# 06.學習內驅力和獎勵 [TOC=3,5] ## 6 Learn drive and reward ## 學習內驅力和獎勵 ### 6.1 The main problem in education ### 教育中的主要問題 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. 關于教育的主要問題是認為學習可能會引起不快，而且應該[忍受](https://supermemo.guru/wiki/The_grind_is_the_glory)這種不快從而學的更多。 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: 有無數的教育者認為學校應該像工作一樣：它令人不快，但它只是需要去做。在這一章中，我將解釋相反的才是對的： > **Good learning is inherently pleasurable**, and without pleasure there is no good learning. > > **好的學習本身就是快樂的**，沒有快樂就沒有好的學習。 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. 不快的錯誤觀念如此普遍，以至于即使是對學習樂趣有廣泛了解的好老師也認為，學生在學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. 在這一章中，我展示了學習的快樂與大腦息息相關，以及我們如何以人類的健康、學習、創造力以及最終的未來為代價，系統地摧毀這種[進化的天賦](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. 教育的主要問題也是社會的主要問題之一。我們破壞了學習的樂趣，也就大大破壞了生活的樂趣。我們建立了一個教育系統，讓數百萬人過上不幸福的生活。 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. 你可能對我的話持懷疑態度，因為不愉快的學習的錯誤觀念是[學校教育](https://supermemo.guru/wiki/Schooling)的一個強有力的副作用。因此，本章試圖說服你。消除這個錯誤觀念所需要的只是理解大腦中編碼新知識的簡單機制。 ### 6.2 Learn drive and entropy ### 學習內驅力和熵 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為什么大多數孩子在學校沒有學到多少。 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). 你們可能從物理課回憶起熵是無序的度量，熱力學第二定律表明孤立系統的熵永遠不會減小。這是一種形象的物理定律，我們會終生銘記。這是[普遍適用的](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. 信息論中有一個姊妹概念叫[香農熵](https://en.wikipedia.org/wiki/Entropy_%28information_theory%29%29)。它可以理解為由一個來源發送的信息的平均值。例如，假設一個渠道連續不斷地將一串相同的字母傳送到無窮遠處（例如，一串 A:「AAAAAA…」）中。這是完全可以預測的，并且熵為零。我們不會從這樣的渠道學習。 [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，科學家們開始假設信息渠道的熵是否會對大腦感知渠道價值的方式產生強大影響。1957 年，[Meyer 假設](https://supermemo.guru/wiki/Music_and_entropy)音樂的熵決定了人們對音樂之美的感知。他的結論是，較高的熵可能會導致主觀緊張，這與更有意思的音樂片段有關。 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[后來被改進](https://supermemo.guru/wiki/Impact_of_syncopation_on_the_pleasure_of_music)，以更好地理解對音樂和普通信息的感知。音樂不僅僅是信息。這通過一個現象來看是很顯然的，因為一首歌被多次回放可以令人愉快好多次。但是書籍很少出現這種情況。 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. 音樂是一種普遍的信息。如果你被允許選擇一個無線頻道，你會很快從嘈雜的靜電干擾中調諧出來，你也不會對零熵的靜音太興奮。但是，大多數人會對有規律的鼓聲做出積極的反應。只要它沒有被敲打在碎玻璃上（這是我們天生不喜歡的），我們就會發現一個有固定鼓點的廣播頻道比無聲的更有趣。這自然只會持續一段時間，直到鼓點本身變得無聊和太容易預測。 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，我們終于可以測試大腦對信息熵的反應了。神經影像顯示[前海馬對視覺信息熵有反應](https://www.ncbi.nlm.nih.gov/pubmed/15896570)，[腹側紋狀體](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3403290/)也有類似的發現。因此，我們現在可以肯定大腦對信息熵有反應。熵感受器在掃描環境尋找學習機會時非常重要。這是獎勵的前奏，而獎勵是[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的基礎。 ### 6.3 Prior knowledge in information seeking ### 信息搜尋中的預備知識 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. 我們需要區分信息和意義。熵不是衡量后者的一個好指標。意義的度量除了度量信息渠道之外，還必須涉及到大腦本身。預備知識在學習中是必不可少的。想象一下，當你在收音機上搜索一個有趣的頻道時，你會發現一個新聞服務。如果服務是用泰語提供的，而你不會說泰語，那么你會更喜歡用英語提供的服務。就信息而言，兩個新聞頻道可能有相同的熵，但是你的預備知識會讓你選擇英語頻道。泰國頻道傳遞聲音流，而英語頻道傳遞[概念](https://supermemo.guru/wiki/Abstract_knowledge)流。如果不了解接受者的知識，信息熵就無法告訴我們什么。我們無法確定信噪比。 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. 每個聽者都有自己偏好的信息熵水平。對于大多數音樂愛好者來說，迪斯科或電子樂的常規節拍要比單獨的鼓聲有趣一些。這種類型的音樂承載著更高的平均信息量。對于一個更老練的聽眾來說，有一點切分音是將更受歡迎的。然而，切分音需要一定程度的預先學習。那些對音樂了解較少的人可能會被增加的節奏復雜性弄糊涂。如果節拍中有太多的信息，[就不可能隨著音樂起舞](https://supermemo.guru/wiki/Impact_of_syncopation_on_the_pleasure_of_music)。對于一般人來說，Wynton Marsalis 的天才可能很難被察覺。頂級爵士音樂只為一小部分受過高等教育的聽眾準備，就像大多數人一樣，隨著復雜性的增加，音樂慢慢向無線電雜音的方向衰變。 ### 6.4 Entropy detectors in the brain ### 大腦中的熵檢測器 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. 大腦無法有效檢測撞擊視網膜或耳膜的信號的熵。就像顯示器上的像素一樣，視網膜細胞也不知道它們傳達了什么。如果檢測器，如海馬體，要根據熵而響應，它必須對來自內嗅皮層的輸入（即海馬體本身的輸入）進行操作。這些輸入將在高度處理后顯示信號。它可能會呈現一個概念，而不是像素。感覺輸入端的高熵信號將在神經選擇、完善和[泛化](https://supermemo.guru/wiki/Generalization)過程的早期失去大部分噪聲成分。信噪比將決定丟失多少信息。噪音越大，損失越大。我們越聰明，這個處理過程就越有選擇性，在那個階段會丟失越多信息。這很好。我們對細節視而不見。模式識別就像一個確定性函數，根據定義，它會導致熵的下降。復雜的模式可能會變成簡單的概念。這些概念將為檢測器提供實際輸入，例如海馬體。 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. 請注意，實驗中產生的視覺流證明[海馬體對信號熵的反應](https://www.ncbi.nlm.nih.gov/pubmed/15896570)具有高度[象征性](https://supermemo.guru/wiki/Abstract_knowledge)。因此，視覺流在處理過程中丟失的信息會少得多。這一高度簡化和[概念化](https://supermemo.guru/wiki/Generalization)的信息將會被掃描為意外，并為整個[學習內驅力](https://supermemo.guru/wiki/Learn_drive)系統提供指引。這就是為什么在這種情況下，海馬體似乎對輸入熵有反應。 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. 上面的推理解釋了為什么低熵和高熵的感覺信號都是無趣的。經過一定程度的處理后，高熵信號可能會失去所有噪聲，并向海馬體傳遞低熵輸入。然后，我們觀察感官輸入時「最佳熵」水平的錯覺。我們需要一個新的概念，**學習熵**，來幫助我們準確地判斷信號的吸引力。學習熵需要考慮信息的高度處理，然后才能激活大腦中的獎勵中樞。本文稍后將討論[學習熵](https://supermemo.guru/wiki/I_would_never_send_my_kids_to_school#Learntropy)。 ### 6.5 Speed of information processing ### 信息處理速度 An under-appreciated factor in sensory information scanning is the speed of information processing in the brain. 感官信息掃描中一個被低估的因素是大腦中信息處理的速度。 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. 對于每一首音樂，都有一個可以接受的播放速度范圍，在其范圍內我們可以欣賞音樂之美。高速播放可能會很煩人，而且音樂可能變得難以解碼，因為高速超出了我們的處理能力。同一首音樂如果放慢了速度，很快就會失去吸引力。演講或課堂講授也是如此。對于相同的信息和相同的熵水平，我們可以實現完全不同的信號吸引力水平。總是有一個最佳的傳授速度，該速度取決于影響[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的所有其他因素，包括預備知識。因此，傳授速度是高度個性化的。 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. 我喜歡以 1.4 倍的速度聽講座。我用 1.3 倍來聽更有挑戰性的片段。雖然我從來沒有加快 [Fareed Zakaria](https://en.wikipedia.org/wiki/Fareed_Zakaria_GPS) 的速度，但是我享受這個節目中的每一條信息。課堂上的學生沒有加速或減速按鈕。即使是暫停按鈕，如果有的話，也很難去點擊，因為這可能會惹惱其他學生。 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"_. 在學校里，傳授的速度常常超過學生的處理能力。這導致了微不足道的學習和強大的壓力。沒有時間[欣賞高速列車窗外的風景](https://supermemo.guru/wiki/Futility_of_schooling)。在麻省理工學院，他們稱之為「[_用消防水管喝水_](https://supermemo.guru/wiki/Futility_of_schooling)」。 ### 6.6 Probability vs. knowledge ### 概率與知識 Low probability events carry more information. Average information determines entropy. Prior knowledge determines the perception of an information channel's entropy. 低概率事件攜帶更多信息。平均信息決定熵。預備知識決定了對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. 如果你碰巧收聽了廣播新聞，并且聽到「_Janet Jackson 遜生了一個孩子_」，你的興趣程度將取決于事件發生的概率。如果你不知道 Janet Jackson 是誰，這是一個高概率事件。如果[每天約有 35 萬名女性分娩](http://www.theworldcounts.com/stories/How-Many-Babies-Are-Born-Each-Day)，這已經不是新聞，也不是新鮮事或有趣的事。一名士兵在戰爭中的第一次死亡將成為新聞，但是當死亡人數增加到成千上萬時，年輕的生命將只是一個統計數字。 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. 如果你碰巧認識 Janet Jackson 或者喜歡她的音樂，那么生孩子的概率會急劇下降到「_一生一次_」的水平（對 Janet 來說）。這會讓你變得有興趣。然而，如果你記得 Janet 是古代情景喜劇中的一個漂亮女孩，她生孩子可能會被歸為「_不可能！_」。如果你意識到 Janet 50 歲了，并且你知道更年期，你可能會立刻對她的情況產生病態的好奇。你預備知識決定了你對信息的反應。信道沒有最佳熵水平，只有最適合特定大腦的熵水平。在這一點上，你可能會看到，我們需要引入一個新的衍生概念，我們在[之后](https://supermemo.guru/wiki/I_would_never_send_my_kids_to_school#Learntropy)將稱之為**學習熵**。學習熵將決定一個特定的通道對一個特定的大腦的吸引力。 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. 如果你喜歡 Janet 式的八卦，那么這個八卦頻道會給你帶來恰到好處的意外。它將提供學習匹配。如果你缺乏相關知識或者你的優先級不同，你就會對它置之不理。你的學習重點也將決定你在特定領域的知識水平，以及你對任何特定渠道及其信息熵的反應。 ### 6.7 Predictability and surprisal ### 可預見性和意外 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. 概率和復雜性不是信息感知的唯一組成部分。我們似乎在尋找可預測性和意外之間的平衡。我喜歡鄉土爵士樂。在這種類型的音樂中，低音部通常是高度可預測的，伴著[最佳的切分音數量](https://supermemo.guru/wiki/Impact_of_syncopation_on_the_pleasure_of_music)。這使得身體運動與節奏同步變得容易。然而，如果沒有意外，鄉土爵士樂就不會有意思。在這里，復雜的爵士樂即興表演會刺激負責檢測「意外」的神經系統。此外，經過幾十年的學習，我的大腦有一個完整的信號數據庫。可能有一個我認識并喜歡的伴唱歌手的聲音。我的大腦已經準備好迎接鄉土爵士樂了。 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. 我喜歡 Ken Robinson 關于創造力的講座。在某種程度上，它們是高度可預測的。我完全同意 Robinson，所以你可以說 Robinson 助長了我的證實性偏見。這很愉快。當人們與我們觀點一致時，我們喜歡說「英雄所見略同」。但是，如果 Robinson 繼續重復同樣陳腔濫調，講述學校如何扼殺創造力，他將失去吸引力。熵可以解釋為平均預期[意外](https://en.wikipedia.org/wiki/Self-information)。Robinson 的講授帶來了很多意外。他可能會以不同的、不同尋常的創造性方式描繪相同的模型。因此，**大腦接收新的信息，產生泛化，并確認現有的模型**。從新的語境中得出的泛化可以增強[知識的連貫性](https://supermemo.guru/wiki/Knowledge_coherence)。在基于已知模型的消息中，這是一種非常令人愉快的補充。 Robinson lectures find a good balance between predictability and surprise. Robinson 的講座在可預測性和意外之間找到了很好的平衡。 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將不斷提供意外，證實現有的模型，并在新的語義分支中武裝它們，在這些分支上可以建立新的知識。一個破壞現有模型的意外起初可能并不令人愉快，但可能會導致一場非常令人愉快的思維革命。 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. 打個比方，你可以想象這是一個信息渠道，修剪你的知識之樹，并添加新的分支，就像一個陶工在他完美塑造的作品上添加[新的粘土層](https://supermemo.guru/wiki/Jigsaw_puzzle_metaphor)一樣。 ### 6.8 Detecting surprisal ### 檢測意外 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 \(when new information requires changes to the model\). 人類的[學習內驅力](https://supermemo.guru/wiki/Learn_drive)是基于對[意外](https://en.wikipedia.org/wiki/Self-information)的檢測。我們已經知道這一點很久了。人類和機器學習模型都以不同的名稱涉及到這個概念。[Piaget](https://en.wikipedia.org/wiki/Jean_Piaget) 寫過，[圖式](https://en.wikipedia.org/wiki/Jean_Piaget#Schema)在意外的影響下陷入失調。[Jeff Hawkins](https://en.wikipedia.org/wiki/Jeff_Hawkins) 在他的新大腦皮層模型中談到了基于學習和智力的預測錯誤。我喜歡談論模型，以及它們的細化（當新數據適合模型時），或者矛盾（當新數據需要改變模型時）。 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. 為了獲得學習的獎勵，一個新的令人意外的信息需要與預先存在的知識（模型、圖式、預測等等）相容。為了獲得獎勵，神經處理是必要的。關于輸入的信息需要被處理，并與存儲在大腦中的信息進行比較。大腦輸入信息的主要處理器之一是海馬體。大腦的信息交換臺能夠將輸入與先前的知識進行比較。 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. 視覺流的熵的測量不一定是信息通道令人快樂的能力的可靠指標。流向海馬體的所有信息都經過高度處理。代表美麗海灘的像素流將被加工成一系列形狀和紋理。反過來，這些模型將依次模擬棕櫚樹、沙子和大海。這種高度壓縮的簡單信息將決定對信息輸入的最初響應。 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. 掃描環境中的信息如同掃描食物的氣味。氣味很誘人，但只有實際去吃才是真正的回報。這就是熵掃描不需要回報的原因。它所需要做的就是獲得獎勵。如前所述，前海馬對熵有反應，然而實驗設計確保熵是指輸入過程中不會丟失太多信息的簡單形狀的組合。與其說是信號熵，不如說是信息比較器（如海馬）的輸入熵。重要的不是視網膜像素，而是比較器輸入上表示的手掌形狀。對于比較器來說，灰度或靜態噪聲的高熵模式與白度或靜音沒有區別。它們在輸入時都會帶來相同的熵：零。這就是為什么我用「[學習熵](https://supermemo.guru/wiki/Learntropy)」這個詞來準確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 \(perhaps an indication of a preceding layer of neural processing\). 響應信號熵的前海馬體因發現 Halle Berry 神經元而聞名（參見[更多](http://phys.org/news/2005-06-single-cell-recognition-halle-berry-neuron.html)）。研究人員將電極植入一名同意接受治療的癲癇患者體內，發現在不同情境下，單個神經元對 Halle Berry 圖像的反應是一致的。同樣的神經元也會對 Halle Berry 的名字做出反應。與此同時，后海馬體對 Jennifer Aniston 的反應可能不那么一致（這可能是前一層神經處理的跡象）。 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. 我們大多數人都不知道 Halle Berry 的氣味如何，她的氣味可能不足以激活海馬體中的 Halle Berry 神經元，然而，即使是氣味信號也可以通過嗅球、嗅結節、松果體皮層和內嗅皮層中的幾個突觸快速到達那里（見下圖）。然而，如果一個人能聽到 Halle 的聲音，聲音信號可能會在嗅結節中出現，加強識別，并導致海馬或更下方的新皮層中 Halle 神經元的激活。  > **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. \(source: Wikipedia\) > > **圖：** 嗅覺系統解剖學。嗅覺信號可以通過嗅球、嗅結節、小齒輪狀皮層和鼻內皮層的幾個突觸快速到達海馬體。（來源：維基百科） 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). 這是否意味著 Halle 會永久地存在于患者的海馬體中？由于海馬體與新記憶的形成有關，我們可能會認為 Halle 出現在海馬神經元中是識別的結果。她在病人心中的永久位置可能位于新皮層的更深一層。我們現在知道，在記憶鞏固的過程中，知識印記[從海馬體轉移到新皮層](http://www.jneurosci.org/content/29/32/10087.full)。我們也非常肯定這個過程是[在睡眠中](http://super-memory.com/articles/sleep.htm#Neural_optimization_in_sleep)發生的。正是在新皮層，我們應該尋找代表 Halle 或某人祖母的概念神經元。最后一種可能性產生了一種假設的神經元類型，稱為[祖母神經元](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，研究人員可以識別出視覺皮層中對面孔做出反應的[祖母神經元](https://en.wikipedia.org/wiki/Grandmother_cell)。在那里，我們可能會發現在看到 Halle 的圖像時神經元會更加穩定地激活。然而，Halle 的概念可能仍然存在于其他地方，并在注意到 Halle 時被視覺皮層細胞激活。 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. 另一個激活途徑可能是從新聞上聽到 Halle 的名字。整個識別過程將由內嗅皮層和海馬體協調進行，而最終的 Halle 神經元將在新皮層的某處激活。 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). 要讓富含信息的信號產生獎勵，必須有一個低概率事件在輸入時被檢測到，并通過聯想將其編碼為大腦皮層中的新知識。當前海馬對熵做出反應時，[廣泛的雙側丘腦皮層網絡的活動會受到意外因素的調節](06.learn-drive-and-reward-xue-xi-nei-qu-li-he-jiang-li.md)。在那里，我們將尋找學習快樂的根源。根據消息的類型，還可能涉及其他比較器中樞。人們還發現，當檢測到新的視覺信號時，杏仁體可能會產生獎勵。對獎勵視覺刺激做出反應的杏仁體神經元也可能對新的視覺刺激做出反應。[Rolls 假設，這可能通過杏仁體來實現對新奇事物的獎勵。](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. 我們知道海馬體直接與伏隔核（大腦快樂中樞）相連。此連接適用于兩種情況： 1. 對快樂的預期； 2. 最終的獎勵。 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! 在預期檢測到一個高[學習熵](https://supermemo.guru/wiki/learntropy)信號后，就會產生對高價值信息的積極追求。海馬體檢測到一條信息后，可能會同時將相關的學習信息發送到大腦皮層，并向快樂中樞發送獎勵信號。這將意味著學習新東西的時刻！ ### 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，在尋找外星智能的時候，SETI 的研究人員發現了一個來自[射手座](https://en.wikipedia.org/wiki/Sagittarius_%28constellation%29)的不尋常的無線電信號。在宇宙空間微弱的低水平噪聲中，信號是極不可能出現的。低概率標志著高意外。天文學家 Jerry Ehman 在打印輸出上圈出 6 個與信號相對應的字母，并在旁邊寫上了「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). \(source: Wikipedia\) > > **圖：** 一張彩色電腦打印出來的原始拷貝掃描件，拍攝于 1977 年[哇！信號](https://en.wikipedia.org/wiki/Wow!_signal)到達后的幾年。（來源：維基百科） "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!」 是大腦對突然發現的反應。這一刻非常愉快。[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的全部目的是在環境中尋找「哇」因子。這些是最有價值的知識，補充了目前已知的：當前的現實模型。[漸進閱讀](https://supermemo.guru/wiki/Incremental_reading)的樂趣來自流入學生大腦的涌動的「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. 到目前為止，我們已經看到熵、意外、可預測性和預備知識對學習的影響。在這種情況下，只有信號的概率并不能完全解釋它的力量。它的解釋在本文的后面（參見：[知識評估網絡](https://supermemo.guru/wiki/Knowledge_valuation_network)）。Ehman 在寫這篇文章的時候，能夠感覺到它所蘊含的巨大意義。這是迄今為止，也是從那時起，證明除了人類文明以外，還有其他文明存在的最有力的證據。如果相同的信號出現在檢測海洋中的沙丁魚，就不會有「哇！」。即使在北極也不會有。 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。如果錯誤率高，[學習內驅力](https://supermemo.guru/wiki/Learn_drive)可能會減弱。當 Penzias 和 Wilson 在 1964 年發現宇宙微波背景輻射時，并沒有「哇！」。困惑的研究人員繼續從他們的無線電天線上移除鴿子糞便。解釋神秘噪音時，他們優先考慮了鴿子糞的原因。1978 年，Penzias 和 Wilson 因為他們的發現獲得了諾貝爾獎。 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. 當一個科學家有了一個發現，他可能會驚呼「尤里卡！」和手舞足蹈。他大腦中某個地方的神經網絡產生了一種[泛化](https://supermemo.guru/wiki/Generalization)，結果發出了獎勵信號。這進一步傳播，讓一個老人像孩子一樣在實驗室里跳躍。 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。一個在空房間里蹣跚學步的孩子會尋找環境中的低概率成分，比如彩色物體、新玩具等等。當一個蹣跚學步的孩子用勺子從桌子上掉下來做實驗時，她就像一個小科學家。然而，當大腦做出「_所有落下的勺子都會發出聲音_」的[泛化](https://supermemo.guru/wiki/Generalization)時，她也會得到獎勵。她可能會以和快樂的科學家完全一樣的方式慶祝，與年齡無關。一個大大的微笑是第一個明確的信號。 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[學習內驅力](https://supermemo.guru/wiki/Learn_drive)控制的各種形式的學習中，同樣的快樂在較小程度上也會發生。不管我們是否了解名人或巖石的化學成分。事情很有趣，因為它們通過學習內驅力機制獎勵大腦。 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. 一個創造性的過程也會產生獎勵。一個被認為有用的聯想是有回報的。一個能解決難題的聯想更有價值。顯然，獎勵是有等級的。該系統可以量化信息、聯想或解決方案的概率。概率越低，獎勵越高。 ### 6.10 Knowledge valuation network ### 知識評估網絡 #### 6.10.1 Knowledge valuations #### 知識評估 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. 大腦處理過的所有知識片段都會立即評估其相關性、[連貫性](https://supermemo.guru/wiki/Coherence)和價值。我們能立即知道這些信息是否可以理解的和有用的。當它們[前后矛盾](https://supermemo.guru/wiki/Consistency)、[不連貫](https://supermemo.guru/wiki/Coherence)或不相關時，我們也常常會立即注意到。 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 \(e.g. the answer to "_Will you marry me?_"\). 不尋常和令人驚訝的知識非常有價值，然而，從大腦的角度來看，概率并非價值的最佳反映。有極不可能發生的低重要性事件（例如，小行星撞擊遙遠的行星系統），也有很可能的發生改變一個人生活的事件（例如，「_你愿意嫁給我嗎_？」）。 #### 6.10.2 The emotional brain and the rational brain #### 感性腦和理性腦 **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. **知識評估網絡**是一個基于感性評估和理性評估相結合的評估系統。感性評估將把信息與原始大腦中樞負責饑餓、口渴、性沖動等的獎勵聯系起來。理性估值將以知識為基礎。純粹感性評估的一個例子是對「_最近的快餐店在哪里？_」的回答。基于知識的評估可能更加復雜和高度網絡化，也就是說，依賴于一個次級評估網絡。對「_哪本書最適合我的考試？_」的回答通過一個人的目標來評估，包括通過考試，獲得影響工作前景的學位，并為終生目標做出貢獻。感性和理性的評估在解剖學上是分開的。感性評估來自于被比喻為三位一體大腦中更古老的部分：爬行動物和古哺乳動物結構。例如，丘腦處理的特定刺激可能會向杏仁體發送單獨的信號進行感性評估，向新皮層發送單獨的信號進行理性評估。感性化的大腦在基因上更古老。個性和教育決定了理性評估是否能控制或超越感性評估。 #### 6.10.3 Decision tree in fast thinking #### 快速思考中的決策樹 **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**. **知識評估網絡**是決定單個知識塊價值的記憶關系網絡。如果將學習解釋為一項任務，評估網絡將決定**感知到的任務價值**。 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. 用計算機的術語，知識評估網絡可以比作[決策樹](https://en.wikipedia.org/wiki/Decision_tree)。目標和情感決定了樹根的核心價值。知識片段之間的語義聯系可以被解釋為從目標到細節的部分價值轉移。精心整合和精心選擇的知識的組織良好的語義網絡需要幾毫秒才能做出專家決策。這就是卡尼曼所說的自動[快速思考](https://supermemo.guru/wiki/Fast_thinking)（如果你對需要緩慢解決問題的棘手問題感興趣，請參見[如何解決任何問題？](https://supermemo.guru/wiki/How_to_solve_any_problem%3F)）中。同樣的過程，作為決策或解決問題的基礎，參與知識評估。像許多專家的決定一樣，評估速度很快，而且通常是在意識參與度很低的情況下進行的。簡而言之，我們有時很快知道一些事情，卻無法完全解釋原因。這個過程幾乎不受我們自己的控制，更不用說學校老師的控制了。為了有效學習，估值必須很高。  > **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) > **圖：** xefer 是一種工具，有助于將知識理解為網絡。它依賴維基百科文章之間的語義鏈接。[試試看](https://xefer.com/wikipedia) #### 6.10.4 Valuation network in education #### 教育中的評估網絡 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_". 大腦在幾年到幾十年的學習過程中建立了一個評估網絡。通過睡眠中的優化和[遺忘](https://supermemo.guru/wiki/Forgetting_curve)，網絡被打磨和拋光，以實現高效運行。這使得采取估值捷徑變得容易。一個學生在選擇一本書時，可能不再把考試放在他整個人生的大背景中去考慮。他可能已經找到了一條捷徑:「_在接下來的 3 個月里，我只想通過地質學_」。 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. 知識評估網絡是高度專業化的，個體間的差異很大。理性和感性之間的平衡會有所不同。目標之間的平衡會有所不同。評估網絡在罪犯的思維中會有不同的形狀，在懷有以人類利益為基礎的崇高目標的研究人員的思維中也會有不同的形狀。 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. 網絡的發展將取決于個性、人生經歷和環境。一些性格特征，例如脾氣暴躁，可能會助長犯罪心理的形成。生命早期的一些創傷性事件可能助長基于單一想法的有偏見的網絡的發展。環境和現有知識將決定激情、興趣、目標和網絡次級評估。 > 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) > > 發展健康的評估網絡的理想途徑是一個[遠離創傷和長期壓力的童年](https://supermemo.guru/wiki/Stress_resilience)，沒有外部壓力影響感性評估，有大量的娛樂活動，在廣闊的[行為空間](https://supermemo.guru/wiki/Behavioral_space)中[自由學習](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. 所有促進大腦健康發展的策略也將促進飽滿、高度個性化和高效的評估網絡。這些都將成為[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的基礎。所有教育工作者都同意，我們希望幫助孩子更好地掌控他們的情感生活，培養聰明、有創造力和知識淵博的大腦。 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. 教育系統的主要問題是一種千篇一律的方法，在這種方法中，所有的孩子都以流水線的方式接受相同的知識，而很少考慮有效學習的關鍵組成部分：[學習內驅力](https://supermemo.guru/wiki/Learn_drive)。學習內驅力是一種完美的計算系統，它能將「大腦中代表知識的語義網絡的當前狀態」與「知識評估網絡響應環境中可用信息而產生的當前輸入」相匹配。如果這個孩子堅持要看 YouTube 上的視頻，那么他自己的大腦就是最好的權威。所有干涉都會影響未來的獨立性和創造性。 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 \(e.g. [13 years of school in a month](https://supermemo.guru/wiki/Schools_are_useless_in_teaching_English!#Inefficiency_of_schooling) or [1600% acceleration of learning during vacation](https://supermemo.guru/wiki/Learning_history:_school_vs._self-directed_learning#Self-directed_acceleration)\). 雖然講課老師可能會花 45 分鐘給孩子灌輸一長串符號，這些符號會產生低估值和可忽略不計的記憶，但是同一個孩子可以在 3 - 5 分鐘內訪問谷歌，識別出具有高價值的信息，并且容易編碼終身保留。對于在這一過程中受過良好訓練的孩子來說，在自學過程中獲得知識的效率可能會提高一個數量級。當我說「數量級」時，我只是謙虛和保守。我不想遭到夸張的指責。我在本文其他地方列舉了幾個具體比較的例子（例如[一個月內學習學校 13 年所教的知識](https://supermemo.guru/wiki/Schools_are_useless_in_teaching_English!#Inefficiency_of_schooling)，或者[在假期學習速度加快 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. 在我談到高價值知識時，Peter Thiel 談到了[冪律](https://supermemo.guru/wiki/Thiel_on_power_law)：經過磨練的一小部分核心技能可以帶來指數級回報。 > **Small investments in learning** can produce **dramatic changes** to individual lives and to the entire planet! > > **對學習的小投資**可以給個人生活和整個地球帶來**巨大的變化**！ #### 6.10.5 Knowledge valuation that affects the course of life #### 影響生命過程的知識評估 > Personal anecdote. [Why use anecdotes?](https://supermemo.guru/wiki/Why_use_anecdotes%3F) > > 個人軼事. [為什么使用個人軼事？](https://supermemo.guru/wiki/Why_use_anecdotes%3F) > > **My school tried to block the best thing in my life** > > **我的學校試圖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: > > 我有我自己鮮明的例子來說明評估網絡在對抗教育系統方面的力量： > > 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，我算出了發展長期記憶所需的知識復習最佳間隔的近似函數。這是就是 [SuperMemo](https://supermemo.guru/wiki/SuperMemo) 的誕生。最初，這個函數是用[筆和紙](http://www.super-memory.com/articles/paper.htm)來實現的。幾個月后，我意識到這個系統非常強大。我知道我可以用電腦把它的力量翻倍。然而，我不知道有誰能根據我的函數來編寫學習軟件。那時候，波蘭的所有程序員是要么是在大型機上寫 Fortran 或 Cobol 的老家伙，要么是在使用 ZX 81、Commodore 64 或 ZX Spectrum 等微型計算機的越來越多的業余愛好者。我決定自己寫這個程序。但是我沒有編程技能。我是計算機科學的學生，我向老師尋求幫助。然而，我們唯一的編程課程是 Datapoint 匯編語言。這些技能對于玩玩寄存器得出 11 \* 11 = 121 非常有用。但我想學習一些對 SuperMemo 編程有用的東西。我的學校一直要求我學習計算電路的電阻，或者學習符號積分。我的知識評估網絡產生了一個簡單的輸出：編程技能——> SuperMemo ——>更快的學習（在所有領域，包括電子或微積分）。我決心學習編程。我的學校決心阻止我\(通過要求選修其他必修課程\)。無奈之下，我報名了經濟大學，該大學開設了算法語言課程。這門課程的重點是 [Pascal](https://en.wikipedia.org/wiki/Pascal_%28programming_language)。我不得不在做平時的功課的同時，在額外的時間里學習\) Pascal。那門課程不錯，但我們都在學習理論。紙上談兵。當時（1986 年）波蘭大學里的個人電腦很少，大多數實用的應用都在名為 [Odra](https://en.wikipedia.org/wiki/Odra_%28computer) 的大型機上運行（從 1960 年開始，為在波蘭的蘇聯集團生產）。當我終于得到了我的第一臺電腦：[ZX Spectrum](https://en.wikipedia.org/wiki/ZX_Spectrum)（1986 年 1 月 4 日），我終于可以開始學習編程語言了。在電腦到來之前，我開始寫我的第一個程序！在紙上。這是一個組織我一天的計劃（類似 SuperMemo 中的[計劃](http://help.supermemo.org/wiki/Plan)）。不久之后，我也學會了 Pascal。首先，我必須減少學校的不良影響，減輕課業負擔。我和我的電路老師達成了協議。我會為他做一些高通濾波計算，這將是提高我 Pascal 技能的機會。這個程序花了很多小時來寫，簡直是浪費時間。這是糟糕學習的完美例子。我幾乎不明白我自己的程序是如何工作的。然而，這仍然比僅僅學習圖解要好。就編程而言，那段學習很好，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. > > 對那些不懂編程的人來說，很難用語言表達出來，但是大學課程和自己編程之間的知識價值差異相當于李子和木星之間的大小差異。當我的同學們在電子學和計量學方面苦于無聊的講座時，我已經可創業了。我在學校什么也學不到。我可以在 Pascal 的課外課程中學到一點。然而，只有激情和明確目標支持的實用知識才是重要的。到了 1987 年 12 月，我的努力達到了頂峰，寫下了第一版 SuperMemo，這徹底改變了我的人生歷程。我的導師 Zbigniew Kierzowski 博士的開明態度讓我把我的碩士論文全部奉獻給 SuperMemo 這個主題。80 歲生日快樂，Kierzkowski 教授 ！對于一個學生來說，在這個尺度上做出自己的決定，然后再加上論文是用英語寫的，這是非常不尋常的。這一事實在今天并不罕見，但它涉及到 1989 年的一場大規模行政和戰術斗爭。 > > 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. > > 我的學校差點毀了 [SuperMemo](https://supermemo.guru/wiki/SuperMemo)，它是我現在快樂的主要來源。這里沒有惡意。我的大部分大學老師都是了不起的人。但學校是一個系統，旨在通過嚴格的課程來擠壓學生，而不是給他們創造表達的空間，然而創造表達的空間是教育的最好的基礎。 **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. **我的學校積極地試圖阻止我完成我整個職業生涯和未來最重要的事情**。如果我更聽話，更順從，更善于忍受社會壓力，我會成為一名「更好」的學生，在電路學、微積分、計量學和抽象代數的理論上投入更多時間。其結果是，這篇文章永遠不會被寫出來。這個網站也將不存在。 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. 我不會用我現在的生活去換取任何研究或行業上的其他職業。通過產生基于強大的知識估值網絡的抵抗，我挺過了否認我的攻擊。 > **We need to design an education system in which kids do not need to battle for the right to develop.** > > **我們需要設計一個教育系統，讓孩子們不需要為發展權利而斗爭。** ### 6.11 Learntropy ### 學習熵 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. 有許多因素會影響大腦對信息和信息渠道的感知和評估。在前面的章節中，我們已經注意到大腦不僅對熵有反應。還有許多因素可以調節單個信息的熵或意外的影響。這些因素包括：編碼、傳授速度、預處理（例如泛化、完善、識別等），預備知識（包括估值、情緒價、渠道可靠性等），最佳水平（受處理速度影響），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. 這一過程的復雜性要求有一個更好的概念來封裝所有這些細微差別。我建議使用學習熵這個術語來描述從特定背景下的個人大腦的角度來看的教育渠道或信號的吸引力。 > **Learntropy is the attractiveness of any educative signal as determined by the learn drive system.** > > **學習熵是由學習內驅力系統決定的任何教育信號的吸引力。** Lectures can be boring or attractive. Learntropy expresses their attractiveness from the point of view of an individual. 講座可能很無聊也可能很吸引人。學習熵從個人的角度來表示它們的吸引力。 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). 雖然熵有一個精確的數學定義，但是學習熵最好用「獎勵系統」對從「分析的信號」中學習的行動的反應來衡量。正如熵取決于單個信息的概率一樣，學習熵也取決于這些信息（圖片、聲音、句子等）的獎勵效應。這種獎勵效應將與概率相關聯，但評估將在很大程度上取決[知識評估網絡](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). 好的學習是有獎勵的。然而，也有不好的學習。因為這里有解碼失敗懲罰。如果一名學生努力解碼一條信息，但失敗了，他將受到懲罰。挫折就是這樣產生的。這就是不喜歡學習的開始。如果學習熵低，獎勵少，懲罰高，最終結果可能是負面的。如果我們把負面的獎勵信號考慮在內，學習熵實際上可以為負值。無聊的講座可能會帶來負值的學習熵。這會導致抑制[學習內驅力](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. 高知識估值導致高[學習熵](https://supermemo.guru/wiki/learntropy)，而學習熵又為長期記憶知識的注意力和語義插入提供了必要條件。在強大的反饋循環中，**學習熵增強了**[**學習內驅力**](https://supermemo.guru/wiki/Learn_drive)**，這是決定學習熵的估值的基礎。**這種反饋循環通過遺忘、習得性無助、衰老、傷害和精神資源的純粹可用性來控制。通過合理的學習和生活方式管理，特別是[自然創造力循環](https://supermemo.guru/wiki/Natural_creativity_cycle)，這種平衡可以在高[學習內驅力](https://supermemo.guru/wiki/Learn_drive)水平上保持幾十年。 ### 6.12 Signal timing vs. learntropy ### 信息時機與學習熵 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. 從學習流中的各個信息獲得的獎勵程度將決定信息學習熵的水平。關于無聊話題的講座的學習熵很低。在網上尋找解決某個特定問題所需的信息會帶來很高的學習熵。 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). 與基于平均值的[香農熵](https://en.wikipedia.org/wiki/Entropy_%28information_theory)不同，學習熵更像是一個趨勢平均數，最新的信息將比之前傳授的信息具有更高的權重。此外，學習熵植根于支配[記憶鞏固](https://supermemo.guru/wiki/Two_component_model_of_memory)的規律，包括[間隔效應](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 \(e.g. during a lecture\). 一旦高價值知識填補了理解上的一個重要空白，枯燥的演講的學習熵就會激增。學習熵的增加與決定知識估值的記憶[穩定性](https://supermemo.guru/wiki/Stability)曲線的表達式成正比（包括[知識估值網絡](https://supermemo.guru/wiki/Knowledge_valuation_network)中的下降曲線）。隨著時間的推移，高價值知識的影響將逐漸減弱。這些令人愉快的發現的累積效應將決定在任何特定時段（例如在演講期間）的學習熵的水平。 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，教育者可以影響學習熵，增強[學習內驅力](https://supermemo.guru/wiki/Learn_drive)，并提高長期學習效果。灌輸知識是一種糟糕的策略。提供答案應該是有選擇性的，應該傾向于回答高度重要的抽象和普遍的問題。自主學習的自由探索是終身保持[學習內驅力](https://supermemo.guru/wiki/Learn_drive)和終身學習的最佳方案。 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. 所有形式的學校教育都傾向于抑制[學習內驅力](https://supermemo.guru/wiki/Learn_drive)。因此，許多成年人可能會發現很難內化關于學習熵在學習中的重要性的信息。然而，在現代世界中，幾乎每個人都需要獨自解決一個小的技術或健康問題。這個問題可能就像在 Facebook 上微調一個選項一樣簡單。問題的解決方案越難找，找到答案的回報就越大。越難找到的答案，其搜索和探索的過程就越持久和廣泛。每個人都應該熟悉這種感覺。然而，對[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的抑制往往會導致知識的減少，自尊心的降低，所有的探索都可能提前結束。換句話說，那些在學校或以后的生活中失去創造力的人會更早放棄，甚至可能永遠不會嘗試。從這個意義上說，計算機、互聯網、技術等帶來的所有技術問題和故障都有一些積極的副作用，即使是在最被動的人身上，也會刺激殘留的[學習內驅力](https://supermemo.guru/wiki/Learn_drive)。唯一的要求是這些任務需要以一定程度的成功結束。否則，可能會出現相反的情況。懲罰信號可能讓我們從探索中退出。 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? 你可以快速回答這個關于你自己的[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的即時測驗。如果你在生活中遇到一個小問題，你是尋求人類專家還是依賴谷歌？如果你的車出故障，或者你的電腦崩潰，或者你受傷，或者你胃痛，你會去哪里？ ### 6.13 Learntropy and learn drive ### 學習熵和學習內驅力 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. 在一個類似[遺忘](https://supermemo.guru/wiki/Forgetting_curve)的過程中，[學習熵](https://supermemo.guru/wiki/Forgetting_curve)的獎勵的影響將隨著時間的推移呈指數級下降。就像[間隔重復](https://supermemo.guru/wiki/Spaced_repetition)復習一樣，新的獎勵將學習熵恢復到更高的水平。就像[間隔效應](https://supermemo.guru/wiki/Spacing_effect)一樣，較長的休息時間可能會導致相同的信息有更多的獎勵。 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. 在學習過程中，決定學習熵的獎勵信號和決定回憶的鞏固信號有很大的區別：一旦你學會了什么，在短時間內重復復習是沒有意義的，一旦你把回憶的概率提高到 100%，你可以在下次復習之前讓時間流逝。學習熵的上限可能很難達到。如果你喜歡一場演講，有一些歪曲的事實或講授，你會更喜歡它。如果你只記住一段記憶，你不可能在短時間內用技巧更好地記住它。你可以使用助記技巧重新構建記憶并影響其持久性，但是一旦回憶概率達到100%，記憶的最好的辦法是讓它閑置一段時間或在不同的上下文中使用它，這可能會導致開發新的記憶，從而與原來的一段記憶形成冗余連接。 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) \(incl. forgetting through brain cell loss\). 學習熵的消失是由于缺乏獎勵信號。[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的消失是一個[遺忘](https://supermemo.guru/wiki/Forgetting)的問題（包括因部分腦細胞死亡而遺忘）。 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 \(see: [Addiction to learning](https://supermemo.guru/wiki/Addiction_to_learning)\). 學習熵將以指數下降和收益遞減的方式疊加在每個信息上。通過優化獎勵信息的出現時機，我們可以將學習熵提高，使學習成為與食物、性、藥物等獎勵相同的最令人愉快的活動之一。如果你對此持懷疑態度，請回憶一下那些沉迷于電子游戲的玩家，他們可以在晚上餓著肚子玩游戲。[電子游戲](https://supermemo.guru/wiki/Videogames)可以劫持[學習內驅力](https://supermemo.guru/wiki/Learn_drive)，并將其與賭博的獎勵結合起來。賭博的獎勵也可能受類似于學習熵的下降和提升規則的支配，然而，它們受制于[可變的獎勵](https://supermemo.guru/wiki/Variable_reward)，這可能導致上癮。重要的是要區分學習的樂趣和有害的上癮（參見：[對學習的上癮](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 \(i.e. health in general\). 學習熵將決定[學習內驅力](https://supermemo.guru/wiki/Learn_drive)，但兩者將以不同的規則來維持。學習內驅力依賴于知識，因此將受到[間隔重復](https://supermemo.guru/wiki/Spaced_repetition)的影響。由于知識是一個網絡，所以說學習內驅力的最佳激勵可能是沒有意義的。為了最大限度地提高學習內驅力，我們應該終身學習，尊重[自然創造力周期](https://supermemo.guru/wiki/Natural_creativity_cycle)，并注意大腦的健康（即一般的健康）。 ### 6.14 Optimum information delivery ### 最佳信息傳授 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. 在學校教育中，我們可以設想一個處于最佳[學習熵](https://supermemo.guru/wiki/learntropy)水平的講課，在這個講課中一個學生不斷地說「哇！哇！」她保持盡可能快地記筆記。但是，更常見的情況是，課堂會發出一個高熵信息或讓人感到無聊。它的學習熵會很低，甚至是負數。 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. 如果最佳學習熵水平取決于學生，老師該如何最好地向全班學生傳授知識？有時候適合所有人的傳授是不可能的。換句話說，這困難到需要天才的教學技能。對大多數老師來說，他們的傳授使大多數孩子感到無聊或沮喪。 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). 在課堂上，少數幸運兒可能會理解大部分信息。對于一小部分有天賦的人來說，這堂課可能沒有帶來多少新知識。對他們來說這很無聊。對于其他孩子來說，信息的復雜性超出了他們的理解水平。在這種情況下，如果他們試圖理解，老師的講課可能會令他們挫敗。一個關于弦理論的講課可能相當于一個隨機打亂排列的英語單詞序列的噪音。講課是一種浪費時間的活動。諾貝爾獎得主 [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. 為了避免負學習熵帶來的挫敗感，學生們會像你從我[前面](https://supermemo.guru/wiki/I_would_never_send_my_kids_to_school#Prior_knowledge_in_information_seeking)提到的泰國頻道換臺一樣「換臺」。孩子們會忽略來自老師的「靜電噪音」，并收聽到其他「頻道」，這些頻道有更合適的學習熵水平（例如桌下的手機上的 Facebook）。即使他們的理解能力很好，所傳授的知識也可能無法與他們目前的知識互補。如果這些知識不能產生[高質量高價值](https://supermemo.guru/wiki/Knowledge_valuation_network)的泛化，就會被認為是顯而易見或毫無關聯的。 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! 低學習熵，即使偶爾發生，也會讓學生習慣性「換臺」。一段時間后，學生們將「進化」一種過濾器，將教師當做一個零熵和零學習熵的無聲廣播頻道。提高講課質量將是徒勞的。老師在學生心中消失了！ 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，學生往往不能集中注意力在更好的信息上。老師向所有學生傳授同樣的信息，他們都可能同樣感到無聊。相比之下，在谷歌上搜索好的關鍵詞，[可以像拼圖游戲一樣，用符合當前知識樹](https://supermemo.guru/wiki/Knowledge_crystallization)的低概率信息連續轟炸大腦。谷歌是一個非常便宜和高效的「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[漸進學習](https://supermemo.guru/wiki/Incremental_learning)中，學習熵檢測器將選擇最佳通道，對這些通道進行優先排序，并利用最佳時機最大限度地提高語義連接和記憶整合。這應該很容易理解為什么**我非常高興，我永遠不會被迫坐在學校的長椅上！**我太愛學習了！ 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. 所有以上的例子說明了信息和大腦在識別值得學習的東西時的相互作用是多么復雜。學習的獎勵是最好的已知的學習質量指標。當學生快樂的時候，我們就走在正確的路上。當學校成為痛苦之地時，我們在社會上就失敗了。 > **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.** > > **唯一可靠的知識互補和一致性檢測器是學習內驅力系統的神經網絡。這就是為什么知識不能預先包裝并強加于學生的原因。** 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). 這是可以用[結晶比喻](https://supermemo.guru/wiki/Knowledge_crystallization)來解釋。獎勵系統的神經學細節在以下一節中介紹：[學習獎勵](https://supermemo.guru/wiki/I_would_never_send_my_kids_to_school#Learning_rewards)。 ### 6.15 Gripping lectures ### 令人全神貫注的講課 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. 我們喜歡學習，但我們通常討厭別人教導我們。這些感覺與創造力相關，這可能可以用這樣一個事實來解釋：創造性的闡述對于[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礎。 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)! 在學習中，我們決定研究什么。[學習熵](https://supermemo.guru/wiki/learntropy)的評估嚴格取決于大腦和當前的記憶激活的狀態。在教學中，知識的傳授是與我們對它的看法無關。許多學生把枯燥的科目列為他們不喜歡學校的首要原因。不是欺凌，不是壓力，也不是早起。而是極度的無聊！我在[這里](https://supermemo.guru/wiki/Learning_history:_school_vs._self-directed_learning)寫了關于自我導向學習和在學校學習之間的極大差異。這一切都源于[學習內驅力](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. 我感到驚訝的是，有這么多的資源浪費在尋找讓孩子們在講課期間保持興趣的方法的研究上，而講課顯然只是一種糟糕的教育工具。眼神交流分析？參與分析？努力量化激情？所有的孩子都有了天生的[學習內驅力](https://supermemo.guru/wiki/Learn_drive)，我們的首要任務應該是確保我們不會破壞這種內驅力。對[學習內驅力](https://supermemo.guru/wiki/Learn_drive)起主要破壞作用的是[強制灌輸知識](https://supermemo.guru/wiki/Schools_suppress_the_learn_drive)。此外，還有許多社會經濟因素使許多兒童即使在最好的環境下也不能茁壯成長。有些孩子永遠不會表現出對學習的熱情。在大多數情況下，這不是他們的錯。只有一小部分孩子受到殘疾、健康問題和不幸的遺傳天賦的限制。[學習內驅力](https://supermemo.guru/wiki/Learn_drive)隨年齡增長呈指數衰減的主要原因是義務教育。被動的授課助長了這一過程。 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. 當然，有些授課是有效的。[可汗學院](https://supermemo.guru/wiki/Khan_Academy)里有很多很好的例子。即使是沒有幻燈片的口頭演講也能起作用。YouTube 上的 TED 演講可能會很有趣。它能滿足[學習內驅力](https://supermemo.guru/wiki/Learn_drive)。[MOOC](https://en.wikipedia.org/wiki/Massive_open_online_course) 是建立在這樣一個原則之上的，即一位搖滾明星教師比數千名重復同樣咒語的普通教師要好。即使你只是一個被動的傾聽者，你也能學到很多東西。但是，也有一些條件：你需要對主題充滿好奇，或者你需要喜歡這個演講者，或者兩者兼而有之。**只有一個確定的機制可以確保講座是有趣的：你需要自己選擇它！**這只是自我導向學習要求的又一個方面。 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. 除了選擇之外，在講課時，你絕對需要一個暫停按鈕，以防你需要上廁所，或緩解饑餓感。沒有什么比爆裂的膀胱更能有效地毀掉一場講課了。最后但并非最不重要的一點是，大多數講課都可以受益于 Netflix 的「跳過開頭」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. 當然，如果你用你自己的創造性思維，甚至是快速的研究來提高講課的效率，講課就會發揮最好的作用。這就是為什么暫停一分鐘或一天可能是學習效率的關鍵。與一些精神科醫生的說法相反，創造性休息和走神與[多動癥](https://supermemo.guru/wiki/ADHD)無關。只要它們是毫不相關的，它們就是高效學習的標志。 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. 我使用兩種方法來漸進地學習講課。我的第一個方法是傾聽和鍛煉。鍛煉可以提高注意力。良好的專注減少了暫停的需要，然而，它也減少了學習的創造性方面。對于優先級最高的主題，我使用[漸進視頻](https://supermemo.guru/wiki/Incremental_video)，我可以在其中暫停和繼續多次。我甚至可以把最重要的講課節選留待將來復習。然而，即使是漸進視頻也不是最好的學習方法。它不能在速度和數量上與[漸進閱讀](https://supermemo.guru/wiki/Incremental_reading)相競爭。有時，使用漸進閱讀處理講課記錄比聽講課本身更有意義。這一點在充滿陳述性知識的講課中尤為明顯。 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)! 我主要根據我喜歡聽的演講者來選擇我的視頻材料。在這篇文章的背景下，我知道你會喜歡 Ken Robinson 的演講！去看看：[Ken Robinson：學校扼殺創造力](https://supermemo.guru/wiki/Robinson:_Schools_kill_creativity)！ ### 6.16 Learning rewards ### 學習獎勵 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. 學習的樂趣可能是最令人滿意的樂趣之一。與吃飯或性不同的是，學習的樂趣不會隨著行為結束而終止。學習的樂趣是可持續的，只有在參與學習的網絡過載的情況下，學習的樂趣才會慢慢減少。可以通過睡眠將其重置回基線。研究表明，學習的快樂與[海洛因或可卡因的快樂](https://supermemo.guru/wiki/Biederman_model)具有相同的機制。與吃或性不同的是，快樂的學習可以占據大部分醒著的時間。在這個意義上來說，學習、創造力、問題解決和生產力的樂趣可能是斯多葛派的享樂療法的主要工具。雖然一個健康的人很容易滿足對食物的需求，但是對學習的需求可能永遠不會停止。[學習內驅力](https://supermemo.guru/wiki/Learn_drive)取決于當前知識的狀態，而這種狀態可以被學習本身所操縱。 > **All people with mood swings should consider learning as therapy.** > > **所有情緒波動的人都應該把學習當作治療。** #### 6.16.1 Learn drive reward #### 學習內驅力獎勵 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. 我已經提到了幾個例子來說明[學習內驅力](https://supermemo.guru/wiki/Learn_drive)是如何在大腦中產生獎勵信號的。我們知道低概率的信息是有獎勵的。提供新知識的泛化也是如此。一個導致理解突破的信息片段是很有價值的。[拼圖中缺失的一塊拼圖](https://supermemo.guru/wiki/Jigsaw_puzzle_metaphor)帶來了巨大的獎勵。一個生僻的詞，一旦被解碼，就能使一整篇長的文本從雜亂無章的句子轉換成清晰的推理。 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. 通過泛化來確認一個模型，或者為一個更好的新模型奠定基礎，這兩種方法都讓人感覺很棒。此外，所有與強烈情感相關的模型驗證肯定都會讓人欣喜若狂：「_我的團隊是世界上最好的！_」或「_是的！我的新生兒真的很健康！_」，或「_耶！我就知道努力工作會使我升職的！_」。然而，當我們討論[學習內驅力](https://supermemo.guru/wiki/Learn_drive)時，我想過濾掉額外的可能會模糊圖景的情緒層。我們需要記住，學習是愉快的，并與它是否能從運用知識中得到獎勵無關。 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. 發現時的_啊哈！_、_哇！_、或者_尤里卡！_是學習中最純粹、最終極的獎賞。它不需要從別人那里得到更多的贊揚或獎勵。在這里，知識本身就是獎勵。 The common denominator of this reward is the encoding of new highly-valued information in memory. 這種獎勵的共同點是對記憶中新的高價值信息進行編碼。 > **The learn drive reward comes from high-value knowledge ready for long-term storage.** > > **學習內驅力的獎勵來自準備長期儲存的高價值知識。** 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.** 在我們尋求理解現實的過程中，隨著大腦中儲存的信息總量增加，儲存的知識的熵就會下降。**隨著不斷學習和建模，理解世界的復雜性所需的努力越來越少。** #### 6.16.2 Evolution of the learn drive #### 學習內驅力的進化 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. 科學家說聰明的動物玩得更多。我說，更有趣的是，那些玩得更多的物種更聰明。我猜想，**人類大腦體積變大的觸發因素可能是學習內驅力**，而不是鳥類或哺乳動物面臨的環境變化需要更多的思考。這并不是說，如果人類大腦皮層沒有變大，人類就會突然面臨滅絕。這可能是因為[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的出現，突然允許更好地使用數量增多的代價高昂的腦細胞。在[學習內驅力](https://supermemo.guru/wiki/Learn_drive)出現之前，增加大腦體積可能會給動物留下多余的頭部重量和一組多余的細胞需要供養。如果沒有[學習內驅力](https://supermemo.guru/wiki/Learn_drive)，多余的大腦空間可能會被閑置，這很可能會導致浪費性萎縮。如果學校教育試圖凌駕于[學習內驅力](https://supermemo.guru/wiki/Learn_drive)之上，它將有助于這種進化優勢被廢棄。學校教育將助長更不聰明、更沒有創造力的社會。 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. 如果我們在人類進化的時間線上畫出大腦的大小，我們可以看到大約兩百萬年前的一個巨大的增加。古人類學家傾向于將這種轉變歸因于飲食、[烹飪](https://en.wikipedia.org/wiki/Catching_Fire:_How_Cooking_Made_Us_Human)等方面為大腦提供更好的營養。 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) \(from weaning to an average of 7 years old\). 如果關于學習內驅力出現的猜想是正確的，那么_能人_就是這一突破的起點。這可能意味著從鳥類和哺乳動物簡單的程序性游戲內驅力向更復雜的陳述性學習內驅力的轉變，這種[學習內驅力](https://supermemo.guru/wiki/Learn_drive)最終導致我們建立現實的抽象模型，而這正是人類智能的基礎。據推測，從_能人_開始，人類有了[以大腦發育為主的童年](https://supermemo.guru/wiki/Homo_habilis:_the_emergence_of_childhood)（從斷奶到 7 歲左右）。 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. [學習內驅力](https://supermemo.guru/wiki/Learn_drive)在進化中的姍姍來遲表明，它不是神經網絡中出現的簡單功能（參見：[Biederman](https://supermemo.guru/wiki/Biederman_model) 模型）。否則，它可能很容易出現在魚類或更早的時候。[學習內驅力](https://supermemo.guru/wiki/Learn_drive)需要一組專用的神經結構，這些神經結構能夠在檢測到對陳述性知識的連貫結構的增量貢獻時，發送獎勵信號。這種信號和潛在結構可能在程序性學習和陳述性學習中有所不同。對于不同類別的感官輸入，它也可能有所不同。 #### 6.16.3 Procedural learning reward #### 程序性學習獎勵 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 世紀 80 年代，我猜想了一種[可能進行程序性學習的神經網絡](http://www.super-memory.com/english/ol/ol_files/refinement_circuitry_in_stochastic_learning.jpg)。在我的[碩士論文](http://super-memory.com/english/ol.htm)中，出于無知，我使用了我自己的術語「[_隨機學習_](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 提出了一個符合我自己想法的小腦皮理論模型。在新世紀里，[有大量的數據證實了這個模型](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. 程序性學習神經網絡的思想是非常簡單的。想象一下你騎自行車。你運用你的意識去學習騎自行車所需的每個動作，然后繼續騎車。然而，一旦你在路上，程序性學習系統確保你可以用最少的神經參與自動執行所有的動作，而不需要有意識的監督或只需要最小限度的監督一組[指令神經元](https://en.wikipedia.org/wiki/Command_neuron)。程序性學習將決定你的[運動程序](https://en.wikipedia.org/wiki/Motor_program)。這個程序性學習系統將對發送到運動系統的信號序列進行微小的隨機調整（因此被稱為「_隨機學習_」）。您可以將這些隨機調整視為程序性創造力。每當你的自行車失去平衡，一個懲罰信號將從錯誤檢測網絡發送，去取消發送的修正。這個懲罰信號將為運動程序起一個指導信號的作用。 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. 在睡眠期間，記憶將被重新組織、簡化、優化，對有意識的輸入的需要會被消除，而對技能貢獻不大的垃圾信號將被駁回。隨著每一公里的騎行，信號序列將通過反復試驗而得到完善。而每次睡眠，褶皺就會變得更光滑。騎自行車將成為一種樂趣。從笨拙的、有意識的騎手到本能的騎手的轉變中，這種樂趣可見一斑。 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. 以類似的方式，在計算機上鍵入每個句子時，你的打字錯誤會越來越少。你知道鍵盤上的「\)」在哪里嗎？「}」呢？你打字越流暢，你就越有可能忘記這個細節。當有意識地控制動作順序消失后時，鍵盤上「\)」位置的陳述性知識可能作為「垃圾」被丟棄。它不再被需要了。 #### 6.16.4 Declarative learning reward #### 陳述性學習獎勵 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. 在解釋陳述性學習內驅力時，情況要復雜一些。陳述性學習有明確的回報。有些事情是有趣的，發現真相是令人愉快的。在神經層面，大腦將掃描輸入并產生神經活躍，以尋找學習熵高的領域，這些領域最大限度地提供與當前記憶狀態相匹配的新知識。任何有意義的低概率信息都會被認為更具吸引力。明亮的分形圖案將被認為是美麗的。灰色的隨機顏色會被認為是無聊的。同樣的情況也會發生在更復雜的視覺信息上。生機勃勃的森林是美麗的。但同樣的森林在冬天、在干旱中，或者在環境污染的影響下，可能看起來并不吸引人。[StevenPinker](https://en.wikipedia.org/wiki/Steven_Pinker) 說，我們被充滿活力的圖像所吸引。但我不同意。因為吸引力要寬泛得多。我們可能同樣被一座死火山或南極洲的冰凍景觀所吸引。我們喜歡使用簡單模型表達復雜信息的環境、信號、信息或大腦活動。美麗海灘的圖片可以用一些簡單的形狀和紋理來表示。 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. 信息熵與數據的可壓縮性有關。信號處理從輸入開始。視網膜對視覺輸入信號進行 100 倍的壓縮。海馬體和視皮層接受形狀和關系的簡單表示。這些可能最終改變皮質長期記憶儲存中單個突觸的狀態。 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. 整個[學習內驅力](https://supermemo.guru/wiki/Learn_drive)都是建立在尋找有效的神經網絡知識表示方法的基礎上的。學習內驅力、睡眠中的記憶優化和遺忘對于最大限度地提高可壓縮性、抽象性、可用性和性能至關重要。這就是大腦如何通過簡單的表示來確保我們能夠看到一個復雜的世界。這是人類智力的核心。如果人工智能研究人員能夠為機器人配備類似人類的學習內驅力，只要有足夠的記憶空間，他們的學習能力可能是取之不盡用之不竭的。 #### 6.16.5 Reward centers in learning #### 學習中的獎勵中樞 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，研究人員報告稱，[在「高度好奇」的狀態下，伏隔核的活動增加](https://supermemo.guru/wiki/Curiosity_improves_learning)。他們還證明了我們所熟知的：這種狀態提高了記憶表現。此外，這種提高的表現也體現在附帶學習上，即不依靠本身激發好奇心的學習。這項研究在媒體上被廣泛報道，但卻以一種錯誤的解釋：「_好奇心會激發大腦，以獲得更好的記憶_」。例如，《科學美國人》的標題為「_神經成像揭示了大腦的獎賞和記憶路徑是如何激發大腦對知識的探究_」。這篇論文本身就提出了「_激發好奇心_」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". 由于獎勵中樞可能涉及快樂的預期，我們更應該把研究的結果看作是[學習內驅力](https://supermemo.guru/wiki/Learn_drive)與快樂相關的一個跡象。正是學習內驅力引發了學習。學習才是令人愉快的。標題應該是「神經成像證實有效的學習是令人愉快的」。換句話說，順序不是「內驅力->快樂->學習」，而是「內驅力->學習->快樂」。 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. 與其說需要「激發好奇心」，不如說需要「培養[學習內驅力](https://supermemo.guru/wiki/Learn_drive)」。關鍵的區別在于，將刺激視為一種可能在課堂上使用的快速解決方法，而不是需要數月甚至數年的長期過程。一次廣告宣傳活動可能會用一些廉價的手段來激發我們的好奇心，而終身的激情則是永不滿足、永不衰退的學習內驅力的配方，這是不斷學習的完美保證。 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. 的確，好奇心的狀態會提高注意力，這會提高整體的學習，但是，這永遠不應該作為一種課堂策略來使用。只有當獎勵來自學習對象，而不是來自學習對象的周邊，學習的游戲化才有意義。許多兒童學習程序使用鮮艷的顏色，不尋常的聲音或微笑的臉來吸引注意，以誘導學習。然而，一旦習慣性了它，這種形式或人工游戲化就不再有效。而且，附帶的知識不會持續很久。任何利用好奇心來激發附帶學習的努力都是不具體的和低效的。同樣，我們可能希望藥物干預，如利他林，可以改善學習。然而于此相反，學習必須基于它本身的獎勵。 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. 伏隔核和腹側被蓋區參與了快樂、對快樂的預測和信號評估。來自[知識評估網絡](https://supermemo.guru/wiki/Knowledge_valuation_network)的信號在[動機和情感](https://supermemo.guru/wiki/Brain_centers_involved_in_valuation_of_anticipated_outcomes:_nucleus_accumbens_and_VTA)兩個方面匯合到這些區域。多巴胺與快樂的預期有關。由于多巴胺參與了注意力，僅僅是對快樂的預期就會導致學習的改善，這是因為更加關注預期傳遞快樂的信息來源。 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，想想你有多討厭你的新聞頻道，當他們耍詭計激起你的興趣時，然后說“休息后再找出答案”。當他們用“突發新聞”毀掉這一切時，你會變得更加憤怒。期待也會導致挫折。只有實際的學習才能提供獎勵。從進化的角度來看，只有實際的學習獎勵才有意義。我們不想將僅僅給動物看食物作為獎勵。 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**. 伏隔核中的嗡嗡聲可以是快樂的直接表達，也可能是尋找快樂的狀態。最后，對于最終的結論，實際的解釋并不重要：**無聊和不快是學習的敵人**。 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! 為了進行用新知識補充現有知識的有效學習，我們需要遵循[學習內驅力](https://supermemo.guru/wiki/Learn_drive)。簡單地說，這意味著學習的快樂在教育中是可取的。我們決不應該在不愉快的狀態下學習（參見：[值得的難度](https://supermemo.guru/wiki/I_would_never_send_my_kids_to_school#Desirable_difficulty)）。大腦讓學生知道，在信息論的意義上，新的知識并不合適，痛苦的學習來自于此！它會被拒絕。而快樂是很好的向導！ 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.** 通過上面的神經學推理，我們得出了一個顯而易見的結論，**有效學習的最好保證就是讓學生自己學習，跟隨自己的激情。** ### 6.17 Biederman model ### Biederman 模型 #### 6.17.1 Pleasure of reading about the pleasure of reading #### 閱讀關于閱讀樂趣的樂趣 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 發表了一篇論文，給我帶來了令人難忘的閱讀樂趣。這篇文章本身向我解釋了閱讀的樂趣。在一篇題為“感知愉悅與大腦”的論文中，Biederman 假設，負責視覺感知的大腦結構中阿片受體的分布梯度可能有助于觀看美麗風景等美好場景的樂趣。Biederman 的想法似乎向我解釋了我多年以來所知道的：**學習是令人愉快的**。我總是喜歡學習，然而，我從來沒有真正理解我的喜歡在腦科學方面的基礎。Biederman 的解釋恰如其分，非常令人愉快。它解釋了一些困擾我一段時間的事情。在閱讀的那一刻，我非常善于自我分析。在閱讀的樂趣時，我試著去“感受”閱讀的啟示是如何提供快樂的。閱讀關于閱讀趣的樂趣變得令人難以忘懷。 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 所提議的是具有里程碑意義的。因此，為了簡單起見，讓我給他們的想法起個名字：**Biederman 模型**（根據資歷選擇名字）。在視覺感知中，連續的神經元層負責對視覺場景進行更抽象的表示。打個比方，它從像素和顏色開始，然后移動到邊緣、紋理和曲面，然后移動到對象，然后是面、位置和集合，然后是有意義的場景，在鏈的末端，這些場景可能會激活一座“美麗的山”的表示，就這樣被記住，只有幾個細節在工作記憶中的第一印象之外永久化。一張數以百萬計的像素的照片將變成一個有意義的場景，只需幾句話就能用語言表達出來，并以這種方式被記憶多年，只需很少的神經代價。 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 模型利用了早些時候的一項發現（[MichaelE.Lewis 等人，1981年](http://supermemo.guru/wiki/Opioid_receptors_form_a_gradient_along_a_processing_hierarchy)），即沿著視覺感知路徑存在μ-阿片受體的分布梯度。神經元攜帶的意義越多，它可能具有的阿片受體就越多。我們知道，阿片類藥物是有益的和上癮的。Biederman 模型基于這樣的假設：阿片受體的分布梯度是感知快感的來源。 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). 在處理語音和音樂方面也有類似的層級系統。顳葉皮質涉及處理從音調到旋律的聲音。然而，處理節奏也涉及到大腦的其他區域。很有可能，所有這些感知網絡都遵循類似的原則。這是[神經美學](https://en.wikipedia.org/wiki/Neuroesthetics)的研究課題。 #### 6.17.2 Opioid vs. dopamine pleasure #### 阿片類藥物與多巴胺的快樂 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 模型有一個小問題。學習的樂趣可以有意識地分析。在我自己的例子中，閱讀 Biederman 模型的樂趣可以分解并追蹤到模型的各個組件。這一事實意味著快樂與有意識的體驗是結合在一起的。眾所周知，意識是神經科學難以破解的難題。我們所知道的關于意識的大部分知識要么是推測的，要么是基于硬而昂貴的實驗，在這些實驗中，植入大腦的電極可以被用來引起效應，這些效應可以稍后或同時由受影響的個體報告。證據似乎集中在意識的綜合模式上，在這種模式中，大腦中幾個結構的激活被整合起來，并被視為有意識的自我。按照這一思路，激活大腦皮層某個部位的 Halle Berry 神經元，并不足以將 Halle 帶到一個人的意識中。數以百萬計的概念神經元可以在同一時間被激活，一個思維只能在感知現實的模型中的一小部分上運作。要將 Halle 帶到一個人的頭腦中，激活必須與意識感知的其他組成部分結合起來，包括感知獎勵。 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). 由于這些原因，大腦皮層神經元中的阿片受體不會對學習的最終回報起到很大的作用。阿片類藥物的拮抗劑，[納洛酮\(Naloxone\)，可以奪走某些人對音樂的樂趣](https://supermemo.guru/wiki/Thrill_of_music_may_be_attenuated_with_opioid_antagonists)。然而，阿片類藥物的學習樂趣應該產生首次使用微量海洛因或嗎啡所產生輕微的幸福。從這個意義上說，內嗎啡肽的釋放和阿片受體的激活可以促進學習的快感。盡管如此，這種快樂還不夠具體到讓人驚呼「哇！」，「啊哈！」或者「尤里卡！」（Biederman 稱其為「理解的點擊」）。對于最終的學習獎勵，必須有來自[大腦中快樂中樞](https://supermemo.guru/wiki/Neural_circuits_involved_in_liking_and_wanting)的綜合獎勵體驗。 #### 6.17.3 Pleasure of association #### 聯想的快樂 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. 發現的最終樂趣震撼將來自于一種有意義的聯想。這可以用理解 Biederman 模型本身的樂趣來解釋。當我們思考這個模型時，我們激活了我們大腦中的兩個重要概念：（1）意義的梯度（來自對視覺感知所涉及的神經結構的理解），（2）愉悅的梯度（來自對視覺通路中阿片受體含量的觀察）。一旦想到這兩個概念，就會有一個類比的粘合劑：「梯度」的概念。這種膠水有助于產生一種令人愉快的啟示的聯想：**意義=快樂**！這正是我在讀 Biederman 的論文時所經歷的。要使這種震撼發生，光有更多的阿片受體與快樂梯度的概念相關聯，而不是與梯度的數學基礎或它與「梯度」一詞的關聯，是不夠的。與通常使用的「意義」一詞相比，與「意義梯度」這一新穎概念相關的阿片受體還不夠多。當這兩個高價值的概念發生碰撞時，就會發生震撼：意義 + 快樂。 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，受體的梯度很遠地延伸到聯想區，包括海馬旁皮質。我們可能還記得，在更遠的下游，我們在海馬中發現了 [Halle Berry 神經元](https://supermemo.guru/wiki/Pleasure_of_learning#Detecting_surprisal)。為了說明阿片類藥物的快樂和聯想的快樂之間的區別，讓我們想象一下在一個美麗的海灘上遇見 Halle。當我們走在海灘上時，我們可能會感受到一股如海洛因一樣的幸福之風，這是因為我們意識到我們的環境很美：「我走在沙灘上感覺很棒」。一旦 Halle 出現在地平線上，視覺分析可能會提供另一種阿片類藥物的快感來自「美麗的女士接近」的信號。然后，視覺處理單元可以確定這位女士是 Halle，這可能激活 Halle 的皮層代表，這可能是阿片類物質豐富的。然而，只有 Halle 和「我的海灘」的最終結合才會引發一個重大發現，也許是一個古老的生殖夢想：「Halle 和我走在同一片沙灘上！」這就是來自腹側紋狀體和伏隔核的獎勵可能會在「喜歡」的情況下發揮作用，而[多巴胺](https://supermemo.guru/wiki/Opioid_rewards_may_depend_on_dopamine_signals)的一擊可能會觸發「想要」的行為程序。該行為「想要」計劃的細節已經從這篇文章中被刪掉了。然而，在高度發育的個體中，來自前額葉皮質的執行信號將不可避免地停止該程序的執行。簡而言之，在大腦的快感中樞注射多巴胺可能會給大腦帶來一些不雅的想法，而阿片肽的釋放可能只會帶來一種聯想的幸福。 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). 學習的樂趣不需要涉及有吸引力的異性代表。Halle 出現在我的例子中，只是因為發現了 [Halle Berry 神經元](https://en.wikipedia.org/wiki/Grandmother_cell)。為了學習的樂趣，所有需要的是一個強大的和高度重視的想法，激活大腦中的快樂中樞的聯想。每次我們學到新的東西，快樂就會發生，而當我們學到高價值的東西時，這種震撼是最強大的。發現 Biederman 模型的樂趣來自于在我的[知識評估網絡](https://supermemo.guru/wiki/Knowledge_valuation_network)中對學習本身的樂趣的高度評價。 #### 6.17.4 Impact of memory on the pleasure of learning #### 記憶對學習快樂的影響 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. 我還想補充 Biederman 關于脫敏的假設，即反復接觸會使樂趣下降。Biederman 認為，孩子們因為賭博的因素喜歡重復的[電子游戲](https://supermemo.guru/wiki/Videogames)。然而，賭博在成年人中同樣有效。我認為孩子們因為[童年失憶癥](https://supermemo.guru/wiki/Childhood_amnesia)更喜歡重復學習。一些重復的快樂可能來自于有限的理解，但有些僅僅是由加速遺忘來解釋。理解能力差和遺忘是成人大腦和兒童大腦的主要區別。 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. 我們還應該注意到，復習樂趣的大幅下降將不是來自競爭性學習，而是來自[長期記憶的鞏固](https://supermemo.guru/wiki/Two_component_model_of_memory)，這可能導致信號在系統中高效地傳遞。競爭性學習在模式識別中可能很重要，但在聯想學習中，[高可提取性](https://supermemo.guru/wiki/Retrievability)會破壞重復接觸的樂趣。 #### 6.17.5 Stages of learn drive evolution #### 學習內驅力進化的幾個階段 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. 當我假設在人類中出現了強大的[學習內驅力](https://supermemo.guru/wiki/Learn_drive)時，我想到了從知識到獎勵中樞的直接渠道。它最終將成為比 Biederman 模型所暗示的更高水平的[學習內驅力](https://supermemo.guru/wiki/Learn_drive)。每一次涉及到受體，進化都有一種簡單而感激的材料可供處理。受體梯度最初是在[恒河猴皮層](https://supermemo.guru/wiki/Opioid_receptors_form_a_gradient_along_a_processing_hierarchy)中發現的。類似的機制可能涉及更簡單的大腦，甚至更原始的、沒有中樞控制的神經系統。我不知道一只螞蟻在想什么，也不知道它的感覺如何，但是找到一個很好的食物來源肯定是某種螞蟻快樂的源泉。由此我們可以得出結論，學習的樂趣可能不會比神經系統本身更年輕。然而，在進化的過程中，驅動建立了新的功能層和效率層。有趣的創造力似乎只出現在一些鳥類和哺乳動物身上。比如人類[學習內驅力](https://supermemo.guru/wiki/Learn_drive)，這種進化過程可能已經達到頂峰。這在某種程度上自然會在思維機器中實現。了解[學習內驅力](https://supermemo.guru/wiki/Learn_drive)對于人類的生存至關重要：無論是對人工智能的需求，還是對人工智能轉向人類的威脅。 ### 6.18 Desirable difficulty ### 值得的困難 **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. **值得的困難**是一個概念，可能成為一個容忍在學校學習的不悅的借口。我在這里解釋為什么這個借口是不公正和危險的。 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 可能是學習理論方面最好的專家。如果他告訴你在學習中困難是值得的，他是對的，而且這與好的學習總是令人愉快的事實并不矛盾。值得的難度是學習中引發更好的學習的障礙的概念組合。讓我們從學習的樂趣出發，逐一解決這些問題： * **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. * **主動回憶**：主動回憶優于被動回憶。主動回憶更難。這是一個值得的困難。我們在學習中需要主動回憶，因為它是在[間隔重復](https://supermemo.guru/wiki/Spaced_repetition)中有效地重新鞏固記憶符號的唯一過程。當我們在實踐中運用有用的知識時，主動回憶就會發生。這種使用是令人愉快的，因為它帶來了生產力，這是一種獨立于學習的回報。人類只是喜歡[實現目標](https://supermemo.guru/wiki/Setting_goals_can_change_your_life)。如果復習是人為計劃的，就像在 [SuperMemo](https://supermemo.guru/wiki/SuperMemo)，它不會產生一個富有成效的行為，它可能很容易失去它的吸引力。SuperMemo 的所有成功用戶都將復習與他們的目標聯系起來。他們認為每一項和每一次重復都是邁向更美好未來的一步。并不是所有的用戶都具有這種想象能力。這就是為什么 SuperMemo 沒有讓人類為之傾倒，盡管它有著驚人的效率。 * **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) * **間隔重復**：如果記憶的[可提取性](https://supermemo.guru/wiki/Retrievability)較低，則記憶[鞏固](https://supermemo.guru/wiki/Consolidation)更有效。這導致了回憶的[困難](https://supermemo.guru/wiki/Recall)。這是一個值得的困難。與[主動回憶](https://supermemo.guru/wiki/Active_recall)一樣，[復習](https://supermemo.guru/wiki/Review)的獎勵來自于知識和生產力的運用。在 [SuperMemo](https://supermemo.guru/wiki/SuperMemo) 中，默認情況下，大多數復習都以成功的回憶結束，而且難度和快樂之間可能存在某種聯系。同樣，只有一部分 SuperMemo 用戶能找到這個過程的樂趣。那些通常不會堅持很久就會輟學的人。我們告訴所有用戶，讓SuperMemo變得有趣，否則它對你無效！另見：[知道的快樂](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. * **漸進復習**：SuperMemo 提倡在空間中學習。從記憶和創造力的角度來看，在較長的時間內閱讀一篇小篇幅的文章更有效率。這同樣指的是看視頻或聽講座。這導致了上下文檢索方面的一些小問題。然而，它在創造性的闡述中帶來了額外的獎勵。它還改進了記憶編碼、泛化和長期記憶鞏固。矛盾的是，這些額外的困難導致了額外的學習效率，這使得[漸進閱讀](https://supermemo.guru/wiki/Incremental_reading)成為最令人愉快的學習形式之一。 * **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. * **學習語境**：在回憶中改變語境是一種非常簡單和有效的值得的困難類型。如果編碼是正確的，回憶將會成功，它將是更有效的，它將是有益的。如果語境的改變導致泛化和更好的記憶編碼，學習的有效性就會提高，學習的回報就會增加。 * **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. * **解決問題**：解決問題可以是非常愉快的。問題越難，解題的樂趣就越大。問題解決涉及一個學習過程，因為解題需要中間步驟，從而將新知識存儲在記憶中。所有這些步驟都是令人愉快的。如果學生在這項任務中掙扎而沒有進步，他將什么也學不到，也得不到任何回報。這些任務太難了。如果學生不能解決問題，但通過中間步驟取得了進步，即使他們與解法無關，學習也是存在的，回報也是存在的。同樣，如果困難是值得的，它將導致回報。如果沒有獎勵，困難似乎是無法克服的。因此，這既不有益，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. * **邊做邊學**：邊做邊學可能涉及游戲、創造力、解決問題等等。邊做邊學需要更多的時間，而且往往會帶來更好的結果和更多的回報。 * **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. * **延遲反饋**：在某些情況下，延遲反饋可能導致更多的處理。簡單地說，如果老師不告訴你做得有多好，你可能會想更長一段時間。這對記憶有好處。如果是這樣的話，最終的效果將是有益的。 * **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. * 幫助退出：我寫的幫助退出是在[學校抑制學習內驅力](https://supermemo.guru/wiki/Schools_suppress_the_learn_drive#Teacher_problem)的背景下。沒有得到答案的孩子可能會變得更加好奇。好奇心會增加學習的回報。在糾正錯誤的現實模型方面得不到幫助的學生，會因為自己解決矛盾而得到更大的回報。 * **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. * **其他困難**：可以改善學習的障礙是無止境的，其中一些可能是荷爾蒙的性質，有些可能涉及激勵的力量。所有這些障礙的共同點似乎是某種形式的更深層次的處理、記憶的鞏固、注意力的提高等等。不可避免地，導致更好學習的障礙也包括更好的回報。 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). 值得的困難并不會剝奪學習的樂趣。恰恰相反，它使學習更有效，更有趣。如果過于困難而導致不愉快，那么困難就不再值得了。這種簡單的等價性來自于[學習內驅力](https://supermemo.guru/wiki/Learn_drive)中獎勵系統的機制。 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. 請注意，由于值得的困難而產生的有效學習的獎勵并不需要對應較高的學習熵。[學習熵](https://supermemo.guru/wiki/learn%20entropy)是信息通道的度量。例如，主動回憶與新穎性無關。它指的是記憶的重新整合。同樣，解決問題的部分原因可能是實現與學習無關的目標的需要，或者是獲得新知識以外的生產力的回報。 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). 還要注意的是，幾乎所有上述值得的困難都內在地與[漸進學習](https://supermemo.guru/wiki/Incremental_learning)的過程相關聯。 ### 6.19 Addiction to learning ### 學習成癮 #### 6.19.1 Inborn addiction #### 天生成癮 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. 我們生來就熱愛學習。在義務教育時期，熱愛通常很快就會消逝。我們對學習的熱愛持續的時間越長，對大腦、健康和人類的益處就越大。對學習的熱愛與[上癮](https://en.wikipedia.org/wiki/Addiction)無關。上癮的定義包括強制參與某項活動所造成的不良后果。 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. 與益處相比，學習的負面影響很小。如果有一定程度的貪婪，甚至是強迫性的沖動，它可以進一步促進積極的影響。提高一個人對學習的熱愛是有可能的。良好的學習為進一步的學習提供了最好的推動力。 #### 6.19.2 Learning and gambling #### 學習和賭博 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. 分別與學習和賭博有關的獎勵機制之間有著密切的聯系。賭博和學習新詞都以[類似的形式](http://www.cell.com/current-biology/fulltext/S0960-9822%2814%2901207-X)激活[腹側紋狀體](https://en.wikipedia.org/wiki/Striatum)。這種與賭博的密切聯系可能會混淆學習的圖景。賭徒在老虎機前學不到什么東西。讓人上癮的電子游戲更好。可能很有教育意義。許多團隊游戲成癮者以前在學校沒有取得任何進步，但都能說一口流利的英語。對體育新聞的上癮也可能涉及到一定程度的學習。我是在非洲國家杯（足球）期間才了解[卡賓達](https://supermemo.guru/wiki/Variable_reward)。對 Facebook 更新的上癮也沒有什么不同。它是建立在預期特定收益的[可變獎勵](https://supermemo.guru/wiki/Variable_reward)的基礎上的，然而，它也可能很大程度涉及到的學習。這種學習可能包括八卦，名人新聞，假新聞，或實際有用的學習。即使是最新的政治民意調查也會讓人上癮。在 Hillary Clinton 和 Donald Trump 之間的較量中，民調平衡得足以產生扣人心弦的效果。強制檢查新的民意測驗都有上癮的特征。然而，這種上癮會導致大量的學習。要靠學生把賭博和學習分開。貪婪的學習是好的。從上癮中獲得的學習也可能是好的。然而，賭博本身并不能給人類的生存帶來什么價值。 #### 6.19.3 Learning and sleep #### 學習和睡眠 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. 強迫性學習可能侵犯睡眠時間，并可能導致失眠癥和[睡眠相位后移綜合癥](https://supermemo.guru/wiki/DSPS)的流行。有強烈[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的有創造力的人可能會熬夜學習，直到凌晨。在電燈出現之前，這種違反睡眠模式的行為是困難的，甚至是不可能的。好消息是，[學習內驅力](https://supermemo.guru/wiki/Learn_drive)往往隨著網絡疲勞而減弱。我們學習的時間越長，記憶回路的飽和程度就越高。只有睡眠才能帶來解脫。這就是為什么即使是最貪得無厭的學習者往往會昏昏欲睡，并在某一點上放棄學習。如果讀者為了一本小說而熬夜，這可能是睡眠內驅力不足、學習減少和懸疑小說中典型的可變獎勵增加的組合。 #### 6.19.4 Learning and exercise #### 學習和鍛煉 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? 我聽說強迫性的學習會導致更少的鍛煉。那就不好了。然而，我認為更有可能產生這種效果的是糟糕的學習。好的學習是快樂的，能激發額外的能量。一個快樂的孩子不應該長時間地坐在一本書或一臺電腦前。一定有辦法釋放能量。也許我們應該說，減少運動是學習成癮的標志，而良好的學習有神經營養作用，應該讓人爆發額外的能量燃燒？ #### 6.19.5 Learning restraint #### 學習限制 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. 學習是有代價的，也是需要時間的。這就是為什么它應該是明智的。然而，良好的學習幾乎總是一個好的長期投資。這就是為什么我們不應該害怕上癮。恰恰相反，我們應該珍惜和激發[學習內驅力](https://supermemo.guru/wiki/Learn_drive)，提供快樂的終身學習。 ### 6.20 Displeasure of learning ### 學習的不快 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. 當我聲稱所有的學習都是令人愉快的時候，我聽到了一種聲音，像是“我不得不經歷一場壓力很大的考試，它給了我很多生活上的好知識”。這些聲音混淆了良好學習的樂趣和使學習成為許多學生恐懼的因素的不快。這些可怕的因素包括糟糕的老師，嚴厲的家長，最后期限，壓力，糟糕的睡眠，糟糕的教科書，過多的書冊，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. 我聽說，如果沒有最后期限或學校強加的目標，學習將被電子游戲、小說、電視、愛好、體育等所取代。出于許多原因，這可能是正確的。其中一些活動可能帶來與學習無關的樂趣。然而，由于學習或鍛煉的原因，它們也是有益的。一個全面發展的學生應該能夠自由地放慢速度，把時間分配給有趣的學習和其他有趣的活動。進展緩慢可能帶來更多好處。 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. 學習的方程式不可能在好的學習之后產生不快樂。責任總是在別的地方。所有負面的東西都應該研究和消除。 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. 歸根結底，即使對考試、證書和職責有不滿意之處，這種不滿也應由學生自己承擔。 **Pleasurable learning can be buried in displeasure caused by stress, bad people, bad schools, bad textbooks, and more**. **快樂的學習可能被埋沒在由壓力、壞人、糟糕的學校、糟糕的教科書等等引起的不快中。** ### 6.21 Learning and procrastination ### 學習和拖延 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. 如果學習是最可持續的快樂形式，為什么一半的學生會拖延呢？這幾乎是一般人口數字的三倍。 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. 答案很簡單也很重要：學生拖延是因為好的學習是一種樂趣，而壞的學習是非常不愉快的。學校甚至大學里的大部分作業都與[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的需求不匹配。這種學習是無效的，也是令人不快的。那些孩子經常在晚上玩電腦游戲，聲稱他們的大腦需要休息。我懷疑他們的大腦不在休息。實際上，他們做的工作都是他們覺得愉快的。這種樂趣很大程度上來自于新的學習。不幸的是，游戲玩得好在學校是沒有學分的，所以瑣事和樂趣的正弦循環從第二天開始，甚至在同一天隨著作業開始。 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. 我從來沒有停止驚訝，有這么多的學生自稱懶惰。同時，只要這些都是愉快的或為自己的目標服務，他們就可以做許多精神上的體力勞動。即使是那些在 SuperMemo 中有數千個記憶項目的人也常常給自己低的責任心分數。學習的目標可能是模糊的，但即使它們是非常清楚的，輸入和先前知識之間的不匹配可能會導致顯著的不快。如果[學習熵](https://supermemo.guru/wiki/learn%20entropy)很低，作業可能會很枯燥。如果它是負的，他們將是令人厭惡的。 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. 高目標估值與不良學習的負面獎勵之間的斗爭將導致拖延。拖延者常常說自己懶惰，即使他們不是。 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. 如果你認為你在學習上懶惰，你需要重新評估你的材料和方法。即使是簡單的違反[自然創造力周期](https://supermemo.guru/wiki/Natural_creativity_cycle)的行為也會扼殺學習的樂趣。 ### 6.22 Learning and depression ### 學習和抑郁 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. 學習是一種可持續的、非上癮的快樂形式，除了時間成本外，幾乎沒有任何副作用。此外，良好的學習傾向于增長知識，并通過提高[學習內驅力](https://supermemo.guru/wiki/Learn_drive)來促進更多的學習。這意味著學習應該作為抑郁癥的治療手段。 #### 6.22.1 Learning at school #### 在校學習 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[學習是快樂和獎勵的源泉](https://supermemo.guru/wiki/Pleasure_of_learning)，為什么我們會在學齡兒童中看到猖獗的抑郁呢？盡管是學習機構，但[學校更有可能導致抑郁癥，而不是作為一種治療措施](https://supermemo.guru/wiki/I_became_so_depressed_that_I_stopped_going_to_school)。沒有學習的自由，就很難獲得好的學習。為了讓學習變得愉快，它需要[學習內驅力](https://supermemo.guru/wiki/Learn_drive)。它不能是強制性的，也不能是強制性的。它一定是自由的。 #### 6.22.2 Impact of memory on mood #### 記憶對情緒的影響 Free learning is fun, however, the pleasure of learning is not what makes it a great tool against depression. 自由學習是有趣的，然而，學習的樂趣并不能使它成為對抗抑郁的強大工具。 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. 記憶是一個可能觸發或抑制抑郁的因素。記憶決定了輸入信號在大腦中是如何被傳送的。記憶決定了哪些概念與輸入或神經激活相關聯。記憶決定了我們對過往汽車的聲音的反應。它可能會讓人想起一個快樂的假期，埃隆·馬斯克的靈感，或者是一場使心愛的人殘廢的車禍的記憶。 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. 要使記憶對情緒產生重大影響，我們需要很多記憶。僅僅與心理治療師坐下來，了解一些關于大腦、我們的生活或應對策略的關鍵事實是不夠的。要想在大腦中形成健康的軌跡，需要幾個月又一年的學習。我們可以建立內在樂觀或內在悲觀的聯想。我們需要數千個這樣的聯想來扭轉局面。然而，即使是多年的學習也很容易被病理或創傷所推翻。神經激素可以立即改變大腦工作的模式。神經激素譜的改變將立即優先考慮可能以負面方式影響情緒的記憶集。創傷可以灌輸記憶，這將激發新的活化源，將覆蓋從其他來源的激活。換句話說，如果開關改變了使用中的軌跡，或者大腦中產生了一種新的活化源，那么良好記憶的裝備就可能一文不值。要減輕一個有學問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. 一旦抑郁發作，受影響的個人將面臨雙重打擊。不僅是關于防守的美好回憶。糟糕的記憶開始盤旋在周圍，促進他們自己的新軌道，并獲得上風。大腦重新編程，使情緒的平衡向錯誤的方向擺動。當這個過程成為失控，我們可能有一個臨床抑郁癥在手邊。完全的壞消息是，抑郁癥患者失去了他們對生活的熱愛和對學習的熱愛。 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. 學習能打破這個循環嗎？這可能是非常困難的！尊重[生理周期](https://supermemo.guru/wiki/Natural_creativity_cycle)是恢復脫軌大腦的第一步。在晝夜周期中，需要抓住創造力高峰窗口來嘗試補救性學習。學習需要多產、強烈、有效和愉快。[漸進閱讀](https://supermemo.guru/wiki/Incremental_reading)如果不是那么困難的話，那就太棒了。對于一個在部門里沒有技能的抑郁的人來說，[SuperMemo](https://supermemo.guru/wiki/SuperMemo) 是不能補救的。太晚了。試圖在糟糕的心態下掌握漸進閱讀只會讓事情變得更糟。這可能會導致對漸進閱讀的厭惡。 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. 如果學習是可能的，它可以作為一個避難所，這可能有助于抑制消極記憶和建立新的聯系。在這一點上，建立新的知識卷軸的過程可能開始。這一應當使人對世界作出更加樂觀的解釋的過程是緩慢和艱苦的。在最嚴重的情況下，可能需要幾個月或幾年的艱苦工作，結果是不能保證的。 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.** 最終的結論是，學習不是靈丹妙藥，但它可以在治療中發揮重要作用。最重要的是，通過豐富而有效的學習，可以提前數年避免患抑郁癥的風險。學習必須在自由和尊重[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的條件下進行。簡而言之，**熱愛學習是走向熱愛生活的好方法。** #### 6.22.3 Anti-depressants #### 抗抑郁藥 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. 我是醫學上的路德派教徒。為了一個健康的身體，我堅持一條規則：「如果它沒有壞，就不要修理它」。我避免任何形式的藥物干預。我相信[自我平衡](https://en.wikipedia.org/wiki/Homeostasis)的力量和干預自我平衡的危險。我用的最厲害的藥是咖啡和啤酒。我甚至不用阿司匹林。我對濫用抗生素、止痛藥、安眠藥和抗抑郁藥物感到非常沮喪。我已經幾十年沒吃抗生素了。長到忘記多久了。如有必要，我會在死亡之床上使用。所有藥物都有其合法的用途，抗抑郁藥物也是如此。由于它們導致受體下調，一旦服用，就會使神經遞質的現狀變得更糟。這通常意味著，服用的藥物越多，就越需要服用才能避免挫折。然而，在嚴重的臨床抑郁癥病例中，藥物可能會阻止失控的過程。它們可以保護大腦免受自我傷害。一旦抑郁癥患者開始失去腦細胞，康復的道路就變得漫長而崎嶇。抗抑郁治療開始的那一刻，如果起作用的話，就是把學習作為治療的最佳時機。只要大腦愿意繼續學習，學習就可以啟動那些微妙的知識卷軸，這些知識將與現實聯系在一起，產生殘留的[學習內驅力](https://supermemo.guru/wiki/Learn_drive)。在理想的情況下，一旦藥物被撤除，學習內驅力應該存留下來，開始一個與抑郁相反的過程：學習、創造力、良好的睡眠和良好的情緒的積極反饋。這并不容易，但它是非常重要的。如果藥物治療是唯一能改變病人生活的東西，它只能作為病理過程中的一種中斷而起作用。它不會使大腦處于比問題開始前更好的狀態。改進需要積極努力。沒有健康的[學習內驅力](https://supermemo.guru/wiki/Learn_drive)，積極向上的記憶就不會開始。 #### 6.22.4 Learn drive and optimism #### 學習內驅力和樂觀 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. 蹣跚學步的孩子似乎表現出最旺盛的學習內驅力。難怪，健康的孩子生來就是樂觀的。樂觀與[學習內驅力](https://supermemo.guru/wiki/Learn_drive)之間存在著相關性。快樂的頭腦可能在神經化學的基礎上起到學習內驅力的作用。悲觀肯定會起到抑制或過濾的作用，阻止[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的表達。從這個意義上說，悲觀的頭腦可能掩蓋了學習內驅力。在抑郁癥中，學習內驅力可能完全消失。難怪[羅伯特·薩波爾斯基博士](https://en.wikipedia.org/wiki/Robert_Sapolsky)稱抑郁癥是世界上最嚴重的疾病。 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. 似乎出現了一種共識，即學校是青少年\(以及以后的生活\)抑郁的主要原因。其機制尚不清楚，但[習得性無助](https://supermemo.guru/wiki/Learned_helplessness)和對[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的抑制可能是導致病理變化的關鍵因素。 #### 6.22.5 Can learning help you? #### 學習可以幫到你嗎？ 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. 如果你正在讀這篇文章，而你不確定學習是否對你有幫助，那么問問自己這個問題：你今天心情好嗎？正如上面提到的，當你在一個下降秋千和尋找一個解決方案，你的解釋是黑暗的，你可能會發現這篇文章不夠安慰。然后記住關于活化能的概念：你需要先邁出一小步，然后你可能會被一系列有趣的東西拉進來學習。 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! 如果你今天對量子力學沒有興趣，那就從名人新聞或者體育新聞開始吧。少量學習總比不學習強！ ### 6.23 Optimization of education: Global or Local? ### 教育的優化：全局還是局部？ Is there a risk in using pleasure as a guiding light in education? 在教育中用快樂作為指引是否有風險？ #### 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. 經過多年的學習，我們慢慢地建立了一個理想的學術學習過程的想象模型，在這個過程中，我們[設定長期目標](https://supermemo.guru/wiki/Setting_goals_can_change_your_life)，遵循課程，增加重要的知識，當我們在給定的領域獲得具有巖石般堅實的知識的大學學位，并得到社會有效運作所需的廣泛的一般知識的支持時。我們在學校系統中待的時間越長，就越難離開，對這種模式有一個客觀的看法。矛盾的是，對于那些在學校表現良好并開始相信自己成功的人來說，這種模式的驗證是最困難的，這要歸功于這種完美的學術學習模式。聰明人在學校遭受的痛苦較少，因此，對學校系統問題的考慮也較少。成功的學生將這種模式內在化，并通過為子孫后代提供相同的固定路徑而使其永久化。 **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. **我們通過課程設計學生知識的模式是錯誤的！**一所完善的學校的模式應歸功于制度和教師，而所有實際的學習都應歸功于學生。當孩子們成群結隊地不能上學時，我們傾向于責怪孩子或他們的父母，而一小部分成功的學生將繼續為自己的孩子夢想完美的學校模式，并繼續將模式推給那些不那么幸運的學生。 #### 6.23.2 Optimization based on the learn drive #### 基于學習內驅力的優化 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. 與課程不同，[學習內驅力](https://supermemo.guru/wiki/Learn_drive)背后的優化機制是在人類進化過程中不斷完善的。它能夠推動個人知識達到理科或工程學的所有復雜問題所需的水平。在義務教育到來之前，人類已經取得了啟動[啟蒙運動](https://en.wikipedia.org/wiki/Age_of_Enlightenment)或[工業革命](https://en.wikipedia.org/wiki/Industrial_Revolution)所需的一切可以想象的突破。義務教育本來有助于把“蒙昧”群眾提高到一個新的水平，但是，它正日益把自己推向用壓制創造性思維取代啟蒙的最佳化的角落。 #### 6.23.3 Designing a child's mind #### 設計一個孩子的思想 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. 我一直從受過高等教育和非常聰明的人那里聽到這樣的話：教育太重要了，不能讓它依賴于自學或[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的盲目性。顯然，教育是如此重要，我們應該用最好的科學工具和最好的專家來規劃和設計它。當我全神貫注于高效率的學習時，在我真正開始思考教育制度之前，我生活在同樣的信念中。默認專家意見是很自然的。 Highly educated people often utter the following claims: 受過高等教育的人常常提出以下主張： * _children are incapable of long-term planning, therefore a curriculum is needed_ * 孩子們沒能力做長期規劃，因此需要一套課程 * _learn drive is a type of local optimization, while we need to plan education globally_ * 學習內驅力是一種局部優化，而我們需要在全局范圍內規劃教育 * _following student interests is a recipe for disaster: they will all end up immersed in mind-numbing videogames_ * 關注學生的興趣是一場災難：他們最終都會沉浸在令人麻木的電子游戲中 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) 問題是，教育的全局優化設定了越來越嚴格的績效目標。全局優化不斷使用同樣低效的學習工具，試圖將更多的“必要”知識轉移到學生的頭腦中。其結果是數百萬學生的痛苦。雖然斯大林為蘇聯的巨大成就進行了全局優化，但正是市場經濟以其簡單的優化算法將西方世界提升到了新的高度。參見：[現代學校教育就像蘇聯的經濟一樣](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. 目前所采用的優化教育使用知識測試作為表現的衡量標準，而是依靠填鴨式和短期記憶來在較短的時間內取得更多的成績。因此，它不斷失去對[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的控制。國與國之間的競爭也采用表現測試。我們不是針對實際的長期知識進行優化，而是對學生頭腦中的知識周轉速度進行優化。其結果是學生對知識的不滿，而這些知識與投入的時間和實際的人類潛力相比是微不足道的。 #### 6.23.4 Reliance on emergence #### 依靠涌現 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. 優化教育可以采用[涌現](https://supermemo.guru/wiki/Emergence)的概念。[學習內驅力](https://supermemo.guru/wiki/Learn_drive)是一種機制，通過這種機制，知識是自組織的，不需要[老師的努力](https://supermemo.guru/wiki/Push_zone)，也不會給孩子帶來痛苦。自然學習可能需要很長的時間，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學習，因為這是他們自己選擇的學習。 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): 在參考基于[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的局部優化學習時，我們應該牢記兩個重要事實： * 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[學習內驅力](https://supermemo.guru/wiki/Learn_drive)，就沒有好的學習。所有覆蓋的嘗試都將被人類記憶所拒絕 * [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) * [學習內驅力](https://supermemo.guru/wiki/Learn_drive)帶來了令人驚訝的學習過程的長期優化。在19世紀50年代之前，幾乎所有的人類成就都是在[學習內驅力](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). 持懷疑態度的人會注意到，自從實行義務教育以來，人類的進步加快了。他是對的。然而，自從 40 億年前第一種生命形式出現以來，我們一直在加速進步。我認為 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) 對這種加速做出了更重要的貢獻。 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[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的局部優化非常不直觀。創造科學來自于對自然選擇機制的一種類似的不直觀的感覺。一個基于隨機突變的局部進化優化如何導致一個人的奇跡呢？經上帝之手的全局設計/優化/指導似乎是不可避免的。如今，接受創造科學的人越來越少，然而，絕大多數人不知道[學習內驅力](https://supermemo.guru/wiki/Learn_drive)背后的機制是什么，為什么忽視它是[普魯士教育體系](https://en.wikipedia.org/wiki/Prussian_education_system)的首要問題。 #### 6.23.5 The tree metaphor #### 樹類比 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. 在沒有教育系統的情況下，只要有足夠的時間和機會進入知識豐富的環境，個人的知識就會成長為一個龐大、全面和[連貫的整體](https://supermemo.guru/wiki/Coherence)。這對所有自由和健康的人都是適用的。樹的大小和質量可能取決于一個人的個性、興趣和智力發展的起點。然而，教育的一個主要錯誤觀念是，知識的有機增長導致多重偏見和[無知領域](https://supermemo.guru/wiki/Ban_on_homeschooling)。據稱，這些空白點比上了幾年學后留下的空白點要多。由于[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的計算能力，以及[涌現](https://supermemo.guru/wiki/Emergence)的現象，情況正好相反。我喜歡用來解釋[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的類比是樹的生長。 > Metaphor. [Why use metaphors?](https://supermemo.guru/wiki/Why_use_metaphors%3F) > > 類比。[為什么要用類比？](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. > > 人類個體知識的自然增長可以比作一棵樹的生長。樹枝[分生組織](https://en.wikipedia.org/wiki/Meristem)中的單個細胞對這棵樹和它的全局生長目標知之甚少。分生組織遵循簡單的激素、生化或生物物理規則（如頂端優勢）。這些簡單的規則引導生長向光是高效率的，樹可以塑造它的樹冠美麗。它也將有效地組織成一個樹冠與其他物種。地心引力是以最佳方式處理的。營養物質的重新分配是容易的。光的吸收很好。所有障礙，如其他樹木、巖石或燈柱，都容易處理。類似的機制保證了植物根系的有效生長。生長錐還利用一套簡單的局部規則在大腦中萌生新的神經連接。 > > 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. > > 知識之樹也遵循類似的原則。[學習內驅力](https://supermemo.guru/wiki/Learn_drive)機制確保個體的記憶之葉渴望新發現的光芒，并在靈感的方向上萌生枝條。在局部，[學習內驅力](https://supermemo.guru/wiki/Learn_drive)可能看起來簡單而盲目。在全局范圍內，我們成長為知識淵博的偉人，以支持人類在社會中的所有重要職能。自學成才的大腦可以適應任何環境，實現所有可以想象到的人類目標。 > > 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 > > 就像樹木需要水、二氧化碳、一些營養物質和光一樣，大腦也需要能量、豐富的輸入和不受約束的自由。所有強迫性調節的嘗試都抑制了[學習內驅力](https://supermemo.guru/wiki/Learn_drive)，知識之樹也不能自己發芽。 > > 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): > > 另一個有助于解釋建立[連貫](https://supermemo.guru/wiki/Coherence)知識的[出現](https://supermemo.guru/wiki/Emergence)的類比是[知識結晶類比](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 > > > > **圖：**在**完美的學校教育**中，我們創造了完美的知識結晶。在大學里，我們又多了一塊專業化的水晶。然而，在現實中，學習看起來并不那么完美。對于大多數孩子來說，知識從來沒有建立足夠的連貫性，并且由于干擾\(即快速遺忘\)而支離破碎。因此，在**現實的學校教育**中，知識漸近達到一定的數量，并從那一刻起就不斷地翻滾，在穩定性和連貫性方面幾乎沒有進展。相反，在**自由學習**中，知識的獲取是混亂和不均衡的。但是，只要它是基于學習內驅力，知識量是非常大的。知識的單個晶體相互碰撞，并建立一致性和連貫性。這反過來又有助于知識的穩定和進一步整合。到了大學時，就體積而言，自由學習者應該比普通學生知道的要多得多。自由知識有多方面的長處，也有多方面的弱點。然而，它在連貫性方面更優越。這就是為什么它更適用于解決問題 #### 6.23.6 Local optimization #### 局部優化 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. [學習內驅力](https://supermemo.guru/wiki/Learn_drive)的局部優化使人的能力與個體的環境和目標完美匹配。學校教育的全局優化抑制了[學習內驅力](https://supermemo.guru/wiki/Learn_drive)，在將個人與其工作匹配時遵循受抑制的[學習內驅力](https://supermemo.guru/wiki/Learn_drive)，并導致了一個不幸福的社會，在這個社會中，大多數人渴望朝九晚五的工作以獲得舒適，而將領導、學習和責任則委托給其他人。相反的情況發生在[民主學校](https://supermemo.guru/wiki/Democratic_schools)，這些學校依靠自我學習來培養自我決定、自我實現和自力更生的人，隨時準備在他們感興趣的領域接受任何挑戰。 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. 在他歷史性的畢業演講中，史蒂夫·喬布斯開玩笑說，在他被診斷為癌癥之前，他不知道胰腺是什么。顯然，他盲目的學習內驅力在他廣博的知識中留下了一個空白。即使這是真的，我也絕不會拿史蒂夫·喬布斯和他的作品來換取局部優化學習的一些失敗。他[鼓舞人心的演講](https://www.youtube.com/watch?v=D1R-jKKp3NA)的要點之一就是追隨自己的[學習內驅力](https://supermemo.guru/wiki/Learn_drive)。用他的話說，「[_做偉大工作的唯一方法就是熱愛你所做的_](http://news.stanford.edu/2005/06/14/jobs-061505/)」。這一真理千百年來一直被所有智者重復。 #### 6.23.7 Is global optimization possible? #### 全局優化可能嗎？ [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. [全局優化](https://en.wikipedia.org/wiki/Global_optimization)為所有輸入值找到最優。在教育部門一級**對學習進行全局優化**，例如通過[共同核心測試](https://supermemo.guru/wiki/Common_core)和[標準化測試](https://supermemo.guru/wiki/Standardized_testing)等工具。全局優化是基于有缺陷的推理，即我們可以設計一個孩子的頭腦。嘗試預測孩子未來的父母也可以進行全局優化。 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. 我們能提前決定孩子的未來嗎？如果父母在全局范圍內選擇最佳的未來，我們就會有多余的律師和醫生。失意的大學輟學者人數也會大幅增加。如果政府能提供一點幫助，并在幼年時以最佳方式重新分配給孩子們，我們最終會得到 [1984](https://en.wikipedia.org/wiki/Nineteen_Eighty-Four) 的一種變體。很少有孩子愿意在 6 歲時發現他們注定要成為一名簿記員或木匠。擇業顯然應該建立在愛和激情的基礎上，而不是政府頒布的法令。 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. 也許孩子們應該被允許在全局范圍內進行優化？這也行不通，我們最終會有多余的搖滾音樂家、專業電子游戲玩家和足球運動員。 Contrast this with optimization via the [learn drive](https://supermemo.guru/wiki/Learn_drive) that has delivered the best of human achievement for centuries. 將其與數百年來為人類帶來最好成就的[學習內驅力](https://supermemo.guru/wiki/Learn_drive)進行對比。 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，[課程](https://supermemo.guru/wiki/Curriculum)就是一種在走向全局最優的過程中尋找中介最優的一種嘗試。將課程作為指導，了解什么是值得了解的，似乎是個好主意。當一個孩子或老師對學習失去熱情時，他們可能會參考課程。不過，如果[學習內驅力](https://supermemo.guru/wiki/Learn_drive)處于超速狀態，為什么還要慢下來呢？這孩子有沒有可能永遠不知道酒精的危害？這不太可能。另一方面，我不知道有一門課程能教會孩子們如何運用[漸進閱讀](https://supermemo.guru/wiki/Incremental_reading)。我可能會有偏見，但我肯定會把這項技能放在需要卡瓦萊克或[塞迪尼亞之戰](https://en.wikipedia.org/wiki/Battle_of_Cedynia)（例子取自我自己的課程）之前。我很感激今天的 Julian Kawalec 。然而，共產黨當局強制要求我讀他的小說，這對我來說是學校折磨的根源。你可能想知道 Kawalec 是誰。我很想告訴你，但是維基百科上有一篇[波蘭語](https://pl.wikipedia.org/wiki/Julian_Kawalec)的關于他的成就的文章。 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. 如果您根據課程測試學生的知識，很容易看出他們掌握了全局優化計劃的一小部分。他們通過自學來獲得了大量關于世界的知識。這導致了良好的學校教育的錯覺。如果課程不是強制性的，教師有更多的適應空間，知識的數量和一致性就會增加。連貫性和速度是自學的兩個標志。更少的孩子可能會選擇解決二次方程，但他們會用他們認為對他們來說重要的其他技能多次填補這個空間。所有那些計劃在 [STEM](https://en.wikipedia.org/wiki/Science,_technology,_engineering,_and_mathematics) 中工作的人，無論如何都會遲早得到二次方程式。其余的將依賴于當前的默認值，即學習方程式并快速忘記它們。大多數人不知道如何處理二次方程。很少有人知道他們的目的。課程中的方程式增加了困境和知識的成本，這些知識可能是在愉快的心態下有機會獲得的。 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). 如果不可能實現全局長期優化，那么課程計劃形式的中間步驟就不那么復雜了。它們仍然偏離了由[學習內驅力](https://supermemo.guru/wiki/Learn_drive)所確定的最佳狀態。 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** \(next\). 有效優化的唯一方法是讓[學習內驅力](https://supermemo.guru/wiki/Learn_drive)通過來自父母、導師、同齡人、陌生人、社交媒體、維基百科、Google 等的溫和推動來確定軌跡。優化教育必須堅持**學習的基本規律**（下一步）。 ### 6.24 Fundamental law of learning ### 學習的基本規律 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). 大多數人都知道學習是令人愉快的。然而，很少有人意識到這一事實對[教育的未來](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. 只有神經科學中源源不斷的寶貴發現，才能幫助我們認識到[快樂在學習中](https://supermemo.guru/wiki/Pleasure_of_learning)的根本重要性。獎勵過程從[感知](https://supermemo.guru/wiki/Biederman_model)水平開始，并通過聯想學習、[創造力](https://supermemo.guru/wiki/Natural_creativity_cycle)、解決問題和實現目標的最終樂趣來進行。在每一站，都有[快樂的信號](https://supermemo.guru/wiki/Pleasure_of_learning)來獎勵腦力的進步。 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. 我也遲遲不能理解快樂的力量。早在 1991 年，[我們](https://supermemo.guru/wiki/SuperMemo_World)保守地寫道：_「有一個確定的方法來判斷一個給定的學生是否會在他的工作中取得成功。如果他在長時間的學習中找到樂趣，他一定會做得很好」_（參見：[Supermemo Decalog](http://www.super-memory.com/articles/decalog.htm)）。今天，我們意識到這種快樂與神經網絡中的所有形式的學習有著內在的聯系，因此它成為衡量學習進度的最好的標準之一。 This makes it possible to formulate the **fundamental law of declarative learning**: 這使得有可能制定**陳述性學習的基本規律**： **When there is no pleasure, there is no good learning.** **沒有快樂，就沒有好的學習。** 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. 當然，這項規律需要修正和細化。好的陳述性學習會帶來快樂。這一事實可以被一些因素所掩蓋，例如一些好的學習會被隱藏在[大量的不好的學習](https://supermemo.guru/wiki/Unpleasant_learning_at_school)中。快樂本身并不是學習的保證。我們發現的事實可能是令人痛苦的。一些陳述性學習可能發生在不愉快的條件下（如恐懼條件反射）。經典的條件反射經常涉及疼痛。臨床抑郁癥會阻礙一個人騎自行車的傾向，但不會破壞在騎自行車時發生的程序性學習。 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. 陳述性學習的基本規律簡單地說，獲得滿足[學習內驅力](https://supermemo.guru/wiki/Learn_drive)的高質量知識將產生獎勵信號。沒有這一信號表明缺乏學習。枯燥的事實可以在沒有樂趣的情況下短期用于陳述性記憶，但如果學習得不到回報，這些事實就不會堅持堅實的現實模式。通過健康的[遺忘](https://supermemo.guru/wiki/Forgetting_curve)系統，這些事實很可能很快就會從記憶中消失。更糟糕的是，[糟糕而持久的學習習慣會在以后的生活中引起學習上的問題](https://supermemo.guru/wiki/Toxic_memory)！記憶中任何連貫模型的出現都會不可避免地產生獎勵信號。 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. 如果你碰巧把痛苦強加在你自己身上，你需要重新思考你的策略。你可能需要放慢速度，或者回到基礎上，學習心理和[睡眠衛生](https://supermemo.guru/wiki/Natural_creativity_cycle)的規則，控制你的[壓力](https://supermemo.guru/wiki/Stress_resilience)，學習[制定知識的 20 條規則](https://supermemo.guru/wiki/20_rules)，或者試一試[漸進閱讀](https://supermemo.guru/wiki/Incremental_reading)。如果你不顧痛苦堅持下去，你將得不到好的結果。[Gladwell 的 10,000 小時](http://www.newyorker.com/news/sporting-scene/complexity-and-the-ten-thousand-hour-rule)規則也需要被認可。沒有一位小提琴演奏家是完全通過幾個小時的練習而誕生的。就像學習一樣，偉大的音樂是愛的結果。 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，這個世界上的大多數學生都沒有自己的過錯。糟糕的學習是從上面強加給他們的！ **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! **全世界的學生團結起來！**你不再需要忍受學習的痛苦。如果你受苦了，你有基本的學生抗議的權利。如果你受苦了，那就是出了問題！你可以停止學習了！如果有人要求你學習，而你無法享受學習，你可以反擊，并要求快樂的學習！這不是你的精英享樂主義玻璃心的要求。這是理性的要求。**沒有快樂，就沒有學習！**你的痛苦是浪費時間，浪費健康，浪費全球人力資源！ ### 6.25 Summary: Pleasure of learning ### 摘要：學習的快樂 * human brain naturally tunes in to "interesting information" in the environment * 人類大腦自然地適應環境中的「有趣的信息」。 * learning and discovering new things is rewarding * 學習和發現新事物是有益的。 * many educators subscribe to the dangerous myth that learning may cause displeasure and still be effective * 許多教育工作者贊同一個危險的錯誤觀念，即學習可能引起不快，而且仍然有效。 * surprisal is highly valued in new knowledge acquisition * 在獲取新知識時，意外被高度看重 * predictability and surprisal may both add to attractiveness of the information channel * 可預測性和可預見性都可能增加信息渠道的吸引力 * attractiveness of the information channel depends on the prior knowledge * 信息渠道的吸引力與預備知識相關 * information delivered to the brain must account for prior knowledge. This factor makes universal delivery, e.g. via lecturing, very difficult * 傳遞給大腦的信息必須考慮到預備知識。這一因素使得通用的傳授（例如通過講課）變得非常困難。 * attractiveness of the information channel depends on the speed of delivery and the speed of processing * 信息渠道的吸引力與傳授速度和處理速度相關。 * the speed and complexity of information delivery in learning must to tailored to individual needs * 學習中信息傳授的速度和復雜性必須適合個人需要。 * the encoding of a new high value associative memory occurs simultaneously with sending a signal to reward centers in the brain * 新的高價值聯想記憶的編碼與發送信號到大腦中的獎勵中樞同時發生。 * failed tailoring of information channels in schooling leads to lack of reward * 學校教育中信息渠道的調整失敗導致缺少獎勵 * learning provides a unique type of sustainable pleasure that may have therapeutic value * 學習提供了一種獨特的可持續的快樂，可能具有治療價值 * for systemic reasons, schooling usually fails to tune in to child interests * 由于系統性原因，學校教育通常不能符合兒童的興趣 * unrewarding nature of schooling is the chief cause of near-universal dislike of "learning" at school * 學校教育的無獎勵本質是幾乎普遍不喜歡在學校「學習」的主要原因。 * by destroying the pleasure of learning we contribute to creating an unhappy society * 通過破壞學習的樂趣，我們為創造一個不快樂的社會作出了貢獻 * the fundamental law of declarative learning states: **No pleasure, no learning!** * 陳述性學習的基本規律是：**沒有快樂，就沒有學習！**