Proactive AI writing assistants need to predict when users want drafting help, yet we lack empirical understanding of what drives preferences. Through a factorial vignette study with 50 participants making 750 pairwise comparisons, we find compositional effort dominates decisions ($ρ= 0.597$) while urgency shows no predictive power ($ρ\approx 0$). More critically, users exhibit a striking perception-behavior gap: they rank urgency first in self-reports despite it being the weakest behavioral driver, representing a complete preference inversion. This misalignment has measurable consequences. Systems designed from users' stated preferences achieve only 57.7\% accuracy, underperforming even naive baselines, while systems using behavioral patterns reach significantly higher 61.3\% ($p < 0.05$). These findings demonstrate that relying on user introspection for system design actively misleads optimization, with direct implications for proactive natural language generation (NLG) systems.

翻译：主动式AI写作助手需要预测用户何时需要草稿协助，然而我们缺乏对偏好驱动因素的实证理解。通过一项包含50名参与者进行750组配对比较的析因情境研究，我们发现构成性努力主导决策（$ρ= 0.597$），而紧急性则无预测能力（$ρ\approx 0$）。更关键的是，用户表现出显著的感知-行为差距：他们在自我报告中将紧急性列为第一要素，但其实际行为驱动力最弱，呈现出完全的偏好倒置。这种错位会产生可量化的后果。基于用户陈述偏好设计的系统仅达到57.7%的准确率，甚至低于朴素基线系统，而采用行为模式的系统则显著达到更高的61.3%（$p < 0.05$）。这些发现表明，依赖用户内省进行系统设计会误导优化方向，对主动式自然语言生成（NLG）系统具有直接启示。