Slide Deck: Unresolved Questions in the Mechanisms of Human Attachment and Emotional Suffering

Subtitle: Gaps that Load Minimization Theory (LMT) & SUQE Aim to Address 【Abstract】 Predictive processing frameworks provide a broad account of emotion as error minimization, yet detailed mechanisms underlying excessive attachment, qualitative emotional suffering, and successful detachment remain underexplored. This document presents four key unresolved questions in the science of human attachment and emotional "stuckness": (1) the quantitative tipping point into pathological attachment, (2) the origin of qualitative "hurt" in anticipatory loss, (3) the computational pathway of effective letting go, and (4) criteria for emergent attachment in artificial agents. Load Minimization Theory (LMT) and the Shiho Unified Qualia Equation (SUQE) are proposed as formal tools that may help bridge these gaps by reframing attachment as a shadow of relational load minimization, and detachment as non-clinging, high-coherence entanglement. The aim is to invite collaboration from predictive processing, affective neuroscience, and AI alignment communities. Keywords: Load Minimization Theory, LMT, Shiho Unified Qualia Equation, SUQE, attachment, pathological obsession, emotional qualia, detachment, letting go, predictive processing, relational load, an-soku, non-clinging entanglement, AI attachment, human-AI interaction, qualia emergence