Abstract: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.
| Comments: | To appear in the Proceedings of the 48th Annual Meeting of the Cognitive Science Society (CogSci 2026) |
| Subjects: | Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2602.03467 [cs.AI] |
| (or arXiv:2602.03467v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2602.03467 arXiv-issued DOI via DataCite |
Submission history
From: Zeynep G. Saribatur [view email]
[v1]
Tue, 3 Feb 2026 12:42:41 UTC (154 KB)
[v2]
Tue, 28 Apr 2026 18:12:21 UTC (153 KB)