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The Bayesian Mutation Sampler Explains Distributions of Causal Judgments.

Ivar R Kolvoort1,2, Nina Temme1, Leendert van Maanen3

  • 1Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.

Open Mind : Discoveries in Cognitive Science
|July 7, 2023
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Summary

People

Keywords:
causal judgmentscausal reasoningconservatismpriorsresponse distributionssampling

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Area of Science:

  • Cognitive Psychology
  • Causal Inference
  • Computational Modeling

Background:

  • Causal judgments exhibit significant variability, deviating from normal distributions and normative responses.
  • Existing models like the Mutation Sampler explain average judgments but not response distributions.

Purpose of the Study:

  • To develop an improved model of causal reasoning that accounts for response distributions.
  • To explain phenomena such as response conservatism and central tendencies in causal judgments.

Main Methods:

  • Developed the Bayesian Mutation Sampler (BMS) by extending the Mutation Sampler with generic prior distributions.
  • Fitted the BMS model to experimental data on causal judgments.

Main Results:

  • The BMS model accurately predicts average causal judgments.
  • The BMS explains distributional characteristics, including response conservatism, lack of extreme responses, and spikes at 50%.

Conclusions:

  • Incorporating generic prior distributions significantly enhances causal reasoning models.
  • The Bayesian Mutation Sampler provides a more comprehensive account of human causal judgment variability.