15%的错误率，是比较好的学习方法

如果人工智能可以作为参考的话，那么15%的错误率，是最好的学习方法

Getting everything right all of the time might sound like the ideal scenario, but such a perfect success rate can mean you're not actually learning anything new.

在任何时候都做正确的事情可能听起来像是一个理想的场景，但是如此完美的成功率可能意味着你实际上没有学到任何新东西。

To make sure you're learning at the optimal rate, new research finds you should be aiming to fail around 15 percent of the time – or 15.87 percent of the time, to be exact.

为了确保你以最佳的速度学习，新的研究发现，你应该把失败的目标定在15%左右，确切地说，是15.87%。

These findings could have implications for training courses, teaching in classrooms, and everywhere that learning happens. It's that sweet spot between finding something too easy and too difficult.

这些发现可能会对培训课程、课堂教学以及学习发生的任何地方产生影响。这是在太容易和太困难之间找到平衡点。

"These ideas that were out there in the education field – that there is this 'zone of proximal difficulty', in which you ought to be maximising your learning – we've put that on a mathematical footing," says psychologist Robert Wilson from the University of Arizona.

亚利桑那大学的心理学家罗伯特·威尔逊说:“在教育领域，有这样一个‘最近困难区’，在这个区域里，你应该最大限度地提高你的学习能力。我们已经把它建立在数学的基础上。”

To come up with the 15/85 percent split, Wilson and his colleagues ran a series of machine learning experiments. The experiments were designed to teach computers how to do simple tasks, such as putting patterns into categories, or recognising the difference between odd and even numbers.

为了得出15/85%的比例，威尔逊和他的同事进行了一系列的机器学习实验。这些实验的目的是教计算机如何做一些简单的任务，比如将模式分类，或者识别奇数和偶数之间的区别。

The computer systems learnt fastest, the researchers found, when they were making the right call 85 percent of the time. That figure seems to match up with previous studies carried out with animals, too.

研究人员发现，当计算机系统有85%的正确率时，学习速度最快。这一数字似乎与之前对动物进行的研究相符。

According to the team, this sort of split is most likely to apply to humans when it comes to perceptual learning, where we gradually learn through experience and examples (not unlike a machine learning algorithm).

据研究小组称，在感知学习方面，这种分裂最有可能适用于人类，在感知学习中，我们通过经验和例子逐步学习(与机器学习算法并无不同)。

Take a radiologist learning to tell the difference between images of tumors and non-tumors, for example: at a level that's too easy, the radiologist would identify 100 percent of the images correctly. At a level that's too difficult, that might drop to somewhere around 50 percent.

以一位放射科医生学习区分肿瘤和非肿瘤图像为例：在一个过于简单的级别上，放射科医生可以100%正确地识别图像。在一个太难的水平上，可能会下降到50%左右。

But if the radiologist is correctly identifying 85 percent of the images and making mistakes with the other 15 percent, that could be the spot where the learning rate is the fastest.

但是，如果放射科医生正确识别85%的图像，而对另外15%的图像出错，那么这可能是学习速度最快的地方。

Of course, as we gain more knowledge, that difficulty level needs to be adjusted again, to keep the learning task at just the right level in terms of how challenging it is.

当然，随着我们获得更多的知识，这个难度水平需要再次调整，以使学习任务在多大的挑战性方面保持在正确的水平。

The researchers are also keen to point out that their study only covers basic, binary choices – it doesn't necessarily follow that we should all be aiming for an 85 percent grade in our future exams.

研究人员还指出，他们的研究只涵盖了基本的二元选择——这并不一定意味着我们在未来的考试中都应该争取85%的分数。

More research is going to be needed to figure out how this applies more broadly to education, outside of computer algorithms. For now though, it's a good starting point for finding that balance between something that's so easy we get bored, and so difficult we give up – a quandary that educators have been thinking about for a long time.

我们还需要更多的研究来弄清楚这是如何在计算机算法之外更广泛地应用于教育的。不过，就目前而言，这是一个很好的起点，有助于在容易让我们感到无聊、也很难让我们放弃的事情之间找到平衡——这是教育工作者长期以来一直在思考的一个窘境。

"If you are taking classes that are too easy and ace-ing 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," says Wilson.

威尔逊说：“如果你所上的课程太简单，而且总是名列前茅，那么你在一门课上获得的收益可能不如一个正在努力奋斗但又设法跟上进度的人。”。

"The hope is we can expand this work and start to talk about more complicated forms of learning."

“希望我们能扩大这项工作，开始讨论更复杂的学习形式。”

The research has been published in Nature Communications.

这项研究已发表在《自然通讯》杂志上。