Reinforcement learning (RL) enables simulations of HCI tasks, yet their validity is questionable when performance is driven by visual rendering artifacts distinct from interaction design. We provide the first systematic analysis of how luminance and contrast affect behavior by training 247 \RV{simulated users using RL} on pointing and tracking tasks. We vary the luminance of task-relevant objects, distractors, and background under no distractor, static distractor, and moving distractor conditions, and evaluate task performance and robustness to unseen luminances. Results show luminance becomes critical with static distractors, substantially degrading performance and robustness, whereas motion cues mitigate this issue. Furthermore, robustness depends on preserving relational ordering between luminances rather than matching absolute values. Extreme luminances, especially black, often yield high performance but poor robustness. Overall, seemingly minor luminance changes can strongly shape learned behavior, revealing critical insights into what RL-driven simulated users actually learn.

翻译：强化学习（RL）能够模拟人机交互（HCI）任务，但当性能由与交互设计无关的视觉渲染伪影驱动时，其有效性值得商榷。我们首次系统分析了亮度和对比度如何影响行为，通过在指向和追踪任务上训练247个\RV{基于强化学习的模拟用户}。我们在无干扰物、静态干扰物和移动干扰物条件下，改变任务相关对象、干扰物和背景的亮度，并评估任务性能以及对未见亮度的鲁棒性。结果表明，在存在静态干扰物时，亮度变得至关重要，会显著降低性能和鲁棒性，而运动线索则能缓解此问题。此外，鲁棒性依赖于保持亮度间相对顺序而非匹配绝对值。极端亮度，尤其是黑色，通常能产生高性能但鲁棒性较差。总体而言，看似微小的亮度变化可能强烈影响习得行为，这揭示了关于强化学习驱动的模拟用户实际学习内容的关键见解。