To read or not to read? Motives for reading negative COVID-19 news.

People were confronted with a barrage of negative news during the COVID-19 crisis. This study investigated how anticipated psychological impact predicted decisions to read personalized and factual COVID-19 news. First, participants chose, based on headlines, whether they wanted to read news articles (or not). Then, all headlines were rated on a set of motivational dimensions. In order to test confirmatory hypotheses, the data were divided into an exploration (n = 398) and validation data set (n = 399). Using multilevel modeling, we found robust support for four preregistered hypotheses: Choice for negative COVID-19 news was positively predicted by (a) personal versus factual news; (b) the anticipated amount of knowledge acquisition; (c) the anticipated relevance to one's own personal situation; and (d) participant's sense of moral duty. Moreover, exploratory findings suggested a positive relationship between headline choice and anticipated compassion, a negative relationship with anticipated inappropriateness and gratitude, and a quadratic relationship with anticipated strength of feelings. These results support the idea that negative content offers informational value, both in terms of understanding negative events and in terms of preparing for these events. Furthermore, engagement with negative content can be motivated by moral values. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Testing, explaining, and exploring models of facial expressions of emotions.

Models are the hallmark of mature scientific inquiry. In psychology, this maturity has been reached in a pervasive question-what models best represent facial expressions of emotion? Several hypotheses propose different combinations of facial movements [action units (AUs)] as best representing the six basic emotions and four conversational signals across cultures. We developed a new framework to formalize such hypotheses as predictive models, compare their ability to predict human emotion categorizations in Western and East Asian cultures, explain the causal role of individual AUs, and explore updated, culture-accented models that improve performance by reducing a prevalent Western bias. Our predictive models also provide a noise ceiling to inform the explanatory power and limitations of different factors (e.g., AUs and individual differences). Thus, our framework provides a new approach to test models of social signals, explain their predictive power, and explore their optimization, with direct implications for theory development.