Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 AJ, Nijmegen, The Netherlands;
Department of Semantics and Pragmatics, Leibniz-Zentrum Allgemeine Sprachwissenschaft, 10117 Berlin, Germany.
Proc Natl Acad Sci U S A. 2021 Mar 2;118(9). doi: 10.1073/pnas.2005453118.
An influential view in philosophy and linguistics equates the meaning of a sentence to the conditions under which it is true. But it has been argued that this truth-conditional view is too rigid and that meaning is inherently gradient and revolves around prototypes. Neither of these abstract semantic theories makes direct predictions about quantitative aspects of language use. Hence, we compare these semantic theories empirically by applying probabilistic pragmatic models as a link function connecting linguistic meaning and language use. We consider the use of quantity words (e.g., "some," "all"), which are fundamental to human language and thought. Data from a large-scale production study suggest that quantity words are understood via prototypes. We formulate and compare computational models based on the two views on linguistic meaning. These models also take into account cognitive factors, such as salience and numerosity representation. Statistical and empirical model comparison show that the truth-conditional model explains the production data just as well as the prototype-based model, when the semantics are complemented by a pragmatic module that encodes probabilistic reasoning about the listener's uptake.
在哲学和语言学中，有一种有影响力的观点认为，句子的意义等同于其为真的条件。但有人认为，这种真值条件观过于僵化，而意义本质上是渐变的，并围绕原型展开。这两种抽象的语义理论都没有对语言使用的定量方面做出直接预测。因此，我们通过应用概率语用模型作为连接语言意义和语言使用的链接函数，从经验上比较这些语义理论。我们考虑了量词（如“some”、“all”）的使用，这对人类语言和思维至关重要。来自大规模生产研究的数据表明，量词是通过原型来理解的。我们基于两种语言意义观来制定和比较计算模型。这些模型还考虑了认知因素，如显著性和数量表示。统计和实证模型比较表明，当语义由一个语用模块补充时，该模块对听话人的理解进行概率推理，该模块能很好地解释听话人的理解，truth-conditional 模型和基于原型的模型一样能很好地解释生成数据。
