Everyone who spends time with children knows how incredibly much they learn. But how can babies and young children possibly learn so much so quickly? In a recent article in Science, I describe a promising new theory about how babies and young children learn and the slew of research that supports it. The idea is that kids learn by thinking like Nate Silver, the polling analyst extraordinaire at the New York Times.
I suspect that most people who, like me, obsessively click his FiveThirtyEight blog throughout the day think of Nate as a semi-divine oracle who can tell you whether your electoral prayers will be answered. But there is a very particular kind of science behind Nate’s predictions. He uses what’s called Bayesian modeling, named after the Rev. Thomas Bayes, an 18th-century mathematician. The latest studies show that kids, at least unconsciously, are Bayesians, too.
The Bayesian idea is simple, but it turns out to be very powerful. It’s so powerful, in fact, that computer scientists are using it to design intelligent learning machines, and more and more psychologists think that it might explain human intelligence. Bayesian inference is a way to use statistical data to evaluate hypotheses and make predictions. These might be scientific hypotheses and predictions or everyday ones.
Read the whole story: Slate
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