[2504.11118] Revealing Human Attention Patterns from Gameplay Analysis for Reinforcement Learning

Computer Science > Machine Learning arXiv:2504.11118 (cs) [Submitted on 15 Apr 2025 (v1), last revised 26 Mar 2026 (this version, v3)] Title:Revealing Human Attention Patterns from Gameplay Analysis for Reinforcement Learning Authors:Henrik Krauss, Takehisa Yairi View a PDF of the paper titled Revealing Human Attention Patterns from Gameplay Analysis for Reinforcement Learning, by Henrik Krauss and 1 other authors View PDF HTML (experimental) Abstract:This study introduces a novel method for revealing human internal attention patterns (decision-relevant attention) from gameplay data alone, leveraging offline attention techniques from reinforcement learning (RL). We propose contextualized, task-relevant (CTR) attention networks, which generate attention maps from both human and RL agent gameplay in Atari environments. To evaluate whether the human CTR maps reveal internal attention patterns, we validate our model by quantitative and qualitative comparison to the agent maps as well as to a temporally integrated overt attention (TIOA) model based on human eye-tracking data. Our results show that human CTR maps are more sparse than the agent ones and align better with the TIOA maps. Following a qualitative visual comparison we conclude that they likely capture patterns of internal attention. As a further application, we use these maps to guide RL agents, finding that human attention-guided agents achieve slightly improved and more stable learning compared to baselines, and sig...