Overcoming underload and overload: The effect of stage and level of autonomy in routine-failure trade-off in human-autonomy teams

被引:8
作者
Xu, Xinran [1 ]
Yu, Ruifeng [1 ]
Yuan, Minhui [1 ]
机构
[1] Tsinghua Univ, Dept Ind Engn, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Mental workload; Information processing stage; Level of autonomy; Lumberjack effect; Human -autonomy team; HUMAN-PERFORMANCE CONSEQUENCES; VISUAL-ATTENTION; AUTOMATION; WORKLOAD; MODEL; SYSTEM; SUPPORT; STRESS; IMPACT;
D O I
10.1016/j.ergon.2023.103424
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
We examined how human mental workload and the corresponding eye movement behaviors are affected by the stages and levels of autonomy in routine and autonomy failure conditions in human-autonomy teams (HAT). Thirty participants performed monitoring and diagnosing tasks with the autonomous agent in a three-factor experiment. The factors included information processing stage, level of autonomy, and agent operation condi-tion. The results indicated that the later the agent-supported information processing stage or the higher the autonomy level, the higher the participants' mental workload following autonomous agent failure. Compared to the continuous manual operation condition, the HAT performance did not decline following autonomous agent failure at the cost of increased mental workload. The eye movement results indicated a top-down compensatory control mechanism of attention, indicating the risk of team performance decline following autonomous agent failure. These findings can be applied in designing autonomous agents and setting human mental workload levels in a HAT.
引用
收藏
页数:9
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共 51 条
[1]   Impact of automation: Measurement of performance, workload and behaviour in a complex control environment [J].
Balfe, Nora ;
Sharples, Sarah ;
Wilson, John R. .
APPLIED ERGONOMICS, 2015, 47 :52-64
[2]   Operator adaptation to changes in system reliability under adaptable automation [J].
Chavaillaz, Alain ;
Sauer, Juergen .
ERGONOMICS, 2017, 60 (09) :1261-1272
[3]  
Cohen J., 1988, Statistical power analyses for behavioral sciences, V2nd, DOI [10.4324/9780203771587, DOI 10.4324/9780203771587]
[4]   Effect of workload history on task performance [J].
Cox-Fuenzalida, Luz-Eugenia .
HUMAN FACTORS, 2007, 49 (02) :277-291
[5]   Assessing mental workload in virtual reality based EOT crane operations: A multi-measure approach [J].
Das, Souvik ;
Maiti, J. ;
Krishna, O. B. .
INTERNATIONAL JOURNAL OF INDUSTRIAL ERGONOMICS, 2020, 80
[6]   A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance [J].
Dehais, Frederic ;
Lafont, Alex ;
Roy, Raphaelle ;
Fairclough, Stephen .
FRONTIERS IN NEUROSCIENCE, 2020, 14
[7]   A conceptual model of team dynamical behaviors and performance in human-autonomy teaming [J].
Demir, Mustafa ;
Cooke, Nancy J. ;
Amazeen, Polemnia G. .
COGNITIVE SYSTEMS RESEARCH, 2018, 52 :497-507
[8]   Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning [J].
Ding, Yi ;
Cao, Yaqin ;
Duffy, Vincent G. ;
Wang, Yi ;
Zhang, Xuefeng .
ERGONOMICS, 2020, 63 (07) :896-908
[9]  
Fairclough S., 2019, Neuroergonomics, P73, DOI 10.1016/B978-0-12811926-6.00012-9
[10]   Can eye-tracking data be measured to assess product design?: Visual attention mechanism should be considered [J].
Guo, Fu ;
Ding, Yi ;
Liu, Weilin ;
Liu, Chang ;
Zhang, Xuefeng .
INTERNATIONAL JOURNAL OF INDUSTRIAL ERGONOMICS, 2016, 53 :229-235