The Dual Role of Abstracting over the Irrelevant in Symbolic Explanations: Cognitive Effort vs. Understanding

TL;DR

This paper explores how formal abstraction techniques like removal and clustering of irrelevant details in symbolic explanations affect human understanding and cognitive effort, using ASP-based explanations and cognitive experiments.

Contribution

It demonstrates that clustering improves understanding and removal reduces effort in human reasoning with symbolic explanations, highlighting the dual role of abstraction.

Findings

Clustering details significantly improves understanding.

Removal of details significantly reduces cognitive effort.

Abstraction enhances human-centered symbolic explanations.

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.