Memory, attention, and decision-making are three major areas of psychology. They are frequently studied in isolation, and using a range of models to understand them. This book brings a unified approach to understanding these three processes. It shows how these fundamental functions for cognitive neuroscience can be understood in a common and unifying computational neuroscience framework. This framework links empirical research on brain function from neurophysiology, functional neuroimaging, and the effects of brain damage, to a description of how neural networks in the brain implement these functions using a set of common principles. The book describes the principles of operation of these networks, and how they could implement such important functions as memory, attention, and decision-making.
The topics covered include
The hippocampus and memory
Reward and punishment related learning: emotion and motivation
Visual object recognition learning
Short term memory
Attention, short term memory, and biased competition
Probabilistic decision-making
Action selection
Decision-making
Also included are tutorial appendices on
Neural networks in the brain
Neural encoding in the brain
'Memory, Attention and Decision-Making' will be valuable for those in the fields of neuroscience, psychology, and cognitive neuroscience from advanced undergraduate level upwards. It will also be of interest to those interested in neuroeconomics, animal behaviour, zoology, evolutionary biology, psychiatry, medicine, and philosophy. The book has been written with modular chapters and sections, making it possible to select particular Chapters for course work.
Rezensionen / Stimmen
It is a majestic work, underpinned by a frightening capacity for memory and research and a sinuous flair for exegesis. The industry behind the text is clear, astonishing, laudable and often breath-taking. * Applied Cognitive Psychology * The main strength of this book, compared to similar computational neuroscience books, is the functional, practical, and clinical links that are made to theoretical and computational models. The overall significance and contribution of computational neuroscience in quite apparent and the marriage of these models with neuropsychology and neuroimaging is well conceived. * Doody's Notes *
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Illustrationen
numerous figures, halftones, and colour plates
Maße
Höhe: 252 mm
Breite: 180 mm
Dicke: 43 mm
Gewicht
ISBN-13
978-0-19-923270-3 (9780199232703)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Edmund T. Rolls is Professor of Experimental Psychology at the University of Oxford, and a Fellow and Tutor of Corpus Christi College, Oxford. He read preclinical medicine at the University of Cambridge, and now performs research in neuroscience at Oxford. His research links neurophysiological and computational neuroscience approaches to human functional neuroimaging and neuropsychological studies in order to provide a fundamental basis for understanding human brain function and its disorders. He is author of The Brain and Emotion (1999, Oxford University Press), with A.Treves of Neural Networks and Brain Function (1998, Oxford University Press), with G.Deco of Computational Neuroscience of Vision (2002, Oxford University Press) and Emotion Explained (2005, Oxford University Press).
Autor*in
Professor of Experimental Psychology, University of Oxford, UK
1. Introduction ; 2. The hippocampus and memory ; 3. Reward- and punishment-related learning; emotion and motivation ; 4. Invariant visual object recognition learning ; 5. Short-term memory ; 6. Attention, short-term memory, and biased competition ; 7. Probabilistic decision-making ; 8. Action selection by biased attractor competition in the pre-frontal cortex ; 9. Reward, decision and action reversal using attractor dynamics ; 10. Decision-making ; Appendix A Introduction to linear algebra for neural networks ; Appendix B Neural network models ; Appendix C Information theory, and neuronal encoding
