When the authors tested out prospective configuration in artificial neural networks they found that they learned in a much more human-like way—more robustly and with less training—than models trained with backprop.

当作者测试人工神经网络中的前瞻性配置时，他们发现与使用反向传播训练的模型相比，它们的学习方式更像人类，而且更加稳健，所需训练更少。