The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Agent Reasoning Transparency’s Effect on Operator Workload
Abstract
We examined how varying the transparency of agent reasoning affected operator workload in a route selection task, and how the differing measures of workload compared in assessing and understanding cognitive workload. Participants guided a three-vehicle convoy safely through a simulated environment of which they had a limited amount of information, while maintaining communication with command and monitoring their surroundings for threats. The intelligent route-planning agent assessed potential threats and suggested changes to the convoy route as needed. Each participant was assigned to one of three agent reasoning transparency conditions. Contrary to our hypothesis, NASA-TLX Global workload measures indicated that workload decreased slightly as access to agent reasoning increased. However, psychophysical measures of workload disagreed with NASA-TLX global results. Comparison of individual NASA-TLX workload factors with the psychophysical measures indicated that performance satisfaction was highest in the intermediary transparency condition, and the addition of ambiguous information in the highest transparency condition increased effort and resulted in increased complacent behavior. Recommendations for future workload analysis are offered.
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“(III) Agent autonomy and reliability interact such that transparency reduces the negative effects of lower levels of reliability.”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Wright et al. (2016)
In the citing paper
“Wright et al. (2016) also reported a trend of increasing workload (measured using self-report and pupil diameter)”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Wright et al., 2016
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“increasing agent transparency was associated with lower perceptions of time pressure and frustration (Wright et al., 2016)”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Wright et al., 2016
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“humans were more complacent (Wright et al., 2016).”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Wright et al., 2016
In the citing paper
“increasing agent transparency was associated with lower perceptions of time pressure and frustration (Wright et al., 2016)”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Wright et al., 2016
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Cites
Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research
(1988) · Advances in Psychology · doi:10.1016/s0166-4115(08)62386-9 · Cited by 15,003
A model for types and levels of human interaction with automation
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/3468.844354 · Cited by 4,195 · cited 3×
“Instead of asking how to reduce workload, perhaps we should instead be asking how much is needed to maximize performance while preventing an out-of-the-loop (OOTL) situation (Parasuraman et al., 2000).”
BackgroundPerformance 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 1×
“The use of intelligent agents (IAs) as decision aids is becoming more commonplace, and operators are reaping benefits from freed up cognitive resources such as increased performance and decreased workload (Parasuraman, Molloy, & Singh, 1993; Parasuraman, Sheridan, & Wickens, 2000).”
BackgroundA Dynamic Model of Stress and Sustained Attention
Hancock, P. A. (1989) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/001872088903100503 · Cited by 874
Self-citation
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Cited by
Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature
O’Neill, T., McNeese, N., Barron, A., & Schelble, B. (2022) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720820960865 · Cited by 695
Zhang, R., Duan, W., Flathmann, C., McNeese, N., Freeman, G., & Williams, A. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610072 · Cited by 134
Metadata
- Authors
- Wright, J. L., Chen, J. Y. C., Barnes, M. J., & Hancock, P. A.
- Affiliations
- U.S. Army Research Laboratory, Orlando, FL
- U.S. Army Research Laboratory, Ft. Huachuca, AZ
- University of Central Florida, Orlando, FL
- Venue
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Publisher
- SAGE Publications
- Published
- 2016-09
- Volume
- 60
- Issue
- 1
- Pages
- 249-253
- ISSN
- 1071-1813
- 2169-5067
- Cited by
- 19
- References
- 4
- Language
- en
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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