网上流传的“西蒙学习法”大约是这样的：

“西蒙学习法”是指诺贝尔经济学奖获得者西蒙教授提出的一个理论：“对于一个有一定基础的人来说，只要真正肯下功夫，在6个月内就可以掌握任何一门学问。”西蒙学习法使用的原理是集中力量将知识分而治之。

赫伯特·西蒙（Herbert A. Simon）（1916年6月15日至2001年2月9日），美国经济学家，政治学家，认知科学家，1978年诺贝尔经济学奖获得者，1975年图灵奖获得者。他创造了术语“有限理性”和“满意度”，也是第一个分析复杂性架构的人。

为什么是6个月？说是这样算的：

一个人1分钟到1分半钟可以记忆一个信息，心理学把这样一个信息称为“块”，估记每一门学问所包含的信息量大约是5万块，如果1分钟能记忆1“块”，那么5万块大约需要1000个小时，以每星期学习40小时计算，要掌握一门学问大约需要用6个月。

西蒙学习法有4个步骤：

1）选择一门学问； 2）拆分这门学问，拆分到可以比较容易学习为止。（降低学习难度，还有类比、联想、溯源等方法） 3）持续学习6个月，各个击破每个被拆分的小部分。（高强度，高压强，但是这中间方法和天赋就很重要了） 4）掌握这门学问。

还有一个黑人的例子……

感觉这个方法出处有点不明不白的，而且即使是拆分成一个一个小知识块（一门学科5万个），也不是一两分钟就能记住的，而且还有一个遗忘曲线的问题，这中间一定是有什么问题的。还有流传更广的一万小时理论是对国际象棋选手的研究（记忆大量棋谱），除了告诉人要努力，对于日常学习的指导意义也不大。

原文在这里：What We Know About Learning*，1997年在教育前沿会议上的主题演讲，后整理发表于Journal of Engineering。

有不少同学已经分析过了，还进行了翻译，也省了不少事。里面确实提到了知识块，这学习方法应该就是从这里演绎出来的（也真是人才），不过原文里真的没有鸡汤：

First, the expert possesses a large indexed memory in the area of expertise. In every field that has been investigated, the expert has a minimum of about 50,000 to 100,000 “chunks” of knowledge. “Chunk” is a technical term in psychology, meaning any unit of knowledge that has become familiarized and has a place in the memory’s index. As it has a place in the index, a chunk is anything you can recognize in your field of expertise. English speakers are experts on the English language — we have stored over 100,000 familiar chunks, which are called words. When we see them in a text, we recognize them and retrieve their meanings from memory.

专家在专业领域拥有大量的索引记忆。在每一个被研究过的领域，专家至少拥有大约5万到10万个 “块（Chuck） “的知识。

Now what does “index” mean? An index is a set of patterns that enable you to recognize things about which you have knowledge, whenever they appear. It’s no use to have knowledge unless you can get access to it when it’s relevant; and getting access to knowledge when it’s relevant uses the process we call recognition. If you say “Hi, mom” and someone says, “How did you do that?” you reply, “Well I can recognize my own mom!” We’re not very good at telling what features we saw in order to recognize her but we can be sure of the act of recognition. 索引是一组模式，它能让你识别你有知识的事物，只要它们出现，你就能识别它们。除非你能在相关的时候获得知识，否则拥有知识是没有用的；而在相关的时候获得知识，使用的是我们称之为识别的过程。

It has been shown by studies of numerous fields of expertise that a large part of the skill, of say, a doctor when you walk into the office is simply the skill of recognizing patterns. Sometimes we use a fancier word: we say that we do it by intuition. Intuition is essentially synonymous with recognition. Having an intuition means you get knowledge about something without quite knowing how you did it: without knowing the underlying process. Usually, intuitions come rather suddenly, and somebody says “How did you know that?” and you say “Oh I had an intuition.” You would sound a lot less mysterious if you just said, “Oh I recognized it. I recognized that chunk.” And having recognized the chunk you do just what you do with the index of an encyclopedia: you get access to all those things you know about it, stored in your brain. That’s one large part of what expertise is all about.

Another part of expertise is the skill of searching through a problem space: of searching from the situation you are in now toward a goal situation, and having skills of asking what to do next. This is where means-ends analysis is used. “I am here; I want to be there. What is the difference between here and there? What operators do I have that sometimes reduce differences of that kind? Now lets apply such an operator and see whether we can make progress.”

从你现在所处的情况向目标情况搜索，并有询问下一步该怎么做的技能。这就是手段-目的分析的运用。

注意到里面的关键词了吗？知识块、索引、搜索，这个网络是专家直觉的源泉，这和现在的说法是一样的，人工智能可能也正是这么做的，而且在专业的领域做得比人类要好。我觉得还要加上一个“创造”，就是知识点连成网，在碰到问题时能迅速检索这个网络，并形成解决方案，再重新塑造这个网络，这才是一个学习进步的过程，这就是象棋人类最早被人工智能超越的原因：大量象棋棋谱的记忆和超快的搜索！

这和所谓的“西蒙学习法”是相反的：不是先拆成几万个块去学习，而是学习成专家之后会有几万个可检索的块！

里面还有一个观点：

We have found that one of the powerful ways for learning is to be given worked-out examples, step-by-step examples of problem solutions [7, 8]. Let the student, by working on these examples, find out how to get from one step to the next. Now, that’s a little like learning from doing — throw the student a problem and let him or her solve it. But by providing examples you are allowing the student to solve a series of sub-problems, step-by-step. You can make each step as long or as short as you like, depending on how hard they are for the students.

给学生提供经过加工的例子，一步步解决问题的例子。让学生通过这些例子，找出如何从一个步骤到下一个步骤。

通过实践学习（Learning from doing），这在自己的学习中也是非常值得借鉴的。