Mechanistic beliefs determine adherence to the Markov property in causal reasoning.

ABSTRACT

What kind of information do people use to make predictions? Causal Bayes nets theory implies that people should follow structural constraints like the Markov property in the form of the screening-off rule, but previous work shows little evidence that people do. We tested six hypotheses that attempt to explain violations of screening off, some by asserting that people use mechanistic knowledge to infer additional latent structure. In three experiments, we manipulated whether the causal relations among variables within a causal structure were supported by the same or different mechanisms. The experiments differed in the type of causal structures (common cause vs. chain), the way that causal structures were presented (verbal description vs. observational learning), how the mechanisms were presented (explicit description vs. implicit description vs. visual hint), and the number of predictions requested (2 vs. 24). The results revealed that the screening-off rule was violated more often when the mechanisms were the same than when they were different. The findings suggest that people use knowledge about underlying mechanisms to infer latent structure for prediction.