Modern command, control, communications, computers, cyber, intelligence, surveillance, and reconnaissance (C5ISR) environments place substantial attentional demands on mission commanders. Failures in attention allocation in these high-risk settings can have severe operational consequences. This study investigates the efficacy of gaze-driven, attention-guided adaptive decision support tools, including visual-only and multimodal designs, in a high-fidelity simulated military command center. To characterize gaze and attentional dynamics during interaction with these tools, recurrence quantification analysis was applied to eye-tracking data. Stepwise regression using the Bayesian information criterion was then used to identify recurrence-based gaze metrics associated with performance. Results showed that the multimodal adaptive decision support tool was associated with significantly higher performance than the visual-only attention-guided tool. Average diagonal line length showed a negative linear association with performance, whereas entropy showed a positive linear association. Recurrence rate, determinism, and entropy also showed nonlinear quadratic relationships with performance. In particular, recurrence rate and determinism followed an inverted-U pattern consistent with the Yerkes-Dodson law. These findings suggest that effective performance in dynamic C5ISR contexts depends on a balance between structured and flexible visual scanning, and that recurrence-based gaze metrics can help characterize attentional dynamics during interaction with adaptive decision support systems.

翻译：现代指挥、控制、通信、计算机、网络、情报、监视与侦察（C5ISR）环境对任务指挥官提出了巨大的注意力需求。在这些高风险情境中，注意力分配失误可能造成严重的操作后果。本研究在逼真的模拟军事指挥中心中，探讨了基于注视驱动、注意力引导的自适应决策支持工具（包括纯视觉与多模态设计）的有效性。为表征与这些工具交互过程中的注视与注意力动态，利用递归量化分析对眼动追踪数据进行了处理。随后，采用基于贝叶斯信息准则的逐步回归法，识别与绩效相关的递归注视指标。结果表明，相比纯视觉注意力引导工具，多模态自适应决策支持工具的绩效显著更高。平均对角线长度与绩效呈负线性关联，而熵与绩效呈正线性关联。递归率、确定性与熵还表现出与绩效的非线性二次关系。特别地，递归率与确定性遵循倒U型模式，与耶克斯-多德森定律一致。这些发现表明，在动态C5ISR环境中，有效绩效依赖于结构化与灵活视觉扫描之间的平衡，而基于递归的注视指标可帮助表征与自适应决策支持系统交互过程中的注意力动态。