TY - JOUR T1 - Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory JF - Proceedings of the National Academy of Sciences JO - Proc Natl Acad Sci USA SP - 2502 LP - 2507 DO - 10.1073/pnas.1720963115 VL - 115 IS - 10 AU - Collins, Anne G. E. AU - Frank, Michael J. Y1 - 2018/03/06 UR - http://www.pnas.org/content/115/10/2502.abstract N2 - A major factor that improves learning in artificial agents is the use of multiple algorithms in parallel to benefit from their complementary strengths across different environments. The human brain performs a similar optimization, balancing the use of resource-intensive but immediately accessible information in working memory and a more reliable slow but steady reinforcement learning to build habits. These parallel computations are evident in neural signal dynamics that unfold across both short- and long-term time scales, which reveal that the two processes compete for decisions but cooperate for learning. These findings further our understanding of human learning and may inspire better artificial learners.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 decision-making, but the nature of their interactions is elusive. Here, we leverage methods for extracting trial-by-trial indices of reinforcement learning (RL) and working memory (WM) in human electro-encephalography to reveal single-trial computations beyond that afforded by behavior alone. Neural dynamics confirmed that increases in neural expectation were predictive of reduced neural surprise in the following feedback period, supporting central tenets of RL models. Within- and 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. ER -