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

A probabilistic causal chain A→B→C may intui- tively appear to be transitive: If A probabilistically causes B, and B probabilistically causes C, A probabilistically causes C. However, probabilistic causal relations can only guaranteed to be transitive if the so-called Markov condition holds. In two experiments, we examined how people make probabilistic judgments about indirect relationships A→C in causal chains A→B→Cthat violate theMarkov condition.We hypothesized that participants would make transitive inferences in accor- dance with the Markov condition although they were present- ed with counterevidence showing intransitive data. For in- stance, participants were successively presented with data entailing positive dependencies A→B and B→C.Atthe same time, the data entailed that A and C were statistically indepen- dent. The results of two experiments showthat transitive reason- ing via a mediating event B influenced and distorted the induc- tion of the indirect relation between A and C. Participants’ judg- ments were affected by an interaction of transitive, causal- model-based inferences and the observed data. Our findings support the idea that people tend to chain individual causal relations intomental causal chains that obey the Markov condi- tion and thus allow for transitive reasoning, even if the observed data entail that such inferences are not warranted.

Sydow, M. v., Hagmayer, Y., & Meder, B. (2016). Transitive reasoning distorts induction in causal chains. Memory & Cognition, 44, 469-487. doi:10.3758/s13421-015-0568-5 (Full text)

Publication year	2016
Document type:	Article
Publication status:	Published
External URL:	http://dx.doi.org/10.3758/s13421-015-0568-5 View
Categories:
Keywords:	causal coherencecausal inductioncausalityknowledge-based inductionlearningmarkov conditionmixing of causal relationshipsprobabilistic reasoningtransitive distortion effectstransitivity