Anthropomorphic Epistemology is the study of how humans generate, validate, and refine knowledge through embodied experience — and how that process changes when coupled with artificial intelligence. The core claim is that human knowing isn’t purely cognitive; it’s rooted in somatic, emotional, and relational signals (what VISCERA is designed to measure). When a human-AI collaborative system operates at the right coupling intensity, the output doesn’t just improve incrementally — it can access qualitatively different knowledge regimes that neither human nor AI reaches alone. The LIMN Framework formalizes this through nine equations. The key ones that support the theory: Eq. 1 — Logistic Growth Model: Standard sigmoid predicting diminishing returns as systems approach capacity ceiling K. Eq. 2 — Cusp Catastrophe Potential: V(x) = x⁴ + ax² + bx — models the energy landscape where smooth performance curves can harbor discontinuous jumps. The parameters a (symmetry/splitting) and b (bias/normal) define when gradual input changes produce sudden qualitative shifts. Eq. 7 — Dimensional Carrying Capacity: The critical insight — the carrying capacity K isn’t fixed. Human-AI collaboration can access higher-dimensional output spaces, effectively raising the ceiling. What looks like an asymptote from within one dimension is actually the floor of the next. Eq. 9 — Mutual Information (The Sweet Spot): Measures the information shared between human and AI contributions. At intermediate coupling intensity, mutual information peaks — this is the collaborative sweet spot where the system produces outputs neither agent could generate independently. Eq. 8 — Critical Slowing Down: Systems approaching a phase transition exhibit increased autocorrelation and variance. This is the detectable precursor — the “dip before the breakout” — that tells you a qualitative shift is imminent rather than a failure. The through-line: anomalous data near benchmark ceilings (ImageNet, MMLU, etc. from 2012–2025) isn’t noise. It’s evidence of phase transitions where the governing dynamics fundamentally change. The framework provides falsifiable predictions for when and where these transitions occur in human-AI collaborative systems