In the machine example, we show children’s patterns of conditional probability, the relationship between certain blocks and the machine turning on or off. If I tried to give you just a description of the sequence of events in one of these experiments in a conversation, I’d probably get it wrong and you wouldn’t be able to remember it — it’s pretty complicated for even adults to describe. But when you give kids these complicated sets of relationships and then just ask them to make the machine go or make the machine stop, they do the right things. Although they can’t consciously track how these conditional probabilities work, they are unconsciously taking that information into account. And they do this in the same way that sophisticated Bayesian network machine-learning programs do.
Would you like to comment?
Join Diigo for a free account, or sign in if you are already a member.