Investigation of the influence of graphical interface elements on the reading of visual information

Cover Page

Cite item

Full Text

Abstract

The article is devoted to the study of how users perceive interfaces designed for remote control of dynamic objects, such as drones and autonomous robots. The object of the study is the user interface of a remote dynamic object control system, and the subject is UX design tools for creating a graphical interface for such systems. The purpose of the work is to establish how color, location (top or left), type (textual or iconographic) and the presence of background (backing) of the controls affect the speed of finding the necessary information by the user in the interfaces of remote control of dynamic objects. The study used the technology of eye-tracking to objectively assess the differences in visual perception of selected interface parameters. Statistical multivariate analysis of variance (ANOVA) was used in data processing. Thirty-eight subjects participated in the experiment. The results showed that the color, location and type of controls significantly affect the speed of information retrieval. Red buttons solved the task faster than blue buttons, as did iconographic controls compared to textual controls. At the same time, it was found that all the highlighted parameters influence each other's perception. The results obtained can be used in the design of interfaces for aircraft and robotics systems, as well as for other highly loaded control environments. The novelty of the work lies in the complex analysis of interface parameters and their influence on the cognitive load of the user. The results of the study confirm that the correct choice of color, location and type of control elements increases the speed of problem solving (finding the necessary element), which is important for creating ergonomic design of interfaces of dynamic control systems.

References

  1. Зыбин, Е. Ю., Косьянчук, В. В., Земкин, В. А. Авиационные человеко-машинные интерфейсы: состояние и перспективы развития // Актуальные проблемы психологии труда, инженерной психологии и эргономики. Москва : Институт психологии РАН, 2021. С. 211-230.
  2. Choi, J.-K., Kwon, Y.-J., Jeon, J., Kim, K., Choi, H., Jang, B. Conceptual Design of Driver-Adaptive Human-Machine Interface for Digital Cockpit // 2018 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea (South), 2018. С. 1005-1007. doi: 10.1109/ICTC.2018.8539644.
  3. Endsley, M. R. Toward a theory of situation awareness in dynamic systems // Human factors. 1995. Т. 37, № 1. С. 32-64. doi: 10.1518/00187209577904954.
  4. Wickens, C. D. Multiple Resources and Mental Workload // Human Factors: The Journal of the Human Factors and Ergonomics Society. 2008. Т. 50, № 3. С. 449-455. doi: 10.1518/001872008X288394.
  5. Parasuraman, R., Riley, V. Humans and automation: Use, misuse, disuse, abuse // Human factors. 1997. Т. 39, № 2. С. 230-253. doi: 10.1518/001872097778543886.
  6. Manoochehri, M. Up to the magical number seven: An evolutionary perspective on the capacity of short term memory // Heliyon. 2021. Т. 7, № 5. doi: 10.1016/j.heliyon.2021.e06955.
  7. Singh, H., Singh, J. Human eye tracking and related issues: A review // International Journal of Scientific and Research Publications. 2012. Т. 2, № 9. С. 1-9. URL: http://www.ijsrp.org/research-paper-0912.php?rp=P09146.
  8. Янчус, В. Э. Информационная модель восприятия визуальной информации человеком // Труды Международной конференции по компьютерной графике и зрению "Графикон". 2023. № 33. С. 969-975. doi: 10.20948/graphicon-2023-969-975. URL: https://www.graphicon.ru/html/2023/papers/paper_101.pdf.
  9. Horizontal Attention Leans Left // Nielsen Norman Group. URL: https://www.nngroup.com/articles/horizontal-attention-leans-left/ (дата обращения: 29.01.2025).
  10. Акрамов, Ш. У., Романова, А. Н., Зюзина, А. И. Закономерность Миллера или пределы кратковременной памяти человека // Вестник Калужского университета. 2022. № 3(56). С. 39-44. doi: 10.54072/18192173_2022_3_5_39.
  11. Шиффман, Х. Р. Ощущение и восприятие. 5-е изд. СПб.: Питер, 2003. 929 с.
  12. Янчус В.Э., Боревич Е.В., Авдеева А.А. Применение технологии ай-трекинга в вопросах исследования восприятия графической информации // Программные системы и вычислительные методы. 2021. № 1. С. 53-62. doi: 10.7256/2454-0714.2021.1.33378 URL: https://nbpublish.com/library_read_article.php?id=33378
  13. Goodman, S. N. Aligning statistical and scientific reasoning // Science. 2016. Т. 352, № 6290. С. 1180-1181. doi: 10.1126/science.aaf5406.
  14. Кулаичев, А. П. Методы и средства комплексного анализа данных. ИНФРА-М, 2006. 512 с.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).