In a recent series of experiments, Boger and Firestone ask: How do we perceive style?’. Their findings suggest that style perception relies on basic perceptual processes involved in differentiating image content from its context. Their research highlights that we need to understand both content and style processing to fully understand perception.

Advances in artificial intelligence offer an enticing solution to a global problem: perhaps interacting with large language models (LLMs) can help all…

It appears simple that every entity in our perceptual array is a thing (object) or stuff (substance). Yet, a recent article by Paulun and colleagues reveals that there are many puzzles to be solved about how we perform this seemingly simple perception.

Longitudinal measurements of brain structure and function are critical for understanding how humans change over time. Traditional longitudinal approaches sample sparsely across large windows of time to estimate coarse, long-term brain changes. This review showcases insights from dense longitudinal neuroimaging (DLN), an emerging approach that samples densely across relatively short windows of time to precisely estimate individual trajectories of brain change. DLN measures multiple samples from individuals throughout critical periods of rapid change. It allows precise estimates of nonlinear trajectories to advance a mechanistic understanding of brain change. Novel findings from this approach are improving our understanding of human cognition, such as the role of the motor system in visual development and learning.

The vision of a universal artificial intelligence (AI) tutor has remained elusive, despite decades of effort. Could large language models (LLMs) be the game-changer? We overview novel issues arising from developing a nationwide AI tutor. We highlight the practical questions that point to specific gaps in our scientific understanding of the learning process.

The role of dopamine in signalling reward prediction errors is well established, but its involvement in more complex decision-making processes is less clear. In recent work, Kocharian et al. demonstrate that dopamine also encodes imagined outcomes and decision confidence, revealing a richer, more nuanced role in learning and behaviour.

We are grateful for Razavi and colleagues’ response [1] to our opinion piece [2]. In our article, we argued that powerful but non-specific belief updating mechanisms play a more important role in explaining paranoia (excessive concerns about others’ intentions) than more specific processes of coalitional threat detection and harm attribution. Razavi and colleagues disagreed.

Neuroscience has probed only a sliver of the rich cognitive, emotional, and social behaviors that enable primates to thrive in the real world. Technological breakthroughs allow us to quantify these behaviors alongside wireless neural recordings. New studies reveal that neural activity is intricately bound to movement and is profoundly modulated by behavioral context, emotional states, and social dynamics. We frame our review of primate neuroethology around Niko Tinbergen’s four foundational questions – function, mechanism, development, and evolution – to unify classic ethological insights with modern neuroscience tools. We demonstrate that investigating natural behavior promises deep insights into primate cognition, which are relevant for advanced brain–machine interfaces, improved therapies for neurological disorders, and deeper understanding of natural and artificial intelligence.

A core strength of computational psychiatry is its focus on theory-driven research, in which cognitive processes are precisely quantified using computational models that formalize specific theoretical mechanisms. However, the data used in these studies often come from traditional laboratory-based cognitive tasks, which have unclear ecological validity. In this review we propose that the same theoretical frameworks and computational models can be applied to real-world data such as experience sampling, passive data, and digital-behavior data (e.g., online activity such as on social media). In turn, modeling real-world data can benefit from a theory-driven computational approach to move from purely predictive to explanatory power. We illustrate these points using emerging studies and discuss the challenges and opportunities of using real-world data in computational psychiatry.

Schone and colleagues reveal surprising stability in the brain’s body map, challenging textbook notions of dramatic remapping. But why were the textbooks wrong? Because what was interpreted as plasticity was only half of the story. In fact, missing limb representations do persist, awaiting the right probe.

Our understanding of the neural basis of consciousness is mostly restricted to large-scale brain activity patterns as measured by methods such as functional magnetic resonance imaging (fMRI) and magneto/electro-encephalography (M/EEG). In contrast, we lack even basic understanding of circuit-level mechanisms supporting consciousness – particularly in humans – despite the fundamental role that such mechanisms likely play in instantiating larger-scale brain activity patterns supporting conscious states and contents. Here, we review what progress has been made on circuit-level theories of consciousness (e.g., apical amplification theory, dendritic integration theory) and argue that such theories can be tested in humans using recently developed, state-of-the-art methods. Doing so will further facilitate translation of consciousness science into clinical settings and strengthen the bridge between circuit- and network-level theories of consciousness.

Several cognitive functions of sleep, including a role in supporting memory consolidation, are conserved across the evolution of animal species. As outlined here, studies of insect behavior and neural circuits have identified key synaptic and circuit mechanisms through which sleep can influence long-term memories.

Although cultural differences in cognition and behavior are well-documented, collecting data from multiple cultural samples to explore relevant cognitive processes remains challenging. Rahal and Schulze Spüntrup address this by applying webcam-based eye-tracking to adults from 20 countries, revealing cultural variations in the cognitive processes underlying in-group favoritism during social decision-making.

The cognitive neuroscience community using M/EEG has not converged on measures of task-related inter-regional brain connectivity that generalize across tasks and laboratories. We call for community-driven efforts to systematically test and validate connectivity metrics using shared datasets and protocols, aiming to establish robust, replicable frameworks for cognitive and clinical applications.

Advances in artificial intelligence offer an enticing solution to a global problem: perhaps interacting with large language models (LLMs) can help alleviate loneliness. Although promising, evidence from cognitive neuroscience suggests that LLM interactions cannot satisfy psychological and physical needs for proximity. Addressing loneliness requires societal action, not simulating human relationships with artificial surrogates.

Insects navigate by integrating a geocentric velocity vector, allowing them to track their position relative to a distant nest. Recent advances reveal in detail the key neural mechanisms supporting this behavior, offering new insight into how complex spatial cognition is implemented in brain circuits.

Paper wasp societies use behaviors like individual face recognition, configural face processing, social eavesdropping, and transitive inference to manage social relationships. Despite their evolutionary distance, wasps and vertebrates share similarities in their social recognition behavior, indicating that these behaviors can be implemented in miniature brains without a neocortex.

Working memory (WM) is crucial for planning, reasoning, and learning, and is one of the most extensively studied topics in cognitive psychology and neuroscience. However, the concept of a WM subsystem for motor content – or 'motor working memory' (MWM) – is generally neglected, even though MWM likely plays an important role in everyday action. Here, we synthesize evidence that the brain both prospectively and retrospectively maintains motor content in WM and propose that MWM carries out multiple key computational functions in motor control and skill learning. A focused research program on MWM is overdue and will deepen our understanding of the links between cognition and action.

We live much of our lives in our heads, detached from the here and now – as when we recollect the past, anticipate future possibilities, lose ourselves in a daydream, a novel, or our favourite corner of science [1]: ‘what sets us apart is…a life in the mind, the ability to imagine’ [2]. For most of us, sensory imagery, which allows us to experience the sensory properties of objects in their absence, is a central element of our imaginings [3]. The realisation that ~4% of people lack visual imagery, facilitated by a term with which to describe this, aphantasia [4], while an even higher proportion, with hyperphantasia, enjoy imagery rivalling the vividness of perception, has triggered a recent surge of research.

Social insects rely on multiple communication channels. These channels have traditionally been considered innate, eliciting stereotyped responses. However, recent research has shown that cognitive modulation occurs in communication contexts long assumed to be entirely genetically encoded, thus revealing a previously unrecognized cognitive plasticity in social insect communication.