Belief change and spread have been studied in many disciplines—from psychology, sociology, economics and philosophy, to biology, computer science and statistical physics—but we still do not have a firm grasp on why some beliefs change more easily and spread faster than others. To fully capture the complex social-cognitive system that gives rise to belief dynamics, we first review insights about structural components and processes of belief dynamics studied within different disciplines. We then outline a unifying quantitative framework that enables theoretical and empirical comparisons of different belief dynamic models. This framework uses a statistical physics formalism, grounded in cognitive and social theory, as well as empirical observations. We show how this framework can be used to integrate extant knowledge and develop a more comprehensive understanding of belief dynamics.

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Skepticism toward childhood vaccines and genetically modified food has grown despite scientific evidence of their safety. Beliefs about scientific issues are difficult to change because they are entrenched within many interrelated moral concerns and beliefs about what others think. We propose a cognitive network model that estimates network ties between all interrelated beliefs to calculate the overall dissonance and interdependence. Using a probabilistic nationally representative longitudinal study, we test whether our model can be used to predict belief change and find support for our model’s predictions: High network dissonance predicts subsequent belief change, and people are driven toward lower network dissonance. We show the advantages of measuring dissonance using the belief network structure compared to traditional measures. This study is the first to combine a unifying predictive model with an experimental intervention and to shed light on the dynamics of dissonance reduction leading to belief change.

Significance Much of online conversation today consists of signaling one’s political identity. Although many signals are obvious to everyone, others are covert, recognizable to one’s ingroup while obscured from the outgroup. This type of covert identity signaling is critical for collaborations in a diverse society, but measuring covert signals has been difficult, slowing down theoretical development. We develop a method to detect covert and overt signals in tweets posted before the 2020 US presidential election and use a behavioral experiment to test predictions of a mathematical theory of covert signaling. Our results show that covert political signaling is more common when the perceived audience is politically diverse and open doors to a better understanding of communication in politically polarized societies.

Most efforts to increase public acceptance of scientific facts have focused on providing transparent factual information. While this is essential for enabling people to make informed decisions, evidence is mixed about the effectiveness of facts alone for modifying beliefs. To enable more effective science communication, we need a better understanding of how moral and social considerations affect belief change beyond facts. In two longitudinal studies with a total of 1,673 participants, we experiment with different educational interventions that present scientific messages about the safety of GM food and childhood vaccines designed to alleviate different moral and social concerns. To aid our understanding of why some interventions are more successful than others, and why only some people change their beliefs, we develop a statistical-physics-inspired quantitative model of belief dynamics. The model incorporates several possible mechanisms of belief change and can provide insights into the cognitive processes underlying successful interventions and individual belief change. Our experimental data support the theory-driven hypothesis that belief change is more likely when facts are presented in a way that lowers their dissonance with people’s moral and social concerns, but only when the particular type of dissonance (moral or social) is considered important. In addition, individuals who pay more attention to how facts relate to their moral and social concerns show more belief change with decreasing dissonance. Taken together, our model and results provide a pathway towards a more rigorous understanding of how to address people’s moral and social concerns when communicating scientific facts.