How Machine (Deep) Learning Helps Us Understand Human Learning: the
  Value of Big Ideas
  I use simulation of two multilayer neural networks to gain intuition into the
determinants of human learning. The first network, the teacher, is trained to
achieve a high accuracy in handwritten digit recognition. The second network,
the student, learns to reproduce the output of the first network. I show that
learning from the teacher is more effective than learning from the data under
the appropriate degree of regularization. Regularization allows the teacher to
distinguish the trends and to deliver "big ideas" to the student. I also model
other learning situations such as expert and novice teachers, high- and
low-ability students and biased learning experience due to, e.g., poverty and
trauma. The results from computer simulation accord remarkably well with
finding of the modern psychological literature. The code is written in MATLAB
and will be publicly available from the author's web page.
