Synthetic-Emotional Calibration (SEC) introduces a governed control interface for stability- and viability-first cognitive systems, particularly in instrumented, symbolic, or hybrid architectures. Unlike affective computing (which simulates human-like emotions for interaction), reward-based optimization (e.g., reinforcement learning), or prompt steering, SEC functions purely as a constraint mechanism. It maps discrete regulatory modes—identified by stable, machine-invariant Unicode emoji code points (e.g., U+1F6D1 for closure)—to bounded, calibrated parameter vectors. These vectors precisely modulate internal dynamics, including risk thresholds, feedback gains, pathway coupling, and hazard filtering, via single-execution-cycle injection without persistent learning, adaptation, or external optimization. The central contribution of this work is the formal specification of SEC as a control formalism, not an affective or psychological model. By decoupling governance from implementation details, SEC externalizes regulatory modulation into an inspectable, static calibration surface (e.g., version-controlled CSV tables). This enables reproducibility through version control, auditability through human inspection, and safety through fail-loud behavior (requiring explicit mode specifications). Calibration parameters are stored in externally versioned artifacts, providing a human-legible governance surface suitable for safety-critical and regulated AI contexts. This paper focuses on the mechanistic definition, vector representation (in a continuous parameter space bounded by a Viability Boundary), emoji-indexed calibration table, and control semantics of SEC. It makes no claims of universality, psychological realism, optimality, or replacement for learned adaptation, and is specific to SpiralBrain-class instrumented cognitive architectures operating under the Regulatory Intelligence (RI) paradigm. Empirical validations—covering stability, scarcity response, phase-lock behavior, attractor integrity collapse, and posture separation—are reported in companion works referenced herein. The work contributes a reusable abstraction for AI governance, positioning regulation as a first-class system property rather than an emergent side effect of optimization. For architectural documentation, reproducibility artifacts, and illustrative SEC calibration excerpts, see the public repository: https://github.com/jhcragin/SpiralBrain-v3.0-public. Keywords: Synthetic-Emotional Calibration, SEC, regulatory intelligence, viability theory, geometric homeostasis, AI governance, safety-critical AI, non-learning constraints, inspectable control, cybernetics
John Cragin (Sun,) studied this question.