals. The decision landscape visualization approach is a novel tool for analysing mouse trajectories during decision execution, which can provide new insights into individual differences in the dynamics of decision making.
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DOI：
10.1098/rstb.2007.2054
发表时间：
2007-09-29
影响因子：
6.3
作者：
Cisek, Paul
通讯作者：
Cisek, Paul
影响因子：
3.8
作者：
Fischer MH;Hartmann M
通讯作者：
Hartmann M
影响因子：
1.7
作者：
Frisch, Simon;Dshemuchadse, Maja;Scherbaum, Stefan
通讯作者：
Scherbaum, Stefan
影响因子：
3.4
作者：
Scherbaum, Stefan;Dshemuchadse, Maja;Goschke, Thomas
通讯作者：
Goschke, Thomas
影响因子：
3.8
作者：
Freeman JB;Dale R;Farmer TA
通讯作者：
Farmer TA