Implicit social associations for geometric-shape agents more strongly influenced by visual form than by explicitly identified social actions

Studies of infants' and adults' social cognition frequently use geometric-shape agents such as coloured squares and circles, but the influence of agent visual-form on social cognition has been little investigated. Here, although adults gave accurate explicit descriptions of interactions between geometric-shape aggressors and victims, implicit association tests for dominance and valence did not detect tendencies to encode the shapes’ social attributes on an implicit level. With regard to valence, the lack of any systematic implicit associations precludes conclusive interpretations. With regard to dominance, participants implicitly associated a yellow square as more dominant than a blue circle, even when the true relationship was the reverse of this and was correctly explicitly described by participants. Therefore, although explicit dominance judgements were strongly influenced by observed behaviour, implicit dominance associations were more clearly influenced by preconceived associations between visual form and social characteristics. This study represents a cautionary tale for those conducting experiments using geometric-shape agents

Medical Applications and Toxicities of Gallium Compounds

Over the past two to three decades, gallium compounds have gained importance in the fields of medicine and electronics. In clinical medicine, radioactive gallium and stable gallium nitrate are used as diagnostic and therapeutic agents in cancer and disorders of calcium and bone metabolism. In addition, gallium compounds have displayed anti-inflammatory and immunosuppressive activity in animal models of human disease while more recent studies have shown that gallium compounds may function as antimicrobial agents against certain pathogens. In a totally different realm, the chemical properties of gallium arsenide have led to its use in the semiconductor industry. Gallium compounds, whether used medically or in the electronics field, have toxicities. Patients receiving gallium nitrate for the treatment of various diseases may benefit from such therapy, but knowledge of the therapeutic index of this drug is necessary to avoid clinical toxicities. Animals exposed to gallium arsenide display toxicities in certain organ systems suggesting that environmental risks may exist for individuals exposed to this compound in the workplace. Although the arsenic moiety of gallium arsenide appears to be mainly responsible for its pulmonary toxicity, gallium may contribute to some of the detrimental effects in other organs. The use of older and newer gallium compounds in clinical medicine may be advanced by a better understanding of their mechanisms of action, drug resistance, pharmacology, and side-effects. This review will discuss the medical applications of gallium and its mechanisms of action, the newer gallium compounds and future directions for development, and the toxicities of gallium compounds in current use

Challenges when Generalizing Psychological Measurements across Populations : Applications in Machine Learning and Cross-Cultural Comparisons

In order to ascertain the validity and applicability of psychological theories, models, and measurements, it is important to examine their generalizability across different assessment situations. In this thesis, I examine how the application of measures outside of their initial domain may cause complications. This is applied to two fields where such considerations of generalizations may be especially beneficial: machine learning models and cross-cultural comparisons. Paper I explored whether text-based machine learning models of personality with a broad set of predictors, or models based on a set of more constrained but more psychologically meaningful predictors, better predicted personality in one of two text domains. The former models provided equal or superior prediction in the same domain in which it was trained compared to the latter models, but equally poor or poorer prediction in the other domain. Paper II reexamined the results of an article that, like the cross-cultural studies re-examined in Paper III, found that over time and across states in the U.S., higher gender equality was associated with larger gender differentiation, here in names given to children. Re-analyses showed that there was no such systematic association across time, and that the differentiation across states was confounded with a more strongly associated cultural/language predictor. Paper III re-examined multiple studies that have assessed that association across countries. Here, it was shown that cultural differences, as indicated by cultural regions, other measures such as individualism, and data quality indicators, better explained the variation in differences across countries. When controlling for cultural/language regions, the association with gender equality disappeared or, sometimes, reversed. These results indicate the degree to which different cultural factors are interrelated, and suggests the need for complementary methods. In conclusion, this thesis exemplifies the importance of considering how models and measures may interact with and generalize across situations. This is true whether it supports greater generality or situational specificity of different psychological measures

No Evidence of a Gender-Equality Paradox in Gendered Names : Comment on Vishkin, Slepian, and Galinsky (2022)

This article reexamines the results of Vishkin, Slepian, and Galinsky (2022), which found larger gender differences in voiced names with higher gender equality over time and across states. I show that the employed statistical methods and calculations led the authors to draw incorrect conclusions. Using more appropriate methods, I show that there is no evidence of a systematic decrease in the proportion of voiced female names over time nor a corresponding increase for male names in Study 1 and that the gender difference has actually decreased. In Study 2, I show that, contrary to the authors’ hypothesis, both men and women have a higher proportion of voiced names in states with higher female leadership scores and that the increased difference disappears when controlling for a cultural confound—states’ proportion of foreign-born inhabitants. I conclude by discussing some overarching issues and thoughts on best practices

No evidence of a gender-equality paradox in gendered names: Comment on Vishkin, Slepian, and Galinsky (2022)

This article reexamines the conclusions of Vishkin, Sleipan, and Galinsky (2022) Study 1, that gender differentiation in the proportion of voiced baby names has increased over a time period of increased gender equality. By plotting the data, it becomes evident that the current differentiation is actually smaller than ever before. I discuss why the methods employed by the authors is problematic for assessing trends in dependent time-series data and illustrate how analyses that address these problems lead to fundamentally different conclusions. I also provide some discussion around their Study 2

Coefficients of determination measured on the same scale as the outcome: Alternatives to R2 that use standard deviations instead of explained variance

The coefficient of determination, R^2, also called the explained variance, is often taken as a proportional measure of the relative determination of model on outcome. However, while R^2 has some attractive statistical properties, its reliance on squared variations (variances) may limit its use as an easily interpretable descriptive statistic of that determination. Here, properties of this coefficient on the squared scale are discussed and generalized to three relative measures on the original scale. These generalizations can all be expressed as transformations of R^2, and alternatives can therefore also be calculated by plugging in related estimates, such as the adjusted R^2. The third coefficient, new for this article, and here termed the CoD_SD (the coefficient of determination in terms of standard deviations), or R_π (R-pi), equals √(R^2)/(√(R^2)+√(1-R^2)). It is argued that this coefficient most usefully captures the relative determination of the model. When the contribution of the error is c times that of the model, the CoD_SD equals 1 / (1 + c), while R^2 equals 1 / (1 + c^2)