Within and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory

Abstract

Learning from rewards and punishments is essential to survival, and facilitates flexible human behavior. It is widely appreciated that multiple cognitive and reinforcement learning systems contribute to behavior, but the nature of their interactions is elusive. Here, we leverage novel methods for extracting trial-by-trial indices of reinforcement learning (RL) and working memory (WM) in human electroencephalography to reveal single trial computations beyond that afforded by behavior alone. Within-trial dynamics confirmed that increases in neural expectation were predictive of reduced neural surprise in the following feedback period, supporting central tenets of RL models. Cross-trial dynamics revealed a cooperative interplay between systems for learning, in which WM contributes expectations to guide RL, despite competition between systems during choice. Together, these results provide a deeper understanding of how multiple neural systems interact for learning and decision making, and facilitate analysis of their disruption in clinical populations.\n\nOne sentence summaryDecoding of dynamical neural signals in humans reveals cooperation between cognitive and habit learning systems.