Abstract
When acquiring a motor skill, learners must practice the skill at a difficulty that is challenging but still manageable in order to gradually improve their performance. In other words, during training the learner must experience success as well as failure. Does there exist an optimal proportion of successes and failures to promote the fastest improvements in skill? Here, we build on a recent theoretical framework for optimal machine learning, extending it to the learning of motor skills. We then designed a custom task whose difficulty dynamically changed along with subjects’ performance, constraining the error rate during training. In a large behavioral dataset, we observe evidence that learning is greatest at around a ∼30% error rate, matching our theoretical predictions.
Author Summary Practicing a motor skill involves successfully performing intended movements and learning from mistakes. Is there an optimal proportion of mistakes during training that leads to fast and efficient skill learning? Inspired by recent theoretical work on principles of machine learning, we mathematically derive an “optimal error rate” for a simple motor skill and then experimentally validate our predictions. We find both theoretical and empirical evidence suggesting that ∼30% is the optimal error rate for motor learning, which has practical implications both for rehabilitation and for sports coaching and training.
Competing Interest Statement
The authors have declared no competing interest.
