Depression: an RL formulation and a behavioural test

abstract Virtually all pharmacological treatments with clinical efficacy in psychiatry have effects on neuromodulatory systems. Reinforcement learning has provided a very detailed and rich ground for the understanding of neuromodulators' effects in normal behaviour. Here, we report a formulation of very specific notions in psychiatry in reinforcement learning terms and a decision making task testing them behaviourally. We choose depression, a mood disorder with intuitive links to affective learning, as a first test case. We argue for a very simple definition of reward sensitivity and behavioural control in a goal-directed setting, design a behavioural decision making task and show that it allows related cognitive constructs to be captured in a parametric and behaviourally specific manner. Finally we show that these tasks allow classification of subjects into healthy and depressed groups based purely on a behavioural measure. We discuss the relevance of this approach to psychitric diagnosis.