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.

翻译：解释是人类认知的核心，然而人工智能系统常产生难以理解的输出。尽管符号人工智能为可解释性提供了透明基础，原始逻辑追踪往往带来较高的外在认知负荷。本研究探讨形式化抽象（特别是细节移除与聚类）如何影响人类推理表现与认知努力。利用答案集编程作为形式化框架，我们定义了需被抽象化的无关细节概念以获得简化解释。通过认知实验（参与者使用源自答案集程序的解释对跨领域刺激进行分类），结果表明：细节聚类显著提升参与者的理解水平，而细节移除则显著降低认知努力，这支持了抽象化能增强以人为中心的符号解释的假设。