How cultural evolution can inform the science of science communication—and vice versa

The science of science communication (SSC) aims to develop evidence-based practices for communicating scientific findings and inspiring behavior consistent with scientific understanding. However, the spread of misinformation poses a significant challenge. SSC has identified factors influencing the spread of information, including content properties, individual conditions, and social dynamics. This paper proposes that cultural evolution research, which studies the spread and change of cultural traits, provides a unifying framework for understanding these factors. Cultural evolution examines how beliefs, knowledge, attitudes, and behaviors spread within and between populations, and the relative 'attractiveness' of certain cultural traits. Its interdisciplinary approach, encompassing formal modeling and empirical methods, makes it particularly relevant to SSC's goal of evidence-based communication strategies. The paper will demonstrate how cultural evolution extends central themes and concepts within SSC and offers a road map for deeper exploration of their synergy.

Existing research has explored several aspects of how cultural traits spread, emphasizing the role of content biases, such as the eliciting of emotions (especially fear and disgust), social relevance, and intentionality. These content biases are understood as stemming from our evolved psychological mechanisms. However, prior applications of cultural evolution to science communication have limitations; they often focus narrowly on content properties, neglecting the interplay of individual conditions and social dynamics. The 'knowledge deficit model'—the idea that increased knowledge automatically leads to behavior change—is largely discredited; individual beliefs, attitudes, and worldviews, strongly influenced by social dynamics, crucially affect information adoption. While some work highlights the role of social influence and prior worldviews, it often fails to translate this understanding into practical recommendations for communicators. Some research has used models to explain how cultural traits, even harmful ones, spread through social norms and incentives, highlighting the role of social learning strategies. Studies examining conformity show the potential for 'spillover effects', where behavior change in some individuals spreads to others. But these social learning models often overlook content properties and individual biases that modify social influence. Therefore, a more integrative approach is needed that considers the complex interactions among all three sets of factors.

This paper employs a synthesis of existing literature. It reviews existing research in both cultural evolution and science communication, identifying key concepts and findings in each field. The paper then demonstrates how these concepts and findings overlap and can be integrated within a cultural evolutionary framework. The methodology is primarily theoretical, drawing on existing empirical studies and models to illustrate the consilience between cultural evolution and science communication. The paper uses examples from various science communication challenges (e.g., vaccination, GMOs, climate change) to illustrate the application of cultural evolutionary principles. The paper's main goal is to build a conceptual framework, not to conduct original empirical research. It uses existing empirical studies to support the theoretical argument, thus highlighting the existing literature as a method for advancing the understanding of the intersection between cultural evolution and science communication. The paper aims at creating a systematic integrative approach and identifying gaps and opportunities for future research.

The paper's key findings show how the three main sets of factors identified in SSC—content properties, individual conditions, and social dynamics—align with and are further illuminated by cultural evolutionary theory. **Content Properties:** Cultural attractiveness is influenced by features like emotional content (fear and disgust often hindering communication), social relevance, and intentionality (a major component in conspiracy theories). These aspects align with the concept of 'content biases' in cultural evolution, suggesting inherent differences in the 'attractiveness' of various information types. **Individual Conditions:** Preexisting beliefs, values, and worldviews profoundly impact information acceptance and can even lead to the 'backfire effect', where corrections increase belief in misinformation. Cultural evolution offers the concept of 'cultural linkage,' wherein certain traits travel together, and 'inductive biases' whereby existing knowledge shapes information acquisition and transmission. This highlights the importance of tailoring communication to avoid direct contradiction with existing beliefs. **Social Dynamics:** Social norms, incentives, and social learning strategies (e.g., imitation of majorities or prestigious individuals) are crucial in spreading information. Cultural evolution provides models that illustrate how social norms can stabilize traits even if they are harmful, similar to the ‘cultural cognition thesis’ in SSC where people choose beliefs to maintain social connection. Social learning strategies, like conformity, can create 'spillover effects' where behavioral change spreads indirectly. The paper critically examines past research, revealing limitations in focusing on only one set of factors (content properties, individual conditions, or social dynamics). A holistic approach that integrates these aspects is needed. The paper highlights the need to understand how these factors interact to influence the success of science communication, and how the relative strength of each factor can be identified and used to tailor effective communication strategies.

This synthesis of cultural evolution and science communication research addresses a critical gap in understanding the dynamics of information spread, especially misinformation. The integrative framework presented here provides a more nuanced understanding of the challenges and opportunities for effective science communication. By integrating the three sets of factors, we move beyond the simplistic 'knowledge deficit model' and acknowledge the complex psychological and social mechanisms involved. The findings suggest that successful science communication strategies must consider not just the content of the message but also individual predispositions and social contexts. The interplay between these factors, including the relative strengths and interactions, requires further empirical investigation. This framework should guide future research to design more effective interventions to combat misinformation and promote evidence-based behaviors.

The paper demonstrates the significant consilience between cultural evolution and science communication research. Cultural evolution provides a powerful framework for understanding the complexities of information spread and behavior change. Conversely, science communication offers a rich testing ground for cultural evolutionary theory. Future research should focus on how the three sets of factors interact to influence the success of science communication, and how the insights from cultural evolution can inform the design of more effective interventions. A deeper understanding of this relationship could significantly enhance our ability to communicate scientific information effectively and promote evidence-based decision-making.

The paper's primary limitation is its reliance on a synthesis of existing literature. While this approach is valuable for building a conceptual framework, it lacks original empirical data to directly test the proposed integrative model. Future research should conduct empirical studies to explore the interactions between content properties, individual conditions, and social dynamics in more detail. The paper primarily focuses on Western cultural contexts, and further research is needed to investigate whether the proposed framework generalizes across diverse cultural settings.