The human brain is arguably the most complex system we know of. Over the past few decades, scientists have developed several methods and theories for studying the functional organization of the brain, and how cognitive/perceptual/emotional processes might arise from the brain's electro-chemical-computational dynamics. These methods facilitated and inspired large literatures on brain-behavior links, and yet there remains a seemingly endless chasm between our simple impoverished models and the unfathomable complexity of the human brain. The purpose of this Research Topic is to ask the question: Are we thinking about thinking about the brain in the right way? In most scientific publications, researchers describe a broad and established theoretical framework and briefly describe new experimental results consistent with that framework. Here, we encourage authors to express ideas that might be radical, controversial, or different from established theories or methodological approaches. Supportive data are highly encouraged. The aim is to spark discussions about the validity and usefulness of current methodological/theoretical approaches in human cognitive neuroscience, with the goal of inspiring new approaches and ways of thinking. Neuroscience is a massive field with myriad methodological and theoretical approaches; we focus this Research Topic on approaches most commonly used in human neuroscience.

A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.

This volume presents recent empirical advances using neuroscience techniques to investigate how culture influences neural processes underlying a wide range of human abilities, from perception and scene processing to memory and social cognition. It also highlights the theoretical and methodological issues with conducting cultural neuroscience research. Section I provides diverse theoretical perspectives on how culture and biology interact are represented. Sections II –VI is to demonstrate how cultural values, beliefs, practices and experience affect neural systems underlying a wide range of human behavior from perception and cognition to emotion, social cognition and decision-making. The final section presents arguments for integrating the study of culture and the human brain by providing an explicit articulation of how the study of culture can inform the study of the brain and vice versa.

The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding sentient behavior—a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston over years of groundbreaking research, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy. Active inference puts the action into perception. This book offers the first comprehensive treatment of active inference, covering theory, applications, and cognitive domains. Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The book emphasizes the implications of the free energy principle for understanding how the brain works. It first introduces active inference both conceptually and formally, contextualizing it within current theories of cognition. It then provides specific examples of computational models that use active inference to explain such cognitive phenomena as perception, attention, memory, and planning.

The burgeoning field of social neuroscience has begun to illuminate the complex biological bases of human social cognitive abilities. However, in spite of being based on the premise of investigating the neural bases of interacting minds, the majority of studies have focused on studying brains in isolation using paradigms that investigate offline social cognition, i.e. social cognition from a detached observer's point of view, asking study participants to read out the mental states of others without being engaged in interaction with them. Consequently, the neural correlates of real-time social interaction have remained elusive and may —paradoxically— represent the 'dark matter' of social neuroscience. More recently, a growing number of researchers have begun to study online social cognition, i.e. social cognition from a participant's point of view, based on the assumption that there is something fundamentally different when we are actively engaged with others in real-time social interaction as compared to when we merely observe them. Whereas, for offline social cognition, interaction and feedback are merely a way of gathering data about the other person that feeds into processing algorithms 'inside’ the agent, it has been proposed that in online social cognition the knowledge of the other —at least in part— resides in the interaction dynamics ‘between’ the agents. Furthermore being a participant in an ongoing interaction may entail a commitment toward being responsive created by important differences in the motivational foundations of online and offline social cognition. In order to promote the development of the neuroscientific investigation of online social cognition, this Frontiers Research Topic aims at bringing together contributions from researchers in social neuroscience and related fields, whose work involves the study of at least two individuals and sometimes two brains, rather than single individuals and brains responding to a social context. Specifically, this Research Topic will adopt an interdisciplinary perspective on what it is that separates online from offline social cognition and the putative differences in the recruitment of underlying processes and mechanisms. Here, an important focal point will be to address the various roles of social interaction in contributing to and —at times— constituting our awareness of other minds. For this Research Topic, we, therefore, solicit reviews, original research articles, opinion and method papers, which address the investigation of social interaction and go beyond traditional concepts and ways of experimentation in doing so. While focusing on work in the neurosciences, this Research Topic also welcomes contributions in the form of behavioral studies, psychophysiological investigations, methodological innovations, computational approaches, developmental and patient studies. By focusing on cutting-edge research in social neuroscience and related fields, this Frontiers Research Topic will create new insights concerning the neurobiology of social interaction and holds the promise of helping social neuroscience to really go social.

In this EBook, we highlight how newly emerging techniques for non-invasive manipulation of the human brain, combined with simultaneous recordings of neural activity, contribute to the understanding of brain functions and neural dynamics in humans. A growing body of evidence indicates that the neural dynamics (e.g., oscillations, synchrony) are important in mediating information processing and networking for various functions in the human brain. Most of previous studies on human brain dynamics, however, show correlative relationships between brain functions and patterns of neural dynamics measured by imaging methods such as electroencephalography (EEG), magnetoencephalography (MEG), near-infrared spectroscopy (NIRS), positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). In contrast, manipulative approaches by non-invasive brain stimulation (NIBS) have been developed and extensively used. These approaches include transcranial magnetic stimulation (TMS) and transcranial electric stimulation (tES) such as transcranial direct current stimulation (tDCS), alternating current stimulation (tACS), and random noise stimulation (tRNS), which can directly manipulate neural dynamics in the intact human brain. Although the neural-correlate approach is a strong tool, we think that manipulative approaches have far greater potential to show causal roles of neural dynamics in human brain functions. There have been technical challenges with using manipulative methods together with imaging methods. However, thanks to recent technical developments, it has become possible to use combined methods such as TMS–EEG coregistration. We can now directly measure and manipulate neural dynamics and analyze functional consequences to show causal roles of neural dynamics in various brain functions. Moreover, these combined methods can probe brain excitability, plasticity and cortical networking associated with information processing in the intact human brain. The contributors to this EBook have succeeded in showcasing cutting-edge studies and demonstrate the huge impact of their approaches on many areas in human neuroscience and clinical applications.

Critical Neuroscience: A Handbook of the Social and Cultural Contexts of Neuroscience brings together multi-disciplinary scholars from around the world to explore key social, historical and philosophical studies of neuroscience, and to analyze the socio-cultural implications of recent advances in the field. This text’s original, interdisciplinary approach explores the creative potential for engaging experimental neuroscience with social studies of neuroscience while furthering the dialogue between neuroscience and the disciplines of the social sciences and humanities. Critical Neuroscience transcends traditional skepticism, introducing novel ideas about ‘how to be critical’ in and about science.