Cognitive Science 35 (5):842-873 (2011)
Abstract |
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
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Keywords | Categorization Causal induction Learning |
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DOI | 10.1111/j.1551-6709.2011.01179.x |
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References found in this work BETA
Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Tijdschrift Voor Filosofie 64 (1):201-202.
The Role of Theories in Conceptual Coherence.Gregory L. Murphy & Douglas L. Medin - 1985 - Psychological Review 92 (3):289-316.
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Citations of this work BETA
Causal Responsibility and Robust Causation.Guy Grinfeld, David Lagnado, Tobias Gerstenberg, James F. Woodward & Marius Usher - 2020 - Frontiers in Psychology 11:1069.
Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment.Samuel G. B. Johnson & Woo-Kyoung Ahn - 2015 - Cognitive Science 39 (7):1468-1503.
The Oxford Handbook of Causal Reasoning.Michael Waldmann (ed.) - 2017 - Oxford, England: Oxford University Press.
Hierarchical Bayesian Models as Formal Models of Causal Reasoning.York Hagmayer & Ralf Mayrhofer - 2013 - Argument and Computation 4 (1):36 - 45.
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