摘要：Recent studies on causal learning have shown that people use covariation information to infer casual structure, and that people have prior assumptions about causal structure and causal strength. Although covariation between two variables is insufficient to induce causal direction, learners give various interpretations to covariation information. Whereas necessity of causality assumes low base rate of the effect, sufficiency of causality expects high causal strength. These viewpoints result in opposite interpretations in causal structure learning. The purpose of the present study is to investigate prior assumptions in inferring preventive causal structure. Participants were asked to observe the states of bacteria (present or absent) and to infer their causal direction. The results found that judgments of causal structure varied as a function of covariation information, and that participants interpreted covariation according to sufficiency of causation. These findings are explained by asymmetries in generative and preventive causal relations. Theoretical implications and future directions are discussed..
关键词：因果推論;因果構造学習;ベイズモデリング;因果ベイズネット;原因の確率;causal reasoning;causal structure learning;Bayesian modeling;causalBayesnets;probability of cause
