用最小认知网格框架量化评估类比与隐喻模型的认知合理性
Structural Ranking of the Cognitive Plausibility of Computational Models of Analogy and Metaphors with the Minimal Cognitive Grid
- 基于最小认知网格框架，从功能结构比、泛化性、性能匹配三维度定量评估
- 揭示SME、CogSketch、METCL和大语言模型在认知合理性上的系统差异
- 为认知模型可比性提供数学标准，适合认知科学与AI交叉研究者参考
本文采用最小认知网格（MCG）框架，对主流类比与隐喻计算模型——结构映射引擎（SME）、CogSketch、METCL及大语言模型（LLMs）进行系统评估。通过形式化与量化MCG框架的三个核心维度——功能/结构比、泛化性与性能匹配，分析各系统与认知理论的契合程度，建立一致且可推广的数学评估标准，实现对模型认知合理性水平的可比性比较。
原文摘要 · Abstract (English)
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.
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