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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, Jiazhong3; Loft, Shayne4
First AuthorZhang, Jingyu
Correspondent Emailzhangjingyu@psych.ac.cn
Contribution Rank1
Abstract

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.

Keywordrelational complexity network workload air traffic controllers conflict resolution network disentangling error operation
2019-10-16
Language英语
DOI10.1177/0018720819880646
Source PublicationHUMAN FACTORS
ISSN0018-7208
Pages14
SubtypeArticle
Indexed BySCI
Funding ProjectNational Key Research and Development Plan[2016YFB1001203] ; National Natural Science Foundation of China[31671148]
PublisherSAGE PUBLICATIONS INC
WOS Research AreaBehavioral Sciences ; Engineering ; Psychology
WOS SubjectBehavioral Sciences ; Engineering, Industrial ; Ergonomics ; Psychology, Applied ; Psychology
WOS IDWOS:000491458600001
QuartileQ1
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/30171
Collection社会与工程心理学研究室
Corresponding AuthorZhang, Jingyu
Affiliation1.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
First Author AffilicationInstitute of Psychology, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Psychology, Chinese Academy of Sciences
Recommended Citation
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.
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