Many previous accounts of causal structure induction have focused on atemporalcontingency data while fewer have described learning on the basis of observations of events unfolding over time. How do people use temporal information to infer causal structures? Here we develop a computational-level framework and propose several algorithmic-level approximations to explain how people impute causal structures from continuous-time event sequences. We compare both normative and process accounts to participant behavior across two experiments. We consider structures combining both generative and preventative causal relationships in the presence of either regular or irregular background noise in the form of spontaneous activations. We find that 1) humans are robustly capable learners in this setting, successfully identifying a variety of ground truth structures but 2) diverging from our computational-level account in ways we can explain with a more tractable simulation and summary statistics approximation scheme. We thus argue that human structure induction from temporal information relies on comparisons between observed patterns and expectations established via mental simulation
