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• W2025218202 startingPage "249" @default.
• W2025218202 abstract "Recent studies have shown that people have the capacity to derive interventional predictions for previously unseen actions from observational knowledge, a finding that challenges associative theories of causal learning and reasoning (e.g., Meder, Hagmayer, & Waldmann, 2008). Although some researchers have claimed that such inferences are based mainly on qualitative reasoning about the structure of a causal system (e.g., Sloman, 2005), we propose that people use both the causal structure and its parameters for their inferences. We here employ an observational trial-by-trial learning paradigm to test this prediction. In Experiment 1, the causal strength of the links within a given causal model was varied, whereas in Experiment 2, base rate information was manipulated while keeping the structure of the model constant. The results show that learners’ causal judgments were strongly affected by the observed learning data despite being presented with identical hypotheses about causal structure. The findings show furthermore that participants correctly distinguished between observations and hypothetical interventions. However, they did not adequately differentiate between hypothetical and counterfactual interventions." @default.
• W2025218202 created "2016-06-24" @default.
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• W2025218202 creator A5082975553 @default.
• W2025218202 creator A5085696258 @default.
• W2025218202 date "2009-04-01" @default.
• W2025218202 modified "2023-09-30" @default.
• W2025218202 title "The role of learning data in causal reasoning about observations and interventions" @default.
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• W2025218202 doi "https://doi.org/10.3758/mc.37.3.249" @default.
• W2025218202 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/19246341" @default.
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