Using reinforcement learning to explain psychodynamics

Abstract

In this thesis we consider the use of reinforcement learning (RL) as a formal framework for understanding psychodynamic phenomena. While the field of psychodynamics has influenced a century of theoretical and clinical thinking about psychology, it also faces a number of scientific challenges. We show how RL can provide new explanations for key psychodynamic constructs by grounding them in the principles of reward maximization. The body of the thesis consists of three chapters in which simulations of adaptive agents offer normative accounts of psychodynamic processes. We address the following three phenomena: i) intrapsychic conflict, ii) the dynamics of grief, and iii) the distinct processing of pleasure and pain. We relate these to RL implementations that consider i) modularity, ii) memory replay and iii) reward learning operators, respectively, as central explanatory factors. By providing a computational foundation for psychodynamic concepts, this thesis offers a path toward a more rigorous science of psychological dynamics and their disruption in clinical conditions.