Display Signaling in Augmented Reality: Effects of Cue Reliability and Image Realism on Attention Allocation and Trust Calibration
Abstract
This experiment seeks to examine the relationships among three advanced technology features (presentation of target cuing, reliability of target cuing, and level of image reality and the attention and trust given to that information). The participants were 16 military personnel who piloted an unmanned air vehicle and searched for targets camouflaged in terrain, which was presented at two levels of image realism. Cuing was available for some targets, and the reliability of this information was manipulated at two levels (100% and 75%). The results showed that the presence of cuing aided target detection for expected targets but drew attention away from the presence of unexpected targets. Cuing benefits and attentional tunneling were both reduced when cuing became less reliable. Increasing image realism was compelling but increased reliance on the cuing information when those data were reliable. Potential applications include a cost-benefit analysis of how trust modulates attention in the use of automated target recognition systems and the extent to which increased realism may influence this trust.
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Yang et al. found likelihood alarms took more time to adjust trust calibration than binary warning signals.
“Yang and colleagues [139] found that likelihood alarms took more time to adjust trust calibration than binary warning signals.”
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 [139]
Uncertainty cues increased workload in some cases but not others and led to instances of over-trust when cues were not reliable.
“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 [140]
One paper found trust is best reflected at each moment in the interaction rather than at the end of the interaction.
“To assess trust, one paper found that trust is best refected at each moment in the interaction rather than at the end of the interaction [139].”
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 [139]
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Raikwar, A., Mifsud, D. M., Wickens, C. D., Batmaz, A. U., Warden, A. C., Kelley, B., Clegg, B. A., & Ortega, F. R. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3372062 · Cited by 12 · see citing works
Self-citationZhang, N., Yan, Y., & Suzuki, R. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713293 · Cited by 12 · see citing works
Trust in haptic assistance: weighting visual and haptic cues based on error history
Gibo, T. L., Mugge, W., & Abbink, D. A. (2017) · Experimental Brain Research · doi:10.1007/s00221-017-4986-4 · Cited by 12 · see citing works
Using Trust to Determine User Decision Making & Task Outcome During a Human-Agent Collaborative Task
Herse, S., Vitale, J., Johnston, B., & Williams, M.-A. (2021) · IEEE/ACM International Conference on Human-Robot Interaction · doi:10.1145/3434073.3444673 · Cited by 17 · see citing works
Augmented Reality Based Traffic Sign Recognition for Improved Driving Safety
Abdi, L., Meddeb, A., & Abdallah, F. B. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-17765-6_9 · Cited by 12 · see citing works
Visual and Haptic Guidance for Enhancing Target Search Performance in Dual-Task Settings
Wang, G., Wang, H.-H., & Ren, G. (2024) · Applied Sciences · doi:10.3390/app14114650 · Cited by 10 · see citing works
On The Security of Mobile Cockpit Information Systems
Lundberg, D. A., Farinholt, B., Sullivan, E., Mast, R., Checkoway, S., Savage, S., Snoeren, A. C., & Levchenko, K. (2014) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2660267.2660375 · Cited by 11 · see citing works
Framing the Human Dimension in Cybersecurity
Nixon, J., & McGuinness, B. (2013) · ICST Transactions on Security and Safety · doi:10.4108/trans.sesa.01-06.2013.e2 · Cited by 10 · see citing works
A content analysis of studies published in the field of augmented reality in education
TURHAN, M. E., Metin, M., & Çevik, E. E. (2022) · Journal of Educational Technology and Online Learning · doi:10.31681/jetol.925340 · Cited by 10 · see citing works
Evidence Report, Risk of Inadequate Design of Human and Automation/Robotic Integration
Zumbado, J. R., Billman, D. O., Feary, M., & Green, C. (2011) · NASA Technical Reports Server (NASA) · Cited by 9 · see citing works
Design of human centered augmented reality for managing chronic health conditions
McLaughlin, A. C., Matalenas, L. A., & Coleman, M. G. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-811272-4.00011-7 · Cited by 9 · see citing works
Automated and Interactive Real‐Time Systems
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Design for Human-Automation and Human-Autonomous Systems
Lee, J. D., & Seppelt, B. (2023) · Springer handbooks · doi:10.1007/978-3-030-96729-1_19 · Cited by 8 · see citing works
Buwaider, A., El-Hajj, V. G., Iop, A., Romero, M., Jean, W. C., Edström, E., & Terander, A. E. (2024) · Virtual Reality · doi:10.1007/s10055-024-01033-9 · Cited by 8 · see citing works
A Closer Look at Warning Cues on the Sustained Attention to Response Task Performance
Dang, J. A., Shaw, T. H., McKnight, P. E., & Helton, W. (2022) · Hum. Factors · doi:10.1177/00187208211060708 · Cited by 9 · see citing works
Müller, R., Reindel, D. F., & Stadtfeld, Y. D. (2023) · Human Factors and Ergonomics in Manufacturing & Service Industries · doi:10.1002/hfm.21032 · Cited by 6 · see citing works
On the Go with AR: Attention to Virtual and Physical Targets while Varying Augmentation Density
Kim, Y.-J., Kumaran, R., Luo, J., Bullock, T., Giesbrecht, B., & Höllerer, T. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714289 · Cited by 7 · see citing works
Adopting AR wayfinding in heritage tourism: extending the UTAUT in cultural contexts
Wang, F., Peng, X., Wang, Y., Fan, W., Wang, K., & Du, Y. (2025) · npj Heritage Science · doi:10.1038/s40494-025-02069-z · Cited by 10 · see citing works
The Effectiveness of Gaze Guidance Lines in supporting JTAC’s Attention Allocation
Mifsud, D. M., Wickens, C. D., Maulbeck, M., Crane, P. M., & Ortega, F. R. (2022) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181322661143 · Cited by 7 · see citing works
Self-citationWorkload in Human-Robot Interaction: A Review of Manipulations and Outcomes
Prewett, M. S., Saboe, K. N., Johnson, R. C., Coovert, M. D., & Elliott, L. R. (2009) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/154193120905301848 · Cited by 7 · see citing works
- Authors
- Yeh, M., & Wickens, C. D.
- Affiliations
- Mitre (United States) (US)
- University of Illinois Urbana-Champaign (US)
- Venue
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Publisher
- SAGE Publications
- Published
- 2001-09
- Volume
- 43
- Issue
- 3
- Pages
- 355-365
- ISSN
- 0018-7208 (ISSN-L)
- 1547-8181
- OpenAlex
- W2129001561
- Cited by
- 175
- References
- 21
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 12 · 2023: 9 · 2022: 11 · 2021: 9 · 2020: 7 · 2019: 6 · 2018: 8 · 2017: 11 · 2016: 5 · 2015: 9 · 2014: 4 · 2013: 5 · 2012: 11
- FWCI
- 4.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Human Factors The Journal of the Human Factors and Ergonomics Society (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Army Research Laboratory (DAAL01-96-2-0003)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
- Physical Sciences › Computer Science › Human-Computer Interaction › Interactive and Immersive Displays
- Keywords
- augmented reality, cue reliability, photorealism, attention allocation, trust calibration, attentional tunneling, automatic target recognition, target detection, military personnel
- MeSH terms
- Adult, Analysis of Variance, Attention, Computer Graphics, Cues, Data Display, Female, Humans, Male, Military Personnel, Reaction Time, Sensitivity and Specificity, Task Performance and Analysis, User-Computer Interface, Visual Perception, Reproducibility of Results
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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