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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.

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https://youtu.be/H1TLZ8bay1k

学校のカリキュラム

読解（文章の内容理解）においては独力で楽しんで読むためには98%の語彙を知っている必要がある

日本語の研究においては20語に1語（95%）が許容範囲

羽鳥博愛(1982)　心理言語学と英語教育