Being triggered: Virtualized explanations for a multi-adaptive network model of post-traumatic stress disorder

Post-Traumatic Stress Disorder (PTSD) flashbacks can be triggered by neutral cues that were present during trauma. This paper presents a second-order adaptive temporal–causal network that models cue-driven replay, metaplastic control of learning speed, and therapy-induced gating of both sequence entry and emotion regulation. The adopted causal and agentic modeling approach used in the paper, serves as a conceptual tool for understanding the structure and dynamics of complex adaptive processes. A four-epoch scenario (trauma, pre-therapy trigger, therapy, post-therapy trigger) is instantiated with an air-raid siren. Simulations show that therapy reduces post-therapy affect and action without erasing memory: replay still initiates, but control rises sooner, peaks lower, and decays faster. The model is compiled into role matrices for direct execution, and an AI-generated short animation is used as a communicative front-end to the dynamics. As a validation check, therapy is split into entry-gating and regulation routes: only regulation produces durable post-therapy gains under current parameters. Together, the model and the animation translate a technical mechanism into an interpretable narrative and yield concrete predictions about timing, generalization, and how to strengthen prevention at the entry point.