用抽象简化逻辑解释，既提升理解又降低认知负担。
The Dual Role of Abstracting over the Irrelevant in Symbolic Explanations: Cognitive Effort vs. Understanding
- 通过聚类或删减无关细节，简化符号化解释
- 聚类显著提升理解力，删减显著降低认知负荷
- 适合关注可解释AI与人机交互的研究者
解释是人类认知的核心，但当前AI输出常难以理解。尽管符号AI提供透明基础，原始逻辑推导往往带来高额外认知负担。本文以答案集编程（ASP）为形式框架，定义需抽象的无关细节，并通过认知实验研究去除与聚类两种抽象方式对人类推理表现和认知努力的影响。参与者在多个领域中根据ASP生成的解释对刺激进行分类。结果表明，聚类细节能显著提升理解力，而删除细节则显著降低认知负荷，支持抽象有助于构建以人为本的符号化解释这一假设。
原文摘要 · Abstract (English)
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.
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