Navigating Dynamic Risks: Outcome Monitoring and Evaluation under Escalating Risk
Abstract
Real-world risk-taking unfolds through sequences of decisions where stakes escalate incrementally. Yet most ERP research examines isolated outcomes, leaving neural adaptation to 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 loss probability while recording EEG in 25 participants. We measured early outcome detection via the feedback-related negativity (FRN) and reward positivity (RewP) and later evaluative processing via the P3b. All three components showed systematic adaptation with component-specific sensitivities to risk parameters. The FRN exhibited context-dependent shifts between habituation and sensitization: valence differentiation was amplified under conditions where monitoring was most critical — when potential gains were modest or losses severe. The RewP showed stake-dependent valence differentiation, with larger loss amounts enhancing the distinction between positive and negative outcomes and increasing sensitivity to relative outcome valence within the current risk context. The P3b consistently prioritized negative outcomes, with sustained attentional resources amplifying under higher stakes. Exploratory analyses revealed fundamentally opposing temporal adaptation strategies as a function of individual risk propensity, emerging specifically under high-stakes conditions. Risk-prone individuals showed progressive sensitization to negative outcomes alongside maintained positive outcome evaluation; risk-averse individuals showed the inverse pattern. Critically, both groups achieved equivalent task performance, suggesting these divergent neural signatures reflect strategic regulatory approaches rather than processing deficits. These findings advance understanding of how the brain navigates dynamic risk environments and when adaptive risk-taking may transition toward maladaptive patterns.
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BibTeX
@misc{lorenz2026navigating,
title = {{Navigating Dynamic Risks: Outcome Monitoring and Evaluation under Escalating Risk}},
author = {Corinna Lorenz and Nicola Ferdinand},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/3ytf6_v4},
url = {https://osf.io/preprints/psyarxiv/3ytf6_v4/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Navigating Dynamic Risks: Outcome Monitoring and Evaluation under Escalating Risk
AU - Corinna Lorenz
AU - Nicola Ferdinand
PY - 2026
DA - 2026/09/04
PB - PsyArXiv
DO - 10.31234/osf.io/3ytf6_v4
UR - https://osf.io/preprints/psyarxiv/3ytf6_v4/
AB - Real-world risk-taking unfolds through sequences of decisions where stakes escalate incrementally. Yet most ERP research examines isolated outcomes, leaving neural adaptation to 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 loss probability while recording EEG in 25 participants. We measured early outcome detection via the feedback-related negativity (FRN) and reward positivity (RewP) and later evaluative processing via the P3b. All three components showed systematic adaptation with component-specific sensitivities to risk parameters. The FRN exhibited context-dependent shifts between habituation and sensitization: valence differentiation was amplified under conditions where monitoring was most critical — when potential gains were modest or losses severe. The RewP showed stake-dependent valence differentiation, with larger loss amounts enhancing the distinction between positive and negative outcomes and increasing sensitivity to relative outcome valence within the current risk context. The P3b consistently prioritized negative outcomes, with sustained attentional resources amplifying under higher stakes. Exploratory analyses revealed fundamentally opposing temporal adaptation strategies as a function of individual risk propensity, emerging specifically under high-stakes conditions. Risk-prone individuals showed progressive sensitization to negative outcomes alongside maintained positive outcome evaluation; risk-averse individuals showed the inverse pattern. Critically, both groups achieved equivalent task performance, suggesting these divergent neural signatures reflect strategic regulatory approaches rather than processing deficits. These findings advance understanding of how the brain navigates dynamic risk environments and when adaptive risk-taking may transition toward maladaptive patterns.
ER -