A new computational and dimensional approach to understanding and
classifying mental disorders: modeling key learning and decision-making
mechanisms across different mental disorders.
Even as researchers look for neurobiological correlates of mental
disorders, many of these disorders are still classified solely according
to the manifestation of clinical symptoms. Neurobiological findings
rarely help diagnose a specific disease or predict its outcome. Although
current diagnostic categories are questionable (sometimes labeling
common states of human suffering as disorders), traditional neuroimaging
approaches are not sophisticated enough to capture the neurobiological
markers of mental disorder. In this book, Andreas Heinz proposes a
computational and dimensional approach to understanding and classifying
mental disorders: modeling key learning and decision-making mechanisms
across different mental disorders. Such an approach focuses on the
malleability and diversity of human behavior and its biological
underpinnings.
Heinz explains basic learning mechanisms and their effects on human
behavior, focusing not on single disorders but on how such mechanisms
work in a multitude of mental states. For example, he traces alterations
in dopamine-reinforcement learning in psychotic, affective, and
addictive disorders. He investigates to what extent these basic
dimensions of mental disorders can account for such syndromes as craving
and loss of control in addiction, positive and negative mood states in
affective disorders, and the altered experience of self and world
associated with psychotic states. Finally, Heinz explores the clinical
and therapeutic implications of such accounts. He argues that a focus on
learning mechanisms, with its emphasis on human creativity and
resilience, should help reduce the stigma of mental disorder.
