What is the “sweet spot” for learning?

What is the “sweet spot” for learning?

February 20, 2022 0 By Rick

Are you failing too little? Are you failing too much? And how are you supposed to know what the right amount of failure is? How much can you fail before it becomes counter-productive? According to a recent study, if you’re having one of those days where it feels like everything’s going wrong, don’t worry about it because failing 15% of the time is the key to success. You might be surprised to hear that there’s a mathematical solution to this enigma. You may know it by the 15% rule or the 85% rule, but whatever the case, it’s still about optimized learning. It’s about finding that “sweet spot.” Whichever way you want to read it, research says that failing about 15% of the time is just right. You are probably not learning anything new if you consistently score 100%. Researchers agree that we learn best when we’re trying to learn something that’s just outside the boundary of our existing knowledge. Unless we’re sufficiently challenged, we don’t learn anything new. But it’s also true that when a challenge is too difficult, we either fail entirely or just give up and don’t learn anything new. Researchers at the University of Arizona, Brown University, Princeton and UCLA conducted a study using artificial intelligence to develop what they call “The Eighty-Five Percent Rule for Optimal Learning.” 

Teaching computers
Robert Wilson, lead author of the study, and his fellow collaborators taught computers such simple tasks 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.” They found that the computers learned optimally in situations where they were accurate 85% of the time. “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,” according to Wilson. What’s more, the 85% rule was true for animals, too. So, what about learning in humans? Wilson thinks that perceptual learning is where the rule is most likely to apply. He uses the case of a radiologist distinguishing between images of non-tumors and tumors as an example. Other examples are tennis players improving their serve or dermatologists learning how to discriminate between images of cancerous and non-cancerous moles. 

Human learning
“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 posited. “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. In contrast, 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.” Wilson stresses that he and his team were only looking at simple tasks with black and white answers, i.e., correct or incorrect. How does this play out in school? Well, if you’re getting As in all your classes, they’re probably too easy for you and you won’t get as much out of them as a student who struggles but keeps up, Wilson speculates. We need to remember that this is all theoretical right now. We need to test it on education and human behavior. Hopefully, we can do this soon. 

Not new
The idea behind this isn’t new. Back in the 1930s, Lev Vygotsky said that “kids learn best when they are in the zone of proximal difficulty where things are just beyond what they can do—where it’s not so easy that it’s trivial but not so far beyond that they can’t do it.” That’s in the classroom, of course, and theory is theory. But it’s not until you get out of the classroom and into the field where you’re making these judgments over and over with many different examples that you are going to get really good at it. It’s a slow, incremental and sometimes frustrating process. Who would have thought that failing could feel so good?