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The Eighty Five Percent Rule for Optimal Learning

View ORCID ProfileRobert C. Wilson, Amitai Shenhav, Mark Straccia, Jonathan D. Cohen
doi: https://doi.org/10.1101/255182
Robert C. Wilson
aDepartment of Psychology and Cognitive Science Program, University of Arizona
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  • For correspondence: bob@arizona.edu
Amitai Shenhav
bCognitive, Linguistic, & Psychological Sciences, Brown University
cBrown Institute for Brain Science, Brown University
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Mark Straccia
dDepartment of Psychology, UCLA
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Jonathan D. Cohen
ePrinceton Neuroscience Institute, Princeton University
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Abstract

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 Five Percent Rule’ for artificial neural networks used in AI and biologically plausible neural networks thought to describe human and animal learning.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted January 27, 2018.
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The Eighty Five Percent Rule for Optimal Learning
Robert C. Wilson, Amitai Shenhav, Mark Straccia, Jonathan D. Cohen
bioRxiv 255182; doi: https://doi.org/10.1101/255182
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The Eighty Five Percent Rule for Optimal Learning
Robert C. Wilson, Amitai Shenhav, Mark Straccia, Jonathan D. Cohen
bioRxiv 255182; doi: https://doi.org/10.1101/255182

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