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  1.  22
    Agents and Causes: Dispositional Intuitions As a Guide to Causal Structure.Ralf Mayrhofer & Michael R. Waldmann - 2015 - Cognitive Science 39 (1):65-95.
    Currently, two frameworks of causal reasoning compete: Whereas dependency theories focus on dependencies between causes and effects, dispositional theories model causation as an interaction between agents and patients endowed with intrinsic dispositions. One important finding providing a bridge between these two frameworks is that failures of causes to generate their effects tend to be differentially attributed to agents and patients regardless of their location on either the cause or the effect side. To model different types of error attribution, we augmented (...)
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  2.  4
    Structure Induction in Diagnostic Causal Reasoning.Björn Meder, Ralf Mayrhofer & Michael R. Waldmann - 2014 - Psychological Review 121 (3):277-301.
  3.  6
    Successful Structure Learning From Observational Data.Anselm Rothe, Ben Deverett, Ralf Mayrhofer & Charles Kemp - 2018 - Cognition 179:266-297.
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  4.  9
    Indicators of Causal Agency in Physical Interactions: The Role of the Prior Context.Ralf Mayrhofer & Michael R. Waldmann - 2014 - Cognition 132 (3):485-490.
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  5.  20
    Sufficiency and Necessity Assumptions in Causal Structure Induction.Ralf Mayrhofer & Michael R. Waldmann - 2016 - Cognitive Science 40 (8):2137-2150.
    Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when deciding between alternative causal structures. In three experiments, we requested subjects to choose which of two observable variables was the cause and which the effect. We found (...)
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  6.  44
    Hierarchical Bayesian Models as Formal Models of Causal Reasoning.York Hagmayer & Ralf Mayrhofer - 2013 - Argument and Computation 4 (1):36 - 45.
    (2013). Hierarchical Bayesian models as formal models of causal reasoning. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 36-45. doi: 10.1080/19462166.2012.700321.
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  7.  8
    Time and Singular Causation—A Computational Model.Simon Stephan, Ralf Mayrhofer & Michael R. Waldmann - 2020 - Cognitive Science 44 (7).
    Causal queries about singular cases, which inquire whether specific events were causally connected, are prevalent in daily life and important in professional disciplines such as the law, medicine, or engineering. Because causal links cannot be directly observed, singular causation judgments require an assessment of whether a co‐occurrence of two events c and e was causal or simply coincidental. How can this decision be made? Building on previous work by Cheng and Novick (2005) and Stephan and Waldmann (2018), we propose a (...)
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