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I use simulation of two multilayer neural networks to gain intuition into thedeterminants of human learning. The first network, the teacher, is trained toachieve a high accuracy in handwritten digit recognition. The second network,the student, learns to reproduce the output of the first network. I show thatlearning from the teacher is more effective than learning from the data underthe appropriate degree of regularization. Regularization allows the teacher todistinguish the trends and to deliver "big ideas" to the student. I also modelother learning situations such as expert and novice teachers, high- andlow-ability students and biased learning experience due to, e.g., poverty andtrauma. The results from computer simulation accord remarkably well withfinding of the modern psychological literature. The code is written in MATLABand will be publicly available from the author's web page.

How Machine (Deep) Learning Helps Us Understand Human Learning: the Value of Big Ideas