It’s been said that Failure is the first step towards success. As much as it hurts, failure is an important part of life. In fact, it is necessary as it builds your strength.
But what’s the right amount of failure? A new study has provided a perfect mathematical answer to this question.
A study by the University of Arizona has recently suggested that learning is optimized when we fail 15% of the time. There is always a sweet spot when it comes to learning. Meanwhile, we adapt best when we are challenged to grasp something directly outside the limits of our current information. At the point when a challenge is too simple, basic, we don’t learn anything new; in like manner, we don’t improve our insight when a challenge is so difficult that we fail or give up.
Where does this sweet spot lie?
The study suggests that it lies when a failure occurs 15% of the time. In other words, it’s when the right answer is given 85% of the time.
UArizona assistant professor of psychology and cognitive science Robert Wilson said, “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. The Eighty-Five Percent Rule for Optimal Learning.”
Scientists came up with so-called 85% rule after conducting a series of machine-learning experiments in which they taught computers simple tasks, such as classifying different patterns into one of two categories or classifying photographs of handwritten digits as odd versus even numbers, or low versus high numbers.
The computers learned fastest in situations in which the difficulty was such that they responded with 85% precision.
Robert Wilson said, “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. When researchers looked at previous studies of animal learning, they found that the 85% Rule held in those instances as well.”
“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 tumors and non-tumors.”
“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. I can imagine giving easy examples and giving difficult examples and giving common 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 scientists were observing simple tasks in which there was a clear correct and incorrect answer. They thus didn’t go so far as to say that students should aim for a B average in school.
Wilson said, “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 that we can expand this work and start to talk about more complicated forms of learning.”
The study is published in the journal Nature Communications.