研究AI助人工作时认知负荷如何影响表现，发现外部负荷危害最大。
Precision Proactivity: Measuring Cognitive Load in Real-World AI-Assisted Work
- 基于任务分解与知识图谱，用对话文本估算认知负荷
- 外部负荷影响是内在负荷的三倍，显著降低任务质量
- 专家更抗压，新手虽受益更多却不愿多用AI
像ChatGPT和Claude这样的系统通过主动提供未请求的任务相关资讯，协助数十亿用户。基于认知负荷理论，我们研究了认知负荷在AI辅助知识工作中对表现的影响。招募34名金融专业人士完成复杂估值任务，使用GPT-4o进行交互。构建基于对话转录的框架，利用计算指标估计内在负荷与外部负荷，其锚点来自任务分解与知识图谱。在1,178个参与者-子任务观察中，AI生成内容的使用与任务质量呈正相关；而外部负荷的负面影响最大，约为内在负荷的三倍。中介分析显示存在补偿路径，部分抵消负荷带来的缺陷，但无法消除。外部负荷在发言者内部持续存在，并不对称地传递至模型响应。模型发起的任务切换是表现下降最强预测因子。经验水平调节这些动态：经验较少的专业人士承受更大代价，从AI内容中获益更多，但并非在高负荷下最积极增加使用的人群。
原文摘要 · Abstract (English)
Systems like ChatGPT and Claude assist billions through proactive dialogue-offering unsolicited, task-relevant information. Drawing on Cognitive Load Theory, we study how cognitive load shapes performance in AI-assisted knowledge work. We recruited 34 financial professionals to complete a complex valuation task using GPT-4o and developed a transcript-based framework estimating intrinsic and extraneous load from computational indicators anchored in a task decomposition and knowledge graph. Across 1,178 participant-subtask observations, AI-generated content usage is positively associated with quality, while extraneous load shows the largest negative association-roughly three times that of intrinsic load. Mediation reveals a compensatory pathway partially offsetting but not eliminating load-related deficits. Extraneous load persists within speakers and spills asymmetrically to model responses. Model-initiated task switching is the strongest predictor of decline. Expertise moderates these dynamics: less experienced professionals face larger penalties and derive greater marginal gains from AI-generated content, yet are not those who most increase uptake under load.
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