Thinking about the RewP as a marker of goal prediction error shifts how we connect brain signals to behavior. If this signal comes from a critic-like system within a broader actor–critic setup, it helps explain how people learn which actions move them closer to goals and when to change strategy. That matters for education, habit formation, and designing tools that support attention and motivation, because the brain’s feedback system may be tuned to abstract progress rather than simple rewards.
This perspective raises practical questions worth exploring: how do different kinds of goals shape the RewP, and can we harness that signal to help people learn more effectively or make environments more inclusive for diverse motivations? Follow the full article to see how these ideas map onto reinforcement-learning models and what they imply for improving human potential.
The Reward Positivity (RewP) is an electroencephalogram (EEG) feature that emerges following performance feedback and is commonly understood to index both positive and negative reward-prediction error (RPE+ and RPE−, respectively) signals. In contrast to this dominant perspective, we argue that the RewP is an independent EEG feature that selectively responds to positive RPE and is superimposed on a common background signal. We further propose that the RewP signals a goal prediction error: it is elicited by abstract signals instead of by hedonic ‘rewards’. This goal prediction error appears to be produced by a critic-like architecture that is associated with the actor–critic framework in reinforcement learning. This perspective emphasizes the role of the RewP in goal attainment and cognitive control as opposed to being a simple indicator of reward receipt.
