Institute für Linguistik, Heinrich Heine Universität Düsseldorf.
Department of Linguistics, University of Washington.
Cogn Sci. 2023 Jan;47(1):e13234. doi: 10.1111/cogs.13234.
According to logical theories of meaning, a meaning of an expression can be formalized and encoded in truth conditions. Vagueness of the language and individual differences between people are a challenge to incorporate into the meaning representations. In this paper, we propose a new approach to study truth-conditional representations of vague concepts. For a case study, we selected two natural language quantifiers most and more than half. We conducted two online experiments, each with 90 native English speakers. In the first experiment, we tested between-subjects variability in meaning representations. In the second experiment, we tested the stability of meaning representations over time by testing the same group of participants in two experimental sessions. In both experiments, participants performed the verification task. They verified a sentence with a quantifier (e.g., "Most of the gleerbs are feezda.") based on the numerical information provided in the second sentence, (e.g., "60% of the gleerbs are feezda"). To investigate between-subject and within-subject differences in meaning representations, we proposed an extended version of the Diffusion Decision Model with two parameters capturing truth conditions and vagueness. We fit the model to responses and reaction times data. In the first experiment, we found substantial between-subject differences in representations of most as reflected by the variability in the truth conditions. Moreover, we found that the verification of most is proportion-dependent as reflected in the reaction time effect and model parameter. In the second experiment, we showed that quantifier representations are stable over time as reflected in stable model parameters across two experimental sessions. These findings challenge semantic theories that assume the truth-conditional equivalence of most and more than half and contribute to the representational theory of vague concepts. The current study presents a promising approach to study semantic representations, which can have a wide application in experimental linguistics.
根据意义的逻辑理论，一个表达式的意义可以形式化为真值条件，并进行编码。语言的模糊性和人与人之间的个体差异是将其纳入意义表示的挑战。在本文中，我们提出了一种新的方法来研究模糊概念的真值条件表示。作为案例研究，我们选择了两个自然语言量词 most（大多数）和 more than half（超过一半）。我们进行了两项在线实验，每项实验都有 90 名以英语为母语的参与者。在第一个实验中，我们测试了意义表示的个体间变异性。在第二个实验中，我们通过在两个实验会话中测试相同的参与者组来测试意义表示的随时间的稳定性。在这两个实验中，参与者都执行了验证任务。他们根据第二句话中提供的数值信息（例如，“gleerbs 的 60%是 feezda”）来验证带有量词的句子（例如，“Most of the gleerbs are feezda.”）。为了研究意义表示的个体间和个体内差异，我们提出了一个扩展的扩散决策模型，该模型有两个参数可以捕捉真值条件和模糊性。我们将模型拟合到响应和反应时间数据上。在第一个实验中，我们发现 most 的表示在个体间存在很大差异，这反映在真值条件的可变性上。此外，我们发现 most 的验证是比例依赖的，这反映在反应时间效应和模型参数中。在第二个实验中，我们表明，在两个实验会话中，跨时间的模型参数稳定，这表明量词的表示是稳定的。这些发现挑战了语义理论，即假设 most 和 more than half 的真值条件等价，并为模糊概念的表示理论做出了贡献。本研究提出了一种有前途的方法来研究语义表示，它可以在实验语言学中得到广泛应用。
