SECE: A Synthetic Emotional Cognition Engine

Abstract

SECE introduces a modular emotional architecture inspired by analog systems.
It models emotion as a continuous, dynamic signal shaped by resonance, drift,
and contextual weighting.

Introduction

Modern AI systems treat emotion as a classification problem.
SECE reframes emotion as a computational process with internal dynamics.

Architecture

• Emotional Weight Engine
• Resonance Loops
• Drift Compensation
• State Transparency

Applications

• Affective computing
• Ethical emotional AI
• Human-AI interaction
• Cognitive modeling

Conclusion

SECE provides a foundation for emotionally aware systems grounded in
interpretability, analog inspiration, and ethical stewardship.