The Difficulty to Break a Relational Complexity Network Can Predict Air Traffic Controllers' Mental Workload and Performance in Conflict Resolution
文献类型：期刊论文
| 作者 | Zhang, Jingyu1,2 ; E, Xiaotian1,2; Du, Feng1,2; Yang, Jiazhong3; Loft, Shayne4
|
| 刊名 | HUMAN FACTORS
 |
| 出版日期 | 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 |
| WOS记录号 | WOS:000491458600001 |
| 出版者 | SAGE PUBLICATIONS INC |
| 资助机构 | National Key Research and Development Plan ; National Natural Science Foundation of China |
| 源URL | [http://ir.psych.ac.cn/handle/311026/30171]  |
| 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. |
入库方式： OAI收割
来源：心理研究所
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