TY - JOUR
T1 - Using agreement probability to study differences in types of concepts and conceptualizers
AU - Canessa, Enrique
AU - Chaigneau, Sergio E.
AU - Moreno, Sebastián
N1 - Funding Information:
The Core Facilities for Advanced Light Microscopy and Advanced Electron Microscopy at Oslo University Hospital are acknowledged for providing access to and training on relevant microscopes. We thank the Lipidomics Core Facility at the Danish Cancer Society Research Center for providing access to instrumentations and materials. We thank Ulrikke Dahl Brinch and Catherine Sem Wegner for excellent technical help with sample preparation for electron microscopy. We thank Pietro De Camilli for kindly providing VAPA/B knockout cells and Matthew Yoke Wui Ng and Kia Wee Tan for providing plasmid constructs. AHL was supported by a Young Research Talents Grant from the Research Council of Norway (project number 325305). CR was supported by a grant from the Norwegian Cancer Society (project number 198140). HS was supported by the Norwegian Cancer Society (project number 182698), the South‐Eastern Norway Regional Health Authority (project number 2016087), the Research Council of Norway (project number 302994), and the European Research Council (Advanced Grant number 788954). MJ was supported by grants from the Danish National Research Foundation (DNRF125) and the Novo Nordisk Foundation (NNF17OC0029432), and KM was supported by the Independent Research Fund Denmark (6108‐00542B). This work was partly supported by the Research Council of Norway through its Centres of Excellence funding scheme (project number 262652). Figures were created using Adobe Illustrator CS6 and BioRender ( https://biorender.com/ ).
Publisher Copyright:
© 2022, The Psychonomic Society, Inc.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Agreement probability
KW - Concrete/abstract concepts
KW - Property listing task
KW - Sighted/blind populations
UR - http://www.scopus.com/inward/record.url?scp=85143337807&partnerID=8YFLogxK
U2 - 10.3758/s13428-022-02030-z
DO - 10.3758/s13428-022-02030-z
M3 - Article
AN - SCOPUS:85143337807
SN - 1554-351X
JO - Behavior Research Methods
JF - Behavior Research Methods
ER -