When Cognitive Graphs Meet LLMs: BDEI Cognitive Pathways for Panic Emotional Arousal Prediction
arXiv:2606.15121v1 Announce Type: new Abstract: 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 dime
The paper leverages recent advancements in LLMs and cognitive graphs to address a critical gap in predicting emotional arousal, which is increasingly relevant for personalized AI interventions.
Predicting panic emotional arousal before manifestation could enable proactive mental health interventions and enhance the resilience of individuals facing high-stress situations.
This research introduces a novel, theory-grounded approach to emotional arousal prediction, moving beyond existing methods that do not explicitly model the process.
- · Mental health tech startups
- · Healthcare AI platforms
- · Emergency services
- · Psychology researchers
- · Traditional emotion detection systems
- · One-size-fits-all psychological interventions
Improved early warning systems for mental health crises.
Personalized AI companions offering real-time emotional support and intervention strategies.
Enhanced overall societal resilience through ubiquitous and proactive emotional well-being management systems.
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