Journal: Trends in cognitive sciences
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.
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.
Decades of research have established two central roles of the hippocampus - memory consolidation and spatial navigation. Recently, a third function of the hippocampus has been proposed: simulating future events. However, claims that the neural patterns underlying simulation occur without prior experience have come under fire in light of newly published data.
Deep reinforcement learning (RL) methods have driven impressive advances in artificial intelligence in recent years, exceeding human performance in domains ranging from Atari to Go to no-limit poker. This progress has drawn the attention of cognitive scientists interested in understanding human learning. However, the concern has been raised that deep RL may be too sample-inefficient - that is, it may simply be too slow - to provide a plausible model of how humans learn. In the present review, we counter this critique by describing recently developed techniques that allow deep RL to operate more nimbly, solving problems much more quickly than previous methods. Although these techniques were developed in an AI context, we propose that they may have rich implications for psychology and neuroscience. A key insight, arising from these AI methods, concerns the fundamental connection between fast RL and slower, more incremental forms of learning.
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.
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.
This review article summarises recently proposed theories on how neural circuits in the brain could approximate the error back-propagation algorithm used by artificial neural networks. Computational models implementing these theories achieve learning as efficient as artificial neural networks, but they use simple synaptic plasticity rules based on activity of presynaptic and postsynaptic neurons. The models have similarities, such as including both feedforward and feedback connections, allowing information about error to propagate throughout the network. Furthermore, they incorporate experimental evidence on neural connectivity, responses, and plasticity. These models provide insights on how brain networks might be organised such that modification of synaptic weights on multiple levels of cortical hierarchy leads to improved performance on tasks.
Democracies assume accurate knowledge by the populace, but the human attraction to fake and untrustworthy news poses a serious problem for healthy democratic functioning. We articulate why and how identification with political parties - known as partisanship - can bias information processing in the human brain. There is extensive evidence that people engage in motivated political reasoning, but recent research suggests that partisanship can alter memory, implicit evaluation, and even perceptual judgments. We propose an identity-based model of belief for understanding the influence of partisanship on these cognitive processes. This framework helps to explain why people place party loyalty over policy, and even over truth. Finally, we discuss strategies for de-biasing information processing to help to create a shared reality across partisan divides.
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.