Return-to-Manual Performance can be Predicted Before Automation Fails

Abstract

Objective

This study aimed to examine operator state variables (workload, fatigue, and trust in automation) that may predict return-to-manual (RTM) performance when automation fails in simulated air traffic control.

Background

Prior research has largely focused on triggering adaptive automation based on reactive indicators of performance degradation or operator strain. A more direct and effective approach may be to proactively engage/disengage automation based on predicted operator RTM performance (conflict detection accuracy and response time), which requires analyses of within-person effects.

Method

Participants accepted and handed-off aircraft from their sector and were assisted by imperfect conflict detection/resolution automation. To avoid aircraft conflicts, participants were required to intervene when automation failed to detect a conflict. Participants periodically rated their workload, fatigue and trust in automation.

Results

For participants with the same or higher average trust than the sample average, an increase in their trust (relative to their own average) slowed their subsequent RTM response time. For participants with lower average fatigue than the sample average, an increase in their fatigue (relative to own average) improved their subsequent RTM response time. There was no effect of workload on RTM performance.

Conclusions

RTM performance degraded as trust in automation increased relative to participants’ own average, but only for individuals with average or high levels of trust.

Applications

Study outcomes indicate a potential for future adaptive automation systems to detect vulnerable operator states in order to predict subsequent RTM performance decrements.

Keywords

automation air traffic control trust in automation mental workload human-automation interaction

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References

Aricò

Borghini

Di Flumeri

Colosimo

Bonelli

Golfetti

Pozzi

Imbert

J. P.

Granger

Benhacene

Babiloni

(2016). Adaptive automation triggered by EEG-based mental workload index: A passive brain-computer interface application in realistic air traffic control environment. Frontiers in Human Neuroscience, 10(OCT2016), 1–13. https://doi.org/10.3389/fnhum.2016.00539

Bagheri

Jamieson

G. A. A.

(2005). The impact of context-related reliability on automation failure detection and scanning behaviour. 2004 IEEE International Conference on Systems, Man and Cybernetics, 212–217. https://doi.org/10.1109/icsmc.2004.1398299

Bailey

N. R.

Scerbo

M. W.

Freeman

F. G.

Mikulka

P. J.

Scott

L. A.

(2006). Comparison of a brain-based adaptive system and a manual adaptable system for invoking automation. Human Factors, 48(4), 693–709. https://doi.org/10.1518/001872006779166280

Bates

Douglas

Maechler

Martin

Bolker

Ben

Walker

Steve

(2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1). https://doi.org/10.18637/jss.v067.i01

Bowden

V. K.

Griffiths

Strickland

Loft

(2021). Detecting a single automation failure: The impact of expected (but not experienced) automation reliability. Human Factors: The Journal of the Human Factors and Ergonomics Society, 001872082110371. https://doi.org/10.1177/00187208211037188

Bryk

Anthony S.

Raudenbush

Stephen W.

(1992). Hierarchical Linear Models. Newbury Park, CA: Sage.

Calhoun

G. L.

Ward

V. B. R. R.

Ruff

H. A.

(2011). Performance-based adaptive automation for supervisory control. Proceedings of the Human Factors and Ergonomics Society, 2059–2063. https://doi.org/10.1177/1071181311551429

Charles

R. L.

Nixon

(2019). Measuring mental workload using physiological measures: A systematic review. Applied Ergonomics, 74, 221–232. https://doi.org/10.1016/j.apergo.2018.08.028

Chen

S. I.

Visser

T. A. W.

Huf

Loft

(2017). Optimizing the balance between task automation and human manual control in simulated submarine track management. Journal of Experimental Psychology: Applied, 23(3), 240–262. https://doi.org/10.1037/xap0000126

10.

Chiappe

Morgan

C. A.

Kraut

Ziccardi

Sturre

Strybel

T. Z.

K. P. L.

(2016). Evaluating probe techniques and a situated theory of situation awareness. Journal of Experimental Psychology: Applied, 22(4), 436–454. https://doi.org/10.1037/xap0000097

11.

Cohen

(1992). A power primer. Psychological Bulletin, 112(July), 155–159. https://doi.org/10.1037/0033-2909.112.1.155

12.

de Visser

E. J.

Peeters

M. M. M.

Jung

M. F.

Kohn

Shaw

T. H.

Pak

Neerincx

M. A.

(2020). Towards a theory of longitudinal trust calibration in human–robot teams. International Journal of Social Robotics, 12(2), 459–478. https://doi.org/10.1007/s12369-019-00596-x

13.

De Visser

Parasuraman

(2011). Adaptive aiding of human-robot teaming: Effects of imperfect automation on performance, trust, and workload. Journal of Cognitive Engineering and Decision Making, 5(2), 209–231. https://doi.org/10.1177/1555343411410160

14.

Endsley

M. R.

(2017). From Here to Autonomy. Human Factors, 59(1), 5–27. https://doi.org/10.1177/0018720816681350

15.

Endsley

M. R.

(2021). A systematic review and meta-analysis of direct objective measures of situation awareness: A comparison of SAGAT and SPAM. Human Factors, 63(1), 124–150. https://doi.org/10.1177/0018720819875376

16.

Endsley

M. R.

Kiris

E. O.

(1995). The out-of-the-loop performance problem and level of control in automation. Human Factors, 37(2), 381–394. https://doi.org/10.1518/001872095779064555

17.

Feigh

K. M.

Dorneich

M. C.

Hayes

C. C.

(2012). Toward a characterization of adaptive systems: A framework for researchers and system designers. Human Factors, 54(6), 1008–1024. https://doi.org/10.1177/0018720812443983

18.

Fothergill

Selina

Loft

Shayne

Neal

Andrew

(2009). ATC-Lab Advanced: An Air Traffic Control Simulator with Realism and Control. Behavior Research Methods, 41(1), 118–127. https://doi.org/10.3758/BRM.41.1.118

19.

Grandjean

(1979). Fatigue in industry. British Journal of Industrial Medicine, 36, 175–186.

20.

Hancock

P. A.

(2007). On the process of automation transition in multitask human-machine systems. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 37(4), 586–598. https://doi.org/10.1109/TSMCA.2007.897610

21.

Hancock

P. A.

Matthews

(2019). Workload and performance: Associations, insensitivities, and dissociations. Human Factors, 61(3), 374–392. https://doi.org/10.1177/0018720818809590

22.

Hancock

P. A.

Warm

J. S.

(1989). A dynamic model of stress & attention. Human Factors, 31(5), 519–537. https://doi.org/10.7771/2327-2937.1024

23.

Hart

S. G.

Staveland

L. E.

(1988). Development of NASA-TLX (task load index). Advances in Psychology, 52, 139–183. https://doi.org/10.1016/S0166-4115(08)62386-9

24.

Hart

S. G.

Wickens

C. D.

(1990). Workload assessment and prediction. In Booher

H. R.

(Ed.), An emerging technology. Advanced concepts for integrating people, machine, and organizations (pp. 257–296). Van Nostrand Reinhold.

25.

Hockey

G. R. J.

(1997). Compensatory control in the regulation of performance under stress and high workload: A cognitive-energetical framework. Biological Psychology, 45(96), 73–93.

26.

Hoff

K. A.

Bashir

(2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human Factors, 57(3), 407–434. https://doi.org/10.1177/0018720814547570

27.

Hofmann

D. A.

Gavin

M. B.

(1998). Centering decisions in hierarchical linear models: Implications for research in organizations. Journal of Management, 24(5), 623–641. https://doi.org/10.1177/014920639802400504

28.

Howard

Z. L.

Innes

Eidels

Loft

(2021). Using past and present indicators of human workload to explain variance in human performance. Psychonomic Bulletin and Review. https://doi.org/10.3758/s13423-021-01961-6

29.

Kaber

D. B.

Riley

J. M.

(1999). Adaptive automation of a dynamic control task based on secondary task workload measurement. International Journal of Cognitive Ergonomics, 3.3, 169–187.

30.

Kaber

D. B.

Wright

M. C.

Prinzel

L. J.

III Clamann

M. P.

(2005). Adaptive automation of human-machine system information-processing functions. Human Factors, 47(4), 730–741. https://doi.org/10.1518/001872005775570989

31.

Karpinsky

N. D.

Chancey

E. T.

Palmer

D. B.

Yamani

(2018). Automation trust and attention allocation in multitasking workspace automation trust and attention allocation in multitasking workspace. Applied Ergonomics, 70(March), 194–201. https://doi.org/10.1016/j.apergo.2018.03.008

32.

Kohn

S. C.

de Visser

E. J.

Wiese

Lee

Y. C.

Shaw

T. H.

(2021). Measurement of trust in automation: A narrative review and reference guide. Frontiers in Psychology, 12(October). https://doi.org/10.3389/fpsyg.2021.604977

33.

Lee

J. D.

Moray

(1994). Trust, self-confidence, and operators’ adaptation to automation. International Journal of Human Computer Studies, 40(1), 153–184. https://doi.org/10.1006/ijhc.1994.1007

34.

Lee

J. D.

See

K. A.

(2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392

35.

Loft

Bolland

Humphreys

M. S.

Neal

(2009). A theory and model of conflict detection in air traffic control: Incorporating environmental constraints. Journal of Experimental Psychology: Applied, 15(2), 106–124. https://doi.org/10.1037/a0016118

36.

Loft

Jooste

Y. R.

Ballard

Huf

Lipp

O. V.

Visser

T. A. W.

(2018). Using situation awareness and workload to predict performance in submarine track management: A multilevel approach. Human Factors, 60(7), 978–991. https://doi.org/10.1177/0018720818784803

37.

Loft

Sanderson

Neal

Mooij

(2007). Modeling and predicting mental workload in en route air traffic control: Critical review and broader implications. Human Factors, 49(3), 376–399. https://doi.org/10.1518/001872007x197017

38.

Sarter

(2019). Eye tracking: A process-oriented method for inferring trust in automation as a function of priming and system reliability. IEEE Transactions on Human-Machine Systems, 49(6), 560–568. https://doi.org/10.1109/THMS.2019.2930980

39.

Manzey

Reichenbach

Onnasch

(2012). Human performance consequences of automated decision aids. Journal of Cognitive Engineering and Decision Making, 6(1), 57–87. https://doi.org/10.1177/1555343411433844

40.

Matthews

(2016). Multidimensional profiling of task stress states for human factors: A brief review. Human Factors. https://doi.org/10.1177/0018720816653688

41.

Matthews

Campbell

S. E.

(2009). Sustained performance under overload: Personality and individual differences in stress and coping. Theoretical Issues in Ergonomics Science, 10(5), 417–422. https://doi.org/10.1080/14639220903106395

42.

Matthews

Szalma

Panganiban

A. R.

Neubauer

Warm

J. S.

(2013). Profiling task stress with the dundee stress state questionnaire. In Psychology of Stress: New Research (pp. 49–91). Nova Science Publishers, Inc.

43.

McGuirl

J. M.

Sarter

N. B.

(2006). Supporting trust calibration and the effective use of decision aids by presenting dynamic system confidence information. Human Factors, 48(4), 656–665. https://doi.org/10.1518/001872006779166334

44.

Merritt

S. M.

Huber

Lachapell-Unnerstall

Lee

(2014). Continuous Calibration of Trust in Automated Systems.

45.

Mracek

D. L.

Arsenault

M. L.

Day

E. A.

Hardy

J. H.

Terry

R. A.

(2014). A multilevel approach to relating subjective workload to performance after shifts in task demand. Human Factors, 56(8), 1401–1413. https://doi.org/10.1177/0018720814533964

46.

Muir

B. M.

Moray

(1996). Trust in automation. Part II. Experimental studies of trust and human intervention in a process control simulation. Ergonomics, 39(3), 429–460.

47.

Nickel

Hockey

G. R. J.

Roberts

A. C.

Roberts

M. H.

(2006). Markers of high risk operator functional state in adaptive control of process automation. Proceedings of International Ergonomics Association.

48.

Onnasch

Wickens

C. D.

Manzey

(2014). Human performance consequences of stages and levels of automation: An integrated meta-analysis. Human Factors, 56(3), 476–488. https://doi.org/10.1177/0018720813501549

49.

Parasuraman

Manzey

D. H.

(2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 52(3), 381–410. https://doi.org/10.1177/0018720810376055

50.

Parasuraman

Sheridan

T. B.

Wickens

C. D.

(2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 30(3), 286–297. https://doi.org/10.1109/3468.844354

51.

Preacher

K. J.

Curran

P. J.

Bauer

D. J.

(2003). Simple intercepts, simple slopes, and regions of significance in HLM 2-way interactions. Quantpsy.Org. http://www.quantpsy.org/interact/hlm2.htm

52.

Sarter

N. B.

Woods

D. D.

(1995). How in the world did we ever get into that mode? Mode error and awareness in supervisory control. Human Factors, 37(1), 5–19. https://doi.org/10.1518/001872095779049516

53.

Saxby

D. J.

Matthews

Hitchcock

E. M.

Warm

J. S.

Funke

G. J.

Gantzer

(2012). Effect of active and passive fatigue on performance using a driving simulator. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 52(21), 1751–1755. https://doi.org/10.1177/154193120805202113

54.

Saxby

D. J.

Matthews

Warm

J. S.

Hitchcock

E. M.

Neubauer

(2013). Active and passive fatigue in simulated driving: discriminating styles of workload regulation and their safety impacts. Journal of Experimental Psychology: Applied, 19(4), 287–300. https://doi.org/10.1016/j.physbeh.2017.03.040

55.

Schaefer

K. E.

Chen

J. Y. C.

Szalma

J. L.

Hancock

P. A.

(2016). A meta-analysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems. Human Factors, 58(3), 377–400. https://doi.org/10.1177/0018720816634228

56.

Sebok

Wickens

C. D.

(2017). Implementing Lumberjacks and Black Swans into model-based tools to support human-automation interaction. Human Factors, 59(2), 189–203. https://doi.org/10.1177/0018720816665201

57.

Warm

J. S.

Parasuraman

Matthews

(2008). Vigilance requires hard mental work and is stressful. Human Factors: The Journal of the Human Factors and Ergonomics Society, 50(3), 433–441. https://doi.org/10.1518/001872008x312152

58.

Wickens

C. D.

Dixon

S. R.

(2007). The benefits of imperfect diagnostic automation: A synthesis of the literature. Theoretical Issues in Ergonomics Science, 8(3), 201–212. https://doi.org/10.1080/14639220500370105

59.

Wickens

C. D.

Kessel

(1979). The effects of participatory mode and task workload on the detection of dynamic system failures. IEEE Transactions on Systems, Man and Cybernetics, 9(1), 24–34. https://doi.org/10.1109/TSMC.1979.4310070

60.

Wilson

G. F.

Russell

C. A.

(2007). Performance enhancement in an uninhabited air vehicle task using psychophysiologically determined adaptive aiding. Human Factors, 49(6), 1005–1018. https://doi.org/10.1518/001872007X249875

61.

Yang

X. J.

Schemanske

Searle

(2021). Toward quantifying trust dynamics: How people adjust their trust after moment-to-moment interaction with automation. Human Factors, 00(0), 1–17. https://doi.org/10.1177/00187208211034716

62.

Yeh

Y.-Y.

Wickens

C. D.

(1988). Dissociation of performance and subjective measures of workload. Human Factors, 30(1), 111–120. https://doi.org/10.1177/001872088803000110

63.

Young

M. S.

Stanton

N. A.

(2002). Attention and automation: New perspectives on mental underload and performance. Theoretical Issues in Ergonomics Science, 3(2), 178–194. https://doi.org/10.1080/14639220210123789

64.

Zhou

Chen

(2019). Physiological indicators for user trust in machine learning with influence enhanced fact-checking. In International cross-domain conference for machine learning and knowledge extraction (pp. 94–113).