Experimental Investigations of Verification Procedures for Quantified Statements

量化陈述验证程序的实验研究

• 批准号: 1027686
• 负责人: Martin Hackl
• 金额: $ 7.87万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-01-20 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1027686&HistoricalAwards=false
• 关键词: Experimental; Investigations; Verification; Procedures; Quantified

Quantificational expressions such as EVERY, SOME, MOST, MORE THAN HALF, etc. play a central role in the scientific study of language because they introduce some of the most fundamental questions about the inventory of combinatorial rules and the expressive power found in natural languages. Consequently, much effort has been devoted to developing theories that explain the formal properties of quantifiers. In contrast, there is relatively little work on how quantifier meanings are processed in real time, specifically how their truth conditional import is used by non-linguistic systems of the mind, for instance in verification tasks. This project investigates real time processing of quantified expressions using a novel experimental paradigm called Self-Paced Counting, which allows researchers to gather fine-grained timing information about how subjects gather information incrementally in verification tasks that involve counting or magnitude estimation. This timing information can reveal subtle differences between verification strategies triggered by quantificational expressions. In particular, verification profiles triggered by semantically equivalent quantificational determiners that have different morphosyntactic structure can be shown to differ systematically. Such findings provide motivation for a theory of quantification that decomposes complex quantifiers into smaller building blocks than is standardly recognized.

定量表达，如EVERY， SOME， MOST， MORE THAN HALF等，在语言科学研究中发挥着核心作用，因为它们引入了一些关于组合规则清单和自然语言表达能力的最基本问题。因此，大量的努力已经投入到发展理论来解释量词的形式性质。相比之下，关于量词含义如何被实时处理的研究相对较少，特别是关于它们的真理条件导入如何被非语言系统所使用的研究，例如在验证任务中。该项目使用一种称为自节奏计数的新颖实验范式来研究量化表达式的实时处理，该实验范式允许研究人员收集细粒度的定时信息，以了解受试者如何在涉及计数或大小估计的验证任务中逐渐收集信息。这个时间信息可以揭示由定量表达式触发的验证策略之间的细微差异。特别是，由具有不同形态句法结构的语义等效定量限定词触发的验证概况可以被系统地显示为不同。这些发现为量化理论提供了动力，该理论将复杂的量词分解成比标准认识到的更小的构建块。