Abstract To study linguistically coded concepts, researchers often resort to the Property Listing Task (PLT). In a PLT, participants are asked to list properties that describe a concept (e.g., for DOG, subjects may list “is a pet”, “has four legs”, etc.), which are then coded into property types (i.e., superficially dissimilar properties such as “has four legs” and “is a quadruped” may be coded as “four legs”). When the PLT is done for many concepts, researchers obtain Conceptual Properties Norms (CPNs), which are used to study semantic content and as a source of control variables. Though the PLT and CPNs are widely used across psychology, there is a lack of a formal model of the PLT, which would provide better analysis tools. Particularly, nobody has attempted analyzing the PLT’s listing process. Thus, in the current work we develop a mathematical description of the PLT. Our analyses indicate that several regularities should be found in the observable data obtained from a PLT. Using data from three different CPNs (from 3 countries and 2 different languages), we show that these regularities do in fact exist and generalize well across different CPNs. Overall, our results suggest that the description of the regularities found in PLT data may be fruitfully used in the study of concepts.

中文翻译：

---

房产上市任务数据的数学规律

摘要 为了研究语言编码的概念，研究人员经常求助于属性列表任务 (PLT)。在 PLT 中，参与者被要求列出描述概念的属性（例如，对于 DOG，受试者可能会列出“是一只宠物”、“有四只腿”等），然后将其编码为属性类型（即，表面上不同的属性，例如“有四条腿”和“是四足动物”可以编码为“四条腿”）。当对许多概念进行 PLT 时，研究人员会获得概念属性范数 (CPN)，用于研究语义内容并作为控制变量的来源。虽然 PLT 和 CPNs 在心理学中被广泛使用，但缺乏正式的 PLT 模型，可以提供更好的分析工具。特别是，没有人尝试分析 PLT 的上市过程。因此，在目前的工作中，我们开发了 PLT 的数学描述。我们的分析表明，应该在从 PLT 获得的可观察数据中找到几个规律。使用来自三个不同 CPN（来自 3 个国家和 2 种不同语言）的数据，我们表明这些规律确实存在，并且在不同的 CPN 中可以很好地概括。总体而言，我们的结果表明，对 PLT 数据中发现的规律的描述可以有效地用于概念研究。