Personality and Social Psychology Review

A core part of human intelligence is the ability to work flexibly with others to achieve goals. The incorporation of artificial agents into human spaces is making increasing demands on artificial intelligence (AI) to demonstrate and facilitate this ability. However, this kind of flexibility is not well understood because existing approaches to intelligence typically construe this either as an individual-difference trait or as a property of groups. We argue that by focusing either on individual or collective intelligence without considering their dynamic interaction, existing conceptualizations of intelligence limit the potential of people and AI systems. To address this impasse, we propose a new kind of intelligence—socially minded intelligence—that can be applied to both individuals and collectives. We outline how socially minded intelligence might be measured and cultivated within people, how it might be modelled in AI agents, and how it might be applied to other intelligent systems.

In psychology, “intelligence” is generally understood to be something that either individuals or groups have. However, the extent to which people can make each other more intelligent by working collectively—and the extent to which groups are smarter for having individuals who can think for themselves—is underexplored. Artificial intelligence (AI) research has a similar problem, meaning that artificial agents lack the ability to engage in this kind of intelligence, both with each other and with people. To address this gap in the literature, we outline a new kind of intelligence for psychology and AI—socially minded intelligence—which can be applied to individuals, groups, and artificial agents. We discuss how socially minded intelligence might be measured, improved, modeled in AI agents, and applied to other intelligent systems such as teams consisting of people and AI agents.

Partisan animosity is on the rise in many nations around the globe. Given its significant implications, it is imperative to establish a clear conceptualization of partisan animosity that can aid efforts to reduce it. To address this need, we present a novel framework that conceptualizes partisan animosity as an attitude of blame directed toward political outgroups. Drawing from the literature on moral psychology, we construct a comprehensive model of the psychology of blame. Then, we use that model as an interpretive lens to understand existing interventions that have reduced partisan animosity. Finally, we suggest a variety of possible future interventions inspired by our framework. By adopting this blame-based perspective, our article sheds light on the underlying mechanisms of partisan animosity, provides a unifying framework for understanding existing work, and stimulates novel ideas for future research.

Partisan animosity, hostility directed toward political outparties, has been growing in many areas of the world, with significant negative impacts on society and politics. This article offers a new perspective on this growing animosity, proposing that partisan animosity reflects an attitude of blame that partisans direct toward each other. Drawing from insights in moral psychology, we present a model of blame, describing how it operates, and use the model to understand both the nature of partisan animosity and potential pathways for intervention. Our model contributes to understanding partisan animosity with the ultimate goal of informing interventions to reduce it.

Valence asymmetries—the tendency for bad stimuli to elicit more processing effort than good ones—have been widely observed but remain theoretically contested. To advance this debate, we present a formalized account integrating two major explanatory perspectives: the intrapsychic (or phylogenetic) approach, which locates the effect in internal evaluative mechanisms, and the ecological (or ontogenetic) approach, which attributes it primarily to environmental factors. We introduce a concise set of parameters to specify key concepts and analyze the argumentative structure of each perspective. This yields three major insights: (a) the traditional labels for these approaches are misleading, and we suggest using valence-driven and distinctiveness-driven instead, (b) theories must specify how exactly good and bad stimuli are defined, and (c) some explanations rely on implicit yet critical assumptions, such as the probability of having contact with stimuli. Clarifying these foundations provides a framework for informative empirical tests in future research.

Why do people pay more attention to bad things than to good ones? Psychologists call this pattern a valence asymmetry. Although the effect is well established, its causes are still debated. To advance this debate, we translate two leading ideas about this bias into a precise mathematical model. This allows us to see how the explanations differ, what each predicts, and where they overlap. This analysis reveals three important insights: First, some widely used terms are misleading, and we suggest using clearer alternatives like valence-driven and distinctiveness-driven instead. Second, researchers need to define more carefully what actually counts as good or bad. Third, many theories rely on hidden assumptions—such as how often people encounter certain kinds of stimuli. Making those assumptions explicit should help future studies test competing explanations more directly.

Children and adolescents are growing up in increasingly ethnically, racially, and culturally diverse and often unequal worlds. Therefore, how schools, as key developmental contexts, approach diversity has important implications at individual, group, and societal levels. Here, we present a review, integration, and expansion of research on cultural diversity climate in schools, focusing on five dimensions of diversity climate (the cultural diversity climate “Big Five”) and their facets. We begin by distinguishing cultural diversity climate from other related terms (e.g., diversity ideologies, diversity policies). Then, we discuss the five core dimensions of diversity climate (intergroup contact theory’s optimal contact conditions, multiculturalism climate, colorblind climate, critical consciousness climate, polyculturalism climate). We examine facets subsumed under the dimensions, propose new theory-based facets, and outline differences and commonalities between them. Next, we review measurement approaches and highlight future research directions. Overall, this integrative, multidimensional, and multifaceted framework advances psychological theory on diversity climate.

Schools play a vital role in how children and adolescents experience and understand diversity. This article reviews the concept of cultural diversity climate in schools, focusing on five key aspects that shape how cultural diversity is approached (i.e., the cultural diversity climate “Big Five”). Our integration of the cultural diversity climate “Big Five” encompasses five main dimensions, such as multiculturalism climate (acknowledging and valuing diversity at school), critical consciousness climate (critically reflecting on and challenging racism and inequities), and polyculturalism climate (raising students’ awareness of connections between groups and cultures). To move towards a more fine-grained understanding of diversity climate, we describe existing facets (i.e., sub-components of the different dimensions) and develop new facets based on theory. We also discuss different ways of measuring cultural diversity climate. This work is important for creating more inclusive, equitable, and critically conscious school environments, thereby supporting all students, especially those from marginalized communities.

Gender differences in sexuality are often attributed to evolved biological differences organized before adolescence or experiential ones learned afterward—neglecting learning that endures because it is evolutionarily expected, and we are biologically sensitized to it. Here, we present the Biodevelopmental Learning Opportunities and Outcomes Model (BLOOM) of gender differences in sexuality, arguing women’s lower interest in sex originates not from unequal capacities to want/desire it, but unequal opportunities to like/enjoy sex when biospsychosocially primed to learn from it. We synthesize evidence indicating sex is least equal in adolescence, offering the greatest costs and fewest rewards to women/girls who debut with men/boys (WDM). Concomitantly, it is most teachable in adolescence, when a window of opportunity for sexual incentive learning may open, particularly among individuals with heightened sexual plasticity/learning aptitude (i.e., women/girls). Implications for distinguishing gender differences in sexuality from experience-contingent similarities, and realizing equal sexual rights, education, and health are discussed.

Gender differences in sexual enjoyment are among the largest in psychology and have remained so over decades despite other advances in gender equality. The gender gap in sexual pleasure, for example, has gained widespread attention and is increasingly discussed as an explanation for gender differences in sexuality. Here, we spotlight the largest, but least discussed, gender gap in sexual enjoyment: the developmental gap. We review evidence that adolescence is not simply a vulnerable period for sexual health, but a window of opportunity for learning to have healthy, enjoyable, and desirable sex—and one wherein equal opportunity is lacking. We propose women get the least equitable sex during this window, when they are primed to learn from it, and this learning informs sexual interest thereafter, generating acquired differences that are often mistaken for inborn ones. We close with recommendations for ensuring equal opportunities for healthy sex and sexual health across genders.

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.

Active 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.

Who 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.

Popular 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.

Popular 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.