Action selection in early stages of psychosis: an active inference approach
Mon-A7-Talk II-02
Presented by: Elisabeth Friederike Sterner
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.
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