概念相似性作为在几何空间中的特征集上的聚合
Karthikeya Kaushik1, Bill D Thompson1
1Department of Psychology, University of California, 2121 Berkeley Way West, Berkeley, CA 94704, USA.
Cognition
|October 4, 2025
概括
这项研究引入了概念相似性的新认知模型,集成了几何和基于集合的方法. 它成功地解释了人类判断的不对称性,并超越了现有的模型.
科学领域:
- 认知科学 认知科学
- 计算语言学 计算语言学
- 心理学 心理学 心理学
背景情况:
- 概念相似性判断是认知,学习和推理的基础.
- 经典模型与判断不对称性作斗争,而现代几何模型则缺乏对这些效应的解释能力.
- 现有的模型未能捕捉到微妙的人类判断,如不对称性和三角不平等侵犯.
研究的目的:
- 开发一个新的建模框架,将古典和当代的概念结构方法整合起来.
- 创建一个相似性函数,能够解释经典的判断效应,如不对称性.
- 为了验证模型的预测与人类的判断和已建立的数据集.
主要方法:
- 将概念表示为来自自然语言描述的高维特征嵌入集.
- 开发一种针对基于集的嵌入的新型相似性函数.
- 使用抽象概念 (国家) 和纳尔逊自由词汇关联数据集的行为研究来评估模型预测.
- 正式化拟议模型与特维尔斯基对比模型之间的联系.
主要成果:
- 拟议的模型成功地考虑了经典的概念相似性判断效应,包括不对称性.
- 该模型在预测人类判断方面,与替代方法相比,表现优越.
- 在新框架和特维尔斯基的对比模型之间建立了正式的联系,将古典和现代理论连接起来.
结论:
- 开发的框架有效地整合了几何和基于集合的方法来建模概念结构.
- 这种方法提供了一个可扩展和数据驱动的替代方案,可以捕捉复杂的人类判断现象.
- 这些发现为理解认知科学中的概念相似性提供了一个普遍适用的框架.
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