von Sydow,M.
Meder,B.
Hagmayer,Y.

In deterministic causal chains the relations „A causes B’ and „B causes C’ imply that „A causes C’. However, this is not necessarily the case for probabilistic causal relationships: A may probabilistically cause B, and B may probabilistically cause C, but A does not probabilistically cause C, but rather ¬C. The normal transitive inference is only valid when the Markov condition holds, a key feature of the Bayes net for- malism. However, it has been objected that the Markov as- sumption does not need to hold in the real world. In our stu- dies we examined how people reason about causal chains that do not obey the Markov condition. Three experiments involv- ing causal reasoning within causal chains provide evidence that transitive reasoning seems to hold psychologically, even when it is objectively not valid. Whereas related research has shown that learners assume the Markov condition in causal chains in the absence of contradictory data, we here demon- strate the use of this assumption for situations in which partic- ipants were directly confronted with evidence contradicting the Markov condition. The results suggest a causal transitivity heuristic resulting from chaining individual causal links into mental causal models that obey the Markov condition.

Sydow, M. v., Meder, B., & Hagmayer, Y. (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 (pp. 803-808). Austin, TX: Cognitive Science Society. (Full text)

Publication year	2009
Document type:	In book
Publication status:	Published
External URL:	http://141.14.165.6/CogSci09/papers/141/paper141.pdf View
Categories:
Keywords:	categorizationcausal chaincausal modelsdeterministic causal chainsdeterministic causal relations implyheuristicsif a causesmarkov conditionsyllogistic reasoningtransitivity