# 06.学习内驱力和奖励 [TOC=3,5] ## 6 Learn drive and reward ## 学习内驱力和奖励 ### 6.1 The main problem in education ### 教育中的主要问题 The main problem with regards to education is the belief that learning may cause displeasure, and that this displeasure should be [endured](https://supermemo.guru/wiki/The_grind_is_the_glory) to achieve more learning. 关于教育的主要问题是认为学习可能会引起不快，而且应该[忍受](https://supermemo.guru/wiki/The_grind_is_the_glory)这种不快从而学的更多。 There are countless educators who believe that school should be like work: it is unpleasant but it just needs to be done. In this chapter, I will explain that the opposite is true: 有无数的教育者认为学校应该像工作一样：它令人不快，但它只是需要去做。在这一章中，我将解释相反的才是对的： > **Good learning is inherently pleasurable**, and without pleasure there is no good learning. > > **好的学习本身就是快乐的**，没有快乐就没有好的学习。 The displeasure myth is so prevalent that even good teachers with an extensive understanding of the pleasures of learning believe that a degree of unhappiness at school is unavoidable. 不快的错误观念如此普遍，以至于即使是对学习乐趣有广泛了解的好老师也认为，学生在学校有一定程度的不快是不可避免的。 In this chapter, I show that the pleasure of learning is wired into the brain, and how we systematically destroy this [gift of evolution](https://supermemo.guru/wiki/Education_counteracts_evolution) at the cost of mankind's health, learning, creativity, and ultimately future. 在这一章中，我展示了学习的快乐与大脑息息相关，以及我们如何以人类的健康、学习、创造力以及最终的未来为代价，系统地摧毁这种[进化的天赋](https://supermemo.guru/wiki/Education_counteracts_evolution)。 The main problem of education is also one of the main problems of society. By destroying the pleasure of learning we are contributing powerfully to the destruction of the pleasure of living. We have built an education system that sets millions of people up for a life of unhappiness. 教育的主要问题也是社会的主要问题之一。我们破坏了学习的乐趣，也就大大破坏了生活的乐趣。我们建立了一个教育系统，让数百万人过上不幸福的生活。 Chances are you are skeptical of my words, as the myth of unpleasant learning is a potent side effect of [schooling](https://supermemo.guru/wiki/Schooling). Therefore this chapter is an attempt to convince you. And all that is necessary to abolish this myth is an understanding of the simple mechanism by which new knowledge is encoded in the brain. 你可能对我的话持怀疑态度，因为不愉快的学习的错误观念是[学校教育](https://supermemo.guru/wiki/Schooling)的一个强有力的副作用。因此，本章试图说服你。消除这个错误观念所需要的只是理解大脑中编码新知识的简单机制。 ### 6.2 Learn drive and entropy ### 学习内驱力和熵 The concept of entropy is helpful in understanding why most kids do not learn much at school. 熵的概念有助于理解为什么大多数孩子在学校没有学到多少。 You may recall from your physics class that entropy is a measure of disorder, and that the second law of thermodynamics states that the entropy of an isolated system never decreases. This is the type of sexy law of physics that we tend to remember for life. It is [universally applicable](https://supermemo.guru/wiki/Applicability). 你们可能从物理课回忆起熵是无序的度量，热力学第二定律表明孤立系统的熵永远不会减小。这是一种形象的物理定律，我们会终生铭记。这是[普遍适用的](https://supermemo.guru/wiki/Applicability)。 There is a sister concept in information theory called [Shannon entropy](https://en.wikipedia.org/wiki/Entropy_%28information_theory). It can be understood as the average value of information transmitted by a source. For example, take a channel that is continually transmitting a string of identical letters into infinity \(e.g. a string of As: "AAAAAA..."\). It is entirely predictable and carries an entropy of zero. We do not learn from such a channel. 信息论中有一个姊妹概念叫[香农熵](https://en.wikipedia.org/wiki/Entropy_%28information_theory%29%29)。它可以理解为由一个来源发送的信息的平均值。例如，假设一个渠道连续不断地将一串相同的字母传送到无穷远处（例如，一串 A:「AAAAAA…」）中。这是完全可以预测的，并且熵为零。我们不会从这样的渠道学习。 [Claude Shannon](https://en.wikipedia.org/wiki/Claude_Shannon) proposed the concept of information entropy in 1948. Soon after, scientists were hypothesizing as to whether the entropy of an information channel may have a powerful impact on how the brain perceives the value of the channel. In 1957, [Meyer hypothesized](https://supermemo.guru/wiki/Music_and_entropy) that the entropy of music determines the perception of its beauty. He concluded that a higher entropy may result in subjective tension, which correlates with more meaningful musical moments. [Claude Shannon](https://en.wikipedia.org/wiki/Claude_Shannon) 在 1948 年提出了信息熵的概念。不久之后，科学家们开始假设信息渠道的熵是否会对大脑感知渠道价值的方式产生强大影响。1957 年，[Meyer 假设](https://supermemo.guru/wiki/Music_and_entropy)音乐的熵决定了人们对音乐之美的感知。他的结论是，较高的熵可能会导致主观紧张，这与更有意思的音乐片段有关。 Meyer's thinking was [later refined](https://supermemo.guru/wiki/Impact_of_syncopation_on_the_pleasure_of_music) to better understand the perception of music and information in general. There is more to music than just information. This is visible through the phenomena of a song being entertaining and fun for many playbacks. But this is rarely the case with books. Meyer 的思想[后来被改进](https://supermemo.guru/wiki/Impact_of_syncopation_on_the_pleasure_of_music)，以更好地理解对音乐和普通信息的感知。音乐不仅仅是信息。这通过一个现象来看是很显然的，因为一首歌被多次回放可以令人愉快好多次。但是书籍很少出现这种情况。 Music is a universal message. If you were given a choice of a radio channel, you would quickly tune out from noisy static and you would also not be too excited about zero entropy silence. However, most people will respond positively to a regular beat of a drum. As long as it wasn't being drummed on broken glass, which we are wired to dislike, we would find a radio channel with a regular drumbeat more interesting than a silent one. This will naturally last only for a while until the drumbeat itself becomes boring and too predictable. 音乐是一种普遍的信息。如果你被允许选择一个无线频道，你会很快从嘈杂的静电干扰中调谐出来，你也不会对零熵的静音太兴奋。但是，大多数人会对有规律的鼓声做出积极的反应。只要它没有被敲打在碎玻璃上（这是我们天生不喜欢的），我们就会发现一个有固定鼓点的广播频道比无声的更有趣。这自然只会持续一段时间，直到鼓点本身变得无聊和太容易预测。 Today, we can finally test the response of the brain to information entropy. Neuroimaging shows that the [anterior hippocampus responds to the entropy of a visual stream](https://www.ncbi.nlm.nih.gov/pubmed/15896570), and similar findings have been confirmed for the [ventral striatum](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3403290/). Therefore we are now certain that the brain responds to information entropy. The entropy sensor is important in scanning the environment for learning opportunities. This is the prelude to the reward that underlies the [learn drive](https://supermemo.guru/wiki/Learn_drive). 今天，我们终于可以测试大脑对信息熵的反应了。神经影像显示[前海马对视觉信息熵有反应](https://www.ncbi.nlm.nih.gov/pubmed/15896570)，[腹侧纹状体](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3403290/)也有类似的发现。因此，我们现在可以肯定大脑对信息熵有反应。熵感受器在扫描环境寻找学习机会时非常重要。这是奖励的前奏，而奖励是[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的基础。 ### 6.3 Prior knowledge in information seeking ### 信息搜寻中的预备知识 We need to distinguish between information and meaning. Entropy is not a good measure of the latter. The measure of meaning must involve the brain itself in addition to the information channel metric. Prior knowledge is essential in learning. Imagine that in your search for an interesting channel on the radio you find a news service. If the service is delivered in Thai and you do not speak Thai, you will prefer a service delivered in English. In information sense, news channels may have the same entropy, yet your prior knowledge will make you opt for the English channel. While the Thai channel delivers a stream of sounds, the English channel delivers a stream of [concepts](https://supermemo.guru/wiki/Abstract_knowledge). Without understanding the knowledge of the recipient, information entropy tells us little. We cannot determine a signal-to-noise ratio. 我们需要区分信息和意义。熵不是衡量后者的一个好指标。意义的度量除了度量信息渠道之外，还必须涉及到大脑本身。预备知识在学习中是必不可少的。想象一下，当你在收音机上搜索一个有趣的频道时，你会发现一个新闻服务。如果服务是用泰语提供的，而你不会说泰语，那么你会更喜欢用英语提供的服务。就信息而言，两个新闻频道可能有相同的熵，但是你的预备知识会让你选择英语频道。泰国频道传递声音流，而英语频道传递[概念](https://supermemo.guru/wiki/Abstract_knowledge)流。如果不了解接受者的知识，信息熵就无法告诉我们什么。我们无法确定信噪比。 Every listener will have his or her own preferred level of information entropy. For most music lovers, the regular beat of a disco or techno will be somewhat more interesting than the isolated beat of a drum. This type of music carries a higher average level of information. For a more sophisticated listener a bit of syncopation will be welcome. However, syncopation requires a degree of prior learning. Those with lesser knowledge of music may get confused with increased rhythmic complexity. If there is too much information in the beat it [may no longer be possible to dance to the music](https://supermemo.guru/wiki/Impact_of_syncopation_on_the_pleasure_of_music). To an average ear, the genius of Wynton Marsalis may be hard to perceive. Top shelf jazz music is reserved for only a small fraction of highly educated listeners, as for most of the population, as the complexity increases, the music slowly disintegrates into the direction of radio static. 每个听者都有自己偏好的信息熵水平。对于大多数音乐爱好者来说，迪斯科或电子乐的常规节拍要比单独的鼓声有趣一些。这种类型的音乐承载着更高的平均信息量。对于一个更老练的听众来说，有一点切分音是将更受欢迎的。然而，切分音需要一定程度的预先学习。那些对音乐了解较少的人可能会被增加的节奏复杂性弄糊涂。如果节拍中有太多的信息，[就不可能随着音乐起舞](https://supermemo.guru/wiki/Impact_of_syncopation_on_the_pleasure_of_music)。对于一般人来说，Wynton Marsalis 的天才可能很难被察觉。顶级爵士音乐只为一小部分受过高等教育的听众准备，就像大多数人一样，随着复杂性的增加，音乐慢慢向无线电杂音的方向衰变。 ### 6.4 Entropy detectors in the brain ### 大脑中的熵检测器 The brain cannot effectively detect the entropy of the signal hitting the retina or the eardrum. Like pixels of a monitor, retinal cells are not aware of what they display. If the detector, such as the hippocampus, is to light up in response to entropy, it must operate on the inputs from the entorhinal cortex \(i.e. the input to the hippocampus itself\). Those inputs will present the signal after a high degree of processing. Instead of pixels, it may present a concept. A high entropy signal at the sensory inputs will lose most of its noise component early in the process of neural selection, completion, and [generalization](https://supermemo.guru/wiki/Generalization). The signal-to-noise ratio will determine how much information is lost. The bigger the noise, the bigger the loss. The smarter we are, the more selective this processing will be and the more information will be lost at that stage. That's good. We become blind to detail. Pattern recognition will act like a deterministic function, which by definition, results in a drop in entropy. Complex patterns may become simple concepts. Those concepts will provide the actual input to the detector, e.g. the hippocampus. 大脑无法有效检测撞击视网膜或耳膜的信号的熵。就像显示器上的像素一样，视网膜细胞也不知道它们传达了什么。如果检测器，如海马体，要根据熵而响应，它必须对来自内嗅皮层的输入（即海马体本身的输入）进行操作。这些输入将在高度处理后显示信号。它可能会呈现一个概念，而不是像素。感觉输入端的高熵信号将在神经选择、完善和[泛化](https://supermemo.guru/wiki/Generalization)过程的早期失去大部分噪声成分。信噪比将决定丢失多少信息。噪音越大，损失越大。我们越聪明，这个处理过程就越有选择性，在那个阶段会丢失越多信息。这很好。我们对细节视而不见。模式识别就像一个确定性函数，根据定义，它会导致熵的下降。复杂的模式可能会变成简单的概念。这些概念将为检测器提供实际输入，例如海马体。 Note that the visual stream produced in experiments that prove the [response of the hippocampus to signal entropy](https://www.ncbi.nlm.nih.gov/pubmed/15896570) has a highly [symbolic nature](https://supermemo.guru/wiki/Abstract_knowledge). As such, the stream will lose far less information in processing. That highly simplified and [conceptualized](https://supermemo.guru/wiki/Generalization) message will be scanned for surprisal and provide guidance to the entire [learn drive](https://supermemo.guru/wiki/Learn_drive) system. This is why, in this case, the hippocampus appears to be responding to input entropy. 请注意，实验中产生的视觉流证明[海马体对信号熵的反应](https://www.ncbi.nlm.nih.gov/pubmed/15896570)具有高度[象征性](https://supermemo.guru/wiki/Abstract_knowledge)。因此，视觉流在处理过程中丢失的信息会少得多。这一高度简化和[概念化](https://supermemo.guru/wiki/Generalization)的信息将会被扫描为意外，并为整个[学习内驱力](https://supermemo.guru/wiki/Learn_drive)系统提供指引。这就是为什么在这种情况下，海马体似乎对输入熵有反应。 The above reasoning explains why both low and high entropy sensory signals can be uninteresting. After a degree of processing, a high entropy signal may lose all its noise and deliver a low entropy input to the hippocampus. We then observe the illusion of an "optimum entropy" level at sensory input. We need a new concept, **learntropy**, that will help us accurately determine the attractiveness of the signal. Learntropy needs to take into account the high degree of processing of information before it can activate reward centers in the brain. Learntropy is discussed [later](https://supermemo.guru/wiki/I_would_never_send_my_kids_to_school#Learntropy) in this text. 上面的推理解释了为什么低熵和高熵的感觉信号都是无趣的。经过一定程度的处理后，高熵信号可能会失去所有噪声，并向海马体传递低熵输入。然后，我们观察感官输入时「最佳熵」水平的错觉。我们需要一个新的概念，**学习熵**，来帮助我们准确地判断信号的吸引力。学习熵需要考虑信息的高度处理，然后才能激活大脑中的奖励中枢。本文稍后将讨论[学习熵](https://supermemo.guru/wiki/I_would_never_send_my_kids_to_school#Learntropy)。 ### 6.5 Speed of information processing ### 信息处理速度 An under-appreciated factor in sensory information scanning is the speed of information processing in the brain. 感官信息扫描中一个被低估的因素是大脑中信息处理的速度。 For every piece of music, there is a tolerable playback range where the beauty of the music is appreciated. A high speed playback can be annoying and the music may become hard to decode, as the high speed goes beyond our processing power. The same piece of music slowed down can quickly lose its appeal. The same happens in speech delivery or in classroom lecturing. For the same information and the same entropy level, we may accomplish highly different levels of signal attractiveness. There is always an optimum speed of delivery and that speed depends on all other factors that power the [learn drive](https://supermemo.guru/wiki/Learn_drive), incl. prior knowledge. As such, speed of delivery is highly individual. 对于每一首音乐，都有一个可以接受的播放速度范围，在其范围内我们可以欣赏音乐之美。高速播放可能会很烦人，而且音乐可能变得难以解码，因为高速超出了我们的处理能力。同一首音乐如果放慢了速度，很快就会失去吸引力。演讲或课堂讲授也是如此。对于相同的信息和相同的熵水平，我们可以实现完全不同的信号吸引力水平。总是有一个最佳的传授速度，该速度取决于影响[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的所有其他因素，包括预备知识。因此，传授速度是高度个性化的。 I like to listen to lectures at 1.4x speed. I use 1.3x for more ambitious pieces. I never speed up [Fareed Zakaria](https://en.wikipedia.org/wiki/Fareed_Zakaria_GPS) though, but rather relish every piece of information in this show. Students in a classroom lecture do not have a speed-up or slow-down button. Even the pause button, if available, is hard to hit as it may annoy other students. 我喜欢以 1.4 倍的速度听讲座。我用 1.3 倍来听更有挑战性的片段。虽然我从来没有加快 [Fareed Zakaria](https://en.wikipedia.org/wiki/Fareed_Zakaria_GPS) 的速度，但是我享受这个节目中的每一条信息。课堂上的学生没有加速或减速按钮。即使是暂停按钮，如果有的话，也很难去点击，因为这可能会惹恼其他学生。 In schools, all too often, the speed of delivery surpasses student's processing capacity. This results in negligible learning and high stress. There is no time to [enjoy the landscapes in the window of a high-speed train](https://supermemo.guru/wiki/Futility_of_schooling). At MIT they call it _"drinking from a firehose"_. 在学校里，传授的速度常常超过学生的处理能力。这导致了微不足道的学习和强大的压力。没有时间[欣赏高速列车窗外的风景](https://supermemo.guru/wiki/Futility_of_schooling)。在麻省理工学院，他们称之为「[_用消防水管喝水_](https://supermemo.guru/wiki/Futility_of_schooling)」。 ### 6.6 Probability vs. knowledge ### 概率与知识 Low probability events carry more information. Average information determines entropy. Prior knowledge determines the perception of an information channel's entropy. 低概率事件携带更多信息。平均信息决定熵。预备知识决定了对信息通道的熵的感知。 If you happen to tune in to radio news and you hear that "_Janet Jackson has delivered a baby_", your degree of interest will depend on the probability of the event. If you have no idea who Janet Jackson is, this is a high probability event. If some [350,000 women deliver babies every single day](http://www.theworldcounts.com/stories/How-Many-Babies-Are-Born-Each-Day), this is no longer news and is not new or interesting. The first death of a soldier in a war makes news, but when deaths incrase into the thousands, young lives become just a statistic. 如果你碰巧收听了广播新闻，并且听到「_Janet Jackson 逊生了一个孩子_」，你的兴趣程度将取决于事件发生的概率。如果你不知道 Janet Jackson 是谁，这是一个高概率事件。如果[每天约有 35 万名女性分娩](http://www.theworldcounts.com/stories/How-Many-Babies-Are-Born-Each-Day)，这已经不是新闻，也不是新鲜事或有趣的事。一名士兵在战争中的第一次死亡将成为新闻，但是当死亡人数增加到成千上万时，年轻的生命将只是一个统计数字。 If you happen to know Janet Jackson or like her music, the probability of a baby delivery drops dramatically to the level of "_once in a lifetime_" \(for Janet\). This can make you become interested. However, if you recall Janet as a beautiful girl from some ancient sitcom, her baby delivery may go into the category of "_Impossible!_". If you realize Janet is 50 years old, and you know about menopause, you may instantly become morbidly curious about her case. Your prior knowledge determines how you respond to the message. There is no optimum entropy level for a channel. There is only an optimum entropy level that fits a specific brain. At this point you may see that we need to introduce a new derived concept, which we will [later](https://supermemo.guru/wiki/I_would_never_send_my_kids_to_school#Learntropy) call **learntropy**. Learntropy will determine the attractiveness of a given channel for a given brain. 如果你碰巧认识 Janet Jackson 或者喜欢她的音乐，那么生孩子的概率会急剧下降到「_一生一次_」的水平（对 Janet 来说）。这会让你变得有兴趣。然而，如果你记得 Janet 是古代情景喜剧中的一个漂亮女孩，她生孩子可能会被归为「_不可能！_」。如果你意识到 Janet 50 岁了，并且你知道更年期，你可能会立刻对她的情况产生病态的好奇。你预备知识决定了你对信息的反应。信道没有最佳熵水平，只有最适合特定大脑的熵水平。在这一点上，你可能会看到，我们需要引入一个新的衍生概念，我们在[之后](https://supermemo.guru/wiki/I_would_never_send_my_kids_to_school#Learntropy)将称之为**学习熵**。学习熵将决定一个特定的通道对一个特定的大脑的吸引力。 If you love Janet-like gossip, the channel rich in that gossip will provide the right level of surprisal for you. It will provide the learntropy match. If you lack knowledge or your priorities differ, you will tune out. Your learning priorities will also determine your level of knowledge in particular areas and your response to any particular channel and its information entropy. 如果你喜欢 Janet 式的八卦，那么这个八卦频道会给你带来恰到好处的意外。它将提供学习匹配。如果你缺乏相关知识或者你的优先级不同，你就会对它置之不理。你的学习重点也将决定你在特定领域的知识水平，以及你对任何特定渠道及其信息熵的反应。 ### 6.7 Predictability and surprisal ### 可预见性和意外 Probability and complexity are not the only components in information perception. We seem to look for a balance between predictability and surprise. I like funk. In this type of music, the bassline is often highly predictable with the [optimum dose of syncopation](https://supermemo.guru/wiki/Impact_of_syncopation_on_the_pleasure_of_music). It makes it easy to synchronize the body motion with the rhythm. However, funk would not be interesting if it did not carry surprise. This is where the sophisticated jazz riffs tickle the neural system responsible for the detection of surprisal. In addition, after decades of learning, there is a whole database of signals that my brain responds to. There may be that one backup singer voice that I recognize and like. My brain is ready for funk. 概率和复杂性不是信息感知的唯一组成部分。我们似乎在寻找可预测性和意外之间的平衡。我喜欢乡土爵士乐。在这种类型的音乐中，低音部通常是高度可预测的，伴着[最佳的切分音数量](https://supermemo.guru/wiki/Impact_of_syncopation_on_the_pleasure_of_music)。这使得身体运动与节奏同步变得容易。然而，如果没有意外，乡土爵士乐就不会有意思。在这里，复杂的爵士乐即兴表演会刺激负责检测「意外」的神经系统。此外，经过几十年的学习，我的大脑有一个完整的信号数据库。可能有一个我认识并喜欢的伴唱歌手的声音。我的大脑已经准备好迎接乡土爵士乐了。 I love Ken Robinson lectures on creativity. In one way, they are highly predictable. I totally agree with Robinson, so you can say that Robinson feeds my confirmation bias. This is pleasurable. When people agree with us, we like to say "_great minds think alike_". But if Robinson just kept repeating the same dry mantras on how schools kill creativity, he would lose his appeal. Entropy can be interpreted as the average expected [surprisal](https://en.wikipedia.org/wiki/Self-information). Robinson's delivery carries a great deal of nice surprises. He may paint the same models in a different and unusually creative way. As a result, **the brain receives new information, produces a generalization, and confirms the existing models**. Generalizations derived from new contexts increase [knowledge coherence](https://supermemo.guru/wiki/Knowledge_coherence). This a very pleasing type of complementarity in a message based on a known model. 我喜欢 Ken Robinson 关于创造力的讲座。在某种程度上，它们是高度可预测的。我完全同意 Robinson，所以你可以说 Robinson 助长了我的证实性偏见。这很愉快。当人们与我们观点一致时，我们喜欢说「英雄所见略同」。但是，如果 Robinson 继续重复同样陈腔滥调，讲述学校如何扼杀创造力，他将失去吸引力。熵可以解释为平均预期[意外](https://en.wikipedia.org/wiki/Self-information)。Robinson 的讲授带来了很多意外。他可能会以不同的、不同寻常的创造性方式描绘相同的模型。因此，**大脑接收新的信息，产生泛化，并确认现有的模型**。从新的语境中得出的泛化可以增强[知识的连贯性](https://supermemo.guru/wiki/Knowledge_coherence)。在基于已知模型的消息中，这是一种非常令人愉快的补充。 Robinson lectures find a good balance between predictability and surprise. Robinson 的讲座在可预测性和意外之间找到了很好的平衡。 The most pleasing information channels will keep delivering surprises that confirm existing models and arm them in new semantic twigs on which new knowledge can be built. A surprise that destroys existing models may not be pleasing at first, but may lead to a highly pleasing revolution in thinking. 最令人愉快的信息渠道将不断提供意外，证实现有的模型，并在新的语义分支中武装它们，在这些分支上可以建立新的知识。一个破坏现有模型的意外起初可能并不令人愉快，但可能会导致一场非常令人愉快的思维革命。 Metaphorically, you can imagine this as the information channel massaging your tree of knowledge and adding new branches like a potter who adds [new layers of clay](https://supermemo.guru/wiki/Jigsaw_puzzle_metaphor) to his perfectly shaped creation. 打个比方，你可以想象这是一个信息渠道，修剪你的知识之树，并添加新的分支，就像一个陶工在他完美塑造的作品上添加[新的粘土层](https://supermemo.guru/wiki/Jigsaw_puzzle_metaphor)一样。 ### 6.8 Detecting surprisal ### 检测意外 Human [learn drive](https://supermemo.guru/wiki/Learn_drive) is based on detecting [surprisal](https://en.wikipedia.org/wiki/Self-information). We have known that for ages. All models of human and machine learning involve that concept under different names. [Piaget](https://en.wikipedia.org/wiki/Jean_Piaget) wrote about [schemata](https://en.wikipedia.org/wiki/Jean_Piaget#Schema) that fall into disequilibrium under the impact of surprisal. In his models of the neocortex, [Jeff Hawkins](https://en.wikipedia.org/wiki/Jeff_Hawkins) speaks of prediction errors that underlie learning and intelligence. I like to speak of models, and their elaboration \(when new information fits the model\), or contradiction \(when new information requires changes to the model\). 人类的[学习内驱力](https://supermemo.guru/wiki/Learn_drive)是基于对[意外](https://en.wikipedia.org/wiki/Self-information)的检测。我们已经知道这一点很久了。人类和机器学习模型都以不同的名称涉及到这个概念。[Piaget](https://en.wikipedia.org/wiki/Jean_Piaget) 写过，[图式](https://en.wikipedia.org/wiki/Jean_Piaget#Schema)在意外的影响下陷入失调。[Jeff Hawkins](https://en.wikipedia.org/wiki/Jeff_Hawkins) 在他的新大脑皮层模型中谈到了基于学习和智力的预测错误。我喜欢谈论模型，以及它们的细化（当新数据适合模型时），或者矛盾（当新数据需要改变模型时）。 For the reward of learning, a new surprising piece of information needs to fit pre-existing knowledge \(models, schemata, predictions, or so\). For the reward to be delivered, neural processing is necessary. Information on the input needs to be processed and compared with information stored in the brain. One of the chief processors of input information in the brain is the hippocampus. It is the brain's information switchboard that is able to compare the input with prior knowledge. 为了获得学习的奖励，一个新的令人意外的信息需要与预先存在的知识（模型、图式、预测等等）相容。为了获得奖励，神经处理是必要的。关于输入的信息需要被处理，并与存储在大脑中的信息进行比较。大脑输入信息的主要处理器之一是海马体。大脑的信息交换台能够将输入与先前的知识进行比较。 Measuring the entropy of the visual stream is not necessarily a reliable indicator of the pleasing power of the information channel. All information streamed to the hippocampus undergoes a high degree of processing. A stream of pixels representing a beautiful beach will be processed into a series of shapes and textures. Those in turn will model palms, sand, and the sea. This highly compressed simple information will determine the original response to the information input. 视觉流的熵的测量不一定是信息通道令人快乐的能力的可靠指标。流向海马体的所有信息都经过高度处理。代表美丽海滩的像素流将被加工成一系列形状和纹理。反过来，这些模型将依次模拟棕榈树、沙子和大海。这种高度压缩的简单信息将决定对信息输入的最初响应。 Scanning for information in the environment is equivalent to scanning for scents of food. The scent is enticing, but only the actual feeding is a true reward. This is why entropy scanning does not need to be rewarding. All it needs to do is to lead to a reward. The anterior hippocampus responds to entropy, as noticed earlier, however experimental design made sure that the entropy refers to the combination of simple shapes that do not lose much information during input processing. Instead of speaking of signal entropy, we should rather focus on the input entropy at the information comparator such as the hippocampus. It is not the retinal pixels that matter, but the shape of the palm as represented on the comparator input. For the comparator, the high entropy pattern of grayness or static noise will not differ from whiteness or silence. They will all bring the same entropy on input: zero. This is why I used the term [learntropy](https://supermemo.guru/wiki/Learntropy) to accurately refer to the attractiveness of the information channel. 扫描环境中的信息如同扫描食物的气味。气味很诱人，但只有实际去吃才是真正的回报。这就是熵扫描不需要回报的原因。它所需要做的就是获得奖励。如前所述，前海马对熵有反应，然而实验设计确保熵是指输入过程中不会丢失太多信息的简单形状的组合。与其说是信号熵，不如说是信息比较器（如海马）的输入熵。重要的不是视网膜像素，而是比较器输入上表示的手掌形状。对于比较器来说，灰度或静态噪声的高熵模式与白度或静音没有区别。它们在输入时都会带来相同的熵：零。这就是为什么我用「[学习熵](https://supermemo.guru/wiki/Learntropy)」这个词来准确地描述信息渠道的吸引力。 The anterior hippocampus that responds to signal entropy is famous for the discovery of the Halle Berry neuron \(see [more](http://phys.org/news/2005-06-single-cell-recognition-halle-berry-neuron.html)\). Using electrodes implanted in a consenting epilepsy patient, researchers were able to pinpoint a single neuron consistently responding to images of Halle Berry in various contexts. The same neuron would also respond to Halle Berry's name. At the same time, posterior hippocampus might respond less consistently to Jennifer Aniston \(perhaps an indication of a preceding layer of neural processing\). 响应信号熵的前海马体因发现 Halle Berry 神经元而闻名（参见[更多](http://phys.org/news/2005-06-single-cell-recognition-halle-berry-neuron.html)）。研究人员将电极植入一名同意接受治疗的癫痫患者体内，发现在不同情境下，单个神经元对 Halle Berry 图像的反应是一致的。同样的神经元也会对 Halle Berry 的名字做出反应。与此同时，后海马体对 Jennifer Aniston 的反应可能不那么一致（这可能是前一层神经处理的迹象）。 Most of us have no idea how Halle Berry smells and her smell might not be unique enough to activate Halle Berry neuron in the hippocampus, however, even the smell signal can get there fast via just a few synapses in the olfactory bulb, olfactory tubercle, piniform cortex, and the entorhinal cortex \(see picture\). However, if one could hear the sound of Halle's voice, it might meet the sound signal in the olfactory tubercle, contribute to recognition, and result in the subsequent activation of the Halle neuron in the hippocampus or further down in the neocortex. 我们大多数人都不知道 Halle Berry 的气味如何，她的气味可能不足以激活海马体中的 Halle Berry 神经元，然而，即使是气味信号也可以通过嗅球、嗅结节、松果体皮层和内嗅皮层中的几个突触快速到达那里（见下图）。然而，如果一个人能听到 Halle 的声音，声音信号可能会在嗅结节中出现，加强识别，并导致海马或更下方的新皮层中 Halle 神经元的激活。  > **Figure:** Olfactory system anatomy. The smell signal can get to the hippocampus fast via just a few synapses in the olfactory bulb, olfactory tubercle, piniform cortex, and the entorhinal cortex. \(source: Wikipedia\) > > **图：** 嗅觉系统解剖学。嗅觉信号可以通过嗅球、嗅结节、小齿轮状皮层和鼻内皮层的几个突触快速到达海马体。（来源：维基百科） Does it all mean that Halle resides permanently in the patient's hippocampus? Due to the association of the hippocampus with formation of new memories, we may rather think that Halle shows up in hippocampal neurons as a result of the recognition. Her permanent place in the heart of the patient is likely situated further downstream in the neocortex. We now know that in the process of memory consolidation, knowledge engrams [move from the hippocampus to the neocortex](http://www.jneurosci.org/content/29/32/10087.full). We are also pretty sure that this process is happening [in sleep](http://super-memory.com/articles/sleep.htm#Neural_optimization_in_sleep). It is in the neocortex that we should look for concept neurons representing Halle or one's grandmother. This last possibility gave rise to a hypothetical type of neuron called [**the grandmother cell**](https://en.wikipedia.org/wiki/Grandmother_cell). 这是否意味着 Halle 会永久地存在于患者的海马体中？由于海马体与新记忆的形成有关，我们可能会认为 Halle 出现在海马神经元中是识别的结果。她在病人心中的永久位置可能位于新皮层的更深一层。我们现在知道，在记忆巩固的过程中，知识印记[从海马体转移到新皮层](http://www.jneurosci.org/content/29/32/10087.full)。我们也非常肯定这个过程是[在睡眠中](http://super-memory.com/articles/sleep.htm#Neural_optimization_in_sleep)发生的。正是在新皮层，我们应该寻找代表 Halle 或某人祖母的概念神经元。最后一种可能性产生了一种假设的神经元类型，称为[祖母神经元](https://en.wikipedia.org/wiki/Grandmother_cell)。 In monkeys, researchers could identify [grandmother cells](https://en.wikipedia.org/wiki/Grandmother_cell) in the visual cortex that respond to faces. There we might find cells that more consistently fire up in contact with Halle's image. However, the concept of Halle might still reside elsewhere and be activated, among others, by visual cortex cells upon noticing Halle. 在猴子身上，研究人员可以识别出视觉皮层中对面孔做出反应的[祖母神经元](https://en.wikipedia.org/wiki/Grandmother_cell)。在那里，我们可能会发现在看到 Halle 的图像时神经元会更加稳定地激活。然而，Halle 的概念可能仍然存在于其他地方，并在注意到 Halle 时被视觉皮层细胞激活。 Another activation route might come from hearing Halle's name on the news. The entire recognition process would be orchestrated by the entorhinal cortex and the hippocampus while the ultimate Halle neuron would light up somewhere in the layers of the neocortex. 另一个激活途径可能是从新闻上听到 Halle 的名字。整个识别过程将由内嗅皮层和海马体协调进行，而最终的 Halle 神经元将在新皮层的某处激活。 For information rich signal to generate a reward, there must be a low probability event detected on input and encoded via association as new knowledge in the cortex. Where anterior hippocampus would respond to the entropy, the [activity of the extensive bilateral thalamo-cortical network would be modulated by the surprise factor](https://www.ncbi.nlm.nih.gov/pubmed/15896570). There we shall search for the roots of the pleasure of learning. There are also other comparator centers that might be involved depending on the type of the message. The amygdala has also been found to likely produce rewards when detecting novel visual signals. The same amygdala neurons that respond to rewarding visual stimuli may respond to novel visual stimuli. [Rolls hypothesized that this may implement the reward of novelty via the amygdala](https://supermemo.guru/wiki/Amygdala_may_be_involved_in_rewarding_novel_input). 要让富含信息的信号产生奖励，必须有一个低概率事件在输入时被检测到，并通过联想将其编码为大脑皮层中的新知识。当前海马对熵做出反应时，[广泛的双侧丘脑皮层网络的活动会受到意外因素的调节](06.learn-drive-and-reward-xue-xi-nei-qu-li-he-jiang-li.md)。在那里，我们将寻找学习快乐的根源。根据消息的类型，还可能涉及其他比较器中枢。人们还发现，当检测到新的视觉信号时，杏仁体可能会产生奖励。对奖励视觉刺激做出反应的杏仁体神经元也可能对新的视觉刺激做出反应。[Rolls 假设，这可能通过杏仁体来实现对新奇事物的奖励。](https://supermemo.guru/wiki/Amygdala_may_be_involved_in_rewarding_novel_input) We know that the hippocampus connects directly with the nucleus accumbens \(the brain pleasure center\). This connection might be used in two contexts: 1. the anticipation of pleasure and 2. the ultimate reward. 我们知道海马体直接与伏隔核（大脑快乐中枢）相连。此连接适用于两种情况： 1. 对快乐的预期； 2. 最终的奖励。 The anticipation would follow the detection of a high [learntropy](https://supermemo.guru/wiki/Learntropy) signal and would result in active pursuit of high value messages. Detecting a message by the hippocampus might then simultaneously send associative learning messages to the neocortex and the reward signal to the pleasure center. That would spell the moment of learning something new! 在预期检测到一个高[学习熵](https://supermemo.guru/wiki/learntropy)信号后，就会产生对高价值信息的积极追求。海马体检测到一条信息后，可能会同时将相关的学习信息发送到大脑皮层，并向快乐中枢发送奖励信号。这将意味着学习新东西的时刻！ ### 6.9 The wow factor ### 「哇」因子 In the summer of 1977, while looking for extraterrestrial intelligence, SETI researchers discovered an unusual radio signal coming from [Sagittarius](https://en.wikipedia.org/wiki/Sagittarius_%28constellation%29). In the bland low-level noise of cosmic space the signal was highly unlikely. Low probability marks high surprisal. Astronomer Jerry Ehman circled 6 letters corresponding with the signal on a printout and mark it with "Wow!". 1977 年夏天，在寻找外星智能的时候，SETI 的研究人员发现了一个来自[射手座](https://en.wikipedia.org/wiki/Sagittarius_%28constellation%29)的不寻常的无线电信号。在宇宙空间微弱的低水平噪声中，信号是极不可能出现的。低概率标志着高意外。天文学家 Jerry Ehman 在打印输出上圈出 6 个与信号相对应的字母，并在旁边写上了「Wow!」（哇！）。  > **Figure:** A scan of a color copy of the original computer printout, taken several years after the 1977 arrival of the [Wow! signal](https://en.wikipedia.org/wiki/Wow!_signal). \(source: Wikipedia\) > > **图：** 一张彩色电脑打印出来的原始拷贝扫描件，拍摄于 1977 年[哇！信号](https://en.wikipedia.org/wiki/Wow!_signal)到达后的几年。（来源：维基百科） "Wow!" is how the brain responds to a sudden discovery. The moment is highly pleasurable. The entire purpose of the [learn drive](https://supermemo.guru/wiki/Learn_drive) is to look for wow factors in the environment. These are the most valuable nuggets of knowledge that complement what is currently known: the current model of reality. The pleasure of [incremental reading](https://supermemo.guru/wiki/Incremental_reading) comes from the condensed power of wows streamed into the student's brain. 「Wow!」 是大脑对突然发现的反应。这一刻非常愉快。[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的全部目的是在环境中寻找「哇」因子。这些是最有价值的知识，补充了目前已知的：当前的现实模型。[渐进阅读](https://supermemo.guru/wiki/Incremental_reading)的乐趣来自流入学生大脑的涌动的「哇」的力量。 Thus far, we have seen the impact of entropy, surprisal, predictability, and current knowledge on learning. In this case, the mere probability of the signal does not fully explain its power. It is the interpretation that stands behind it \(see: [Knowledge valuation network](https://supermemo.guru/wiki/Knowledge_valuation_network)\). At the moment of making his note, Ehman could sense the enormity of its implications. This had been the most powerful evidence thus far and ever since for the existence of intelligence other than human intelligence. If the same signal represented detecting sardines in the ocean, there would be no "wow!". Not even in the Arctic. 到目前为止，我们已经看到熵、意外、可预测性和预备知识对学习的影响。在这种情况下，只有信号的概率并不能完全解释它的力量。它的解释在本文的后面（参见：[知识评估网络](https://supermemo.guru/wiki/Knowledge_valuation_network)）。Ehman 在写这篇文章的时候，能够感觉到它所蕴含的巨大意义。这是迄今为止，也是从那时起，证明除了人类文明以外，还有其他文明存在的最有力的证据。如果相同的信号出现在检测海洋中的沙丁鱼，就不会有「哇！」。即使在北极也不会有。 The reliability of the information channel is important. If the error rate is high, the [learn drive](https://supermemo.guru/wiki/Learn_drive) may weaken. When Penzias and Wilson discovered cosmic microwave background radiation in 1964, there was no "wow!". Perplexed researchers went on to remove pigeon droppings from their radio antenna. Pigeon droppings received a priority in their explanation of the mysterious noise. In 1978, for their discovery, Penzias and Wilson received a Nobel Prize. 信息渠道的可靠性很重要。如果错误率高，[学习内驱力](https://supermemo.guru/wiki/Learn_drive)可能会减弱。当 Penzias 和 Wilson 在 1964 年发现宇宙微波背景辐射时，并没有「哇！」。困惑的研究人员继续从他们的无线电天线上移除鸽子粪便。解释神秘噪音时，他们优先考虑了鸽子粪的原因。1978 年，Penzias 和 Wilson 因为他们的发现获得了诺贝尔奖。 When a scientist makes a discovery, he may exclaim "_Eureka!_" and punch the air. A neural network somewhere in his brain has produced a [generalization](https://supermemo.guru/wiki/Generalization) that results in sending a reward signal. This propagates further and makes an old man jump around the lab like a child. 当一个科学家有了一个发现，他可能会惊呼「尤里卡！」和手舞足蹈。他大脑中某个地方的神经网络产生了一种[泛化](https://supermemo.guru/wiki/Generalization)，结果发出了奖励信号。这进一步传播，让一个老人像孩子一样在实验室里跳跃。 The same happens early in life. A toddler in an empty room will scan the environment for low probability components like colorful objects, new toys, etc. When a toddler experiments with a spoon dropping off the table, she is like a little scientist. However, when the brain makes a [generalization](https://supermemo.guru/wiki/Generalization) "_all falling spoons make noise_", she is rewarded too. She may celebrate in the exactly same way as the happy scientist, independent of the age. A big smile is the first clear sign. 人生早期也是如此。一个在空房间里蹒跚学步的孩子会寻找环境中的低概率成分，比如彩色物体、新玩具等等。当一个蹒跚学步的孩子用勺子从桌子上掉下来做实验时，她就像一个小科学家。然而，当大脑做出「_所有落下的勺子都会发出声音_」的[泛化](https://supermemo.guru/wiki/Generalization)时，她也会得到奖励。她可能会以和快乐的科学家完全一样的方式庆祝，与年龄无关。一个大大的微笑是第一个明确的信号。 The same happy thing occurs to a lesser degree in all forms of learning controlled by the [learn drive](https://supermemo.guru/wiki/Learn_drive). It does not matter if we learn about a celebrity or the chemical composition of a rock. Things are interesting because they reward the brain through the learn drive mechanism. 在由[学习内驱力](https://supermemo.guru/wiki/Learn_drive)控制的各种形式的学习中，同样的快乐在较小程度上也会发生。不管我们是否了解名人或岩石的化学成分。事情很有趣，因为它们通过学习内驱力机制奖励大脑。 A creative process will also produce rewards. An association deemed useful is rewarding. An association that leads to a solution to a difficult problem is even more rewarding. Clearly there is a gradation of rewards. The system can quantify the probability of information, association, or a solution. The lower the probability, the higher the reward. 一个创造性的过程也会产生奖励。一个被认为有用的联想是有回报的。一个能解决难题的联想更有价值。显然，奖励是有等级的。该系统可以量化信息、联想或解决方案的概率。概率越低，奖励越高。 ### 6.10 Knowledge valuation network ### 知识评估网络 #### 6.10.1 Knowledge valuations #### 知识评估 All granular pieces of knowledge processed by the brain are instantly evaluated for their relevance, [coherence](https://supermemo.guru/wiki/Coherence), and value. We instantly know if information is understandable and useful. We also often instantly notice when it is [inconsistent](https://supermemo.guru/wiki/Consistency), [incoherent](https://supermemo.guru/wiki/Coherence) or irrelevant. 大脑处理过的所有知识片段都会立即评估其相关性、[连贯性](https://supermemo.guru/wiki/Coherence)和价值。我们能立即知道这些信息是否可以理解的和有用的。当它们[前后矛盾](https://supermemo.guru/wiki/Consistency)、[不连贯](https://supermemo.guru/wiki/Coherence)或不相关时，我们也常常会立即注意到。 Unusual and surprising bits of knowledge are highly valued, however, the probability isn't the best reflection of value from the brain's point of view. There are highly unlikely events of low significance \(e.g. asteroid strike in a remote planetary system\), and likely events that change one's life \(e.g. the answer to "_Will you marry me?_"\). 不寻常和令人惊讶的知识非常有价值，然而，从大脑的角度来看，概率并非价值的最佳反映。有极不可能发生的低重要性事件（例如，小行星撞击遥远的行星系统），也有很可能的发生改变一个人生活的事件（例如，「_你愿意嫁给我吗_？」）。 #### 6.10.2 The emotional brain and the rational brain #### 感性脑和理性脑 **Knowledge valuation network** is an evaluation system based on a resultant of emotional and rational valuations. Emotional valuations will connect information with rewards in primitive brain centers responsible for hunger, thirst, sex drive, etc. Rational valuations will be knowledge-based. An example of pure emotional valuation comes from an answer to "_Where is the nearest fast food shop?_". Knowledge-based valuations may be more complex and highly networked, i.e. dependent on a network of subvaluations. Answer to "_Which book is best for my exam?_" is evaluated through one's goals that include passing exam leading to getting a degree affecting job prospects and contributing to lifetime goals. Emotional and rational valuations segregate anatomically. The emotional valuations come from what has metaphorically been described as older portions of the triune brain: reptilian and paleommamalian structures. For example, a specific stimulus processed by the thalamus may send separate signals to the amygdala for an emotional valuation and to the neocortex for a rational valuation. The emotional brain is philogenetically older. Personality and education determine if rational valuations can control or override emotional valuations. **知识评估网络**是一个基于感性评估和理性评估相结合的评估系统。感性评估将把信息与原始大脑中枢负责饥饿、口渴、性冲动等的奖励联系起来。理性估值将以知识为基础。纯粹感性评估的一个例子是对「_最近的快餐店在哪里？_」的回答。基于知识的评估可能更加复杂和高度网络化，也就是说，依赖于一个次级评估网络。对「_哪本书最适合我的考试？_」的回答通过一个人的目标来评估，包括通过考试，获得影响工作前景的学位，并为终生目标做出贡献。感性和理性的评估在解剖学上是分开的。感性评估来自于被比喻为三位一体大脑中更古老的部分：爬行动物和古哺乳动物结构。例如，丘脑处理的特定刺激可能会向杏仁体发送单独的信号进行感性评估，向新皮层发送单独的信号进行理性评估。感性化的大脑在基因上更古老。个性和教育决定了理性评估是否能控制或超越感性评估。 #### 6.10.3 Decision tree in fast thinking #### 快速思考中的决策树 **Knowledge valuation network** is the network of memory connections that determine the value of an individual piece of knowledge. If learning is interpreted as a task, valuation network will determine the **perceived task value**. **知识评估网络**是决定单个知识块价值的记忆关系网络。如果将学习解释为一项任务，评估网络将决定**感知到的任务价值**。 In computational terms, knowledge valuation network can be compared to a [decision tree](https://en.wikipedia.org/wiki/Decision_tree). Goals and emotions determine core values at the root of the tree. Semantic connections between pieces of knowledge can be interpreted as fractional value transfer from goals to details. Well-organized semantic network of well-consolidated and well-chosen knowledge needs milliseconds to make expert decisions. This is what Kahneman calls automatic [fast thinking](https://supermemo.guru/wiki/Fast_thinking) \(if you are interested in tough problems that require _slow problem solving_, see _How to solve any problem?_\). The same kind of processes, that underlie decision making or problem solving, participate in knowledge valuation. Like many expert decisions, the valuation is fast and it is often running with low participation of conscious intentionality. In short, we sometime die to know things without fully being able to explain why. This process is hardly under our own control, let alone the control of the teacher at school. For efficient learning, valuations must be high. 用计算机的术语，知识评估网络可以比作[决策树](https://en.wikipedia.org/wiki/Decision_tree)。目标和情感决定了树根的核心价值。知识片段之间的语义联系可以被解释为从目标到细节的部分价值转移。精心整合和精心选择的知识的组织良好的语义网络需要几毫秒才能做出专家决策。这就是卡尼曼所说的自动[快速思考](https://supermemo.guru/wiki/Fast_thinking)（如果你对需要缓慢解决问题的棘手问题感兴趣，请参见[如何解决任何问题？](https://supermemo.guru/wiki/How_to_solve_any_problem%3F)）中。同样的过程，作为决策或解决问题的基础，参与知识评估。像许多专家的决定一样，评估速度很快，而且通常是在意识参与度很低的情况下进行的。简而言之，我们有时很快知道一些事情，却无法完全解释原因。这个过程几乎不受我们自己的控制，更不用说学校老师的控制了。为了有效学习，估值必须很高。  > **Figure:** xefer is a tool that helps understand knowledge as a network. It relies on semantic links between Wikipedia articles.[Try it](https://xefer.com/wikipedia) > **图：** xefer 是一种工具，有助于将知识理解为网络。它依赖维基百科文章之间的语义链接。[试试看](https://xefer.com/wikipedia) #### 6.10.4 Valuation network in education #### 教育中的评估网络 The brain builds a valuation network in the course of learning over years and decades. Through optimization in sleep and via [forgetting](https://supermemo.guru/wiki/Forgetting_curve), the network is polished and smoothed up for efficient operation. This makes it easy to take valuation shortcuts. A student choosing a book may no longer see his exam in the full context of his whole life. He might have developed a quick shortcut: "_In the next 3 months, all I want to do is to pass geology_". 大脑在几年到几十年的学习过程中建立了一个评估网络。通过睡眠中的优化和[遗忘](https://supermemo.guru/wiki/Forgetting_curve)，网络被打磨和抛光，以实现高效运行。这使得采取估值捷径变得容易。一个学生在选择一本书时，可能不再把考试放在他整个人生的大背景中去考虑。他可能已经找到了一条捷径:「_在接下来的 3 个月里，我只想通过地质学_」。 Knowledge valuation network is highly specialized and very different from individual to individual. The balance between reason and emotions will differ. The balance between goals will differ. The valuation network will shape differently in the mind of a criminal, and differently in the mind of a researcher with lofty goals based on the good of mankind. 知识评估网络是高度专业化的，个体间的差异很大。理性和感性之间的平衡会有所不同。目标之间的平衡会有所不同。评估网络在罪犯的思维中会有不同的形状，在怀有以人类利益为基础的崇高目标的研究人员的思维中也会有不同的形状。 The development of the network will depend on the personality, lifetime experience, and the environment. Some personality characteristics, e.g. short temper, may favor developing a more criminal mindset. Some traumatic events in early life may favor developing biased networks based on single-minded obsessions. The environment and the available knowledge will determine passions, interests, goals, and network subvaluations. 网络的发展将取决于个性、人生经历和环境。一些性格特征，例如脾气暴躁，可能会助长犯罪心理的形成。生命早期的一些创伤性事件可能助长基于单一想法的有偏见的网络的发展。环境和现有知识将决定激情、兴趣、目标和网络次级评估。 > The ideal path towards developing healthy network valuations is a [childhood sheltered from trauma and chronic stress](https://supermemo.guru/wiki/Stress_resilience), with no external stressors shaping emotional valuations, plenty of play, and [free learning](https://supermemo.guru/wiki/Free_learning) in large [behavioral spaces](https://supermemo.guru/wiki/Behavioral_space) > > 发展健康的评估网络的理想途径是一个[远离创伤和长期压力的童年](https://supermemo.guru/wiki/Stress_resilience)，没有外部压力影响感性评估，有大量的娱乐活动，在广阔的[行为空间](https://supermemo.guru/wiki/Behavioral_space)中[自由学习](https://supermemo.guru/wiki/Free_learning) All strategies that promote healthy brain development will also promote rich, highly-individualized, and efficient valuation network. Those will underlie a sparkling [learn drive](https://supermemo.guru/wiki/Learn_drive). All educators agree that we want to help kids have a good grip on their emotional life and build smart, creative, and knowledgeable brains. 所有促进大脑健康发展的策略也将促进饱满、高度个性化和高效的评估网络。这些都将成为[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的基础。所有教育工作者都同意，我们希望帮助孩子更好地掌控他们的情感生活，培养聪明、有创造力和知识渊博的大脑。 The chief problem of educational system is a cookie-cutter approach in which all kids are fed the same knowledge in an industrial fashion with little respect to the key component of efficient learning: the [learn drive](https://supermemo.guru/wiki/Learn_drive). Learn drive is a perfect computational device that matches the current status of the semantic network representing knowledge in the brain with current input produced by the knowledge valuation network in response to information available in the environment. If the kid insists he must see that YouTube video, his own brain is the best authority. All interference will affect future independence and creativity. 教育系统的主要问题是一种千篇一律的方法，在这种方法中，所有的孩子都以流水线的方式接受相同的知识，而很少考虑有效学习的关键组成部分：[学习内驱力](https://supermemo.guru/wiki/Learn_drive)。学习内驱力是一种完美的计算系统，它能将「大脑中代表知识的语义网络的当前状态」与「知识评估网络响应环境中可用信息而产生的当前输入」相匹配。如果这个孩子坚持要看 YouTube 上的视频，那么他自己的大脑就是最好的权威。所有干涉都会影响未来的独立性和创造性。 While a lecturing teacher may spend 45 minutes to feed a child with a long string of symbols that produce low valuations, and negligible memories, the same kid, with access to Google, within 3-5 minutes, will identify pieces of information with high valuations, and easy coding for lifetime retention. For kids well trained in the process, the efficiency of knowledge acquisition may be an order of magnitude higher in self-learning. When I say "order of magnitude", I am just being modest and conservative. I do not want to run into accusations of hyperbole. I included a couple of examples of specific comparisons in this text elsewhere \(e.g. [13 years of school in a month](https://supermemo.guru/wiki/Schools_are_useless_in_teaching_English!#Inefficiency_of_schooling) or [1600% acceleration of learning during vacation](https://supermemo.guru/wiki/Learning_history:_school_vs._self-directed_learning#Self-directed_acceleration)\). 虽然讲课老师可能会花 45 分钟给孩子灌输一长串符号，这些符号会产生低估值和可忽略不计的记忆，但是同一个孩子可以在 3 - 5 分钟内访问谷歌，识别出具有高价值的信息，并且容易编码终身保留。对于在这一过程中受过良好训练的孩子来说，在自学过程中获得知识的效率可能会提高一个数量级。当我说「数量级」时，我只是谦虚和保守。我不想遭到夸张的指责。我在本文其他地方列举了几个具体比较的例子（例如[一个月内学习学校 13 年所教的知识](https://supermemo.guru/wiki/Schools_are_useless_in_teaching_English!#Inefficiency_of_schooling)，或者[在假期学习速度加快 1600%](https://supermemo.guru/wiki/Learning_history:_school_vs._self-directed_learning#Self-directed_acceleration) ）。 Where I speak of golden nuggets of knowledge, Peter Thiel speaks of the [power law](https://supermemo.guru/wiki/Thiel_on_power_law): a small set of core skills honed to perfection can produce power returns. 在我谈到高价值知识时，Peter Thiel 谈到了[幂律](https://supermemo.guru/wiki/Thiel_on_power_law)：经过磨练的一小部分核心技能可以带来指数级回报。 > **Small investments in learning** can produce **dramatic changes** to individual lives and to the entire planet! > > **对学习的小投资**可以给个人生活和整个地球带来**巨大的变化**！ #### 6.10.5 Knowledge valuation that affects the course of life #### 影响生命过程的知识评估 > Personal anecdote. [Why use anecdotes?](https://supermemo.guru/wiki/Why_use_anecdotes%3F) > > 个人轶事. [为什么使用个人轶事？](https://supermemo.guru/wiki/Why_use_anecdotes%3F) > > **My school tried to block the best thing in my life** > > **我的学校试图阻止我一生中最好的事情** > > I have my own striking example of the power of the valuation network in confrontation with the education system: > > 我有我自己鲜明的例子来说明评估网络在对抗教育系统方面的力量： > > In 1985, I computed the approximate function of optimum intervals for knowledge review needed for developing long-term memories. This was the birth to [SuperMemo](https://supermemo.guru/wiki/SuperMemo). Originally, the function was applicable using a [pen and paper](http://www.super-memory.com/articles/paper.htm). Within a few months, I realized the system was extremely powerful. I knew I could double its power with the use of a computer. However, I did not know anyone who could write learning software based on my math. In those days, the entire population of programmers in Poland was made of old timers doing Fortran or Cobol on mainframes, or a growing mass of amateur enthusiasts working with microcomputers such as ZX 81, Commodore 64, or ZX Spectrum. I decided to write the program myself. I had no programming skills though. I was a student of computer science and I asked my teachers for help. However, our only course of programming was the assembly language of Datapoint. Those skills were great for playing with registers and coming up with 11\*11=121. I wanted to learn something more useful for programming SuperMemo. My school kept demanding that I learn to compute the resistance of an electronic circuit, or learn symbolic integration. My knowledge valuation network produced a simple output: programming skills -> SuperMemo -> faster learning \(in all fields, incl. electronics or calculus\). I was determined to learn programming. My school was determined to stop me \(by loading other compulsory courses\). In desperation, I enrolled in University of Economics, which had a course of algorithmic languages. The course focused on [Pascal](https://en.wikipedia.org/wiki/Pascal_%28programming_language). I had to do my normal load of classes and do my Pascal in extra time. That course was nice, but we did all learning in theory. On paper. There were very few PCs at Polish universities in those days \(1986\) and most practical applications run on mainframes called [Odra](https://en.wikipedia.org/wiki/Odra_%28computer) \(produced for Soviet block in Poland as of 1960\). When I finally got my first computer: [_ZX Spectrum_](https://en.wikipedia.org/wiki/ZX_Spectrum) \(Jan 4, 1986\), I could finally start learning programming languages. Before the computer arrived, I started writing my first program! On paper. It was a program for organizing my day \(sort of [_Plan_](http://help.supermemo.org/wiki/Plan) in SuperMemo\). Not much later, I was able to learn Pascal too. First I had to reduce the bad impact of school and cut the load of classes. I struck a deal with my teacher of electronic circuits. I would do some high-pass filter calculations for him, and this would be a chance to improve my Pascal skills. The program took many hours to write and was a monumental waste of time. It was a perfect example of bad learning. I hardly understood how my own program worked. However, it was still better than just learning diagrams. For programming, the learning was good and I improved my skills a lot. > > 1985 年，我算出了发展长期记忆所需的知识复习最佳间隔的近似函数。这是就是 [SuperMemo](https://supermemo.guru/wiki/SuperMemo) 的诞生。最初，这个函数是用[笔和纸](http://www.super-memory.com/articles/paper.htm)来实现的。几个月后，我意识到这个系统非常强大。我知道我可以用电脑把它的力量翻倍。然而，我不知道有谁能根据我的函数来编写学习软件。那时候，波兰的所有程序员是要么是在大型机上写 Fortran 或 Cobol 的老家伙，要么是在使用 ZX 81、Commodore 64 或 ZX Spectrum 等微型计算机的越来越多的业余爱好者。我决定自己写这个程序。但是我没有编程技能。我是计算机科学的学生，我向老师寻求帮助。然而，我们唯一的编程课程是 Datapoint 汇编语言。这些技能对于玩玩寄存器得出 11 \* 11 = 121 非常有用。但我想学习一些对 SuperMemo 编程有用的东西。我的学校一直要求我学习计算电路的电阻，或者学习符号积分。我的知识评估网络产生了一个简单的输出：编程技能——> SuperMemo ——>更快的学习（在所有领域，包括电子或微积分）。我决心学习编程。我的学校决心阻止我\(通过要求选修其他必修课程\)。无奈之下，我报名了经济大学，该大学开设了算法语言课程。这门课程的重点是 [Pascal](https://en.wikipedia.org/wiki/Pascal_%28programming_language)。我不得不在做平时的功课的同时，在额外的时间里学习\) Pascal。那门课程不错，但我们都在学习理论。纸上谈兵。当时（1986 年）波兰大学里的个人电脑很少，大多数实用的应用都在名为 [Odra](https://en.wikipedia.org/wiki/Odra_%28computer) 的大型机上运行（从 1960 年开始，为在波兰的苏联集团生产）。当我终于得到了我的第一台电脑：[ZX Spectrum](https://en.wikipedia.org/wiki/ZX_Spectrum)（1986 年 1 月 4 日），我终于可以开始学习编程语言了。在电脑到来之前，我开始写我的第一个程序！在纸上。这是一个组织我一天的计划（类似 SuperMemo 中的[计划](http://help.supermemo.org/wiki/Plan)）。不久之后，我也学会了 Pascal。首先，我必须减少学校的不良影响，减轻课业负担。我和我的电路老师达成了协议。我会为他做一些高通滤波计算，这将是提高我 Pascal 技能的机会。这个程序花了很多小时来写，简直是浪费时间。这是糟糕学习的完美例子。我几乎不明白我自己的程序是如何工作的。然而，这仍然比仅仅学习图解要好。就编程而言，那段学习很好，我的技能提高了很多。 > > It is hard to express it in words to those who do not know programming, but the difference of knowledge valuations between university courses and doing one's own programming is comparable to the size difference between the plum and the Jupiter. While my colleagues suffered through boring lectures in electronics and metrology, I could make my start. I would learn nothing at school. I would learn a bit in my extracurricular course of Pascal. However, only the practical knowledge backed up by passion and clear goals mattered. By December 1987, my effort culminated in writing the first version of SuperMemo, which totally changed the course of my life. Open mind of my supervisor Dr Zbigniew Kierzowski let me devote my whole Master's Thesis to the subject of SuperMemo. Happy 80th birthday Professor Kierzkowski! It was pretty unusual for a student to make his own determination on that scale, and then compound it with the fact that the thesis was written in English. This fact is not unusual today, but it involved a big administrative and tactical battle back in 1989. > > 对那些不懂编程的人来说，很难用语言表达出来，但是大学课程和自己编程之间的知识价值差异相当于李子和木星之间的大小差异。当我的同学们在电子学和计量学方面苦于无聊的讲座时，我已经可创业了。我在学校什么也学不到。我可以在 Pascal 的课外课程中学到一点。然而，只有激情和明确目标支持的实用知识才是重要的。到了 1987 年 12 月，我的努力达到了顶峰，写下了第一版 SuperMemo，这彻底改变了我的人生历程。我的导师 Zbigniew Kierzowski 博士的开明态度让我把我的硕士论文全部奉献给 SuperMemo 这个主题。80 岁生日快乐，Kierzkowski 教授 ！对于一个学生来说，在这个尺度上做出自己的决定，然后再加上论文是用英语写的，这是非常不寻常的。这一事实在今天并不罕见，但它涉及到 1989 年的一场大规模行政和战术斗争。 > > My school almost destroyed [SuperMemo](https://supermemo.guru/wiki/SuperMemo), i.e. the major source of my present joy. There was no malice involved. Most of my college teachers were fantastic people. It was the system that was designed to squeeze students through a rigid curriculum rather than give them space for creative expression that is the best basis of education. > > 我的学校差点毁了 [SuperMemo](https://supermemo.guru/wiki/SuperMemo)，它是我现在快乐的主要来源。这里没有恶意。我的大部分大学老师都是了不起的人。但学校是一个系统，旨在通过严格的课程来挤压学生，而不是给他们创造表达的空间，然而创造表达的空间是教育的最好的基础。 **My school was actively trying to block me from accomplishing the most important thing that underlay my entire professional life and future**. If I was a bit more compliant, more conformist, more prone to social pressures, I would be a "better" student, invest more time in the theory of electronic circuits, calculus, metrology, and abstract algebra. As a result, this article would have never been written. This site would not exist. **我的学校积极地试图阻止我完成我整个职业生涯和未来最重要的事情**。如果我更听话，更顺从，更善于忍受社会压力，我会成为一名「更好」的学生，在电路学、微积分、计量学和抽象代数的理论上投入更多时间。其结果是，这篇文章永远不会被写出来。这个网站也将不存在。 I would not trade my present life for any other type of career in research or industry. I survived the denial attack by providing resistance based on strong knowledge valuation network. 我不会用我现在的生活去换取任何研究或行业上的其他职业。通过产生基于强大的知识估值网络的抵抗，我挺过了否认我的攻击。 > **We need to design an education system in which kids do not need to battle for the right to develop.** > > **我们需要设计一个教育系统，让孩子们不需要为发展权利而斗争。** ### 6.11 Learntropy ### 学习熵 There are many factors that affect how messages and information channels are perceived and valued by the brain. In preceding sections we have noticed that the brain does not respond just to entropy. There are many factors that modulate the impact of entropy or surprisal of individual messages. Those factors include: encoding, speed of delivery, pre-processing \(e.g. generalization, completion, recognition, etc.\), prior knowledge \(incl. valuation, emotional valence, channel reliability, etc.\), optimum level \(affected by speed of processing\), and more. 有许多因素会影响大脑对信息和信息渠道的感知和评估。在前面的章节中，我们已经注意到大脑不仅对熵有反应。还有许多因素可以调节单个信息的熵或意外的影响。这些因素包括：编码、传授速度、预处理（例如泛化、完善、识别等），预备知识（包括估值、情绪价、渠道可靠性等），最佳水平（受处理速度影响），等等。 The complexity of the process calls for a better concept that can encapsulate all those nuances. I suggest the use of the term learntropy to describe the attractiveness of an educational channel or signal from the point of view of an individual brain in a specific context. 这一过程的复杂性要求有一个更好的概念来封装所有这些细微差别。我建议使用学习熵这个术语来描述从特定背景下的个人大脑的角度来看的教育渠道或信号的吸引力。 > **Learntropy is the attractiveness of any educative signal as determined by the learn drive system.** > > **学习熵是由学习内驱力系统决定的任何教育信号的吸引力。** Lectures can be boring or attractive. Learntropy expresses their attractiveness from the point of view of an individual. 讲座可能很无聊也可能很吸引人。学习熵从个人的角度来表示它们的吸引力。 While entropy has a precise mathematical definition, learntropy would probably best be measured by the response of the reward system to the act of learning from the analyzed signal. As much as entropy depends on the probability of individual messages, learntropy will depend on the rewarding power of these messages \(pictures, sounds, sentences, etc\). That rewarding power will be associated with probability, but the valuation will largely depend on the [knowledge valuation network](https://supermemo.guru/wiki/Knowledge_valuation_network). 虽然熵有一个精确的数学定义，但是学习熵最好用「奖励系统」对从「分析的信号」中学习的行动的反应来衡量。正如熵取决于单个信息的概率一样，学习熵也取决于这些信息（图片、声音、句子等）的奖励效应。这种奖励效应将与概率相关联，但评估将在很大程度上取决[知识评估网络](https://supermemo.guru/wiki/Knowledge_valuation_network)。 For good learning there is a reward. However, there is also bad learning. There is a decoding failure penalty. If a student makes an effort to decode a message and fails, he is penalized. This is how frustration is born. This is how the dislike of learning begins. If learntropy is low, reward is little, penalty is high, and the net result may be negative. If we take negative reward signals into account, learntropy could actually assume negative values. A boring lecture could carry negative learntropy. It will result in suppressing the [learn drive](https://supermemo.guru/wiki/Learn_drive). 好的学习是有奖励的。然而，也有不好的学习。因为这里有解码失败惩罚。如果一名学生努力解码一条信息，但失败了，他将受到惩罚。挫折就是这样产生的。这就是不喜欢学习的开始。如果学习熵低，奖励少，惩罚高，最终结果可能是负面的。如果我们把负面的奖励信号考虑在内，学习熵实际上可以为负值。无聊的讲座可能会带来负值的学习熵。这会导致抑制[学习内驱力](https://supermemo.guru/wiki/Learn_drive)。 High knowledge valuations contribute to high [learntropy](https://supermemo.guru/wiki/Learntropy), which in turn is necessary for attention and semantic slotting in of knowledge for long-term retention. In a powerful feedback loop, **learntropy enhances the learn drive, which underlies valuations that determine learntropy**. This feedback loop is kept in check by forgetting, learned helplessness, aging, injury, and the sheer availability of mental resources. With rational learning and lifestyle management, esp. with respect to the [natural creativity cycle](https://supermemo.guru/wiki/Natural_creativity_cycle), the equilibrium can be maintained at the high [learn drive](https://supermemo.guru/wiki/Learn_drive) level for decades. 高知识估值导致高[学习熵](https://supermemo.guru/wiki/learntropy)，而学习熵又为长期记忆知识的注意力和语义插入提供了必要条件。在强大的反馈循环中，**学习熵增强了**[**学习内驱力**](https://supermemo.guru/wiki/Learn_drive)**，这是决定学习熵的估值的基础。**这种反馈循环通过遗忘、习得性无助、衰老、伤害和精神资源的纯粹可用性来控制。通过合理的学习和生活方式管理，特别是[自然创造力循环](https://supermemo.guru/wiki/Natural_creativity_cycle)，这种平衡可以在高[学习内驱力](https://supermemo.guru/wiki/Learn_drive)水平上保持几十年。 ### 6.12 Signal timing vs. learntropy ### 信息时机与学习熵 The degree of reward obtained from individual messages in the learning stream will determine the level of signal learntropy. A lecture on a boring topic will carry low learntropy. Surfing the net for titbits of information needed to solve a specific problem will carry high learntropy. 从学习流中的各个信息获得的奖励程度将决定信息学习熵的水平。关于无聊话题的讲座的学习熵很低。在网上寻找解决某个特定问题所需的信息会带来很高的学习熵。 Unlike [Shannon entropy](https://en.wikipedia.org/wiki/Entropy_%28information_theory) that is based on averages, learntropy will be more of a trailing average where recent messages will carry a higher weight than messages delivered earlier in time. In addition, learntropy is rooted in rules that govern the [consolidation of memory](https://supermemo.guru/wiki/Two_component_model_of_memory), incl. the [spacing effect](https://supermemo.guru/wiki/Spacing_effect). 与基于平均值的[香农熵](https://en.wikipedia.org/wiki/Entropy_%28information_theory)不同，学习熵更像是一个趋势平均数，最新的信息将比之前传授的信息具有更高的权重。此外，学习熵植根于支配[记忆巩固](https://supermemo.guru/wiki/Two_component_model_of_memory)的规律，包括[间隔效应](https://supermemo.guru/wiki/Spacing_effect)。 The learntropy of a boring lecture will shoot up once a golden nugget of fills an important gap in understanding. The increase in learntropy will be proportional to the expression of the [stability](https://supermemo.guru/wiki/Stability) of the memory trace determining knowledge valuation \(incl. descending traces in the [knowledge valuation network](https://supermemo.guru/wiki/Knowledge_valuation_network)\). The impact of a golden nugget will wane in time. The cumulative effect of those happy discoveries will determine the level of learntropy at any given time \(e.g. during a lecture\). 一旦高价值知识填补了理解上的一个重要空白，枯燥的演讲的学习熵就会激增。学习熵的增加与决定知识估值的记忆[稳定性](https://supermemo.guru/wiki/Stability)曲线的表达式成正比（包括[知识估值网络](https://supermemo.guru/wiki/Knowledge_valuation_network)中的下降曲线）。随着时间的推移，高价值知识的影响将逐渐减弱。这些令人愉快的发现的累积效应将决定在任何特定时段（例如在演讲期间）的学习熵的水平。 The above shows that educators can influence learntropy, enhance the [learn drive](https://supermemo.guru/wiki/Learn_drive), and enhance long-term learning outcomes. Feeding passive knowledge is a bad strategy. Providing answers should be selective and should favor high importance abstract and universal questions. Free explorations of self-directed learning are the best formula for lifelong sustainable [learn drive](https://supermemo.guru/wiki/Learn_drive) and lifelong learning. 以上表明，教育者可以影响学习熵，增强[学习内驱力](https://supermemo.guru/wiki/Learn_drive)，并提高长期学习效果。灌输知识是一种糟糕的策略。提供答案应该是有选择性的，应该倾向于回答高度重要的抽象和普遍的问题。自主学习的自由探索是终身保持[学习内驱力](https://supermemo.guru/wiki/Learn_drive)和终身学习的最佳方案。 All forms of schooling tend to suppress the [learn drive](https://supermemo.guru/wiki/Learn_drive). As a result, many adults may find it difficult to internalize the message on the importance of learntropy in learning. However, in the modern world, nearly everyone is faced with the need to solve a minor technical or health problem on their own. The problem may be as simple as a trivial change to setup in Facebook options. The harder it is to find the solution to a problem, the greater the reward in finding answers. The harder it is to find answers, the more persistent and extensive the search and exploration. Those feelings should be familiar to everyone. However, suppression of the [learn drive](https://supermemo.guru/wiki/Learn_drive) always results in lesser knowledge, lower self-esteem, and all explorations might come to an end earlier. In other words, those who lost their creative drive at school, or later in life, will give up earlier, or perhaps never even try. In that sense, all technical problems and glitches that come with computers, Internet, technology, etc. have some positive side effect of stimulating the vestiges of the lost [learn drive](https://supermemo.guru/wiki/Learn_drive) even in the most passive individuals. The only requirement is that those quests need to end with a degree of success. Otherwise, the opposite may happen. The penalty signal may lead to conditioning a withdrawal from exploration. 所有形式的学校教育都倾向于抑制[学习内驱力](https://supermemo.guru/wiki/Learn_drive)。因此，许多成年人可能会发现很难内化关于学习熵在学习中的重要性的信息。然而，在现代世界中，几乎每个人都需要独自解决一个小的技术或健康问题。这个问题可能就像在 Facebook 上微调一个选项一样简单。问题的解决方案越难找，找到答案的回报就越大。越难找到的答案，其搜索和探索的过程就越持久和广泛。每个人都应该熟悉这种感觉。然而，对[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的抑制往往会导致知识的减少，自尊心的降低，所有的探索都可能提前结束。换句话说，那些在学校或以后的生活中失去创造力的人会更早放弃，甚至可能永远不会尝试。从这个意义上说，计算机、互联网、技术等带来的所有技术问题和故障都有一些积极的副作用，即使是在最被动的人身上，也会刺激残留的[学习内驱力](https://supermemo.guru/wiki/Learn_drive)。唯一的要求是这些任务需要以一定程度的成功结束。否则，可能会出现相反的情况。惩罚信号可能让我们从探索中退出。 You can quickly answer this instant quiz about your own [learn drive](https://supermemo.guru/wiki/Learn_drive). If you face a minor problem in life, do you seek a human expert or you rely on Google? If your car fails, or you computer crashes, or you get injured, or you got a stomach ache, where do you go? 你可以快速回答这个关于你自己的[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的即时测验。如果你在生活中遇到一个小问题，你是寻求人类专家还是依赖谷歌？如果你的车出故障，或者你的电脑崩溃，或者你受伤，或者你胃痛，你会去哪里？ ### 6.13 Learntropy and learn drive ### 学习熵和学习内驱力 In a process similar to [forgetting](https://supermemo.guru/wiki/Forgetting_curve), the impact of [learntropy](https://supermemo.guru/wiki/Learntropy) reward will decline exponentially over time. Like in [spaced repetition](https://supermemo.guru/wiki/Spaced_repetition) review, the new reward will bring back learntropy to a high level. Like in a [spacing effect](https://supermemo.guru/wiki/Spacing_effect), longer breaks may result in the same message being more rewarding. 在一个类似[遗忘](https://supermemo.guru/wiki/Forgetting_curve)的过程中，[学习熵](https://supermemo.guru/wiki/Forgetting_curve)的奖励的影响将随着时间的推移呈指数级下降。就像[间隔重复](https://supermemo.guru/wiki/Spaced_repetition)复习一样，新的奖励将学习熵恢复到更高的水平。就像[间隔效应](https://supermemo.guru/wiki/Spacing_effect)一样，较长的休息时间可能会导致相同的信息有更多的奖励。 There is a major difference between the reward signal determining learntropy and the consolidation signal determining recall in learning: once you learn something, repeated review in short spaces of time is pointless, once you drive recall probability to 100%, you can let time pass before the next review. The upper limit on learntropy might be hard to reach. If you love a lecture, with some twists of facts or delivery, you can love it more. If you remember a singular memory, you cannot remember it better by tricks employed in a short space of time. You can reformulate the memory using mnemonic techniques and affect its durability, but once the probability of recall is 100%, the best thing to do for the memory might be to leave it unused for a while or employ it in varying context, which may essentially lead to developing new memories that will form redundant connections to the original singular memory. 在学习过程中，决定学习熵的奖励信号和决定回忆的巩固信号有很大的区别：一旦你学会了什么，在短时间内重复复习是没有意义的，一旦你把回忆的概率提高到 100%，你可以在下次复习之前让时间流逝。学习熵的上限可能很难达到。如果你喜欢一场演讲，有一些歪曲的事实或讲授，你会更喜欢它。如果你只记住一段记忆，你不可能在短时间内用技巧更好地记住它。你可以使用助记技巧重新构建记忆并影响其持久性，但是一旦回忆概率达到100%，记忆的最好的办法是让它闲置一段时间或在不同的上下文中使用它，这可能会导致开发新的记忆，从而与原来的一段记忆形成冗余连接。 Extinction of learntropy occurs via lack of reward signal. Extinction of [learn drive](https://supermemo.guru/wiki/Learn_drive) is a matter of [forgetting](https://supermemo.guru/wiki/Forgetting) \(incl. forgetting through brain cell loss\). 学习熵的消失是由于缺乏奖励信号。[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的消失是一个[遗忘](https://supermemo.guru/wiki/Forgetting)的问题（包括因部分脑细胞死亡而遗忘）。 Learntropy will be additive over individual messages with exponential decline and diminishing returns. By optimizing the timing of rewarding messages, we can drive learntropy high and make learning become one of the most pleasurable activities on the par with rewards of food, sex, drugs, etc. If you are skeptical, recall obsessive videogamers who can literally starve while playing nights. [Videogames](https://supermemo.guru/wiki/Videogames) can highjack the [learn drive](https://supermemo.guru/wiki/Learn_drive) and combine it with the reward of gambling. Rewards of gambling might also be governed by similar rules of decline and boost as learntropy, however, they are subject to [variable reward](https://supermemo.guru/wiki/Variable_reward) which can lead to addiction. It is important to distinguish between the pleasure of learning and harmful addictions \(see: [Addiction to learning](https://supermemo.guru/wiki/Addiction_to_learning)\). 学习熵将以指数下降和收益递减的方式叠加在每个信息上。通过优化奖励信息的出现时机，我们可以将学习熵提高，使学习成为与食物、性、药物等奖励相同的最令人愉快的活动之一。如果你对此持怀疑态度，请回忆一下那些沉迷于电子游戏的玩家，他们可以在晚上饿着肚子玩游戏。[电子游戏](https://supermemo.guru/wiki/Videogames)可以劫持[学习内驱力](https://supermemo.guru/wiki/Learn_drive)，并将其与赌博的奖励结合起来。赌博的奖励也可能受类似于学习熵的下降和提升规则的支配，然而，它们受制于[可变的奖励](https://supermemo.guru/wiki/Variable_reward)，这可能导致上瘾。重要的是要区分学习的乐趣和有害的上瘾（参见：[对学习的上瘾](https://supermemo.guru/wiki/Addiction_to_learning)）。 Learntropy will determine the [learn drive](https://supermemo.guru/wiki/Learn_drive), but both will be sustained with different rules. Learn drive is knowledge dependent, and as such will be subject to [spaced repetition](https://supermemo.guru/wiki/Spaced_repetition). As knowledge is a network, speaking of optimum stimulation of [learn drive](https://supermemo.guru/wiki/Learn_drive) is probably pointless. To maximize learn drive, we should engage in lifelong learning, respect [natural creativity cycle](https://supermemo.guru/wiki/Natural_creativity_cycle), and take care of the brain health \(i.e. health in general\). 学习熵将决定[学习内驱力](https://supermemo.guru/wiki/Learn_drive)，但两者将以不同的规则来维持。学习内驱力依赖于知识，因此将受到[间隔重复](https://supermemo.guru/wiki/Spaced_repetition)的影响。由于知识是一个网络，所以说学习内驱力的最佳激励可能是没有意义的。为了最大限度地提高学习内驱力，我们应该终身学习，尊重[自然创造力周期](https://supermemo.guru/wiki/Natural_creativity_cycle)，并注意大脑的健康（即一般的健康）。 ### 6.14 Optimum information delivery ### 最佳信息传授 In schooling, we might envisage a lecture delivered at optimum [learntropy](https://supermemo.guru/wiki/Learntropy) level, in which a student keeps saying "_wow! wow!_". She keeps taking down notes as fast as humanly possible. More often though, the lecture will buzz a high entropy signal or ooze boredom. Its learntropy will be low or even negative. 在学校教育中，我们可以设想一个处于最佳[学习熵](https://supermemo.guru/wiki/learntropy)水平的讲课，在这个讲课中一个学生不断地说「哇！哇！」她保持尽可能快地记笔记。但是，更常见的情况是，课堂会发出一个高熵信息或让人感到无聊。它的学习熵会很低，甚至是负数。 If optimum learntropy levels depend on the student, how can a teacher optimally deliver knowledge to a classroom? Sometimes universal delivery is impossible. In other cases, it is difficult enough to require genius teaching skills. For most teachers, lecture delivery keeps most kids bored or frustrated. 如果最佳学习熵水平取决于学生，老师该如何最好地向全班学生传授知识？有时候适合所有人的传授是不可能的。换句话说，这困难到需要天才的教学技能。对大多数老师来说，他们的传授使大多数孩子感到无聊或沮丧。 In lecture delivery, a lucky few may get most of the message. For a fraction of the gifted, the lecture may carry nothing new. For them it is boring. For other kids, message complexity goes above their comprehension level. In such cases, the lecture can be frustrating if they try to decode it. A lecture on string theory might be comparable to a noise of randomly shuffled English words. Lecturing is an exercise in timewasting. Nobel Prize winner [Carl Wieman](https://supermemo.guru/wiki/Carl_Wieman)compared it to [blood-letting](https://supermemo.guru/wiki/Wieman:_Lectures_make_no_sense). 在课堂上，少数幸运儿可能会理解大部分信息。对于一小部分有天赋的人来说，这堂课可能没有带来多少新知识。对他们来说这很无聊。对于其他孩子来说，信息的复杂性超出了他们的理解水平。在这种情况下，如果他们试图理解，老师的讲课可能会令他们挫败。一个关于弦理论的讲课可能相当于一个随机打乱排列的英语单词序列的噪音。讲课是一种浪费时间的活动。诺贝尔奖得主 [Carl Wieman](https://supermemo.guru/wiki/Carl_Wieman) 把它比作[放血](https://supermemo.guru/wiki/Wieman:_Lectures_make_no_sense)。 To avoid the frustration of negative learntropy, students will tune out like you tuned out from that Thai channel I mentioned [earlier](https://supermemo.guru/wiki/I_would_never_send_my_kids_to_school#Prior_knowledge_in_information_seeking). Children will ignore the static noise coming from the teacher and tune in to other channels that carry more appropriate levels of learntropy \(e.g. Facebook on a phone under the desk\). Even if their comprehension is good, the knowledge delivered may not complement their current knowledge. If it does not generate [high-quality high-value](https://supermemo.guru/wiki/Knowledge_valuation_network) generalization, it will be considered obvious or irrelevant. 为了避免负学习熵带来的挫败感，学生们会像你从我[前面](https://supermemo.guru/wiki/I_would_never_send_my_kids_to_school#Prior_knowledge_in_information_seeking)提到的泰国频道换台一样「换台」。孩子们会忽略来自老师的「静电噪音」，并收听到其他「频道」，这些频道有更合适的学习熵水平（例如桌下的手机上的 Facebook）。即使他们的理解能力很好，所传授的知识也可能无法与他们目前的知识互补。如果这些知识不能产生[高质量高价值](https://supermemo.guru/wiki/Knowledge_valuation_network)的泛化，就会被认为是显而易见或毫无关联的。 Low learntropy, even if occurring occasionally, conditions the student to tune out. After a while, students will develop a filter that will turn a teacher into a silent radio channel carrying zero entropy and zero learntropy. Improvements to lecture quality will become futile. The teacher disappears! 低学习熵，即使偶尔发生，也会让学生习惯性「换台」。一段时间后，学生们将「进化」一种过滤器，将教师当做一个零熵和零学习熵的无声广播频道。提高讲课质量将是徒劳的。老师在学生心中消失了！ In a classroom setting, a student will often not be able to zero in on a better signal. The same signal is dished out to all students and they all may get equally bored. In contrast, Googling for good keywords can bombard the brain with perfectly timed low probability messages that will [fit the current knowledge tree like a jigsaw puzzle](https://supermemo.guru/wiki/Knowledge_crystallization). Google is a very cheap and efficient generator of "wow!". 在教室里，学生往往不能集中注意力在更好的信息上。老师向所有学生传授同样的信息，他们都可能同样感到无聊。相比之下，在谷歌上搜索好的关键词，[可以像拼图游戏一样，用符合当前知识树](https://supermemo.guru/wiki/Knowledge_crystallization)的低概率信息连续轰炸大脑。谷歌是一个非常便宜和高效的「哇！」生成器。 In [incremental learning](https://supermemo.guru/wiki/Incremental_learning), the learntropy scanner will pick best channels, prioritize those and employ perfect timing for maximizing semantic connectivity and memory consolidation. This should make it easy to understand why **I am extremely happy, I will never ever be forced to sit in a school bench!** I love learning too much! 在[渐进学习](https://supermemo.guru/wiki/Incremental_learning)中，学习熵检测器将选择最佳通道，对这些通道进行优先排序，并利用最佳时机最大限度地提高语义连接和记忆整合。这应该很容易理解为什么**我非常高兴，我永远不会被迫坐在学校的长椅上！**我太爱学习了！ All the above examples illustrate how intricate the interaction between the signal and the brain is in recognizing things worth learning. The reward of learning is the best known indicator of learning quality. When students are happy, we are on the right track. When schools are the place of misery, we are failing on a societal scale. 所有以上的例子说明了信息和大脑在识别值得学习的东西时的相互作用是多么复杂。学习的奖励是最好的已知的学习质量指标。当学生快乐的时候，我们就走在正确的路上。当学校成为痛苦之地时，我们在社会上就失败了。 > **The only reliable detector of knowledge complementarity and coherence are the neural networks of the learn drive system. This is why knowledge cannot be prepackaged and imposed on students.** > > **唯一可靠的知识互补和一致性检测器是学习内驱力系统的神经网络。这就是为什么知识不能预先包装并强加于学生的原因。** This is explained using a [crystallization metaphor](https://supermemo.guru/wiki/Knowledge_crystallization). The neural details of the reward system follow in the section: [Learning rewards](https://supermemo.guru/wiki/I_would_never_send_my_kids_to_school#Learning_rewards). 这是可以用[结晶比喻](https://supermemo.guru/wiki/Knowledge_crystallization)来解释。奖励系统的神经学细节在以下一节中介绍：[学习奖励](https://supermemo.guru/wiki/I_would_never_send_my_kids_to_school#Learning_rewards)。 ### 6.15 Gripping lectures ### 令人全神贯注的讲课 We love learning, but we usually hate to be taught. Those feelings correlate with creativity, which can probably be explained by the fact that creative elaboration is essential for [pattern completion](https://supermemo.guru/wiki/Pattern_completion) that underlies comprehension. 我们喜欢学习，但我们通常讨厌别人教导我们。这些感觉与创造力相关，这可能可以用这样一个事实来解释：创造性的阐述对于[模式完成](https://supermemo.guru/wiki/Pattern_completion)是必不可少的，而模式完成正是理解的基础。 In learning, we decide what to investigate. The [learntropy](https://supermemo.guru/wiki/Learntropy) evaluation strictly depends on the status of the brain and current memory activations. In teaching, knowledge is dished out independent of what we think of it. Many students list boring subjects as their number one reason for disliking school. Not bullying, stress, or early waking. Excruciating boredom! I write about the astronomical difference between self-directed learning and learning at school [here](https://supermemo.guru/wiki/Learning_history:_school_vs._self-directed_learning). It is all about the [learn drive](https://supermemo.guru/wiki/Learn_drive)! 在学习中，我们决定研究什么。[学习熵](https://supermemo.guru/wiki/learntropy)的评估严格取决于大脑和当前的记忆激活的状态。在教学中，知识的传授是与我们对它的看法无关。许多学生把枯燥的科目列为他们不喜欢学校的首要原因。不是欺凌，不是压力，也不是早起。而是极度的无聊！我在[这里](https://supermemo.guru/wiki/Learning_history:_school_vs._self-directed_learning)写了关于自我导向学习和在学校学习之间的极大差异。这一切都源于[学习内驱力](https://supermemo.guru/wiki/Learn_drive)！ I am amazed with how many resources are wasted on research that looks for ways to keep kids interested during lectures, while it should be obvious that lectures are just a poor educational tool. Eye contact analysis? Engagement analysis? Efforts to quantify passion? All kids are equipped with natural [learn drive](https://supermemo.guru/wiki/Learn_drive) and our priority should be to ensure we do not destroy that drive. [Force-feeding knowledge](https://supermemo.guru/wiki/Schools_suppress_the_learn_drive) is the prime destroyer of the [learn drive](https://supermemo.guru/wiki/Learn_drive). In addition, there are many socioeconomic factors that prevent a great chunk of kids to thrive even in the best circumstances. Some kids will never show passion for learning. In most cases, it is not their fault. Only a tiny fraction are limited by disabilities, health, and less fortunate genetic endowments. The exponential decay in the [learn drive](https://supermemo.guru/wiki/Learn_drive) with age is caused primarily by compulsory schooling. Passive lecturing is a huge contributor to that process. 我感到惊讶的是，有这么多的资源浪费在寻找让孩子们在讲课期间保持兴趣的方法的研究上，而讲课显然只是一种糟糕的教育工具。眼神交流分析？参与分析？努力量化激情？所有的孩子都有了天生的[学习内驱力](https://supermemo.guru/wiki/Learn_drive)，我们的首要任务应该是确保我们不会破坏这种内驱力。对[学习内驱力](https://supermemo.guru/wiki/Learn_drive)起主要破坏作用的是[强制灌输知识](https://supermemo.guru/wiki/Schools_suppress_the_learn_drive)。此外，还有许多社会经济因素使许多儿童即使在最好的环境下也不能茁壮成长。有些孩子永远不会表现出对学习的热情。在大多数情况下，这不是他们的错。只有一小部分孩子受到残疾、健康问题和不幸的遗传天赋的限制。[学习内驱力](https://supermemo.guru/wiki/Learn_drive)随年龄增长呈指数衰减的主要原因是义务教育。被动的授课助长了这一过程。 Naturally, there are lectures that work. [Khan Academy](https://supermemo.guru/wiki/Khan_Academy) is jam-packed with good examples. Even a spoken lecture with no slides can work. A TED talk on YouTube can be fun. It can satisfy the [learn drive](https://supermemo.guru/wiki/Learn_drive). [MOOCs](https://en.wikipedia.org/wiki/Massive_open_online_course) are founded on the principle that one rock-star teacher is better than thousands of rank-and-file teachers repeating the same mantra. You can learn a lot even if you are just a passive listener. There are conditions though: you need to be intensely curious about the subject, or you need to love the speaker, or both. **There is only one sure mechanism for ensuring the lecture is interesting: you need to choose it on your own!** This is just one more aspect of the need for self-directed learning. 当然，有些授课是有效的。[可汗学院](https://supermemo.guru/wiki/Khan_Academy)里有很多很好的例子。即使是没有幻灯片的口头演讲也能起作用。YouTube 上的 TED 演讲可能会很有趣。它能满足[学习内驱力](https://supermemo.guru/wiki/Learn_drive)。[MOOC](https://en.wikipedia.org/wiki/Massive_open_online_course) 是建立在这样一个原则之上的，即一位摇滚明星教师比数千名重复同样咒语的普通教师要好。即使你只是一个被动的倾听者，你也能学到很多东西。但是，也有一些条件：你需要对主题充满好奇，或者你需要喜欢这个演讲者，或者两者兼而有之。**只有一个确定的机制可以确保讲座是有趣的：你需要自己选择它！**这只是自我导向学习要求的又一个方面。 In addition to choice, in lecturing, you definitely need a pause button in case you need to take a toilet break, or quiet the hunger pangs. Nothing can ruin a lecture as effectively as a bursting bladder. Last but not least, most lectures could benefit from Netflix's Skip Intro feature. 除了选择之外，在讲课时，你绝对需要一个暂停按钮，以防你需要上厕所，或缓解饥饿感。没有什么比爆裂的膀胱更能有效地毁掉一场讲课了。最后但并非最不重要的一点是，大多数讲课都可以受益于 Netflix 的「跳过开头」功能。 Naturally, the lecture will work best if you enhance it with your own creative thinking or even quick research. This is why pausing for a minute, or for a day might be essential for learning efficiency. Against the claims of some psychiatrists, creative breaks and a wandering mind have nothing to do with [ADHD](https://supermemo.guru/wiki/ADHD). As long as they are remotely relevant, they are hallmarks of great learning. 当然，如果你用你自己的创造性思维，甚至是快速的研究来提高讲课的效率，讲课就会发挥最好的作用。这就是为什么暂停一分钟或一天可能是学习效率的关键。与一些精神科医生的说法相反，创造性休息和走神与[多动症](https://supermemo.guru/wiki/ADHD)无关。只要它们是毫不相关的，它们就是高效学习的标志。 I use two methods for consuming lectures incrementally. My first method is to listen and exercise. Exercise improves focus. Good focus reduces the need for a pause, however, it also reduces the creative aspect of learning. For subjects of highest priority, I use [incremental video](https://supermemo.guru/wiki/Incremental_video) where I can pause and resume multiple times. I can even keep the most important lecture extracts for future review. However, even incremental video isn't the best approach to learning. It cannot compete in speed and volume with [incremental reading](https://supermemo.guru/wiki/Incremental_reading). Sometimes it makes better sense to employ incremental reading and process the lecture transcript than to listen to the lecture itself. This is particularly visible in fact-rich lecturing. 我使用两种方法来渐进地学习讲课。我的第一个方法是倾听和锻炼。锻炼可以提高注意力。良好的专注减少了暂停的需要，然而，它也减少了学习的创造性方面。对于优先级最高的主题，我使用[渐进视频](https://supermemo.guru/wiki/Incremental_video)，我可以在其中暂停和继续多次。我甚至可以把最重要的讲课节选留待将来复习。然而，即使是渐进视频也不是最好的学习方法。它不能在速度和数量上与[渐进阅读](https://supermemo.guru/wiki/Incremental_reading)相竞争。有时，使用渐进阅读处理讲课记录比听讲课本身更有意义。这一点在充满陈述性知识的讲课中尤为明显。 I choose my video materials mostly on the basis of speakers who I just love to listen to. In the context of this article, I know you would love Ken Robinson lectures! Go and see: [Robinson: Schools kill creativity](https://supermemo.guru/wiki/Robinson:_Schools_kill_creativity)! 我主要根据我喜欢听的演讲者来选择我的视频材料。在这篇文章的背景下，我知道你会喜欢 Ken Robinson 的演讲！去看看：[Ken Robinson：学校扼杀创造力](https://supermemo.guru/wiki/Robinson:_Schools_kill_creativity)！ ### 6.16 Learning rewards ### 学习奖励 The pleasure of learning might be one of the most satisfying possible pleasures. As opposed to eating or having sex, the pleasure of learning does not terminate with the act. The pleasure of learning is sustainable and wanes slowly only with the overload of networks involved in learning. It can be reset back to the baseline with sleep. The pleasure of learning has been shown to involve the same mechanisms as the [pleasure of heroin or cocaine](https://supermemo.guru/wiki/Biederman_model). Unlike feeding or sex, pleasurable learning can fill most of the waking time. In that sense, the pleasures of learning, creativity, problem solving, and productivity might be great tools in stoic hedonic therapy. Whereas the need for food is easily satisfied in a healthy individual, the need for learning may never end. The [learn drive](https://supermemo.guru/wiki/Learn_drive) depends on the status of current knowledge and this status can be manipulated with learning itself. 学习的乐趣可能是最令人满意的乐趣之一。与吃饭或性不同的是，学习的乐趣不会随着行为结束而终止。学习的乐趣是可持续的，只有在参与学习的网络过载的情况下，学习的乐趣才会慢慢减少。可以通过睡眠将其重置回基线。研究表明，学习的快乐与[海洛因或可卡因的快乐](https://supermemo.guru/wiki/Biederman_model)具有相同的机制。与吃或性不同的是，快乐的学习可以占据大部分醒着的时间。在这个意义上来说，学习、创造力、问题解决和生产力的乐趣可能是斯多葛派的享乐疗法的主要工具。虽然一个健康的人很容易满足对食物的需求，但是对学习的需求可能永远不会停止。[学习内驱力](https://supermemo.guru/wiki/Learn_drive)取决于当前知识的状态，而这种状态可以被学习本身所操纵。 > **All people with mood swings should consider learning as therapy.** > > **所有情绪波动的人都应该把学习当作治疗。** #### 6.16.1 Learn drive reward #### 学习内驱力奖励 I have mentioned a couple of examples of how the [learn drive](https://supermemo.guru/wiki/Learn_drive) leads to a reward signal in the brain. We know that low probability information can be rewarding. So can a generalization that contributes new knowledge. A snippet of information that leads to a great goal of understanding is highly valued. A [missing piece in a jigsaw puzzle](https://supermemo.guru/wiki/Jigsaw_puzzle_metaphor) carries a great reward. One obscure word, once decoded, can make a whole long text switch from a tangle of sentences into a clear line of reasoning. 我已经提到了几个例子来说明[学习内驱力](https://supermemo.guru/wiki/Learn_drive)是如何在大脑中产生奖励信号的。我们知道低概率的信息是有奖励的。提供新知识的泛化也是如此。一个导致理解突破的信息片段是很有价值的。[拼图中缺失的一块拼图](https://supermemo.guru/wiki/Jigsaw_puzzle_metaphor)带来了巨大的奖励。一个生僻的词，一旦被解码，就能使一整篇长的文本从杂乱无章的句子转换成清晰的推理。 Confirming a model via a generalization or laying foundations for a new better model both feel great. In addition, all model confirmations associated with strong emotions can lead to euphoria: "_My team is the best in the world!_", or "_Yes! My newborn is healthy indeed!_", or "_Yeah! I knew that hard work will earn me that promotion!_". However, when discussing the [learn drive](https://supermemo.guru/wiki/Learn_drive), I would like to filter out that extra emotional layer that may obscure the picture. We need to remember that learning is pleasurable independent of whether it brings rewards from employing the knowledge. 通过泛化来确认一个模型，或者为一个更好的新模型奠定基础，这两种方法都让人感觉很棒。此外，所有与强烈情感相关的模型验证肯定都会让人欣喜若狂：「_我的团队是世界上最好的！_」或「_是的！我的新生儿真的很健康！_」，或「_耶！我就知道努力工作会使我升职的！_」。然而，当我们讨论[学习内驱力](https://supermemo.guru/wiki/Learn_drive)时，我想过滤掉额外的可能会模糊图景的情绪层。我们需要记住，学习是愉快的，并与它是否能从运用知识中得到奖励无关。 The _Aha!_, _Wow!_ or _Eureka!_ of discovery is the purest and ultimate prize in learning. It does not need to entail further reward in accolades or praise from others. Here, the knowledge is its own reward. 发现时的_啊哈！_、_哇！_、或者_尤里卡！_是学习中最纯粹、最终极的奖赏。它不需要从别人那里得到更多的赞扬或奖励。在这里，知识本身就是奖励。 The common denominator of this reward is the encoding of new highly-valued information in memory. 这种奖励的共同点是对记忆中新的高价值信息进行编码。 > **The learn drive reward comes from high-value knowledge ready for long-term storage.** > > **学习内驱力的奖励来自准备长期储存的高价值知识。** In our quest to understand reality, while the total amount of information stored in the brain increases, the entropy of stored knowledge drops. **With learning and modeling, it takes less and less effort to understand the complexity of the world.** 在我们寻求理解现实的过程中，随着大脑中储存的信息总量增加，储存的知识的熵就会下降。**随着不断学习和建模，理解世界的复杂性所需的努力越来越少。** #### 6.16.2 Evolution of the learn drive #### 学习内驱力的进化 Scientists say that smart animals play more. I say that it is even more interesting to note that species that play more are smarter. I hypothesize that the **learn drive may have been the trigger factor in the explosion of the human brain size**. It is not that birds or mammals faced a change in environment that required more thinking. It is not that humans suddenly faced extinction had they not blown up the size of their cortex. It may have been the emergence of the [learn drive](https://supermemo.guru/wiki/Learn_drive) that suddenly allowed better usage of the expensive increase in the number of brain cells. Before there was the [learn drive](https://supermemo.guru/wiki/Learn_drive), adding brain size might leave an animal with an extra head weight to carry and an extra set of cells to feed. Without the [learn drive](https://supermemo.guru/wiki/Learn_drive), the extra brain space might remain unused and likely undergo wasteful atrophy. If schooling attempts to override the [learn drive](https://supermemo.guru/wiki/Learn_drive), it will contribute to the disuse of that evolutionary advantage. It will contribute to society that is less smart and less creative. 科学家说聪明的动物玩得更多。我说，更有趣的是，那些玩得更多的物种更聪明。我猜想，**人类大脑体积变大的触发因素可能是学习内驱力**，而不是鸟类或哺乳动物面临的环境变化需要更多的思考。这并不是说，如果人类大脑皮层没有变大，人类就会突然面临灭绝。这可能是因为[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的出现，突然允许更好地使用数量增多的代价高昂的脑细胞。在[学习内驱力](https://supermemo.guru/wiki/Learn_drive)出现之前，增加大脑体积可能会给动物留下多余的头部重量和一组多余的细胞需要供养。如果没有[学习内驱力](https://supermemo.guru/wiki/Learn_drive)，多余的大脑空间可能会被闲置，这很可能会导致浪费性萎缩。如果学校教育试图凌驾于[学习内驱力](https://supermemo.guru/wiki/Learn_drive)之上，它将有助于这种进化优势被废弃。学校教育将助长更不聪明、更没有创造力的社会。 If we plot the brain size over the timeline of human evolution, we can see a powerful upswing around 2 million years ago. Paleoanthropologists tend to attribute that swing to better brain nutrients in the diet, [cooking](https://en.wikipedia.org/wiki/Catching_Fire:_How_Cooking_Made_Us_Human), and the like. 如果我们在人类进化的时间线上画出大脑的大小，我们可以看到大约两百万年前的一个巨大的增加。古人类学家倾向于将这种转变归因于饮食、[烹饪](https://en.wikipedia.org/wiki/Catching_Fire:_How_Cooking_Made_Us_Human)等方面为大脑提供更好的营养。 If the hypothesis on the emergence of the [learn drive](https://supermemo.guru/wiki/Learn_drive) is correct, _Homo habilis_ would be a candidate for the starting point of the breakthrough. This could point to the transition from a simple procedural play drive of birds and mammals towards a more sophisticated declarative learn drive that ultimately leads us to building abstract models of reality, which underlie human intelligence. _Homo habilis_ has also been hypothesized to lead to the emergence of [childhood dominated by brain growth](https://supermemo.guru/wiki/Homo_habilis:_the_emergence_of_childhood) \(from weaning to an average of 7 years old\). 如果关于学习内驱力出现的猜想是正确的，那么_能人_就是这一突破的起点。这可能意味着从鸟类和哺乳动物简单的程序性游戏内驱力向更复杂的陈述性学习内驱力的转变，这种[学习内驱力](https://supermemo.guru/wiki/Learn_drive)最终导致我们建立现实的抽象模型，而这正是人类智能的基础。据推测，从_能人_开始，人类有了[以大脑发育为主的童年](https://supermemo.guru/wiki/Homo_habilis:_the_emergence_of_childhood)（从断奶到 7 岁左右）。 The late arrival of the [learn drive](https://supermemo.guru/wiki/Learn_drive) in evolution would suggest that it is not a simple property emergent in neural networks \(see: [Biederman model](https://supermemo.guru/wiki/Biederman_model)\). Otherwise it might easily show up in fish or earlier. The [learn drive](https://supermemo.guru/wiki/Learn_drive) requires a dedicated set of neural structures that are able to send a reward signal at the point of detecting an incremental contribution to a coherent structure of declarative knowledge. This signal and the underlying structure might differ in procedural learning and declarative learning. It might also differ for different classes of sensory input. [学习内驱力](https://supermemo.guru/wiki/Learn_drive)在进化中的姗姗来迟表明，它不是神经网络中出现的简单功能（参见：[Biederman](https://supermemo.guru/wiki/Biederman_model) 模型）。否则，它可能很容易出现在鱼类或更早的时候。[学习内驱力](https://supermemo.guru/wiki/Learn_drive)需要一组专用的神经结构，这些神经结构能够在检测到对陈述性知识的连贯结构的增量贡献时，发送奖励信号。这种信号和潜在结构可能在程序性学习和陈述性学习中有所不同。对于不同类别的感官输入，它也可能有所不同。 #### 6.16.3 Procedural learning reward #### 程序性学习奖励 I hypothesized about [circuits that might run procedural learning](http://www.super-memory.com/english/ol/ol_files/refinement_circuitry_in_stochastic_learning.jpg) back in the 1980s. In my [Master's Thesis](http://super-memory.com/english/ol.htm), out of ignorance, I used my own term ["_stochastic learning_"](http://www.super-memory.com/english/ol/ol_memory.htm). I had no idea that two decades earlier, back in 1969, David Marr proposed a theoretical model of the cerebellar cortex that fit my own thinking. In the new millennium, [there is a lot of data to confirm the model](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2805361/). 在 20 世纪 80 年代，我猜想了一种[可能进行程序性学习的神经网络](http://www.super-memory.com/english/ol/ol_files/refinement_circuitry_in_stochastic_learning.jpg)。在我的[硕士论文](http://super-memory.com/english/ol.htm)中，出于无知，我使用了我自己的术语「[_随机学习_](http://www.super-memory.com/english/ol/ol_memory.htm)」。我不知道的是，早在二十年前，也就是 1969 年，David Marr 提出了一个符合我自己想法的小脑皮理论模型。在新世纪里，[有大量的数据证实了这个模型](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2805361/)。 The idea of a procedural learning circuit is very simple. Imagine you ride a bicycle. You apply your conscious mind to learn individual moves needed to mount the bike and to then continue pedalling. However, once you are on the way, the procedural learning system makes sure you can execute all moves automatically with minimum neural effort without participation of conscious supervision or minimal supervision over a set of [command neurons](https://en.wikipedia.org/wiki/Command_neuron). Procedural learning will determine your [motor program](https://en.wikipedia.org/wiki/Motor_program). This procedural learning system will make minor random adjustments to the sequence of signals sent to the motor system \(hence the name "_stochastic learning_"\). You can view those random changes as procedural creativity. Each time your bike loses balance, a penalty signal will be sent from the error-detecting network to cancel proposed corrections. That penalty signal will play the role of a teaching signal for the motor program. 程序性学习神经网络的思想是非常简单的。想象一下你骑自行车。你运用你的意识去学习骑自行车所需的每个动作，然后继续骑车。然而，一旦你在路上，程序性学习系统确保你可以用最少的神经参与自动执行所有的动作，而不需要有意识的监督或只需要最小限度的监督一组[指令神经元](https://en.wikipedia.org/wiki/Command_neuron)。程序性学习将决定你的[运动程序](https://en.wikipedia.org/wiki/Motor_program)。这个程序性学习系统将对发送到运动系统的信号序列进行微小的随机调整（因此被称为「_随机学习_」）。您可以将这些随机调整视为程序性创造力。每当你的自行车失去平衡，一个惩罚信号将从错误检测网络发送，去取消发送的修正。这个惩罚信号将为运动程序起一个指导信号的作用。 During sleep, memories will be reorganized to eliminate the need for conscious input, simplified, optimized, and garbage signals that have a low contribution to the skill will be rejected. With each kilometer cycled, the sequence of signals will be perfected by trial and error. With each bout of sleep the wrinkles will get smoother. Riding a bike will become a pleasure. That pleasure seems to peek in the transition from clumsy conscious rider to a natural. 在睡眠期间，记忆将被重新组织、简化、优化，对有意识的输入的需要会被消除，而对技能贡献不大的垃圾信号将被驳回。随着每一公里的骑行，信号序列将通过反复试验而得到完善。而每次睡眠，褶皱就会变得更光滑。骑自行车将成为一种乐趣。从笨拙的、有意识的骑手到本能的骑手的转变中，这种乐趣可见一斑。 In a similar fashion, with each sentence typed on the computer, you will strike fewer typos. Do you know where "\)" is on the keyboard? How about "}"? The more fluent you are in typing, the more likely you are to forget this detail. When the conscious control of motor sequences is taken away, declarative knowledge of the position of "\)" on the keyboard may be thrown away as "garbage". It is no longer needed. 以类似的方式，在计算机上键入每个句子时，你的打字错误会越来越少。你知道键盘上的「\)」在哪里吗？「}」呢？你打字越流畅，你就越有可能忘记这个细节。当有意识地控制动作顺序消失后时，键盘上「\)」位置的陈述性知识可能作为「垃圾」被丢弃。它不再被需要了。 #### 6.16.4 Declarative learning reward #### 陈述性学习奖励 Things are a bit more complex in explaining declarative learn drive. There is a definite reward to declarative learning. Some things are just interesting, and finding out the truth is pleasing. At the neural level, the brain will scan inputs and neural activations to look for areas of high learntropy with maximum delivery of new knowledge matching the current status of memory. Any meaningful message of low probability will be deemed more attractive. A bright fractal pattern will be deemed beautiful. A gray randomness of colors will be deemed boring. The same will occur in the case of a more complex visual message. A vibrant forest is beautiful. The same forest may seem unattractive in winter, in draught, or under the impact of environmental pollution. [Steven Pinker](https://en.wikipedia.org/wiki/Steven_Pinker) remarked that we are attracted to images that ooze vitality. I disagree. The attraction is much wider. We may be equally well attracted to a deathly volcano or a frozen landscape of Antarctica. We love environments, signals, messages, or brain activations that can express complex information using simple models. The picture of a beautiful beach can be represented by a couple of simple shapes and textures. 在解释陈述性学习内驱力时，情况要复杂一些。陈述性学习有明确的回报。有些事情是有趣的，发现真相是令人愉快的。在神经层面，大脑将扫描输入并产生神经活跃，以寻找学习熵高的领域，这些领域最大限度地提供与当前记忆状态相匹配的新知识。任何有意义的低概率信息都会被认为更具吸引力。明亮的分形图案将被认为是美丽的。灰色的随机颜色会被认为是无聊的。同样的情况也会发生在更复杂的视觉信息上。生机勃勃的森林是美丽的。但同样的森林在冬天、在干旱中，或者在环境污染的影响下，可能看起来并不吸引人。[StevenPinker](https://en.wikipedia.org/wiki/Steven_Pinker) 说，我们被充满活力的图像所吸引。但我不同意。因为吸引力要宽泛得多。我们可能同样被一座死火山或南极洲的冰冻景观所吸引。我们喜欢使用简单模型表达复杂信息的环境、信号、信息或大脑活动。美丽海滩的图片可以用一些简单的形状和纹理来表示。 Entropy of information is related to compressability of data. Signal processing begins on the input. The retina performs a 100-fold compression of the visual input signal. The hippocampus and the visual cortex receive simple representations of shapes and relationships. Those may end up changing the status of a single synapse in cortical long-term memory storage. 信息熵与数据的可压缩性有关。信号处理从输入开始。视网膜对视觉输入信号进行 100 倍的压缩。海马体和视皮层接受形状和关系的简单表示。这些可能最终改变皮质长期记忆储存中单个突触的状态。 The whole learn drive is based on seeking effective ways of representing knowledge in neural networks. [Learn drive](https://supermemo.guru/wiki/Learn_drive), memory optimization in sleep, and forgetting are essential to maximize compressibility, abstractness, usability, and performance. This is how the brain makes sure that we can see a complex world using simple representations. That's the core of human intelligence. If artificial intelligence researchers could equip robots with a human-like learn drive, given sufficient memory, their learning capacity might be inexhaustible. 整个[学习内驱力](https://supermemo.guru/wiki/Learn_drive)都是建立在寻找有效的神经网络知识表示方法的基础上的。学习内驱力、睡眠中的记忆优化和遗忘对于最大限度地提高可压缩性、抽象性、可用性和性能至关重要。这就是大脑如何通过简单的表示来确保我们能够看到一个复杂的世界。这是人类智力的核心。如果人工智能研究人员能够为机器人配备类似人类的学习内驱力，只要有足够的记忆空间，他们的学习能力可能是取之不尽用之不竭的。 #### 6.16.5 Reward centers in learning #### 学习中的奖励中枢 In 2014, researchers reported that the [activity in the nucleus accumbens was increased in the state of "high curiosity"](https://supermemo.guru/wiki/Curiosity_improves_learning). They have also demonstrated what we have always known: this state improved memory performance. In addition, that improved performance spilled onto incidental learning, i.e. learning that would not spark curiosity on its own. This research was widely reported in media with a wrong interpretation: "_curiosity primes the brain for better memory_". For example, Scientific American headlined "_Neuroimaging reveals how the brain’s reward and memory pathways prime inquiring minds for knowledge_". The paper itself suggested the need for _"stimulating curiosity"_. 在 2014 年，研究人员报告称，[在「高度好奇」的状态下，伏隔核的活动增加](https://supermemo.guru/wiki/Curiosity_improves_learning)。他们还证明了我们所熟知的：这种状态提高了记忆表现。此外，这种提高的表现也体现在附带学习上，即不依靠本身激发好奇心的学习。这项研究在媒体上被广泛报道，但却以一种错误的解释：「_好奇心会激发大脑，以获得更好的记忆_」。例如，《科学美国人》的标题为「_神经成像揭示了大脑的奖赏和记忆路径是如何激发大脑对知识的探究_」。这篇论文本身就提出了「_激发好奇心_」的必要性。 As reward centers can be involved in the anticipation of pleasure, we should rather see the results of the research as an indicator that the [learn drive](https://supermemo.guru/wiki/Learn_drive) is associated with pleasure. It is the learn drive that causes learning. It is learning that is pleasurable. The headline should be "_Neuroimaging confirms that efficient learning is pleasurable_". In other words, the sequence is not "drive -> pleasure -> learning", but "drive -> learning -> pleasure". 由于奖励中枢可能涉及快乐的预期，我们更应该把研究的结果看作是[学习内驱力](https://supermemo.guru/wiki/Learn_drive)与快乐相关的一个迹象。正是学习内驱力引发了学习。学习才是令人愉快的。标题应该是「神经成像证实有效的学习是令人愉快的」。换句话说，顺序不是「内驱力->快乐->学习」，而是「内驱力->学习->快乐」。 Instead of speaking of the need to "stimulate curiosity", which should rather speak of the need to "develop the [learn drive](https://supermemo.guru/wiki/Learn_drive)". The key difference is in perceiving stimulation as quick-fix approach that might be used in a classroom as opposed to a long-term process that takes months and years. An advertising campaing may use cheap tricks to stimulate our curiosity, while a lifelong passion is a formula for insatiable and unwaning [learn drive](https://supermemo.guru/wiki/Learn_drive), which is a perfect warranty for unceasing learning. 与其说需要「激发好奇心」，不如说需要「培养[学习内驱力](https://supermemo.guru/wiki/Learn_drive)」。关键的区别在于，将刺激视为一种可能在课堂上使用的快速解决方法，而不是需要数月甚至数年的长期过程。一次广告宣传活动可能会用一些廉价的手段来激发我们的好奇心，而终身的激情则是永不满足、永不衰退的学习内驱力的配方，这是不断学习的完美保证。 It is true that the state of curiosity will improve attention and this will improve overall learning, however, this should not ever be used as a classroom strategy. Gamification of learning makes sense only if rewards come from target learning, not from learning that surrounds the target. Many learning programs for children use bright colors, unusual sounds or smiling faces to attract attention to induce learning. However, once habituation sets it, this form or artificial gamification stops being effective. Moreover, incidental knowledge does not last. Any effort to employ curiosity to spark incidental learning is non-specific and inefficient. Equally well we might hope that pharmacological intervention, e.g. with Ritalin, could improve learning. Instead, learning must be its own reward. 的确，好奇心的状态会提高注意力，这会提高整体的学习，但是，这永远不应该作为一种课堂策略来使用。只有当奖励来自学习对象，而不是来自学习对象的周边，学习的游戏化才有意义。许多儿童学习程序使用鲜艳的颜色，不寻常的声音或微笑的脸来吸引注意，以诱导学习。然而，一旦习惯性了它，这种形式或人工游戏化就不再有效。而且，附带的知识不会持续很久。任何利用好奇心来激发附带学习的努力都是不具体的和低效的。同样，我们可能希望药物干预，如利他林，可以改善学习。然而于此相反，学习必须基于它本身的奖励。 The nucleus accumbens and the ventral tegmental area are involved in pleasure, in anticipation of pleasure, and in signal evaluation. The signals from the [knowledge valuation network](https://supermemo.guru/wiki/Knowledge_valuation_network) converge into those areas in both their [motivational and affective valence](https://supermemo.guru/wiki/Brain_centers_involved_in_valuation_of_anticipated_outcomes:_nucleus_accumbens_and_VTA). Dopamine is involved in the anticipation of pleasure. As dopamine is involved in attention, anticipation of pleasure alone would lead to improved learning due to a better focus on the source of information that is expected to deliver the pleasure. 伏隔核和腹侧被盖区参与了快乐、对快乐的预测和信号评估。来自[知识评估网络](https://supermemo.guru/wiki/Knowledge_valuation_network)的信号在[动机和情感](https://supermemo.guru/wiki/Brain_centers_involved_in_valuation_of_anticipated_outcomes:_nucleus_accumbens_and_VTA)两个方面汇合到这些区域。多巴胺与快乐的预期有关。由于多巴胺参与了注意力，仅仅是对快乐的预期就会导致学习的改善，这是因为更加关注预期传递快乐的信息来源。 If you are unconvinced, think of how much you hate your news channel when they do their tricks to pique your interest, and then say "_find out after the break_". You can get even more livid when they ruin it all with "_Breaking News!_". Anticipation can lead to frustration too. Only actual learning provides the reward. Only actual learning reward makes sense from the point of view of evolution. We do not want to reward an animal for the mere sight of food. 如果你不相信，想想你有多讨厌你的新闻频道，当他们耍诡计激起你的兴趣时，然后说“休息后再找出答案”。当他们用“突发新闻”毁掉这一切时，你会变得更加愤怒。期待也会导致挫折。只有实际的学习才能提供奖励。从进化的角度来看，只有实际的学习奖励才有意义。我们不想将仅仅给动物看食物作为奖励。 The buzz in the nucleus accumbens can be a direct expression of pleasure or might also indicate the state of pleasure seeking. In the end, the actual interpretation does not matter for the ultimate conclusion: **boredom and displeasure are the enemies of learning**. 伏隔核中的嗡嗡声可以是快乐的直接表达，也可能是寻找快乐的状态。最后，对于最终的结论，实际的解释并不重要：**无聊和不快是学习的敌人**。 For efficient learning in which new knowledge complements current knowledge, we need to follow the [learn drive](https://supermemo.guru/wiki/Learn_drive). In simple terms, this means that the pleasure of learning is desirable in education. We should never learn in the state of displeasure \(cf. [Desirable difficulty](https://supermemo.guru/wiki/I_would_never_send_my_kids_to_school#Desirable_difficulty)\). Painful learning comes from the brain letting the student know that, in information theoretic sense, the new knowledge does not fit! It will be rejected. Pleasure is a good guide! 为了进行用新知识补充现有知识的有效学习，我们需要遵循[学习内驱力](https://supermemo.guru/wiki/Learn_drive)。简单地说，这意味着学习的快乐在教育中是可取的。我们决不应该在不愉快的状态下学习（参见：[值得的难度](https://supermemo.guru/wiki/I_would_never_send_my_kids_to_school#Desirable_difficulty)）。大脑让学生知道，在信息论的意义上，新的知识并不合适，痛苦的学习来自于此！它会被拒绝。而快乐是很好的向导！ From the above neural reasoning we derive the obvious, **the best warranty of efficient learning is to let students learn on their own and follow their own passions.** 通过上面的神经学推理，我们得出了一个显而易见的结论，**有效学习的最好保证就是让学生自己学习，跟随自己的激情。** ### 6.17 Biederman model ### Biederman 模型 #### 6.17.1 Pleasure of reading about the pleasure of reading #### 阅读关于阅读乐趣的乐趣 In 2006, Irving Biederman and Edward A. Vessel, published a paper that gave me unforgettable pleasure to read. The article itself explained the pleasure of reading to me. In a paper titled "_Perceptual pleasure and the brain_", Biederman hypothesized that a gradient of opioid receptors in brain structures responsible for visual perception might contribute to the pleasure of viewing nice scenes such as beautiful landscapes. Biederman's idea seemed to explain to me what I have known for ages: **learning is pleasurable**. I always liked to learn, however, I never truly understood what underlies my liking in terms of brain science. Biederman's explanation was a perfect fit and it was powerfully pleasurable. It explained something that bothered my mind for a longer while. At the moment of reading, I was very self-analytical. While reading about the pleasure of reading I was trying to "feel" how the enlightenment of reading provides the pleasure. The pleasure of reading about the pleasure of reading became unforgettable. 2006年，Irving Biederman 和 Edward A. Vile 发表了一篇论文，给我带来了令人难忘的阅读乐趣。这篇文章本身向我解释了阅读的乐趣。在一篇题为“感知愉悦与大脑”的论文中，Biederman 假设，负责视觉感知的大脑结构中阿片受体的分布梯度可能有助于观看美丽风景等美好场景的乐趣。Biederman 的想法似乎向我解释了我多年以来所知道的：**学习是令人愉快的**。我总是喜欢学习，然而，我从来没有真正理解我的喜欢在脑科学方面的基础。Biederman 的解释恰如其分，非常令人愉快。它解释了一些困扰我一段时间的事情。在阅读的那一刻，我非常善于自我分析。在阅读的乐趣时，我试着去“感受”阅读的启示是如何提供快乐的。阅读关于阅读趣的乐趣变得令人难以忘怀。 What Biederman and Vessel proposed is monumental. Let me therefore name their thinking for simplicity: the **Biederman model** \(name choice by seniority\). In visual perception, successive layers of neurons are responsible for more abstract representations of the visual scene. Metaphorically speaking, it starts from pixels and colors, then it moves on to edges, textures and surfaces, then to objects, then to faces, places, and collections, and then to meaningful episodic scenes that, at the end of the chain, may activate a representation of a "beautiful mountain", and be remembered as such with only a few details perpetuated beyond the first impression in working memory. Millions of pixels of a photograph will turn into a meaningful scene that can be verbalized in just a few sentences and remembered as such for years, at a very little neural cost. Biederman 和 Vessel 所提议的是具有里程碑意义的。因此，为了简单起见，让我给他们的想法起个名字：**Biederman 模型**（根据资历选择名字）。在视觉感知中，连续的神经元层负责对视觉场景进行更抽象的表示。打个比方，它从像素和颜色开始，然后移动到边缘、纹理和曲面，然后移动到对象，然后是面、位置和集合，然后是有意义的场景，在链的末端，这些场景可能会激活一座“美丽的山”的表示，就这样被记住，只有几个细节在工作记忆中的第一印象之外永久化。一张数以百万计的像素的照片将变成一个有意义的场景，只需几句话就能用语言表达出来，并以这种方式被记忆多年，只需很少的神经代价。 Biederman model capitalizes on an earlier discovery \([Michael E. Lewis et al., 1981](http://supermemo.guru/wiki/Opioid_receptors_form_a_gradient_along_a_processing_hierarchy)\) that there is a gradient of mu-opioid receptors along the visual perception pathway. The more meaning the neuron carries, the more opioid receptors it is likely to have. We know that opiates are rewarding and addictive. Biederman model is based on the hypothesis that this [gradient of opioid receptors is the source of perceptive pleasure](https://supermemo.guru/wiki/Opioid_receptors_are_involved_in_the_pleasure_of_learning). Biederman 模型利用了早些时候的一项发现（[MichaelE.Lewis 等人，1981年](http://supermemo.guru/wiki/Opioid_receptors_form_a_gradient_along_a_processing_hierarchy)），即沿着视觉感知路径存在µ-阿片受体的分布梯度。神经元携带的意义越多，它可能具有的阿片受体就越多。我们知道，阿片类药物是有益的和上瘾的。Biederman 模型基于这样的假设：阿片受体的分布梯度是感知快感的来源。 There is a similar hierarchical system for processing speech and music. A temporal cortex involves processing sounds from pitch to melody. However, processing rhythm involves other areas of the brain too. Chances are, all those perceptive networks work along similar principles. This is the subject of study of [neuroesthetics](https://en.wikipedia.org/wiki/Neuroesthetics). 在处理语音和音乐方面也有类似的层级系统。颞叶皮质涉及处理从音调到旋律的声音。然而，处理节奏也涉及到大脑的其他区域。很有可能，所有这些感知网络都遵循类似的原则。这是[神经美学](https://en.wikipedia.org/wiki/Neuroesthetics)的研究课题。 #### 6.17.2 Opioid vs. dopamine pleasure #### 阿片类药物与多巴胺的快乐 There is a slight problem with the Biederman model though. The pleasure of learning can be analyzed consciously. The pleasure of reading about Biederman model, in my own case, could be decomposed and tracked down to individual components of the model. This fact implies that the pleasure is integrated with conscious experience. Consciousness is a notoriously hard nut to crack for neuroscience. Most of what we know about consciousness is either speculative or based on hard and expensive experiments in which electrodes implanted in the brain can be used to elicits effects that can later, or concurrently, be reported by the affected individual. The evidence seems to be converging on the integrative model of consciousness in which an activation of several structures in the brain gets integrated and perceived as conscious self. In that line of thinking, activating a Halle Berry neuron somewhere in the cortex is not enough to bring Halle to one's consciousness. Millions of concept neurons can get activated at the same time and a thinking mind can only operate on a few pieces of the model of the perceived reality. To bring Halle to one's mind, the activation must get integrated with other components of conscious perception, including the reward of the perception. Biederman 模型有一个小问题。学习的乐趣可以有意识地分析。在我自己的例子中，阅读 Biederman 模型的乐趣可以分解并追踪到模型的各个组件。这一事实意味着快乐与有意识的体验是结合在一起的。众所周知，意识是神经科学难以破解的难题。我们所知道的关于意识的大部分知识要么是推测的，要么是基于硬而昂贵的实验，在这些实验中，植入大脑的电极可以被用来引起效应，这些效应可以稍后或同时由受影响的个体报告。证据似乎集中在意识的综合模式上，在这种模式中，大脑中几个结构的激活被整合起来，并被视为有意识的自我。按照这一思路，激活大脑皮层某个部位的 Halle Berry 神经元，并不足以将 Halle 带到一个人的意识中。数以百万计的概念神经元可以在同一时间被激活，一个思维只能在感知现实的模型中的一小部分上运作。要将 Halle 带到一个人的头脑中，激活必须与意识感知的其他组成部分结合起来，包括感知奖励。 For those reasons, opioid receptors in cortical neurons will not do much for the ultimate reward of learning. An opiod antagonist, [naloxone, can take away some of the pleasure of music in some people](https://supermemo.guru/wiki/Thrill_of_music_may_be_attenuated_with_opioid_antagonists). However, the opioid pleasure of learning should rather produce a mild bliss of first-time micro-dose heroin or morphine use. In that sense, release of endomorphins and activation of opioid receptors can make a contribution to the pleasure of learning. Nevertheless, this pleasure isn't specific enough to give one a jolt of "wow!", "aha!" or "eureka!" \(Biederman calls it "click of comprehension"\). For that ultimate learning reward, there must be an integrative reward experience coming from the [pleasure centers in the brain](https://supermemo.guru/wiki/Neural_circuits_involved_in_liking_and_wanting). 由于这些原因，大脑皮层神经元中的阿片受体不会对学习的最终回报起到很大的作用。阿片类药物的拮抗剂，[纳洛酮\(Naloxone\)，可以夺走某些人对音乐的乐趣](https://supermemo.guru/wiki/Thrill_of_music_may_be_attenuated_with_opioid_antagonists)。然而，阿片类药物的学习乐趣应该产生首次使用微量海洛因或吗啡所产生轻微的幸福。从这个意义上说，内吗啡肽的释放和阿片受体的激活可以促进学习的快感。尽管如此，这种快乐还不够具体到让人惊呼「哇！」，「啊哈！」或者「尤里卡！」（Biederman 称其为「理解的点击」）。对于最终的学习奖励，必须有来自[大脑中快乐中枢](https://supermemo.guru/wiki/Neural_circuits_involved_in_liking_and_wanting)的综合奖励体验。 #### 6.17.3 Pleasure of association #### 联想的快乐 That ultimate pleasure jolt of discovery will come from a meaningful association. It can be explained using the pleasure of understanding the Biederman model itself. When thinking about the model, we activate two important concepts in our minds: \(1\) a gradient of meaning \(derived from understanding neural structures involved in visual perception\), and \(2\) a gradient of pleasure \(derived from the observation on the content of opioid receptors in visual pathways\). Once these two concept come up in mind, there is a glue of analogy: the concept of _"gradient"_. That glue helps bring up the association that gives a jolt of pleasant enlightenment: **MEANING = PLEASURE**! That's exactly what I experienced when reading Biederman's paper. For that jolt to happen, it is not enough that there are more opiate receptors associated with the concept of the gradient of pleasure than with gradient's mathematical underpinnings or its association with the word "gradient". It is not enough that there is more opiate associated with the novel concept of "gradient of meaning" than with the often used term "meaning". The jolt happens when those two highly priced concepts collide: meaning + pleasure. 发现的最终乐趣震撼将来自于一种有意义的联想。这可以用理解 Biederman 模型本身的乐趣来解释。当我们思考这个模型时，我们激活了我们大脑中的两个重要概念：（1）意义的梯度（来自对视觉感知所涉及的神经结构的理解），（2）愉悦的梯度（来自对视觉通路中阿片受体含量的观察）。一旦想到这两个概念，就会有一个类比的粘合剂：「梯度」的概念。这种胶水有助于产生一种令人愉快的启示的联想：**意义=快乐**！这正是我在读 Biederman 的论文时所经历的。要使这种震撼发生，光有更多的阿片受体与快乐梯度的概念相关联，而不是与梯度的数学基础或它与「梯度」一词的关联，是不够的。与通常使用的「意义」一词相比，与「意义梯度」这一新颖概念相关的阿片受体还不够多。当这两个高价值的概念发生碰撞时，就会发生震撼：意义 + 快乐。 Biederman noticed that the gradient of receptors proceeds far into the associative areas, incl. the parahippocampal cortex. We may remember that further downstream, in the hippocampus we have found the [Halle Berry neuron](https://supermemo.guru/wiki/Pleasure_of_learning#Detecting_surprisal). To illustrate the difference between the opioid pleasure and the associative pleasure, let us imagine meeting Halle on a beautiful beach. While walking on a beach, we may experience a delicate heroin-like breeze of bliss, which comes from the realization that our environment is perceptively beautiful: _"the beach I walk on feels great"_. Once Halle shows up on a horizon, visual analysis may provide another breeze of opioid pleasure coming from the signal _"beautiful lady approaching"_. Then the visual processing unit may identify the lady as Halle, which might activate cortical representation of Halle, which could be opioid-rich. However, only the ultimate association of Halle and _"my beach"_ would trigger a major discovery, perhaps an atavistic reproductive dream: _"Halle walks the same sand like me!"_. This is where the reward from the ventral striatum and the nucleus accumbens might come to play in "liking" the situation, and a jolt of [dopamine](https://supermemo.guru/wiki/Opioid_rewards_may_depend_on_dopamine_signals) might trigger a behavioral program of "wanting". The details of that behavioral "wanting" program have been cut out from this text by censorship. Nevertheless, execution of that program would inevitably be halted in highly-developed individuals by executive signals from the prefrontal cortex. In short, an injection of dopamine in the pleasure centers of the brain may give the brain some indecent ideas, while the release of opioid peptides might just result in an associative bliss. Biederman 注意到，受体的梯度很远地延伸到联想区，包括海马旁皮质。我们可能还记得，在更远的下游，我们在海马中发现了 [Halle Berry 神经元](https://supermemo.guru/wiki/Pleasure_of_learning#Detecting_surprisal)。为了说明阿片类药物的快乐和联想的快乐之间的区别，让我们想象一下在一个美丽的海滩上遇见 Halle。当我们走在海滩上时，我们可能会感受到一股如海洛因一样的幸福之风，这是因为我们意识到我们的环境很美：「我走在沙滩上感觉很棒」。一旦 Halle 出现在地平线上，视觉分析可能会提供另一种阿片类药物的快感来自「美丽的女士接近」的信号。然后，视觉处理单元可以确定这位女士是 Halle，这可能激活 Halle 的皮层代表，这可能是阿片类物质丰富的。然而，只有 Halle 和「我的海滩」的最终结合才会引发一个重大发现，也许是一个古老的生殖梦想：「Halle 和我走在同一片沙滩上！」这就是来自腹侧纹状体和伏隔核的奖励可能会在「喜欢」的情况下发挥作用，而[多巴胺](https://supermemo.guru/wiki/Opioid_rewards_may_depend_on_dopamine_signals)的一击可能会触发「想要」的行为程序。该行为「想要」计划的细节已经从这篇文章中被删掉了。然而，在高度发育的个体中，来自前额叶皮质的执行信号将不可避免地停止该程序的执行。简而言之，在大脑的快感中枢注射多巴胺可能会给大脑带来一些不雅的想法，而阿片肽的释放可能只会带来一种联想的幸福。 The pleasure of learning does not need to involve attractive representatives of the opposite sex. Halle showed up in my example only because of the discovery of the [Halle Berry neuron](https://en.wikipedia.org/wiki/Grandmother_cell). For the pleasure of learning, all that is needed is a powerful and highly-valued association of ideas that activates the pleasure centers in the brain. The pleasure happens each time we learn something new, and the jolt is most powerful when we learn something of high value. The pleasure of discovering the Biederman model came from high valuations of the pleasure of learning itself in my [knowledge valuation network](https://supermemo.guru/wiki/Knowledge_valuation_network). 学习的乐趣不需要涉及有吸引力的异性代表。Halle 出现在我的例子中，只是因为发现了 [Halle Berry 神经元](https://en.wikipedia.org/wiki/Grandmother_cell)。为了学习的乐趣，所有需要的是一个强大的和高度重视的想法，激活大脑中的快乐中枢的联想。每次我们学到新的东西，快乐就会发生，而当我们学到高价值的东西时，这种震撼是最强大的。发现 Biederman 模型的乐趣来自于在我的[知识评估网络](https://supermemo.guru/wiki/Knowledge_valuation_network)中对学习本身的乐趣的高度评价。 #### 6.17.4 Impact of memory on the pleasure of learning #### 记忆对学习快乐的影响 I would also add to Biederman's hypotheses on desensitization, i.e. the decline in pleasure with repeated exposure. Biederman suggests that children love repetitive [videogames](https://supermemo.guru/wiki/Videogames) because of the gambling factor. However, gambling is no less potent in adults. I posit that children enjoy repetitive learning more because of [childhood amnesia](https://supermemo.guru/wiki/Childhood_amnesia). Some of the repeat pleasure may come from limited comprehension, but some will simply be explained by accelerated forgetting. Poor comprehension and forgetting are the primary differentiators between the adult and the child brains. 我还想补充 Biederman 关于脱敏的假设，即反复接触会使乐趣下降。Biederman 认为，孩子们因为赌博的因素喜欢重复的[电子游戏](https://supermemo.guru/wiki/Videogames)。然而，赌博在成年人中同样有效。我认为孩子们因为[童年失忆症](https://supermemo.guru/wiki/Childhood_amnesia)更喜欢重复学习。一些重复的快乐可能来自于有限的理解，但有些仅仅是由加速遗忘来解释。理解能力差和遗忘是成人大脑和儿童大脑的主要区别。 We should also notice that a great deal of decline in pleasure of review will come not from competitive learning but from [long term-memory consolidation](https://supermemo.guru/wiki/Two_component_model_of_memory) that might result in signals flowing efficiently in the system. Competitive learning may be important in pattern recognition but in associative learning, it will be high [retrievability](https://supermemo.guru/wiki/Retrievability) that will undermine the pleasure of repeated exposure. 我们还应该注意到，复习乐趣的大幅下降将不是来自竞争性学习，而是来自[长期记忆的巩固](https://supermemo.guru/wiki/Two_component_model_of_memory)，这可能导致信号在系统中高效地传递。竞争性学习在模式识别中可能很重要，但在联想学习中，[高可提取性](https://supermemo.guru/wiki/Retrievability)会破坏重复接触的乐趣。 #### 6.17.5 Stages of learn drive evolution #### 学习内驱力进化的几个阶段 When I hypothesized on the emergence of powerful [learn drive](https://supermemo.guru/wiki/Learn_drive) in humans, I had in mind the direct channel from knowledge to reward centers. It would ultimately be a higher level of [learn drive](https://supermemo.guru/wiki/Learn_drive) than the one implied by the Biederman model. Each time receptors are involved, evolution has a simple and grateful material to work with. Receptor gradient has originally been discovered in a [rhesus cortex](https://supermemo.guru/wiki/Opioid_receptors_form_a_gradient_along_a_processing_hierarchy). Similar mechanisms might be involved in simpler brains or even more primitive nervous systems deprived of central control. I have no idea what an ant thinks or how it feels, but finding a great food source must definitely be a source of some kind of ant pleasure. From this we can conclude that the pleasure of learning might not be much phylogenetically younger than the nervous system itself. However, in the course of evolution, the drive has built up new layers of functionality and efficiency. Playful creativity seems to emerge only with some birds and with mammals. That evolutionary process might have ultimately peaked as human [learn drive](https://supermemo.guru/wiki/Learn_drive). This will naturally, at some point, be implemented in thinking machines. Understanding the power of the [learn drive](https://supermemo.guru/wiki/Learn_drive) will be vital for survival of humanity: both in its need for artificial intelligence and the threat of having AI turn against mankind. 当我假设在人类中出现了强大的[学习内驱力](https://supermemo.guru/wiki/Learn_drive)时，我想到了从知识到奖励中枢的直接渠道。它最终将成为比 Biederman 模型所暗示的更高水平的[学习内驱力](https://supermemo.guru/wiki/Learn_drive)。每一次涉及到受体，进化都有一种简单而感激的材料可供处理。受体梯度最初是在[恒河猴皮层](https://supermemo.guru/wiki/Opioid_receptors_form_a_gradient_along_a_processing_hierarchy)中发现的。类似的机制可能涉及更简单的大脑，甚至更原始的、没有中枢控制的神经系统。我不知道一只蚂蚁在想什么，也不知道它的感觉如何，但是找到一个很好的食物来源肯定是某种蚂蚁快乐的源泉。由此我们可以得出结论，学习的乐趣可能不会比神经系统本身更年轻。然而，在进化的过程中，驱动建立了新的功能层和效率层。有趣的创造力似乎只出现在一些鸟类和哺乳动物身上。比如人类[学习内驱力](https://supermemo.guru/wiki/Learn_drive)，这种进化过程可能已经达到顶峰。这在某种程度上自然会在思维机器中实现。了解[学习内驱力](https://supermemo.guru/wiki/Learn_drive)对于人类的生存至关重要：无论是对人工智能的需求，还是对人工智能转向人类的威胁。 ### 6.18 Desirable difficulty ### 值得的困难 **Desirable difficulty** is a concept that might be an excuse for tolerating the displeasure of learning at school. Here I explain why this excuse is unjust and dangerous. **值得的困难**是一个概念，可能成为一个容忍在学校学习的不悦的借口。我在这里解释为什么这个借口是不公正和危险的。 Robert Bjork might be the best expert on learning theory. If he tells you that difficulties can be desirable in learning, he is right and it does not stand in contradiction to the fact that good learning is always pleasurable. Desirable difficulty is a conglomerate of concepts in which obstacles in learning lead to better learning. Let's tackle those one by one in the light of the pleasure of learning: Robert Bjork 可能是学习理论方面最好的专家。如果他告诉你在学习中困难是值得的，他是对的，而且这与好的学习总是令人愉快的事实并不矛盾。值得的难度是学习中引发更好的学习的障碍的概念组合。让我们从学习的乐趣出发，逐一解决这些问题： * **active recall**: active recall is superior to passive review. Active recall is harder. This is a desirable difficulty. We need active recall in learning because it is the only procedure by which a memory engram can be effectively reconsolidated in [spaced repetition](https://supermemo.guru/wiki/Spaced_repetition). Active recall occurs each time we employ useful knowledge in practice. This use is pleasurable because it leads to productivity, which is a reward independent of learning. Humans simply love to [achieve goals](https://supermemo.guru/wiki/Setting_goals_can_change_your_life). If review is planned artificially, like in [SuperMemo](https://supermemo.guru/wiki/SuperMemo), it does not lead to a productive act and it may easily lose its appeal. All successful users of SuperMemo link the review with their goals. They see each item and each repetition as a step to a better future. Not all users have this imaginative capacity. This is why SuperMemo has not swept mankind off its feet despite its amazing efficiency. * **主动回忆**：主动回忆优于被动回忆。主动回忆更难。这是一个值得的困难。我们在学习中需要主动回忆，因为它是在[间隔重复](https://supermemo.guru/wiki/Spaced_repetition)中有效地重新巩固记忆符号的唯一过程。当我们在实践中运用有用的知识时，主动回忆就会发生。这种使用是令人愉快的，因为它带来了生产力，这是一种独立于学习的回报。人类只是喜欢[实现目标](https://supermemo.guru/wiki/Setting_goals_can_change_your_life)。如果复习是人为计划的，就像在 [SuperMemo](https://supermemo.guru/wiki/SuperMemo)，它不会产生一个富有成效的行为，它可能很容易失去它的吸引力。SuperMemo 的所有成功用户都将复习与他们的目标联系起来。他们认为每一项和每一次重复都是迈向更美好未来的一步。并不是所有的用户都具有这种想象能力。这就是为什么 SuperMemo 没有让人类为之倾倒，尽管它有着惊人的效率。 * **spaced repetition**: memory [consolidation](https://supermemo.guru/wiki/Consolidation) is more effective if [retrievability](https://supermemo.guru/wiki/Retrievability) of memory is less. This leads to difficulty in [recall](https://supermemo.guru/wiki/Recall). This is a desirable difficulty. Like with [active recall](https://supermemo.guru/wiki/Active_recall), the reward of [review](https://supermemo.guru/wiki/Review) comes from the employment of knowledge and productivity. In [SuperMemo](https://supermemo.guru/wiki/SuperMemo), by default, most of review ends with successful recall and there might be some link between difficulty and pleasure. Again, only a subset of users of SuperMemo can find this process pleasurable. Those who don't usually do not last long and drop out. We tell all users, make SuperMemo fun, or it won't work for you! See also: [Pleasure of knowing](https://supermemo.guru/wiki/Pleasure_of_knowing) * **间隔重复**：如果记忆的[可提取性](https://supermemo.guru/wiki/Retrievability)较低，则记忆[巩固](https://supermemo.guru/wiki/Consolidation)更有效。这导致了回忆的[困难](https://supermemo.guru/wiki/Recall)。这是一个值得的困难。与[主动回忆](https://supermemo.guru/wiki/Active_recall)一样，[复习](https://supermemo.guru/wiki/Review)的奖励来自于知识和生产力的运用。在 [SuperMemo](https://supermemo.guru/wiki/SuperMemo) 中，默认情况下，大多数复习都以成功的回忆结束，而且难度和快乐之间可能存在某种联系。同样，只有一部分 SuperMemo 用户能找到这个过程的乐趣。那些通常不会坚持很久就会辍学的人。我们告诉所有用户，让SuperMemo变得有趣，否则它对你无效！另见：[知道的快乐](https://supermemo.guru/wiki/Pleasure_of_knowing) * **incremental review**: SuperMemo advocates learning in spaces. It is more efficient from the point of view of memory and creativity to read an article in small portions over a longer period of time. The same refers to watching a video or listening to a lecture. This results in minor battles for context retrieval. However, it brings an extra bonus in creative elaboration. It also improves memory encoding, generalization, and long-term memory consolidation. Paradoxically, those extra difficulties result in extra learning efficiency that makes [incremental reading](https://supermemo.guru/wiki/Incremental_reading) one of the most pleasurable forms of learning. * **渐进复习**：SuperMemo 提倡在空间中学习。从记忆和创造力的角度来看，在较长的时间内阅读一篇小篇幅的文章更有效率。这同样指的是看视频或听讲座。这导致了上下文检索方面的一些小问题。然而，它在创造性的阐述中带来了额外的奖励。它还改进了记忆编码、泛化和长期记忆巩固。矛盾的是，这些额外的困难导致了额外的学习效率，这使得[渐进阅读](https://supermemo.guru/wiki/Incremental_reading)成为最令人愉快的学习形式之一。 * **learning context**: changing the context in retrieval is a very simple and effective type of desirable difficulty. If the encoding is correct, retrieval will be successful, it will be more effective and it will be rewarding. If context change leads to generalization and better memory encoding, the effectiveness of learning will increase and the reward of learning will increase. * **学习语境**：在回忆中改变语境是一种非常简单和有效的值得的困难类型。如果编码是正确的，回忆将会成功，它将是更有效的，它将是有益的。如果语境的改变导致泛化和更好的记忆编码，学习的有效性就会提高，学习的回报就会增加。 * **problem solving**: solving problems can be very pleasurable. The harder the problem, the greater the pleasure of a solution. Problem solving involves a learning process as the solution requires intermediary steps that result in storing new knowledge in memory. All those steps are pleasurable. If the student struggles with the task and makes no progress, he will learn nothing and receive no reward. The tasks turns out too difficult. If the students fails to solve the problem, but makes progress with intermediary steps, even if they are unrelated to the solution, the learning will be there and the reward will be there. Again, if the difficulty is desirable, it will lead to a reward. If there is no reward, the difficulty appeared insurmountable. As such, it is neither rewarding nor desirable. * **解决问题**：解决问题可以是非常愉快的。问题越难，解题的乐趣就越大。问题解决涉及一个学习过程，因为解题需要中间步骤，从而将新知识存储在记忆中。所有这些步骤都是令人愉快的。如果学生在这项任务中挣扎而没有进步，他将什么也学不到，也得不到任何回报。这些任务太难了。如果学生不能解决问题，但通过中间步骤取得了进步，即使他们与解法无关，学习也是存在的，回报也是存在的。同样，如果困难是值得的，它将导致回报。如果没有奖励，困难似乎是无法克服的。因此，这既不有益，也不值得。 * **learning by doing**: learning by doing may involve play, creativity, problem solving and more. Learning by doing takes more time and often brings better results and more reward. * **边做边学**：边做边学可能涉及游戏、创造力、解决问题等等。边做边学需要更多的时间，而且往往会带来更好的结果和更多的回报。 * **delayed feedback**: delayed feedback, in some circumstances, may result in more processing. In simplest terms, if the teacher does not tell you how well you have done, you may wonder for a while longer. This can benefit memory. If it does, the ultimate effect will be rewarding. * **延迟反馈**：在某些情况下，延迟反馈可能导致更多的处理。简单地说，如果老师不告诉你做得有多好，你可能会想更长一段时间。这对记忆有好处。如果是这样的话，最终的效果将是有益的。 * **help withdrawal**: I write about help withdrawal in the context of [schools suppressing the learning drive](https://supermemo.guru/wiki/Schools_suppress_the_learn_drive#Teacher_problem). Kids who receive no answers may become more curious. Curiosity increases the reward of learning. Students who do not receive assistance in correcting their false models of reality, get stronger rewards for resolving inconsistencies on their own. * 帮助退出：我写的帮助退出是在[学校抑制学习内驱力](https://supermemo.guru/wiki/Schools_suppress_the_learn_drive#Teacher_problem)的背景下。没有得到答案的孩子可能会变得更加好奇。好奇心会增加学习的回报。在纠正错误的现实模型方面得不到帮助的学生，会因为自己解决矛盾而得到更大的回报。 * **other difficulties**: the number of obstacles that can improve learning is endless, some of those can be hormonal in nature, some can involve motivational forces. The common denominator of all those obstacles seems to be some form of deeper processing, memory consolidation, improved attention, and more. Inevitably, obstacles that lead to better learning also involve better reward. * **其他困难**：可以改善学习的障碍是无止境的，其中一些可能是荷尔蒙的性质，有些可能涉及激励的力量。所有这些障碍的共同点似乎是某种形式的更深层次的处理、记忆的巩固、注意力的提高等等。不可避免地，导致更好学习的障碍也包括更好的回报。 Desirable difficulty does not take away the pleasure of learning. Just the opposite, it makes learning more effective and more fun. If difficulty goes too far, and it results in displeasure then the difficulty is no longer desirable. This simple equivalence comes from the mechanics of the reward system in [learn drive](https://supermemo.guru/wiki/Learn_drive). 值得的困难并不会剥夺学习的乐趣。恰恰相反，它使学习更有效，更有趣。如果过于困难而导致不愉快，那么困难就不再值得了。这种简单的等价性来自于[学习内驱力](https://supermemo.guru/wiki/Learn_drive)中奖励系统的机制。 Note that reward bonus for efficient learning due to desirable difficulty does not need to correspond to high [learn entropy](https://supermemo.guru/wiki/learn%20entropy). learn entropy is a metric for an information channel. Active recall, for example, is unrelated to novelty. It refers to memory reconsolidation. Similarly, problem solving may in part come from the need to achieve goals unrelated to learning, or be rewarded by productivity other than gains in new knowledge. 请注意，由于值得的困难而产生的有效学习的奖励并不需要对应较高的学习熵。[学习熵](https://supermemo.guru/wiki/learn%20entropy)是信息通道的度量。例如，主动回忆与新颖性无关。它指的是记忆的重新整合。同样，解决问题的部分原因可能是实现与学习无关的目标的需要，或者是获得新知识以外的生产力的回报。 Note also that nearly all of the above desirable difficulties are inherently wired into the process of [incremental learning](https://supermemo.guru/wiki/Incremental_learning). 还要注意的是，几乎所有上述值得的困难都内在地与[渐进学习](https://supermemo.guru/wiki/Incremental_learning)的过程相关联。 ### 6.19 Addiction to learning ### 学习成瘾 #### 6.19.1 Inborn addiction #### 天生成瘾 We are born in love with learning. That love usually wanes fast during the years of compulsory schooling. The longer we can sustain the love of learning, the bigger the benefit for the brain, health and mankind. Love of learning has nothing to do with addiction. The definition of [addiction](https://en.wikipedia.org/wiki/Addiction) includes adverse consequences that are a result of compulsive engagement in an activity. 我们生来就热爱学习。在义务教育时期，热爱通常很快就会消逝。我们对学习的热爱持续的时间越长，对大脑、健康和人类的益处就越大。对学习的热爱与[上瘾](https://en.wikipedia.org/wiki/Addiction)无关。上瘾的定义包括强制参与某项活动所造成的不良后果。 Negative side effects of learning are tiny in comparison to benefits. If there is a degree of voracity or even compulsion, it can boost the positive effects even further. It is possible to boost one's love of learning. Good learning provides the best boost to further learning. 与益处相比，学习的负面影响很小。如果有一定程度的贪婪，甚至是强迫性的冲动，它可以进一步促进积极的影响。提高一个人对学习的热爱是有可能的。良好的学习为进一步的学习提供了最好的推动力。 #### 6.19.2 Learning and gambling #### 学习和赌博 There is a close connection between the reward systems involved in learning and in gambling. Gambling and learning new words both activate the [ventral striatum](https://en.wikipedia.org/wiki/Striatum) in a [similar fashion](http://www.cell.com/current-biology/fulltext/S0960-9822%2814%2901207-X). This close connection with gambling may confuse the picture for learning. A gambler at a slot machine does not learn much. Addictive videogaming is better. It can be pretty educational. Many team game addicts achieve fluency in English having made no progress at school before. Addiction to sports news may also involve a degree of learning. I learned about [Cabinda](http://en.wikipedia.org/wiki/Republic_of_Cabinda) only during the Africa Cup of Nations \(football\). Addiction to Facebook updates is not different either. It is based on [variable reward](https://supermemo.guru/wiki/Variable_reward) in anticipation of specific gains, however, it can also involve a great degree of learning. That learning may involve gossip, celebrity news, fake news, or actual useful learning. Even political poll updates can cause an addiction. In the battle between Hillary Clinton and Donald Trump, the polls were balanced enough to produce the cliffhanger effect. Compulsive checks for new polls have all hallmarks of an addiction. This kind of addiction, however, can lead to a great deal of learning. It is up to the student to separate gambling from learning. Voracious learning is good. Learning derived from an addiction may be good too. However, gambling on its own brings little value to human existence. 分别与学习和赌博有关的奖励机制之间有着密切的联系。赌博和学习新词都以[类似的形式](http://www.cell.com/current-biology/fulltext/S0960-9822%2814%2901207-X)激活[腹侧纹状体](https://en.wikipedia.org/wiki/Striatum)。这种与赌博的密切联系可能会混淆学习的图景。赌徒在老虎机前学不到什么东西。让人上瘾的电子游戏更好。可能很有教育意义。许多团队游戏成瘾者以前在学校没有取得任何进步，但都能说一口流利的英语。对体育新闻的上瘾也可能涉及到一定程度的学习。我是在非洲国家杯（足球）期间才了解[卡宾达](https://supermemo.guru/wiki/Variable_reward)。对 Facebook 更新的上瘾也没有什么不同。它是建立在预期特定收益的[可变奖励](https://supermemo.guru/wiki/Variable_reward)的基础上的，然而，它也可能很大程度涉及到的学习。这种学习可能包括八卦，名人新闻，假新闻，或实际有用的学习。即使是最新的政治民意调查也会让人上瘾。在 Hillary Clinton 和 Donald Trump 之间的较量中，民调平衡得足以产生扣人心弦的效果。强制检查新的民意测验都有上瘾的特征。然而，这种上瘾会导致大量的学习。要靠学生把赌博和学习分开。贪婪的学习是好的。从上瘾中获得的学习也可能是好的。然而，赌博本身并不能给人类的生存带来什么价值。 #### 6.19.3 Learning and sleep #### 学习和睡眠 Obsessive learning may encroach on sleep time, and may contribute to the epidemic of insomnia and [DSPS](https://supermemo.guru/wiki/DSPS). Creative minds with powerful [learn drive](https://supermemo.guru/wiki/Learn_drive) may stay up learning till the early morning hours. This violation of sleep pattern was difficult or impossible before the arrival of electric lighting. The good news is that the [learn drive](https://supermemo.guru/wiki/Learn_drive) tends to wane with network fatigue. The longer we learn, the greater the degree of saturation in memory circuits. Only sleep can bring relief. This is why even most voracious learners tend to get sleepy and give up learning at some point. If a reader skips the night over a novel, this may be a likely combination of insufficient sleep drive, reduced learning, and increased variable reward that is typical of suspenseful fiction. 强迫性学习可能侵犯睡眠时间，并可能导致失眠症和[睡眠相位后移综合症](https://supermemo.guru/wiki/DSPS)的流行。有强烈[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的有创造力的人可能会熬夜学习，直到凌晨。在电灯出现之前，这种违反睡眠模式的行为是困难的，甚至是不可能的。好消息是，[学习内驱力](https://supermemo.guru/wiki/Learn_drive)往往随着网络疲劳而减弱。我们学习的时间越长，记忆回路的饱和程度就越高。只有睡眠才能带来解脱。这就是为什么即使是最贪得无厌的学习者往往会昏昏欲睡，并在某一点上放弃学习。如果读者为了一本小说而熬夜，这可能是睡眠内驱力不足、学习减少和悬疑小说中典型的可变奖励增加的组合。 #### 6.19.4 Learning and exercise #### 学习和锻炼 I hear that obsessive learning can lead to less exercise. That would be bad. However, I think that it is bad learning that is more likely to have this effect. Good learning is joyous and sparks extra energy. A happy kid should not survive long sitting over a book or over a computer. There must be a way to vent energy. Perhaps we should rather say that reduced exercise is a hallmark of learning addiction, while good learning has neurotrophic effects and should make one burst with extra energy to burn? 我听说强迫性的学习会导致更少的锻炼。那就不好了。然而，我认为更有可能产生这种效果的是糟糕的学习。好的学习是快乐的，能激发额外的能量。一个快乐的孩子不应该长时间地坐在一本书或一台电脑前。一定有办法释放能量。也许我们应该说，减少运动是学习成瘾的标志，而良好的学习有神经营养作用，应该让人爆发额外的能量燃烧？ #### 6.19.5 Learning restraint #### 学习限制 Learning has its cost and it takes time. This is why it should be judicious. However, good learning is nearly always a good long-term investment. This is why we should never fear an addiction. Just the opposite, we should cherish and stoke up the [learn drive](https://supermemo.guru/wiki/Learn_drive) to provide for happy lifelong learning. 学习是有代价的，也是需要时间的。这就是为什么它应该是明智的。然而，良好的学习几乎总是一个好的长期投资。这就是为什么我们不应该害怕上瘾。恰恰相反，我们应该珍惜和激发[学习内驱力](https://supermemo.guru/wiki/Learn_drive)，提供快乐的终身学习。 ### 6.20 Displeasure of learning ### 学习的不快 When I claim that all learning is pleasurable, I hear a chorus of voices like "_I had to go through an awfully stressful exam that gave me lots of good knowledge for life_". Those voices confuse the pleasure of good learning with the displeasure of factors that turn learning into a horror for many students. Those horror factors are bad teachers, harsh parents, deadlines, stress, bad sleep, awful textbooks, excess volume, and more. 当我声称所有的学习都是令人愉快的时候，我听到了一种声音，像是“我不得不经历一场压力很大的考试，它给了我很多生活上的好知识”。这些声音混淆了良好学习的乐趣和使学习成为许多学生恐惧的因素的不快。这些可怕的因素包括糟糕的老师，严厉的家长，最后期限，压力，糟糕的睡眠，糟糕的教科书，过多的书册，等等。 I hear that without deadlines or school-imposed goals, the learning would be replaced with videogames, novels, TV, hobbies, sports, etc. This might be true for many reasons. Some of those activities may carry pleasures unrelated to learning. However, they will also be beneficial for reasons of learning or exercise. A well-rounded student should be free to slow down, allocate his time for fun learning and other fun activities. Slow progress might bring more benefit. 我听说，如果没有最后期限或学校强加的目标，学习将被电子游戏、小说、电视、爱好、体育等所取代。出于许多原因，这可能是正确的。其中一些活动可能带来与学习无关的乐趣。然而，由于学习或锻炼的原因，它们也是有益的。一个全面发展的学生应该能够自由地放慢速度，把时间分配给有趣的学习和其他有趣的活动。进展缓慢可能带来更多好处。 There is no way the equation of learning could produce unhappiness in the wake of good learning. The blame will always be elsewhere. All negatives should be studied and eliminated. 学习的方程式不可能在好的学习之后产生不快乐。责任总是在别的地方。所有负面的东西都应该研究和消除。 In the ultimate account, even if there is a displeasure related to exams, certificates and duties, this displeasure should be imposed on the student by herself. 归根结底，即使对考试、证书和职责有不满意之处，这种不满也应由学生自己承担。 **Pleasurable learning can be buried in displeasure caused by stress, bad people, bad schools, bad textbooks, and more**. **快乐的学习可能被埋没在由压力、坏人、糟糕的学校、糟糕的教科书等等引起的不快中。** ### 6.21 Learning and procrastination ### 学习和拖延 If learning is the most sustainable form of pleasure, why do half of the students procrastinate? This is nearly a triple of the figure for the general population. 如果学习是最可持续的快乐形式，为什么一半的学生会拖延呢？这几乎是一般人口数字的三倍。 The answer is simple and important: students procrastinate because as much as good learning is a pleasure, bad learning is highly unpleasant. Most of assignments at school or even college carry a great deal of mismatch with the needs of the [learn drive](https://supermemo.guru/wiki/Learn_drive). This kind of learning is ineffective and unpleasant. Those kids will often play computer games in the evening claiming they _need to rest their brains_. I doubt their brains are at rest. They actually do jobs that they find pleasurable. A great deal of that pleasure comes from new learning. Unfortunately, there are no credits at school for good gaming, so the sinusoidal cycle of chores-and-fun begins on the next day or even the same day with homework. 答案很简单也很重要：学生拖延是因为好的学习是一种乐趣，而坏的学习是非常不愉快的。学校甚至大学里的大部分作业都与[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的需求不匹配。这种学习是无效的，也是令人不快的。那些孩子经常在晚上玩电脑游戏，声称他们的大脑需要休息。我怀疑他们的大脑不在休息。实际上，他们做的工作都是他们觉得愉快的。这种乐趣很大程度上来自于新的学习。不幸的是，游戏玩得好在学校是没有学分的，所以琐事和乐趣的正弦循环从第二天开始，甚至在同一天随着作业开始。 I never stop being amazed how many students call themselves lazy. At the same time they can do many heroic feats of physical of mental work as long as these are enjoyable or serve their own goals. Even those with thousands of memorized items in SuperMemo often give themselves low conscientiousness scores. Goals of learning can be hazy, but even if they are crystal clear, poor match between the input and prior knowledge can result in significant displeasure. If [learn entropy](https://supermemo.guru/wiki/learn%20entropy) is low, assignments can be boring. If it is negative, they will be repulsive. 我从来没有停止惊讶，有这么多的学生自称懒惰。同时，只要这些都是愉快的或为自己的目标服务，他们就可以做许多精神上的体力劳动。即使是那些在 SuperMemo 中有数千个记忆项目的人也常常给自己低的责任心分数。学习的目标可能是模糊的，但即使它们是非常清楚的，输入和先前知识之间的不匹配可能会导致显著的不快。如果[学习熵](https://supermemo.guru/wiki/learn%20entropy)很低，作业可能会很枯燥。如果它是负的，他们将是令人厌恶的。 The battle between high goal valuations and negative rewards of bad learning will result in procrastination. Procrastinators often call themselves lazy even if they are nothing but. 高目标估值与不良学习的负面奖励之间的斗争将导致拖延。拖延者常常说自己懒惰，即使他们不是。 If you think you are lazy about learning, you need to re-evaluate your materials and your methodology. Even simple violations of the [natural creativity cycle](https://supermemo.guru/wiki/Natural_creativity_cycle) can kill the fun of learning. 如果你认为你在学习上懒惰，你需要重新评估你的材料和方法。即使是简单的违反[自然创造力周期](https://supermemo.guru/wiki/Natural_creativity_cycle)的行为也会扼杀学习的乐趣。 ### 6.22 Learning and depression ### 学习和抑郁 Learning is a sustainable and non-addictive form of pleasure with hardly any side effects other than cost in time. In addition, good learning tends to absorb the mind, and promote more learning by boosting the [learn drive](https://supermemo.guru/wiki/Learn_drive). This means that learning should be employable as therapy in depression. 学习是一种可持续的、非上瘾的快乐形式，除了时间成本外，几乎没有任何副作用。此外，良好的学习倾向于增长知识，并通过提高[学习内驱力](https://supermemo.guru/wiki/Learn_drive)来促进更多的学习。这意味着学习应该作为抑郁症的治疗手段。 #### 6.22.1 Learning at school #### 在校学习 If [learning is a source of pleasure and reward](https://supermemo.guru/wiki/Pleasure_of_learning), why do we see [rampant depression](https://supermemo.guru/wiki/Incremental_increase_in_depression) in kids of school age? Despite being institutions of learning, schools are [more likely to contribute to depression than to act as a remedy](https://supermemo.guru/wiki/I_became_so_depressed_that_I_stopped_going_to_school). Without the freedom to learn, it is hard to achieve good learning. For learning to be pleasurable, it needs to be powered by the [learn drive](https://supermemo.guru/wiki/Learn_drive). It cannot be coercive or mandatory. It must be free. 如果[学习是快乐和奖励的源泉](https://supermemo.guru/wiki/Pleasure_of_learning)，为什么我们会在学龄儿童中看到猖獗的抑郁呢？尽管是学习机构，但[学校更有可能导致抑郁症，而不是作为一种治疗措施](https://supermemo.guru/wiki/I_became_so_depressed_that_I_stopped_going_to_school)。没有学习的自由，就很难获得好的学习。为了让学习变得愉快，它需要[学习内驱力](https://supermemo.guru/wiki/Learn_drive)。它不能是强制性的，也不能是强制性的。它一定是自由的。 #### 6.22.2 Impact of memory on mood #### 记忆对情绪的影响 Free learning is fun, however, the pleasure of learning is not what makes it a great tool against depression. 自由学习是有趣的，然而，学习的乐趣并不能使它成为对抗抑郁的强大工具。 Memory is a factor that may trigger or suppress depression. Memories determine how input signals get routed in the brain. Memory determines what concepts get associated with inputs or neural activations. Memories determine how we react to the sound of a passing car. It may bring up the memories of a happy vacation, the inspiration of Elon Musk, or memories of a car accident that crippled a loved one. 记忆是一个可能触发或抑制抑郁的因素。记忆决定了输入信号在大脑中是如何被传送的。记忆决定了哪些概念与输入或神经激活相关联。记忆决定了我们对过往汽车的声音的反应。它可能会让人想起一个快乐的假期，埃隆·马斯克的灵感，或者是一场使心爱的人残废的车祸的记忆。 For memories to have a significant impact on mood, we need many of them. It is not enough to sit down a session with psychotherapist and learn a few key facts about the brain, our lives, or coping strategies. It takes months and years of learning to develop healthy tracks in the brain. We may build associations that are inherently optimistic or inherently pessimistic. We need thousands of such associations to swing the balance. However, even years of learning may easily be overturned by a pathology or trauma. Neurohormones can instantly change the mode in which the brain works. A switch in neurohormonal profile will instantly give preference to a subset of memories that may affect mood in a negative way. Trauma can plant memories that will stoke up new source of activation that will override activation from other sources. In other words, an armament of good memories may count for nothing if a switch changes the tracks in use or if a new source of activation is born in the brain. It is hardly possible to mitigate the death of a close person with learning. 要使记忆对情绪产生重大影响，我们需要很多记忆。仅仅与心理治疗师坐下来，了解一些关于大脑、我们的生活或应对策略的关键事实是不够的。要想在大脑中形成健康的轨迹，需要几个月又一年的学习。我们可以建立内在乐观或内在悲观的联想。我们需要数千个这样的联想来扭转局面。然而，即使是多年的学习也很容易被病理或创伤所推翻。神经激素可以立即改变大脑工作的模式。神经激素谱的改变将立即优先考虑可能以负面方式影响情绪的记忆集。创伤可以灌输记忆，这将激发新的活化源，将覆盖从其他来源的激活。换句话说，如果开关改变了使用中的轨迹，或者大脑中产生了一种新的活化源，那么良好记忆的装备就可能一文不值。要减轻一个有学问的人的死亡是不可能的。 Once depression hits, the affected individual faces a double whammy. Not only are good memories on defense. Bad memories start circling around facilitating their own new tracks and gaining upper hand. The brain reprograms itself and swings the balance of mood in a wrong direction. When this process becomes a runaway, we may have a clinical depression at hand. To complete bad news, depressed patients lose their love of life and their love of learning. 一旦抑郁发作，受影响的个人将面临双重打击。不仅是关于防守的美好回忆。糟糕的记忆开始盘旋在周围，促进他们自己的新轨道，并获得上风。大脑重新编程，使情绪的平衡向错误的方向摆动。当这个过程成为失控，我们可能有一个临床抑郁症在手边。完全的坏消息是，抑郁症患者失去了他们对生活的热爱和对学习的热爱。 Can learning disrupt this cycle? It can be extremely hard! Respect for [circadian cycle](https://supermemo.guru/wiki/Natural_creativity_cycle) is the first step towards recovering the derailed brain. In the circadian cycle, peak creativity window needs to be captured to attempt remedial learning. Learning needs to be prolific, intense, effective, and pleasurable. [Incremental reading](https://supermemo.guru/wiki/Incremental_reading) would be fantastic if it was not that difficult. For a depressed individual with no skills in the department, [SuperMemo](https://supermemo.guru/wiki/SuperMemo) is no remedy. It is too late. Trying to master incremental reading in a bad state of mind could only make matters worse. It could result in a hate of incremental reading. 学习能打破这个循环吗？这可能是非常困难的！尊重[生理周期](https://supermemo.guru/wiki/Natural_creativity_cycle)是恢复脱轨大脑的第一步。在昼夜周期中，需要抓住创造力高峰窗口来尝试补救性学习。学习需要多产、强烈、有效和愉快。[渐进阅读](https://supermemo.guru/wiki/Incremental_reading)如果不是那么困难的话，那就太棒了。对于一个在部门里没有技能的抑郁的人来说，[SuperMemo](https://supermemo.guru/wiki/SuperMemo) 是不能补救的。太晚了。试图在糟糕的心态下掌握渐进阅读只会让事情变得更糟。这可能会导致对渐进阅读的厌恶。 If learning is possible, it can act as a refuge, which might help suppress negative memories and build new connections. As of that point, the process of building new tendrils of knowledge may begin. This process that should take the mind towards a more optimistic interpretation of the world is slow and laborious. In most severe cases, it may take months or years of hard work and the outcome is not guaranteed. 如果学习是可能的，它可以作为一个避难所，这可能有助于抑制消极记忆和建立新的联系。在这一点上，建立新的知识卷轴的过程可能开始。这一应当使人对世界作出更加乐观的解释的过程是缓慢和艰苦的。在最严重的情况下，可能需要几个月或几年的艰苦工作，结果是不能保证的。 The ultimate conclusion is that learning is not a panacea, however, it can play an important role in therapy. Most of all, the risk of depression can be staved off years in advance by rich and effective learning. That learning must proceed in conditions of freedom and respect for the [learn drive](https://supermemo.guru/wiki/Learn_drive). In short, **love of learning is a good way towards the love of life.** 最终的结论是，学习不是灵丹妙药，但它可以在治疗中发挥重要作用。最重要的是，通过丰富而有效的学习，可以提前数年避免患抑郁症的风险。学习必须在自由和尊重[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的条件下进行。简而言之，**热爱学习是走向热爱生活的好方法。** #### 6.22.3 Anti-depressants #### 抗抑郁药 I am a medical Luddite. For a healthy body, I stick to the rule "_if it ain't broke, don't fix it_". I avoid all forms of pharmacological intervention. I believe in powers of [homeostasis](https://en.wikipedia.org/wiki/Homeostasis) and dangers of homeostatic intervention. The strongest drugs I use are coffee and beer. I do not even use aspirin. I am most dismayed by the misuse of antibiotics, painkillers, sleeping pills and anti-depressants. It has been decades since I last took an antibiotic. Long enough to forget. I will use one on a death bed if necessary. All drugs have their legitimate use and so do anti-depressants. As they result in receptor downregulation, once taken, they make the neurotransmitter status quo worse. This usually means, the more the drug is taken, the more it needs to be taken to avoid a setback. However, in severe cases of clinical depression, the drugs may stop the runaway process. They may protect the brain from self-injury. Once a depressed patient starts losing brain cells, the road to recovery becomes long and bumpy. The moment anti-depressant therapy begins, if it works, is the best moment to use learning as therapy. As long as the brain is willing to proceed, learning can start up those delicate tendrils of knowledge that will hook onto reality to produce vestigial [learn drive](https://supermemo.guru/wiki/Learn_drive). In the ideal case, once the drugs are withdrawn, that learn drive should survive to begin a process that is a reverse of depression: positive feedback of learning, creativity, good sleep, and good mood. This is not easy, but it is very important. If drug therapy is the only thing that changes in a patient's life, it will work only as a break in the pathological process. It will not set the brain in a better state than the one from before the problem started. Improvements require active effort. Without a healthy [learn drive](https://supermemo.guru/wiki/Learn_drive), building up positive memories will not begin. 我是医学上的路德派教徒。为了一个健康的身体，我坚持一条规则：「如果它没有坏，就不要修理它」。我避免任何形式的药物干预。我相信[自我平衡](https://en.wikipedia.org/wiki/Homeostasis)的力量和干预自我平衡的危险。我用的最厉害的药是咖啡和啤酒。我甚至不用阿司匹林。我对滥用抗生素、止痛药、安眠药和抗抑郁药物感到非常沮丧。我已经几十年没吃抗生素了。长到忘记多久了。如有必要，我会在死亡之床上使用。所有药物都有其合法的用途，抗抑郁药物也是如此。由于它们导致受体下调，一旦服用，就会使神经递质的现状变得更糟。这通常意味着，服用的药物越多，就越需要服用才能避免挫折。然而，在严重的临床抑郁症病例中，药物可能会阻止失控的过程。它们可以保护大脑免受自我伤害。一旦抑郁症患者开始失去脑细胞，康复的道路就变得漫长而崎岖。抗抑郁治疗开始的那一刻，如果起作用的话，就是把学习作为治疗的最佳时机。只要大脑愿意继续学习，学习就可以启动那些微妙的知识卷轴，这些知识将与现实联系在一起，产生残留的[学习内驱力](https://supermemo.guru/wiki/Learn_drive)。在理想的情况下，一旦药物被撤除，学习内驱力应该存留下来，开始一个与抑郁相反的过程：学习、创造力、良好的睡眠和良好的情绪的积极反馈。这并不容易，但它是非常重要的。如果药物治疗是唯一能改变病人生活的东西，它只能作为病理过程中的一种中断而起作用。它不会使大脑处于比问题开始前更好的状态。改进需要积极努力。没有健康的[学习内驱力](https://supermemo.guru/wiki/Learn_drive)，积极向上的记忆就不会开始。 #### 6.22.4 Learn drive and optimism #### 学习内驱力和乐观 Toddlers seem to show the most exuberant learn drive. No wonder, healthy children are born optimistic. There is a correlation between optimism and the [learn drive](https://supermemo.guru/wiki/Learn_drive). Happy mind might act as an energizer of the learn drive on the neurochemical basis. Pessimism will definitely act as a suppressant or filter that will prevent the expression of the [learn drive](https://supermemo.guru/wiki/Learn_drive). In that sense, pessimistic mind may mask the learn drive. In depression, the learn drive may disappear entirely. No wonder [Dr Robert Sapolsky](https://en.wikipedia.org/wiki/Robert_Sapolsky) called depression the worst disease in the world. 蹒跚学步的孩子似乎表现出最旺盛的学习内驱力。难怪，健康的孩子生来就是乐观的。乐观与[学习内驱力](https://supermemo.guru/wiki/Learn_drive)之间存在着相关性。快乐的头脑可能在神经化学的基础上起到学习内驱力的作用。悲观肯定会起到抑制或过滤的作用，阻止[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的表达。从这个意义上说，悲观的头脑可能掩盖了学习内驱力。在抑郁症中，学习内驱力可能完全消失。难怪[罗伯特·萨波尔斯基博士](https://en.wikipedia.org/wiki/Robert_Sapolsky)称抑郁症是世界上最严重的疾病。 A consensus seems to emerge that schools are a major contributor to depression among teenagers \(and later in life\). The mechanism isn't clear, but [learned helplessness](https://supermemo.guru/wiki/Learned_helplessness) and the suppression of the [learn drive](https://supermemo.guru/wiki/Learn_drive) emerge as possible keys to the pathology. 似乎出现了一种共识，即学校是青少年\(以及以后的生活\)抑郁的主要原因。其机制尚不清楚，但[习得性无助](https://supermemo.guru/wiki/Learned_helplessness)和对[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的抑制可能是导致病理变化的关键因素。 #### 6.22.5 Can learning help you? #### 学习可以帮到你吗？ If you are reading this, and you are not sure learning can help you, ask yourself the question: _Are you in a good mood today?_ As mentioned above, when you are on a downswing and looking for a solution, your interpretations are darker, and you may not find this text comforting enough. Remember then about the concept of activation energy: you need a little first step to begin, and you may then be pulled in by a vortex of interesting things to learn. 如果你正在读这篇文章，而你不确定学习是否对你有帮助，那么问问自己这个问题：你今天心情好吗？正如上面提到的，当你在一个下降秋千和寻找一个解决方案，你的解释是黑暗的，你可能会发现这篇文章不够安慰。然后记住关于活化能的概念：你需要先迈出一小步，然后你可能会被一系列有趣的东西拉进来学习。 If you are in no mood for quantum mechanics today, start from petty celebrity news, or sports news. Lowly learning is better than no learning! 如果你今天对量子力学没有兴趣，那就从名人新闻或者体育新闻开始吧。少量学习总比不学习强！ ### 6.23 Optimization of education: Global or Local? ### 教育的优化：全局还是局部？ Is there a risk in using pleasure as a guiding light in education? 在教育中用快乐作为指引是否有风险？ #### 6.23.1 Perfect model of education #### 完美的教育模型 Over long years of schooling, we slowly develop an imaginary model of a perfect academic learning process in which we [set long-term goals](https://supermemo.guru/wiki/Setting_goals_can_change_your_life), follow the curriculum, add important pieces of knowledge, and get to the point when we receive a college degree with rock solid knowledge in a given area supported by extensive general knowledge needed for an efficient function in society. The longer we stay in the school system, the harder it is to step away and have an objective view of that model. Paradoxically, verification of that model comes hardest to those minds who do well at school and start believing they have succeeded thanks to that perfect model of academic learning. Smart people suffer less pain at school, and, as a result, think less of the problem of the school system. Successful students internalize the model and perpetuate it by providing the same fixed path for future generations. 经过多年的学习，我们慢慢地建立了一个理想的学术学习过程的想象模型，在这个过程中，我们[设定长期目标](https://supermemo.guru/wiki/Setting_goals_can_change_your_life)，遵循课程，增加重要的知识，当我们在给定的领域获得具有岩石般坚实的知识的大学学位，并得到社会有效运作所需的广泛的一般知识的支持时。我们在学校系统中待的时间越长，就越难离开，对这种模式有一个客观的看法。矛盾的是，对于那些在学校表现良好并开始相信自己成功的人来说，这种模式的验证是最困难的，这要归功于这种完美的学术学习模式。聪明人在学校遭受的痛苦较少，因此，对学校系统问题的考虑也较少。成功的学生将这种模式内在化，并通过为子孙后代提供相同的固定路径而使其永久化。 **The model in which we design student's knowledge via curriculum is wrong!** The model of a perfect school gives credit to the system and the teachers, while all actual learning should be credited to the student. When kids fail school in droves, we tend to blame the kids, or their parents, while a small fraction of successful students will continue dreaming of the perfect school model for their own kids, and keep pushing the model on the less fortunate ones. **我们通过课程设计学生知识的模式是错误的！**一所完善的学校的模式应归功于制度和教师，而所有实际的学习都应归功于学生。当孩子们成群结队地不能上学时，我们倾向于责怪孩子或他们的父母，而一小部分成功的学生将继续为自己的孩子梦想完美的学校模式，并继续将模式推给那些不那么幸运的学生。 #### 6.23.2 Optimization based on the learn drive #### 基于学习内驱力的优化 Unlike the curriculum, the optimization mechanism behind the [learn drive](https://supermemo.guru/wiki/Learn_drive) has been perfected in the course of human evolution. It is capable of driving individual knowledge to the level needed to disentangle all complexities of science or engineering. Before the arrival of compulsory schooling, mankind has achieved all imaginable breakthroughs needed to start [Enlightenment](https://en.wikipedia.org/wiki/Age_of_Enlightenment) or [Industrial Revolution](https://en.wikipedia.org/wiki/Industrial_Revolution). Compulsory schooling has originally helped to lift the "unenlightened" masses to a new level, however, it is increasingly driving itself into the optimization corner in which enlightenment is replaced by suppression of creative minds. 与课程不同，[学习内驱力](https://supermemo.guru/wiki/Learn_drive)背后的优化机制是在人类进化过程中不断完善的。它能够推动个人知识达到理科或工程学的所有复杂问题所需的水平。在义务教育到来之前，人类已经取得了启动[启蒙运动](https://en.wikipedia.org/wiki/Age_of_Enlightenment)或[工业革命](https://en.wikipedia.org/wiki/Industrial_Revolution)所需的一切可以想象的突破。义务教育本来有助于把“蒙昧”群众提高到一个新的水平，但是，它正日益把自己推向用压制创造性思维取代启蒙的最佳化的角落。 #### 6.23.3 Designing a child's mind #### 设计一个孩子的思想 I hear this all the time from highly educated and very smart people that education is too important to let it rely on self-learning or on the blindness of the [learn drive](https://supermemo.guru/wiki/Learn_drive). Apparently, education is so important that we should plan it and design it globally with the best tools of science and using the best experts. While I was preoccupied with efficient learning, and before I really started thinking about the education system, I lived with the same conviction. It is quite natural to default to expert opinion. 我一直从受过高等教育和非常聪明的人那里听到这样的话：教育太重要了，不能让它依赖于自学或[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的盲目性。显然，教育是如此重要，我们应该用最好的科学工具和最好的专家来规划和设计它。当我全神贯注于高效率的学习时，在我真正开始思考教育制度之前，我生活在同样的信念中。默认专家意见是很自然的。 Highly educated people often utter the following claims: 受过高等教育的人常常提出以下主张： * _children are incapable of long-term planning, therefore a curriculum is needed_ * 孩子们没能力做长期规划，因此需要一套课程 * _learn drive is a type of local optimization, while we need to plan education globally_ * 学习内驱力是一种局部优化，而我们需要在全局范围内规划教育 * _following student interests is a recipe for disaster: they will all end up immersed in mind-numbing videogames_ * 关注学生的兴趣是一场灾难：他们最终都会沉浸在令人麻木的电子游戏中 The problem is that global optimization of education sets performance targets that keep getting tighter. Global optimization keeps employing the same inefficient learning tools in an attempt to transfer more "necessary" knowledge to student minds. The outcome is misery for millions of students. While Stalin optimized globally for massive achievements of the Soviet Union, it was the market economics with its simple optimization algorithms that lifted the western world to new heights. See: [Modern schooling is like Soviet economy](https://supermemo.guru/wiki/Modern_schooling_is_like_Soviet_economy) 问题是，教育的全局优化设定了越来越严格的绩效目标。全局优化不断使用同样低效的学习工具，试图将更多的“必要”知识转移到学生的头脑中。其结果是数百万学生的痛苦。虽然斯大林为苏联的巨大成就进行了全局优化，但正是市场经济以其简单的优化算法将西方世界提升到了新的高度。参见：[现代学校教育就像苏联的经济一样](https://supermemo.guru/wiki/Modern_schooling_is_like_Soviet_economy) Currently employed optimization of education uses knowledge tests as the measure of performance, but relies on cramming and short-term memory to achieve more in a shorter period of time. As a result, it keeps losing its grip on the [learn drive](https://supermemo.guru/wiki/Learn_drive). Competition between nations also employs performance tests. Instead of optimizing for actual long-term knowledge, we optimize for the speed of knowledge turnover in student heads. The result is unhappy students with knowledge that is tiny relative to the time invested and to the actual human potential. 目前所采用的优化教育使用知识测试作为表现的衡量标准，而是依靠填鸭式和短期记忆来在较短的时间内取得更多的成绩。因此，它不断失去对[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的控制。国与国之间的竞争也采用表现测试。我们不是针对实际的长期知识进行优化，而是对学生头脑中的知识周转速度进行优化。其结果是学生对知识的不满，而这些知识与投入的时间和实际的人类潜力相比是微不足道的。 #### 6.23.4 Reliance on emergence #### 依靠涌现 Optimization of education can employ the concept of [emergence](https://supermemo.guru/wiki/Emergence). The [learn drive](https://supermemo.guru/wiki/Learn_drive) is a mechanism by which knowledge is self-organizing with no [effort from teachers](https://supermemo.guru/wiki/Push_zone), and no pain from a child. Natural learning may take long hours, but it is [pleasurable](https://supermemo.guru/wiki/Pleasure_of_learning), and healthy kids don't mind learning all day long as long as this is learning of their own choosing. 优化教育可以采用[涌现](https://supermemo.guru/wiki/Emergence)的概念。[学习内驱力](https://supermemo.guru/wiki/Learn_drive)是一种机制，通过这种机制，知识是自组织的，不需要[老师的努力](https://supermemo.guru/wiki/Push_zone)，也不会给孩子带来痛苦。自然学习可能需要很长的时间，但它是[令人愉快的](https://supermemo.guru/wiki/Pleasure_of_learning)，健康的孩子不介意整天学习，因为这是他们自己选择的学习。 There are two vital facts we should hold in mind in reference to the local optimization of learning based on the [learn drive](https://supermemo.guru/wiki/Learn_drive): 在参考基于[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的局部优化学习时，我们应该牢记两个重要事实： * without a reliance on the [learn drive](https://supermemo.guru/wiki/Learn_drive), there is no good learning. All attempts at override will be massively rejected by human memory * 不依靠[学习内驱力](https://supermemo.guru/wiki/Learn_drive)，就没有好的学习。所有覆盖的尝试都将被人类记忆所拒绝 * [learn drive](https://supermemo.guru/wiki/Learn_drive) brings amazingly efficient long-term optimization of the learning process. Nearly all human achievement before the 1850s has been accomplished with the guidance of the [learn drive](https://supermemo.guru/wiki/Learn_drive) * [学习内驱力](https://supermemo.guru/wiki/Learn_drive)带来了令人惊讶的学习过程的长期优化。在19世纪50年代之前，几乎所有的人类成就都是在[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的指引下完成的 A skeptic would notice that human progress has accelerated since the introduction of compulsory schooling. He would be right. However, we have been on an accelerating ascent of progress ever since the emergence of the first forms of life 4 billion years ago. I see Guttenberg and [Tim Berners-Lee](https://supermemo.guru/wiki/Tim_Berners-Lee) as more significant contributors to that acceleration than that of the respectable [Johann Julius Hecker](https://en.wikipedia.org/wiki/Johann_Julius_Hecker). 持怀疑态度的人会注意到，自从实行义务教育以来，人类的进步加快了。他是对的。然而，自从 40 亿年前第一种生命形式出现以来，我们一直在加速进步。我认为 Guttenberg 和 [Tim Berners-Lee](https://supermemo.guru/wiki/Tim_Berners-Lee) 比受人尊敬的 [Johann Julius Hecker](https://en.wikipedia.org/wiki/Johann_Julius_Hecker) 对这种加速做出了更重要的贡献。 Local optimization based on the [learn drive](https://supermemo.guru/wiki/Learn_drive) is highly unintuitive. Creation science comes from a similar unintuitive feelings about the mechanism of natural selection. How can a local evolutionary optimization based on random mutations lead to a marvel of a human being? Global design/optimization/guidance by the hand of God seems unavoidable. Fewer people subscribe to the creation science today, however, a vast majority of the population has no idea what mechanism underlies the [learn drive](https://supermemo.guru/wiki/Learn_drive), and why ignoring it is the chief problem of the [Prussian education system](https://en.wikipedia.org/wiki/Prussian_education_system). 基于[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的局部优化非常不直观。创造科学来自于对自然选择机制的一种类似的不直观的感觉。一个基于随机突变的局部进化优化如何导致一个人的奇迹呢？经上帝之手的全局设计/优化/指导似乎是不可避免的。如今，接受创造科学的人越来越少，然而，绝大多数人不知道[学习内驱力](https://supermemo.guru/wiki/Learn_drive)背后的机制是什么，为什么忽视它是[普鲁士教育体系](https://en.wikipedia.org/wiki/Prussian_education_system)的首要问题。 #### 6.23.5 The tree metaphor #### 树类比 Given enough time and access to knowledge-rich environments, without the need for an education system, the knowledge of an individual grows into a large, comprehensive, and [coherent body](https://supermemo.guru/wiki/Coherence). This is true of all free, and healthy individuals. The size and the quality of the tree may depend on one's personality, interests, and the starting point of the intellectual development. However, one of the chief myths of education is that the organic growth of knowledge leads to multiple biases and [areas of ignorance](https://supermemo.guru/wiki/Ban_on_homeschooling). Those blank spots are allegedly larger than those that remain after years of schooling. Due to the computational power of the [learn drive](https://supermemo.guru/wiki/Learn_drive), and the phenomenon of [emergence](https://supermemo.guru/wiki/Emergence), the opposite is true. The metaphor I like to use to explain the power of the [learn drive](https://supermemo.guru/wiki/Learn_drive) is that of a tree growth. 在没有教育系统的情况下，只要有足够的时间和机会进入知识丰富的环境，个人的知识就会成长为一个庞大、全面和[连贯的整体](https://supermemo.guru/wiki/Coherence)。这对所有自由和健康的人都是适用的。树的大小和质量可能取决于一个人的个性、兴趣和智力发展的起点。然而，教育的一个主要错误观念是，知识的有机增长导致多重偏见和[无知领域](https://supermemo.guru/wiki/Ban_on_homeschooling)。据称，这些空白点比上了几年学后留下的空白点要多。由于[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的计算能力，以及[涌现](https://supermemo.guru/wiki/Emergence)的现象，情况正好相反。我喜欢用来解释[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的类比是树的生长。 > Metaphor. [Why use metaphors?](https://supermemo.guru/wiki/Why_use_metaphors%3F) > > 类比。[为什么要用类比？](https://supermemo.guru/wiki/Why_use_metaphors%3F) > > Natural growth of individual human knowledge can be compared to a growth of a tree. Individuals cells in the [meristem](https://en.wikipedia.org/wiki/Meristem) of a tree twig know very little of the tree and its global growth goals. The meristem follows simple hormonal, biochemical, or biophysical rules \(e.g. apical dominance\). Those simple rules guiding growth towards light are highly efficient and the tree can shape its crowns beautifully. It will also efficiently organize into a canopy with other species. Force of gravity is tackled optimally. Redistribution of nutrients is easy. Absorption of light is excellent. All obstacles, e.g. other trees, rocks or lamp posts, are handled with ease. Similar mechanisms ensure an efficient growth of a plant root system. A simple set of local rules is also employed by the growth cone in sprouting new neural connections in the brain. > > 人类个体知识的自然增长可以比作一棵树的生长。树枝[分生组织](https://en.wikipedia.org/wiki/Meristem)中的单个细胞对这棵树和它的全局生长目标知之甚少。分生组织遵循简单的激素、生化或生物物理规则（如顶端优势）。这些简单的规则引导生长向光是高效率的，树可以塑造它的树冠美丽。它也将有效地组织成一个树冠与其他物种。地心引力是以最佳方式处理的。营养物质的重新分配是容易的。光的吸收很好。所有障碍，如其他树木、岩石或灯柱，都容易处理。类似的机制保证了植物根系的有效生长。生长锥还利用一套简单的局部规则在大脑中萌生新的神经连接。 > > The tree of knowledge works along similar principles. The [learn drive](https://supermemo.guru/wiki/Learn_drive) mechanism makes sure that individual leaves of memory crave light of new discovery and sprout branches in the direction of inspiration. Locally, the [learn drive](https://supermemo.guru/wiki/Learn_drive) may seem simple and blind. Globally we grow great individuals with erudite knowledge needed to support all vital human functions in society. Self-learning brains can fit any environment and fulfill all imaginable human goals. > > 知识之树也遵循类似的原则。[学习内驱力](https://supermemo.guru/wiki/Learn_drive)机制确保个体的记忆之叶渴望新发现的光芒，并在灵感的方向上萌生枝条。在局部，[学习内驱力](https://supermemo.guru/wiki/Learn_drive)可能看起来简单而盲目。在全局范围内，我们成长为知识渊博的伟人，以支持人类在社会中的所有重要职能。自学成才的大脑可以适应任何环境，实现所有可以想象到的人类目标。 > > As much as trees need water, CO2, some nutrients and light, brains need energy, rich input, and unconstrained freedom. All attempts at coercive regulation suppress the [learn drive](https://supermemo.guru/wiki/Learn_drive) and the tree of knowledge fails to germinate on its own > > 就像树木需要水、二氧化碳、一些营养物质和光一样，大脑也需要能量、丰富的输入和不受约束的自由。所有强迫性调节的尝试都抑制了[学习内驱力](https://supermemo.guru/wiki/Learn_drive)，知识之树也不能自己发芽。 > > Another metaphor that can help explain the [emergence](https://supermemo.guru/wiki/Emergence) in building up [coherent](https://supermemo.guru/wiki/Coherence) knowledge is the [Knowledge crystallization metaphor](https://supermemo.guru/wiki/Knowledge_crystallization): > > 另一个有助于解释建立[连贯](https://supermemo.guru/wiki/Coherence)知识的[出现](https://supermemo.guru/wiki/Emergence)的类比是[知识结晶类比](https://supermemo.guru/wiki/Knowledge_crystallization)： > >  > > > **Figure:** In **perfect schooling** we create a perfect crystal of knowledge. In college, we add an extra crystal of specialization. In reality though, learning looks a bit less perfect. For most kids, knowledge never builds sufficient coherence and falls apart due to interference \(i.e. fast forgetting\). As a result, in **real schooling**, knowledge asymptotically reaches a certain volume and keeps churning around from that point on with little progress in stability or coherence. In contrast, in **free learning**, the acquisition of knowledge is chaotic and uneven. However, as long as it is based on the learn drive, the volume of knowledge is very large. Individual crystals of knowledge collide, and build consistency and coherence. This in turn helps stability and further integration of knowledge. By the time of college, in terms of volume, free learners should know far more than ordinary students. Free knowledge has multiple areas of strength, and multiple areas of weakness. However, it superior in coherence. This is why it is more applicable in problem solving > > > > **图：**在**完美的学校教育**中，我们创造了完美的知识结晶。在大学里，我们又多了一块专业化的水晶。然而，在现实中，学习看起来并不那么完美。对于大多数孩子来说，知识从来没有建立足够的连贯性，并且由于干扰\(即快速遗忘\)而支离破碎。因此，在**现实的学校教育**中，知识渐近达到一定的数量，并从那一刻起就不断地翻滚，在稳定性和连贯性方面几乎没有进展。相反，在**自由学习**中，知识的获取是混乱和不均衡的。但是，只要它是基于学习内驱力，知识量是非常大的。知识的单个晶体相互碰撞，并建立一致性和连贯性。这反过来又有助于知识的稳定和进一步整合。到了大学时，就体积而言，自由学习者应该比普通学生知道的要多得多。自由知识有多方面的长处，也有多方面的弱点。然而，它在连贯性方面更优越。这就是为什么它更适用于解决问题 #### 6.23.6 Local optimization #### 局部优化 Local optimization of the [learn drive](https://supermemo.guru/wiki/Learn_drive) leads to a perfect match between human ability and individual's environment and goals. Global optimization of schooling suppresses the [learn drive](https://supermemo.guru/wiki/Learn_drive), defers to the suppressed [learn drive](https://supermemo.guru/wiki/Learn_drive) when matching individuals with their jobs, and results in an unhappy society where most individuals crave 9-5 jobs for their comfort where the leadership, learning, and responsibility are delegated to someone else. The opposite happens in [democratic schools](https://supermemo.guru/wiki/Democratic_schools) which rely on self-learning to produce self-determined, self-fulfilled and self-reliant individuals ready to accept any challenge in their chosen area of interest. [学习内驱力](https://supermemo.guru/wiki/Learn_drive)的局部优化使人的能力与个体的环境和目标完美匹配。学校教育的全局优化抑制了[学习内驱力](https://supermemo.guru/wiki/Learn_drive)，在将个人与其工作匹配时遵循受抑制的[学习内驱力](https://supermemo.guru/wiki/Learn_drive)，并导致了一个不幸福的社会，在这个社会中，大多数人渴望朝九晚五的工作以获得舒适，而将领导、学习和责任则委托给其他人。相反的情况发生在[民主学校](https://supermemo.guru/wiki/Democratic_schools)，这些学校依靠自我学习来培养自我决定、自我实现和自力更生的人，随时准备在他们感兴趣的领域接受任何挑战。 In his historic commencement speech, Steve Jobs joked that before he was diagnosed with cancer, he did not know what the pancreas was. Apparently, his blind learn drive left a gap in his extensive knowledge. Even if this was true, I would never trade Steve Jobs and his opus vitae for a few failures of the local optimization of learning. One of the main points of his [inspiring speech](https://www.youtube.com/watch?v=D1R-jKKp3NA) was to follow one's [learn drive](https://supermemo.guru/wiki/Learn_drive). In his words "[_the only way to do great work is to love what you do_](http://news.stanford.edu/2005/06/14/jobs-061505/)". This truth has been repeated by all wise people for millennia. 在他历史性的毕业演讲中，史蒂夫·乔布斯开玩笑说，在他被诊断为癌症之前，他不知道胰腺是什么。显然，他盲目的学习内驱力在他广博的知识中留下了一个空白。即使这是真的，我也绝不会拿史蒂夫·乔布斯和他的作品来换取局部优化学习的一些失败。他[鼓舞人心的演讲](https://www.youtube.com/watch?v=D1R-jKKp3NA)的要点之一就是追随自己的[学习内驱力](https://supermemo.guru/wiki/Learn_drive)。用他的话说，「[_做伟大工作的唯一方法就是热爱你所做的_](http://news.stanford.edu/2005/06/14/jobs-061505/)」。这一真理千百年来一直被所有智者重复。 #### 6.23.7 Is global optimization possible? #### 全局优化可能吗？ [Global optimization](https://en.wikipedia.org/wiki/Global_optimization) finds an optimum for all input values. **Global optimization of learning** is done at the level of the department of education, e.g. by means of tools such as [common core](https://supermemo.guru/wiki/Common_core) and [standardized testing](https://supermemo.guru/wiki/Standardized_testing). Global optimization is based on the flawed reasoning that we can design a child's mind. Global optimization can also be done by parents who attempt to predict a child's future. [全局优化](https://en.wikipedia.org/wiki/Global_optimization)为所有输入值找到最优。在教育部门一级**对学习进行全局优化**，例如通过[共同核心测试](https://supermemo.guru/wiki/Common_core)和[标准化测试](https://supermemo.guru/wiki/Standardized_testing)等工具。全局优化是基于有缺陷的推理，即我们可以设计一个孩子的头脑。尝试预测孩子未来的父母也可以进行全局优化。 Can we determine a child's future in advance? If parents were to choose future globally and optimally, we would have a surplus of lawyers and doctors. We would also have a major increase in frustrated college dropouts. If governments were to help a bit and redistribute the jobs for kids optimally at early age, we would end up with a variant of [1984](https://en.wikipedia.org/wiki/Nineteen_Eighty-Four). Few kids would love to find out at the age of 6 they are set for a life as a book-keeper or a carpenter. Job selection should obviously be based on love and passion, not a government decree. 我们能提前决定孩子的未来吗？如果父母在全局范围内选择最佳的未来，我们就会有多余的律师和医生。失意的大学辍学者人数也会大幅增加。如果政府能提供一点帮助，并在幼年时以最佳方式重新分配给孩子们，我们最终会得到 [1984](https://en.wikipedia.org/wiki/Nineteen_Eighty-Four) 的一种变体。很少有孩子愿意在 6 岁时发现他们注定要成为一名簿记员或木匠。择业显然应该建立在爱和激情的基础上，而不是政府颁布的法令。 Perhaps kids should then be allowed to optimize globally? That would not work either, we would end up with a surplus of rock musicians, professional videogamers, and football players. 也许孩子们应该被允许在全局范围内进行优化？这也行不通，我们最终会有多余的摇滚音乐家、专业电子游戏玩家和足球运动员。 Contrast this with optimization via the [learn drive](https://supermemo.guru/wiki/Learn_drive) that has delivered the best of human achievement for centuries. 将其与数百年来为人类带来最好成就的[学习内驱力](https://supermemo.guru/wiki/Learn_drive)进行对比。 Is then a [curriculum](https://supermemo.guru/wiki/Curriculum) an attempt to find an intermediary optimum on the way to a global optimum. Curriculum as a guide to what is worth knowing seems like a good idea. When a kid or a teacher runs out of enthusiasm for learning, they might consult the curriculum. If the [learn drive](https://supermemo.guru/wiki/Learn_drive) is in overdrive though, why slow down? Is there a risk the kid will never learn the dangers of alcohol? This isn't too likely. On the other hand, I am not aware of a curriculum that teaches kids how to employ [incremental reading](https://supermemo.guru/wiki/Incremental_reading). I might be biased, but I would definitely put that skill ahead of the need to cram Kawalec or [Battle of Cedynia](https://en.wikipedia.org/wiki/Battle_of_Cedynia) \(examples taken from my own curriculum\). I can appreciate late Julian Kawalec today. However, mandatory reading of his novels imposed by the communist authorities was a source of school torture for me. You probably wonder who Kawalec was. I would love to tell you but Wikipedia has an article on his achievements in [Polish only](https://pl.wikipedia.org/wiki/Julian_Kawalec). 那么，[课程](https://supermemo.guru/wiki/Curriculum)就是一种在走向全局最优的过程中寻找中介最优的一种尝试。将课程作为指导，了解什么是值得了解的，似乎是个好主意。当一个孩子或老师对学习失去热情时，他们可能会参考课程。不过，如果[学习内驱力](https://supermemo.guru/wiki/Learn_drive)处于超速状态，为什么还要慢下来呢？这孩子有没有可能永远不知道酒精的危害？这不太可能。另一方面，我不知道有一门课程能教会孩子们如何运用[渐进阅读](https://supermemo.guru/wiki/Incremental_reading)。我可能会有偏见，但我肯定会把这项技能放在需要卡瓦莱克或[塞迪尼亚之战](https://en.wikipedia.org/wiki/Battle_of_Cedynia)（例子取自我自己的课程）之前。我很感激今天的 Julian Kawalec 。然而，共产党当局强制要求我读他的小说，这对我来说是学校折磨的根源。你可能想知道 Kawalec 是谁。我很想告诉你，但是维基百科上有一篇[波兰语](https://pl.wikipedia.org/wiki/Julian_Kawalec)的关于他的成就的文章。 If you test student knowledge against the curriculum, it is easy to see they master a tiny subset of that globally optimized plan. They add to this a great deal of their own knowledge about the world obtained via self-learning. This leads to the illusion of good schooling. If curriculum was not obligatory, and teachers had more room to adapt, the volume of knowledge and its coherence would increase. Coherence and speed are two hallmarks of self-learning. Fewer kids might choose to solve quadratic equations, but they would fill up that space many times over with other skills they consider important to them. All those who plan careers in [STEM](https://en.wikipedia.org/wiki/Science,_technology,_engineering,_and_mathematics) would get to quadratic equations anyway, sooner or later. The rest would fall back on current default, which is to learn the equations and forget them fast. Most people do not know how to tackle quadratic equations. Few know of their purpose. Equations in the curriculum add distress and the cost of knowledge that might have been opportunistically acquired efficiently in a happy state of mind. 如果您根据课程测试学生的知识，很容易看出他们掌握了全局优化计划的一小部分。他们通过自学来获得了大量关于世界的知识。这导致了良好的学校教育的错觉。如果课程不是强制性的，教师有更多的适应空间，知识的数量和一致性就会增加。连贯性和速度是自学的两个标志。更少的孩子可能会选择解决二次方程，但他们会用他们认为对他们来说重要的其他技能多次填补这个空间。所有那些计划在 [STEM](https://en.wikipedia.org/wiki/Science,_technology,_engineering,_and_mathematics) 中工作的人，无论如何都会迟早得到二次方程式。其余的将依赖于当前的默认值，即学习方程式并快速忘记它们。大多数人不知道如何处理二次方程。很少有人知道他们的目的。课程中的方程式增加了困境和知识的成本，这些知识可能是在愉快的心态下有机会获得的。 If the global long-term optimization is not possible, intermediate steps in the form of a curriculum plan are only less complex. They are still a departure from the optimum determined by the [learn drive](https://supermemo.guru/wiki/Learn_drive). 如果不可能实现全局长期优化，那么课程计划形式的中间步骤就不那么复杂了。它们仍然偏离了由[学习内驱力](https://supermemo.guru/wiki/Learn_drive)所确定的最佳状态。 The only way to optimize efficiently is to let the [learn drive](https://supermemo.guru/wiki/Learn_drive) determine the trajectory with gentle nudges from parents, mentors, peers, strangers, social media, wikipedia, Google, and more. Optimization of education must adhere to the **fundamental law of learning** \(next\). 有效优化的唯一方法是让[学习内驱力](https://supermemo.guru/wiki/Learn_drive)通过来自父母、导师、同龄人、陌生人、社交媒体、维基百科、Google 等的温和推动来确定轨迹。优化教育必须坚持**学习的基本规律**（下一步）。 ### 6.24 Fundamental law of learning ### 学习的基本规律 Most people know that learning can be pleasurable. However, very few people appreciate how important this fact is for the [future of education](https://supermemo.guru/wiki/Education_reform). 大多数人都知道学习是令人愉快的。然而，很少有人意识到这一事实对[教育的未来](https://supermemo.guru/wiki/Education_reform)有多么重要。 Only a constant stream of precious findings in neuroscience helps us see the fundamental importance of [pleasure in learning](https://supermemo.guru/wiki/Pleasure_of_learning). The reward process begins at the level of [perception](https://supermemo.guru/wiki/Biederman_model), and proceeds via associative learning, to [creativity](https://supermemo.guru/wiki/Natural_creativity_cycle), to [problem solving](https://supermemo.guru/wiki/How_to_solve_any_problem%3F), and the ultimate pleasure of achieving goals. At each station there are [pleasure signals](https://supermemo.guru/wiki/Pleasure_of_learning) to reward the progress of brainwork. 只有神经科学中源源不断的宝贵发现，才能帮助我们认识到[快乐在学习中](https://supermemo.guru/wiki/Pleasure_of_learning)的根本重要性。奖励过程从[感知](https://supermemo.guru/wiki/Biederman_model)水平开始，并通过联想学习、[创造力](https://supermemo.guru/wiki/Natural_creativity_cycle)、解决问题和实现目标的最终乐趣来进行。在每一站，都有[快乐的信号](https://supermemo.guru/wiki/Pleasure_of_learning)来奖励脑力的进步。 I was slow to understand the power of pleasure too. Back in 1991, [we](https://supermemo.guru/wiki/SuperMemo_World) wrote conservatively: _"There is a sure way to tell if a given student will be successful in his work. If he finds pleasure in long-lasting learning sessions, he is bound to do a terrific job"_ \(see: [SuperMemo Decalog](http://www.super-memory.com/articles/decalog.htm)\). Today, we realize that the pleasure is so inherently associated with all forms of learning in neural networks that it emerges as one of the best yardsticks in measuring learning progress. 我也迟迟不能理解快乐的力量。早在 1991 年，[我们](https://supermemo.guru/wiki/SuperMemo_World)保守地写道：_「有一个确定的方法来判断一个给定的学生是否会在他的工作中取得成功。如果他在长时间的学习中找到乐趣，他一定会做得很好」_（参见：[Supermemo Decalog](http://www.super-memory.com/articles/decalog.htm)）。今天，我们意识到这种快乐与神经网络中的所有形式的学习有着内在的联系，因此它成为衡量学习进度的最好的标准之一。 This makes it possible to formulate the **fundamental law of declarative learning**: 这使得有可能制定**陈述性学习的基本规律**： **When there is no pleasure, there is no good learning.** **没有快乐，就没有好的学习。** Naturally, this law needs to be qualified to be precise. Good declarative learning results in pleasure. This fact can be masked by factors such as the fact that a bit of good learning can hide in [a mass of bad learning](https://supermemo.guru/wiki/Unpleasant_learning_at_school). Pleasure itself is no warranty of learning. Facts that we discover can be distressing. Some declarative learning may occur in conditions of displeasure \(e.g. fear conditioning\). Classical conditioning often involves pain. Clinical depression will impede one's inclination to take on biking, but will not ruin the procedural learning that occurs while biking. 当然，这项规律需要修正和细化。好的陈述性学习会带来快乐。这一事实可以被一些因素所掩盖，例如一些好的学习会被隐藏在[大量的不好的学习](https://supermemo.guru/wiki/Unpleasant_learning_at_school)中。快乐本身并不是学习的保证。我们发现的事实可能是令人痛苦的。一些陈述性学习可能发生在不愉快的条件下（如恐惧条件反射）。经典的条件反射经常涉及疼痛。临床抑郁症会阻碍一个人骑自行车的倾向，但不会破坏在骑自行车时发生的程序性学习。 The fundamental law of declarative learning simply states that the acquisition of quality knowledge that satisfies the [learn drive](https://supermemo.guru/wiki/Learn_drive) will produce a reward signal. Absence of that signal is an indication of the absence of learning. Dry facts can be committed short-term to declarative memory without having fun, but those facts will not adhere to solid models of reality if there is no reward from learning. Those facts are likely to be eliminated from memory fast by a healthy system of [forgetting](https://supermemo.guru/wiki/Forgetting_curve). Even worse, [bad and persistent engrams can cause problems with learning later in life](https://supermemo.guru/wiki/Toxic_memory)! The emergence of any coherent model in memory will inevitably produce a reward signal. 陈述性学习的基本规律简单地说，获得满足[学习内驱力](https://supermemo.guru/wiki/Learn_drive)的高质量知识将产生奖励信号。没有这一信号表明缺乏学习。枯燥的事实可以在没有乐趣的情况下短期用于陈述性记忆，但如果学习得不到回报，这些事实就不会坚持坚实的现实模式。通过健康的[遗忘](https://supermemo.guru/wiki/Forgetting_curve)系统，这些事实很可能很快就会从记忆中消失。更糟糕的是，[糟糕而持久的学习习惯会在以后的生活中引起学习上的问题](https://supermemo.guru/wiki/Toxic_memory)！记忆中任何连贯模型的出现都会不可避免地产生奖励信号。 If you happen to impose the suffering on yourself on your own, you need to rethink your strategies. You may need to slow down, or go back to basics, learn the rules of mental and [sleep hygiene](https://supermemo.guru/wiki/Natural_creativity_cycle), manage your [stress](https://supermemo.guru/wiki/Stress_resilience), learn the [20 rules of formulating knowledge](https://supermemo.guru/wiki/20_rules) or perhaps give [incremental reading](https://supermemo.guru/wiki/Incremental_reading) a try. If you persist despite pain, you will not be rewarded with good results. [Gladwell's 10,000 hour rule](http://www.newyorker.com/news/sporting-scene/complexity-and-the-ten-thousand-hour-rule) also needs to be qualified. No violin virtuoso has ever been born out of sheer suffering through hours of practice. Like with learning, great music is a child of love. 如果你碰巧把痛苦强加在你自己身上，你需要重新思考你的策略。你可能需要放慢速度，或者回到基础上，学习心理和[睡眠卫生](https://supermemo.guru/wiki/Natural_creativity_cycle)的规则，控制你的[压力](https://supermemo.guru/wiki/Stress_resilience)，学习[制定知识的 20 条规则](https://supermemo.guru/wiki/20_rules)，或者试一试[渐进阅读](https://supermemo.guru/wiki/Incremental_reading)。如果你不顾痛苦坚持下去，你将得不到好的结果。[Gladwell 的 10,000 小时](http://www.newyorker.com/news/sporting-scene/complexity-and-the-ten-thousand-hour-rule)规则也需要被认可。没有一位小提琴演奏家是完全通过几个小时的练习而诞生的。就像学习一样，伟大的音乐是爱的结果。 On the other hand, most of students of this world suffer of no fault of their own. Bad learning is imposed on them from above! 另一方面，这个世界上的大多数学生都没有自己的过错。糟糕的学习是从上面强加给他们的！ **Students of the world unite!** You no longer need to suffer the pain of learning. If you suffer, you have your basic student right to protest. If you suffer, something is going wrong! You can stop learning! If anyone demands learning from you, and you do not enjoy it, you can strike back, and demand pleasurable learning! This is not your elitist hedonistic weak heart demand. This is a demand of reason. **No pleasure, no learning!** Your suffering is a waste of time, a waste of health, and a waste of human global resources! **全世界的学生团结起来！**你不再需要忍受学习的痛苦。如果你受苦了，你有基本的学生抗议的权利。如果你受苦了，那就是出了问题！你可以停止学习了！如果有人要求你学习，而你无法享受学习，你可以反击，并要求快乐的学习！这不是你的精英享乐主义玻璃心的要求。这是理性的要求。**没有快乐，就没有学习！**你的痛苦是浪费时间，浪费健康，浪费全球人力资源！ ### 6.25 Summary: Pleasure of learning ### 摘要：学习的快乐 * human brain naturally tunes in to "interesting information" in the environment * 人类大脑自然地适应环境中的「有趣的信息」。 * learning and discovering new things is rewarding * 学习和发现新事物是有益的。 * many educators subscribe to the dangerous myth that learning may cause displeasure and still be effective * 许多教育工作者赞同一个危险的错误观念，即学习可能引起不快，而且仍然有效。 * surprisal is highly valued in new knowledge acquisition * 在获取新知识时，意外被高度看重 * predictability and surprisal may both add to attractiveness of the information channel * 可预测性和可预见性都可能增加信息渠道的吸引力 * attractiveness of the information channel depends on the prior knowledge * 信息渠道的吸引力与预备知识相关 * information delivered to the brain must account for prior knowledge. This factor makes universal delivery, e.g. via lecturing, very difficult * 传递给大脑的信息必须考虑到预备知识。这一因素使得通用的传授（例如通过讲课）变得非常困难。 * attractiveness of the information channel depends on the speed of delivery and the speed of processing * 信息渠道的吸引力与传授速度和处理速度相关。 * the speed and complexity of information delivery in learning must to tailored to individual needs * 学习中信息传授的速度和复杂性必须适合个人需要。 * the encoding of a new high value associative memory occurs simultaneously with sending a signal to reward centers in the brain * 新的高价值联想记忆的编码与发送信号到大脑中的奖励中枢同时发生。 * failed tailoring of information channels in schooling leads to lack of reward * 学校教育中信息渠道的调整失败导致缺少奖励 * learning provides a unique type of sustainable pleasure that may have therapeutic value * 学习提供了一种独特的可持续的快乐，可能具有治疗价值 * for systemic reasons, schooling usually fails to tune in to child interests * 由于系统性原因，学校教育通常不能符合儿童的兴趣 * unrewarding nature of schooling is the chief cause of near-universal dislike of "learning" at school * 学校教育的无奖励本质是几乎普遍不喜欢在学校「学习」的主要原因。 * by destroying the pleasure of learning we contribute to creating an unhappy society * 通过破坏学习的乐趣，我们为创造一个不快乐的社会作出了贡献 * the fundamental law of declarative learning states: **No pleasure, no learning!** * 陈述性学习的基本规律是：**没有快乐，就没有学习！**