The spread of false and misleading information in online social networks is a global problem in need of urgent solutions. It is also a policy problem because misinformation can harm both the public and democracies. To address the spread of misinformation, policymakers require a successful interface between science and policy, as well as a range of evidence-based solutions that respect fundamental rights while efficiently mitigating the harms of misinformation online. In this article, we discuss how regulatory and nonregulatory instruments can be informed by scientific research and used to reach EU policy objectives. First, we consider what it means to approach misinformation as a policy problem. We then outline four building blocks for cooperation between scientists and policymakers who wish to address the problem of misinformation: understanding the misinformation problem, understanding the psychological drivers and public perceptions of misinformation, finding evidence-based solutions, and co-developing appropriate policy measures. Finally, through the lens of psychological science, we examine policy instruments that have been proposed in the EU, focusing on the strengthened Code of Practice on Disinformation 2022.

This investigation was conceptually framed within the theory of marginal deviations (Caprara & Zimbardo, 1996) and sought evidence for the general hypothesis that some children who initially show marginal behavioral problems may, over time, develop more serious problems depending partly on other personal and behavioral characteristics. To this end, the findings of two studies conducted, respectively, with American elementary school children and Italian middle school students are reviewed. These two studies show that hyperactivity, cognitive difficulties, low special preference, and lack of prosocial behavior increase a child's risk for growth in aggressive behavior over several school years. More importantly, they also show that equivalent levels of these risk factors have a greater impact on the development of children who, early on, were marginally aggressive.

In this article, we introduce the "Campbell Paradigm" as a novel variant of Rosenberg and Hovland's (1960) tripartite model of attitudes. The Campbell Paradigm is based on a highly restricted measurement model that speaks of a compensatory relation between a person's latent attitude and the costs that come with any specific behavior. It overcomes the overarching weakness of the original tripartite model (i.e., its relative irrelevance for actual behavior) and offers a parsimonious explanation for behavior. Even though this seems attractive, we also discuss why the paradigm has not gained momentum in the 50 years since it was originally proposed by Donald T. Campbell. To demonstrate the paradigm's suitability even when implemented with an unrefined instrument in a domain where it has not been used previously, we apply the paradigm to a classic data example from attitude research from the 1984 US presidential election to account for the electorate's voting intentions and actual voting behaviors.

More people than ever are living longer with chronic conditions such as obesity, type 2 diabetes, and heart disease. Behavior change for effective self-management can improve health outcomes and quality of life in people living with such chronic illnesses. The science of developing behavior change interventions with impact for patients aims to optimize the reach, effectiveness, adoption, implementation, and maintenance of interventions and rigorous evaluation of outcomes and processes of behavior change. The development of new services and technologies offers opportunities to enhance the scope of delivery of interventions to support behavior change and self-management at scale. Herein, we review key contemporary approaches to intervention development, provide a critical overview, and integrate these approaches into a pragmatic, user-friendly framework to rigorously guide decision-making in behavior change intervention development. Moreover, we highlight novel emerging methods for rapid and agile intervention development. On-going progress in the science of intervention development is needed to remain in step with such new developments and to continue to leverage behavioral science's capacity to contribute to optimizing interventions, modify behavior, and facilitate self-management in individuals living with chronic illness.