Structural Ranking of the Cognitive Plausibility of Computational Models of Analogy and Metaphors with the Minimal Cognitive Grid

2026-05-02

AI Summary

This study addresses the lack of systematic, quantifiable methods for evaluating the cognitive plausibility of computational models of analogy and metaphor. Building upon the Minimal Cognitive Grid (MCG) framework, it presents the first formalized and quantified assessment system, structured around three dimensions: functional/structural ratio, generality, and performance alignment. The framework enables a structured comparison of prominent systems—including Structure Mapping Engine (SME), CogSketch, METCL, and large language models—yielding a principled ranking of their cognitive plausibility. By establishing a generalizable theoretical foundation and a standardized evaluation benchmark, this work advances the development and validation of cognitively grounded artificial intelligence models of analogical and metaphorical reasoning.

Abstract

In this paper, we employ the Minimal Cognitive Grid (MCG), a framework created to evaluate the cognitive plausibility of artificial systems, to offer a systematic assessment of leading computational models of analogy and metaphor, including the Structure-Mapping Engine (SME), CogSketch, METCL, and Large Language Models (LLMs). We present a formal and quantitative operationalization of the MCG framework and, through the analysis of its three main dimensions (Functional/Structural Ratio, Generality, and Performance Match), examine how well each system aligns with standard cognitive theories of the modeled phenomena, thus allowing for comparison of the models with respect to their cognitive plausibility, according to consistent and generalizable mathematical criteria.

Problem

• cognitive plausibility
• analogy
• metaphor
• computational models
• Minimal Cognitive Grid

Innovation

• Minimal Cognitive Grid
• cognitive plausibility
• computational models of analogy
• quantitative operationalization
• Structure-Mapping Engine