One of the problems that occur during (artificial) neural network training is called overfitting. The error on the training set is driven to a very small value, but when new data is presented to the network the error is large. The network has memorized the training examples, but it has not learned to generalize to new situations.
The human brain does not overfit when training at some task, but becomes better and better instead. Why? Or conversely, why do artificial neural networks overfit when are trained too much, in contrast with the human brain?