Proceedings of the Annual Meeting of the Cognitive Science Society

The present paper examines what domain-general causalknowledge reasoners need for at least some outcome-variabletypes to construct useable content-specific causal knowledge.In particular, it explains why it is essential to have analyticknowledge of causal-invariance integration functions:knowledge for predicting the expected outcome assuming thatthe empirical knowledge acquired regarding a causal relationholds across the learning context and an application context.The paper reports two studies that support the hypothesis thatpreschool children have such knowledge regarding binarycauses and effects, enabling them to generalize acrosscontexts rationally, favoring the causal-invariance hypothesisover alternative hypotheses, including interaction (e.g., linear)integration functions, heuristics, and biases.