Navigating Dynamic Risks: Outcome Monitoring and Evaluation under Escalating Risk
Year: 2026
Type: preprint
Abstract: Real-world risk-taking unfolds through sequences of decisions where stakes escalate incrementally. Yet most ERP research examines isolated outcomes, leaving neural processing of accumulating risk poorly understood. We developed an ERP-adapted Columbia Card Task enabling precise temporal analysis of feedback processing across consecutive outcomes, systematically manipulating gain amounts, loss amounts, and lossprobability while recording EEG in 25 participants. We measured early outcome detection via the feedbackrelated negativity (FRN) and reward positivity (RewP) and later evaluative processing via the P3b.Early outcome processing depended on the potential loss amount. FRN amplitudes differentiated betweenpositive and negative outcomes when large losses were at stake but not when losses were small, an effect carried by more pronounced responses to negative outcomes under large losses. The RewP showed a similar but less well supported pattern. Gain amount and loss probability did not modulate early components, and we found no evidence that they changed across the feedback sequence. In contrast, the P3b was larger for negative than for positive outcomes, and this difference increased across the feedback sequence as risk accumulated, without evidence for a dependence on the stakes. Exploratory analyses did not provide consistent evidence that individual risk propensity modulated these patterns.With early processing scaling with the consequences at stake and later evaluation increasingly focusing on negative outcomes as the feedback sequence progresses, these findings suggest that outcome monitoring under escalating risk is not a fixed response to outcome valence.
Authors: Corinna Lorenz, Nicola Kristina Ferdinand
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7223036410