13:30 - 15:00
Mon-A7-Talk II-
Mon-Talk II-
Room: A7
Chair/s:
Franziska Knolle
Computational psychiatry provides a direct approach for the investigation and development of mechanistic explanations for psychiatric illnesses, through the mathematical description of processes underlying behaviour. Alterations in decision-making, a cognitive process relevant for the successful interaction with the environment, have been reported in disorders including psychosis, obsessive-compulsive disorder (OCD), and eating disorders, and have been linked to the development of symptoms. Importantly, we describe a transdiagnostic approach using similar tasks and models to show that specific alterations in mathematical parameters express disease-specific dysfunction and symptom associations. Kelly Diederen (King’s College London) used a novel gamified task in conjunction with computational modelling to demonstrate that decision-confidence and belief-updating can be measured at scale using online assessment, and that these processes are altered in people at increased risk of psychosis. Elisabeth Sterner (LMU/TUM) will show that deficits in punishment learning in early psychosis are linked to increased forgetting and reduced confidence in policy selection using an Active Inference model of the Go/NoGo-Task. Pritha Sen (LMU/TUM) will present first-time imaging data investigating model-based (MB) and model-free (MF) decision making in OCD using hierarchical Bayesian modelling of the Two-Step task. Computational result show differences between patients and controls with links to symptom strength. Margaret Westwater (Oxford/Yale) will report data from both laboratory-based and online assessments of learning under uncertainty, which used computational modelling to
demonstrate that impaired learning in individuals with eating disorders is linked to altered reward sensitivity. Franziska Knolle (TUM/Cambridge) will discuss the effect of dopaminergic treatment on probabilistic reward learning in OCD using Rescorla-Wagner-modelling, showing that exaggerated cingulate reward prediction errors in patients are remediated by dopaminergic modulation. This state-of-the-art symposium demonstrates that computational models of decision making provide mechanistic explanations for dysfunctions underlying three common psychiatric conditions: psychosis, OCD, and eating disorders, and may provide starting points for treatment development.
Action selection in early stages of psychosis: an active inference approach
Mon-A7-Talk II-02
Presented by: Elisabeth Friederike Sterner
Elisabeth Friederike Sterner, Franziska Knolle
Department of Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany
In psychosis, alterations in value-based action selection and action-outcome learning can be observed often even before disease onset. Computational models of decision-making allow the investigation of whether and how these impairments contribute mechanistically to psychotic symptoms and, thus, have great potential to improve early identification and intervention.
The active inference (AI) framework conceptualizes decision-making as a process of Bayesian inference in which the brain predicts the consequences of an action based on both past experiences and the structure of the task to choose the action to produce the most preferred outcomes. Using AI modelling, we wanted to explore (1) whether AI parameters of a modelled orthogonalized Go/NoGo task differ between at-risk-mental-state (ARMS) individuals, first-episode psychosis patients (FEP) and healthy controls and (2) whether task performance and modelling parameters would be suitable for identifying group associations.
We observed reduced performance in patients with specific deficits in punishment learning. In addition, AI-modelling showed that FEP patients displayed increased forgetting and less optimal general choice behavior, with poorer action-state association (1). Using ROC analysis, we were able to demonstrate that combining the specific expression of modelling parameters and the individual performances measures revealed fair-to-good classification performances of all groups which is especially relevant for the distinction of controls and ARMS individuals (2).
Taken together, our findings show that AI-modelling of an orthogonalized Go/No-Go task does not only provide further explanation for dysfunctional mechanisms underlying decision-making in psychosis, but may also be highly relevant for future research on the development of diagnostic biomarkers.
Keywords: action selection, psychosis, computational modelling, active inference, classification, computational psychiatry