Publikationen
2018
- Rothe, A., Deverett, B., Mayrhofer, R. & Kemp, C. (2018). Successful structure learning from observational data. Cognition, 179, 266-297.
- Stephan, S., Mayrhofer, R. & Waldmann, M. (2018). Assessing singular causation: The role of causal latencies. In T.T. Rogers, M. Rau, X. Zhu, & C. W. Kalish (Eds.), (pp. 1080-1085). : Cognitive Science Society.
2017
- Bramley, N., Mayrhofer, R., Gerstenberg, T. & Lagnado, D. (2017). Causal learning from interventions and dynamics in continuous time. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), (pp. 150-155). : Cognitive Science Society.
- Meder, B. & Mayrhofer, R. (2017). Diagnostic reasoning. In M. R. Waldmann (Ed.), (pp. 433-458). : Oxford University Press.
- Meder, B. & Mayrhofer, R. (2017). Diagnostic causal reasoning with verbal information. Cognitive Psychology, 96, 54-84. https://dx.doi.org/10.1016/j.cogpsych.2017.05.002, ISSN: 0010-0285.
2016
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Mayrhofer, R. & Waldmann, M.
(2016).
Sufficiency and necessity assumptions in causal structure induction.
Cognitive Science,
40(8),
2137-2150.
https://dx.doi.org/10.1111/cogs.12318, ISSN: 0364-0213.
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Mayrhofer, R. & Waldmann, M.
(2016).
Causal agency and the perception of force.
Psychonomic Bulletin & Review,
23(3),
789-796.
https://dx.doi.org/10.3758/s13423-015-0960-y, ISSN: 1069-9384.
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Waldmann, M. R. & Mayrhofer, R.
(2016).
Hybrid causal representations.
In B. Ross (Ed.),
The Psychology of Learning and Motivation.
(pp. 85-127). New York: Academic Press.
ISBN: 978-0-12-804790-3.
2014
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Mayrhofer, R. & Waldmann, M.
(2014).
Indicators of causal agency in physical interactions: The role of the prior context.
Cognition,
132(3),
485-490.
https://dx.doi.org/10.1016/j.cognition.2014.05.013, ISSN: 0010-0277.
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Meder, B., Mayrhofer, R. & Waldmann, M.
(2014).
Structure induction in diagnostic causal reasoning.
Psychological Review,
121(3),
277-301.
https://dx.doi.org/10.1037/a0035944, ISSN: 0033-295X.
- Neth, H., Engelmann, N. & Mayrhofer, R. (2014). Foraging for alternatives: Ecological rationality in keeping options viable. (). : .
2013
- Hagmayer, Y. & Mayrhofer, R. (2013). Hierarchical Bayesian models as formal models of causal reasoning. Argument & Computation, 4, 36-45.
- Mayrhofer, R. & Waldmann, M. R. (2013). Agency intuitions in physical interactions. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society. (pp. 996-1001). Austin, TX: Cognitive Science Society. ISBN: 978-0-97-683189-1.
- Meder, B. & Mayrhofer, R. (2013). Sequential diagnostic reasoning with verbal information. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), (pp. 1014-1019). : Cognitive Science Society.
2010
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Mayrhofer, R., Hagmayer, Y. & Waldmann, M.
(2010).
Agents and causes: A Bayesian error attribution model of causal reasoning.
In S. Ohlsson & R. Catrambone (Eds.),
(pp. 925-930). : Cognitive Science Society.
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Mayrhofer, R., Nagel, J. & Waldmann, M.
(2010).
The role of causal schemas in inductive reasoning.
In S. Ohlsson & R. Catrambone (Eds.),
(pp. 1082-1087). : Cognitive Science Society.
2009
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Griffiths, O., Mayrhofer, R., Nagel, J. & Waldmann, M. R.
(2009).
Causal schema-based inductive reasoning.
In N. Taatgen, H. van Rijn, L. Schomaker & J. Nerbonne (Eds.),
Do Voters Use Episodic Knowledge to Rely on Recognition?.
(pp. 691-696). Austin, TX: Cognitive Science Society.
ISBN: 978-0-9768318-5-3.
- Mayrhofer, R. (2009). Kausales Denken, Bayes-Netze und die Markov-Bedingung.
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Meder, B., Mayrhofer, R. & Waldmann, M. R.
(2009).
A rational model of elemental diagnostic inference.
In N. Taatgen, H. van Rijn, L. Schomaker & J. Nerbonne (Eds.),
Proceedings of the 31st Annual Conference of the Cognitive Science Society.
(pp. 2176-2181). Austin, TX: Cognitive Science Society.
ISBN: 978-0-9768318-5-3.