What Can Reinforcement Learning Models of Dopamine and Serotonin Tell Us about the Action of Antidepressants?

Abstract

Although evidence suggests that antidepressants are effective at treating depression, the mechanisms behind antidepressant action remain unclear, especially at the cognitive/computational level. In recent years, reinforcement learning (RL) models have increasingly been used to characterise the roles of neurotransmitters and to probe the computations that might be altered in psychiatric disorders like depression. Hence, RL models might present an opportunity for us to better understand the computational mechanisms underlying antidepressant effects. Moreover, RL models may also help us shed light on how these computations may be implemented in the brain (e.g., in midbrain, striatal, and prefrontal regions) and how these neural mechanisms may be altered in depression and remediated by antidepressant treatments. In this paper, we evaluate the ability of RL models to help us understand the processes underlying antidepressant action. To do this, we review the preclinical literature on the roles of dopamine and serotonin in RL, draw links between these findings and clinical work investigating computations altered in depression, and appraise the evidence linking modification of RL processes to antidepressant function. Overall, while there is no shortage of promising ideas about the computational mechanisms underlying antidepressant effects, there is insufficient evidence directly implicating these mechanisms in the response of depressed patients to antidepressant treatment. Consequently, future studies should investigate these mechanisms in samples of depressed patients and assess whether modifications in RL processes mediate the clinical effect of antidepressant treatments.

Keywords: antidepressants; depression; dopamine; reinforcement learning; serotonin.

Figure 1

Schematic diagram illustrating the RL framework. At every time step, the agent receives information about the state they are in (i.e., a representation of their current environment, such as the speed and position of the car when driving) and the amount of reward they have received. Based on this feedback, the agent aims to adjust its action selection policy to maximise the amount of reward obtained in the future.