Human psychoneuroimmunology research has demonstrated that stress, depression, and close relationships reliably shape immune and endocrine function in ways that matter for health. Across studies of examination stress, laboratory stressors, marital discord, cancer survivorship, and dementia caregiving, psychosocial adversity predicts more infections, weaker vaccine responses and faster erosion of vaccine protection, slower wound healing, heightened inflammation, and accelerated cellular aging. Depression also sensitizes immune function, producing larger inflammatory responses when individuals encounter stressors. Loneliness, low support, and distressed relationships can amplify stress reactivity and are linked to greater inflammatory and metabolic vulnerability, including postprandial inflammatory and endothelial responses. More recent work has extended these pathways to the gut microbiome and intestinal permeability (leaky gut), integrating microbial, neuroendocrine, and immune mechanisms. Collectively, the evidence supports a biobehavioral model in which social stress accelerates immune aging and increases risk for inflammation-related disease, while behavioral and nutritional interventions can modify these trajectories.
Question asking, a foundational aspect of human communication, is an integral part of information-seeking behavior. We delve into the complex landscape of question-asking theories, exploring their diverse intersections with a myriad of fields-such as early development, artificial intelligence, and education. Synthesizing decades of research on question asking across different domains, we introduce the Question Asking in Information Seeking (QuInS) framework. QuInS provides a unified perspective on the iterative, dynamic processes that drive inquiry through question asking. We examine the early childhood origins of question asking, theories of inquiry, and the role of questions in fostering creativity, learning, problem solving, and social contexts. Finally, we explore how recent computational advancements contribute to formulate and empirically assess questions. Given the ubiquity of question asking in daily life, it is critical to elucidate its nature and mechanistic role in identifying and filling knowledge gaps. Such an endeavor will drive us to be more efficient, effective, and creative learners and question askers.
Changing behavior is a central challenge in domains ranging from health and finance to voting and education. Drawing on decades of research from psychology and related fields, this review synthesizes high-quality evidence-particularly randomized controlled trials with objective behavioral outcomes-to identify effective strategies for changing behavior. We propose a unifying three-phase framework that conceptualizes behavior change as a process involving: (a) building motivation, (b) following through, and (c) forming durable habits. For each phase, we identify key psychological challenges and review interventions designed to overcome them. By organizing a large and fragmented literature within a coherent framework, this review aims to clarify when and why behavior change interventions succeed and to inform more effective, behaviorally informed policies and practices.
Psychological wisdom research has shifted from characterizing rare exemplars and desired outcomes to specifying processes that support sound judgment under uncertainty. Yet it has advanced along two siloed research tracks: one on folk theories-cultural tools such as exemplars, narratives, heuristics (including proverbs and maxims), and standards of judgment-and the other on the mechanisms involved in wise judgment. This review bridges these research tracks using a situated metacognitive lens: Folk theories provide candidate attributes or strategies for action, whereas perspectival metacognition-the capacity to recognize epistemic limits, coordinate viewpoints, and track uncertainty and change-regulates their context-sensitive selection and use. We synthesize evidence connecting wisdom-related processes to emotional balance, relational well-being, cooperation, and reduced polarization, while noting boundary conditions. We show how this synthesis sharpens measurement trade-offs, highlighting the limits of global self-report and the advances in situated assessment. Finally, we summarize work on wisdom development and cultivation and consider the socioecological implications of wisdom research in an AI-shaped world.
Implicit bias has been an influential concept in psychology for the same reasons that it has been controversial: It suggests that processes outside of individuals’ control, and possibly outside their awareness, may lead to biased and discriminatory behavior. Such a mechanistic explanation for such morally fraught behavior was bound to be controversial. This article overviews the current state of the implicit bias literature with an emphasis on criticisms, both conceptual and empirical, and the ways that implicit bias research has changed in light of those criticisms. I argue that successive methods and theories have gradually improved on earlier efforts in a way that has produced cumulative progress in this field. The article ends by sketching questions for future research and with a call to better integrate person-focused and context-focused theories.
Large language models (LLMs) are entering psychological research both as tools and as objects of inquiry. Yet many studies apply human instruments to LLMs without establishing that the outputs are reliable or interpretable, raising the risk of measurement phantoms-statistical regularities mistaken for genuine psychological phenomena. This review argues that robust AI psychological research requires integrating two methodological traditions: psychometric validation of what a score means and causal inference standards for what the results warrant. It develops a dual-validity framework in which evidentiary demands scale with scientific ambition: from tool use through behavioral characterization and human simulation to cognitive modeling. Classifying text may require only accuracy and reliability; claiming that an LLM simulates anxiety or illuminates cognitive mechanisms requires additional evidence, including construct validity evidence and experimental controls. Progress depends on developing computational analogs of psychological constructs rather than assuming human measures automatically apply to language models.
This paper focuses on the problem of short video addiction among adolescents, deeply analyzes its causes, and provides coping strategies for parents. The study finds that short video addiction originates from multiple factors, including plat-form characteristics, adolescents’ psychological traits, family environment, and social environment. To prevent children from becoming addicted, parents should strengthen parent–child communication and companionship, set a good example, cultivate children’s media literacy, and establish family rules. If chil-dren are already addicted, parents need to be keenly aware, communicate openly with them, enrich their lives, pay attention to their psychological needs, and seek professional help when necessary. It is expected that families, schools, and society will form a joint force to guide adolescents in using short videos correctly and promote their healthy development.
Social robotics is a rapidly advancing field dedicated to the development of embodied artificial agents capable of social interaction with humans. These systems are deployed across domains such as health care, education, service, and entertainment-contexts that demand nuanced social competence. Yet, the social dimension of social robotics remains insufficiently conceptualized and empirically grounded. Many companies have failed as their robots struggle to sustain meaningful, long-term engagement with users. Understanding human responses to these agents requires robust psychological frameworks. While prior work has emphasized emotion expression and affective cues, human social interaction is shaped by broader constructs, including individual goals and roles, self-presentation, and culture. Generative artificial intelligence is reshaping human-robot interaction but has yet to resolve foundational challenges in social engagement. Addressing these gaps necessitates deeper integration of psychological theory, methodology, and data. A sustained dialogue between psychology and robotics holds promise not only for advancing socially adept machines but also for enriching psychological science itself.
Intensive longitudinal methods (ILMs) represent a class of longitudinal designs used to understand the flow of people's thoughts, feelings, physiology, and behaviors in their natural settings. This term encompasses daily diaries, experience sampling, ecological momentary assessment, ambulatory assessment, and related methods. Research on ILMs has grown exponentially, evolving into a core approach that complements more traditional designs. This article builds on this journal's first review on this topic, published in 2003. In the quarter-century since, there have been marked advances in design, technology, and statistical modeling. Three core ideas permeate this review: To build adequate theories of psychological functioning in natural settings, researchers must focus on ( a ) kinematics, ( b ) dynamics, and ( c ) heterogeneity. Kinematics answers the question, What happened? Dynamics answers the question, Why did it happen? Heterogeneity answers the question, How much do people vary in the whats and whys? ILMs can address these three goals of psychological science.
This article provides a critical overview of research on human rationality. Rationality research poses a number of unusual challenges to psychologists. For one, it is unusually interdisciplinary and involves research conducted in adjacent disciplines (e.g., economics, education, communication, computer science and philosophy), not all of which are accessible with psychological training. What underlies this diversity, however, is an arguably even more unusual feature: the fact that even purely descriptive research, focused on what we actually do, cannot proceed without reference to normative considerations, that is, considerations of what we ought to do. Empirical results can thus be understood only with some understanding of the relevant norms of rationality. This article introduces the range of relevant frameworks, followed by examples of the different ways these frameworks are put to use. The bulk of the article then surveys research findings on human rationality across the core areas of (probability) judgment, reasoning, decision-making, and argumentation. Two final sections provide cross-cutting themes, one on the contrast (and interrelationship) between individual and collective rationality and one on the unique challenges of linking rationality research to real-world concerns.
My career began with the exciting beginnings of cognitive psychology. It took me to memory, mental representations, categorization, spatial cognition, language, event cognition, stories, discourse, visualizations, comics, gesture, joint action, creativity, design, and more. On the way I enjoyed collaborations with friends and students in many areas and many countries. I am slowing down just as brain, AI, computational models, and big data are taking over the field, bringing new methods and new ways of thinking and, with that, new talent and inspired minds.
The advent of noninvasive imaging methods like functional magnetic resonance imaging (fMRI) transformed cognitive neuroscience, providing insights into large-scale brain networks and their link to cognition. In the decades since, the majority of fMRI studies have employed a group-level approach, which has characterized the average brain—a construct that emphasizes features aligned across individuals but obscures the idiosyncrasies of any single person's brain. This is a critical limitation, as each brain is unique, including in the topography (i.e., arrangement) of large-scale brain networks. Recently, a new precision fMRI movement, emphasizing extensive scanning of single subjects, has spurred another leap in progress, allowing fMRI researchers to reliably map whole-brain network organization within individuals. Precision fMRI reveals a more detailed picture of functional neuroanatomy, unveiling common features that are obscured at the group level as well as forms of individual variation. However, this presents conceptual hurdles. For instance, if all brains are unique, how do we identify commonalities? And what forms of variation in functional organization are meaningful for understanding cognition? Which sources of variability are stochastic, and which are due to measurement noise? Here, we review recent findings and describe how precision fMRI can be used ( a ) to account for variation across individuals to identify core principles of brain organization and ( b ) to characterize how and why human brains vary. We argue that, as we dive deeper into the individual, overarching principles of brain organization emerge from fine-scale features, even when these vary across individuals.
Our memories shape our perception of the world and guide adaptive behavior. Rather than being a veridical record of experiences, memory is selective. An accumulating body of work suggests that motivational states, emerging from the interplay between internal and external demands, play a critical role in determining what information is encoded in memory and how. Central to the regulation of motivational states are dopaminergic and noradrenergic neuromodulatory systems that can coordinate brain activity to determine how information is propagated, shaping memory outcomes. In this review, we propose that motivational states supported by the dopaminergic ventral tegmental area would facilitate the formation of flexible associative memory, while the noradrenergic locus coeruleus would facilitate unitized goal-relevant memory. By considering how neuromodulatory systems can support different neural contexts, we aim to explain how motivation enables an adaptive memory system, and in bridging motivation and memory, we aim to offer a framework for insights applicable to education and clinical practice.
Originally postulated in 2001, the impaired response inhibition and salience attribution (iRISA) model of addiction highlights the prefrontal cortex (especially the orbitofrontal, dorsolateral, anterior cingulate, and inferior frontal regions) as central to drug addiction symptomatology. Accordingly, drug cues assume a heightened salience and value that overpower alternative reinforcers, with a concomitant decrease in inhibitory control, especially in a drug-related context. These processes may manifest in metacognitive impairments (e.g., self-awareness of choice), obstructing insight into illness, as a function of recency of drug use. In this review, we update the neurobehavioral evidence for iRISA two decades later, emphasizing the robust measurement of the iRISA interaction (between a drug-related cue/context and a cognitive-behavioral function), and highlight relevant individual differences (e.g., drug use severity, craving). Crucially, we describe data suggesting functional recovery (with abstinence, treatment, and other emerging modalities) and the need for identifying valid outcome biomarkers. We end by highlighting recent developments in artificial intelligence (e.g., natural language processing applied to spontaneous speech) and computational modeling, and call for enhanced ecological validity to facilitate dynamic and clinically meaningful neural explorations in drug addiction.
How a developing nervous system discovers meaning in complex sensory inputs has typically been examined separately for each sensory modality. Even as studies have uncovered modality-specific strategies, it remains unclear whether common principles underlie such discovery. Here, we pursue the thesis that the detection and exploitation of temporal regularities may provide a unifying mechanism for sensory organization across modalities. We synthesize research spanning neurophysiology and cognitive neuroscience and incorporate results from theoretical computer science. This integration supports the conclusion that time may be the fundamental dimension along which the brain organizes its sensorium and that the computational complexity of this problem is rendered tractable by ecologically appropriate heuristics. This proposal suggests the centrality of temporal processing in perceptual development, with implications for studies of typical and atypical development, clinical populations, and computational modeling.
This review synthesizes and critiques research on early life adversity and stress effects on multidomain health outcomes in child samples to fill a gap in the literature that has largely focused on adults. Prioritizing evidence from meta-analytic and systematic reviews as well as findings from (quasi-)experimental or large prospective longitudinal studies, we integrate interdisciplinary findings to characterize patterns of evidence for stress associations with child outcomes, including mental, physical, and positive health; academic, social, and justice system-related domains; and intermediary phenotypes that may predict disease, including biomarkers. We note cohesive evidence for sensitive periods of susceptibility to stress exposure and describe key mediators and moderators of stress effects, especially family-level factors. Then we highlight interventions targeting malleable factors that hold promise for ameliorating the effects of stress on children. Leveraging a developmental lens, we conclude with field-wide limitations and propose future directions for stress and health research that centers child development.
A robust approach to understanding dyadic emotion regulation needs to incorporate insights from affective science and relationship science. To date, research emerging from these two traditions has largely unfolded separately with limited cross-disciplinary collaboration. Here we review research from these two disciplinary perspectives, focusing on social support and dyadic coping in the close relationship literature and on extrinsic interpersonal emotion regulation in the affective science literature. We also present a framework of dyadic emotion regulation. This framework includes both affect-improving and affect-worsening processes that can be motivated by hedonic or instrumental goals and that can have effects not only on the emotions targeted for regulation but also on the relationship dynamics of the dyadic partners. We identify key gaps in the literature and directions for future research, and we conclude that recognition of the complex interplay between emotion regulation and relationship processes allows for deeper and more nuanced models of dyadic emotion regulation.
The past decade has seen a surge in developing just-in-time adaptive interventions (JITAIs)—an intervention approach that leverages advancements in digital technologies to address the rapidly changing needs of individuals in daily life. This article provides an overview of the state of science on JITAI development and highlights important directions for future research. We explain what a JITAI is (and what it is not) and review the scientific and practical rationales underlying this approach. We also call attention to three key challenges relating to the development of JITAIs. The first challenge is that individuals may not be able to engage with (i.e., invest energy in) an intervention when they need it most in daily life. The second concerns the generally suboptimal engagement of individuals in interventions that leverage digital technologies as currently implemented. The third concerns the paucity of research on ways to harness the power of social relationships in JITAIs. We conclude that much research effort is needed to build more sophisticated and effective JITAIs.
Domain-general object recognition ( o ) is the ability to discriminate between objects at the subordinate level. It describes the general ability that applies across object categories, in contrast to abilities that apply only to a specific category. Interest in this ability emerged from vision research and cognitive neuroscience. However, research into high-level visual abilities has been relatively independent of the wider literature on individual differences in abilities. This review seeks to bridge this gap. To assess whether o represents a novel construct, we compare it with the closest preexisting constructs. We argue that abilities such as visual memory and perceptual speed share conceptual overlap with o , but none of these abilities have the kind of subordinate-level discrimination at their core that o does. Despite theoretical differences, some tests of these constructs may serve as adequate indicators of o . We also connect o to theory about the structure of cognitive abilities.
