Academic AbstractPopular lay theories shape how millions of people understand and communicate about romantic relationships, yet they are rarely examined scientifically. Rather than dismissing these frameworks as misconceptions, this review argues that engaging with lay theories can advance a science of relationships that is both empirically rigorous and publicly resonant. Using the love languages as a case study, this review introduces a bidirectional framework in which lay theories inform scientific inquiry and scientific insights are translated back to the public. Engaging with lay theories can refine theory, clarify core relational processes, and reveal contextual and cultural blind spots in lay theories. In turn, examining why lay theories resonate highlights unmet public needs, opportunities for improved scientific communication, and implications for clinical practice, relationship education, and public policy. The result is a science of relationships that is rigorous and resonant with the lived experiences of those it aims to serve.Public AbstractPopular ideas about relationships-such as the love languages-shape how millions of people understand love, communicate needs, and decide whether their romantic relationships are working. Yet these ideas are rarely examined by scientists, even though they influence real-life relationship decisions. This article argues that popular relationship theories should not simply be dismissed as wrong, but carefully studied as windows into what people need, value, and struggle with in their relationships. Using the love languages as an example, this review shows how engaging with popular ideas can help scientists improve theories of love, communicate research more clearly, and design guidance that better fits people's lives and relationships. This review also highlights potential risks of oversimplified frameworks, especially when they ignore cultural context. By building a science of relationships that is both rigorous and relatable, researchers can offer guidance that is more inclusive, useful, and responsive to the public.
Threatening a man's masculinity may lead to severe negative consequences at individual (e.g., anxiety) and societal levels (e.g., discrimination). Existing research is extensive and requires synthesis. In a comprehensive, systematic literature search, we identified and categorized peer-reviewed studies experimentally manipulating masculinity threat (MT) according to triggers, internalized responses, compensatory reactions, and moderators. We meta-analyzed studies with obtainable effect sizes (Nk = 442, Nparticipants = 19,448 predominantly heterosexual, mainly Western men) using three-level random effects models. The mean MT effect was small to medium, d = .45, 95% CI [.38, .51]. Self-induced prototypicality threat triggers (men concluding they do not meet masculine ideals) showed the greatest impact. Negative emotions and reduced self-perceived masculinity emerged as the strongest responses. Effects weakened after initial responding, suggesting successful compensation reduces the need for further efforts. Drawing on available evidence, we separate empirically supported MT components from open questions, offering a foundation for theory and research.Public Abstract Men are often expected to demonstrate masculinity. Questioning their masculinity can lead to masculinity threat. These threats can have negative consequences for the men themselves (e.g., emotional pain) and society (e.g., discrimination of gay men, women). To better understand when and how such threats and the following consequences occur, we conducted a meta-analysis on 123 studies involving 19,448 men. On average, studies showed that masculinity threats indeed influenced men's feelings, thoughts, and behaviors. Men were most threatened when they themselves concluded they did not conform to masculine ideals. Negative emotions (e.g., fear) and reduced feelings of masculinity emerged as the strongest consequences. Interestingly, when men initially showed masculine behaviors to reduce the threat, such as risk-taking, they were less likely to feel the need to prove their masculinity again later. We present a visual summary showing which aspects of masculinity threat are supported by evidence and which questions remain open.
Academic Abstract Advances in AI require a revision of the psychological and socio-technical dynamics by which individuals are radicalized to embrace violent extremism. This review synthesizes process models of radicalization with research on social and personality risk factors, AI, and psychological mechanisms to propose a four-stage framework mapping the AI architecture of radicalization: (1) Exposure, where recommender systems and virality features create initial attraction to extreme content; (2) Reinforcement, where filter bubbles and group recommendations leverage biases to strengthen extremist beliefs and create echo chambers; (3) Group Integration, where ideologically homogenous clusters, AI bot swarms and companions foster group belonging and readiness for action; cumulatively resulting in (4) Violent Extremist Action. We examine how established social, cognitive, personality, and contextual vulnerability factors heighten psychological risk in the AI-driven radicalization process, as well as the emerging role of generative AI. We conclude by outlining a stage-based framework for governance and future research.Public Abstract AI-driven algorithms designed to maximize engagement on social media, compounded by generative AI, can unintentionally set the stage for radicalization. It begins with Exposure, where algorithms push users toward extreme content because it captures attention. Next, during Reinforcement, algorithms feed users personalized content while AI swarms can create a synthetic consensus that reinforces emerging biases, normalizes extremity, and insulates users from alternative views. Third, during Group Integration, individuals are absorbed into extremist networks, reinforced by human peers, AI companions, and bot swarms that validate radical beliefs and deepen identity ties. By exploiting psychological needs for belonging and certainty, this stage becomes particularly pernicious, potentially opening the door for violence. We propose policy measures that can reduce radicalization at each stage.
Academic AbstractActive inference is an integrative theoretical framework that models the nervous system as a statistical engine for predicting and regulating sensory input. Within this framework, perception and behavior work toward the same imperative: minimizing uncertainty. The current article extends this approach to the inferences social agents make about themselves as both subjects and objects of experience. The resulting model conceptualizes "The Game of Self" as a continuous Bayesian updating of episodic and semantic self-representations in order to reduce self-related uncertainty. The model proposes a bidirectional predictive loop that evolves over time-semantic representations of identity guide the construction of episodic experience, while those experiences, in turn, shape semantic self-categorization. In both directions, the self-representations that emerge through active inference are those with the highest posterior probabilities given situational evidence. The article explores how episodic and semantic self-representations are continuously shaped by a dynamic and adaptive process of Bayesian inference.Public AbstractWho am I? What am I feeling? What should I do? These are fundamental questions that people ask themselves throughout their lives-and the answers can shape everything from small decisions to major life changes. But how do we come to know ourselves in the face of social and personal uncertainty? This article examines how the brain uses statistical modeling to make sense of identity and experience in an uncertain world. It introduces the concept of "The Game of Self"-an ongoing cycle between who we think we are and what we're experiencing. Our beliefs about who we are shape what we experience, and our experiences shape who we think we are. In each moment, our sense of self is the brain's best statistical guess about our current identity and lived experience. This framework offers new ways to think about selfhood-not as fixed, but as adaptive and responsive.
