The Difficulty to Break a Relational Complexity Network Can Predict Air Traffic Controllers' Mental Workload and Performance in Conflict Resolution

doi:10.1177/0018720819880646

	The Difficulty to Break a Relational Complexity Network Can Predict Air Traffic Controllers' Mental Workload and Performance in Conflict Resolution
	Zhang, Jingyu1,2 ; E, Xiaotian1,2 ; Du, Feng1,2 ; Yang, Jiazhong 3; Loft, Shayne 4
刊名	HUMAN FACTORS
	2019-10-16
页码	14
关键词	relational complexity network workload air traffic controllers conflict resolution network disentangling error operation
ISSN号	0018-7208
DOI	10.1177/0018720819880646
通讯作者	Zhang, Jingyu(zhangjingyu@psych.ac.cn)
英文摘要	Objective: To test the network disentangling model for explaining air traffic controllers' (ATCos) conflict resolution performance. The network rigidity index (NRI), and the steps to break the relational complexity network following a central-available-node-first rule, was hypothesized to explain the overall task demand, whereas marginal-effort-decrease rule was expected to explain the actual operational outcome. Background: Understanding the conflict resolution process of ATCos is important for aviation safety and efficiency. However, linear models are insufficient. We proposed a new model that ATCos behavior can be largely considered as a process to break the relational complexity network, in which nodes represent the aircraft while links represent the cognitive complexity to understand the aircraft dyad relationship. Method: Twenty-one professional ATCos completed 27 conflict resolution scenarios that varied in the NRI and other control variables. Multilevel regression analyses were performed to understand the influence of the NRI on the number of interventions, mental workload, and unresolved rate. A cross-validation was performed to evaluate the predictive power of the model. Results: NRI influenced ATCos intervention number in a curvilinear manner, which further leads to ATCo's mental workload. The deviance between the number of interventions and the NRI was strongly linked with unresolved rate. Cross-validation suggests that the models predictions are robust. Conclusion: The network disentangling model provides a useful theory-driven way to explain controllers' conflict resolution workload and other important performance outcomes such as intervention probability. Application: The proposed model can potentially be used for workload management, sector design, and intelligent decision support tool development.
资助项目	National Key Research and Development Plan[2016YFB1001203] ; National Natural Science Foundation of China[31671148]
WOS研究方向	Behavioral Sciences ; Engineering ; Psychology
语种	英语
出版者	SAGE PUBLICATIONS INC
WOS记录号	WOS:000491458600001
资助机构	National Key Research and Development Plan ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.psych.ac.cn/handle/311026/30170]
专题	心理研究所_社会与工程心理学研究室
通讯作者	Zhang, Jingyu
作者单位	1.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China 3.Civil Aviat Flight Univ China, Guanghan, Peoples R China 4.Univ Western Australia, Perth, WA, Australia
推荐引用方式 GB/T 7714	Zhang, Jingyu,E, Xiaotian,Du, Feng,et al. The Difficulty to Break a Relational Complexity Network Can Predict Air Traffic Controllers' Mental Workload and Performance in Conflict Resolution[J]. HUMAN FACTORS,2019:14.
APA	Zhang, Jingyu,E, Xiaotian,Du, Feng,Yang, Jiazhong,&Loft, Shayne.(2019).The Difficulty to Break a Relational Complexity Network Can Predict Air Traffic Controllers' Mental Workload and Performance in Conflict Resolution.HUMAN FACTORS,14.
MLA	Zhang, Jingyu,et al."The Difficulty to Break a Relational Complexity Network Can Predict Air Traffic Controllers' Mental Workload and Performance in Conflict Resolution".HUMAN FACTORS (2019):14.