arXiv：cs.CL· Shane Steinert-Threlkeld, Jakub Szymanik·· 3 小时前
认知温度计：机器学习与逻辑复杂度
Cognitive Thermometers: Machine Learning and Logical Complexity
AI 导读
研究者提出用机器学习模型作为“认知温度计”来衡量语义复杂度，因为逻辑可定义性对逻辑语言的选择高度敏感。已有证据显示，逻辑与机器学习在相对复杂度及其对语义类型学的影响上常得出趋同结果；两者分歧时，学习比逻辑复杂度更具解释力。该框架试图统一符号逻辑与连接主义 AI 的复杂度研究路径。
正文
Abstract:How does the human mind represent semantic categories? Why do natural languages favor certain meanings over others? Prior explanations have relied on logical definability and complexity, but these are highly sensitive to the choice of logical language, rendering some design choices unmotivated. In this article, we propose that machine learning provides a somewhat more agnostic approach to measuring semantic complexity. We review emerging evidence that logic and machine learning often yield converging results on relative complexity and its resulting effects in semantic typology. Where they diverge, learning appears to be a better explanation than logical complexity. We argue that treating machine learning models as ``cognitive thermometers'' enables a unified approach to complexity that bridges symbolic logic and connectionist AI.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.10724 [cs.CL] |
| (or arXiv:2610.10724v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10724 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jakub Szymanik [view email]
[v1]
Wed, 7 Oct 2026 18:04:45 UTC (3,052 KB)
来源：arXiv：cs.CL · arxiv.org