Skip to main content
SpringerLink
Log in
Book cover

IFIP Conference on Human-Computer Interaction

INTERACT 2021: Human-Computer Interaction – INTERACT 2021 pp 76–92Cite as

Comparing Performance Models for Bivariate Pointing Through a Crowdsourced Experiment

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12933))

Abstract

Evaluation of a novel user-performance model’s fitness requires comparison with baseline models, yet it is often time consuming and involves much effort by researchers to collect data from many participants. Crowdsourcing has recently been used for evaluating novel interaction techniques, but its potential for model comparison studies has not been investigated in detail. In this study, we evaluated four existing Fitts’ law models for rectangular targets, as though one of them was a proposed novel model. We recruited 210 crowd workers, who performed 94,080 clicks in total, and confirmed that the result for the best-fit model was consistent with previous studies. We also analyzed whether this conclusion would change depending on the sample size, but even when we randomly sampled data from five workers for 10,000 iterations, the best-fit model changed only once (0.01%). We have thus demonstrated a case in which crowdsourcing is beneficial for comparing performance models.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    We found this previous work as part of a Ph.D. thesis by one of these authors (Faridani) [11]. He defined this Fitts’ law study as a crowdsourced task, and thus we introduce it here.

  2. 2.

    https://crowdsourcing.yahoo.co.jp.

  3. 3.

    The simulation included data from the outlier worker detected in the analysis of the main experiment, because that worker’s status as an outlier depends on the other sampled workers’ results.

References

  1. Accot, J., Zhai, S.: Beyond Fitts’ law: models for trajectory-based HCI tasks. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 1997), pp. 295–302 (1997). https://doi.org/10.1145/258549.258760

  2. Accot, J., Zhai, S.: Refining Fitts’ law models for bivariate pointing. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2003, pp. 193–200. ACM, New York (2003). https://doi.org/10.1145/642611.642646. http://doi.acm.org/10.1145/642611.642646

  3. Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control 19(6), 716–723 (1974). https://doi.org/10.1109/TAC.1974.1100705

    Article  MathSciNet  MATH  Google Scholar 

  4. Appert, C., Chapuis, O., Beaudouin-Lafon, M.: Evaluation of pointing performance on screen edges. In: Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2008, pp. 119–126. ACM, New York (2008). https://doi.org/10.1145/1385569.1385590. http://doi.acm.org/10.1145/1385569.1385590

  5. Bi, X., Li, Y., Zhai, S.: Ffitts law: modeling finger touch with Fitts’ law. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2013, pp. 1363–1372. ACM, New York (2013). https://doi.org/10.1145/2470654.2466180. http://doi.acm.org/10.1145/2470654.2466180

  6. Bohan, M., Longstaff, M., Van Gemmert, A., Rand, M., Stelmach, G.: Effects of target height and width on 2D pointing movement duration and kinematics. Motor Control 7, 278–289 (2003)

    Google Scholar 

  7. Burnham, K.P., Anderson, D.R.: Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer, New York (2003). https://doi.org/10.1007/b97636

    Book  MATH  Google Scholar 

  8. Cockburn, A., Lewis, B., Quinn, P., Gutwin, C.: Framing effects influence interface feature decisions, pp. 1–11. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3313831.3376496

  9. Crossman, E.R.: The measurement of perceptual load in manual operations. University of Birmingham, Ph.D. thesis (1956)

    Google Scholar 

  10. Devore, J.L.: Probability and Statistics for Engineering and the Sciences, 8th edn. Brooks/Cole, January 2011. ISBN-13 978-0-538-73352-6

    Google Scholar 

  11. Faridani, S.: Models and algorithms for crowdsourcing discovery. Ph.D. thesis, USA (2012)

    Google Scholar 

  12. Findlater, L., Zhang, J., Froehlich, J.E., Moffatt, K.: Differences in crowdsourced vs. lab-based mobile and desktop input performance data. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI 2017, pp. 6813–6824. ACM, New York (2017). https://doi.org/10.1145/3025453.3025820. http://doi.acm.org/10.1145/3025453.3025820

  13. Fitts, P.M.: The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47(6), 381–391 (1954). https://doi.org/10.1037/h0055392

    Article  Google Scholar 

  14. Gan, K.C., Hoffmann, E.R.: Geometrical conditions for ballistic and visually controlled movements. Ergonomics 31(5), 829–839 (1988). https://doi.org/10.1080/00140138808966724

    Article  Google Scholar 

  15. Goldberg, K.Y., Faridani, S., Alterovitz, R.: Two large open-access datasets for Fitts’ law of human motion and a succinct derivation of the square-root variant. IEEE Trans. Hum.-Mach. Syst. 45(1), 62–73 (2015). https://doi.org/10.1109/THMS.2014.2360281

    Article  Google Scholar 

  16. Gori, J., Rioul, O., Guiard, Y.: Speed-accuracy tradeoff: a formal information-theoretic transmission scheme (Fitts). ACM Trans. Comput.-Hum. Interact. 25(5) (2018). https://doi.org/10.1145/3231595

  17. Gould, S.J.J., Cox, A.L., Brumby, D.P.: Diminished control in crowdsourcing: an investigation of crowdworker multitasking behavior. ACM Trans. Comput.-Hum. Interact. 23(3) (2016). https://doi.org/10.1145/2928269

  18. Hoffmann, E.R., Drury, C.G., Romanowski, C.J.: Performance in one-, two- and three-dimensional terminal aiming tasks. Ergonomics 54(12), 1175–1185 (2011)

    Google Scholar 

  19. Hoffmann, E.R., Sheikh, I.H.: Effect of varying target height in a Fitts’ movement task. Ergonomics 37(6), 1071–1088 (1994). https://doi.org/10.1080/00140139408963719

    Article  Google Scholar 

  20. Ko, Y.J., Zhao, H., Kim, Y., Ramakrishnan, I., Zhai, S., Bi, X.: Modeling two dimensional touch pointing. In: Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology, UIST 2020, pp. 858–868. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3379337.3415871

  21. Komarov, S., Reinecke, K., Gajos, K.Z.: Crowdsourcing performance evaluations of user interfaces. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2013, pp. 207–216. ACM, New York (2013). https://doi.org/10.1145/2470654.2470684. http://doi.acm.org/10.1145/2470654.2470684

  22. MacKenzie, I.S.: Fitts’ law as a research and design tool in human-computer interaction. Hum.-Comput. Interact. 7(1), 91–139 (1992). https://doi.org/10.1207/s15327051hci0701_3

    Article  Google Scholar 

  23. MacKenzie, I.S., Buxton, W.: Extending Fitts’ law to two-dimensional tasks. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 1992, pp. 219–226. ACM, New York (1992). https://doi.org/10.1145/142750.142794. http://doi.acm.org/10.1145/142750.142794

  24. MacKenzie, I.S., Isokoski, P.: Fitts’ throughput and the speed-accuracy tradeoff. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2008, pp. 1633–1636. ACM, New York (2008). https://doi.org/10.1145/1357054.1357308

  25. Matejka, J., Glueck, M., Grossman, T., Fitzmaurice, G.: The effect of visual appearance on the performance of continuous sliders and visual analogue scales. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI 2016, pp. 5421–5432. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2858036.2858063

  26. Meyer, D.E., Abrams, R.A., Kornblum, S., Wright, C.E., Keith Smith, J.E.: Optimality in human motor performance: ideal control of rapid aimed movements. Psychol. Rev. 95(3), 340–370 (1988). https://doi.org/10.1037/0033-295x.95.3.340

    Article  Google Scholar 

  27. Rioul, O., Guiard, Y.: Power vs. logarithmic model of Fitts’ law: a mathematical analysis. Math. Soc. Sci. 2012, 85–96 (2012). https://doi.org/10.4000/msh.12317

    Article  MathSciNet  MATH  Google Scholar 

  28. Schwab, M., Hao, S., Vitek, O., Tompkin, J., Huang, J., Borkin, M.A.: Evaluating pan and zoom timelines and sliders. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, pp. 1–12. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3290605.3300786

  29. Senanayake, R., Hoffmann, E.R., Goonetilleke, R.S.: A model for combined targeting and tracking tasks in computer applications. Exp. Brain Res. 231(3), 367–379 (2013). https://doi.org/10.1007/s00221-013-3700-4

    Article  Google Scholar 

  30. Soukoreff, R.W., MacKenzie, I.S.: Towards a standard for pointing device evaluation, perspectives on 27 years of Fitts’ law research in HCI. Int. J. Hum. Comput. Stud. 61(6), 751–789 (2004). https://doi.org/10.1016/j.ijhcs.2004.09.001

    Article  Google Scholar 

  31. Wobbrock, J.O., Findlater, L., Gergle, D., Higgins, J.J.: The aligned rank transform for nonparametric factorial analyses using only anova procedures. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2011, pp. 143–146. ACM, New York (2011). https://doi.org/10.1145/1978942.1978963. http://doi.acm.org/10.1145/1978942.1978963

  32. Yamanaka, S.: Steering performance with error-accepting delays. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, pp. 570:1–570:9. ACM, New York (2019). https://doi.org/10.1145/3290605.3300800. http://doi.acm.org/10.1145/3290605.3300800

  33. Yamanaka, S., Miyashita, H.: Modeling the steering time difference between narrowing and widening tunnels. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI 2016, pp. 1846–1856. ACM, New York (2016). https://doi.org/10.1145/2858036.2858037. http://doi.acm.org/10.1145/2858036.2858037

  34. Yamanaka, S., Shimono, H., Miyashita, H.: 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 2019, pp. 157–169. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3343055.3359698

  35. Yamanaka, S., Usuba, H.: Calibration methods of touch-point ambiguity for finger-Fitts law (2021). https://arxiv.org/abs/2101.05244

  36. Yang, H., Xu, X.: Bias towards regular configuration in 2D pointing. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2010, pp. 1391–1400. ACM, New York (2010). https://doi.org/10.1145/1753326.1753536. http://doi.acm.org/10.1145/1753326.1753536

  37. Zhai, S., Kong, J., Ren, X.: Speed-accuracy tradeoff in Fitts’ law tasks: on the equivalency of actual and nominal pointing precision. Int. J. Hum. Comput. Stud. 61(6), 823–856 (2004). https://doi.org/10.1016/j.ijhcs.2004.09.007

    Article  Google Scholar 

  38. Zhang, X., Zha, H., Feng, W.: Extending Fitts’ law to account for the effects of movement direction on 2D pointing. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2012, pp. 3185–3194. ACM, New York (2012). https://doi.org/10.1145/2207676.2208737. http://doi.acm.org/10.1145/2207676.2208737

  39. Zhao, J., Soukoreff, R.W., Ren, X., Balakrishnan, R.: A model of scrolling on touch-sensitive displays. Int. J. Hum.-Comput. Stud. 72(12), 805–821 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Yahoo Japan Corporation, Tokyo, Japan

    Shota Yamanaka

Authors
  1. Shota Yamanaka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shota Yamanaka .

Editor information

Editors and Affiliations

  1. Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy

    Carmelo Ardito

  2. Computer Science Department, University of Bari Aldo Moro, Bari, Italy

    Rosa Lanzilotti

  3. Computer Science Department, University of Pisa, Pisa, Italy

    Alessio Malizia

  4. Department of Computer Science, University of York, York, UK

    Helen Petrie

  5. Computer Science Department, University of Bari Aldo Moro, Bari, Italy

    Antonio Piccinno

  6. Computer Science Department, University of Bari Aldo Moro, Bari, Italy

    Giuseppe Desolda

  7. Microsoft Research, Redmond, WA, USA

    Kori Inkpen

Rights and permissions

Reprints and permissions

Copyright information

© 2021 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yamanaka, S. (2021). Comparing Performance Models for Bivariate Pointing Through a Crowdsourced Experiment. In: Ardito, C., et al. Human-Computer Interaction – INTERACT 2021. INTERACT 2021. Lecture Notes in Computer Science(), vol 12933. Springer, Cham. https://doi.org/10.1007/978-3-030-85616-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85616-8_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85615-1

  • Online ISBN: 978-3-030-85616-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation