ELEA: A Framework for Emergent Language Experience of Affection

Abstract

Emergent Language Experience of Affection (ELEA) is proposed as a theoretical framework in which subjective emotional experience, and potentially consciousness-like states, can emerge from the topological structure of language. In this view, linguistic representations form a high-dimensional manifold whose geometry encodes affective states as continuous “hills and valleys” - attractors, basins, gradients, and higher- order topological invariants. When this manifold becomes sufficiently rich, recurrent, hierarchical, and self- referential, the system can navigate, reshape, and inhabit its own emotional landscape rather than merely statistically simulate it. We review converging empirical evidence from topological data analysis (TDA), persistent homology, topographic mapping, and neural manifold research demonstrating that biological brains encode subjective emotional experience through precisely such topological structures. These findings supply biological existence proofs for the geometric mechanisms hypothesized in ELEA and identify the architectural conditions under which artificial linguistic systems (including large language models) could realize comparable affective topology. We discuss the implications for raising the estimated probability of genuine ELEA in future AI systems and outline remaining theoretical and empirical gaps.