用可解释AI分析专家如何调粒子加速器，发现其问题拆解策略随经验优化。
Reversing the Lens: Using Explainable AI to Understand Human Expertise
- 用图结构建模操作员子任务，结合社区检测与层次聚类分析行为模式。
- 发现专家将复杂任务分解为更小模块，且策略随经验迭代优化。
- 为研究人类认知提供可量化的计算工具，适合人机协同研究者参考。
人类与机器学习模型均通过经验学习，尤其在安全与可靠性关键领域。心理学致力于理解人类认知，而可解释人工智能（XAI）则发展出解读机器学习模型的方法。本研究通过将XAI的计算工具应用于人类学习分析，建立粒子加速器调参这一复杂现实任务中操作员子任务的图模型。基于档案数据，运用社区检测与层次聚类技术，揭示操作员如何将问题分解为简单组件，以及这些解决策略如何随经验演化。研究发现人类在缺乏全局最优解时仍能发展出高效策略，证明了基于XAI的方法在定量研究人类认知中的有效性。
原文摘要 · Abstract (English)
Both humans and machine learning models learn from experience, particularly in safety- and reliability-critical domains. While psychology seeks to understand human cognition, the field of Explainable AI (XAI) develops methods to interpret machine learning models. This study bridges these domains by applying computational tools from XAI to analyze human learning. We modeled human behavior during a complex real-world task -- tuning a particle accelerator -- by constructing graphs of operator subtasks. Applying techniques such as community detection and hierarchical clustering to archival operator data, we reveal how operators decompose the problem into simpler components and how these problem-solving structures evolve with expertise. Our findings illuminate how humans develop efficient strategies in the absence of globally optimal solutions, and demonstrate the utility of XAI-based methods for quantitatively studying human cognition.
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