Single Trial Analyses Of Behavioural And Neuroimaging Data In Perception And Decision Making

The cognitive psychology of perception and decision-making is at a cross-road. Most studies still employ categorical designs, a priori classified stimuli and perform statistical evaluations across subjects. However, a shift has been observed in recent years towards parametric designs in which the information content of stimuli is systematically manipulated to study the single-trial dynamics of behaviour (reaction times, eye movements) and brain activity (EEG, MEG, fMRI). By using the information contained in the variance of individual trials, the single-trial approach goes beyond the activity of the average brain: it reveals the specificity of information processing in individual subjects, across tasks and stimulus space, revealing both inter-individual commonalties and differences. This Research Topic provides theoretical and empirical support for the study of single-trial data. Topics of particular interest include: 1. description of the richness of information in single-trials and how it can be successfully extracted; 2. statistical issues related to measures of central tendency, control for multiple comparisons, multivariate approaches, hierarchical modelling and characterization of individual differences; 3. how manipulation of the stimulus space can allow for a direct mapping of stimulus properties onto brain activity to infer dynamics of information processing and information content of brain states; 4. how results from different brain imaging techniques can be integrated at the single-trial level.

In the years since it first published, Neuroeconomics: Decision Making and the Brain has become the standard reference and textbook in the burgeoning field of neuroeconomics. The second edition, a nearly complete revision of this landmark book, will set a new standard. This new edition features five sections designed to serve as both classroom-friendly introductions to each of the major subareas in neuroeconomics, and as advanced synopses of all that has been accomplished in the last two decades in this rapidly expanding academic discipline. The first of these sections provides useful introductions to the disciplines of microeconomics, the psychology of judgment and decision, computational neuroscience, and anthropology for scholars and students seeking interdisciplinary breadth. The second section provides an overview of how human and animal preferences are represented in the mammalian nervous systems. Chapters on risk, time preferences, social preferences, emotion, pharmacology, and common neural currencies—each written by leading experts—lay out the foundations of neuroeconomic thought. The third section contains both overview and in-depth chapters on the fundamentals of reinforcement learning, value learning, and value representation. The fourth section, “The Neural Mechanisms for Choice, integrates what is known about the decision-making architecture into state-of-the-art models of how we make choices. The final section embeds these mechanisms in a larger social context, showing how these mechanisms function during social decision-making in both humans and animals. The book provides a historically rich exposition in each of its chapters and emphasizes both the accomplishments and the controversies in the field. A clear explanatory style and a single expository voice characterize all chapters, making core issues in economics, psychology, and neuroscience accessible to scholars from all disciplines. The volume is essential reading for anyone interested in neuroeconomics in particular or decision making in general. Editors and contributing authors are among the acknowledged experts and founders in the field, making this the authoritative reference for neuroeconomics Suitable as an advanced undergraduate or graduate textbook as well as a thorough reference for active researchers Introductory chapters on economics, psychology, neuroscience, and anthropology provide students and scholars from any discipline with the keys to understanding this interdisciplinary field Detailed chapters on subjects that include reinforcement learning, risk, inter-temporal choice, drift-diffusion models, game theory, and prospect theory make this an invaluable reference Published in association with the Society for Neuroeconomics—www.neuroeconomics.org Full-color presentation throughout with numerous carefully selected illustrations to highlight key concepts

This book constitutes the refereed proceedings of the 13th International Conference on Brain Informatics, BI 2020, held in Padua, Italy, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 33 full papers were carefully reviewed and selected from 57 submissions. The papers are organized in the following topical sections: cognitive and computational foundations of brain science; investigations of human information processing systems; brain big data analytics, curation and management; informatics paradigms for brain and mental health research; and brain-machine intelligence and brain-inspired computing.

Neuroscience has paid only little attention to decision-making for many years. Although no field of science has cohered around this topic, a variety of researchers in different areas of neuroscience ranging from cellular physiology to neuropsychology and computational neuroscience have been engaged in working on this issue. Thus, the time seemed to be ripe to bring these researchers together and discuss the state of the art of the neurobiology of decision-making in a broad forum. This book is a collection of contributions presented at that forum in Paris in October 1994 organized by the Fondation IPSEN.

Essentials of Cognitive Neuroscience introduces and explicates key principles and concepts in cognitive neuroscience in such a way that the reader will be equipped to critically evaluate the ever-growing body of findings that the field is generating. For some students this knowledge will be needed for subsequent formal study, and for all readers it will be needed to evaluate and interpret reports about cognitive neuroscience research that make their way daily into the news media and popular culture. New to the 2nd Edition New chapter on methodology Updated content considers the growing influence of perspectives from predictive coding, reinforcement learning, deep neural networks, and AI on cognitive neuroscience; as well as important empirical results from the past few years ranging from object and face recognition to perceptual decision making to working memory to language comprehension

Most decisions in life are based on incomplete information and have uncertain consequences. To successfully cope with real-life situations, the nervous system has to estimate, represent and eventually resolve uncertainty at various levels. A common tradeoff in such decisions involves those between the magnitude of the expected rewards and the uncertainty of obtaining the rewards. For instance, a decision maker may choose to forgo the high expected rewards of investing in the stock market and settle instead for the lower expected reward and much less uncertainty of a savings account. Little is known about how different forms of uncertainty, such as risk or ambiguity, are processed and learned about and how they are integrated with expected rewards and individual preferences throughout the decision making process. With this Research Topic we aim to provide a deeper and more detailed understanding of the processes behind decision making under uncertainty.

Mood disorders, such as depression and bipolar disorder, are a global public health problem because of their high prevalence, chronicity, and recurrence throughout the lifespan as well as increased risk of mortality. These disorders are often associated with anatomical and functional brain alterations. In the last few decades, brain imaging research has made it possible to investigate these alternations in patients with neuropsychiatric illnesses. In particular, novel neuroimaging methods (e.g., structural/functional magnetic resonance imaging), have allowed researchers to further understand both anatomical and functional deficits in patients with mood disorders.