toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
   print
  Records Links
Author Canessa, E.; Chaigneau, S.E. doi  openurl
  Title Mathematical regularities of data from the property listing task Type Journal Article
  Year 2020 Publication Journal Of Mathematical Psychology Abbreviated Journal J. Math. Psychol.  
  Volume 97 Issue Pages 19 pp  
  Keywords Concepts; Property listing task; Conceptual properties norms; Semantic access  
  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.  
  Address [Canessa, Enrique; Chaigneau, Sergio E.] Univ Adolfo Ibanez, Ctr Cognit Res CINCO, Sch Psychol, Av Presidente Errazuriz 3328, Santiago, Chile, Email: ecanessa@uai.cl  
  Corporate Author Thesis  
  Publisher Academic Press Inc Elsevier Science Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0022-2496 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000539438000007 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 1192  
Permanent link to this record
 

 
Author Canessa, E.; Chaigneau, S.E.; Lagos, R.; Medina, F.A. doi  openurl
  Title How to carry out conceptual properties norming studies as parameter estimation studies: Lessons from ecology Type Journal Article
  Year 2020 Publication Behavior Research Methods Abbreviated Journal Behav. Res. Methods  
  Volume to appear Issue Pages 17 pp  
  Keywords Conceptual properties norming studies; Property listing task; Parameter estimation; Sample size determination; Sample coverage  
  Abstract Conceptual properties norming studies (CPNs) ask participants to produce properties that describe concepts. From that data, different metrics may be computed (e.g., semantic richness, similarity measures), which are then used in studying concepts and as a source of carefully controlled stimuli for experimentation. Notwithstanding those metrics' demonstrated usefulness, researchers have customarily overlooked that they are only point estimates of the true unknown population values, and therefore, only rough approximations. Thus, though research based on CPN data may produce reliable results, those results are likely to be general and coarse-grained. In contrast, we suggest viewing CPNs as parameter estimation procedures, where researchers obtain only estimates of the unknown population parameters. Thus, more specific and fine-grained analyses must consider those parameters' variability. To this end, we introduce a probabilistic model from the field of ecology. Its related statistical expressions can be applied to compute estimates of CPNs' parameters and their corresponding variances. Furthermore, those expressions can be used to guide the sampling process. The traditional practice in CPN studies is to use the same number of participants across concepts, intuitively believing that practice will render the computed metrics comparable across concepts and CPNs. In contrast, the current work shows why an equal number of participants per concept is generally not desirable. Using CPN data, we show how to use the equations and discuss how they may allow more reasonable analyses and comparisons of parameter values among different concepts in a CPN, and across different CPNs.  
  Address [Canessa, Enrique; Chaigneau, Sergio E.] Univ Adolfo Ibanez, Sch Psychol, Ctr Cognit Res CINCO, Av Presidente Errazuriz 3328, Santiago, Chile, Email: ecanessa@uai.cl  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1554-351x ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000551760700002 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 1210  
Permanent link to this record
 

 
Author Canessa, E.; Chaigneau, S.E.; Moreno, S.; Lagos, R. doi  openurl
  Title Informational content of cosine and other similarities calculated from high-dimensional Conceptual Property Norm data Type Journal Article
  Year 2020 Publication Cognitive Processing Abbreviated Journal Cogn. Process.  
  Volume to appear Issue Pages 14 pp  
  Keywords Cosine similarity; Euclidean distance; Chebyshev distance; Clustering; Conceptual properties  
  Abstract To study concepts that are coded in language, researchers often collect lists of conceptual properties produced by human subjects. From these data, different measures can be computed. In particular, inter-concept similarity is an important variable used in experimental studies. Among possible similarity measures, the cosine of conceptual property frequency vectors seems to be a de facto standard. However, there is a lack of comparative studies that test the merit of different similarity measures when computed from property frequency data. The current work compares four different similarity measures (cosine, correlation, Euclidean and Chebyshev) and five different types of data structures. To that end, we compared the informational content (i.e., entropy) delivered by each of those 4 x 5 = 20 combinations, and used a clustering procedure as a concrete example of how informational content affects statistical analyses. Our results lead us to conclude that similarity measures computed from lower-dimensional data fare better than those calculated from higher-dimensional data, and suggest that researchers should be more aware of data sparseness and dimensionality, and their consequences for statistical analyses.  
  Address [Canessa, Enrique; Chaigneau, Sergio E.] Univ Adolfo Ibanez, Sch Psychol, Ctr Cognit Res CINCO, Ave Presidente Errazuriz 3328, Santiago, Chile, Email: ecanessa@uai.cl  
  Corporate Author Thesis  
  Publisher Springer Heidelberg Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1612-4782 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000546845700001 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 1180  
Permanent link to this record
Select All    Deselect All
 |   | 
Details
   print

Save Citations:
Export Records: