Davis: Errors are key to learning and the proper ratio of errors to successful trials for optimal learning is very clear from machine learning. If you want to optimize learning, he says, make it difficult enough so that 15% error rate orjitsu isn't too hard. That's what the machine learning algorithm is based on human learning; that's what the animal data show.
Andrew Huberman, Ph.D., is a neuroscientist and tenured Professor in the Department of Neurobiology at the Stanford University School of Medicine. He has made numerous significant contributions to the fields of brain development, brain function and neural plasticity, which is the ability of our nervous system to rewire and learn new behaviors, skills and cognitive functioning.
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