Researcher Proposes 'Hormonal Computing' to Regulate How AI Thinks Under Uncertainty

A researcher has proposed a conceptual framework called Hormonal Computing, which would give AI systems a dynamic internal regulatory state modeled loosely on the biological endocrine system. Rather than treating uncertainty as a static confidence score, the approach introduces a persistent signal called 'epistemic cortisol' that rises when neural and symbolic reasoning conflict, evidence contradicts itself, or reasoning chains break down. This internal state would then alter system behavior — triggering more verification, retrieval, or abstention instead of proceeding with a potentially flawed answer. The framework also identifies a failure mode at the opposite extreme: excessively high epistemic stress could cause the system to over-verify, refuse to act, or fall into what the author terms epistemic paralysis. The proposal frames cognitive regulation in AI as a control problem requiring dynamic equilibrium between exploration, verification, action, and abstention.