Predictive Emotional Selfhood in Artificial Minds (PESAM): A Unified and Synergistic Variational Framework

A grand challenge in artificial intelligence and neuroscience is to formally integrate emotion and selfhood into a unified, predictive model of the mind. Without such a framework, creating truly adaptive agents or understanding the computational basis of psychiatric disorders remains elusive. This paper introduces \textbf{Predictive Emotional Selfhood in Artificial Minds (PESAM)}, a variational framework demonstrating that an emotional self emerges from the synergistic interaction of three core mechanisms: (1) \textbf{Affective Precision Control (APC)}, the emotional modulation of sensory gain; (2) \textbf{Self-as-Hyperprior (SaH)}, a deep, stabilizing self-model; and (3) \textbf{Affective Homeostatic Objectives (AHO)}, intrinsic drives for internal stability. We argue that a true test of a unified framework lies in its ability to solve complex problems that are intractable for any single mechanism alone. To this end, we introduce a novel \textbf{Social Threat \& Body-Boundary Task}. Results from this unified task show that the complete PESAM agent achieves significantly higher performance than both lesioned variants and strong alternative models (e.g., reinforcement learning), providing quantitative evidence for a genuinely synergistic—rather than merely additive—account of emotional selfhood and a principled foundation for adaptive AI and computational psychiatry.