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. (C) 2020 Elsevier Inc. All rights reserved.

Abstract: Agreement probability p(a) is a homogeneity measure of lists of properties produced by participants in a Property Listing Task (PLT) for a concept. Agreement probability's mathematical properties allow a rich analysis of property-based descriptions. To illustrate, we use p(a) to delve into the differences between concrete and abstract concepts in sighted and blind populations. Results show that concrete concepts are more homogeneous within sighted and blind groups than abstract ones (i.e., exhibit a higher p(a) than abstract ones) and that concrete concepts in the blind group are less homogeneous than in the sighted sample. This supports the idea that listed properties for concrete concepts should be more similar across subjects due to the influence of visual/perceptual information on the learning process. In contrast, abstract concepts are learned based mainly on social and linguistic information, which exhibit more variability among people, thus, making the listed properties more dissimilar across subjects. Relative to abstract concepts, the difference in p(a) between sighted and blind is not statistically significant. Though this is a null result, and should be considered with care, it is expected because abstract concepts should be learned by paying attention to the same social and linguistic input in both, blind and sighted, and thus, there is no reason to expect that the respective lists of properties should differ. Finally, we used p(a) to classify concrete and abstract concepts with a good level of certainty. All these analyses suggest that p(a) can be fruitfully used to study data obtained in a PLT.