The study explores associations of visually induced motion sickness (VIMS) with emergency braking reaction times (RTs) in driving simulator studies. It examines the effects over the progression of multiple simulated drives.
Background:
Driving simulator usage has many advantages for RT studies; however, if it induces VIMS, the observed driving behavior might deviate from real-world driving, potentially masking or skewing results. Possible effects of VIMS on RT have long been entertained, but the progression of VIMS across simulated drives has so far not been sufficiently considered.
Method:
Twenty-eight adults completed six drives on 2 days in a fixed-base driving simulator. At five points during each drive, pedestrians entered the road, necessitating emergency braking maneuvers. VIMS severity was assessed every minute using the 20-point Fast Motion Sickness Scale. The progression of VIMS was considered in mixed model analyses.
Results:
RT predictions were improved by considering VIMS development over time. Here, the relationship of VIMS and RT differed across days and drives. Increases in VIMS symptom severity predicted more prolonged RT after repeated drives on a given day and earlier within each drive.
Conclusion:
The assessment of VIMS in RT studies can be beneficial. In this context, VIMS measurements in close temporal proximity to the behaviors under study are promising and offer insights into VIMS and its consequences, which are not readily obtainable through questionnaires.
Application:
Driving simulator–based RT studies should consider cumulative effects of VIMS on performance. Measurement and analysis strategies that consider the time-varying nature of VIMS are recommended.
AkaikeH. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.
2.
AkerstedtT.GillbergM. (1990). Subjective and objective sleepiness in the active individual. International Journal of Neuroscience, 52, 29–37.
3.
BatesD.KlieglR.VasishthS.BaayenR. H. (2015). Parsimonious mixed models. arXiv, 2, 1–21. Retrieved from https://arxiv.org/abs/1506.04967
4.
BatesD.MaechlerM.BolkerB.WalkerS. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48.
5.
BittnerA. C.GoreB. F.HooeyB. L. (1997). Meaningful assessments of simulator performance and sickness: Can’t have one without the other?Proceedings of 41st annual meeting of the Human Factors and Ergonomics Society (pp. 1089–1092). Santa Monica, CA: Human Factors and Ergonomics Society.
6.
BockO. L.OmanC. M. (1982). Dynamics of subjective discomfort in motion sickness as measured with a magnitude estimation method. Aviation, Space, and Environmental Medicine, 53, 773–777.
7.
ClassenS.BewernitzM.ShechtmanO. (2011). Driving simulator sickness: An evidence-based review of the literature. American Journal of Occupational Therapy, 65(2), 179–188.
8.
CohenJ. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
9.
DavisS. L.NesbittK.NalivaikoE. (2015). Comparing the onset of cybersickness using the Oculus Rift and two virtual roller coasters. In PisanY.NesbittK.BlackmoreK. L. (Eds.), Proceedings of the 11th Australasian Conference on Interactive Entertainment (IE 2015) (pp. 3–14). Sydney: Australian Computer Society.
10.
DonmezB.BoyleL.LeeJ. D. (2008). Accounting for time dependent covariates in driving simulator studies. Theoretical Issues in Ergonomics Science, 9(3), 189–199.
11.
EdwardsC. J.CreaserJ. I.CairdJ. K.LamsdaleA. M.ChisholmS. L. (2003). Older and younger driver performance at complex intersections: Implications for using perception response time and driving simulation. In RizzoM.LeeJ. D.McGeheeD. V. (Eds.), Proceedings of the second international driving symposium on human factors in driver assessment, training and vehicle design (pp. 33–37). Iowa City: University of Iowa.
12.
ForbesC.EvansM.HastingsN.PeacockB. (2011). Statistical distributions (4th ed.). Hoboken, NJ: John Wiley and Sons.
13.
GreenM. (2000). How long does it take to stop? Methodological analysis of driver perception–brake times. Transportation Human Factors, 2, 195–216.
14.
GwytherH.HollandC. (2012). The effect of age, gender and attitudes on self-regulation in driving. Accident Analysis & Prevention, 45, 19–28.
15.
HellandA.LydersenS.LervågL.-E.JenssenG.MørlandJ.SlørdalL. (2016). Driving simulator sickness: Impact on driving performance, influence of blood alcohol concentration, and effect of repeated simulator exposures. Accident Analysis & Prevention, 94, 180–187.
16.
JohnsonM. J.ChahalT.StinchcombeA.MullenN.WeaverB.BédardM. (2011). Physiological responses to simulated and on-road driving. International Journal of Psychophysiology, 81, 203–208.
17.
JureckiR. S.StańczykT. L. (2014). Driver reaction time to lateral entering pedestrian in a simulated crash traffic situation. Transportation Research Part F: Traffic Psychology and Behaviour, 27(1), 22–36.
18.
KaplanJ.VenturaJ.BakshiA.PierobonA.LacknerJ. R.DiZioP. (2017). The influence of sleep deprivation and oscillating motion on sleepiness, motion sickness, and cognitive and motor performance. Autonomic Neuroscience: Basic and Clinical, 202, 86–96.
19.
KarlI.BergG.RügerF.FärberB. (2013). Driving behavior and simulator sickness while driving the vehicle in the loop: Validation of longitudinal driving behavior. IEEE Intelligent Transportation Systems Magazine, 5(1), 42–57.
20.
KennedyR. S.LaneN. E.BerbaumK. S.LilienthalM. G. (1993). Simulator sickness questionnaire: An enhanced method for quantifying simulator sickness. International Journal of Aviation Psychology, 3(3), 203–220.
21.
KeshavarzB.HechtH. (2011). Validating an efficient method to quantify motion sickness. Human Factors: The Journal of the Human Factors and Ergonomic Society, 53(4), 415–426.
22.
KeshavarzB.HechtH.LawsonB. D. (2014). Visually-induced motion sickness: Causes, characteristics, and countermeasures. In KaleK. S.StanneyK. M. (Eds.), Handbook of virtual environments. Design, implementation, and applications (pp. 647–698). New York, NY: CRC Taylor & Francis.
23.
KlüverM.HerrigelC.HeinrichC.SchönerH.-P.HechtH. (2016). The behavioral validity of dual-task driving performance in fixed and moving base driving simulators. Transportation Research Part F: Traffic Psychology and Behaviour, 37, 78–96.
LawsonB. D. (2014a). Motion sickness scaling. In HaleK. S.StanneyK. M. (Eds.), Handbook of virtual environments: Design, implementation, and applications (pp. 601–626). New York, NY: CRC.
26.
LawsonB. D. (2014b). Motion sickness symptomatology and origins. In HaleK. S.StanneyK. M. (Eds.), Handbook of virtual environments: Design, implementation, and applications (pp. 531–600). New York, NY: CRC.
27.
LawtherA.GriffinM. J. (1986). The motion of a ship at sea and the consequent motion sickness amongst passengers. Ergonomics, 29, 535–552.
28.
LoS.AndrewsS. (2015). To transform or not to transform: Using generalized linear mixed models to analyse reaction time data. Frontiers in Psychology, 6, 1–16.
29.
LoomisJ.BlascovichJ.BeallA. (1999). Immersive virtual environment technology as a basic research tool in psychology. Behavior Research Methods, Instruments, & Computers, 31, 557–564.
30.
MullenN.CharltonJ.DevlinA.BédardM. (2011). Simulator validity: Behaviors observed on the simulator and on the road. In FisherD. L.RizzoM.CairdJ.LeeJ. D. (Eds.), Handbook of driving simulation for engineering, medicine, and psychology (Chap. 13). Boca Raton, FL: CRC.
31.
NalivaikoE.DavisS. L.BlackmoreK. L.VakulinA.NesbittK. V. (2015). Cybersickness provoked by head-mounted display affects cutaneous vascular tone, heart rate and reaction. Physiology & Behavior, 151, 583–590.
32.
NesbittK. V.DavisS.BlackmoreK. L.NalivaikoE. (2017). Correlating reaction time and nausea measures with traditional measures of cybersickness. Displays, 48, 1–8.
33.
ReasonJ. T.GraybielA. (1970). Progressive adaptation to Coriolis accelerations associated with 1-rpm increments in the velocity of the slow rotation room. Aerospace Medicine, 41, 73–79.
34.
ReinhardR.KleerM.DreßlerK. (in press). The impact of individual simulator experiences on usability and driving behavior in a moving base driving simulator. Transportation Research Part F: Traffic Psychology and Behaviour, SI: Driving Simulation. Retrieved from https://doi.org/10.1016/j.trf.2018.01.004
35.
ReinhardR.RutrechtH. M.HengstenbergP.TutulmazE.GeisslerB.HechtH.MuttrayA. (2017). The best way to assess visually induced motion sickness in a fixed-base driving simulator. Transportation Research Part F: Traffic Psychology and Behaviour, 48, 74–88.
36.
RolnickA.LubowR. E. (1991). Why is the driver rarely motion sick? The role of controllability in motion sickness. Ergonomics, 34(7), 867–879.
37.
StonerH. A.FisherD. L.MollenhauerM. A. (2011). Simulator and scenario factors influencing simulator sickness. In FisherD. L.RizzoM.CairdJ.LeeJ. D. (Eds.), Handbook of driving simulation for engineering, medicine, and psychology (Chap. 14). Boca Raton, FL: CRC.
38.
TörnrosJ. (1995). Effect of driving speed on reaction time during motorway driving. Accident Analysis and Prevention, 27(4), 435–442.
39.
VenablesW. N.RipleyB. D. (2002). Modern applied statistics with S (4th ed.). New York, NY: Springer.
40.
WagenmakersE.-J.FarrellS. (2004). AIC model selection using Akaike weights. Psychonomic Bulletin & Review, 11, 192–196.
41.
YoungK. L.ReganM. A.LeeJ. D. (2008). Measuring the effects of driver distraction: Direct driving performance methods and measures. In ReganM. A.LeeJ. D.YoungK. L. (Eds.), Driver distraction: Theory, effects, and mitigation (pp. 85–106). New York, NY: CRC.
