I'll have to refer you to my forthcoming paper for the full argument, but basically, humans (and all animals) experience surprise and then we attribute that surprise to a cause, and then we update (learn).

In ANNs we backprop uniformly, so the error correction is distributed over the whole network. This is why LLM training is inefficient.