- Title
- Causal explanation improves judgment under uncertainty, but rarely in a Bayesian way
- Creator
- Hayes, Brett K.; Ngo, Jeremy; Hawkins, Guy E.; Newell, Ben R.
- Relation
- Memory & Cognition Vol. 46, Issue 1, p. 112-131
- Publisher Link
- http://dx.doi.org/10.3758/s13421-017-0750-z
- Publisher
- Springer
- Resource Type
- journal article
- Date
- 2018
- Description
- Three studies reexamined the claim that clarifying the causal origin of key statistics can increase normative performance on Bayesian problems involving judgment under uncertainty. Experiments 1 and 2 found that causal explanation did not increase the rate of normative solutions. However, certain types of causal explanation did lead to a reduction in the magnitude of errors in probability estimation. This effect was most pronounced when problem statistics were expressed in percentage formats. Experiment 3 used process-tracing methods to examine the impact of causal explanation of false positives on solution strategies. Changes in probability estimation following causal explanation were the result of a mixture of individual reasoning strategies, including non-Bayesian mechanisms, such as increased attention to explained statistics and approximations of subcomponents of Bayes' rule. The results show that although causal explanation of statistics can affect the way that a problem is mentally represented, this does not necessarily lead to an increased rate of normative responding.
- Subject
- judgment; intuitive statistics; Bayesian reasoning
- Identifier
- http://hdl.handle.net/1959.13/1396397
- Identifier
- uon:34036
- Identifier
- ISSN:0090-502X
- Language
- eng
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