冗余(工程)
图形模型
计算机科学
可视化
图形显示
数据挖掘
机器学习
人工智能
操作系统
计算机图形学(图像)
作者
Tao Jin,Chunpeng Chen,Yuting Xia,Xinyu Liu,Xiaoxu Liu
出处
期刊:Ergonomics
[Informa]
日期:2024-02-12
卷期号:: 1-10
标识
DOI:10.1080/00140139.2024.2316314
摘要
Multiple time-series graphs are commonly used for data visualisation, but few scholars have investigated the impact of graphical attributes on decision-making efficiency. This study explores the effects of graphical attributes of varying redundancy conditions on decision-making efficiency. Two experimental conditions were developed for the experiment: non-redundant (independent graphical attributes: colour, linear and marker) and redundant (combinations of two and more graphical attributes: colour and linear, colour and marker, etc.). A total of 60 people took part in both experiments and performed two tasks: maximisation and discrimination. The experiments revealed that the addition of attributes, such as colour, marker or linear, decreased response time (RT), but the combination of colour & linear & marker increased RT. This is more significant in discrimination tasks. We provide empirical evidence for the design of time-series data visualisations and encourage the combination of two of these graphical attributes, such as colour & linear, colour & marker or linear & marker, when conditions allow, to improve decision-making efficiency.Few scholars have studied the impact of graphical attributes on decision-making efficiency in data visualisation. This study explores the effect of graphical attributes with different redundancy levels on decision-making efficiency through behavioural experiments. It has been found that moderately redundant graphical attributes in difficult tasks can significantly improve decision-making efficiency.
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