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Asymmetries in predictive and diagnostic reasoning.

Philip M Fernbach1, Adam Darlow, Steven A Sloman

  • 1Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI. philip_fernbach@brown.edu

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Summary
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People’s probability judgments from causal beliefs align with diagnostic reasoning but neglect alternative causes in predictive reasoning. Causal Bayes nets reveal sophisticated, yet not fully accurate, human probability judgments.

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Area of Science:

  • Cognitive Science
  • Decision Making
  • Artificial Intelligence

Background:

  • Causal Bayes nets (CBNs) model uncertainty judgments from causal beliefs.
  • Empirical evidence suggests systematic errors in these judgments.

Purpose of the Study:

  • Investigate discrepancies between CBNs and human probability judgments.
  • Examine biases in predictive versus diagnostic reasoning.

Main Methods:

  • Developed a normative CBN model for predictive and diagnostic judgments.
  • Conducted experiments eliciting judgments of probability and causal parameters.
  • Tested model predictions against human judgments across varying causal structures.

Main Results:

  • Model predictions closely matched diagnostic judgments.
  • Predictive judgments showed systematic neglect of alternative causes.
  • Further experiments confirmed neglect of alternatives and ruled out pragmatic explanations.

Conclusions:

  • Humans utilize causal structure for probability judgments.
  • Reasoning is sophisticated but not entirely veridical, particularly in predictive tasks.
  • Predictive reasoning biases warrant further investigation.