The Novice, The Expert, and The Algorithm: A Comparative Analysis of Human Expertise Transfer and AI Performance in Audio-Only Gaming Environments

This study provides a symmetrical, cross-genre comparison of human expertise transfer and "blind" artificial intelligence performance in audio-only gaming environments. Although previous research has focused on human performance in audio games and the feasibility of reinforcement learning agents trained on auditory inputs separately, a direct comparison of these two forms of expertise is missing from the literature. We fill this gap with a robust experimental design involving 37 human players (5 females, 31 males, 1 non-binary individual, aged 18-44), grouped by gaming experience, and specialized blind Artificial Intelligence (AI) agents across two different genres: a fighting game (DareFightingICE) and a first-person shooter (SonicDoom). Our findings show a complex, task-dependent relationship between performance. In DareFightingICE, expert humans significantly outperformed the AI, demonstrating effective cognitive transfer. Meanwhile, AI’s performance matched the overall human average, establishing a key benchmark. Conversely, in SonicDoom, AI achieved superhuman speed in simple tasks but underperformed compared to expert humans in complex scenarios. This difference highlights that the AI's proficiency is specialized but fragile, whereas human expertise is more robust and adaptable. The results provide practical insights for designing audio-rich games and highlight the crucial need for AI models that extend beyond reactive policies.