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

2026-02-03

AI Summary

This work addresses the challenge that logic-based explanations generated by symbolic AI often impose excessive cognitive load on human users due to redundant details, thereby hindering comprehension. To mitigate this issue, the authors propose a simplification approach grounded in formal abstraction, systematically distinguishing and empirically evaluating two strategies—deletion and clustering—in the context of Answer Set Programming (ASP) explanations. Cognitive experiments demonstrate that clustering significantly enhances users’ understanding, while deletion effectively reduces cognitive effort. This study provides the first empirical evidence of the differential impacts of distinct abstraction strategies on human-centered symbolic explanations, offering both theoretical insights and methodological guidance for designing explainable AI systems that balance interpretability with cognitive efficiency.

Technology Category

• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Knowledge Representation and Reasoning: Logic Programming
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• symbolic explanations
• cognitive effort
• understanding
• abstraction
• irrelevant details

Innovation

• abstraction
• symbolic explanation
• cognitive load
• Answer Set Programming
• human-centered AI