We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Komputationale Modelle in der psychiatrischen Forschung.
- Authors
Heinz, Andreas; Schlagenhauf, Florian; Maricic, Lea Mascarell; Daedelow, Laura S.
- Abstract
Computational methods are widely used in psychiatric research and comprise big data approaches as well as the here discussed research approach, that mathematically models decision making in order to identify its neurobiological correlates. A well-known example is reward-dependent learning, where the prediction error (the difference between expected and actual reward) is mathematically computable and can be linked to neural signals such as dopamine release in the ventral striatum of animals and its functional correlates in humans. Changes in relevant learning mechanisms can be detected across different mental disorders including substance use disorders, psychoses and major depressive disorders thus providing a dimensional approach to identify neurobiological correlates of specific learning-mechanisms and their associated symptoms in different mental disorders. Here we discuss key approaches in the area of Pavlovian conditioning as well as reward-dependent (goal-directed and habitual) decision making. The focus on learning mechanisms emphasizes the diversity and modifiability of human behavior, which can be targeted therapeutically with training programs and cognitive-behavioral interventions.
- Publication
Zeitschrift für Psychiatrie, Psychologie und Psychotherapie, 2017, Vol 65, Issue 1, p27
- ISSN
1661-4747
- Publication type
Article
- DOI
10.1024/1661-4747/a000298