The default network is known to engage more strongly "by default" during rest compared to many cognitive tasks. The network is also reliably associated with social cognition, suggesting some forms of social cognition may occur "by default" during rest. The goal of this opinion piece is to review accumulating evidence suggesting the default network performs two social cognitive processes during rest: social guiding and social consolidation. Social guiding refers to the observation that the immediate brain state participants enter in the default network as soon as they rest shapes their immediately following social cognition and behavior. Social consolidation refers to the observation that after participants are exposed to new social information, the default network commits the social information to memory during rest. After reviewing findings in support of social guiding and social consolidation, we offer directions for new research on these topics.

The default network (DN) is associated with a variety of introspective cognitive processes. Recent developments support that the 'canonical' DN comprises at least two parallel distributed networks, DN-A and DN-B, which are closely interdigitated in the individual brain. However, the two networks can be clearly distinguished by their connections with different medial temporal lobe (MTL) structures which help explain each network's involvement in different aspects of introspection: DN-A plays a role in mental scene construction and is prominently connected to the anterior hippocampus, parahippocampal cortex, and medial entorhinal cortex. Meanwhile, DN-B plays a role in social cognition and is prominently connected to the amygdala and more lateral entorhinal cortex. These different MTL connections help explain the heterogeneity of functions within the canonical DN, and putatively may shape the fractionation of these distinct networks during brain development, such that different cortical networks end up handling interactions with different MTL regions.

Loneliness is a growing public health concern, and several genetically informed research designs have been applied to studying its etiology. These approaches have revealed that loneliness has a highly polygenic architecture (a great many genetic variants each accounting for tiny percentages of between-person variability), a large impact of environmental factors, and links with gene regulatory processes. With this strong genetic component and high within-person stability, traditional loneliness scales behave surprisingly "trait-like", overlapping with personality constructs like neuroticism. We argue that greater use of idiographic designs and attention to construct contamination within nomothetic designs can improve inquiry into the etiology of loneliness as a dynamic and distinctive phenomenon. However, even with improved measurement, the biological pathways towards the development of complex behavioral traits are notoriously difficult to unravel. We highlight promising emerging methods and conclude with recommendations for studies that leverage multiple genetically informed paradigms (e.g., combining genomic and epigenomic approaches).

The human mind constructs and updates models of events during comprehension. Event models are multidimensional, multi-timescale, and structured. They enable prediction, shape memory formation, and facilitate action control. Event models may be updated incrementally by replacing feature information as it changes or globally by constructing an entirely new model; there is evidence for both mechanisms. Default mode network components, particularly medial prefrontal cortex, are thought to implement key event model functions, utilizing a temporally graded architecture in which regions with longer timescales perform more integration and abstraction. Two signatures of event model representations are phasic changes in overall activity at event boundaries and shifts in neural patterns at those boundaries. Current theories propose multiple control structures for event model updating, including monitoring the quality of event model-driven predictions. Event model updating during comprehension has important consequences not only for processing information in the moment, but also for forming long-term memories.

The posterior cingulate cortex (PCC) is an intriguing yet understudied brain region implicated in diverse cognitive functions and neurological disorders. Progress in understanding the human PCC has been hindered by the absence of a clear rodent homolog, inconsistent lesion-behavior deficits in humans, and limitations in studying the region with noninvasive electrophysiological methods. However, the advent of functional neuroimaging has highlighted the PCC's central role within the default mode network (DMN) and its broader functional role as an associative, transmodal, cortical region. Recent advances in precision imaging have further refined the functional neuroanatomy of the PCC, revealing its complex subregional organization and network connectivity profiles. For example, the PCC is a convergence point for dorsal executive and ventral mnemonic systems, with distinct subregions (dorsal PCC and ventral PCC) differentially contributing to cognitive control, decision-making, and episodic memory. This emphasis on higher-order cognition highlights the often-striking dissociation of the PCC/DMN from primary sensory-motor processing. However, emerging evidence suggests that the PCC operates at the apex of cortical processing hierarchies, supporting temporally extended cognitive behaviors while also integrating sensory updates relevant to ongoing tasks. This review synthesizes recent advances in understanding the human PCC, emphasizing its functional connections to various cognitive systems beyond the DMN and its relative separation, though not isolation, from primary sensory-motor systems. Together, these facets allow the PCC to support the representation of past and future behavioral scenarios by integrating prior experience with ongoing sensory feedback.

Cognitive flexibility, the capacity to adapt behaviors in response to changing environments, is impaired across mental illnesses, including depression, anxiety, addiction, and obsessive-compulsive disorder. Cortico-striatal-cortical circuits are integral to cognition and goal-directed behavior and disruptions in these circuits are linked to cognitive inflexibility in mental illnesses. We review evidence that neurostimulation of these circuits can improve cognitive flexibility and ameliorate symptoms, and that this may be a mechanism of action of current clinical therapies. Further, we discuss how animal models can offer insights into the mechanisms underlying cognitive flexibility and effects of neurostimulation. We review research from animal studies that may, if translated, yield better approaches to modulating flexibility. Future research should focus on refining definitions of cognitive flexibility, improving detection of impaired flexibility, and developing new methods for optimizing neurostimulation parameters. This could enhance neurostimulation therapies through more personalized treatments that leverage cognitive flexibility to improve patient outcomes.

To understand how the brain generates behavior, both brain activity and behavior must be measured accurately. Although neuroscience has developed powerful tools for measuring brain activity, its behavioral measures are far more primitive, in part because it lacks a coherent conceptual framework for analyzing and interpreting behavior. Here I shall review key limitations in current studies of behavior, such as categorical measures and input/output analysis, which are manifested in conventional behavioral measures and experimental designs. I shall discuss how these limitations stem from the dominant linear causation paradigm which has impeded progress in understanding the relationship between neural activity and behavior. Finally, I shall review recent studies that use alternative strategies for studying how the brain generates behavior, and experimental results that challenge the linear causation paradigm. These results suggest hierarchical feedback control with intrinsic reference states, circular causation, and simultaneous reciprocal interactions between the organism and the environment.

Dopamine is heavily studied for its role in reward learning, but it is becoming increasingly appreciated that dopamine can also enable learning from aversion. Dopamine neurons modulate their firing and neurotransmitter release patterns in response to aversive outcomes. However, there is considerable heterogeneity in the timing and directionality of the modulation. Open questions remain as to the factors that determine this heterogeneity and how varying patterns of responses to aversion in different dopamine-receptive brain regions contribute to value learning, decision-making, and avoidance. Here, we review recent progress in this area and highlight important future directions.

Dopaminergic (DA) neurons of the ventral tegmental area (VTA) have long been studied for their role in reward prediction and goal-directed behaviors. However, appreciation is growing for a complementary role of VTA DA neurons in responding to aversive stimuli and as critical substrates for behavioral sequelae of stressful experiences. As is the case across neuroscience, the majority of our knowledge about VTA DA neurons comes from studies in male subjects. Recent years have seen an increase in inclusion of female subjects and exploration of sex differences. There is now an emerging body of literature showing that although there are minimal basal structural and functional sex differences in VTA DA neurons, experience-dependent changes in these neurons can differ significantly between males and females. Here, we discuss potential implications of sex differences in VTA function and review recent data on sex differences and similarities of DA neurons at baseline and following stress.

New techniques for largescale neural recordings from diverse animals are reshaping comparative systems neuroscience. This growth necessitates fresh conceptual paradigms for comparing neural circuits and activity patterns. Here, we take a systems neuroscience approach to early neural evolution, emphasizing the importance of considering nervous systems as multiply modulated, continuous dynamical systems. We argue that endogenous neural activity likely arose early in evolution to organize behaviors and internal states at the organismal level. This connects to a rich literature on the physiology of endogenous activity in small neural circuits: a field that has built links between data and dynamical systems models. Such models offer mechanistic insight and have robust predictive power. Using these tools, we suggest that the emergence of intrinsically active neurons and periodic dynamics played a critical role in the ascendancy of nervous systems, and that dynamical systems presents an appealing framework for comparing across species.

The tail of the striatum (TS) is increasingly recognized as a unique subdivision of the striatum, characterized by its dense sensory inputs and projections received from a distinct group of dopamine neurons. Separate lines of research have characterized the functional role of TS, and TS-projecting dopamine neurons, in three realms: saccadic eye movement towards valuable visual stimuli; tone-guided choice between two options; and defensive responses to threatening stimuli. We propose a framework for reconciling these diverse roles as varied implementations of a conserved response to salient stimuli, with dopamine in TS providing a teaching signal to promote quick attentional shifts that facilitate stimulus-driven orientation and/or avoidance.

Brain and behavior undergo measurable changes in their underlying state and neuromodulators are thought to contribute to these fluctuations. Why do we undergo such changes, and what function could the underlying neuromodulatory systems perform? Here we examine theoretical answers to these questions with respect to the locus coeruleus/norepinephrine system focusing on peripheral markers for arousal, such as pupil diameter, that are thought to provide a window into brain wide noradrenergic signaling. We explore a computational role for arousal systems in facilitating internal state transitions that facilitate credit assignment and promote accurate perceptions in non-stationary environments. We summarize recent work that supports this idea and highlight open questions as well as alternative views of how arousal affects cognition.

Cognitive flexibility exhibits dynamic changes throughout development, with different forms of flexibility showing dissociable developmental trajectories. In this review, we propose that an adolescent-specific mode of flexibility in the face of changing environmental contingencies supports the emergence of adolescent-to-adult gains in cognitive shifting efficiency. We first describe how cognitive shifting abilities monotonically improve from childhood to adulthood, accompanied by increases in brain state flexibility, neural variability, and excitatory/inhibitory balance. We next summarize evidence supporting the existence of a dopamine-driven, adolescent peak in flexible behavior that results in reward seeking, undirected exploration, and environmental sampling. We propose a neurodevelopmental framework that relates these adolescent behaviors to the refinement of neural phenotypes relevant to mature cognitive flexibility, and thus highlight the importance of the adolescent period in fostering healthy neurocognitive trajectories.

This article reviews the prevalence of stress and explores resilience factors in children and adolescents with specific learning disabilities (SLDs). We show that the increased stress and emotional challenges faced by this group are likely due to societal pressures and stigma. Recent findings on neuroendocrine changes in this population are discussed, suggesting a predisposition to psychiatric disorders. This review advocates for a societal shift towards the neurodiversity paradigm, which recognizes SLDs as natural variations in brain function, emphasizing individual strengths and promoting a more inclusive approach that values cognitive diversity. Such advocacy is likely important to combat stress and stigma in those with SLDs. This article also reviews recent work identifying resilience-promoting factors, such as perception of self and peer/teacher relationships, for enhancing emotional well-being and mental health for children and adolescents with SLD.

Methodological advances have facilitated extensive revision of traditional views of thalamic and cholinergic contributions to cognition and behavior. Increasing attention to the integrative capabilities of the thalamus highlights its role beyond a simple sensory relay, recognizing its complex connectivity and role in orchestrating different phases of attention. Correspondingly, modern conceptualizations position the cholinergic system as key in integrating sensory information with attention and goals. These theoretical developments have occurred largely in parallel, but have large conceptual overlap. We review this overlap, including evidence from animal, patient, neuroimaging, and computational studies, and suggest thalamo-cholinergic cognition plays a key role in coordinating stable and flexible attention.

Neural networks trained with deep reinforcement learning can perform many complex tasks at similar levels to humans. However, unlike people, neural networks converge to a fixed solution during optimisation, limiting their ability to adapt to new challenges. In this opinion, we highlight three key new methods that allow neural networks to be posed as models of human cognitive flexibility. In the first, neural networks are trained in ways that allow them to learn complementary 'habit' and 'goal'-based policies. In another, flexibility is 'meta-learned' during pre-training from large and diverse data, allowing the network to adapt 'in context' to novel inputs. Finally, we discuss work in which deep networks are meta-trained to adapt their behaviour to the level of control they have over the environment. We conclude by discussing new insights about cognitive flexibility obtained from the training of large generative models with reinforcement learning from human feedback.

Animal brains are probably the most complex computational machines on our planet, and like everything in biology, they are the product of evolution. Advances in developmental and palaeobiology have been expanding our general understanding of how nervous systems can change at a molecular and structural level. However, how these changes translate into altered function - that is, into 'computation' - remains comparatively sparsely explored. What, concretely, does it mean for neuronal computation when neurons change their morphology and connectivity, when new neurons appear or old ones disappear, or when transmitter systems are slowly modified over many generations? And how does evolution use these many possible knobs and dials to constantly tune computation to give rise to the amazing diversity in animal behaviours we see today? Addressing these major gaps of understanding benefits from choosing a suitable model system. Here, I present the vertebrate retina as one perhaps unusually promising candidate. The retina is ancient and displays highly conserved core organisational principles across the entire vertebrate lineage, alongside a myriad of adjustments across extant species that were shaped by the history of their visual ecology. Moreover, the computational logic of the retina is readily interrogated experimentally, and our existing understanding of retinal circuits in a handful of species can serve as an anchor when exploring the visual circuit adaptations across the entire vertebrate tree of life, from fish deep in the aphotic zone of the oceans to eagles soaring high up in the sky.

Cognition relies on the flexible organization of neural activity. In this discussion, we explore how many aspects of this organization can be described as emergent properties, not reducible to their constituent parts. We discuss how electrical fields in the brain can serve as a medium for propagating activity nearly instantaneously, and how population-level patterns of neural activity can organize computations through subspace coding.

Repetitive negative thinking (RNT) is a transdiagnostic construct that encompasses rumination and worry, yet what precisely is shared between rumination and worry is unclear. To clarify this, we develop a meta-control account of RNT. Meta-control refers to the reinforcement and control of mental behavior via similar computations as reinforce and control motor behavior. We propose rumination and worry are coarse terms for failure in meta-control, just as tripping and falling are coarse terms for failure in motor control. We delineate four meta-control stages and risk factors increasing the chance of failure at each, including open-ended thoughts (stage 1), individual differences influencing subgoal execution (stage 2) and switching (stage 3), and challenges inherent to learning adaptive mental behavior (stage 4). Distinguishing these stages therefore elucidates diverse processes that lead to the same behavior of excessive RNT. Our account also subsumes prominent clinical accounts of RNT into a computational cognitive neuroscience framework.

Ultrasound neuromodulation is a promising technology that could revolutionize study and treatment of brain conditions ranging from mood disorders to Alzheimer's disease and stroke. An understanding of how ultrasound directly modulates specific ion channels could provide a roadmap for targeting specific neurological circuits and achieving desired neurophysiological outcomes. Although experimental challenges make it difficult to unambiguously identify which ion channels are sensitive to ultrasound , recent progress indicates that there are likely several different ion channels involved, including members of the K2P, Piezo, and TRP channel families. A recent result linking TRPM2 channels in the hypothalamus to induction of torpor by ultrasound in rodents demonstrates the feasibility of targeting a specific ion channel in a specific population of neurons.

Cognitive flexibility denotes the ability to disengage from a current task and shift one's focus to a different activity. An individual's level of flexibility is not fixed; rather, people adapt their readiness to switch tasks to changing circumstances. We here review recent studies in the task-switching literature that have produced new insights into the contextual factors that drive this adaptation of flexibility, as well as proposals regarding the underlying cognitive mechanisms and learning processes. A fast-growing literature suggests that there are several different means of learning the need for, and implementing, changes in one's level of flexibility. These, in turn, have distinct consequences for the degree to which adjustments in cognitive flexibility are transferrable to new stimuli and tasks.

Medical innovation and technologic advances enrich daily living and occur within our normative worlds, that are socially constructed. These advances confront society with critical questions about the nature of human life, laying bare the inadequacies of extant norms and boundaries. Yet, society has been unable to develop consensus about when life ends. Scientific studies highlight that life is best characterized by continua without natural boundaries. Thus, scientific information alone cannot be employed to justify the socially constructed health categories required for setting norms and boundaries. An iterative process that integrates a broad range of non-scientific data with advancing scientific information is needed to facilitate consensus for updating social norms and boundaries. This can lead to a new taxonomy of living across the measurable continuum of life and align our normative worlds with the dizzying pace of medical innovation and advances in technologies transforming the world in which we live.

Animals face behavioral problems that can be conceptualized in terms of a gradient of spatial and temporal proximity. I propose that solving close-proximity behavioral problems involves integrating disparate types of information in complex and flexible ways. In this framework, the midbrain periaqueductal gray (PAG) is understood as a key region involved in close-proximity motivated cognition. Anatomically, the PAG has access to signals across the neuroaxis via extensive connectivity with cortex, subcortex, and brainstem. However, the flow of signals is not unidirectional, as the PAG projects to the cortex directly, and further ascending signal flow is attained via the midline thalamus. Overall, the anatomical organization of the PAG allows is to be a critical hub engaged in cognition "here and now".

Understanding behavior in aging has benefited greatly from cognitive neuroscience. Our foundational understanding of the brain in advanced age is based on what now amounts to several decades of work demonstrating differences in brain structure, network organization, and function. Earlier work in this field was focused primarily on the prefrontal cortex and hippocampus. More recent evidence has expanded our understanding of the aging brain to also implicate the cerebellum. Recent frameworks have suggested that the cerebellum may act as scaffolding for cortical function, and there is an emerging literature implicating the structure in Alzheimer's disease. At this juncture, there is evidence highlighting the potential importance of the cerebellum in advanced age, though the field of study is relatively nascent. Here, we provide an overview of key findings in the literature as it stands now and highlight several key future directions for study with respect to the cerebellum in aging.

Epilepsy is highly prevalent and notoriously pharmacoresistant. New therapeutic interventions are urgently needed, both for preventing the seizures themselves as well as negative outcomes and comorbidities associated with chronic epilepsy. While the cerebellum is not traditionally associated with epilepsy or seizures, research over the past decade has outlined the cerebellum as a brain region that is uniquely suited for both therapeutic needs. This review discusses our current understanding of the cerebellum as a key node within seizure networks, capable of both attenuating seizures in several animal models, and conversely, prone to altered structure and function in chronic epilepsy. Critical next steps are to advance therapeutic modulation of the cerebellum more towards translation, and to provide a more comprehensive characterization of how the cerebellum is impacted by chronic epilepsy, in order to subvert negative outcomes.

Dehumanization has been characterized as common in medical settings, despite limited work directly examining this. In this context, everyday dehumanization is believed to be largely unconscious and unintentional, resulting from a variety of factors often related to structural and organizational aspects of healthcare. This article adopts the patients' and the healthcare providers' perspective to explore how dehumanization can have helpful and hurtful effects on patient outcomes and provider well-being. Future directions include more direct assessment of dehumanization in healthcare settings, centering the needs and experiences of people with mental illness and comorbid conditions, and improving our understanding of dehumanization relative to emotion regulation processes.

People from low- and middle-income countries (LMICs) represent large portions of the world population, often occupy less favorable living conditions, and typically suffer greater health risks, yet frequently receive little research and global health attention. The present study reviews emerging evidence on problematic use of the Internet (PUI) in LMICs prior/during the COVID-19 pandemic. Analyzed studies mainly focused on general properties of PUI in university students, problematic gaming in youth, or problematic use of social media in adults, registering higher prevalence estimates, as compared with earlier reports. Research mainly focused on initially affected regions and COVID-exposed populations. Overall, unfavorable circumstances, including poor social support, family relationships, and lifestyle tendencies/habits, may present potential risk for PUI in LMICs, likely exacerbated during the pandemic.

This Current Opinion in Behavioral Sciences article reviews trials that evaluated an obesity treatment that combines response-inhibition training with high-calorie foods and training designed to reduce attention for high-calorie foods. Two randomized controlled trials suggest that food-response inhibition and attention training produced significant body-fat loss, along with a reduction in valuation of, and reward-region response to, high-calorie foods. However, these effects did not emerge in a third trial, potentially because this trial used more heterogeneous food images, which reduced inhibition learning and attentional learning. Collectively, results suggest that food-response inhibition and attention training can devalue high-calorie foods and result in weight loss, but only if a homogeneous set of high-calorie and low-calorie food images is used.

Although the orbitofrontal cortex (OFC) robustly encodes value during preference decisions, it also encodes multiple non-value features of choice options. The role of this information, and its relationship to the options' overall value remain open questions. In this opinion, we attempt to disentangle oft-studied categories of option information - identity and attributes - in the context of both classic theories of economic choice and contradicting evidence of choice biases in multi-attribute decisions. In doing so, we aim to set forth considerations for understanding the wide variety of decision-relevant information encoded by the OFC during preference decisions.

Reinforcement learning is a powerful framework for modelling the cognitive and neural substrates of learning and decision making. Contemporary research in cognitive neuroscience and neuroeconomics typically uses value-based reinforcement-learning models, which assume that decision-makers choose by comparing learned values for different actions. However, another possibility is suggested by a simpler family of models, called . Policy-gradient models learn by optimizing a behavioral policy directly, without the intermediate step of value-learning. Here we review recent behavioral and neural findings that are more parsimoniously explained by policy-gradient models than by value-based models. We conclude that, despite the ubiquity of 'value' in reinforcement-learning models of decision making, policy-gradient models provide a lightweight and compelling alternative model of operant behavior.