Hopelessness and Controllability: Computational Model

Active inference model (Karvelis & Diaconescu 2022) implementing computational distinction between hopelessness (negative instrumental state-action beliefs, ACC-mediated, LC-NE modulated) and controllability (vmPFC-DRN-Amy network, 5-HT modulated).

Key contribution

These are distinct but coupled through projections from LC to DRN that regulate 5-HT release. Provides formal computational framework for how perceived controllability modulates stress response, and how NE-modulated belief updates interact with 5-HT-modulated controllability inference.

Relevance to architecture

This is the kind of computational model that the plant model for the architecture would need to build on.

Source verification

Karvelis & Diaconescu, "A Computational Model of Hopelessness and Active-Escape Bias in Suicidality" (Computational Psychiatry) -- verified.