When We Fail 15% Of The Time We Learn The Fastest


To learn new things, we must sometimes fail. But what’s the right amount of failure? New research led by the University of Arizona proposes a mathematical answer to that question.

Educators and educational scholars have long recognized that there is something of a “sweet spot” for learning. That is, we learn best when we are challenged to grasp something just outside our existing knowledge.

When a challenge is too simple, we don’t learn anything new; likewise, we don’t enhance our knowledge when a problem is so tricky that we fail or give up.

The Sweet Spot

So, where does the sweet spot lie? According to the new study, it’s when a failure occurs 15% of the time.

In other words, it’s when the right answer is given 85% of the time.

“These ideas that were out there in the education field – that there is this ‘zone of proximal difficulty,’ in which you ought to be maximizing your learning – we’ve put that on a mathematical footing,”

said lead author Robert Wilson, U of Arizona assistant professor of psychology and cognitive science.

Wilson collaborated with colleagues at Brown University, the University of California, Los Angeles, and Princeton. They came up with the so-called “85% Rule” after conducting a series of machine-learning experiments in which they taught computers simple tasks.

Examples included classifying different patterns into one of two categories or classifying photographs of handwritten digits as odd versus even numbers or low versus high numbers.

Fast Perceptual Learning

The computers learned fastest in situations in which the difficulty was such that they responded with 85% accuracy.

“If you have an error rate of 15% or accuracy of 85%, you are always maximizing your rate of learning in these two-choice tasks,”

Wilson said.

When researchers looked at previous studies of animal learning, they found that the 85% rule held in those instances as well, Wilson said.

Wilson said that when we think about how humans learn, the 85% Rule would most likely apply to perceptual learning, in which we gradually learn through experience and examples. Imagine, for instance, a radiologist learning to tell the difference between images of tumours and non-tumours.

“You get better at figuring out there’s a tumor in an image over time, and you need experience, and you need examples to get better,” Wilson said. “I can imagine giving easy examples and giving difficult examples and giving intermediate examples. If I give really easy examples, you get 100% right all the time, and there’s nothing left to learn. If I give really hard examples, you’ll be 50% correct and still not learning anything new, whereas if I give you something in between, you can be at this sweet spot where you are getting the most information from each particular example.”

Since Wilson and his collaborators were looking only at simple tasks in which there was a clear correct and incorrect answer, Wilson wouldn’t go so far as to say that students should aim for a B average in school. However, he thinks there might be some educational lessons that are worth further exploration.

“If you are taking classes that are too easy and acing them all the time, then you probably aren’t getting as much out of a class as someone who’s struggling but managing to keep up. The hope is we can expand this work and start to talk about more complicated forms of learning,”

he said.

  1. Robert C. Wilson, Amitai Shenhav, Mark Straccia & Jonathan D. Cohen. The Eighty Five Percent Rule for optimal learning. Nature Communications volume 10, Article number: 4646 (2019)


Last Updated on September 13, 2023