Optimal design method to minimize users' thinking mapping load in human-machine interactions.
Optimal design method to minimize users' thinking mapping load in human-machine interactions.
复制标题
人机交互中最小化用户思维映射负荷的优化设计方法。
作者：
Yanqun Huang;Xu Li;Jie Zhang
背景
人类认知与机器需求/行为之间的差异通常会导致产品运行中严重的思维映射负荷甚至灾难。在当今脑力劳动被掌握的社会中，帮助人们避免人机交互的困惑和困难是很重要的。
目的
提高产品的可用性，最大限度地减少用户在人机交互中的思维映射和解释负荷。
方法
提出了一种基于最小化用户意图与产品界面状态可供性之间思维映射过程中心理负荷的人机界面优化设计方法。通过对用户思维映射问题的分析，构建了操作行为模型。根据人的本能和已有的知识，首先唯一地确定一个期望的理想设计，使思维负荷最小化。然后，创造性的替代品，在人类获得操作信息的方式，提供数字接口状态数据集。最后，利用聚类分析方法，通过计算两个数据集之间的距离，从备选方案中选出最优解。
结果
在人车交互设计案例中，考虑多个因素以最小化用户的思维映射负荷，找到了最接近理想值的解决方案。
结论
聚类结果表明，该方法能有效地解决人机交互设计中的心理负荷最小化问题。
BACKGROUND
The discrepancy between human cognition and machine requirements/behaviors usually results in serious mental thinking mapping loads or even disasters in product operating. It is important to help people avoid human-machine interaction confusions and difficulties in today's mental work mastered society.
OBJECTIVE
Improving the usability of a product and minimizing user's thinking mapping and interpreting load in human-machine interactions.
METHODS
An optimal human-machine interface design method is introduced, which is based on the purpose of minimizing the mental load in thinking mapping process between users' intentions and affordance of product interface states. By analyzing the users' thinking mapping problem, an operating action model is constructed. According to human natural instincts and acquired knowledge, an expected ideal design with minimized thinking loads is uniquely determined at first. Then, creative alternatives, in terms of the way human obtains operational information, are provided as digital interface states datasets. In the last, using the cluster analysis method, an optimum solution is picked out from alternatives, by calculating the distances between two datasets.
RESULTS
Considering multiple factors to minimize users' thinking mapping loads, a solution nearest to the ideal value is found in the human-car interaction design case.
CONCLUSIONS
The clustering results show its effectiveness in finding an optimum solution to the mental load minimizing problems in human-machine interaction design.
影响因子：
2.7
作者：
Goodman-Deane, Joy;Langdon, Patrick;Clarkson, John
通讯作者：
Clarkson, John