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Clarifying the Effect of Edge Targets in Touch Pointing through Crowdsourced Experiments

Published:01 November 2023Publication History
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Abstract

A prior work has recommended adding a 4-mm gap between a target and the edge of a screen, as tapping a target located at the screen edge takes longer than tapping non-edge targets. However, it is possible that this recommendation was created based on statistical errors, and unexplored situations existed in the prior work. In this study, we re-examine the recommendation by utilizing crowdsourced experiments to resolve the issues. If we observe the same results as the prior work through experiments including diversities, we can verify that the recommendation is suitable. We found that increasing the gap between the target and the screen edge decreased the movement time, which was consistent with the prior work. In addition, we newly found that increasing the gap decreased the error rate as well. On the basis of these results, we discuss how the gap and the target should be designed.

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Supplemental Material

iss23main-p6532-p-video.mp4

This is a presentation of our talk at ISS 2023. A prior work has recommended adding a 4-mm gap between a target and the edge of a screen, as tapping a target located at the screen edge takes longer than tapping non-edge targets. However, it is possible that this recommendation was created based on statistical errors, and unexplored situations existed in the prior work. In this study, we re-examine the recommendation by utilizing crowdsourced experiments to resolve the issues. If we observe the same results as the prior work through experiments including diversities, we can verify that the recommendation is suitable. We found that increasing the gap between the target and the screen edge decreased the movement time, which was consistent with the prior work. In addition, we newly found that increasing the gap decreased the error rate as well. On the basis of these results, we discuss how the gap and the target should be designed.

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References

  1. Jos J. Adam, Robin Mol, Jay Pratt, and Martin H. Fischer. 2006. Moving Farther but Faster: An Exception to Fitts’s Law. Psychological Science, 17, 9 (2006), 794–798. https://doi.org/10.1111/j.1467-9280.2006.01784.x PMID: 16984297 Google ScholarGoogle ScholarCross RefCross Ref
  2. Caroline Appert, Olivier Chapuis, and Michel Beaudouin-Lafon. 2008. Evaluation of Pointing Performance on Screen Edges. In Proceedings of the Working Conference on Advanced Visual Interfaces (AVI ’08). Association for Computing Machinery, New York, NY, USA. 119–126. isbn:9781605581415 https://doi.org/10.1145/1385569.1385590 Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Daniel Avrahami. 2015. The Effect of Edge Targets on Touch Performance. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI ’15). Association for Computing Machinery, New York, NY, USA. 1837–1846. isbn:9781450331456 https://doi.org/10.1145/2702123.2702439 Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Nikola Banovic, Tovi Grossman, and George Fitzmaurice. 2013. The Effect of Time-Based Cost of Error in Target-Directed Pointing Tasks. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’13). Association for Computing Machinery, New York, NY, USA. 1373–1382. isbn:9781450318990 https://doi.org/10.1145/2470654.2466181 Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Xiaojun Bi, Yang Li, and Shumin Zhai. 2013. FFitts Law: Modeling Finger Touch with Fitts’ Law. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’13). Association for Computing Machinery, New York, NY, USA. 1363–1372. isbn:9781450318990 https://doi.org/10.1145/2470654.2466180 Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Xiaojun Bi and Shumin Zhai. 2013. Bayesian Touch: A Statistical Criterion of Target Selection with Finger Touch. In Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology (UIST ’13). Association for Computing Machinery, New York, NY, USA. 51–60. isbn:9781450322683 https://doi.org/10.1145/2501988.2502058 Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Xiaojun Bi and Shumin Zhai. 2016. Predicting Finger-Touch Accuracy Based on the Dual Gaussian Distribution Model. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST ’16). Association for Computing Machinery, New York, NY, USA. 313–319. isbn:9781450341899 https://doi.org/10.1145/2984511.2984546 Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Renaud Blanch and Michael Ortega. 2011. Benchmarking Pointing Techniques with Distractors: Adding a Density Factor to Fitts’ Pointing Paradigm. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’11). Association for Computing Machinery, New York, NY, USA. 1629–1638. isbn:9781450302289 https://doi.org/10.1145/1978942.1979180 Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Ana C. Bradi, Jos J. Adam, Martin H. Fischer, and Jay Pratt. 2009. Modulating Fitts’s Law: the effect of disappearing allocentric information. Experimental Brain Research, 194, 4 (2009), 571–576. issn:1432-1106 https://doi.org/10.1007/s00221-009-1733-5 Google ScholarGoogle ScholarCross RefCross Ref
  10. Joshua R. de Leeuw. 2015. jsPsych: A JavaScript library for creating behavioral experiments in a Web browser. Behavior Research Methods, 47, 1 (2015), 01 Mar, 1–12. issn:1554-3528 https://doi.org/10.3758/s13428-014-0458-y Google ScholarGoogle ScholarCross RefCross Ref
  11. Apple Developer. 2021. Guides and safe areas. https://developer.apple.com/design/human-interface-guidelines/foundations/layout/##guides-and-safe-areas Google ScholarGoogle Scholar
  12. Peter Dixon. 2008. Models of accuracy in repeated-measures designs. Journal of Memory and Language, 59, 4 (2008), 447–456. Google ScholarGoogle ScholarCross RefCross Ref
  13. Coskun Dizmen, Errol R. Hoffmann, and Alan H.S. Chan. 2013. Movement time to edge and non-edge targets. Ergonomics, 57, 1 (2013), Nov., 130–135. https://doi.org/10.1080/00140139.2013.855824 Google ScholarGoogle ScholarCross RefCross Ref
  14. J. Shawn Farris, Keith S. Jones, and Brent A. Anders. 2001. Acquisition Speed with Targets on the Edge of the Screen: An Application of Fitts’ Law to Commonly Used Web Browser Controls. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 45, 15 (2001), 1205–1209. https://doi.org/10.1177/154193120104501511 arxiv:https://doi.org/10.1177/154193120104501511. Google ScholarGoogle ScholarCross RefCross Ref
  15. Niels Henze, Enrico Rukzio, and Susanne Boll. 2011. 100,000,000 Taps: Analysis and Improvement of Touch Performance in the Large. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI ’11). Association for Computing Machinery, New York, NY, USA. 133–142. isbn:9781450305419 https://doi.org/10.1145/2037373.2037395 Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Florian Lehmann and Michael Kipp. 2018. How to Hold Your Phone When Tapping: A Comparative Study of Performance, Precision, and Errors. ISS ’18. Association for Computing Machinery, New York, NY, USA. 115–127. isbn:9781450356947 https://doi.org/10.1145/3279778.3279791 Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Yan Ma, Shumin Zhai, IV Ramakrishnan, and Xiaojun Bi. 2021. Modeling Touch Point Distribution with Rotational Dual Gaussian Model. In The 34th Annual ACM Symposium on User Interface Software and Technology (UIST ’21). Association for Computing Machinery, New York, NY, USA. 1197–1209. isbn:9781450386357 https://doi.org/10.1145/3472749.3474816 Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. I. Scott MacKenzie. 1992. Fitts’ Law as a Research and Design Tool in Human-Computer Interaction. Hum.-Comput. Interact., 7, 1 (1992), mar, 91–139. issn:0737-0024 https://doi.org/10.1207/s15327051hci0701_3 Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. I. Scott MacKenzie and Poika Isokoski. 2008. Fitts’ Throughput and the Speed-Accuracy Tradeoff. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’08). ACM, New York, NY, USA. 1633–1636. isbn:9781605580111 https://doi.org/10.1145/1357054.1357308 Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Blanca Mena, M José, Rafael Alarcón, Jaume Arnau Gras, Roser Bono Cabré, and Rebecca Bendayan. 2017. Non-normal data: Is ANOVA still a valid option? Psicothema, 29, 4 (2017), 552–557. Google ScholarGoogle Scholar
  21. Keith B. Perry and Juan Pablo Hourcade. 2008. Evaluating One Handed Thumb Tapping on Mobile Touchscreen Devices. In Proceedings of Graphics Interface 2008 (GI ’08). Canadian Information Processing Society, CAN. 57–64. isbn:9781568814230 Google ScholarGoogle Scholar
  22. Jay Pratt, Jos J. Adam, and Martin H. Fischer. 2007. Visual layout modulates Fitts’s law: The importance of first and last positions. Psychonomic Bulletin & Review, 14, 2 (2007), 01 Apr, 350–355. issn:1531-5320 https://doi.org/10.3758/BF03194076 Google ScholarGoogle ScholarCross RefCross Ref
  23. Petre V. Radulescu, Jos J. Adam, Martin H. Fischer, and Jay Pratt. 2010. Fitts’s Law violation and motor imagery: are imagined movements truthful or lawful? Experimental Brain Research, 201, 3 (2010), 01 Mar, 607–611. issn:1432-1106 https://doi.org/10.1007/s00221-009-2072-2 Google ScholarGoogle ScholarCross RefCross Ref
  24. R. William Soukoreff and I. Scott MacKenzie. 2004. Towards a Standard for Pointing Device Evaluation, Perspectives on 27 Years of Fitts’ Law Research in HCI. Int. J. Hum.-Comput. Stud., 61, 6 (2004), dec, 751–789. issn:1071-5819 https://doi.org/10.1016/j.ijhcs.2004.09.001 Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Hiroki Usuba, Shota Yamanaka, and Homei Miyashita. 2020. A Model for Pointing at Targets with Different Clickable and Visual Widths and with Distractors. In 32nd Australian Conference on Human-Computer Interaction (OzCHI ’20). Association for Computing Machinery, New York, NY, USA. 1–10. isbn:9781450389754 https://doi.org/10.1145/3441000.3441019 Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Daniel Vogel and Patrick Baudisch. 2007. Shift: A Technique for Operating Pen-Based Interfaces Using Touch. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’07). Association for Computing Machinery, New York, NY, USA. 657–666. isbn:9781595935939 https://doi.org/10.1145/1240624.1240727 Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Shota Yamanaka. 2018. Effect of Gaps with Penal Distractors Imposing Time Penalty in Touch-Pointing Tasks. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA. Article 21, 11 pages. isbn:9781450358989 https://doi.org/10.1145/3229434.3229435 Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Shota Yamanaka. 2018. Risk Effects of Surrounding Distractors Imposing Time Penalty in Touch-Pointing Tasks. ISS ’18. Association for Computing Machinery, New York, NY, USA. 129–135. isbn:9781450356947 https://doi.org/10.1145/3279778.3279781 Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Shota Yamanaka, Hiroaki Shimono, and Homei Miyashita. 2019. Towards More Practical Spacing for Smartphone Touch GUI Objects Accompanied by Distractors. In Proceedings of the 2019 ACM International Conference on Interactive Surfaces and Spaces (ISS ’19). Association for Computing Machinery, New York, NY, USA. 157–169. isbn:9781450368919 https://doi.org/10.1145/3343055.3359698 Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Shota Yamanaka and Hiroki Usuba. 2020. Rethinking the Dual Gaussian Distribution Model for Predicting Touch Accuracy in On-Screen-Start Pointing Tasks. Proc. ACM Hum.-Comput. Interact., 4, ISS (2020), Article 205, nov, 20 pages. https://doi.org/10.1145/3427333 Google ScholarGoogle ScholarDigital LibraryDigital Library

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      cover image Proceedings of the ACM on Human-Computer Interaction
      Proceedings of the ACM on Human-Computer Interaction  Volume 7, Issue ISS
      December 2023
      482 pages
      EISSN:2573-0142
      DOI:10.1145/3554314
      Issue’s Table of Contents

      Copyright © 2023 ACM

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      • Published: 1 November 2023
      Published in pacmhci Volume 7, Issue ISS

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