Name: Florian Schlagenhauf
Affiliation: Charité Universitätsmedizin Berlin
Title: Altered reinforcement learning in psychosis and addiction - A computational psychiatry approach
The ability to assign values to environmental stimuli, form predictions about future outcomes and use those predictions to generate appropriate behavioral responses is a cognitive process which is fundamental for successful interaction with the environment. Reinforcement learning offers the possibility to mechanistically describe such processes by combining computational description of behavior and underlying neural activation using multimodal imaging. Reinforcement learning is a central topic of the emerging field of computational psychiatry which aims to bridge the gap between basic and clinical neuroscience. In schizophrenia patients, altered prediction error signaling and value representation in fronto-striatal circuits have been proposed to contribute to central aspects of the disorder including aberrant salience attribution as well as motivational impairments. In addiction, an imbalance between goal-directed and habitual control as well as a heightened influence of drug-associated Pavlovian stimuli might contribute to loss of control over substance intake. I will present results from multimodal imaging studies combining fMRI and FDOPA PET in controls and patient groups and discuss how alterations in those basic reinforcement learning processes can contribute to psychopathology and maladaptive decision making.
Dec 10, 2018 | 04:00 PM
KL 32/ 202