
ArayaLetelier, G., Parra, P. F., LopezGarcia, D., GarciaValdes, A., Candia, G., & Lagos, R. (2019). Collapse risk assessment of a Chilean dual wallframe reinforced concrete office building. Eng. Struct., 183, 770–779.
Abstract: Several codeconforming reinforced concrete buildings were severely damaged during the 2010 moment magnitude (Mw) 8.8 Chile earthquake, raising concerns about their real collapse margin. Although critical updates were introduced into the Chilean design codes after 2010, guidelines for collapse risk assessment of Chilean buildings remain insufficient. This study evaluates the collapse potential of a typical dual system (shear walls and moment frames) office building in Santiago. Collapse fragility functions were obtained through incremental dynamic analyses using a stateoftheart finite element model of the building. Sitespecific seismic hazard curves were developed, which explicitly incorporated epistemic uncertainty, and combined with the collapse fragility functions to estimate the mean annual frequency of collapse (lambda(c)) values and probabilities of collapse in 50years (Pc(50)). Computed values of lambda(c) and Pc(50) were on the order of 10(5)10(4), and 0.10.7%, respectively, consistent with similar studies developed for buildings in the US. The results also showed that the deaggregation of lambda(c) was controlled by small to medium earthquake intensities and that different models of the collapse fragility functions and hazard curves had a nonnegligible effect on lambda(c) and Pc(50), and thus, propagation of uncertainty in risk assessment problems must be adequately taken into account.



Canessa, E., Chaigneau, S. E., Lagos, R., & Medina, F. A. (2021). How to carry out conceptual properties norming studies as parameter estimation studies: Lessons from ecology. Behav. Res. Methods, 53, 354–370.
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 coarsegrained. In contrast, we suggest viewing CPNs as parameter estimation procedures, where researchers obtain only estimates of the unknown population parameters. Thus, more specific and finegrained 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.



Canessa, E., Chaigneau, S. E., Moreno, S., & Lagos, R. (2020). Informational content of cosine and other similarities calculated from highdimensional Conceptual Property Norm data. Cogn. Process., 21, 601–614.
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, interconcept 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 lowerdimensional data fare better than those calculated from higherdimensional data, and suggest that researchers should be more aware of data sparseness and dimensionality, and their consequences for statistical analyses.



Canessa, E., Chaigneau, S. E., Moreno, S., & Lagos, R. (2022). CPNCoverageAnalysis: An R package for parameter estimation in conceptual properties norming studies. Behav. Res. Methods, Early Access.
Abstract: In conceptual properties norming studies (CPNs), participants list properties that describe a set of concepts. From CPNs, many different parameters are calculated, such as semantic richness. A generally overlooked issue is that those values are
only point estimates of the true unknown population parameters. In the present work, we present an R package that allows us to treat those values as population parameter estimates. Relatedly, a general practice in CPNs is using an equal number of participants who list properties for each concept (i.e., standardizing sample size). As we illustrate through examples, this procedure has negative effects on data�s statistical analyses. Here, we argue that a better method is to standardize coverage (i.e., the proportion of sampled properties to the total number of properties that describe a concept), such that a similar coverage is achieved across concepts. When standardizing coverage rather than sample size, it is more likely that the set of concepts in a CPN all exhibit a similar representativeness. Moreover, by computing coverage the researcher can decide whether the
CPN reached a sufficiently high coverage, so that its results might be generalizable to other studies. The R package we make available in the current work allows one to compute coverage and to estimate the necessary number of participants to reach a target coverage. We show this sampling procedure by using the R package on real and simulated CPN data.



Chaigneau, S. E., Canessa, E., Barra, C., & Lagos, R. (2018). The role of variability in the property listing task. Behav. Res. Methods, 50(3), 972–988.
Abstract: 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 nonlinear 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.



Lagos, R., Canessa, E., & Chaigneau, S. E. (2019). Modeling stereotypes and negative selfstereotypes as a function of interactions among groups with power asymmetries. J. Theory Soc. Behav., 49(3), 312–333.
Abstract: Stereotypes is one of the most researched topics in social psychology. Within this context, negative selfstereotypes pose a particular challenge for theories. In the current work, we propose a model that suggests that negative selfstereotypes can theoretically be accounted for by the need to communicate in a social system made up by groups with unequal power. Because our theory is dynamic, probabilistic, and interactionist, we use a computational simulation technique to show that the proposed model is able to reproduce the phenomenon of interest, to provide novel accounts of related phenomena, and to suggest novel empirical predictions. We describe our computational model, our variables' dynamic behavior and interactions, and link our analyses to the literature on stereotypes and selfstereotypes, the stability of stereotypes (in particular, gender and racial stereotypes), the effects of power asymmetries, and the effects of intergroup contact.

