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  1. York Hagmayer & Ralf Mayrhofer (2013). Hierarchical Bayesian Models as Formal Models of Causal Reasoning. 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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  2. York Hagmayer & Magda Osman (2012). From Colliding Billiard Balls to Colluding Desperate Housewives: Causal Bayes Nets as Rational Models of Everyday Causal Reasoning. Synthese 189 (S1):17-28.
    Many of our decisions pertain to causal systems. Nevertheless, only recently has it been claimed that people use causal models when making judgments, decisions and predictions, and that causal Bayes nets allow us to formally describe these inferences. Experimental research has been limited to simple, artificial problems, which are unrepresentative of the complex dynamic systems we successfully deal with in everyday life. For instance, in social interactions, we can explain the actions of other's on the fly and we can generalize (...)
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  3. York Hagmayer, Björn Meder, Momme von Sydow & Michael R. Waldmann (2011). Category Transfer in Sequential Causal Learning: The Unbroken Mechanism Hypothesis. Cognitive Science 35 (5):842-873.
    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 (...)
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  4. Steven A. Sloman, Philip M. Fernbach & York Hagmayer (2010). Self-Deception Requires Vagueness. Cognition 115 (2):268-281.
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  5. Björn Meder & York Hagmayer (2009). Causal Induction Enables Adaptive Decision Making. In. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  6. Momme von Sydow, Björn Meder & York Hagmayer (2009). A Transitivity Heuristic of Probabilistic Causal Reasoning. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  7. York Hagmayer & Björn Meder (2008). Causal Learning Through Repeated Decision Making. In. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. 179--184.
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  8. Michael R. Waldmann, Patricia W. Cheng, York Hagmayer & Aaron P. Blaisdell (2008). Causal Learning in Rats and Humans: A Minimal Rational Model. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oup Oxford.
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  9. York Hagmayer, Steven A. Sloman, David A. Lagnado & Michael R. Waldmann (2007). Causal Reasoning Through Intervention. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press.
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  10. David A. Lagnado, Michael R. Waldmann, York Hagmayer & Steven A. Sloman (2007). Beyond Covariation. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press.
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  11. Steven A. Sloman & York Hagmayer (2006). The Causal Psycho-Logic of Choice. Trends in Cognitive Sciences 10 (9):407-412.
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  12. Michael R. Waldmann & York Hagmayer (2001). Estimating Causal Strength: The Role of Structural Knowledge and Processing Effort. Cognition 82 (1):27-58.
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