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Automation transparency: implications of uncertainty communication for human-automation interaction and interfaces
Open accessAbstract
Operators of highly automated driving systems may exhibit behaviour characteristic for overtrust issues due to an insufficient awareness of automation fallibility. Consequently, situation awareness in critical situations is reduced and safe driving performance following emergency takeovers is impeded. A driving simulator study was used to assess the impact of dynamically communicating system uncertainties on monitoring, trust, workload, takeovers, and physiological responses. The uncertainty information was conveyed visually using a stylised heart beat combined with a numerical display and users were engaged in a visual search task. Multilevel analysis results suggest that uncertainty communication helps operators calibrate their trust and gain situation awareness prior to critical situations, resulting in safer takeovers. In addition, eye tracking data indicate that operators can adjust their gaze behaviour in correspondence with the level of uncertainty. However, conveying uncertainties using a visual display significantly increases operator workload and impedes users in the execution of non-driving related tasks. Practitioner Summary: This article illustrates how the communication of system uncertainty information helps operators calibrate their trust in automation and, consequently, gain situation awareness. Multilevel analysis results of a driving simulator study affirm the benefits for trust calibration and highlight that operators adjust their behaviour according to multiple uncertainty levels.
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“Most commonly, the trust calibration was measured as the statistical diferences of trustworthiness perceptions of systems with high reliability and trustworthiness perceptions of systems with low-reliability groups, examined through analysis of variance (see, e.g., [4, 15, 26, 47, 58, 61, 68-70, 82, 84, 89, 90, 107, 117, 130, 140, 145]).”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [70]
In the citing paper
“Yet, uncertainty cues also increased workload in some cases [70] but not in others [92] and led to instances of over-trust when the cues were not reliable [140].”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [70]
Citations
Cites
lmerTest Package: Tests in Linear Mixed Effects Models
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017) · Journal of Statistical Software · doi:10.18637/jss.v082.i13 · Cited by 24,346
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
A feature-integration theory of attention.
Treisman, A. M., & Gelade, G. (1980) · Cognitive Psychology · doi:10.1016/0010-0285(80)90005-5 · Cited by 12,366
An integrative model of organizational trust
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995) · The Academy of Management Review · doi:10.2307/258792 · Cited by 10,736
Toward a Theory of Situation Awareness in Dynamic Systems
Endsley, M. R. (1995) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872095779049543 · Cited by 8,875
Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605 · cited 5×
“Detail describes the entity that is to be trusted, ranging from the overall system to its single functions and modes (Lee and See, 2004).”
BackgroundInfluentialGUIDE TO THE EXPRESSION OF UNCERTAINTY IN MEASUREMENT
Standard, S. (2006) · Cited by 6,460
Linear Mixed-Effects Models using 'Eigen' and S4
Bates, D. M., Maechler, M., Bolker, B. M., & Walker, S. C. (2015) · Cited by 5,924
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
Humans and automation: Use, misuse, disuse, abuse
Parasuraman, R., & Riley, V. (1997) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872097778543886 · Cited by 4,554
Research works printed but not linked: 12
- 10.1007/978-1-84628-618-6_141 · DOI
- 10.1007/978-3-642-13505-71 · DOI
- Endsley M. R., 2012, Designing for
- Gal, Y. 2016. “Uncertainty in Deep Learning.” PhD Thesis, University of Cambridge.
- 10.1016/S0166-4115(08)62386-91 · DOI
- 10.1518/hfes.46.1.50.30392 · DOI · Paper page
- May P., 1993, Paper Presented at the Annual Meeting of the Human Factors Society, Santa Monica, CA
- 10.5465/amr.1995.9508080335 · DOI · Paper page
- Nguyen T., 2018, Eprint ArXiv, 1803, 1
- Snijders T. A. B., 2012, Multilevel Analysis
- Steptoe A., 1991, Circulation, 83, 14
- 10.13140/RG.2.2.33648.56326 · DOI
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Metadata
- Authors
- Kunze, A., Summerskill, S. J., Marshall, R., & Filtness, A. J.
- Affiliations
- Loughborough University (GB)
- Corresponding author
- Kunze, Alexander
- Venue
- Ergonomics
- Publisher
- Informa UK Limited
- Published
- 2019-02-05
- Volume
- 62
- Issue
- 3
- Pages
- 345-360
- ISSN
- 0014-0139 (ISSN-L)
- 1366-5847
- OpenAlex
- W2902480251
- Cited by
- 165
- References
- 87
- Citations by year
- 2026: 19 · 2025: 35 · 2024: 29 · 2023: 18 · 2022: 25 · 2021: 19 · 2020: 14 · 2019: 6
- FWCI
- 13.20
- Type
- journal article
- Language
- en
- Open access
- green
- dspace.lboro.ac.uk
- Licence
- cc-by-nc-nd
- Locations
- Ergonomics (publishedVersion)
- PubMed (publishedVersion)
- Loughborough University Institutional Repository (Loughborough University) (submittedVersion, cc-by-nc-nd)
- Loughborough University Research Repository (Loughborough University) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Risk and Safety Analysis
- Keywords
- uncertainty communication, human-automation interaction, trust in automation, overtrust, situational awareness, operator workload, gaze behavior, physiological responses, non-driving related tasks
- MeSH terms
- Adult, Attention, Automobile Driving, Communication, Computer Simulation, Eye Movements, Female, Heart Rate, Humans, Male, Man-Machine Systems, Middle Aged, Reaction Time, Task Performance and Analysis, User-Computer Interface, Workload, Uncertainty, Trust, Multilevel Analysis
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