Are 100 ms Fast Enough? Characterizing Latency Perception Thresholds in Mouse-Based Interaction

  • Valentin Forch
  • Thomas Franke
  • Nadine Rauh
  • Josef F. Krems
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10276)


The claim that 100 ms system latency is fast enough for an optimal interaction with highly interactive computer systems has been challenged by several studies demonstrating that users are able to perceive latencies well below the 100 ms mark. Although a high amount of daily computer interactions is still characterized by mouse-based interaction, to date only few studies about latency perception thresholds have employed a corresponding interaction paradigm. Therefore, we determined latency perception thresholds in a mouse-based computer interaction task. We also tested whether user characteristics, such as experience with latency in computer interaction and interaction styles, might be related to inter-individual differences in latency perception thresholds, as results of previous studies indicate that there is considerable inter-individual variance in latency perception thresholds. Our results show that latency perception thresholds for a simple mouse-based computer interaction lie in the range of 60 ms and that inter-individual differences in latency perception can be related to user characteristics.


Latency System response time Human-computer interaction Mouse-based interaction Latency perception 



This research was funded by the German Federal Ministry of Education and Research (03ZZ0504H) in the context of the project fast-realtime. Statements in this paper reflect the authors’ views and do not necessarily reflect those of the funding body or of the project partners.


  1. 1.
    Alcala-Quintana, R., Garcia-Perez, M.A.: The role of parametric assumptions in adaptive Bayesian estimation. Psychol. Methods 9, 250–271 (2004). doi: 10.1037/1082-989X.9.2.250 CrossRefGoogle Scholar
  2. 2.
    Anderson, G., Doherty, R., Ganapathy, S.: User perception of touch screen latency. In: Marcus, A. (ed.) DUXU 2011. LNCS, vol. 6769, pp. 195–202. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-21675-6_23 CrossRefGoogle Scholar
  3. 3.
    Annett, M., Ng, A., Dietz, P.H., Bischof, W.F., Gupta, A.: How low should we go? Understanding the perception of latency while inking. In: Proceedings of Graphics Interface 2014, pp. 167–174. Canadian Information Processing Society (2014)Google Scholar
  4. 4.
    Attig, C., Rauh, N., Franke, T., Krems, J.F.: System latency guidelines then and now – is zero latency really considered necessary? In: Paper Presented at HCI International 2017 (2017)Google Scholar
  5. 5.
    Card, S.K., Robertson, G.G., Mackinlay, J.D.: The information visualizer, an information workspace. In: CHI 1991 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 181–186. ACM (1991). doi: 10.1145/108844.108874
  6. 6.
    Deber, J., Jota, R., Forlines, C., Wigdor, D.: How much faster is fast enough? User perception of latency & latency improvements in direct and indirect touch. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 1827–1836. ACM (2015). doi: 10.1145/2702123.2702300
  7. 7.
    de la Malla, C., Lopez-Moliner, J., Brenner, E.: Dealing with delays does not transfer across sensorimotor tasks. J. Vis. 14, 8 (2014). doi: 10.1167/14.12.8 CrossRefGoogle Scholar
  8. 8.
    Fitts, P.M.: The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47, 381–391 (1954). doi: 10.1037/h0055392 CrossRefGoogle Scholar
  9. 9.
    Honda, T., Hirashima, M., Nozaki, D.: Adaptation to visual feedback delay influences visuomotor learning. PLoS ONE 7, e37900 (2012). doi: 10.1371/journal.pone.0037900 CrossRefGoogle Scholar
  10. 10.
    Ivkovic, Z., Stavness, I., Gutwin, C., Sutcliffe, S.: Quantifying and mitigating the negative effects of local latencies on aiming in 3d shooter games. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 135–144. ACM (2015). doi: 10.1145/2702123.2702432
  11. 11.
    Jota, R., Ng, A., Dietz, P.H., Wigdor, D.: How fast is fast enough? A study of the effects of latency in direct-touch pointing tasks. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2291–2300. ACM (2013). doi: 10.1145/2470654.2481317
  12. 12.
    King-Smith, P.E., Grigsby, S.S., Vingrys, A.J., Benes, S.C., Supowit, A.: Efficient and unbiased modifications of the QUEST threshold method: theory, simulations, experimental evaluation and practical implementation. Vis. Res. 34, 885–912 (1994)CrossRefGoogle Scholar
  13. 13.
    Mackenzie, I.S., Ware, C.: Lag as a determinant of human performance in interactive systems. In: Proceedings of the INTERACT 1993 and CHI 1993 Conference on Human Factors in Computing Systems. pp. 488–493. ACM (1993). doi: 10.1145/169059.169431
  14. 14.
    Miller, R.B.: Response time in man-computer conversational transactions. In: Proceedings of the December 9–11, 1968, Fall Joint Computer Conference, Part I, pp. 267–277. ACM (1968). doi: 10.1145/1476589.1476628
  15. 15.
    Ng, A., Dietz, P.H.: The effects of latency and motion blur on touch screen user experience. J. Soc. Inform. Display 22, 449–456 (2014). doi: 10.1002/jsid.243 CrossRefGoogle Scholar
  16. 16.
    Ng, A., Lepinski, J., Wigdor, D., Sanders, S., Dietz, P.H.: Designing for low-latency direct-touch input. In: Proceedings of the 25th Annual ACM Symposium on User Interface Software and Technology, pp. 453–464. ACM (2012). doi: 10.1145/2380116.2380174
  17. 17.
    Pavlovych, A., Gutwin, C.: Assessing target acquisition and tracking performance for complex moving targets in the presence of latency and jitter. In: Proceedings of Graphics Interface 2012, pp. 109–116. Canadian Information Processing Society (2012)Google Scholar
  18. 18.
    Potter, J.J., Singhose, W.E.: Effects of input shaping on manual control of flexible and time-delayed systems. Hum. Factors 56, 1284–1295 (2014). doi: 10.1177/0018720814528004 CrossRefGoogle Scholar
  19. 19.
    Seow, S.C.: Designing and Engineering Time: the Psychology of Time Perception in Software. Pearson Education, Boston (2008)Google Scholar
  20. 20.
    Shneiderman, B., Plaisant, C.: Designing the User Interface: Strategies for Effective Human-Computer Interaction. Pearson, Boston (1987)Google Scholar
  21. 21.
    Ulrich, R., Vorberg, D.: Estimating the difference limen in 2AFC tasks: pitfalls and improved estimators. Atten. Percept. Psychophys. 71, 1219–1227 (2009). doi: 10.3758/APP.71.6.1219 CrossRefGoogle Scholar
  22. 22.
    Watson, A.B., Pelli, D.G.: QUEST: a Bayesian adaptive psychometric method. Atten. Percept. Psychophys. 33, 113–120 (1983). doi: 10.3758/BF03202828 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Valentin Forch
    • 1
  • Thomas Franke
    • 2
  • Nadine Rauh
    • 1
  • Josef F. Krems
    • 1
  1. 1.Department of Psychology, Cognitive and Engineering PsychologyChemnitz University of TechnologyChemnitzGermany
  2. 2.Institute for Multimedia and Interactive Systems, Engineering Psychology and Cognitive ErgonomicsUniversity of LübeckLübeckGermany

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