Examine the impact of expected automation reliability on trust, workload, task disengagement, nonautomated task performance, and the detection of a single automation failure in simulated air traffic control.
Background
Prior research has focused on the impact of experienced automation reliability. However, many operational settings feature automation that is reliable to the extent that operators will seldom experience automation failures. Despite this, operators must remain aware of when automation is at greater risk of failing.
Method
Participants performed the task with or without conflict detection/resolution automation. Automation failed to detect/resolve one conflict (i.e., an automation miss). Expected reliability was manipulated via instructions such that the expected level of reliability was (a) constant or variable, and (b) the single automation failure occurred when expected reliability was high or low.
Results
Trust in automation increased with time on task prior to the automation failure. Trust was higher when expecting high relative to low reliability. Automation failure detection was improved when the failure occurred under low compared with high expected reliability. Subjective workload decreased with automation, but there was no improvement to nonautomated task performance. Automation increased perceived task disengagement.
Conclusions
Both automation reliability expectations and task experience played a role in determining trust. Automation failure detection was improved when the failure occurred at a time it was expected to be more likely. Participants did not effectively allocate any spared capacity to nonautomated tasks.
Applications
The outcomes are applicable because operators in field settings likely form contextual expectations regarding the reliability of automation.
BagheriN.JamiesonG. A. (2004). Considering subjective trust and monitoring behavior in assessing automation-induced “complacency”. Human Performance, Situation Awareness, and Automation: Current Research and Trends, 1, 54–59.
2.
BaileyN. R.ScerboM. W. (2007). Automation-induced complacency for monitoring highly reliable systems: The role of task complexity, system experience, and operator trust. Theoretical Issues in Ergonomics Science, 8, 321–348.doi:10.1080/14639220500535301
BoagC.NealA.LoftS.HalfordG. S. (2006). An analysis of relational complexity in an air traffic control conflict detection task. Ergonomics, 49, 1508–1526.doi:10.1080/0014013060077974417050391
5.
CalhounG. L.RuffH. A.BehymerK. J.FrostE. M. (2018). Human-autonomy teaming interface design considerations for multi-unmanned vehicle control. Theoretical Issues in Ergonomics Science, 19, 321–352.doi:10.1080/1463922X.2017.1315751
6.
DaviesD. R.ParasuramanR. (1982). The psychology of vigilance. Academic Press.
7.
DismukesK.BermanB. A.LoukopoulosL. D. (2007). The limits of expertise: Rethinking pilot error and the causes of airline accidents. Ashgate Publishing, Ltd.
8.
DixonS. R.WickensC. D. (2006). Automation reliability in unmanned aerial vehicle control: A reliance-compliance model of automation dependence in high workload. Human Factors, 48, 474–486.doi:10.1518/00187200677860682217063963
9.
FahlmanS. A.Mercer-LynnK. B.FloraD. B.EastwoodJ. D. (2013). Development and validation of the multidimensional state boredom scale. Assessment, 20, 68–85.doi:10.1177/107319111142130321903683
10.
FeighK. M.DorneichM. C.HayesC. C. (2012). Toward a characterization of adaptive systems: A framework for researchers and system designers. Human Factors, 54, 1008–1024.doi:10.1177/001872081244398323397810
11.
FothergillS.LoftS.NealA. (2009). ATC-lab (Advanced): An air traffic control simulator with realism and control. Behavior Research Methods, 41, 118–127.doi:10.3758/BRM.41.1.11819182131
12.
GouraudJ.DelormeA.BerberianB. (2018). Influence of automation on mind wandering frequency in sustained attention. Consciousness and Cognition, 66, 54–64.doi:10.1016/j.concog.2018.09.01230396034
13.
HartS. G.StavelandL. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. InHancockP. A.MeshkatiN. (Eds.), Advances in psychology (Vol. 52, pp.139–183).
14.
JamiesonG. A.SkraaningG. (2020). The absence of degree of automation trade-offs in complex work settings. Human Factors:, 62, 516–529.doi:10.1177/001872081984270931348685
15.
LangerE. (1989). Mindfulness. Addison-Wesley.
16.
LeeJ.MorayN. (1992). Trust, control strategies and allocation of function in human-machine systems. Ergonomics, 35, 1243–1270.doi:10.1080/001401392089673921516577
17.
MasalonisA. J.LeM. A.KlingeJ. C.GalsterS. M.DuleyJ. A.HancockP. A.HilburnB. G.ParasuramanR. (1997). Air traffic control workstation mock-up for free flight experimentation: Lab development and capabilities. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 41, 1379–1379.doi:10.1177/1071181397041002166
18.
McGuirlJ. M.SarterN. B. (2006). Supporting trust calibration and the effective use of decision aids by presenting dynamic system confidence information. Human Factors, 48, 656–665.doi:10.1518/00187200677916633417240714
19.
MerloJ. L.WickensC. D.YehM. (2000). Effect of reliability on cue effectiveness and display signaling. [Symposium]. Proceedings of the 4th Annual Army Federated Laboratory Symposium, College Park, MD, pp.27–31.
20.
MetzgerU.ParasuramanR. (2005). Automation in future air traffic management: Effects of decision aid reliability on controller performance and mental workload. Human Factors, 47, 35–49.doi:10.1518/001872005365380215960085
21.
MeyerJ. (2001). Effects of warning validity and proximity on responses to warnings. Human Factors, 43, 563–572.doi:10.1518/00187200177587039512002005
22.
MeyerJ. (2004). Conceptual issues in the study of dynamic hazard warnings. Human Factors, 46, 196–204.doi:10.1518/hfes.46.2.196.3733515359670
23.
MolloyR.ParasuramanR. (1996). Monitoring an automated system for a single failure: Vigilance and task complexity effects. Human Factors, 38, 311–322.doi:10.1177/001872089606380211
24.
MorayN. (2003). Monitoring, complacency, scepticism and eutactic behaviour. International Journal of Industrial Ergonomics, 31, 175–178.doi:10.1016/S0169-8141(02)00194-4
25.
MorayN.InagakiT. (2000). Attention and complacency. Theoretical Issues in Ergonomics Science, 1, 354–365.doi:10.1080/14639220052399159
26.
MuirB. M. (1994). Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems. Ergonomics, 37, 1905–1922.doi:10.1080/00140139408964957
27.
OnnaschL.WickensC. D.LiH.ManzeyD. (2014). Human performance consequences of stages and levels of automation: An integrated meta-analysis. Human factors, 56, 476–488.doi:10.1177/001872081350154924930170
28.
ParasuramanR.ManzeyD. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 52, 381–410.doi:10.1177/001872081037605521077562
29.
ParasuramanR.MolloyR.SinghI. L. (1993). Performance consequences of automation-induced ‘complacency’. The International Journal of Aviation Psychology, 3, 1–23.doi:10.1207/s15327108ijap0301_1
30.
RemingtonR. W.JohnstonJ. C.RuthruffE.GoldM.RomeraM. (2000). Visual search in complex displays: Factors affecting conflict detection by air traffic controllers. Human Factors, 42, 349–366.doi:10.1518/00187200077969810511132797
31.
SaxbyD. J.MatthewsG.WarmJ. S.HitchcockE. M.NeubauerC. (2013). Active and passive fatigue in simulated driving: Discriminating styles of workload regulation and their safety impacts. Journal of Experimental Psychology: Applied, 19, 287–300.doi:10.1037/a003438624041288
32.
SheridanT. B. (2015). Automation. InBoehm-DavisD. A.DursoF. T.LeeJ. D. (Eds.), APA handbooks in psychology. APA handbook of human systems integration (pp.449–465). American Psychological Association.
33.
SinghI. L.MolloyR.ParasuramanR. (1997). Automation-induced monitoring inefficiency: Role of display location. International Journal of Human-Computer Studies, 46, 17–30.doi:10.1006/ijhc.1996.0081
34.
SmallwoodJ.SchoolerJ. W. (2006). The restless mind. Psychological Bulletin, 132, 946–958.doi:10.1037/0033-2909.132.6.94617073528
35.
TatascioreM.BowdenV. K.VisserT. A. W.MichailovsS.LoftS. (2020). The benefits and costs of low and high degree of automation. Human Factors, 62, 874–896.doi:10.1177/0018720819867181
36.
U.S. Air Force. (2015). Autonomous horizons. U.S. Air Force Office of the Chief Scientist.
37.
WesleyD.DauL. A. (2017). Complacency and automation bias in the enbridge pipeline disaster. Ergonomics in Design: The Quarterly of Human Factors Applications, 25, 17–22.doi:10.1177/1064804616652269
38.
WickensC. D.DixonS. R. (2007). The benefits of imperfect diagnostic automation: A synthesis of the literature. Theoretical Issues in Ergonomics Science, 8, 201–212.doi:10.1080/14639220500370105
39.
WickensC. D.HooeyB. L.GoreB. F.SebokA.KoenickeC. S. (2009). Identifying black swans in NextGen: Predicting human performance in off-nominal conditions. Human Factors, 51, 638–651.doi:10.1177/001872080934970920196290
40.
WickensC. D.MavorA. S.ParasuramanR.McGeeP. (1998). Airspace system integration: The concept of free flight. InWickensC. D.MavorA. S.McGeeJ. P. (Eds.), The future of air traffic control: Human operators and automation (pp.225–245). National Academy.
41.
WickensC. D.RiceS.KellerD.HutchinsS.HughesJ.ClaytonK. (2009). False alerts in air traffic control conflict alerting system: Is there a “cry wolf” effect?Human Factors, 51, 446–462.doi:10.1177/001872080934472019899356
42.
YoungM. S.StantonN. A. (2002). Malleable attentional resources theory: A new explanation for the effects of mental underload on performance. Human Factors, 44, 365–375.doi:10.1518/0018720024497709
