We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...) construct new causal maps and that their learning is consistent with the Bayes net formalism. (shrink)

This study examined the idea that reasoning involves construction of mental representations of premises and that there is a developmental progression in the ability of Ss to reason with models containing concrete and abstract elements. Exp 1 found that for 13- and 16-yr-old Ss, reasoning with abstract content was more difficult than with concrete content. Younger Ss appeared to rely more on concrete representations that used real-world knowledge than on more general abstract representations. Exp 2 explored order effects in the (...) presentation of concrete and abstract problems.followed by concrete problems led to reduced concrete-problem performance for high school students but did not affect performance for university students. These results support the hypotheses and suggest that development of formal reasoning abilities goes through 2 levels. (shrink)