Proceedings of the Annual Meeting of the Cognitive Science Society

Many pieces of information are potentially important to causal inference. Determining whether vitamin C preventscolds may entail knowing the frequency with which colds occur without vitamin C, other cold inhibitors, and the frequencyof vitamin C use. Do reasoners integrate all this information to create coherent beliefs? In contrast to models emphasizingmodular causal learning (e.g., Cheng, 1997), McDonnell, Tsividis, & Rehder (2013) proposed an integrated model, positingthat individuals simultaneously update their beliefs about all components of a causal network. We tested modular versusintegrated learning in two experiments using a retrospective inhibition design. In both, participants learned about two causesof headaches sequentially across two phases. We manipulated the base rate of headaches in phase II to be either consistentor inconsistent with phase I learning. Across experiments, participants failed to use base rate information as predicted by theintegrated model, supporting modular causal modular learning.