注意力分配策略对界面任务绩效和视觉行为的影响机制
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国家自然科学基金(52065010); 贵州省科技计划(黔科合基础-ZK[2021]一般341, 筑科合同[2021]7-3, 黔科合支撑[2021]一般397)


Effect Mechanism of Attention Allocation Strategy on Task Performance and Visual Behavior of Interface
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    摘要:

    在工程领域, 作业人员通常需要面对刺激分布不均的复杂信息界面, 并执行相关的交互任务. 作业人员的视觉注意力分配已被证明与任务绩效密切相关, 但对于复杂界面中基于不同信息分配策略的多优先级刺激对作业人员的视觉注意力分配及任务绩效间的潜在联系仍亟待研究. 对此, 本文基于多优先级注意力分配策略实验对作业人员在不同负荷条件下的任务绩效和视觉行为的影响机制展开研究. 实验结果表明, 差异性的分配策略和信息优先级划分提升了任务绩效表现, 不同分配策略和优先级划分条件下的视觉行为存在显著差异, 并受脑力负荷的影响. 该结论能够为人机交互界面的设计和优化提供参考, 从而提高作业人员在任务中的绩效表现.

    Abstract:

    In the engineering field, operators need to face complex information interfaces with unevenly distributed stimuli and perform related interactive tasks. Visual attention allocation of operators has been proved to be closely related to task performance. However, the potential connection between visual attention allocation by multi-priority stimuli based on different information allocation strategies and task performance in complex interfaces requires further investigation. In this study, task performance and visual behavior of operators under different load conditions are studied on the basis of the experiment of the multi-priority attention allocation strategy. The experimental results indicate that the differential allocation strategy and information priority division improve the task performance, and the visual behavior differs significantly under different allocation strategies and priorities and is affected by mental loads. This conclusion can provide a reference for the design and optimization of human-computer interfaces and thus improve the task performance of operators.

    参考文献
    [1] Endsley MR. A taxonomy of situation awareness errors. In: Fuller R, Johnston N, McDonald N, eds. Human Factors in Aviation Operations. Aldershot: Ashgate Publishing Ltd., 1995. 287–292.
    [2] Peters RJ, Itti L. Beyond bottom-up: Incorporating task-dependent influences into a computational model of spatial attention. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis: IEEE, 2007. 1–8.
    [3] Ehinger KA, Hidalgo-Sotelo B, Torralba A, et al. Modeling search for people in 900 scenes: A combined source model of eye guidance. Visual Cognition, 2009, 17(6–7): 945–978. [doi: 10.1080/13506280902834720
    [4] 冯传宴, 完颜笑如, 刘双, 等. 负荷条件下注意力分配策略对情境意识的影响. 航空学报, 2020, 41(3): 124–133
    [5] Awh E, Belopolsky AV, Theeuwes J. Top-down versus bottom-up attentional control: A failed theoretical dichotomy. Trends in Cognitive Sciences, 2012, 16(8): 437–443. [doi: 10.1016/j.tics.2012.06.010
    [6] Maljkovic V, Nakayama K. Priming of pop-out: I. Role of features. Memory & Cognition, 1994, 22(6): 657–672
    [7] Anderson BA, Laurent PA, Yantis S. Value-driven attentional capture. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108(25): 10367–10371. [doi: 10.1073/pnas.1104047108
    [8] Wickens C, McCarley J, Thomas L. Attention-situation awareness (A-SA) model. Proceedings of the 2003 Conference on Human Performance Modeling of Approach and Landing with Augmented Displays. Moffett Field: NASA, 2003. 189–225.
    [9] Wickens CD. Noticing events in the visual workplace: The SEEV and NSEEV models. The Cambridge Handbook of Applied Perception Research. In: Hoffman RR, Hancock PA, Scerbo MW, et al. eds. Cambridge: Cambridge University Press, 2015. 749–768.
    [10] Koch C, Ullman S. Shifts in selective visual attention: Towards the underlying neural circuitry. In: Vaina LM, ed. Matters of Intelligence. Dordrecht: Springer, 1987. 115–141.
    [11] Yarbus AL. Eye Movements and Vision. Boston: Springer, 2013. 209–211.
    [12] Treisman AM. Strategies and models of selective attention. Psychological Review, 1969, 76(3): 282–299. [doi: 10.1037/h0027242
    [13] 吴晓莉, 薛澄岐, Tom G, 等. 数字化监控任务界面中信息特征的视觉搜索实验. 东南大学学报(自然科学版), 2018, 48(5): 807–814. [doi: 10.3969/j.issn.1001-0505.2018.05.005
    [14] Behroozi M, Lui A, Moore I, et al. Dazed: Measuring the cognitive load of solving technical interview problems at the whiteboard. Proceedings of the 40th International Conference on Software Engineering: New Ideas and Emerging Results. Gothenburg: ACM, 2018. 93–96.
    [15] Keskin M, Ooms K, Dogru AO, et al. Exploring the cognitive load of expert and novice map users using EEG and eye tracking. ISPRS International Journal of Geo-Information, 2020, 9(7): 429. [doi: 10.3390/ijgi9070429
    [16] Di Stasi LL, Antolí A, Cañas JJ. Main sequence: An index for detecting mental workload variation in complex tasks. Applied Ergonomics, 2011, 42(6): 807–813. [doi: 10.1016/j.apergo.2011.01.003
    [17] Wickens CD, Helton WS, Hollands JG, et al. Engineering Psychology and Human Performance. New York: Routledge, 2021. 596.
    [18] Kahneman D, Peavler WS. Incentive effects and pupillary changes in association learning. Journal of Experimental Psychology, 1969, 79(2): 312–318. [doi: 10.1037/h0026912
    [19] Hahnemann D, Beatty J. Pupillary responses in a pitch-discrimination task. Perception & Psychophysics, 1967, 2(3): 101–105
    [20] Lowenstein O, Loewenfeld IE. The sleep-waking cycle and pupillary activity. Annals of the New York Academy of Sciences, 1964, 117(1): 142–156
    [21] Goldberg JH, Kotval XP. Computer interface evaluation using eye movements: Methods and constructs. International Journal of Industrial Ergonomics, 1999, 24(6): 631–645. [doi: 10.1016/S0169-8141(98)00068-7
    [22] Baddeley AD. Selective attention and performance in dangerous environments. British Journal of Psychology, 1972, 63(4): 537–546. [doi: 10.1111/j.2044-8295.1972.tb01304.x
    [23] Reimer B. Impact of cognitive task complexity on drivers’ visual tunneling. Transportation Research Record: Journal of the Transportation Research Board, 2009, 2138(1): 13–19. [doi: 10.3141/2138-03
    [24] Rantanen EM, Goldberg JH. The effect of mental workload on the visual field size and shape. Ergonomics, 1999, 42(6): 816–834. [doi: 10.1080/001401399185315
    [25] Williams LJ. Tunnel vision or general interference? Cognitive load and attentional bias are both important. The American Journal of Psychology, 1988, 101(2): 171–191. [doi: 10.2307/1422833
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王傲然,吕健,刘翔.注意力分配策略对界面任务绩效和视觉行为的影响机制.计算机系统应用,2022,31(11):10-20

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  • 收稿日期:2022-03-06
  • 最后修改日期:2022-04-02
  • 在线发布日期: 2022-07-14
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