SciCombinator

A recent study demonstrates that intersubject variability in functional connectivity is heterogeneous across the cortex, with significantly higher variability in multimodal association cortex. Rather than being ‘noise’, intersubject variability is invaluable for understanding principles of brain evolution and ontogenetic development, and for interpreting statistical maps in task-based functional neuroimaging studies.

Music is used to regulate mood and arousal in everyday life and to promote physical and psychological health and well-being in clinical settings. However, scientific inquiry into the neurochemical effects of music is still in its infancy. In this review, we evaluate the evidence that music improves health and well-being through the engagement of neurochemical systems for (i) reward, motivation, and pleasure; (ii) stress and arousal; (iii) immunity; and (iv) social affiliation. We discuss the limitations of these studies and outline novel approaches for integration of conceptual and technological advances from the fields of music cognition and social neuroscience into studies of the neurochemistry of music.

An enduring aim of research in the psychological and brain sciences is to understand the nature of individual differences in human intelligence, examining the stunning breadth and diversity of intellectual abilities and the remarkable neurobiological mechanisms from which they arise. This Opinion article surveys recent neuroscience evidence to elucidate how general intelligence, g, emerges from individual differences in the network architecture of the human brain. The reviewed findings motivate new insights about how network topology and dynamics account for individual differences in g, represented by the Network Neuroscience Theory. According to this framework, g emerges from the small-world topology of brain networks and the dynamic reorganization of its community structure in the service of system-wide flexibility and adaptation.

Many brain regions have been defined, but a comprehensive formalization of each region’s function in relation to human behavior is still lacking. Current knowledge comes from various fields, which have diverse conceptions of ‘functions’. We briefly review these fields and outline how the heterogeneity of associations could be harnessed to disclose the computational function of any region. Aggregating activation data from neuroimaging studies allows us to characterize the functional engagement of a region across a range of experimental conditions. Furthermore, large-sample data can disclose covariation between brain region features and ecological behavioral phenotyping. Combining these two approaches opens a new perspective to determine the behavioral associations of a brain region, and hence its function and broader role within large-scale functional networks.

Experiences affect mood, which in turn affects subsequent experiences. Recent studies suggest two specific principles. First, mood depends on how recent reward outcomes differ from expectations. Second, mood biases the way we perceive outcomes (e.g., rewards), and this bias affects learning about those outcomes. We propose that this two-way interaction serves to mitigate inefficiencies in the application of reinforcement learning to real-world problems. Specifically, we propose that mood represents the overall momentum of recent outcomes, and its biasing influence on the perception of outcomes ‘corrects’ learning to account for environmental dependencies. We describe potential dysfunctions of this adaptive mechanism that might contribute to the symptoms of mood disorders.

Based on modern theories of signal evolution and animal communication, the behavioral ecology view of facial displays (BECV) reconceives our ‘facial expressions of emotion’ as social tools that serve as lead signs to contingent action in social negotiation. BECV offers an externalist, functionalist view of facial displays that is not bound to Western conceptions about either expressions or emotions. It easily accommodates recent findings of diversity in facial displays, their public context-dependency, and the curious but common occurrence of solitary facial behavior. Finally, BECV restores continuity of human facial behavior research with modern functional accounts of non-human communication, and provides a non-mentalistic account of facial displays well-suited to new developments in artificial intelligence and social robotics.

At the centenary of D'Arcy Thompson’s seminal work ‘On Growth and Form’, pioneering the description of principles of morphological changes during development and evolution, recent experimental advances allow us to study change in anatomical brain networks. Here, we outline potential principles for connectome development. We will describe recent results on how spatial and temporal factors shape connectome development in health and disease. Understanding the developmental origins of brain diseases in individuals will be crucial for deciding on personalized treatment options. We argue that longitudinal studies, experimentally derived parameters for connection formation, and biologically realistic computational models are needed to better understand the link between brain network development, network structure, and network function.

Financial decisions are among the most important life-shaping decisions that people make. We review facts about financial decisions and what cognitive and neural processes influence them. Because of cognitive constraints and a low average level of financial literacy, many household decisions violate sound financial principles. Households typically have underdiversified stock holdings and low retirement savings rates. Investors overextrapolate from past returns and trade too often. Even top corporate managers, who are typically highly educated, make decisions that are affected by overconfidence and personal history. Many of these behaviors can be explained by well-known principles from cognitive science. A boom in high-quality accumulated evidence-especially how practical, low-cost ‘nudges’ can improve financial decisions-is already giving clear guidance for balanced government regulation.

Self-control refers to the mental processes that allow people to override thoughts and emotions, thus enabling behavior to vary adaptively from moment to moment. Dominating contemporary research on this topic is the viewpoint that self-control relies upon a limited resource, such that engaging in acts of restraint depletes this inner capacity and undermines subsequent attempts at control (i.e., ego depletion). Noting theoretical and empirical problems with this view, here we advance a competing model that develops a non-resource-based account of self-control. We suggest that apparent regulatory failures reflect the motivated switching of task priorities as people strive to strike an optimal balance between engaging cognitive labor to pursue ‘have-to’ goals versus preferring cognitive leisure in the pursuit of ‘want-to’ goals.

Assumptions on the neural basis of cognition usually focus on cortical mechanisms. Birds have no cortex, but recent studies in parrots and corvids show that their cognitive skills are on par with primates. These cognitive findings are accompanied by neurobiological discoveries that reveal avian and mammalian forebrains are homologous, and show similarities in connectivity and function down to the cellular level. But because birds have a large pallium, but no cortex, a specific cortical architecture cannot be a requirement for advanced cognitive skills. During the long parallel evolution of mammals and birds, several neural mechanisms for cognition and complex behaviors may have converged despite an overall forebrain organization that is otherwise vastly different.