Center for Cognition Research (CINCO), School of Psychology, Universidad Adolfo Ibáñez, Av. Presidente Errázuriz 3328, Las Condes, Santiago, Chile.
Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Las Condes, Santiago, Chile.
Behav Res Methods. 2018 Jun;50(3):972-988. doi: 10.3758/s13428-017-0920-8.
It is generally believed that concepts can be characterized by their properties (or features). When investigating concepts encoded in language, researchers often ask subjects to produce lists of properties that describe them (i.e., the Property Listing Task, PLT). These lists are accumulated to produce Conceptual Property Norms (CPNs). CPNs contain frequency distributions of properties for individual concepts. It is widely believed that these distributions represent the underlying semantic structure of those concepts. Here, instead of focusing on the underlying semantic structure, we aim at characterizing the PLT. An often disregarded aspect of the PLT is that individuals show intersubject variability (i.e., they produce only partially overlapping lists). In our study we use a mathematical analysis of this intersubject variability to guide our inquiry. To this end, we resort to a set of publicly available norms that contain information about the specific properties that were informed at the individual subject level. Our results suggest that when an individual is performing the PLT, he or she generates a list of properties that is a mixture of general and distinctive properties, such that there is a non-linear tendency to produce more general than distinctive properties. Furthermore, the low generality properties are precisely those that tend not to be repeated across lists, accounting in this manner for part of the intersubject variability. In consequence, any manipulation that may affect the mixture of general and distinctive properties in lists is bound to change intersubject variability. We discuss why these results are important for researchers using the PLT.
人们普遍认为，概念可以通过其属性（或特征）来表征。在研究语言中编码的概念时，研究人员通常会要求受试者列出描述它们的属性（即属性列表任务，PLT）。这些列表被累积起来以产生概念属性规范（CPN）。CPN 包含了个体概念的属性频率分布。人们普遍认为，这些分布代表了这些概念的潜在语义结构。在这里，我们不关注潜在的语义结构，而是旨在描述 PLT。PLT 一个经常被忽视的方面是个体之间存在变异性（即，他们只生成部分重叠的列表）。在我们的研究中，我们使用对这种个体间变异性的数学分析来指导我们的研究。为此，我们求助于一组公开的规范，这些规范包含了关于在个体主体水平上被通知的特定属性的信息。我们的研究结果表明，当个体执行 PLT 时，他或她会生成一个由一般属性和独特属性混合而成的属性列表，因此存在产生比独特属性更多的一般属性的非线性趋势。此外，低普遍性属性正是那些在列表之间不太可能重复的属性，以这种方式解释了个体间变异性的一部分。因此，任何可能影响列表中一般属性和独特属性混合的操作都必然会改变个体间变异性。我们讨论了为什么这些结果对使用 PLT 的研究人员很重要。
