Do individual differences in face recognition ability moderate the other ethnicity effect?

Individuals are better at recognizing faces from their own ethnic group compared with other ethnicity faces-the other-ethnicity effect (OEE). This finding is said to reflect differences in experience and familiarity to faces from other ethnicities relative to faces corresponding with the viewers' ethnicity. However, own-ethnicity face recognition performance ranges considerably within a population, from very poor to extremely good. In addition, within-population recognition performance on other-ethnicity faces can also vary considerably with some individuals being classed as "other ethnicity face blind" (Wan et al., 2017). Despite evidence for considerable variation in performance within population for faces of both types, it is currently unclear whether the magnitude of the OEE changes as a function of this variability. By recruiting large-scale multinational samples, we investigated the size of the OEE across the full range of own and other ethnicity face performance while considering measures of social contact. We find that the magnitude of the OEE is remarkably consistent across all levels of within-population own- and other-ethnicity face recognition ability, and this pattern was unaffected by social contact measures. These findings suggest that the OEE is a persistent feature of face recognition performance, with consequences for models built around very poor, and very good face recognizers. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Adaptation of Chain Event Graphs for use with Case-Control Studies in Epidemiology.

Case-control studies are used in epidemiology to try to uncover the causes of diseases, but are a retrospective study design known to suffer from non-participation and recall bias, which may explain their decreased popularity in recent years. Traditional analyses report usually only the odds ratio for given exposures and the binary disease status. Chain event graphs are a graphical representation of a statistical model derived from event trees which have been developed in artificial intelligence and statistics, and only recently introduced to the epidemiology literature. They are a modern Bayesian technique which enable prior knowledge to be incorporated into the data analysis using the agglomerative hierarchical clustering algorithm, used to form a suitable chain event graph. Additionally, they can account for missing data and be used to explore missingness mechanisms. Here we adapt the chain event graph framework to suit scenarios often encountered in case-control studies, to strengthen this study design which is time and financially efficient. We demonstrate eight adaptations to the graphs, which consist of two suitable for full case-control study analysis, four which can be used in interim analyses to explore biases, and two which aim to improve the ease and accuracy of analyses. The adaptations are illustrated with complete, reproducible, fully-interpreted examples, including the event tree and chain event graph. Chain event graphs are used here for the first time to summarise non-participation, data collection techniques, data reliability, and disease severity in case-control studies. We demonstrate how these features of a case-control study can be incorporated into the analysis to provide further insight, which can help to identify potential biases and lead to more accurate study results.

Learning Through Chain Event Graphs: The Role of Maternal Factors in Childhood Type 1 Diabetes.

Chain event graphs (CEGs) are a graphical representation of a statistical model derived from event trees. They have previously been applied to cohort studies but not to case-control studies. In this paper, we apply the CEG framework to a Yorkshire, United Kingdom, case-control study of childhood type 1 diabetes (1993-1994) in order to examine 4 exposure variables associated with the mother, 3 of which are fully observed (her school-leaving-age, amniocenteses during pregnancy, and delivery type) and 1 with missing values (her rhesus factor), while incorporating previous type 1 diabetes knowledge. We conclude that the unknown rhesus factor values were likely to be missing not at random and were mainly rhesus-positive. The mother's school-leaving-age and rhesus factor were not associated with the diabetes status of the child, whereas having at least 1 amniocentesis procedure and, to a lesser extent, birth by cesarean delivery were associated; the combination of both procedures further increased the probability of diabetes. This application of CEGs to case-control data allows for the inclusion of missing data and prior knowledge, while investigating associations in the data. Communication of the analysis with the clinical expert is more straightforward than with traditional modeling, and this approach can be applied retrospectively or when assumptions for traditional analyses are not held.