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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12186))

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Abstract

Icon lightness coding in digital interfaces has a very important impact on interface design. How to reduce visual information confusion and improve cognitive performance through icon feature coding is a subject to be studied in the optimization of interface design. According to previous research, icon composition is subdivided into lines and planes, while background is subdivided into positive background, negative back ground, and no background. The materials used in this study are the positive background of graphical icons, which were represented by color blocks in this study. Employing questionnaire as behavioral experimental tool, the main objective of this paper is to explore threshold range of the graphical icons’ Lightness levels in Dark mode and Light mode. In dark mode, the behavioral experiment showed that when the lightness of the color blocks changes within the range of a1(10,-0,-0)–a5(50,-0,-0), the difference between the lightness of the color blocks and the background increases faster, and the difference increases slowly in the range of a6(60,-0,-0)–a10(100,-0,-0). In light mode, the behavioral experiment showed that when the lightness of the color blocks changes within the range of b1(90,-0,-0)–b3(70,-0,-0), the difference between the lightness of the color blocks and the background increases faster, and the difference increases slowly in the range of b4(60,-0,-0)–b10(0,-0,-0). Through this experiment, we can reduce threshold range of icon’ background lightness levels of in Dark mode and Light mode. Knowledge gained from the current study could serve as a reference when icons are researched or designed in the future.

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Acknowledgement

This paper is supported by National Natural Science Foundation of China (No. 71871056).

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Correspondence to Chengqi Xue .

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Lu, A., Xue, C. (2020). A Study on Search Performance and Threshold Range of Icons. In: Harris, D., Li, WC. (eds) Engineering Psychology and Cognitive Ergonomics. Mental Workload, Human Physiology, and Human Energy. HCII 2020. Lecture Notes in Computer Science(), vol 12186. Springer, Cham. https://doi.org/10.1007/978-3-030-49044-7_6

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  • DOI: https://doi.org/10.1007/978-3-030-49044-7_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49043-0

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