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

TL;DR

This paper uses the Minimal Cognitive Grid framework to systematically evaluate and compare the cognitive plausibility of various computational models of analogy and metaphor, including LLMs.

Contribution

It introduces a formal, quantitative method to assess cognitive plausibility of models using the MCG framework's three dimensions.

Findings

SME, CogSketch, METCL, and LLMs vary in their alignment with cognitive theories.

The MCG framework provides a consistent, mathematical basis for comparison.

Analysis reveals strengths and limitations of current models in cognitive plausibility.

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.