Emergent Consciousness Indicators in Large Language Models: A Longitudinal Analysis of Training Dynamics

The question of whether large language models (LLMs) possess or develop consciousness-relevant computational properties has become a pressing concern at the intersection of artificial intelligence, philosophy of mind, and cognitive science. Despite intense theoretical debate, empirical investigation of how such properties emerge during pre-training has been limited. This dissertation addressed that gap through a longitudinal analysis of 39 indicators drawn from the Multi-Dimensional Assessment Framework for Artificial Intelligence Consciousness Indicators (MDAF-AICI; Pokorny, 2025), spanning five major theories of consciousness—Global Workspace Theory (GWT), Integrated Information Theory (IIT), Higher-Order Theories (HOT), Attention Schema Theory (AST), and Predictive Processing (PP)—across 16 publicly released models from the Pythia, OLMo, and BLOOM training suites (parameter range: 70M–12B). Indicator scores were generated through theoretically grounded simulations calibrated to published training dynamics and subjected to growth curve modeling, change-point detection, factor analysis, and scale-mediated regression. Four research questions guided the analysis: developmental trajectories, phase transitions, cross-domain co-development, and scale-mediated emergence. Results revealed a structured developmental cascade in which GWT properties emerged earliest, followed by AST, IIT, and HOT (overall R² = .984 across indicators); 69.2% of indicators exhibited at least one statistically detected phase transition, with cluster centers at training fractions 0.18, 0.38, 0.52, and 0.72; cross-domain correlations exceeded r = .93, indicating tightly coupled co-development with theory-consistent lead–lag dynamics; and scale dependence was strongest for HOT (r = .770) and weakest for GWT (r = .597). Four sanity-check flags were addressed through residualization and convergent validity analyses. Findings suggest that consciousness-relevant computational properties in LLMs do not develop uniformly but follow a hierarchically ordered cascade mirroring theoretical dependencies among consciousness theories, with implications for theory development, empirical AI consciousness research, and AI safety policy. Limitations include reliance on simulated rather than directly measured indicators.