抽象在符号解释中的双重作用：认知努力与理解
The Dual Role of Abstracting over the Irrelevant in Symbolic Explanations: Cognitive Effort vs. Understanding
AI总结：
研究探讨形式抽象（去除与聚类）对人类推理表现和认知努力的影响，发现聚类提升理解，去除降低认知负担，支持抽象增强人中心符号解释的假设。
摘要：
解释对人类认知至关重要，但AI系统常产生难以理解的输出。尽管符号AI为可解释性提供了透明基础，但原始逻辑轨迹常导致高额外认知负荷。我们研究形式抽象（特别是去除和聚类）如何影响人类推理表现和认知努力。利用答案集编程（ASP）作为形式框架，我们定义了可抽象的无关细节以获得简化解释。我们的认知实验显示，聚类细节显著提升参与者理解，而去除细节显著降低认知努力，支持抽象增强人中心符号解释的假设。
英文摘要：
Explanations are central to human cognition, yet AI systems often produce outputs that are difficult to understand. While symbolic AI offers a transparent foundation for interpretability, raw logical traces often impose a high extraneous cognitive load. We investigate how formal abstractions, specifically removal and clustering, impact human reasoning performance and cognitive effort. Utilizing Answer Set Programming (ASP) as a formal framework, we define a notion of irrelevant details to be abstracted over to obtain simplified explanations. Our cognitive experiments, in which participants classified stimuli across domains with explanations derived from an answer set program, show that clustering details significantly improve participants' understanding, while removal of details significantly reduce cognitive effort, supporting the hypothesis that abstraction enhances human-centered symbolic explanations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
