语义不确定性的独特的人类动力学:上下文偏见加快了词汇歧义
Yang Lei1, Linyan Liu1, Jie Chen1
1State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
Behavioral sciences (Basel, Switzerland)
|September 27, 2025
概括
这项研究揭示了人类如何在阅读时解决词语含义的不确定性,显示上下文指导更快的理解. 人工智能模型模仿了这一广泛,但错过了在人类语义处理中看到的细微的上下文效应.
科学领域:
- 认知心理学 认知心理学
- 计算语言学 计算语言学
- 神经科学是一个神经科学.
背景情况:
- 词汇和语义的模糊性对句子的理解构成了挑战.
- 了解情境信息如何在实时中解决模糊性至关重要.
- 现有的语义处理模型在它们的动态和上下文敏感方面有所不同.
研究的目的:
- 在句子理解过程中调查词汇-语义模两可的解决方案的时间动态.
- 用一种新的基于的测量方法量化语义不确定性.
- 检查上下文偏差强度对明确化速度和轨迹的影响.
主要方法:
- 使用时间分辨率的眼睛追踪来捕获细粒度的时间数据.
- 一个基于的新型测量量量化了从小组级语义选择分布的语义不确定性.
- 用对语境语义偏差强度的参数操纵来检测模型灵敏度.
主要成果:
- 语义不确定性随着时间的推移逐渐减少,并且在模两可的词后急剧减少,表明渐进的整合和语法定.
- 较强的上下文偏差加速了不确定性减少,显示出近线性关系.
- 一个中国的BERT模型 (RoBERTa-wwm-ext) 复制了一般的不确定性减少趋势,但缺乏对上下文偏差强度的敏感性.
结论:
- 人类的语义处理是动态的,对上下文敏感,并向预期的含义持续趋同.
- 当前的语言模型虽然具有广泛的能力,但并不能完全捕捉人类观察到的语义处理的细微,上下文驱动的调制.
- 这些发现提供了对明确化动态的新的经验性描述,并突出了人类和人工语义系统之间的潜在架构差异.
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