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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
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
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.
Keywords:
Causal Bayesian networksCausal inferenceExplaining awayProbability interpretationPropensityMore Related Videos
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