Motivation, Emotion, and Goal Direction in

The articles gathered in this volume represent examples of a unique approach to the study of mental phenomena: a blend of theory and experiment, informed not just by easily measurable laboratory data but also by human introspection. Subjects such as approach and avoidance, desire and fear, and novelty and habit are studied as natural events that may not exactly correspond to, but at least correlate with, some (known or unknown) electrical and chemical events in the brain.

Preface	p. vii
References for Preface	p. xv
List of Contributors	p. xvii
Theories of Pavlovian Conditioning	p. xix
Propagation Controls for True Pavlovian Conditioning	p. 1
Appendix	p. 27
Appendix	p. 33
Associative Learning And Selective Forgetting in ANeural Network Regulated By Reinforcement and Attentive Feedback	p. 37
Conclusion	p. 60
Acknowledgments	p. 60
Versatility in Conditioning A Layered Networ Model	p. 63
References	p. 87
Acknowledgments	p. 87
4 Behavioral Geodesics	p. 91
References	p. 110
Neuromodulatory Mechanisms in Neural Networks and Their Influence On Interstimulus Interval Effects in Pavlovian Conditioning	p. 115
Appendix: Numerical Methods And Parameters	p. 159
Appendix: Numerical Methods And Parameters	p. 160
Complex Motivational-Cognitive Circuits In The Brain	p. 167
Top-Down Processes Attention, and Motivation In Cognitive Tasks	p. 169
References	p. 203
A Neural Network Theory Of Manic-Depressive ILlness	p. 209
Acknowledgments	p. 253
References	p. 253
Learned Helplessness Memory, and The Dynamics of Hope	p. 259
Acknowledgments	p. 295
References	p. 295
Integration, Disintegration, And the Frontal Lobes	p. 301
Familiarity and Novelty: The Contributions of the Limbic Forebrain to Valuation and The Processing of Relevance	p. 337
Applications of Goal Direction in Artificial Neural Systems	p. 367
Knowledge-Representation Networks: Goal Direction In Intelligent Neural Systems	p. 369
Perfect Memory	p. 411
Acknowledgment	p. 424
References	p. 424
Author Index	p. 427
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Motivation, Emotion, and Goal Direction in Neural Networks

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