Subjective Probability Correction for Uncertainty Representations

Fumeng Yang, Maryam Hedayati, Matthew Kay

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We propose a new approach to uncertainty communication: we keep the uncertainty representation fixed, but adjust the distribution displayed to compensate for biases in people's subjective probability in decision-making. To do so, we adopt a linear-in-probit model of subjective probability and derive two corrections to a Normal distribution based on the model's intercept and slope: one correcting all right-tailed probabilities, and the other preserving the mode and one focal probability. We then conduct two experiments on U.S. demographically-representative samples. We show participants hypothetical U.S. Senate election forecasts as text or a histogram and elicit their subjective probabilities using a betting task. The first experiment estimates the linear-in-probit intercepts and slopes, and confirms the biases in participants' subjective probabilities. The second, preregistered follow-up shows participants the bias-corrected forecast distributions. We find the corrections substantially improve participants' decision quality by reducing the integrated absolute error of their subjective probabilities compared to the true probabilities. These corrections can be generalized to any univariate probability or confidence distribution, giving them broad applicability. Our preprint, code, data, and preregistration are available at

Original languageEnglish (US)
Title of host publicationCHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450394215
StatePublished - Apr 19 2023
Event2023 CHI Conference on Human Factors in Computing Systems, CHI 2023 - Hamburg, Germany
Duration: Apr 23 2023Apr 28 2023

Publication series

NameConference on Human Factors in Computing Systems - Proceedings


Conference2023 CHI Conference on Human Factors in Computing Systems, CHI 2023


  • election forecasts
  • perception
  • subjective probability
  • uncertainty visualization

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software


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