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The Eighty Five Percent Rule for optimal learning.


ABSTRACT: Researchers and educators have long wrestled with the question of how best to teach their clients be they humans, non-human animals or machines. Here, we examine the role of a single variable, the difficulty of training, 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. For all of these stochastic 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 Five Percent Rule' for artificial neural networks used in AI and biologically plausible neural networks thought to describe animal learning.

SUBMITTER: Wilson RC 

PROVIDER: S-EPMC6831579 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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The Eighty Five Percent Rule for optimal learning.

Wilson Robert C RC   Shenhav Amitai A   Straccia Mark M   Cohen Jonathan D JD  

Nature communications 20191105 1


Researchers and educators have long wrestled with the question of how best to teach their clients be they humans, non-human animals or machines. Here, we examine the role of a single variable, the difficulty of training, 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 bina  ...[more]

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