Evaluating Visual Consistency of Icon Usage in Across-Devices

References

• Alemerien, K., Kenneth, M. (2014). GUIEvaluator: A metric-tool for evaluating the complexity of Graphical User Interfaces. In Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE, 2014 (pp. 13–18).
   Google Scholar
• Alexander, E. C., Chang, C.-C., Shimabukuro, M., Franconeri, S., Collins, C., & Gleicher, M. (2018). Perceptual biases in font size as a data encoding. IEEE Transactions on Visualization and Computer Graphics, 24(8), 2397–2410. https://doi.org/10.1109/TVCG.2017.2723397
   PubMed Web of Science ®Google Scholar
• Ali, A. X., McAweeney, E., & Wobbrock, J. O. (2021). Anachronism by design: Understanding young adults’ perceptions of computer iconography. International Journal of Human–Computer Studies, 151, 102599. https://doi.org/10.1016/j.ijhcs.2021.102599
   Web of Science ®Google Scholar
• Allen, B. (2000). Individual differences and the conundrums of user-centered design: Two experiments. Journal of the American Society for Information Science, 51(6), 508–520. https://doi.org/10.1002/(SICI)1097-4571(2000)51:6<508::AID-ASI3>3.0.CO;2-Q
   Google Scholar
• Al-Samarraie, H., Eldenfria, A., & Dawoud, H. (2017). The impact of personality traits on users’ information-seeking behavior. Information Processing & Management, 53(1), 237–247. https://doi.org/10.1016/j.ipm.2016.08.004
   Web of Science ®Google Scholar
• Balachandran, D., Tan, G. W.-H., Ooi, K.-B., & Wei, J. (2022). What’s stopping you from migrating to mobile payment? International Journal of Human–Computer Interaction, 1–20. https://doi.org/10.1080/10447318.2022.2115334
   Web of Science ®Google Scholar
• Bernard, M., Liao, C. H., & Mills, M. (2001). The effects of font type and size on the legibility and reading time of online text by older adults. In CHI ’01 Extended Abstracts on Human Factors in Computing Systems (pp. 175–176). https://doi.org/10.1145/634067.634173
   Google Scholar
• Blake, A., Stapleton, G., Rodgers, P., Cheek, L., & Howse, J. (2014). The impact of shape on the perception of Euler diagrams. In T. Dwyer, H. Purchase, & A. Delaney (Eds.), Diagrammatic representation and inference (Vol. 8578, pp. 123–137). Springer. https://doi.org/10.1007/978-3-662-44043-8_16
   Google Scholar
• Borgo, R., Abdul-Rahman, A., Mohamed, F., Grant, P. W., Reppa, I., Floridi, L., & Chen, M. (2012). An empirical study on using visual embellishments in visualization. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2759–2768. https://doi.org/10.1109/TVCG.2012.197
   PubMed Web of Science ®Google Scholar
• Brudy, F., Holz, C., Rädle, R., Wu, C.-J., Houben, S., Klokmose, C., & Marquardt, N. (2019, May 1). Cross-device taxonomy: Survey, opportunities and challenges of interactions spanning across multiple devices. In ACM CHI 2019. https://www.microsoft.com/en-us/research/publication/cross-device-taxonomy-survey-opportunities-and-challenges-of-interactions-spanning-across-multiple-devices/ https://doi.org/10.1145/3290605.3300792
   Google Scholar
• Buchner, A., & Baumgartner, N. (2007). Text – Background polarity affects performance irrespective of ambient illumination and colour contrast. Ergonomics, 50(7), 1036–1063. https://doi.org/10.1080/00140130701306413
   PubMed Web of Science ®Google Scholar
• Büring, T., Gerken, J., & Reiterer, H. (2006). Usability of overview-supported zooming on small screens with regard to individual differences in spatial ability. In Proceedings of the Working Conference on Advanced Visual Interfaces – AVI ’06 (p. 233). https://doi.org/10.1145/1133265.1133310
   Google Scholar
• Cao, Y., Ding, Y., Deng, Y., & Zhang, X. (2020). Effects of mobile application icon complexity and border on college students’ cognition. In S. Fukuda (Ed.), Advances in affective and pleasurable design (Vol. 952, pp. 273–279). Springer International Publishing. https://doi.org/10.1007/978-3-030-20441-9_29
   Google Scholar
• Cao, Y., Zhang, Y., Ding, Y., Duffy, V. G., & Zhang, X. (2021). Is an anthropomorphic app icon more attractive? Evidence from neuroergonomomics. Applied Ergonomics, 97, 103545. https://doi.org/10.1016/j.apergo.2021.103545
   PubMed Web of Science ®Google Scholar
• Chen, R., Huang, J., & Zhou, J. (2020). Skeuomorphic or flat icons for an efficient visual search by younger and older adults? Applied Ergonomics, 85, 103073. https://doi.org/10.1016/j.apergo.2020.103073
   PubMed Web of Science ®Google Scholar
• Conati, C., Carenini, G., Hoque, E., Steichen, B., & Toker, D. (2014). Evaluating the impact of user characteristics and different layouts on an interactive visualization for decision making. Computer Graphics Forum, 33(3), 371–380. https://doi.org/10.1111/cgf.12393
   Web of Science ®Google Scholar
• de Oliveira, R., & da Rocha, H. V. (2007). Consistency on multi-device design. In C. Baranauskas, P. Palanque, J. Abascal, & S. D. J. Barbosa (Eds.), Human–computer interaction – INTERACT 2007 (pp. 617–623). Springer. https://doi.org/10.1007/978-3-540-74800-7_61
   Google Scholar
• Ding, S. Y., Wang, H. Y., & Xue, C. Q. (2020). Experimental study on visual variables influencing icon similarity. In 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 240–243). https://doi.org/10.1109/IEEM45057.2020.9309961
   Google Scholar
• Easterby, R. S. (1970). The perception of symbols for machine displays. Ergonomics, 13(1), 149–158. https://doi.org/10.1080/00140137008931127
   PubMedGoogle Scholar
• Fleetwood, M. D., & Byrne, M. D. (2002). Modeling icon search in ACT-R/PM. Cognitive Systems Research, 3(1), 25–33. https://doi.org/10.1016/S1389-0417(01)00041-9
   Google Scholar
• Gobert, C., Todi, K., Bailly, G., & Oulasvirta, A. (2019). SAM: A modular framework for self-adapting web menus. In Proceedings of IUI 2019 (p. 481). https://doi.org/10.1145/3301275.3302314
   Google Scholar
• Green, T. M., & Fisher, B. (2010). Towards the personal equation of interaction: The impact of personality factors on visual analytics interface interaction. In 2010 IEEE Symposium on Visual Analytics Science and Technology (pp. 203–210). https://doi.org/10.1109/VAST.2010.5653587
   Google Scholar
• Grobelny, J., Karwowski, W., & Drury, C. (2005). Usability of graphical icons in the design of human–computer interfaces. International Journal of Human–Computer Interaction, 18(2), 167–182. https://doi.org/10.1207/s15327590ijhc1802_3
   Web of Science ®Google Scholar
• Guilford, J. P. (1932). A generalized psychophysical law. Psychological Review, 39(1), 73–85. https://doi.org/10.1037/h0070969
   Google Scholar
• Gustafson, A. (2011). Adaptive web design: Crafting rich experiences with progressive enhancement. http://adaptivewebdesign.info/
   Google Scholar
• Gutwin, C., & Fedak, C. (2004). Interacting with big interfaces on small screens: A comparison of fisheye, zoom, and panning techniques. In Proceedings of Graphics Interface, 2004 (pp. 145–152).
   Google Scholar
• Harrison, L., Yang, F., Franconeri, S., & Chang, R. (2014). Ranking visualizations of correlation using Weber’s law. IEEE Transactions on Visualization and Computer Graphics, 20(12), 1943–1952. https://doi.org/10.1109/TVCG.2014.2346979
   PubMed Web of Science ®Google Scholar
• Healey, C. G., & Enns, J. T. (2012). Attention and visual memory in visualization and computer graphics. IEEE Transactions on Visualization and Computer Graphics, 18(7), 1170–1188. https://doi.org/10.1109/TVCG.2011.127
   PubMed Web of Science ®Google Scholar
• Hochheiser, H., & Lazar, J. (2007). HCI and societal issues: A framework for engagement. International Journal of Human–Computer Interaction, 23(3), 339–374. https://doi.org/10.1080/10447310701702717
   Web of Science ®Google Scholar
• Hou, M., Zhu, H., Zhou, M., & Arrabito, G. R. (2011). Optimizing operator-agent interaction in intelligent adaptive interface design: A conceptual framework. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(2), 161–178. https://doi.org/10.1109/TSMCC.2010.2052041
   Web of Science ®Google Scholar
• Houben, S., Marquardt, N., Vermeulen, J., Klokmose, C., Schöning, J., Reiterer, H., & Holz, C. (2017). Opportunities and challenges for cross-device interactions in the wild. Interactions, 24(5), 58–63. https://doi.org/10.1145/3121348
   Google Scholar
• Hsieh, T.-J. (T.). (2017). Multiple roles of color information in the perception of icon-type images. Color Research & Application, 42(6), 740–752. https://doi.org/10.1002/col.22140
   Web of Science ®Google Scholar
• Huang, K.-C., & Chiu, T.-L. (2007). Visual search performance on an LCD monitor: Effects of color combination of figure and icon background, shape of icon, and line width of icon border. Perceptual and Motor Skills, 104(2), 562–574. https://doi.org/10.2466/pms.104.2.562-574
   PubMed Web of Science ®Google Scholar
• Huang, S.-M. (2007). A study of the consistence of subjective ratings for icon-background color combinations for small computer icons. In 2007 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 462–466). https://doi.org/10.1109/IEEM.2007.4419232
   Google Scholar
• Iqbal, M. W., Ahmad, N., Khuram, S., Feroz, I., & Ali, N. (2018). Towards adaptive user interfaces for mobile-phone in smart world. International Journal of Advanced Computer Science and Applications, 9(11), 11. https://doi.org/10.14569/IJACSA.2018.091177
   Web of Science ®Google Scholar
• Jylhä, H., & Hamari, J. (2019). An icon that everyone wants to click: How perceived aesthetic qualities predict app icon successfulness. International Journal of Human–Computer Studies, 130, 73–85. https://doi.org/10.1016/j.ijhcs.2019.04.004
   Web of Science ®Google Scholar
• Karsvall, A. (2002). Personality preferences in graphical interface design. In Proceedings of the Second Nordic Conference on Human–Computer Interaction – NordiCHI ’02 (p. 217). https://doi.org/10.1145/572020.572049
   Google Scholar
• Kim, K., Erickson, A., Lambert, A., Bruder, G., & Welch, G. (2019). Effects of dark mode on visual fatigue and acuity in optical see-through head-mounted displays. In Symposium on Spatial User Interaction (pp. 1–9). https://doi.org/10.1145/3357251.3357584
   Google Scholar
• Ko, Y.-H. (2017). The effects of luminance contrast, colour combinations, font, and search time on brand icon legibility. Applied Ergonomics, 65, 33–40. https://doi.org/10.1016/j.apergo.2017.05.015
   PubMed Web of Science ®Google Scholar
• Kwon, O.-H., Muelder, C., Lee, K., & Ma, K.-L. (2016). A study of layout, rendering, and interaction methods for immersive graph visualization. IEEE Transactions on Visualization and Computer Graphics, 22(7), 1802–1815. https://doi.org/10.1109/TVCG.2016.2520921
   PubMed Web of Science ®Google Scholar
• Langner, R., Kister, U., & Dachselt, R. (2019). Multiple coordinated views at large displays for multiple users: Empirical findings on user behavior, movements, and distances. IEEE Transactions on Visualization and Computer Graphics, 25(1), 608–618. https://doi.org/10.1109/TVCG.2018.2865235
   Web of Science ®Google Scholar
• Legge, G. E., Rubin, G. S., & Luebker, A. (1987). Psychophysics of reading—V. The role of contrast in normal vision. Vision Research, 27(7), 1165–1177. https://doi.org/10.1016/0042-6989(87)90028-9
   PubMed Web of Science ®Google Scholar
• Li, Y., Huang, Y., Li, X., Ma, J., Zhang, J., & Li, J. (2022). The influence of brightness combinations and background colour on legibility and subjective preference under negative polarity. Ergonomics, 65(8), 1046–1056. https://doi.org/10.1080/00140139.2021.2013546
   PubMed Web of Science ®Google Scholar
• Lin, H., Hsieh, Y.-C., & Wu, F.-G. (2016). A study on the relationships between different presentation modes of graphical icons and users’ attention. Computers in Human Behavior, 63, 218–228. https://doi.org/10.1016/j.chb.2016.05.008
   Web of Science ®Google Scholar
• Lin, H., Lin, W., Tsai, W.-C., Cheng, Y.-Y., & Wu, F.-G. (2014). Effect of the color tablet computer’s polarity and character size on legibility. Universal Access in Human–Computer Interaction, 8514, 132–143. https://doi.org/10.1007/978-3-319-07440-5_13
   Google Scholar
• Lindberg, T., & Näsänen, R. (2003). The effect of icon spacing and size on the speed of icon processing in the human visual system. Displays, 24(3), 111–120. https://doi.org/10.1016/S0141-9382(03)00035-0
   Web of Science ®Google Scholar
• Lu, M., Lanir, J., Wang, C., Yao, Y., Zhang, W., Deussen, O., & Huang, H. (2022). Modeling just noticeable differences in charts. IEEE Transactions on Visualization and Computer Graphics, 28(1), 718–726. https://doi.org/10.1109/TVCG.2021.3114874
   PubMed Web of Science ®Google Scholar
• Ming, C., Wu, Z., Gu, H., Chen, C., & Zhang, Y. (2022). The effects of luminance contrast and color combination on icon cognitive performance. Color Research & Application, 47(2), 498–506. https://doi.org/10.1002/col.22720
   Web of Science ®Google Scholar
• Mu, D., Huang, Y., Wang, Y., Yang, J., Li, J., & Kang, Z. (2022). Neumorphic or flat? Impact of icon depth on user attention and visual search efficiency. International Journal of Industrial Ergonomics, 89, 103281. https://doi.org/10.1016/j.ergon.2022.103281
   Web of Science ®Google Scholar
• Nathan, Y., Rosenblatt, J. D., & Bitan, Y. (2022). Effect of the user input method on response time and accuracy in a binary data labeling task. International Journal of Human–Computer Interaction, 1–9. https://doi.org/10.1080/10447318.2022.2128943
   Web of Science ®Google Scholar
• Nowell, L., Schulman, R., & Hix, D. (2002). Graphical encoding for information visualization: An empirical study. In IEEE Symposium on Information Visualization, 2002, INFOVIS 2002 (pp. 43–50). https://doi.org/10.1109/INFVIS.2002.1173146
   Google Scholar
• Punchoojit, L., & Hongwarittorrn, N. (2017). Usability studies on mobile user interface design patterns: A systematic literature review. Advances in Human–Computer Interaction, 2017, 1–22. https://doi.org/10.1155/2017/6787504
   Web of Science ®Google Scholar
• Qian, K., & Mitsudo, H. (2016). Eggs illusion: Local shape deformation generated by a grid pattern. Journal of Vision, 16(15), 27. https://doi.org/10.1167/16.15.27
   PubMed Web of Science ®Google Scholar
• Ragan, E. D., Bowman, D. A., Kopper, R., Stinson, C., Scerbo, S., & McMahan, R. P. (2015). Effects of field of view and visual complexity on virtual reality training effectiveness for a visual scanning task. IEEE Transactions on Visualization and Computer Graphics, 21(7), 794–807. https://doi.org/10.1109/TVCG.2015.2403312
   PubMed Web of Science ®Google Scholar
• Rowland, C. (2015). Designing for the internet of things. O’Reilly Media, Inc. https://www.oreilly.com/library/view/designing-for-the/9781491971468/
   Google Scholar
• Saket, B., Endert, A., & Stasko, J. (2016). Beyond usability and performance: A review of user experience-focused evaluations in visualization. In Proceedings of the beyond Time and Errors on Novel Evaluation Methods for Visualization – BELIV ’16 (pp. 133–1420. https://doi.org/10.1145/2993901.2993903
   Google Scholar
• Sanchez, C. A., Read, T., & Crawford, A. (2021). Smartphone display size can cause distortions in perceptual estimates of size. Applied Ergonomics, 97, 103524. https://doi.org/10.1016/j.apergo.2021.103524
   PubMed Web of Science ®Google Scholar
• Sanders, M. S., & McCormick, E. J. (1998). Human factors in engineering and design. Industrial Robot: An International Journal, 25(2), 153–153. https://doi.org/10.1108/ir.1998.25.2.153.2
   Google Scholar
• Sarsam, S. M., & Al-Samarraie, H. (2018). Towards incorporating personality into the design of an interface: A method for facilitating users’ interaction with the display. User Modeling and User-Adapted Interaction, 28(1), 75–96. https://doi.org/10.1007/s11257-018-9201-1
   Web of Science ®Google Scholar
• Satcharoen, K. (2019). The effect of icon entropy, icon concreteness and time on human selection accuracy. In 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) (pp. 379–382). https://doi.org/10.1109/CCOMS.2019.8821672
   Google Scholar
• Shen, Z., Xue, C., Li, J., & Zhou, X. (2015). Effect of icon density and color contrast on users’ visual perception in human computer interaction. In D. Harris (Ed.), Engineering psychology and cognitive ergonomics (Vol. 9174, pp. 66–76). Springer International Publishing. https://doi.org/10.1007/978-3-319-20373-7_7
   Google Scholar
• Shen, Z., Xue, C., & Wang, H. (2018). Effects of users’ familiarity with the objects depicted in icons on the cognitive performance of icon identification. i-Perception, 9(3), 2041669518780807. https://doi.org/10.1177/2041669518780807
   Web of Science ®Google Scholar
• Smeets, J. B. J., & Brenner, E. (2008). Grasping Weber’s law. Current Biology, 18(23), R1089–R1090. https://doi.org/10.1016/j.cub.2008.10.008
   PubMed Web of Science ®Google Scholar
• Soui, M., Chouchane, M., Gasmi, I., & Mkaouer, M. W. (2017). PLAIN: PLugin for predicting the usAbility of mobile user INterface. In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (pp. 127–136). https://doi.org/10.5220/0006171201270136
   Google Scholar
• Soui, M., Chouchane, M., Mkaouer, M. W., Kessentini, M., & Ghedira, K. (2020). Assessing the quality of mobile graphical user interfaces using multi-objective optimization. Soft Computing, 24(10), 7685–7714. https://doi.org/10.1007/s00500-019-04391-8
   Web of Science ®Google Scholar
• Talbot, J., Setlur, V., & Anand, A. (2014). Four experiments on the perception of bar charts. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2152–2160. https://doi.org/10.1109/TVCG.2014.2346320
   PubMed Web of Science ®Google Scholar
• Toker, D., Conati, C., & Carenini, G. (2019). Gaze analysis of user characteristics in magazine style narrative visualizations. User Modeling and User-Adapted Interaction, 29(5), 977–1011. https://doi.org/10.1007/s11257-019-09244-5
   Web of Science ®Google Scholar
• Trapp, A. K., & Wienrich, C. (2018). App icon similarity and its impact on visual search efficiency on mobile touch devices. Cognitive Research: Principles and Implications, 3(1), 39. https://doi.org/10.1186/s41235-018-0133-4
   PubMed Web of Science ®Google Scholar
• Wäljas, M., Segerståhl, K., Väänänen-Vainio-Mattila, K., & Oinas-Kukkonen, H. (2010). Cross-platform service user experience: A field study and an initial framework. In Proceedings of the 12th International Conference on Human Computer Interaction with Mobile Devices and Services (pp. 219–228). https://doi.org/10.1145/1851600.1851637
   Google Scholar
• Wall, E., Blaha, L. M., Franklin, L., & Endert, A. (2017). Warning, bias may occur: A proposed approach to detecting cognitive bias in interactive visual analytics. In 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) (pp. 104–115). https://doi.org/10.1109/VAST.2017.8585669
   Google Scholar
• Wei, Y., Mei, H., Zhao, Y., Zhou, S., Lin, B., Jiang, H., & Chen, W. (2020). Evaluating perceptual bias during geometric scaling of scatterplots. IEEE Transactions on Visualization and Computer Graphics, 26(1), 321–331. https://doi.org/10.1109/TVCG.2019.2934208
   PubMed Web of Science ®Google Scholar
• Wickens, C. (2021). Attention: Theory, principles, models and applications. International Journal of Human–Computer Interaction, 37(5), 403–417. https://doi.org/10.1080/10447318.2021.1874741
   Web of Science ®Google Scholar
• Wong, W. (1993). Principles of form and design. John Wiley & Sons.
   Google Scholar
• Wu, X., Yan, H., Jiaran, N., & Gu, Z. (2022). Study on semantic-entity relevance of industrial icons and generation of metaphor design. Journal of the Society for Information Display, 30(3), 209–223. https://doi.org/10.1002/jsid.1088
   Web of Science ®Google Scholar
• Ya-Feng, N., Jin, L., Jia-Qi, C., Wen-Jun, Y., Hong-Rui, Z., Jia-Xin, H., Lang, X., Jia-Hao, W., Guo-Rui, M., Zi-Jian, H., Cheng-Qi, X., Xiao-Zhou, Z., & Tao, J. (2022). Research on visual representation of icon colour in eye-controlled systems. Advanced Engineering Informatics, 52, 101570. https://doi.org/10.1016/j.aei.2022.101570
   Web of Science ®Google Scholar
• Yanagisawa, H., Miyazaki, C., & Nakano, S. (2016). Kansei modeling for multimodal user experience (visual expectation effect on product sound perception) (p. 11).
   Google Scholar
• Yang, C., Peng, Y., & Zeng, J. (2021). Research on cognition and application of icon complexity based on EEG. CCF Transactions on Pervasive Computing and Interaction, 3(2), 170–185. https://doi.org/10.1007/s42486-021-00058-2
   Google Scholar
• Yang, L., Li, Y., & Fu, Y. (2016). Study on the change rules of visual threshold perspectives of different background grayscales under the ideal illumination. In E. Qi (Ed.), Proceedings of the 6th International Asia Conference on Industrial Engineering and Management Innovation (pp. 107–116). Atlantis Press. https://doi.org/10.2991/978-94-6239-148-2_11
   Google Scholar
• Zhang, J., Xue, C., Shen, Z., Chen, X., Shao, J., Zhou, L., & Zhou, X. (2016). Study on the effects of semantic memory on icon complexity in cognitive domain. In D. Harris (Ed.), Engineering psychology and cognitive ergonomics (pp. 147–157). Springer International Publishing. https://doi.org/10.1007/978-3-319-40030-3_16
   Google Scholar