Quantifying the Effects of Reduced Update Rate on Motor Performance and User Experience

  • Sung Hun Sim
  • Bing Wu
  • Kyle Brady
  • Andinet Enquobahrie
  • Ricardo Ortiz
  • Sreekanth Arikatla
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 486)


We report two experiments that investigated the impact of reduced visual updating speed on users’ motor performance in a Fitts’-law task. The update rate of visual feedback was set between 10 and 30 Hz. In Experiment 1, the trials were blocked by update rate, allowing participants to get adapted to the reduced visual feedback. In Experiment 2, all trials of different update rates were intermixed and presented in random order. Both experiments found that movement time increased with decreasing update rate. Regression analyses revealed that the Fitts’-law model could be extended to accommodate the findings by including a multiplicative component of frame interval (reciprocal of update rate). The participants’ subjective experience reduced rapidly when the update rate was lower than 20 Hz, and the rating data could be modelled using movement time. The results were discussed in the context of implications for developing VR/AR applications.


Human performance modeling Update rate Fitts’ law Feedback delay 



The work was supported in part by NIH grants 5R00EB008710 & 9R44OD018334.


  1. 1.
    Arikatla, V., Ortiz, R., Thompson, D., Adams, D., Enquobahrie, A., De, S.: A hybrid approach to simulate tissue behavior during surgical simulation. In: 4th International Conference on Computational and Mathematical Biomedical Engineering—CMBE2015 (2015)Google Scholar
  2. 2.
    Sheridan, T.B.: Telerobotics, Automation, and Human Supervisory Control. MIT Press, Cambridge (1992)Google Scholar
  3. 3.
    Apteker, R., Fisher, J., Kisimov, V., Neishlos, H.: Video acceptability and frame rate. IEEE Multimedia 2(3), 32–40 (1995)CrossRefGoogle Scholar
  4. 4.
    Steinmetz, R.: Human perception of jitter and media synchronization. IEEE J. Sel. Areas Commun. 14(1), 61–72 (1996)CrossRefGoogle Scholar
  5. 5.
    Steinicke, F., Bruder, G.A.: Self-experimentation report about long-term use of fully-immersive technology. In: Proceedings of the 2nd ACM Symposium on Spatial User Interaction, pp. 66–69 (2014)Google Scholar
  6. 6.
    Aaron, S.L., Stein, R.B.: Comparison of an EMG-controlled prosthesis and the normal human biceps brachii muscle. Am. J. Phys. Med. 55, 1–14 (1976)Google Scholar
  7. 7.
    Brooks, T.: Telerobotic response requirements. In: Proceedings of the International Conference on Systems, Man and Cybernetics, pp. 113–120 (1990)Google Scholar
  8. 8.
    Airey, J., Rohlf, J., Frederick, P.: Towards image realism with interactive update rates in complex virtual building environments. In: SI3D ’90: Proceedings of the 1990 Symposium on Interactive 3D Graphics, pp. 41–50 (1990)Google Scholar
  9. 9.
    Pausch, R.: Virtual reality on five dollars a day. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 265–269 (1991)Google Scholar
  10. 10.
    Mark, W.R., Randolph, S.C., Finch, M., Verth, J.: Adding force feedback to graphics system: issues and solutions. In: Proceedings of the 23rd Conference on Computer Graphics, pp. 447–452 (1996)Google Scholar
  11. 11.
    Bryson, S.: Effects of lag and frame rate on various tracking tasks. In: Proceedings of SPIE, Stereoscopic Displays Appl. IV, vol. 1925, pp. 155–166 (1993)Google Scholar
  12. 12.
    Ware, C., Balakrishnan, R.: Reaching for objects in VR displays: Lag and frame rate. ACM Trans. Comput. Hum. Interact. 1(4), 331–357 (1994)CrossRefGoogle Scholar
  13. 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)CrossRefGoogle Scholar
  14. 14.
    MacKenzie, S., Ware, C.: Lag as a determinant of human performance in interactive systems. In: Proceedings of the ACM Conference on Human Factors in Computing Systems—INTERCHI, pp. 488–493 (1993)Google Scholar
  15. 15.
    Brady, K.: Taking Fitts’ slow: the effects of delayed visual feedback on human motor performance and user experience. M.S. thesis, Arizona State Univ., Tempe, AZ (2015)Google Scholar
  16. 16.
    Watson, B., Spaulding, V., Walker, N., Ribarsky, W.: Evaluation of the effects of frame rate variation on VR task performance. In: Proceedings of IEEE Virtual Reality Annual Symposium, pp. 38–44 (1997)Google Scholar
  17. 17.
    Watson, B., Walker, N., Ribarsky, W., Spaulding, V.: Effects of variation in system responsiveness on user performance in virtual environments. Hum. Factors 40(3), 403–414 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Sung Hun Sim
    • 1
  • Bing Wu
    • 1
  • Kyle Brady
    • 1
  • Andinet Enquobahrie
    • 2
  • Ricardo Ortiz
    • 2
  • Sreekanth Arikatla
    • 2
  1. 1.Human Systems Engineering ProgramArizona State UniversityMesaUSA
  2. 2.Medical Computing TeamKitware Inc.CarrboroUSA

Personalised recommendations