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The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behavior with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic, and also highlight important gaps researchers should move quickly to fill in the coming weeks and months.

Although past research has demonstrated a positive relationship between collective identification and normative conformity, there may be circumstances in which strongly identified members do not conform but instead choose to challenge group norms. This article proposes a normative conflict model, which distinguishes between nonconformity due to dissent (challenging norms to change them) and nonconformity due to disengagement (distancing oneself from the group). The normative conflict model predicts that strongly identified members are likely to challenge group norms when they experience conflict between norms and important alternate standards for behavior, in particular when they perceive norms as being harmful to the group. Data in support of the model are reviewed, mechanisms by which external variables may influence dissent in social groups are elaborated, and the model is linked to contemporary perspectives on collective identity.

Dual-process models of attitudes highlight the fact that evaluative processes are complex and multifaceted. Nevertheless, many of these models typically neglect important interactions among processes that can contribute to an evaluation. In this article, we propose a multilevel model informed by neuroscience in which current evaluations are constructed from relatively stable attitude representations through the iterative reprocessing of information. Whereas initial iterations provide relatively quick and dirty evaluations, additional iterations accompanied by reflective processes yield more nuanced evaluations and allow for phenomena such as ambivalence. Importantly, this model predicts that the processes underlying relatively automatic evaluations continue to be engaged across multiple iterations, and that they influence and are influenced by more reflective processes. We describe the Iterative Reprocessing Model at the computational, algorithmic, and implementational levels of analysis (Marr, 1982) to more fully characterize its premises and predictions.Recent advances in neuroscientific methods have provided researchers with an unprecedented opportunity to examine the neural correlates of 736

Implicit self-esteem is the automatic, nonconscious aspect of self-esteem. This study demonstrated that implicit self-esteem can be increased using a computer game that repeatedly pairs self-relevant information with smiling faces. These findings, which are consistent with principles of classical conditioning, establish the associative and interpersonal nature of implicit self-esteem and demonstrate the potential benefit of applying basic learning principles in this domain.