How do people represent social networks in their minds? Inspired by work on spatial navigation, recent research reveals how people use domain-general computational principles to build cognitive maps for navigating their social environments. However, some aspects of our social worlds, such as the densely interconnected networks we are embedded in—and the dynamics of information flow within them—challenge the particular construct of a Euclidean cognitive map that has evolved in the study of spatial navigation. Recent research reveals different types of abstract representations people can use to build efficient cognitive maps for navigating social networks. We argue that to solve challenges inherent to navigating social relationships (e.g., figuring out whom to trust or gossip with, building coalitions made up of weak ties), people build cognitive maps of both the direct and indirect relational ties surrounding them. Although the incorporation of indirect ties makes these maps nonveridical, their addition aids in flexible, adaptive behavior, which can be used for successfully navigating any complex social environment.

Maintaining focus is a critical component of attention. Our ability to do so, however, changes over time—developing across the life span, declining as tasks drag on, fluctuating from one second to the next, and even oscillating at the subsecond level. Research in psychology and neuroscience has made great strides in understanding how and why attention varies—and how this variation affects other mental processes—at each timescale. To motivate the integration of findings across scales and facilitate theoretical advances and practical interventions to enhance focus, I propose a nested hierarchy in which changes are considered in the context of dynamics at all other scales.

The ability to learn from and remember experiences (episodic memory) depends on multiple neurocognitive systems. In this article, we highlight recent advances in methods and theory that are unveiling how mechanisms of attention impact episodic memory. We first provide a high-level overview of the construct and neural substrates underlying attention and related goal-state processes, along with their interactions with memory. We then highlight budding evidence supporting the rhythmic nature of memory and attention, raising key questions about the role that the oscillatory phase of attention rhythms plays on memory encoding and retrieval. Third, we consider how understanding age-related changes in memory and attention can be further advanced by assaying the precision of memory. Last, we illustrate how real-time closed-loop experiments provide opportunities to test causal relationships between attention and memory. Along the way, we raise open questions and future research directions about how attention-memory interactions enable learning and remembering in the mind and brain.

A genome-wide association study (GWAS) tests whether each of several million sites in the human genome is correlated with a trait of interest. For a number of reasons, including replication of GWAS results within families, we can be confident that significant correlations reflect in part the causal effects of DNA-level variation on the studied trait. This level of causal inference, much stronger than in most observational studies, enables some far-reaching conclusions about the antecedents and structure of human intelligence. We discuss some of these conclusions regarding whether brain size affects intelligence and the long-debated issue of how different intelligence tests are related to each other.

Vaccines are an important tool for preventing serious illness and avoiding deaths. Vaccine hesitancy, the delay or refusal of vaccines when available or offered, is one of the top 10 threats to global public health. The acceptance and uptake, delay, or refusal of vaccines has direct health implications for individuals, their close contacts, and indirectly for others in their environment and wider social networks. Vaccination uptake/hesitancy is the product of human decision-making and is influenced by various psychological and social factors, including perceived social norms. Individuals will often consider others’ vaccine-related attitudes and/or behaviors to guide their own decision-making. One potential way of reducing vaccine hesitancy is by changing people’s (mis)perceptions of these vaccine-related social norms through feedback interventions that highlight the actual vaccination norms (e.g., that most others would take a vaccine if offered). This article takes a social norms perspective toward understanding vaccine hesitancy, discusses how and why perceived social norms may be influential in hesitancy, and outlines ways psychological science can better understand the perceived social norms implicated in vaccine hesitancy.

Positive affect is a fundamental component of well-being, influencing multiple domains of psychological and physical functioning. This article synthesizes empirical research on positive affect dynamics in naturalistic contexts, emphasizing their associations with mental- and physical-health outcomes. Although a substantial amount of research has investigated positive emotional experiences through trait-based and state-based measurement paradigms, recent methodological innovations highlight the temporal dynamics of affective experiences within individuals across multiple timescales. Here, we examine how key temporal properties—including variability, instability, inertia, and reactivity—relate to adaptive functioning and health-relevant outcomes. These dynamic approaches extend traditional assessment frameworks, offering greater predictive utility for understanding health trajectories beyond static measures. Despite these advances, significant challenges remain in measuring, modeling, and integrating affective processes across diverse temporal resolutions and contexts. Addressing these issues requires refined methodological approaches that enhance precision and interpretability. We conclude by outlining a forward-looking agenda for advancing positive affect dynamics research, emphasizing its potential applications for promoting health and resilience.

Hearing a single word can initiate a sequence of activation that spreads from the representation of the word (e.g., “candy”) to words that share auditory and visual form (e.g., “candle”) and the concepts those words reference (e.g., the idea of a “candle”). In bilinguals, this coactivation spreads both within and across languages to words that share form or meaning in either or both languages. This parallel activation across two languages has cascading effects on higher order cognitive functions such as attention (e.g., what people focus on in a visual scene), memory (e.g., what people remember seeing), and semantic organization (e.g., how concepts are represented and grouped on the basis of their meanings). Here, we consider how the consequences of language coactivation extend beyond the linguistic domain to impact the broader cognitive system and conclude that the interactivity of languages in the bilingual mind fundamentally transforms mental operations.

People appear to excel at generalization: They require little experience to generalize their knowledge to new situations. But can we confidently make such a conclusion? To make progress toward a better understanding, we characterize human generalization by introducing three proposed cognitive mechanisms allowing people to generalize: applying simple rules, judging new objects by considering their similarity to previously encountered objects, and applying abstract rules. We highlight the systematicity with which people use these three mechanisms by, perhaps surprisingly, focusing on failures of generalization. These failures show that people prefer simple ways to generalize, even when simple is not ideal. Together, these results can be subsumed under two proposed stages: First, people infer what aspects of an environment are task relevant, and second, while repeatedly carrying out the task, the mental representations required to solve the task change. In this article, we compare humans to contemporary AI systems. This comparison shows that AI systems use the same generalization mechanisms as humans. However, they differ from humans in the way they abstract patterns from observations and apply these patterns to previously unknown objects—often resulting in generalization performance that is superior to, but sometimes inferior to, that of humans.

When people feel bad, how much effort should they invest to make themselves feel better? Should they try harder, or might this even make matters worse? In other domains of self-regulation, effort contributes to goal-related behaviors and success. However, it is unclear whether, when, or for whom effort is beneficial, irrelevant, or harmful for emotion regulation success and psychological health. In this article, building on theories of motivation, we first discuss how and when effort determines success in emotion regulation and review relevant empirical evidence. We then provide an analysis of two key factors that might predict effort in emotion regulation. Finally, we highlight open questions and future directions.