Awe arises in reaction to exceeded rather than disconfirmed expectancies.

Awe is a fascinating emotion, associated with positive consequences such as greater prosociality, generosity, and epistemic openness. Unfortunately, in spite of the weighty consequences of awe, the exact way in which it arises, and what it entails, is still a puzzle. Particularly puzzling is the question of whether awe is the result of expectancy violation. While awe is thought to arise in reaction to expectancy-violating objects or events, classical expectancy violations (e.g., a red queen of spades playing card) do not tend to cause awe. To shed light on this problem, we distinguished two types of expectancy violations-those that disconfirm and those that exceed one's expectancies-and we investigated whether awe is more likely to arise in reaction to one versus the other. We also looked at what appraisals constitute and are most important to the awe experience and how they structurally interact. To do this, we utilized network analysis and mapped out the network structure of appraisals linked to awe and to expectancy violations. Across two experimental studies (N = 823), we demonstrated that awe arises in reaction to exceeded (rather than disconfirmed) expectancies and that appraisals linked to exceeded expectancies (vastness and uniqueness) are central to awe, while appraisals linked to disconfirmed expectancies (uncertainty and inconsistency) are peripheral to the awe experience. Taken together, our investigation sheds new light on psychologists' understanding of expectancy violations and reveals when and how awe arises and what it entails. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A structured open dataset of government interventions in response to COVID-19.

In response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020.