What are the origins of intelligence? We argue that intelligence develops when flexible, domain-general brains adapt to experiences produced by structured, domain-optimized bodies. Brains are highly plastic and can assume many possible functional configurations, whereas bodies (e.g., retina, cochlea, skin, muscles) form relatively stable structures that shape the information available for learning. By filtering raw input into structured patterns, bodies generate the experiences that allow domain-general brains to develop domain-specific knowledge. Drawing on controlled-rearing studies of newborn animals and embodied AI models, we show how object perception—a core mental skill often viewed as innate—develops automatically when flexible neural networks adapt to first-person sensory experience. The body contributes at multiple stages: generating prenatal activity that establishes initial cortical organization, generating postnatal experiences that drive object learning, and structuring visual input to enhance object perception. Intelligence across biological and artificial systems emerges from the interaction between flexible brains and structured bodies.
The theory of sexuality as gendered holds that sexual orientation and gender are inseparably linked, shaping individual experience and perceptions as well as sociocultural systems. Although long recognized in feminist and queer theory and analyzed in some domains in the psychological sciences, this connection has the potential for much broader reach. This article advances the framework by outlining three broad hypotheses: Beliefs and behaviors toward lesbian, gay, and bisexual people will be grounded in gender norms; institutional treatment of sexual minorities will mirror patterns of gender equality and treatment of women; and known gender differences will reveal analogous differences by sexual orientation. Together, these hypotheses highlight the generative scientific potential of viewing sexuality and gender as intertwined rather than separate dimensions.
Generative AI (GenAI) has transformed many aspects of modern life. In this article, we explore the potential of GenAI for personalized persuasion: the process of tailoring the content of a persuasive message to an individual’s unique preferences, needs, and motivations. First, we describe how GenAI addresses the “identification-customization” challenge in personalized persuasion, and we offer a scoping review of the research supporting its effectiveness to date. Second, we introduce a framework that allows researchers to classify a study’s empirical approach according to several critical design choices, including (a) the data sources and types of personal information used to determine the personalized elements, (b) the complexity of the personalization approach, and (c) the delivery of the personalized content. This framework facilitates the integration of existing work and identifies important gaps in our current knowledge. We discuss promising avenues for future research throughout and conclude with a discussion of important ethical considerations.
Social anhedonia, the inability to experience social pleasure, is a transdiagnostic symptom. Current research on social anhedonia adopts a monolithic approach to evaluate and treat this complex symptom. In this article, we reconceptualize social anhedonia using a triune framework encompassing functional, ecological, and ethological validity. This framework can better guide the future development of assessment tools and interventions. Moreover, it addresses the online-versus-offline, contextualized-versus-decontextualized aspects of social anhedonia in the digital era. We argue that future assessment tools should adopt virtual reality and multimodal data-collection platforms. Personalized, dimension-specific interventions are likely to be more effective. Our timely revisit paves ways for more sophisticated, context-sensitive evaluations and interventions for social anhedonia.
Institutional rearing in infancy and early childhood followed by adoption into well-resourced homes provides information on the long-term sequelae of adverse care limited to the earliest years of life. The current article synthesizes research on cortisol stress reactivity and emotional functioning among previously institutionalized youth from early childhood through young adulthood. It examines potential mechanisms through which alterations in cortisol reactivity following early institutional care may contribute to behavioral and mental-health outcomes. Evidence is summarized showing that blunted cortisol reactivity is linked to emotional difficulties in previously institutionalized children during early and middle childhood, followed by recent findings that pubertal increases in cortisol reactivity toward more typical levels are paradoxically associated with heightened internalizing symptoms. The article concludes by outlining directions for future research to clarify the mechanisms connecting cortisol stress reactivity with psychological adjustment, with the goal of advancing understanding of emotional development following early adverse rearing.
Youth mental health problems have increased dramatically in the past decade, whereas the availability of mental health providers has not. This workforce shortage reflects complex dynamics that make it difficult to increase the supply of providers through existing graduate-training pipelines. Task shifting involves transferring the delivery of healthcare services to providers with less specialized training and is one strategy with potential for growing the behavioral health workforce. Here we review evidence that nonspecialist providers can effectively deliver behavioral health services and argue that a bachelor’s-level mental health profession with standardized training in evidence-based practices may be a solution to growing workforce shortages. We describe the undergraduate program at the Ballmer Institute for Children’s Behavioral Health as a model for how to grow the youth behavioral health workforce through the creation of a bachelor’s-level profession with a scope of practice emphasizing the early identification and prevention of mental health problems. The replication of this program at other universities is underway alongside program evaluation and policy work to create the credentialing and billing mechanisms needed to support sustainable employment pathways for a new profession. We invite partnership with universities interested in joining the movement to create a coordinated workforce development solution to the youth mental health crisis.
This article reviews two case studies in which AI systems were evaluated for abstraction and analogy-making capabilities and compared with those of humans. These studies illustrate how AI systems should be evaluated not only for accuracy on benchmark tasks but also for robustness to task variations and for insight into how the system is solving the tasks. These studies also illuminate the need for transparency, interpretability, and scientifically informed experimental methodology in AI evaluations.
Whether people of color compete or coalesce has significant social and political ramifications. The Racial Position Model, which reveals that people of color in the United States are subordinated along the dimensions of perceived status and cultural foreignness, provides a theoretical framework through which to study intraminority relations. Asian, Black, and Latino Americans are divided from each other along both dimensions of the Racial Position Model, contributing to the perception of distinct group interests and goals that can impede coalition building across racial and ethnic lines. At the same time, by strategically emphasizing groups' experiences with discrimination along shared or "matched" dimensions of subordination, group positions in the Racial Position Model can be leveraged to promote greater solidarity.
How do children learn in everyday situations? This article reviews recent findings of cultural variation in effective support for infants' learning and navigating challenging situations to illustrate a culture-first approach to studying early learning. It contrasts the paradigm that treats culture as a variable added to a generic process for differentiating outcomes across groups. Culture-first research begins with analyzing cultural frameworks of how learning is defined, practiced, and fostered by the focal community, which informs the selection of behaviors to measure. The culture-first approach shifts the perspective from deficit-oriented to strengths-based to identify different profiles of parental guidance that effectively support early learning and various strengths that children begin to develop during infancy. The article underscores the need to bridge domains and cross disciplinary lines toward an understanding of learning that is culturally construed from the outset to inform strategies to support all learners.
Many innovations have come from people working together as partners in thought. These partnerships, however, are not restricted to single encounters. Some of the most meaningful collaborations evolve over weeks, months, or even lifetimes. What are the core computations that enable long-term thought partnerships? Prior work in cognitive science has made initial progress by investigating how people construct mental models of their partners on the fly, establish common ground using language and other modalities, and generate joint plans that lead to successful outcomes. However, it remains unknown what cognitive mechanisms enable such interactions to evolve into genuine partnerships over longer timescales, especially under measures of success that extend beyond task performance. Theoretical and empirical progress on these issues could be instrumental for defining and designing AI systems that may even be capable of establishing long-term thought partnerships with humans. This article outlines several promising avenues for leveraging approaches from cognitive science and AI to study enriching intellectual partnerships.
For more than a century, experimental research on human memory has focused on individuals learning and remembering in isolation; memory scientists began a study of social influences in earnest only in the last 3 decades. A key phenomenon driving this research is collective memory, or defined in cognitive terms, memories that a group of people share. While attention has focused on the overlap in the contents of memory, we focus on deeper representations of these shared memories, namely overlap in their recall organization. Individual memory research has extensively examined how memories become organized, but this is a new arena of research on social remembering. We describe two novel applications of quantitative tools that capture at a global level the overlap in how people organize the contents of their collective memory. These two tools provide different approaches for capturing the ways in which elements become organized and give us holistic views of how people represent the interconnections in memory for a particular episode or theme. This initial research assessing the development of overlapping memory organization offers a stepping stone toward understanding how collective memory narratives and schemata emerge and potential consequences for future learning.
Behavioral scientists aim to explain and predict behavior. In principle, these goals align; in practice, common approaches to pursuing them have become distinct traditions in tension with one another. The explanatory tradition often examines causal factors in isolation, establishing that they have some effect but not how much or how they combine. The predictive tradition learns how factors combine, but these patterns may not reflect a causal structure or hold when conditions change. Answering how much each factor matters, and how they combine across settings, requires both predictive accuracy and causal interpretation. This article examines three developments toward this integration: evaluation frameworks that emphasize generalization, systematic experimentation and flexible models, and interpretation tools. We present recent empirical examples that demonstrate how this integration enables the discovery of generalizable patterns and provides a path toward cumulative behavioral science.
An extensive body of research on human and nonhuman animals has examined responses to clear valence, including stimuli that either represent a relatively clear threat (e.g., electric shock) or a clear reward (e.g., money). But daily life is replete with events or situations that are ambiguous-they could be threatening and/or rewarding. This article describes work that examines the wide interindividual variability with which humans respond to this dual-valence ambiguity. Although some individuals more readily categorize these events as negative, others are prone to arrive at more positive categorizations. This predilection to lean in a more negative versus positive direction represents a stable, trait-like difference and is referred to as "valence bias." Valence bias is generalizable across categories of dual-valence ambiguity and has important implications for health and well-being. Here I focus on extensive findings that lend support for the initial negativity hypothesis, which posits that the initial or default response is negative across people and that positivity relies on an additional regulatory mechanism that helps to overcome the initial negativity. I also describe the cognitive and brain mechanisms underlying the valence bias, including emphasizing the importance of a broad set of systems that support human responses to ambiguity.
Individual variations in face-perception expertise become apparent by the second year of life. We propose that infants' "face diet"-the nature and quantity of their visual interactions with faces-provides a useful lens for understanding how individual differences in face perception arise. In this article, we discuss how the diversity of an infant's face diet and their interactions with caregivers shape their face-perception and social-learning skills, how a masked face diet may influence infants' face perception, and how neurodiversity may affect infants' face diets and learning about faces. These components underscore how face perception develops through both shared and individual pathways, with implications for identifying early-emerging challenges and designing supportive interventions. Future research opportunities include incorporating diverse contexts, improving measurement tools, and examining developmental periods beyond infancy.
The place-value concept is fundamental to understanding the symbolic number system. It dictates that the value of a digit in a number is based on its position or place within the number (e.g., the "5" in "510" is five units of 100, whereas the "5" in "51" is five units of 10). Place value is central to understanding multidigit numbers, performing arithmetic, and learning more complex math. Despite its significance, relatively little research has systematically examined the developmental trajectory and cognitive underpinnings of the place-value concept. In this article, we synthesize prior findings and propose a conceptual framework that delineates the core properties of the place-value concept and characterizes its developmental trajectory. We also identify key cognitive factors that may underpin individual differences in its acquisition. This framework can guide future research to understand how children acquire the place-value concept and how best to support this learning. It also has broad implications for understanding the cognitive architecture of human compositional symbol systems.
Human behavior is fundamentally generative: People create pictures, write stories, compose music, and engage in conversation. Traditional approaches in psychology and cognitive science have not focused on this open-endedness, instead favoring more constrained task settings that admit a limited set of outcomes. Although those approaches have been fruitful, new approaches might be needed to develop a unified understanding of the generative, open-ended behaviors that are so emblematic of human cognition. This article demonstrates the value of generative behaviors as targets for cognitive modeling by providing rich behavioral data that reveal how multiple cognitive processes coordinate. Drawing production serves as a case study illustrating this approach, showing how perception, memory, social inference, and motor control coordinate flexibly on the basis of communicative context. Recent advances in generative artificial intelligence offer both new tools for modeling open-ended human behavior and new comparative targets for understanding similarities and differences between human and machine intelligence. However, applying these tools effectively might require new experimental paradigms, larger data sets, and careful consideration of what mechanistic correspondence between models and human cognition is necessary for scientific progress. Embracing the open-ended nature of human thought and behavior poses methodological challenges but offers a promising path toward understanding the most distinctive aspects of human intelligence.
Greater numbers of people are turning to artificial intelligence (AI) for empathy and emotional support. Here, we review and synthesize recent empirical work on how people perceive empathy from AI versus from humans. Growing evidence points to two dueling effects and a paradox: AI produces language that is rated higher in empathy than language written by humans, but when people perceive text as coming from AI versus a human, they rate it as less empathic. However, despite sometimes rating AI more empathic or having to wait for human empathy, people still show a preference for human empathy. This emerging literature carries significant implications for fundamental research on empathy and for public discourse as the use of AI for emotional support continues to grow.
A consistent pattern emerges from the history of psychology: Technological advances change the way that we understand ourselves. We argue that, in addition to various uses that are already common (e.g., qualitative coding), large language models can be integrated into survey software and act as a virtual research assistant that can generate tailored stimuli on the fly. This creates unprecedented flexibility in developing materials for psychological theory testing. We present an illustrative case study to show how a major lingering debate in the field-that is, whether people really change their mind according to evidence or, instead, rely on motivated reasoning-was pushed forward by using artificial intelligence (AI) to administer personalized experimental treatments. We discuss various potential uses of AI to test hypotheses in psychological science and argue that psychologists should seriously consider using AI to better understand human intelligence.
This article calls for complementary human-AI intelligence. Rather than redefining intelligence to fit machine capabilities, we argue for designing AI that complements and extends human cognition. We distinguish between cognitive AI, which is grounded in cognitive science to model human perception, learning, and decision-making, and machine AI, which achieves large-scale performance through data-driven optimization. Building on advances in machine learning alignment and human-AI complementarity, we propose an integrative framework that connects cognitive and machine AI across four routes: embedding integration, aligning human and machine representations; instruction encoding, using machine AI to translate goals into cognitive AI; training agents, using cognitive AI to guide and train machine AI through human-like data; and coevolving agents, enabling cognitive and machine AI to coadapt and improve together over time. These integration routes provide a foundation for complementary intelligence: systems that combine human interpretability with machine scalability and precision to enhance trust, adaptability, and human agency in complex sociotechnical environments.
Persistence is essential for learning, but children cannot and should not persist at everything. How do young children decide what is worth their effort? We build a theory of young children's state persistence as the outcome of a socially guided decision-making process between children and caregivers. Integrating research from metacognition, decision-making, and social learning, we show how caregivers shape two key beliefs that guide children's effort: What children think they are capable of and whether their effort is worthwhile. Caregivers' actions, in turn, are guided by their own beliefs about children's abilities and the value of tasks, creating a dynamic social system of effort calibration. By reframing persistence as a dynamic coconstructed process, we uncover how motivation is built-and where it can break down.
