Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.

Major biases and stereotypes in group judgments are reviewed and modeled from a recurrent connectionist perspective. These biases are in the areas of group impression formation (illusory correlation), group differentiation (accentuation), stereotype change (dispersed vs. concentrated distribution of inconsistent information), and group homogeneity. All these phenomena are illustrated with well-known experiments, and simulated with an autoassociative network architecture with linear activation update and delta learning algorithm for adjusting the connection weights. All the biases were successfully reproduced in the simulations. The discussion centers on how the particular simulation specifications compare with other models of group biases and how they may be used to develop novel hypotheses for testing the connectionist modeling approach and, more generally, for improving theorizing in the field of social biases and stereotype change.

The authors investigated the degree of discounting and augmentation of a target cause by an alternative cause given a varying number of observations on the alternative cause while holding its degree of covariation constant. Two experiments showed that more observations of the alternative cause resulted in greater discounting or augmentation of a target cause. This sample size effect cannot be explained by current attribution theories based on statistical notions or belief updating but can be accounted for by a connectionist framework. In addition, the authors found that the sample size effect was stronger when the information was presented in a sequential trial-by-trial format as opposed to a summarized format but found no effect of information order. Possible extensions of statistical models with confidence weights that take account of sample size were considered and simulated but none accommodated the data as well as connectionist models.

The statistical law of large numbers prescribes that estimates are more reliable and accurate when based on a larger sample of observations. This effect of sample size was investigated on causal attributions. Subjects received ®xed levels of consensus and distinctiveness covariation, and attributions were measured after a varying number of trials. Whereas prominent statistical models of causality (e.g. Cheng & Novick, 1990;Fo Èrsterling, 1992) predict no effect of sample size, adaptive connectionist models (McClelland & Rumelhart, 1988) predict that subjects will incrementally adjust causal ratings in the direction of the true covariation the more observations are made. In three experiments, sample size effects were found consistent with the connectionist prediction. Possible extensions of statistical models were considered and simulated, but none of them accommodated the data as well as connectionist models.

Although essential to professional competence in psychology, quantitative research methods are a known area of weakness for many undergraduate psychology students. Students find selecting appropriate statistical tests and procedures for different types of research questions, hypotheses and data types particularly challenging, and these skills are not often practiced in class. Decision trees (a type of graphic organizer) are known to facilitate this decision making process, but extant trees have a number of limitations. Furthermore, emerging research suggests that mobile technologies offer many possibilities for facilitating learning. It is within this context that we have developed StatHand, a free cross-platform application designed to support students’ statistical decision making. Developed with the support of the Australian Government Office for Learning and Teaching, StatHand guides users through a series of simple, annotated questions to help them identify a statistical test or procedure appropriate to their circumstances. It further offers the guidance necessary to run these tests and procedures, then interpret and report their results. In this Technology Report we will overview the rationale behind StatHand, before describing the feature set of the application. We will then provide guidelines for integrating StatHand into the research methods curriculum, before concluding by outlining our road map for the ongoing development and evaluation of StatHand.