Childhood maltreatment shapes brain development in ways that vary by adversity type, developmental timing, and genetic vulnerability to psychiatric illness—a heterogeneity that conventional approaches capture poorly. We contend that normative modeling, multivariate methods, and polygenic risk scores offer complementary frameworks for parsing this variability and advancing mechanistic insights and clinical stratification.

Constructing novel states from experience underpins imagination and abstraction. The hippocampal formation is thought to contribute by capturing the relational structure of experiences, but it remains unclear how its abstract relational codes are eventually translated into cortical content representations. I highlight that hippocampal–cortical indexing may scaffold the generation of new states.

Generalizing from past experience is a fundamental feature of cognition. Although generalization has been of long-standing interest to diverse domains in psychological science, the past decade has seen a rapid acceleration of fear generalization research in particular. The field of human fear generalization is at a pivotal juncture where it is now possible to synthesize extensive behavioral work, competing theoretical models, animal and human neuroscience, and clinical and individual-differences research to address enduring gaps. In this review, we provide an updated account of where the field of human fear generalization currently resides and offer concrete avenues for new research emphasizing tighter integration between animal and human neuroscience, prioritizing cross-disciplinary perspectives, and updating conceptual frameworks to align with rapidly advancing methods.

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Language plays a central role in the development of executive functions (EFs). This forum article discusses how language, as a structured environmental stimulus, can shape EF systems and impact EF outcomes. This has implications for developmental and clinical conditions in which language and EFs are jointly affected, and for congenitally deaf children and adults, who often experience reduced access to natural language during development.

The nativist–empiricist debate has long organized cognitive science, framing cognitive mechanisms as either innate or learned. Recent advances in controlled rearing and scalable domain-general neural networks now allow this debate to be tested with unprecedented rigor, by raising newborn animals and deep neural networks under tightly matched developmental conditions. Strikingly, when pushed to this empirical limit, the debate does not resolve: the same developmental phenomena support both radical empiricism and radical nativism. We argue that this stalemate reflects a conceptual error. The debate conflates explanatory levels and ontological orientations, with nativists emphasizing stable symbolic structures and empiricists emphasizing the subsymbolic processes that produce those structures. We propose dissolving the dichotomy within a multilevel framework of cognitive development.

Evidence shows that aesthetic evaluations are underpinned by mechanisms in the reward system that compute value for objects of interest. In turn, recent behavioral, computational, and neuroimaging evidence suggests that creativity involves a similar valuation process, as people generate and evaluate the originality and adequacy of their ideas—two standard criteria of creativity. These findings suggest that creativity and aesthetic evaluation may be supported by a domain-general mechanism for computing subjective value. We offer a proposal in which creators’ value computations are conveyed by the structure and content of their works and recovered during aesthetic evaluation through shared reward-system mechanisms and associated processes. On this view, creating and appreciating are two related expressions of a shared value-based process.

Meditation research is increasingly moving beyond mindfulness and other early-stage practices toward advanced meditation, encompassing states, stages, and endpoints that progressively unfold with increasing expertise through meditative development. This emerging subfield consists of interdisciplinary research that brings advanced meditation within reach of systematic scientific investigation. Here we articulate this research subfield, including its constitutive conceptual and methodological components and interrelated translational domains: philosophical and theoretical foundations, computational phenomenology, cognitive neuroscience, epidemiology and public health, clinical and well-being outcomes, and relations with psychedelic experiences. This subfield represents an important development for meditation research and the mind sciences more broadly, with implications for health and well-being and for empirical understanding of the human mind.

How a scientific community is organized can affect whether disagreement drives progress or leads to isolation. Students and postdocs are uniquely placed to build communities that bridge theoretical and methodological divides. Drawing on a student-led society in consciousness science, we show how empowering early-career researchers can turn fragmentation into collaboration.

Cognitive flexibility allows us to adjust behavior when environmental rules change and to recombine prior knowledge in novel situations. However, the computations underlying rule discovery remain unclear: how do rule representations emerge as we interact with the environment? While it is often considered a hallmark of human behavior, converging evidence highlights comparable forms of abstract learning in rodents. Across species, prefrontal activity patterns encode structured representations that support generalization and monitor for rule switches. In this review, we synthesize recent behavioral, neural, and computational work highlighting models that formalize rule inference and creation. We consider how a translational framework linking circuit-level mechanisms to computational principles can help us understand disruptions in cognitive flexibility in disorders such as schizophrenia.

Cognitive maps are essential for navigation, but to what extent are they involved in thinking more broadly? In this opinion article, we examine whethe…

Cognitive maps are essential for navigation, but to what extent are they involved in thinking more broadly? In this opinion article, we examine whether cognitive maps constitute a genuine vehicle for thought and how they relate to the notion of a language of thought. Integrating work from philosophy, psychology, and neuroscience, we suggest that map-like representations offer a distinct, flexible medium for encoding information and exhibit compositionality in both spatial and nonspatial domains. We then consider how dynamic neural mechanisms—replay, theta sequences, and vector computation—may transform map-based representations into sequential routelike formats that interface with cortical systems supporting propositional thinking. Cognitive maps may be complementary to symbolic cognition, jointly enabling flexible human reasoning.

Spontaneous fluctuations in pupil size are correlated with neural activity and behavior in many ways. This is generally attributed to a single underlying cause: arousal. This unitary account disregards other potential underlying causes. To address this gap, we propose a comprehensive framework of four mechanisms that jointly underlie the complex and context-dependent relationships between spontaneous pupil-size fluctuations, neural activity, and behavior. In addition to arousal, we consider optical effects, brightness constancy, and self-generated visual stimulation. Based on this framework, we argue that pupil fluctuations should be considered beyond arousal, as a functionally important, yet still poorly understood, component of visual processing.

Children tend to explore broadly, and, despite its costs, exploration confers important benefits, especially early in development, when they know little. What drives this exploratory behavior? In contrast to accounts that emphasize heightened curiosity or increased randomness, we propose that early exploration reflects the immaturity of the working memory–selective attention system.

The weight of body parts is largely hidden from conscious experience. People underestimate the weight of their limbs, whereas individuals in clinical populations often experience them as abnormally heavy. We argue that limb weight is a fundamental dimension of internal body representations and a conscious reflection of the functional state of sensorimotor processing.

Understanding human decision-making processes in everyday life is a central, yet rarely addressed, challenge in psychology. Either real-life complexity is reduced by isolating specific aspects of decision-making in highly constrained experimental settings, yielding insights into specific cognitive mechanisms under idealized conditions, or decision-making is studied in real-life contexts, using high-level descriptions of behavior that do not afford fine-grained, process-level insights. Bridging this gap poses a challenge of both measurement and inference. Recent advances in high-resolution tracking technologies provide novel solutions to many measurement challenges but are rarely integrated with formal psychological theory. In this article, we review tracking technologies and statistical tools, proposing a cognitive–computational framework that uses high-resolution spatiotemporal data to investigate the mechanisms of real-life decision-making in a theory-driven manner.

Memory routinely transforms continuous experience into discrete episodes that support flexible retrieval. We propose that these structured representations are established via rapid neural reactivation occurring at the completion of an event. A growing body of evidence across tasks and recording modalities shows that event termination elicits rapid reinstatement of recently encoded neural representations. We review findings revealing that end-of-event reactivation is a robust and functionally meaningful phenomenon, describing when it occurs, how it is measured, and which neural signatures characterize it. We propose that neural reactivation at event offsets offers an efficient neural mechanism for stabilizing newly formed traces against interference, binding relational features into a coherent event index, integrating episodes into pre-existing schemas, and preserving temporal continuity across events.

Differences between musicians and nonmusicians are routinely attributed to training-induced plasticity and transfer, even though pre-existing factors provide equally plausible explanations. We propose repositioning correlational research not to infer training effects, but to constrain causal accounts, reveal the neurocognitive organization of abilities, guide experimental studies, and integrate with genetically informed designs.

Recent studies reveal striking representational alignment between artificial neural networks (ANNs) and biological brains, leading to proposals that all sufficiently capable systems converge on universal representations of reality. We argue that this claim of Universality is premature. We introduce the Umwelt Representation Hypothesis, which proposes that alignment arises not from convergence toward a single global optimum but from overlap in the ecological constraints under which systems develop. We review empirical evidence showing that representational differences between species, individuals, and ANNs are systematic and adaptive, which is difficult to reconcile with Universality. Finally, we reframe ANN model comparison as a method for mapping clusters of alignment in the ecological constraint space rather than as a search for a single optimal world model.

In recent years, several claims have been put forth suggesting that animals of different species address each other using receiver-specific calls that function as individual names. If accurate, this w...

Science chats with cognitive psychologist Kelly Jaakkola about why this complex behavior is so difficult to find in the animal kingdom

Cognitive science faces ongoing challenges in research integration, formalization, conceptual clarity, and other areas, partly due to its multifaceted and interdisciplinary nature. Recent advances in artificial intelligence, particularly the development of language models, offer tools that may help address these longstanding issues. Specifically, they can help map fragmented literatures, formalize verbal theories, identify overlap among constructs and measures, generate predictions across tasks, and extract cultural or ecological structure from naturalistic data. However, these opportunities come with risks, including oversimplification, opacity, deskilling, and bias. Taken together, we conclude that language models could serve as tools for a more integrative and cumulative cognitive science when used judiciously to complement, rather than replace, human agency.

In recent years, several claims have been put forth suggesting that animals of different species address each other using receiver-specific calls that function as individual names. If accurate, this would demonstrate a capacity for symbolic social reference that has rarely been documented in nonhuman communication systems. To evaluate such claims, we propose three minimal criteria for identifying names: individual reference, symbolic representation, and shared meaning. We then review the evidence relevant to these criteria in the species for which name-like calls have been suggested: spectacled parrotlets, common marmosets, African elephants, and bottlenose dolphins. Together, these criteria provide a clear and theoretically grounded framework for exploring the use of name-like calls in nonhuman animals.

People change across daily situations and life transformations, yet maintain a stable sense of self. Explaining this stability requires understanding how identities, traits, values, and memories interact—what changes together, what resists change, and how. We propose that the self is represented as a dynamic causal network of self-aspects linked by beliefs about how they influence one another. Coherence arises when activated self-aspects align with internal beliefs and contextual demands. When disrupted, the network restores coherence through activation shifts that preserve structure or through structural reorganization that reshapes it. This perspective distinguishes the self from associative, categorical, or hierarchical accounts by specifying directional dependencies that constrain change, and highlights how modifying connections among self-aspects promotes coherence and well-being.

Music is pervasive across human history, cultures, and development, yet its evolutionary origins remain unresolved due to the interplay of cultural and biological influences. We argue that identifying uniquely human musicality requires cross-primate comparative research that integrates rhythm and melody processing with exposure, computational principles, and intrinsic reward.

Brain–computer interfaces (BCIs) have achieved transformative success in restoring movement and communication. However, extending these approaches to decoding or recovery of cognitive function, such as attention or memory, poses fundamentally new challenges. Cognitive BCIs will need to contend with distributed and dynamic neural processes that differ sharply from the more localized, stable representations underlying motor and language control, imposing new technical and conceptual demands. Conversely, neuromodulation, long used in neurological and psychiatric therapies, offers a complementary methodological path and initial translational applications through causal modulation of cognitive circuits. Integrating these approaches into adaptive, closed-loop systems could allow cognitive BCIs to restore mental function and bridge systems neuroscience and next-generation neurotherapeutics capable of monitoring and shaping human cognition in real time.

Over the past decade, comparisons between deep neural networks (DNNs) and the human brain have become central to cognitive neuroscience. Early work focused on vision, driven by the success of convolutional neural networks in object recognition, before such comparisons later gained traction in language with the rise of large-scale language models. These comparisons have validated existing hypotheses and generated new ones, challenging views of information processing, connectivity, and computational goals. Despite progress, debates persist over the interpretability and validity of mapping DNNs to brains, underscoring the need for more refined models and methods. Looking ahead, integrating cross-modal insights from vision and language, together with improved modeling and experimental frameworks, promises to advance the mechanistic understanding of cognition.

Adolescence is a period of vulnerability for internalizing mental health disorders. One underappreciated mechanism of internalizing psychopathology is higher-order social cognition (i.e., cognitive processing of information about the self and others), which is highly salient during adolescence. We propose that the protracted development of social cognition during adolescence results in highly individualized, multidimensional profiles that can be characterized across four core dimensions (Propensity, Degree, Positivity, and Negativity). A social cognitive profile with greater Propensity, Degree, and Negativity, and decreased Positivity, along with associated decreases in Accuracy, may confer greater risk for internalizing psychopathology, particularly if applied inflexibly across contexts. Future research may test the utility of these profiles for stratification, prediction, or intervention targeting.

Many worry that AI use will erode human cognition. We discuss evidence suggesting that offloading cognition to AI can impede skill acquisition and lead to skill decay, but risks depend on how AI is used. We also consider whether our basic cognitive abilities might prove more resilient to this erosion.

We recently proposed a modification to an influential conceptual framework in consciousness science: Ned Block’s distinction between phenomenal (P) consciousness and access (A) consciousness [1]. Instead, we proposed that there is only one type of consciousness, requiring two necessary conditions [2]. Importantly, we did not propose a new definition of consciousness; we agree with the current consensus that it is a ‘subjective experience’ in the here and now, or ‘what it is like’ from a first-person perspective.

Mudrik et al. [1] raise an important concern: that research on consciousness risks fragmentation. They partly attribute this to Block’s distinction between phenomenal (P-) and access (A-) consciousness [2] and propose treating them as jointly necessary conditions of any conscious state. This ‘tectonic’ move is rhetorically appealing. Our concern is that it risks resolving a central empirical question by definition. In doing so, it (i) introduces a substantive theoretical commitment under the guise of a neutral clarification, (ii) constrains the space of admissible theories, and (iii) sets a high, anthropocentric threshold for consciousness.

In their timely opinion piece, Liad Mudrik et al. [1] propose to reconceptualize the longstanding distinction between access (A) and phenomenal (P) consciousness. Rather than constituting two types of consciousness, they argue, A and P should be reframed as two conditions that are jointly necessary for consciousness. This change, we are told, offers the promise of progress on entrenched debates between sensory and cognitive theories of consciousness and can move us toward theoretical unification.

Thirty years ago, Block proposed a hugely influential distinction between phenomenal (P) and access (A) consciousness [1,2]. A state is P-conscious if there is something it is like to be in it, and A-conscious if its content is available for reasoning, rational action guidance, and report. Block’s distinction is principally conceptual, but Block has since offered introspective, behavioral, and neuroscientific grounds for thinking that A-consciousness and P-consciousness actually dissociate. For instance, he has argued that iconic and fragile short-term memories are often P-conscious but not A-conscious; that ‘exclusion’ paradigms evidence contents which are P-conscious but not A-conscious; and that local recurrent processing in posterior regions realizes P-consciousness without the anterior activity required for A-consciousness [3–6].

Why do some people remain mentally healthy under stress, while others develop symptoms? Recent observational and interventional evidence indicates that positive appraisal style—the tendency to interpret threats in a mildly positive fashion—is a key resilience factor and a promising target for preventive interventions.

Noninvasive methods are making it possible to study neural replay in humans. Although focused on alignment with rodent findings, human research often tacitly assumes that replay is linked to conscious, deliberate thought. This assumption represents a missed opportunity to investigate the links between replay and consciousness, as well as cross-species differences more broadly.

Reciprocity theory holds a hallowed status in evolutionary social sciences because it provides a way to explain cooperation among unrelated individuals. Theorists have developed a large body of mathematical work based on the iterated prisoner’s dilemma game that explores which strategies are evolutionarily stable in a range of conditions, including scenarios involving behavioral errors. However, there is a mismatch between the vigorous pace of theoretical research and the trickle of empirical studies that have tested predictions generated by the theoretical work. Furthermore, findings from the empirical work on dyadic cooperation in humans diverge from theoretical predictions in significant ways. This discrepancy indicates that the standard theoretical framework is poorly equipped to account for key aspects of real-world cooperation. To better align with real-world observations, theoretical explorations of reciprocity need to shift their focus toward understanding how actors choose partners and maintain relationships.

Misinformation influences cognition—shaping memory, beliefs, attitudes, reasoning, and decision-making. While intentionally disseminated misinformation (i.e., disinformation) has long served persuasive functions, its strategic use to generate epistemic uncertainty has grown in prominence. Such strategic manipulation by incentivised actors serves to construct ‘alternative realities’ that undermine shared truth and institutional trust. This review analyses recent evidence on the direct cognitive and behavioural impacts of misinformation, examines the transformative roles of social media and generative AI, and considers broader epistemological consequences, including the erosion of truth norms and implications for democratic discourse. Addressing these challenges requires coordinated individual and systemic interventions that integrate psychological, technological, educational, and regulatory approaches to strengthen epistemic resilience and safeguard evidence-informed public reasoning and decision-making.

Psychological traits are not uniformly distributed across space; they are geographically clustered. The emerging interdisciplinary field of geographical psychology integrates insights from psychology and the social sciences to understand the causes and consequences of this clustering. By leveraging big data, geospatial analysis, and computational modeling, researchers have identified robust geographical patterns across regions and nations in personality, values, well-being, and prejudice. This review synthesizes key findings on these geographical differences and how they relate to societal-level and individual-level outcomes. Opportunities and challenges for a cognitive science that incorporates a geographical perspective are discussed. Overall, a geographical perspective highlights the dynamic interplay between place and psychology, offering new insights into human behavior and pathways for integrating geography into cognitive science.

Multi-brain neurofeedback offers new possibilities for guiding social interaction by capturing and modulating interpersonal neural dynamics in real time. We propose a hierarchical framework where neurofeedback targets shared sensory dynamics (signal), socio-cognitive processes (functional), or social outcomes (system). We highlight key methodological challenges and potential real-world therapeutic and pedagogical applications.

Developmental voice change exposes a gap in models of self-voice processing: these models explain predictive control and self-voice recognition but not how vocal signals integrate into self-representations. I propose a hierarchical framework in which recursive interactions across sensorimotor predictions, self-voice representations, and higher-order self-representations support the emergence of the vocal self.