The role of learning data in causal reasoning about observations and interventions.

Full 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.

Author information

Author/s: Meder, Björn (B); Hagmayer, York (Y); Waldmann, Michael R (MR);

Affiliation: University of Göttingen, Göttingen, Germany. meder(-atsign-)mpib-berlin.mpg.de

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: Memory & cognition (Mem Cognit), published in United States. (Language: eng)

Reference: 2009-Apr; vol 37 (issue 3) : pp 249-64

Dates: Created 2009/02/27; Completed 2009/05/18;

PMID: 19246341, status: MEDLINE (last retrieval date: 5/18/2009, IMS Date: 18 May 2009 00:00:00)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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