The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Supporting Decision Making and Action Selection under Time Pressure and Uncertainty: The Case of In-Flight Icing
Abstract
Operators in high-risk domains such as aviation often need to make decisions under time pressure and uncertainty. One way to support them in this task is through the introduction of decision support systems (DSSs). The present study examined the effectiveness of two different DSS implementations: status and command displays. Twenty-seven pilots (9 pilots each in a baseline, status, and command group) flew 20 simulated approaches involving icing encounters. Accuracy of the decision aid (a smart icing system), familiarity with the icing condition, timing of icing onset, and autopilot usage were varied within subjects. Accurate information from either decision aid led to improved handling of the icing encounter. However, when inaccurate information was presented, performance dropped below that of the baseline condition. The cost of inaccurate information was particularly high for command displays and in the case of unfamiliar icing conditions. Our findings suggest that unless perfect reliability of a decision aid can be assumed, status displays may be preferable to command displays in high-risk domains (e.g., space flight, medicine, and process control), as the former yield more robust performance benefits and appear less vulnerable to automation biases.
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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
MUIR, B. M. (1994) · Ergonomics · doi:10.1080/00140139408964957 · Cited by 873 · cited 2×
“…technologies, can lead to the opposite, but equally undesirable, effect: Pilots may not use the system-provided information or follow its recommendations when they should do so (Endsley, 1996; Endsley & Kiris, 1995; Lee & Moray, 1992, 1994; Muir, 1994; Parasuraman & Riley, 1997; Riley, 1996).”
BackgroundStatus or Recommendation: Selecting the Type of Information for Decision Aiding
Crocoll, W. M., & Coury, B. G. (1990) · Proceedings of the Human Factors Society Annual Meeting · doi:10.1177/154193129003401922 · Cited by 68
Cited by
Complacency and Bias in Human Use of Automation: An Attentional Integration
Parasuraman, R., & Manzey, D. H. (2010) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720810376055 · Cited by 1,710
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
From Here to Autonomy: Lessons Learned From Human–Automation Research
Endsley, M. R. (2017) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720816681350 · Cited by 898
Human Performance Consequences of Stages and Levels of Automation: An Integrated Meta-Analysis
Onnasch, L., Wickens, C. D., Li, H., & Manzey, D. (2014) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720813501549 · Cited by 524
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
Humans: 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
Manzey, D., Reichenbach, J., & Onnasch, L. (2012) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343411433844 · Cited by 277
Automation Bias in Intelligent Time Critical Decision Support Systems
Cummings, M. (2004) · AIAA 1st Intelligent Systems Technical Conference · doi:10.2514/6.2004-6313 · Cited by 269
Automation bias in intelligent time critical decision support systems
Cummings, M. L. (2017) · Decision Making in Aviation · doi:10.4324/9781315095080-17 · Cited by 233
McGuirl, J. M., & Sarter, N. B. (2006) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872006779166334 · Cited by 212
Self-citationAutomation and Accountability in Decision Support System Interface Design
Cummings, M. L. (2006) · Journal of Technology Studies · doi:10.21061/jots.v32i1.a.4 · Cited by 155
“Ethical and social impact issues can arise during all phases of design, and identifying and addressing these issues as early as possible can help the designer to both analyze the domain more comprehensively as well as suggest specific design guidance.”
BackgroundChugunova, M., & Sele, D. (2022) · Journal of Behavioral and Experimental Economics · doi:10.1016/j.socec.2022.101897 · Cited by 118
Automation bias in public administration – an interdisciplinary perspective from law and psychology
Ruschemeier, H., & Hondrich, L. J. (2024) · Government Information Quarterly · doi:10.1016/j.giq.2024.101953 · Cited by 59
Accuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressure
Swaroop, S., Buçinca, Z., Gajos, K. Z., & Doshi-Velez, F. (2024) · Proceedings of the 29th International Conference on Intelligent User Interfaces · doi:10.1145/3640543.3645206 · Cited by 49
Who can spot an online romance scam?
Whitty, M. T. (2019) · Journal of Financial Crime · doi:10.1108/jfc-06-2018-0053 · Cited by 43
Personalising AI Assistance Based on Overreliance Rate in AI-Assisted Decision Making
Swaroop, S., Buçinca, Z., Gajos, K. Z., & Doshi-Velez, F. (2025) · Proceedings of the 30th International Conference on Intelligent User Interfaces · doi:10.1145/3708359.3712128 · Cited by 20
Metadata
- Authors
- Sarter, N. B., & Schroeder, B.
- Venue
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Publisher
- SAGE Publications
- Published
- 2001-12
- Volume
- 43
- Issue
- 4
- Pages
- 573-583
- ISSN
- 0018-7208 (ISSN-L)
- 1547-8181
- OpenAlex
- W2149408972
- Cited by
- 194
- References
- 3
- FWCI
- 8.70
- Type
- journal article
- Language
- en
- Open access
- closed
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