眼动追踪技术在人员心理选拔中的研究综述
A Review of the Application of Eye Tracking Technology in Personnel Psychological Selection
DOI: 10.12677/ap.2024.148584, PDF,   
作者: 綦家兴, 胡锦隆:武警后勤学院研究生大队,天津;陈旭义, 王振国:武警特色医学中心研究部,天津
关键词: 眼动追踪技术心理选拔注意力认知能力Eye Tracking Technology Psychological Selection Attention Cognitive Competence
摘要: 眼动追踪技术通过精密地追踪和解析个体的视线移动,为心理选拔提供了一种客观可靠的测量方法。通过记录分析个体的眼动特征,能够准确选拔出符合岗位心理需求的人员,提高选拔效率,降低因人选不当而带来的潜在风险。本文系统梳理了眼动追踪技术在人员心理选拔方面的研究进展,明确了该技术在注意力、认知能力、情绪稳定性和人格特质等研究中的关键作用,以期为军事人员心理选拔研究提供有价值的思路和参考。
Abstract: Eye tracking technology provides an objective and reliable measurement method for psychological selection by accurately tracking and analyzing individual eye movements. By recording and analyzing individual eye movement characteristics, we can accurately select the personnel who meet the psychological needs of the post, improve selection efficiency, and reduce the potential risks caused by improper selection. This paper systematically reviews the research progress of eye-tracking technology in the psychological selection of military personnel, and identifies the key role of this technology in the study of attention, cognitive ability, emotional stability and personality traits, so as to provide valuable ideas and references for the study of psychological selection of military personnel.
文章引用:綦家兴, 胡锦隆, 陈旭义, 王振国 (2024). 眼动追踪技术在人员心理选拔中的研究综述. 心理学进展, 14(8), 578-583. https://doi.org/10.12677/ap.2024.148584

参考文献

[1] 陈庆荣, 周曦, 韩静, 安静(2012). 眼球追踪: 模式、技术和应用. 实验室研究与探索, 31(10), 10-15.
[2] 程建梅, 陈强, 章超, 欧居尚(2018). 城市道路不同驾驶环境下汽车驾驶员注意力分配定量研究. 科学技术与工, 18(25), 286-295.
[3] 冯宇(2023). 基于视觉特征与对抗学习的大五人格评估. 硕士学位论文, 合肥: 安徽医科大学.
[4] 李瑞(2021). 情绪稳定性对飞行学员驾驶行为的影响分析. 硕士学位论文, 天津: 中国民航大学.
[5] 牛四芳(2014). 飞行员在飞行动作模拟练习下的眼动模式的分析. 硕士学位论文, 西安: 第四军医大学.
[6] 申天啸(2022). 基于深度学习的驾驶员眼动识别和分析研究. 硕士学位论文, 西安: 西安电子科技大学.
[7] 沈胤宏, 郑秀娟, 张昀, 苗丹民(2023). 基于眼动特征的辅助心理测量方法. 空军军医大学学报, 44(10), 942-947.
[8] 田建全(2006). ProjectA对我军士兵心理选拔研究的启示. 心理科学进展, (2), 164-168.
[9] 王碧梅(2022). 不同经验水平教师教学反思认知加工机制研究——基于眼动和访谈的证据. 教师教育研究, 34(4), 77-85.
[10] 王莉莉, 许凌鹏(2023). 基于眼动数据的管制员注意力特征评价. 中国安全科学学报, 33(2), 217-224.
[11] 王雪松, 邓涛, 房清霆, 赵顾灏(2020). 基于眼动识别的塔台飞行管制员注意力评估系统设计. 中国航班, (1), 48.
[12] 王燕青, 周士琦, 李瑞(2022). 不同时间压力条件下飞行学员的情绪稳定性对决策绩效的影响. 科学技术与工程, 22(13), 5513-5518.
[13] 吴林, 叶宗华, 刘小东(2020). 眼动数据分析在飞行培训中的应用研究. 价值工程, 39(25), 189-191.
[14] 薛志超, 巩渭华, 杨波, 等(2018). 常见驾驶行为下驾驶人注意力分配特征. 济南大学学报(自然科学版), 32(6), 469-475.
[15] 张益凡, 王宇超, 张琴喻, 葛贤亮, 徐杰(2022). 基于眼动指标的飞行员注意状态识别可行性研究. 航空科学技, 33(4), 39-46.
[16] 张志龙, 刘飞虎, 卢宏亮, 等(2023). 认知能力评估工具的研究进展. 职业与健康, 39(5), 715-720.
[17] Ban, S., Lee, Y. J., Kim, K. R., Kim, J., & Yeo, W. (2022). Advances in Materials, Sensors, and Integrated Systems for Monitoring Eye Movements. Biosensors, 12, Article 1039.[CrossRef] [PubMed]
[18] Black, M. H., Chen, N. T. M., Iyer, K. K., Lipp, O. V., Bölte, S., Falkmer, M. et al. (2017). Mechanisms of Facial Emotion Recognition in Autism Spectrum Disorders: Insights from Eye Tracking and Electroencephalography. Neuroscience & Biobehavioral Reviews, 80, 488-515.[CrossRef] [PubMed]
[19] Carniglia, E., Caputi, M., Manfredi, V., Zambarbieri, D., & Pessa, E. (2012). The Influence of Emotional Picture Thematic Content on Exploratory Eye Movements. Journal of Eye Movement Research, 5, 1-9.[CrossRef
[20] Claudio, A., Sebastian, B., Vaclav, S. et al. (2015). Neural Networks for Emotion Recognition Based on Eye Tracking Data. In 2015 IEEE International Conference on Systems, Man, and Cybernetics (pp. 2632-2637). IEEE.
[21] Hayes, T. R., & Henderson, J. M. (2017). Scan Patterns during Real-World Scene Viewing Predict Individual Differences in Cognitive Capacity. Journal of Vision, 17, Article 23.[CrossRef] [PubMed]
[22] Hayes, T. R., Petrov, A. A., & Sederberg, P. B. (2015). Do We Really Become Smarter When Our Fluid-Intelligence Test Scores Improve? Intelligence, 48, 1-14.[CrossRef] [PubMed]
[23] Lim, J. Z., Mountstephens, J., & Teo, J. (2021). Eye-Tracking Feature Extraction for Biometric Machine Learning. Frontiers in Neurorobotics, 15, Article 796895.[CrossRef] [PubMed]
[24] Motta, D. C., Carvalho, B. C., Castilho, P. et al. (2019). Assessment of Neurocognitive Function and Social Cognition with Computerized Batteries: Psychometric Properties of the Portuguese PennCNB in Healthy Controls. Current Psychology, 38, 1-12.
[25] Rauthmann, J. F., Seubert, C. T., Sachse, P., & Furtner, M. R. (2012). Eyes as Windows to the Soul: Gazing Behavior Is Related to Personality. Journal of Research in Personality, 46, 147-156.[CrossRef
[26] Sargezeh, B., Ayatollahi, A., & Daliri, M. R. (2019). Investigation of Eye Movement Pattern Parameters of Individuals with Different Fluid Intelligence. Experimental Brain Research, 237, 15-28.[CrossRef] [PubMed]
[27] van Meeuwen, L. W., Jarodzka, H., Brand-Gruwel, S., Kirschner, P. A., de Bock, J. J. P. R., & van Merriënboer, J. J. G. (2014). Identification of Effective Visual Problem Solving Strategies in a Complex Visual Domain. Learning and Instruction, 32, 10-21.[CrossRef
[28] Wang, W., Kofler, L., Lindgren, C., Lobel, M., Murphy, A., Tong, Q. et al. (2023). AI for Psychometrics: Validating Machine Learning Models in Measuring Emotional Intelligence with Eye-Tracking Techniques. Journal of Intelligence, 11, 170.[CrossRef] [PubMed]
[29] Wu, Y., Kosinski, M., & Stillwell, D. (2015). Computer-Based Personality Judgments Are More Accurate than Those Made by Humans. Proceedings of the National Academy of Sciences of the United States of America, 112, 1036-1040.[CrossRef] [PubMed]