Large Language Model (LLM) surprisal is increasingly used as a proxy for human sentence processing, yet whether model expectations reflect the cognitive factors governing real-time human behavior remains uncertain. We examine Korean subject–verb honorific agreement in subject- and object-control constructions, where human eye-tracking data reveal a prominence-driven asymmetry. Evaluating three Korean-capable models, we find that surprisal was sensitive to honorific-feature mismatches, yielding attraction-like effects, but was not modulated by distractor prominence. Although surprisal selectively improved reading-time predictions, this reflected item-level predictive overlap rather than prominence sensitivity. GPT models thus capture surface regularities but do not reproduce the construction-sensitive, prominence-driven asymmetry of human dependency resolution, suggesting that LLMs diverge from humans precisely where processing depends on working-memory constraints rather than distributional expectation.