Cited By
View all- Ben-Yaakov YMeyer JWang XAn B(2020)User detection of threats with different security measures2020 IEEE International Conference on Human-Machine Systems (ICHMS)10.1109/ICHMS49158.2020.9209426(1-6)Online publication date: Sep-2020
Levels of automation in a binary categorization task
Pages 233 - 236
Abstract
Motivation -- To study the effect of levels of automation on binary categorization decisions.
Research approach -- A laboratory experiment was conducted on 80 students, employing a simulated production control task that involved binary categorizations of situations.
Findings/Design -- The performance with the lower level of automation tended to be less affected by the quality of the aid and overall better than performance with the higher level of automation.
Research limitations/Implications -- The system is fairly abstract, and additional validation of the findings in more realistic settings may be desirable.
Originality/Value -- The study is one of a fairly small number of empirical studies on the effect of levels of automation on performance.
Take away message -- Lower levels of automation may actually lead to better results in a wide range of conditions.
References
[1]
Endsley, M. R., & Kaber, D. B. (1999). Level of automation effects on performance, situation awareness and workload in a dynamic control task. Ergonomics, 42, 462--492.
[2]
Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. New York, NY: Wiley.
[3]
Kaber, D. B., & Endsley, M. R. (2004). The effects of levels of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task. Theoretical Issues in Ergonomic Science, 5, 113--153.
[4]
Maltz, M., & Meyer, J. (2001). Reliance on warnings in an attentionally demanding signal detection task. Human Factors, 43, 217--226.
[5]
Meyer, J. (2001). Effects of warning validity and proximity on the response to warnings. Human Factors, 43, 563--572.
[6]
Meyer, J. (2004). Conceptual issues in the study of dynamic hazard warnings. Human Factors, 46, 196--204.
[7]
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of interaction with automation. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, SMC-30, 286--297.
[8]
Sorkin, R. D. & Woods, D. D. (1985). Systems with human monitors: A signal detection analysis. Human-Computer Interaction, 1, 49--75.
- Levels of automation in a binary categorization task
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Published In
August 2007
334 pages
ISBN:9781847998491
DOI:10.1145/1362550
- Conference Chairs:
- Willem Paul Brinkman,
- Dong Han Ham,
- B. L. William Wong
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- ACM: Association for Computing Machinery
- SIGCHI: Specialist Interest Group in Computer-Human Interaction of the ACM
- Interactions, the Human-Computer Interaction Specialist Group of the BCS
- Middlesex University, London, School of Computing Science
- European Office of Aerospace Research and Development, Air Force Office of Scientific Research, United States Air Force Research Laboratory
- EACE: European Association of Cognitive Ergonomics
- Brunel University, West London, Department of Information Systems and Computing
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New York, NY, United States
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Published: 28 August 2007
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View all- Ben-Yaakov YMeyer JWang XAn B(2020)User detection of threats with different security measures2020 IEEE International Conference on Human-Machine Systems (ICHMS)10.1109/ICHMS49158.2020.9209426(1-6)Online publication date: Sep-2020
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