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Information sampling and Bayesian belief formation in statistical judgment.

Lisheng He1, Hongyi Wang2, Yiwen Bian1

  • 1SILC Business School, Shanghai University, Shanghai 201800, China.

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|October 15, 2025
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Summary
This summary is machine-generated.

Decision makers exhibit biases when interpreting scatterplots due to biased information sampling. A Bayesian learning model accurately predicts these judgment errors, offering insights into cognitive mechanisms and data visualization.

Keywords:
Bayesian cognitioncomputational modelingcorrelation judgmentdata visualizationinformation sampling

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Area of Science:

  • Cognitive Science
  • Statistics
  • Data Visualization

Background:

  • Statistical graphs like scatterplots are crucial for data communication.
  • Decision makers often exhibit systematic errors when interpreting visual data.
  • Understanding these errors is vital for science, medicine, and policy.

Purpose of the Study:

  • To propose and test a Bayesian learning model for understanding judgment errors in scatterplot interpretation.
  • To investigate the role of biased information sampling in statistical graph perception.
  • To quantitatively predict and explain common biases in correlation judgments.

Main Methods:

  • Four eye-tracking experiments (N=421) were conducted.
  • Participants made correlation judgments from scatterplots with manipulated and real data.
  • Bayesian belief updating and information sampling computational models were employed.

Main Results:

  • Participant judgments showed known biases, like underestimating correlations and sensitivity to irrelevant features.
  • The Bayesian model accurately predicted participant judgments and biases.
  • A combined computational model replicated observed behavioral regularities.

Conclusions:

  • Judgment errors in scatterplot interpretation stem from biased information sampling by Bayesian learners.
  • Cognitive mechanisms of belief formation can be elucidated through computational modeling.
  • Findings offer insights for improving data visualization and statistical communication.