“The Eighty Five Percent Rule for Optimal Learning”, Robert C. Wilson, Amitai Shenhav, Mark Straccia, Jonathan D. Cohen2018-01-27 (neuroscience, active learning)⁠:

Researchers and educators have long wrestled with the question of how best to teach their clients, be they human, animal, or machine. Here we focus on the role of a single variable, the difficulty of training, and examine its effect on the rate of learning.

In many situations, we find that there is a sweet spot in which training is neither too easy nor too hard, and where learning progresses most quickly. We derive conditions for this sweet spot for a broad class of learning algorithms in the context of binary classification tasks, in which ambiguous stimuli must be sorted into one of two classes.

For all of these gradient-descent based learning algorithms, we find that the optimal error rate for training is around 15.87% or, conversely, that the optimal training accuracy is about 85%.

We demonstrate the efficacy of this ‘Eighty 5 Percent Rule’ for artificial neural networks used in AI and biologically plausible neural networks thought to describe human and animal learning.