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The propensity interpretation of probability and diagnostic split in explaining away.

Marko Tešić1, Alice Liefgreen2, David Lagnado2

  • 1Department of Psychological Sciences, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK.

Cognitive Psychology
|May 11, 2020
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Summary

People often fail to sufficiently explain away competing causes for an effect. This study suggests this cognitive bias stems from interpreting probabilities as propensities and using a

Keywords:
Causal Bayesian networksCausal inferenceExplaining awayProbability interpretationPropensity

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Area of Science:

  • Cognitive psychology
  • Decision-making
  • Causal reasoning

Background:

  • Explaining-away situations, where multiple causes compete for an effect, are common.
  • Previous research shows people 'insufficiently' explain away competing causes.
  • Existing theories struggle to fully account for this observed insufficiency.

Purpose of the Study:

  • To investigate novel explanations for insufficient explaining away.
  • To test if interpreting probabilities as propensities drives this bias.
  • To examine if a 'diagnostic split' strategy contributes to insufficient explaining away.

Main Methods:

  • Manipulated cover story characteristics to vary the propensity interpretation of probability.
  • Adjusted prior probabilities of causes to alter normative explaining away.
  • Empirically tested participants' causal judgments in explaining-away scenarios.

Main Results:

  • Confirmed the prevalent finding of insufficient explaining away.
  • Found empirical support for the propensity interpretation hypothesis.
  • Provided evidence for the 'diagnostic split' strategy as a contributing factor.

Conclusions:

  • Interpreting probabilities as propensities and employing a 'diagnostic split' likely drive insufficient explaining away.
  • These cognitive strategies offer a compelling explanation for previously observed results.
  • Further research can explore interventions to mitigate these biases in causal reasoning.