Normative Accounts of Illusory Correlations

When learning about the joint occurrence of different variables, individuals often manifest biases in the associations they infer. In some cases, they infer an association when none is present in the observed sample. In other cases, they infer an association that is contrary to the one that is in fa...

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Bibliographic Details
Published in:Psychological review 2021-10, Vol.128 (5), p.856-878
Main Authors: Bott, Franziska M., Kellen, David, Klauer, Karl Christoph
Format: Article
Language:English
Subjects:
Associations
Bayesian analysis
Bias
Correlation analysis
Dismissal
Heuristic
Heuristics
Human
Learning
Rationality
Statistical Data
Statistical Probability
Succession
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Summary:When learning about the joint occurrence of different variables, individuals often manifest biases in the associations they infer. In some cases, they infer an association when none is present in the observed sample. In other cases, they infer an association that is contrary to the one that is in fact observed. These illusory correlations are often interpreted as being the byproduct of selective processing or as the outcome of an "illogical" pseudocontingency heuristic. More recently, a normative account of illusory correlations has been proposed, according to which they can be given a normative underpinning in terms of an application of Laplace's Rule of Succession. The present work will discuss the empirical and theoretical limitations associated with this normative account, and argue for its dismissal. As an alternative, we propose a normative account that casts illusory correlations as the expected outcome of a Bayesian reasoner relying on marginal frequencies. We show that this account succeeds in capturing the qualitative patterns found in a corpus of published studies.
ISSN:0033-295X
1939-1471
DOI:10.1037/rev0000273