The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Automation Reliability in Unmanned Aerial Vehicle Control: A Reliance-Compliance Model of Automation Dependence in High Workload
Abstract
Objective: Two experiments were conducted in which participants navigated a simulated unmanned aerial vehicle (UAV) through a series of mission legs while searching for targets and monitoring system parameters. The goal of the study was to highlight the qualitatively different effects of automation false alarms and misses as they relate to operator compliance and reliance, respectively. Background: Background data suggest that automation false alarms cause reduced compliance, whereas misses cause reduced reliance. Method: In two studies, 32 and 24 participants, including some licensed pilots, performed in-lab UAV simulations that presented the visual world and collected dependent measures. Results: Results indicated that with the low-reliability aids, false alarms correlated with poorer performance in the system failure task, whereas misses correlated with poorer performance in the concurrent tasks. Conclusion: Compliance and reliance do appear to be affected by false alarms and misses, respectively, and are relatively independent of each other. Application: Practical implications are that automated aids must be fairly reliable to provide global benefits and that false alarms and misses have qualitatively different effects on performance.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Bainbridge, L. (1983) · Automatica · doi:10.1016/0005-1098(83)90046-8 · Cited by 2,205
Performance Consequences of Automation-Induced 'Complacency'
Parasuraman, R., Molloy, R., & Singh, I. L. (1993) · The International Journal of Aviation Psychology · doi:10.1207/s15327108ijap0301_1 · Cited by 875 · cited 4×
“SF automation miss rate correlates with TOO miss rate, r = .50; RT to TOO, r = .73; repeats, r = .76; and RT to SF misses, r = –0.97 – that is, higher miss rate less reliance poorer concurrent task performance and faster response to the automation miss (Meyer, 2001; Parasuraman et al., 1993).”
BackgroundInfluentialThe benefits of imperfect diagnostic automation: a synthesis of the literature
Wickens, C. D., & Dixon, S. R. (2007) · Theoretical Issues in Ergonomics Science · doi:10.1080/14639220500370105 · Cited by 423 · cited 3×
“Such resource scarcity may result either when the task itself is difficult (Maltz & Shinar, 2003) or when the automated task is carried out in a multitask context (C. D. Wickens & Dixon, 2005).”
BackgroundResultSelf-citation
Cited by
Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust
Hoff, K. A., & Bashir, M. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814547570 · Cited by 2,817
Automation bias: a systematic review of frequency, effect mediators, and mitigators
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000089 · Cited by 1,162
Automation bias and verification complexity: a systematic review
Lyell, D., & Coiera, E. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw105 · Cited by 451
The benefits of imperfect diagnostic automation: a synthesis of the literature
Wickens, C. D., & Dixon, S. R. (2007) · Theoretical Issues in Ergonomics Science · doi:10.1080/14639220500370105 · Cited by 423
Self-citationHumans: Still Vital After All These Years of Automation
Parasuraman, R., & Wickens, C. D. (2008) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872008x312198 · Cited by 401
Self-citationOn the Independence of Compliance and Reliance: Are Automation False Alarms Worse Than Misses?
Dixon, S. R., Wickens, C. D., & McCarley, J. S. (2007) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872007x215656 · Cited by 265
Self-citationImpact of robot failures and feedback on real-time trust
Desai, M., Kaniarasu, P., Medvedev, M., Steinfeld, A., & Yanco, H. (2013) · 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2013.6483596 · Cited by 234
Effects of Anthropomorphism and Accountability on Trust in Human Robot Interaction
Natarajan, M., & Gombolay, M. (2020) · Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/3319502.3374839 · Cited by 248
de Visser, E., & Parasuraman, R. (2011) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343411410160 · Cited by 207
Complacency and Automation Bias in the Use of Imperfect Automation
Wickens, C. D., Clegg, B. A., Vieane, A. Z., & Sebok, A. L. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720815581940 · Cited by 176
Self-citationMerritt, S. M., Lee, D., Unnerstall, J. L., & Huber, K. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814561675 · Cited by 83
Understanding human management of automation errors
McBride, S. E., Rogers, W. A., & Fisk, A. D. (2014) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2013.817625 · Cited by 82
Gutzwiller, R. S., Chiou, E. K., Craig, S. D., Lewis, C. M., Lematta, G. J., & Hsiung, C.-P. (2019) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181319631201 · Cited by 54
Chen, J., Mishler, S., Hu, B., Li, N., & Proctor, R. W. (2018) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2018.05.010 · Cited by 50
Effects of trust, self-confidence, and feedback on the use of decision automation
Wiczorek, R., & Meyer, J. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00519 · Cited by 46
Prospective memory in an air traffic control simulation: External aids that signal when to act.
Loft, S., Smith, R. E., & Bhaskara, A. (2011) · Journal of Experimental Psychology: Applied · doi:10.1037/a0022845 · Cited by 40
The relationship of trust and dependence
Patton, C. E., & Wickens, C. D. (2024) · Ergonomics · doi:10.1080/00140139.2024.2342436 · Cited by 44
Self-citationAutopilots in the Operating Room: Safe Use of Automated Medical Technology
Ruskin, K. J., Corvin, C., Rice, S. C., & Winter, S. R. (2020) · Anesthesiology · doi:10.1097/aln.0000000000003385 · Cited by 38
Kim, J. Y., Lester, C., & Yang, X. J. (2025) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208251326795 · Cited by 21
Supporting operator reliance on automation through continuous feedback
Seppelt, B. D. (2018) · doi:10.17077/etd.90t9uqnh · Cited by 11
Metadata
- Authors
- Dixon, S. R., & Wickens, C. D.
- Venue
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Publisher
- SAGE Publications
- Published
- 2006-09
- Volume
- 48
- Issue
- 3
- Pages
- 474-486
- ISSN
- 0018-7208 (ISSN-L)
- 1547-8181
- OpenAlex
- W2142610581
- Cited by
- 280
- References
- 4
- FWCI
- 16.44
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
