Hypothesis-Space Constraints in Causal Learning

Abstract

How do children identify promising hypotheses worth testing? Many studies have shown that preschoolers can use patterns of covariation together with prior knowledge to learn causal rela- tionships. However, covariation data are not always available and myriad hypotheses may be commensurate with substantive knowledge about content domains. We propose that children can identify high-level abstract features common to effects and their candidate causes and use these to guide their search. We investigate children’s sensitivity to two such high-level features — proportion and dynamics, and show that preschoolers can use these to link effects and candidate causes, even in the absence of other disambiguating information.


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