A Mathematical Model of Motivated Emotional Mind - Cognitive Embodied System

The paper introduces a mathematical model of the Motivated Emotional Mind cognitive architecture for embodied intelligent systems, describing how the system learns to maintain homeostasis via motivated learning—a reinforcement‑learning variant driven by internal motivations. It formalizes a re‑entrant loop that integrates feedforward processing, lateral interactions, and feedback pathways, and details how exteroceptive and interoceptive signals, bodily context, and memory traces form associative structures called semblions that compete for processing and reconstruction. The model incorporates need thresholds, goal dynamics, bodily state, resource constraints, and action uncertainty, and posits global affect as a central control signal modulating learning rate, representational valence, and exploration‑exploitation balance.

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• reinforcement-learning
• robotics