David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Cognitive Science 35 (5):842-873 (2011)
The goal of the present set of studies is to explore the boundary conditions of category transfer in causal learning. Previous research has shown that people are capable of inducing categories based on causal learning input, and they often transfer these categories to new causal learning tasks. However, occasionally learners abandon the learned categories and induce new ones. Whereas previously it has been argued that transfer is only observed with essentialist categories in which the hidden properties are causally relevant for the target effect in the transfer relation, we here propose an alternative explanation, the unbroken mechanism hypothesis. This hypothesis claims that categories are transferred from a previously learned causal relation to a new causal relation when learners assume a causal mechanism linking the two relations that is continuous and unbroken. The findings of two causal learning experiments support the unbroken mechanism hypothesis.
|Keywords||Categorization Causal induction Learning|
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Citations of this work BETA
Samuel G. B. Johnson & Woo‐Kyoung Ahn (2015). Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment. Cognitive Science 39 (7):1468-1503.
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