Inferring Cognitive Style from Eye Gaze Behavior During Information Visualization Usage

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2020-07-07 DOI:10.1145/3340631.3394881

B. Steichen, Bo Fu, Tho Nguyen

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引用次数: 6

Abstract

Information Visualization is a key technique to assist users in data analysis tasks, by creating visual representations of data to amplify human cognition. However, while human cognitive abilities and styles have been shown to differ significantly, Information Visualizations have traditionally been designed in a manner that does not consider such individual user differences. Recent research has started to address this issue, by identifying individual user characteristics that influence individual users' interactions with Information Visualizations, as well as developing novel Information Visualization systems that provide more personalized support. This paper presents a set of experiments aimed towards building such User-Adaptive Information Visualization systems, by studying the extent to which a user's cognitive style can be inferred from a user's interaction with an Information Visualization system. Results show that a user's eye gaze data can be used to infer a user's cognitive style during information visualization usage with up to 86% accuracy, and that the most informative features relate to a user's saccade angles and fixation durations.

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从信息可视化使用中眼睛注视行为推断认知风格

信息可视化是帮助用户完成数据分析任务的一项关键技术，它通过创建数据的可视化表示来增强人类的认知。然而，虽然人类的认知能力和风格已被证明存在显著差异，但信息可视化的传统设计方式并未考虑到这种个体用户差异。最近的研究已经开始解决这个问题，通过确定影响个人用户与信息可视化交互的个人用户特征，以及开发提供更多个性化支持的新型信息可视化系统。本文通过研究从用户与信息可视化系统的交互中推断用户认知风格的程度，提出了一组旨在构建这种用户自适应信息可视化系统的实验。结果表明，在信息可视化使用过程中，用户的眼睛注视数据可以用来推断用户的认知风格，准确率高达86%，并且最具信息量的特征与用户的扫视角度和注视持续时间有关。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

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