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Effects of low-range latency on performance and perception in a virtual, unstable second-order control task

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Abstract

System latency (i.e., time between user action and system response) has known detrimental effects, particularly in the increasingly prevalent complex human–computer interaction types (e.g., higher control order like in games or teleoperation). The objective of the present research was to examine the impact of low-range latencies on behaviour (performance, control) and perception (perceived control difficulty, latency) in an unstable system with second-order control. Furthermore, the influence of the controller gain (affecting the system’s sensitivity to user input) on latency effects was investigated. The study extends existing research through examining multiple parameters of human–computer interaction in relation to differing levels of low-range latencies (14–198 ms). For this aim, participants across two experiments performed a second-order control task consisting of balancing a ball on a beam using a low-latency computer system with varying levels of added latency. Latency affected performance even with the smallest added latency of 14 ms (d = 0.61). A larger controller gain increased latency effects on control indicators (all d ≥ 0.68) and perceived control difficulty (d = 0.85). Latency impacted performance, control parameters and perceived control difficulty prior to participants’ awareness of the latency. Thus, optimizing latency can be important regardless of whether the user perceives the latency. The diverse effects regarding the different parameters emphasize the usefulness of a comprehensive latency effect assessment and indicate human adaptation to latency.

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References

  1. Casiez G, Conversy S, Falce M et al (2015) Looking through the eye of the mouse. In: Proceedings of the 28th annual ACM symposium on user interface software & technology—UIST’15. ACM Press, New York, pp 629–636

  2. Ivkovic Z, Stavness I, Gutwin C, Sutcliffe S (2015) 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—CHI’15. ACM Press, New York, pp 135–144

  3. Pavlovych A, Stuerzlinger W (2011) Target following performance in the presence of latency, jitter, and signal dropouts. In: Proceedings of graphics interface, pp 33–40

  4. Cooper JR, Wernke MM, Reed KB (2012) The effects of incongruent feedback on bimanual task performance. In: 2012 IEEE haptics symposium (HAPTICS). IEEE, Vancouver, pp 301–305

  5. Davis J, Smyth C, McDowell K (2010) The effects of time lag on driving performance and a possible mitigation. IEEE Trans Robot 26:590–593. https://doi.org/10.1109/TRO.2010.2046695

    Article  Google Scholar 

  6. Zhai S (2004) Mouse. In: Bainbridge WS (ed) Berkshire encyclopedia of human–computer interaction. Berkshire Publishing Group, Great Barrington, pp 461–465

    Google Scholar 

  7. Hertzum M, Hornbæk K (2013) The effect of target precuing on pointing with mouse and touchpad. Int J Hum Comput Interact 29:338–350. https://doi.org/10.1080/10447318.2012.711704

    Article  Google Scholar 

  8. Foremann N, Korallo L (2014) Past and future applications of 3-D (Virutal Reality) technology. Sci Tech J Inf Technol Mech Opt 6:1–8

    Google Scholar 

  9. Chan S, Conti F, Salisbury K, Blevins NH (2013) Virtual reality simulation in neurosurgery: technologies and evolution. Haptics 72:154–164. https://doi.org/10.1227/NEU.0b013e3182750d26

    Article  Google Scholar 

  10. Kaber DB, Li Y, Clamann M, Lee YS (2012) Investigating human performance in a virtual reality haptic simulator as influenced by fidelity and system latency. IEEE Trans Syst Man Cybern Part A Syst Hum 42:1562–1566. https://doi.org/10.1109/TSMCA.2012.2201466

    Article  Google Scholar 

  11. Sachsse H (2013) Einführung in die Kybernetik: unter besonderer Berücksichtigung von technischen und biologischen Wirkungsgefügen. Lehrbuch für Studenten aller Fachrichtungen. Springer, Wiesbaden

    MATH  Google Scholar 

  12. Berge L, Dubois E, Houry-panchetti M et al (2015) Towards an engineering approach for advanced interaction techniques in 3D environments. In: Workshop HCI engineering in the sixth ACM SIGCHI symposium on engineering interactive computing systems

  13. Wickens CD, Hollands JG, Banbury S, Parasuraman R (2013) Engineering psychology & human performance. Pearson, London

    Google Scholar 

  14. Jagacinski RJ, Flach JM (2003) Control theory for humans: quantitative approaches to modeling performance. Lawrence Erlbaum Associates Publishers, Mahwah

    Google Scholar 

  15. Wickens CD, Hollands JG, Banbury S, Parasuraman R (2015) Engineering psychology & human performance. Taylor & Francis, London

    Google Scholar 

  16. Friston S, Karlström P, Steed A (2015) The effects of low latency on pointing and steering tasks. IEEE Trans Vis Comput Graph 22:1605–1615. https://doi.org/10.1109/TVCG.2015.2446467

    Article  Google Scholar 

  17. Loram ID, Lakie M, Gawthrop PJ (2009) Visual control of stable and unstable loads: What is the feedback delay and extent of linear time-invariant control? J Physiol 587:1343–1365. https://doi.org/10.1113/jphysiol.2008.166173

    Article  Google Scholar 

  18. Lupu MF, Sun M, Askey D et al (2010) Human strategies in balancing an inverted pendulum with time delay. In: 32nd annual international conference of the IEEE EMBS, pp 5246–5249

  19. Sweeney M, Maguire M, Shackel B (1993) Evaluating user-computer interaction a framework. Man-Machine Stud 38:689–711

    Article  Google Scholar 

  20. Lazar J, Feng JH, Hochheiser H (2010) Research methods in human–computer interaction. Wiley, Hoboken

    Google Scholar 

  21. Möller S, Schmidt S, Beyer J (2013) Gaming taxonomy: an overview of concepts and evaluation methods for computer gaming QoE. In: 2013 5th international workshop on quality of multimedia experience QoMEX 2013, pp 236–241. https://doi.org/10.1109/QoMEX.2013.6603243

  22. Fiedler M, Hossfeld T, Tran-Gia P (2010) A generic quantitative relationship between quality of experience and quality of service. Blekinge Tek Hogsk 24:36–41. https://doi.org/10.1109/MNET.2010.5430142

    Article  Google Scholar 

  23. Wechsung I, Engelbrecht K-P, Kühnel C et al (2012) Measuring the quality of service and quality of experience of multimodal human–machine interaction. J Multimodal User Interfaces 6:73–85. https://doi.org/10.1007/s12193-011-0088-y

    Article  Google Scholar 

  24. Brunnström K, Beker SA, De Moor K, et al (2013) Qualinet white paper on definitions of quality of experience. In: Experience output from the fifth Qualinet meeting

  25. Galletta DF, Henry RM, McCoy S, Polak P (2006) When the wait isn’t so bad: the interacting effects of website delay, familiarity, and breadth. Inf Syst Res 17:20–37. https://doi.org/10.1287/isre.1050.0073

    Article  Google Scholar 

  26. Downing CE, Liu C (2011) Assessing web site usability in retail electronic commerce. In: 2011 IEEE 35th annual computer software and applications conference. IEEE, pp 144–151

  27. Jarschel M, Schlosser D, Scheuring S, Hoßfeld T (2011) An evaluation of QoE in cloud gaming based on subjective tests. In: 2011 fifth international conference on innovative mobile and internet services in ubiquitous computing. IEEE, pp 330–335

  28. Wu W, Arefin A, Huang Z et al (2010) “I’m the Jedi!”—a case study of user experience in 3D tele-immersive gaming. In: 2010 IEEE international symposium on multimedia. IEEE, pp 220–227

  29. Wang S, Dey S (2009) Modeling and characterizing user experience in a cloud server based mobile gaming approach. In: GLOBECOM—IEEE global telecommunications conference. https://doi.org/10.1109/GLOCOM.2009.5425784

  30. Brooks P, Hestnes B (2010) User measures of quality of experience: why being objective and quantitative is important. IEEE Netw 24:8–13. https://doi.org/10.1109/MNET.2010.5430138

    Article  Google Scholar 

  31. Cloete S, Zupanc C, Burgess-Limerick R, Wallis G (2014) Control order and visuomotor strategy development for joystick-steered underground shuttle cars. Hum Factors J Hum Factors Ergon Soc 56:1177–1188. https://doi.org/10.1177/0018720814522295

    Article  Google Scholar 

  32. de la Malla C, Lopez-Moliner J, Brenner E (2014) Dealing with delays does not transfer across sensorimotor tasks. J Vis 14:1–17. https://doi.org/10.1167/14.12.8

    Article  Google Scholar 

  33. Farshchiansadegh A, Ranganathan R, Casadio M, Mussa-Ivaldi FA (2012) Adaptation to visual feedback delay influences visuomotor learning. J Neurophysiol 7:426–433. https://doi.org/10.1371/journal.pone.0037900

    Article  Google Scholar 

  34. Lupu MF (2013) Human manual control as an information processing channel. University of Pittsburgh, Pittsburgh

    Google Scholar 

  35. Cheung A (2016) The effect of visuohaptic delays on task performance and human control strategy in manual control tasks. Delft University of Technology, Delft

    Google Scholar 

  36. Jagacinski RJ (1977) A qualitative look at feedback control theory as a style of describing behavior. Hum Factors J Hum Factors Ergon Soc 19:331–347. https://doi.org/10.1177/001872087701900403

    Article  Google Scholar 

  37. Mulder M, Van Paassen R, Flach JM, Jagacinski RJ (2005) Fundamentals of manual control theory. In: Marras WS, Karwowski W (eds) The occupational ergonimics handbook—fundamentals and assessment tools for occupational ergonomics. CRC Press, Taylor & Francis, London, pp 1–26

    Google Scholar 

  38. Waltemate T, Senna I, Hülsmann F et al (2016) The impact of latency on perceptual judgments and motor performance in closed-loop interaction in virtual reality. In: Proceedings of 22nd ACM conference on virtual reality software and technology—VRST’16, pp 27–35. https://doi.org/10.1145/2993369.2993381

  39. Potter JJ, Singhose WE (2014) Effects of input shaping on manual control of flexible and time-delayed systems. Hum Factors 56:1284–1295. https://doi.org/10.1177/0018720814528004

    Article  Google Scholar 

  40. Raaen K, Gronli T-M (2014) Latency thresholds for usability in games: a survey. In: Norsk Informatikkonferanse (NIK)

  41. Claypool M, Claypool K (2010) Latency can kill: precision and deadline in online games. In: Proceedings of the first annual ACM SIGMM conference on multimedia systems—MMSys’10. ACM Press, New York, pp 215–222

  42. Beigbeder T, Coughlan R, Lusher C et al (2004) The effects of loss and latency on user performance in unreal tournament 2003®. In: Proceeding NetGames’04 proceedings of 3rd ACM SIGCOMM workshop on network and system support for games. ACM Press, New York, pp 144–151

  43. Quax P, Monsieurs P, Lamotte W et al (2004) Objective and subjective evaluation of the influence of small amounts of delay and jitter on a recent first person shooter game. In: Proceedings of ACM SIGCOMM 2004 work NetGames’04 network and system support games—SIGCOMM 2004 Work 152. https://doi.org/10.1145/1016540.1016557

  44. Claypool M, Finkel D (2014) The effects of latency on player performance in cloud-based games. In: 2014 13th annual workshop on network and systems support for games. IEEE, pp 1–6

  45. Rank M, Shi Z, Muller HJ, Hirche S (2016) Predictive communication quality control in haptic teleoperation with time delay and packet loss. IEEE Trans Hum Mach Syst 46:581–592. https://doi.org/10.1109/THMS.2016.2519608

    Article  Google Scholar 

  46. Kumcu A, Vermeulen L, Elprama SA et al (2017) Effect of video lag on laparoscopic surgery: correlation between performance and usability at low latencies. Int J Med Robot Comput Assist Surg. https://doi.org/10.1002/rcs.1758

    Article  Google Scholar 

  47. Wildzunas RM, Barron TL, Wiley RW (1996) Visual display delay effects on pilot performance. Aviat Sp Environ Med 67:214–221

    Google Scholar 

  48. Jennings S, Craig G, Reid L, Kruk R (2000) The effect of visual system time delay on helicopter control. Proc Hum Factors Ergon Soc Annu Meet 44:69–72. https://doi.org/10.1177/154193120004401318

    Article  Google Scholar 

  49. Franklin GF, Powell DJ, Emami-Naeini A (2011) Feedback control of dynamic systems. Pearson Education Ltd., London

    MATH  Google Scholar 

  50. Jex HR, McDonnell JD, Phatak AV (1966) A “Critical’’ tracking task for manual control research. IEEE Trans Hum Factors Electron HFE 7:138–145. https://doi.org/10.1109/THFE.1966.232660

    Article  Google Scholar 

  51. Szameitat AJ, Rummel J, Szameitat DP, Sterr A (2009) Behavioral and emotional consequences of brief delays in human–computer interaction. Int J Hum Comput Stud 67:561–570. https://doi.org/10.1016/j.ijhcs.2009.02.004

    Article  Google Scholar 

  52. Ellis SR, Mania K, Adelstein BD, Hill MI (2004) Generalizeability of latency detection in a variety of virtual environments. Proc Hum Factors Ergon Soc Annu Meet 48:2632–2636. https://doi.org/10.1177/154193120404802306

    Article  Google Scholar 

  53. Brunnström K, Sjöströmb M, Imran M et al (2018) Quality of experience for a virtual reality simulator. In: Human vision and electronic imaging 2018

  54. Chanel G, Rebetez C, Bétrancourt M, Pun T (2008) Boredom, engagement and anxiety as indicators for adaptation to difficulty in games. In: Proceedings of 12th international conference on entertainment and media ubiquitous era—MindTrek’08, p 13. https://doi.org/10.1145/1457199.1457203

  55. Alexander JT, Sear J, Oikonomou A (2013) An investigation of the effects of game difficulty on player enjoyment. Entertain Comput 4:53–62. https://doi.org/10.1016/j.entcom.2012.09.001

    Article  Google Scholar 

  56. Qin H, Rau PLP, Salvendy G (2010) Effects of different scenarios of game difficulty on player immersion. Interact Comput 22:230–239. https://doi.org/10.1016/j.intcom.2009.12.004

    Article  Google Scholar 

  57. Schmierbach M, Chung MY, Wu M, Kim K (2014) No one likes to lose: the effect of game difficulty on competency, flow, and enjoyment. J Media Psychol 26:105–110. https://doi.org/10.1027/1864-1105/a000120

    Article  Google Scholar 

  58. Normoyle A, Guerrero G, Jörg S (2014) Player perception of delays and jitter in character responsiveness. In: Proceedings of the ACM symposium on applied perception—SAP’14. ACM Press, New York, pp 117–124

  59. Beznosyk A, Quax P, Coninx K, Lamotte W (2011) Influence of network delay and jitter on cooperation in multiplayer games expertise centre for digital media. In: Proceedings of the 10th international conference on virtual reality continuum and its applications in industry, pp 351–354

  60. Khajouei R, Wierenga PC, Hasman A, Jaspers MWM (2011) Clinicians satisfaction with CPOE ease of use and effect on clinicians’ workflow, efficiency and medication safety. Int J Med Inform 80:297–309. https://doi.org/10.1016/j.ijmedinf.2011.02.009

    Article  Google Scholar 

  61. Reynolds N, Ruiz de Maya S (2013) The impact of complexity and perceived difficulty on consumer revisit intentions. J Mark Manag 29:625–645. https://doi.org/10.1080/0267257X.2013.774290

    Article  Google Scholar 

  62. Pitts MJ, Burnett G, Skrypchuk L et al (2012) Visual-haptic feedback interaction in automotive touchscreens. Displays 33:7–16. https://doi.org/10.1016/j.displa.2011.09.002

    Article  Google Scholar 

  63. Young J, Lin M, Bick A et al (2013) Gestural workspaces for computer interaction: configuration and performance. In: Proceedings of the human factors and ergonomics society 57th annual meeting, pp 424–428

    Article  Google Scholar 

  64. Liu S, Bahn S, Choi H, Nam CS (2013) Behavioral characteristics of users with visual impairment in haptically enhanced virtual environments. In: Kurosu M (ed) Human-computer interaction. Interaction modalities and techniques. Springer, Berlin, Heidelberg, pp 618–625

    Chapter  Google Scholar 

  65. Fuller R (2011) Driver control theory: from task difficulty homeostasis to risk allostasis. In: Porter BE (ed) Handbook of traffic psychology, pp 13–26

    Chapter  Google Scholar 

  66. Tulga MK, Sheridan TB (1980) Dynamic decisions and work load in multitask supervisory control. IEEE Trans Syst Man Cybern 10:217–232. https://doi.org/10.1109/TSMC.1980.4308481

    Article  Google Scholar 

  67. Deber J, Jota R, Forlines C (2015) How much faster is fast enough? User perception of latency & latency improvements in direct and indirect touch. In: Conference on human factors in computing systems, pp 1827–1836

  68. Annett M, Ng A, Dietz P et al (2014) How low should we go? Understanding the perception of latency while inking. In: Proceedings of graphics interface, pp 167–174

  69. Knörlein B, Di Luca M, Harders M (2009) Influence of visual and haptic delays on stiffness perception in augmented reality. In: IEEE international symposium on mixed and augmented reality. IEEE, pp 49–52

  70. Kaaresoja T, Brewster S, Lantz V (2014) Towards the temporally perfect virtual button: touch-feedback simultaneity and perceived quality in mobile touchscreen press interactions. ACM Trans Appl Percept 11:1–25. https://doi.org/10.1145/2611387

    Article  Google Scholar 

  71. Ng A, Annett M, Dietz P et al (2014) In the blink of an eye: investigating latency perception during stylus interaction. In: Conference on human factors in computing systems, pp 1103–1112

  72. Caldwell BS, Lafayette W, Wang E (2009) Delays and user performance in human-computer-network interaction tasks. Hum Factors 51:813–830. https://doi.org/10.1177/0018720809359349

    Article  Google Scholar 

  73. Hauser J, Sastry S, Kokotovic P (1992) Nonlinear control via approximate input–output linearization: the ball and beam example. IEEE Trans Autom Contr 37:392–398. https://doi.org/10.1109/9.119645

    Article  MathSciNet  Google Scholar 

  74. Ryu K, Oh Y (2011) Balance control of ball-beam system using redundant manipulator. In: International conference on mechatronics. Istanbul, Turkey, pp 403–408

  75. Huang FC, Gillespie RB, Kuo A (2002) Haptic feedback and human performance in a dynamic task. In: 10th symposium on haptic interfaces for virtual environment and teleoperator systems, pp 24–31

  76. Flach JM (1990) Control with an eye for perception: precursors to an active psychophysics. Ecol Psychol 2:83–111. https://doi.org/10.1207/s15326969eco0202_1

    Article  Google Scholar 

  77. Field A (2012) Repeated measures ANOVA. In: Discovery statistics www.statisticshell.com/docs/repeatedmeasures.pdf. Accessed 20 May 2016

  78. Lüpsen H (2015) Voraussetzungen der parametrischen Varianzanalyse. In: Varianzanalysen—Prüfen der Voraussetzungen und nichtparametrische Methoden sowie praktische Anwendungen mit R und SPSS, pp 53–55

  79. Grubbs FE (1969) Procedures for detecting outlying observations in samples. Technometrics 11:1–21

    Article  Google Scholar 

  80. Kennedy JS, Buehner MJ, Rushton SK (2009) Adaptation to sensory-motor temporal misalignment: instrumental or perceptual learning? Q J Exp Psychol 62:453–469. https://doi.org/10.1080/17470210801985235

    Article  Google Scholar 

  81. Pöppel E (1997) A hierarchical model of temporal perception. Trends Cogn Sci 1:56–61. https://doi.org/10.1016/S1364-6613(97)01008-5

    Article  Google Scholar 

  82. Rank M, Shi Z, Hirche S (2010) Perception of delay in haptic telepresence systems. Presence Teleoperators Virtual Environ 19:389–399. https://doi.org/10.1162/pres_a_00021

    Article  Google Scholar 

  83. Claypool M, Claypool K (2006) On latency and player actions in online games. Commun ACM 49:40. https://doi.org/10.1145/1167838.1167860

    Article  Google Scholar 

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Acknowledgements

This work was supported by the German Federal Ministry of Education and Research (03ZZ0504H) in the frame 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.

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  1. Department of Psychology, Cognitive and Engineering Psychology, Technische Universität Chemnitz, 09107, Chemnitz, Germany

    Judith Martens, Nadine Rauh & Josef F. Krems

  2. Institute for Multimedia and Interactive Systems, Engineering Psychology and Cognitive Ergonomics, Universität zu Lübeck, Lübeck, Germany

    Thomas Franke

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Appendices

Appendix A: Scales for assessing perceived control difficulty and latency

See Tables 7, 8 and 9.

Table 7 Translated items used in the present research
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Table 8 Translated scale labelling used in the present research
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Table 9 Inter-item correlations and correlations with performance
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Appendix B: Experimental procedure

See Fig. 5 and Table 10.

Fig. 5
figure 5

Ball and Beam task used in the experiment. Vertical beam is only for illustration and was only present in the first exercise trials

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Table 10 Sequence of the experiment
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Martens, J., Franke, T., Rauh, N. et al. Effects of low-range latency on performance and perception in a virtual, unstable second-order control task. Qual User Exp 3, 10 (2018). https://doi.org/10.1007/s41233-018-0023-z

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