Distinct Neural Dynamics Underlying Reward Expectation Formation and Outcome Processing: Insights From ERP and...

Reward expectations are fundamental to theories of decision making and reinforcement learning. While prior research has focused on how expectations can influence outcome processing, far fewer studies have investigated how these expectations are actually formed. To address this gap, we measured EEG activity from participants as they completed a two-stage binary-choice task designed to separate the formation of expectations about outcome probabilities from the processing of actual reward outcomes. To more fully examine the neural mechanisms underlying each stage, we measured the Reward Positivity (RewP) and P3b time-domain event-related potential (ERP) components, as well as the delta- and theta-band activity underlying each ERP. During the Outcome Probability stage, participants learned the likelihood of their choice winning on that trial. Each measure of RewP-latency activity (ERP, delta, theta) was larger for outcomes that were certain to occur, but each measure diverged in its relationship to outcome valence. Conversely, all P3b-latency measures were increased for losses that were certain to occur. Notably, changes in RewP-Theta, not in ERP components, provided the earliest marker of sensitivity to certain losses. At the Actual Outcome stage, the RewP and P3b-ERPs were larger for unexpected wins, consistent with theories of reward prediction errors and context updating. Delta activity generally followed the patterns observed in its temporally matched ERP but displayed an inconsistent relationship with outcome valence, suggesting that it may reflect contextualized feedback processing rather than a specific win-related signal. Theta was insensitive to outcome valence and only sensitive to expectations at longer latencies, indicating a shift from valence-sensitive processing during expectation formation to a broader role in monitoring expectancy violations. Together, the results underscore the importance of temporally and functionally distinguishing between the expectation formation and outcome phases, while demonstrating the value of multimethodological analytical approaches to fully capture the dynamic nature of reward-based decision-making.