Machines Like Us

How does the human brain construct intelligent behavior? Computational models have proposed several mechanisms to accomplish this: the most well known is "Hebbian learning," a process mathematically similar to both principal components analysis and Bayesian statistics. But other neural learning algorithms must exist -- how else could the brain disentangle mere correlations from true causation?

Temporal precedence helps to some extent -- and does seem to play a large role in Hebbian learning (e.g., spike-timing dependent plasticity). But the smell of rain does not actually cause rain -- although it can both precede and follow it -- so temporal precedence is not a complete answer to disambiguating causality from correlation.

Read full story at Developing Intelligence.