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

## TL;DR

This paper uses neural network simulations to explore how big ideas and regularization improve human learning, aligning with psychological research and highlighting the role of expert teachers and diverse learning conditions.

## Contribution

It demonstrates how regularization in neural networks models the importance of big ideas in human learning and compares different teaching scenarios.

## Key findings

- Regularization enhances the transmission of big ideas.
- Learning from a teacher outperforms data-only learning.
- Simulation results align with psychological literature.

## Abstract

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.