We outline a new computational account of learning in children using the causal Bayes net formalism. We also present evidence that children as young as two years old use something like causal Bayes net learning mechanisms to infer the causal structure of the world around them. This kind of learning may play an important role in the development of intuitive theories. Finally we contrast causal Bayes net and neural net learning mechanisms.

As a key speaker at the National Autism Conference in London concluded, we still don’t understand what autism actually is (Frith, 2002). Numerous behavioural patterns are associated with the condition(s) and there are various explanations or models of these patterns in the literature. However, it is a characteristic of