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：在个体恐慌情绪显化前预测其唤起时机，对于主动式应急干预至关重要。现有方法虽融入了认知要素，但均未对情绪唤起过程进行显式建模，因而难以胜任情绪唤起时机预测任务。我们认为，基于评价情绪理论进行预测十分必要——该理论对此过程提供了显式建模，但必须解决三个问题：（1）评价理论指出，情绪源于对多个威胁维度的同步评估，但现有工作未能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，导致威胁评价与情绪唤起脱钩，迫使情绪必须通过行为间接推断；（3）当前方法因依赖认知推理的泛化能力，采用大语言模型作为主要决策者，却忽视了其输出易产生幻觉与脆弱的特性。为解决上述问题，我们提出恐慌认知路径框架——该框架同时攻克三大挑战：基于心理距离理论构建的心理安全距离模型，将四域信号映射为统一风险度量，作为后续认知推理的入口条件；在BDI架构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-意图认知路径。当智能体的风险度量超过PSD阈值时，将进入该路径，实现威胁评价与情绪唤起的直接耦合。BDEI路径控制所有状态转换，而LLM仅用于评估信念到欲望转换的参数估计，从而将幻觉限制在单一步骤内，防止错误传播。在飓风桑迪数据集上的实验表明，PCP将情绪唤起时机预测准确率提升10.68%，峰值计数误差降至7.07%。