Failing 15% of the time is the best way to learn, say scientists
The paper is interesting because they calculate the theoretically optimal difficulty for a specific class of learning algorithms: <a href="https://dx.doi.org/10.1038/s41467-019-12552-4" rel="nofollow">https://dx.doi.org/10.1038/s41467-019-12552-4</a> (I think the method might be applicable for scheduling flashcards better than the rule-of-thumb spacing of Anki et al.)<p>The Independent article is unfounded speculation about this applying to the way humans learn, without any discussion of whether the model is actually applicable. (Most things humans are trying to learn aren't binary classification tasks.)