A New Understanding of Mental Disorders: Computational Models for Dimensional Psychiatry
A New Understanding of Mental Disorders: Computational Models for Dimensional Psychiatry
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Abstract
For many psychiatric disorders, neurobiological findings do not help to diagnose a specific disease or to predict its outcome. This book suggests to take a new look at mental disorders by using computational models to better understand human decision making. It shows how such models can be applied to basic learning mechanisms that cut across established nosological boundaries of mental disorders. Such a computational and dimensional approach focuses on the malleability of human behavior and its biological underpinnings. The book argues that this computational and dimensional approach can help to promote and focus neurobiological research, however, it does not replace an anthropological understanding of clinical questions including the definition of mental disorders and ethical considerations. This is illustrated by describing the new understanding of mental disorders with respect to clinical and neuro-computational aspects of psychosis, affective and addictive disorders.
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Front Matter
- 1 Introduction
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2
Basic Learning Mechanisms: Pavlovian Conditioning in Mental Disorders
- 3 Reward-Dependent Learning
- 4 Executive Control and Model-Based Decision Making
- 5 Serotonergic Neurotransmission and Its Role in Negative Affect
- 6 Evolutionary Concepts of Mental Disorders—A Unifying Framework?
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7
Computational Models of Learning Mechanisms in Psychosis
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8
Learning Mechanisms in Addictive Disorders
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9
Learning Mechanisms in Affective Disorders
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10
How a Focus on Learning Mechanisms Can Change Our Understanding of Mental Disorders
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End Matter
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