Predicting individual panic emotional arousal timing before manifestation is essential for proactive emergency intervention. Existing methods incorporate cognitive elements but none explicitly model the emotional arousal process, making them ill-suited for emotional arousal timing prediction. We argue that grounding prediction in appraisal emotion theory is necessary because it explicitly models this process, but three problems must be solved. (1) Appraisal theory posits that emotion arises from simultaneous evaluation across multiple threat dimensions, yet no prior work fuses these inputs into risk perception. (2) Existing cognitive models lack an Emotion node, decoupling threat appraisal from emotional arousal and forcing emotions to be inferred indirectly from behaviors. (3) Given their generalizable cognitive reasoning, current approaches adopt LLMs as the primary decision-maker, yet overlook the fragility and hallucination-proneness of their outputs. To address these issues, we introduce PanicCognitivePath (PCP), a framework that addresses all three. A Psychological Safety Distance (PSD) model, grounded in psychological distance theory, maps four-domain signals into a unified risk metric as the entry condition for subsequent cognitive reasoning. An explicit Emotion node grounded in appraisal emotion theory is introduced into BDI, forming a Belief-Desire-Emotion-Intention (BDEI) pathway. Agents whose risk metric exceeds the PSD threshold enter this pathway, coupling threat appraisal directly to emotional arousal. The BDEI pathway governs all state transitions while the LLM is confined to parameter estimation for the Belief-to-Desire transition, confining hallucinations to a single step and preventing error propagation. Experiments on Hurricane Sandy show PCP improves arousal timing accuracy by 10.68% over baselines, reduces peak count error to 7.07%.

翻译：在个体恐慌情绪显现前预测其唤醒时机，对于主动式应急干预至关重要。现有方法虽融入认知要素，但均未明确建模情绪唤醒过程，因此不适用于情绪唤醒时序预测。我们主张必须基于评价情绪理论进行预测，因其能显式刻画该过程，但需解决三个问题：（1）评价理论认为情绪源于多威胁维度的同步评估，现有工作未将这些输入融合为风险感知；（2）现有认知模型缺乏情绪节点，导致威胁评估与情绪唤醒分离，迫使情绪只能从行为中间接推断；（3）当前方法利用大语言模型的泛化认知推理能力将其作为主要决策者，却忽视了其输出的脆弱性与易幻觉特性。针对上述问题，我们提出PanicCognitivePath（PCP）框架，三者皆可解决：基于心理距离理论的心理安全距离（PSD）模型将四域信号映射为统一风险度量，作为后续认知推理的触发条件；在BDI模型中引入基于评价情绪理论的显式情绪节点，形成信念-欲望-情绪-意图（BDEI）认知路径；当风险度量超过PSD阈值时，智能体进入该路径，将威胁评估与情绪唤醒直接耦合。BDEI路径控制所有状态转移，而大语言模型仅负责信念到欲望转移的参数估计，将幻觉限制在单一步骤并防止错误传播。在飓风桑迪数据集上的实验表明，PCP将唤醒时序准确率提升10.68%，峰值计数误差降低至7.07%。