What’s Your Uncertainty Story? From Narrative Themes to FRN/P3 and Real-World Health Decisions

Xu et al. (2024) show that contextualized neural topic models can extract distinct uncertainty narratives (e.g., health vs. structural constraints) from open-ended text. Li et al. (2021) demonstrate that perceived uncertainty and negative emotions jointly shape vaccine intentions amid conflicting information. We propose eliciting open-ended narratives about uncertain personal decisions (fertility, health, career) and using contextualized topic modeling to derive individual-level narrative profiles. Participants then complete lab paradigms manipulating expected vs. unexpected uncertainty (as in Khosravi et al., 2025), while we measure FRN/P3 and fit RPE. We test whether specific narrative themes predict greater neural sensitivity to unexpected negative feedback (larger FRN) and stronger arousal, and whether emotion-regulation capacity (eWMT; Emadi Chashmi et al., 2023) modulates these links. The novelty is integrating qualitative, narrative-derived individual differences with quantitative neural markers of prediction error processing. This synthesis could explain why some individuals remain susceptible to conflicting health information: their narrative frame amplifies affective responses to uncertainty, altering neural error monitoring and downstream choices. Findings would inform tailored interventions—e.g., portfolio framing (Reeck & LaBar, 2024) or norm-based messaging (Bergquist & Ekelund, 2025)—matched to a person’s narrative “uncertainty style.”

References:

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