Measurable Decision Making with GSR and Pupillary Analysis for Intelligent User Interface
Measurable Decision Making with GSR and Pupillary Analysis for Intelligent User Interface
复制标题
通过 GSR 和智能用户界面的瞳孔分析进行可衡量的决策
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Zhidong Li
中科院分区：
文献类型：
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作者：
Jianlong Zhou;Jinjun Sun;Fang Chen;Yang Wang;R. Taib;Ahmad Khawaji;Zhidong Li
本文通过改变决策因素的类型、数量和值，为多属性决策制定（MADM）提供了一个自适应的、可测量的决策制定框架。在这个框架下，决策是通过皮肤电反应（GSR）和眼动追踪等生理传感器来测量的，而用户则要承受不同的决策质量和难度水平。根据这种可量化的决策，用户可以细化几个决策因素，以便做出高质量、低难度的决策。以汽车行驶路线选择为例，建立了一个实验来验证我们的假设。本研究中，GSR特征在索引决策质量上表现最佳。这些结果可用于指导人机交互中决策相关应用的智能用户界面设计，以适应用户行为和决策绩效。
This article presents a framework of adaptive, measurable decision making for Multiple Attribute Decision Making (MADM) by varying decision factors in their types, numbers, and values. Under this framework, decision making is measured using physiological sensors such as Galvanic Skin Response (GSR) and eye-tracking while users are subjected to varying decision quality and difficulty levels. Following this quantifiable decision making, users are allowed to refine several decision factors in order to make decisions of high quality and with low difficulty levels. A case study of driving route selection is used to set up an experiment to test our hypotheses. In this study, GSR features exhibit the best performance in indexing decision quality. These results can be used to guide the design of intelligent user interfaces for decision-related applications in HCI that can adapt to user behavior and decision-making performance.