发表机构
University of Washington; University of Trento（华盛顿大学; 特伦托大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对语义范畴表征与自然语言意义偏好问题，提出将机器学习模型作为“认知温度计”，构建弥合符号逻辑与联结主义AI的统一复杂性研究方法，发现逻辑与机器学习在语义复杂性研究中常结果一致，分歧时学习更具解释力。
AI 中文摘要
人类心智如何表征语义范畴？为何自然语言更偏好某些意义而非其他？此前的解释依赖逻辑可定义性与复杂性，但这些高度敏感于逻辑语言的选择，致使部分设计选择缺乏动机。本文提出，机器学习提供了一种更具不可知性的语义复杂性测量方法。我们综述新兴证据：逻辑与机器学习在相对复杂性及其对语义类型学的影响上常得出一致结果；当二者出现分歧时，学习似乎比逻辑复杂性更具解释力。我们认为，将机器学习模型视为“认知温度计”，可构建一种统一的复杂性研究方法，弥合符号逻辑与联结主义AI之间的鸿沟。
英文摘要
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.
