The role of learning data in causal reasoning about observations and interventions |
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Authors: | Bjöörn Meder York Hagmayer Michael R Waldmann |
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Institution: | 1.University of G?ttingen,G?ttingen,Germany |
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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. |
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