Main description:

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.

Contents:

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