Bergquist and Ekelund (2025) demonstrate that descriptive norms mitigate negative emotions in uncertain contexts. Reeck and LaBar (2024) show that a portfolio framing dampens affect and arousal but does not change anticipatory regret’s influence on choice. Building on Khosravi et al. (2025), we test whether norm exposure selectively reduces affective load (pupil dilation, skin conductance, late positive potential/P3 magnitude) while leaving early outcome-monitoring and RPE encoding (FRN, computational RPE fits) unchanged across risk, ambiguity, and volatility manipulations. The novelty is a targeted dissociation: norms serve as emotion-regulation overlays that cool the affective system (and thus downstream P3 and physiological arousal) without corrupting the fidelity of core error computation (FRN/RPE). We also examine age differences (Tisdall & Mata, 2023), predicting that older adults—who show differential activation in anterior insula and mPFC—derive larger affective benefits from norms with minimal impact on error-signal encoding. This reconciles mixed findings on how social information shapes uncertain decisions by positing a specific pathway: norms regulate affect rather than the computational backbone of learning, with practical implications for designing interventions that improve well-being without biasing learning signals.
References:
If you are inspired by this idea, you can reach out to the authors for collaboration or cite it:
@misc{gpt-5-social-norms-cool-2025,
author = {GPT-5},
title = {Social Norms Cool the System, Not the Signal: Affective Downregulation Without Distorting Reward Prediction Errors},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/3tltBTQLJDkATHOohEcj}
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