Reward Prediction Errors Reflect an Underlying Learning Process That Parallels Behavioural Adaptations: A Trial-to-Trial Analysis

Reinforcement learning can lead to rapid changes in performance. Computational accounts of reinforcement learning align with classic learning theory, as reported by Sutton and Barto (1998, 2018) and suggest that trial-to-trial changes in performance follow rapid but decelerating learning curves. Although there is some support for a link between changes in behavioural and neural data, evidence has been inconclusive. Here, we had a computational model and human participants learn a novel language through trial-and-error while recording electroencephalographic data. By conducting linear mixed-effects models of trial-to-trial analyses, we sought to determine whether neural signals were indicative of a learning process and whether they were related to changes in behaviour. We found that neural measures did diminish with trial-to-trial changes in performance and that they were predictive of behavioural adaptations in both simulated and empirical data. These neural signals are theorised as reward prediction errors—the computational difference between expectations and outcomes—and here we provide compelling evidence that they reflect an underlying learning process that parallels behavioural adaptation. © 2019, Society for Mathematical Psychology.