Your brain does not process information, retrieve knowledge or store memories. In short: your brain is not a computer
你的大腦不處理資訊、檢索知識或存儲記憶。簡而言之：您的大腦不是一台計算機

No matter how hard they try, brain scientists and cognitive psychologists will never find a copy of Beethoven’s 5th Symphony in the brain – or copies of words, pictures, grammatical rules or any other kinds of environmental stimuli. The human brain isn’t really empty, of course. But it does not contain most of the things people think it does – not even simple things such as ‘memories’.
無論他們多麼努力，大腦科學家和認知心理學家都無法在大腦中找到貝多芬第五交響曲的副本——或者單詞、圖片、語法規則或任何其他類型的環境刺激的副本。當然，人類的大腦並不是真的空的。但它並不包含人們認為它所包含的大部分事物——甚至不包含諸如“記憶”之類的簡單事物。

Our shoddy thinking about the brain has deep historical roots, but the invention of computers in the 1940s got us especially confused. For more than half a century now, psychologists, linguists, neuroscientists and other experts on human behaviour have been asserting that the human brain works like a computer.
我們對大腦的粗製濫造的想法有著深厚的歷史根源，但 1940 年代計算機的發明讓我們特別困惑。半個多世紀以來，心理學家、語言學家、神經科學家和其他人類行為專家一直在斷言，人腦就像計算機一樣工作。

To see how vacuous this idea is, consider the brains of babies. Thanks to evolution, human neonates, like the newborns of all other mammalian species, enter the world prepared to interact with it effectively. A baby’s vision is blurry, but it pays special attention to faces, and is quickly able to identify its mother’s. It prefers the sound of voices to non-speech sounds, and can distinguish one basic speech sound from another. We are, without doubt, built to make social connections.
要了解這個想法有多空洞，請想想嬰兒的大腦。多虧了進化，人類新生兒和所有其他哺乳動物物種的新生兒一樣，進入世界時已經準備好與它進行有效的互動。嬰兒的視力模糊，但它特別注意面部，並且能夠很快識別出母親的視力。與非語音聲音相比，它更喜歡語音，並且可以區分一種基本語音和另一種基本語音。毫無疑問，我們是為了建立社會聯繫而生的。

A healthy newborn is also equipped with more than a dozen reflexes – ready-made reactions to certain stimuli that are important for its survival. It turns its head in the direction of something that brushes its cheek and then sucks whatever enters its mouth. It holds its breath when submerged in water. It grasps things placed in its hands so strongly it can nearly support its own weight. Perhaps most important, newborns come equipped with powerful learning mechanisms that allow them to change rapidly so they can interact increasingly effectively with their world, even if that world is unlike the one their distant ancestors faced.
一個健康的新生兒還配備了十多種反射——對某些對其生存很重要的刺激的現成反應。它把頭轉向某個東西的方向，那個東西擦過它的臉頰，然後吸吮任何進入它嘴裏的東西。浸入水中時它會屏住呼吸。它緊緊抓住放在手裡的東西，幾乎可以支撐自己的重量。也許最重要的是，新生兒配備了強大的學習機制，使他們能夠快速改變，這樣他們就可以越來越有效地與他們的世界互動，即使那個世界與他們遙遠的祖先所面對的世界不同。

Senses, reflexes and learning mechanisms – this is what we start with, and it is quite a lot, when you think about it. If we lacked any of these capabilities at birth, we would probably have trouble surviving.
感官、反應和學習機制 – 這就是我們開始的，當你仔細想想時，它相當多。如果我們在出生時缺乏這些能力中的任何一個，我們可能難以生存。

But here is what we are not born with: information, data, rules, software, knowledge, lexicons, representations, algorithms, programs, models, memories, images, processors, subroutines, encoders, decoders, symbols, or buffers – design elements that allow digital computers to behave somewhat intelligently. Not only are we not born with such things, we also don’t develop them – ever.
但這不是我們與生俱來的：資訊、數據、規則、軟體、知識、詞典、表示、演算法、程式、模型、記憶、圖像、處理器、子程式、編碼器、解碼器、符號或緩衝區——這些設計元素允許數字計算機在某種程度上智慧地運行。我們不僅不是天生就有這些東西，而且我們也不會發展它們——從來沒有。

We don’t store words or the rules that tell us how to manipulate them. We don’t create representations of visual stimuli, store them in a short-term memory buffer, and then transfer the representation into a long-term memory device. We don’t retrieve information or images or words from memory registers. Computers do all of these things, but organisms do not.
我們不存儲單詞或告訴我們如何操作它們的規則。我們不會創建視覺刺激的表示，將它們存儲在短期記憶緩衝區中，然後將表示轉移到長期記憶設備中。我們不會從記憶體寄存器中檢索資訊、圖像或單詞。計算機可以做所有這些事情，但生物體不會。

Computers, quite literally, process information – numbers, letters, words, formulas, images. The information first has to be encoded into a format computers can use, which means patterns of ones and zeroes (‘bits’) organised into small chunks (‘bytes’). On my computer, each byte contains 8 bits, and a certain pattern of those bits stands for the letter d, another for the letter o, and another for the letter g. Side by side, those three bytes form the word dog. One single image – say, the photograph of my cat Henry on my desktop – is represented by a very specific pattern of a million of these bytes (‘one megabyte’), surrounded by some special characters that tell the computer to expect an image, not a word.
從字面上看，計算機處理資訊——數位、字母、單詞、公式、圖像。首先，信息必須編碼為計算機可以使用的格式，這意味著 1 和 0 （'bits'） 的模式被組織成小塊 （'bytes'）。在我的計算機上，每個位元組包含8位，這些位的特定模式代表字母 d，另一個代表字母 o，另一個代表字母 g。這三個字節並排構成單詞 dog。一張圖像 - 比如，我的貓 Henry 在我桌面上的照片 - 由一百萬個字節（“1 兆字節”）組成的非常特定的模式表示，周圍有一些特殊字元，這些字元告訴計算機期待的是圖像，而不是單詞。

Computers, quite literally, move these patterns from place to place in different physical storage areas etched into electronic components. Sometimes they also copy the patterns, and sometimes they transform them in various ways – say, when we are correcting errors in a manuscript or when we are touching up a photograph. The rules computers follow for moving, copying and operating on these arrays of data are also stored inside the computer. Together, a set of rules is called a ‘program’ or an ‘algorithm’. A group of algorithms that work together to help us do something (like buy stocks or find a date online) is called an ‘application’ – what most people now call an ‘app’.
毫不誇張地說，計算機將這些圖案從一個地方移動到另一個地方，這些圖案蝕刻在電子元件上的不同物理存儲區域。有時他們也會複製這些圖案，有時他們會以各種方式改變它們——比如，當我們糾正手稿中的錯誤或修飾照片時。計算機移動、複製和操作這些數位所遵循的規則也存儲在計算機內部。一組規則一起稱為“程式”或“演算法”。一組協同工作以幫助我們做某事（例如購買股票或在線查找約會）的演算法稱為“應用程式”——現在大多數人稱之為“應用程式”。

Forgive me for this introduction to computing, but I need to be clear: computers really do operate on symbolic representations of the world. They really store and retrieve. They really process. They really have physical memories. They really are guided in everything they do, without exception, by algorithms.
請原諒我對計算的介紹，但我需要明確一點：計算機確實對世界的符號表示進行操作。他們真的會儲存和檢索。他們真的會處理。他們真的有身體記憶。他們所做的一切都無一例外地受到演算法的指導。

Humans, on the other hand, do not – never did, never will. Given this reality, why do so many scientists talk about our mental life as if we were computers?
另一方面，人類沒有——從來沒有，也永遠不會。鑒於這一現實，為什麼這麼多科學家把我們的精神生活當作計算機來談論呢？

In his book In Our Own Image (2015), the artificial intelligence expert George Zarkadakis describes six different metaphors people have employed over the past 2,000 years to try to explain human intelligence.
人工智慧專家喬治·扎卡達基斯 （George Zarkadakis） 在他的著作《以我們自己的形象》（In Our Own Image，2015 年）中描述了人們在過去 2000 年中用來解釋人類智慧的六種不同的隱喻。

In the earliest one, eventually preserved in the Bible, humans were formed from clay or dirt, which an intelligent god then infused with its spirit. That spirit ‘explained’ our intelligence – grammatically, at least.
在最早的一次中，人類是由粘土或泥土形成的，然後一個聰明的神將其精神注入其中。那個精神“解釋了”我們的智力——至少在語法上是這樣。

The invention of hydraulic engineering in the 3rd century BCE led to the popularity of a hydraulic model of human intelligence, the idea that the flow of different fluids in the body – the ‘humours’ – accounted for both our physical and mental functioning. The hydraulic metaphor persisted for more than 1,600 years, handicapping medical practice all the while.
西元前 3 世紀水利工程的發明導致了人類智慧水力模型的流行，該模型認為體內不同液體的流動——“體液”——解釋了我們的身體和心理功能。水力的比喻持續了 1,600 多年，一直阻礙著醫療實踐。

By the 1500s, automata powered by springs and gears had been devised, eventually inspiring leading thinkers such as René Descartes to assert that humans are complex machines. In the 1600s, the British philosopher Thomas Hobbes suggested that thinking arose from small mechanical motions in the brain. By the 1700s, discoveries about electricity and chemistry led to new theories of human intelligence – again, largely metaphorical in nature. In the mid-1800s, inspired by recent advances in communications, the German physicist Hermann von Helmholtz compared the brain to a telegraph.
到 1500 年代，由彈簧和齒輪驅動的自動機已經被設計出來，最終激發了勒內·笛卡爾等領先思想家斷言人類是複雜的機器。在 1600 年代，英國哲學家湯瑪斯·霍布斯 （Thomas Hobbes） 提出，思考源於大腦中的微小機械運動。到 1700 年代，關於電和化學的發現導致了人類智慧的新理論——同樣，本質上主要是隱喻性的。在 1800 年代中期，受到通信最新進展的啟發，德國物理學家赫爾曼·馮·亥姆霍茲 （Hermann von Helmholtz） 將大腦比作電報。

The mathematician John von Neumann stated flatly that the function of the human nervous system is ‘prima facie digital’, drawing parallel after parallel between the components of the computing machines of the day and the components of the human brain
數學家約翰·馮·諾依曼 （John von Neumann） 直截了當地指出，人類神經系統的功能是“表面數位的”，在當時的計算機機器元件和人腦的元件之間一接一秒地平行

Each metaphor reflected the most advanced thinking of the era that spawned it. Predictably, just a few years after the dawn of computer technology in the 1940s, the brain was said to operate like a computer, with the role of physical hardware played by the brain itself and our thoughts serving as software. The landmark event that launched what is now broadly called ‘cognitive science’ was the publication of Language and Communication (1951) by the psychologist George Miller. Miller proposed that the mental world could be studied rigorously using concepts from information theory, computation and linguistics.
每個隱喻都反映了孕育它的那個時代最先進的思想。不出所料，就在 1940 年代計算機技術誕生後的幾年內，據說大腦像計算機一樣運作，大腦本身扮演著物理硬體的角色，而我們的思想則充當軟體。開創現在廣義上稱為「認知科學」的里程碑事件是心理學家喬治·米勒 （George Miller） 出版的《語言與交流》（1951 年）。米勒提出，可以使用資訊論、計算和語言學的概念對心理世界進行嚴格的研究。

This kind of thinking was taken to its ultimate expression in the short book The Computer and the Brain (1958), in which the mathematician John von Neumann stated flatly that the function of the human nervous system is ‘prima facie digital’. Although he acknowledged that little was actually known about the role the brain played in human reasoning and memory, he drew parallel after parallel between the components of the computing machines of the day and the components of the human brain.
這種想法在短書《計算機與大腦》（1958 年）中得到了最終的表達，數學家約翰·馮·諾依曼 （John von Neumann） 在書中直截了當地指出，人類神經系統的功能是“表面數位的”。儘管他承認人們對大腦在人類推理和記憶中的作用知之甚少，但他將當時的計算機元件與人腦的元件進行了一次又一次的平行比較。

Propelled by subsequent advances in both computer technology and brain research, an ambitious multidisciplinary effort to understand human intelligence gradually developed, firmly rooted in the idea that humans are, like computers, information processors. This effort now involves thousands of researchers, consumes billions of dollars in funding, and has generated a vast literature consisting of both technical and mainstream articles and books. Ray Kurzweil’s book How to Create a Mind: The Secret of Human Thought Revealed (2013), exemplifies this perspective, speculating about the ‘algorithms’ of the brain, how the brain ‘processes data’, and even how it superficially resembles integrated circuits in its structure.
在計算機技術和大腦研究的後續進步的推動下，一項雄心勃勃的多學科研究逐漸發展起來，其根深蒂固的理念是人類和計算機一樣，都是信息處理器。這項工作現在涉及數千名研究人員，消耗了數十億美元的資金，併產生了由技術和主流文章和書籍組成的大量文獻。Ray Kurzweil 的著作《如何創造心智：揭示人類思想的秘密》（2013 年）體現了這一觀點，推測了大腦的“演算法”、大腦如何“處理數據”，甚至它在結構上如何表面上類似於積體電路。

The information processing (IP) metaphor of human intelligence now dominates human thinking, both on the street and in the sciences. There is virtually no form of discourse about intelligent human behaviour that proceeds without employing this metaphor, just as no form of discourse about intelligent human behaviour could proceed in certain eras and cultures without reference to a spirit or deity. The validity of the IP metaphor in today’s world is generally assumed without question.
人類智慧的信息處理 （IP） 隱喻現在主導著人類的思維，無論是在街頭還是在科學領域。幾乎沒有任何形式的關於智慧人類行為的討論是不使用這個比喻進行的，就像在某些時代和文化中，任何形式的關於智慧人類行為的討論都不可能不涉及精神或神靈。在當今世界，人們通常認為IP隱喻的有效性是毫無疑問的。

But the IP metaphor is, after all, just another metaphor – a story we tell to make sense of something we don’t actually understand. And like all the metaphors that preceded it, it will certainly be cast aside at some point – either replaced by another metaphor or, in the end, replaced by actual knowledge.
但IP的隱喻畢竟只是另一個隱喻——我們講述一個故事來理解我們實際上並不理解的東西。就像之前的所有隱喻一樣，它肯定會在某個時候被拋棄——要麼被另一個隱喻取代，要麼最終被實際的知識所取代。

Just over a year ago, on a visit to one of the world’s most prestigious research institutes, I challenged researchers there to account for intelligent human behaviour without reference to any aspect of the IP metaphor. They couldn’t do it, and when I politely raised the issue in subsequent email communications, they still had nothing to offer months later. They saw the problem. They didn’t dismiss the challenge as trivial. But they couldn’t offer an alternative. In other words, the IP metaphor is ‘sticky’. It encumbers our thinking with language and ideas that are so powerful we have trouble thinking around them.
就在一年多前，在訪問世界上最負盛名的研究機構之一時，我向那裡的研究人員提出挑戰，要求他們在不提及智慧財產權隱喻的任何方面的情況下解釋人類的智能行為。他們做不到，當我在隨後的電子郵件通信中禮貌地提出這個問題時，幾個月後他們仍然沒有什麼可以提供的。他們看到了問題所在。他們並沒有將挑戰視為微不足道。但他們無法提供替代方案。換句話說，IP 的隱喻是「粘性的」。它用如此強大的語言和想法阻礙我們的思考，以至於我們很難圍繞它們思考。

The faulty logic of the IP metaphor is easy enough to state. It is based on a faulty syllogism – one with two reasonable premises and a faulty conclusion. Reasonable premise #1: all computers are capable of behaving intelligently. Reasonable premise #2: all computers are information processors. Faulty conclusion: all entities that are capable of behaving intelligently are information processors.
IP 隱喻的錯誤邏輯很容易說出來。它基於一個錯誤的三段論——一個有兩個合理的前提和一個錯誤的結論。合理的前提 #1：所有計算機都能夠智慧地運行。合理的前提 #2：所有計算機都是信息處理器。錯誤的結論：所有能夠智慧行為的實體都是信息處理器。

Setting aside the formal language, the idea that humans must be information processors just because computers are information processors is just plain silly, and when, some day, the IP metaphor is finally abandoned, it will almost certainly be seen that way by historians, just as we now view the hydraulic and mechanical metaphors to be silly.
撇開正式語言不談，僅僅因為計算機是信息處理器，就認為人類必須是資訊處理器的想法簡直是愚蠢的，當有一天，智慧財產權的隱喻最終被拋棄時，歷史學家幾乎肯定會這樣看，就像我們現在認為液壓和機械的隱喻是愚蠢的一樣。

If the IP metaphor is so silly, why is it so sticky? What is stopping us from brushing it aside, just as we might brush aside a branch that was blocking our path? Is there a way to understand human intelligence without leaning on a flimsy intellectual crutch? And what price have we paid for leaning so heavily on this particular crutch for so long? The IP metaphor, after all, has been guiding the writing and thinking of a large number of researchers in multiple fields for decades. At what cost?
如果 IP 的比喻如此愚蠢，為什麼它如此粘稠？是什麼阻止了我們把它撇到一邊，就像我們可能撇開擋住我們去路的樹枝一樣？有沒有辦法在不依賴脆弱的智力拐杖的情況下理解人類的智力？我們這麼長時間地依賴這個特殊的拐杖付出了什麼代價？畢竟，幾十年來，智慧財產權的隱喻一直在指導著多個領域的大量研究人員的寫作和思考。代價是什麼？

In a classroom exercise I have conducted many times over the years, I begin by recruiting a student to draw a detailed picture of a dollar bill – ‘as detailed as possible’, I say – on the blackboard in front of the room. When the student has finished, I cover the drawing with a sheet of paper, remove a dollar bill from my wallet, tape it to the board, and ask the student to repeat the task. When he or she is done, I remove the cover from the first drawing, and the class comments on the differences.
多年來，我曾多次進行課堂練習，我首先招募一名學生在教室前面的黑板上畫出一美元鈔票的詳細圖片——我說“盡可能詳細”。學生完成後，我用一張紙蓋住這幅畫，從錢包里拿出一張美元鈔票，用膠帶貼在黑板上，然後讓學生重複這個任務。當他或她完成後，我從第一幅畫中去除封面，全班同學對差異進行評論。

Because you might never have seen a demonstration like this, or because you might have trouble imagining the outcome, I have asked Jinny Hyun, one of the student interns at the institute where I conduct my research, to make the two drawings. Here is her drawing ‘from memory’ (notice the metaphor):
因為你可能從未見過這樣的演示，或者因為你可能難以想像結果，所以我請我進行研究的研究所的一名學生實習生 Jinny Hyun 製作了這兩幅圖畫。這是她「憑記憶」畫的畫（注意這個比喻）：

And here is the drawing she subsequently made with a dollar bill present:
這是她後來用一美元鈔票畫的畫：

Jinny was as surprised by the outcome as you probably are, but it is typical. As you can see, the drawing made in the absence of the dollar bill is horrible compared with the drawing made from an exemplar, even though Jinny has seen a dollar bill thousands of times.
Jinny 對結果可能和你一樣感到驚訝，但這是典型的。正如你所看到的，在沒有美元鈔票的情況下繪製的圖畫與從樣本中繪製的圖畫相比是可怕的，即使 Jinny 已經看到一美元鈔票數千次。

What is the problem? Don’t we have a ‘representation’ of the dollar bill ‘stored’ in a ‘memory register’ in our brains? Can’t we just ‘retrieve’ it and use it to make our drawing?
問題是什麼？我們大腦中不是有一美元鈔票的「表示」「存儲」在「記憶寄存器」中嗎？我們不能直接 「檢索 」它並用它來畫畫嗎？

Obviously not, and a thousand years of neuroscience will never locate a representation of a dollar bill stored inside the human brain for the simple reason that it is not there to be found.
顯然不是，一千年的神經科學永遠無法找到存儲在人腦中的美元鈔票的表示，原因很簡單，它不存在。

The idea that memories are stored in individual neurons is preposterous: how and where is the memory stored in the cell?
記憶存儲在單個神經元中的想法是荒謬的：記憶如何以及在哪裡存儲在細胞中？

A wealth of brain studies tells us, in fact, that multiple and sometimes large areas of the brain are often involved in even the most mundane memory tasks. When strong emotions are involved, millions of neurons can become more active. In a 2016 study of survivors of a plane crash by the University of Toronto neuropsychologist Brian Levine and others, recalling the crash increased neural activity in ‘the amygdala, medial temporal lobe, anterior and posterior midline, and visual cortex’ of the passengers.
事實上，大量的大腦研究告訴我們，即使是最平凡的記憶任務，大腦的多個區域，有時甚至是很大的區域也經常參與。當涉及強烈的情緒時，數百萬個神經元會變得更加活躍。在 2016 年多倫多大學神經心理學家布賴恩·萊文 （Brian Levine） 等人對飛機失事倖存者進行的一項研究中，回憶起墜機事件，乘客“杏仁核、內側顳葉、前後中線和視覺皮層”的神經活動增加。

The idea, advanced by several scientists, that specific memories are somehow stored in individual neurons is preposterous; if anything, that assertion just pushes the problem of memory to an even more challenging level: how and where, after all, is the memory stored in the cell?
由幾位科學家提出的觀點，即特定記憶以某種方式存儲在單個神經元中是荒謬的;如果有的話，這個斷言只是將記憶體問題推向了一個更具挑戰性的水準：畢竟，記憶體是如何以及在哪裡存儲在細胞中的？

So what is occurring when Jinny draws the dollar bill in its absence? If Jinny had never seen a dollar bill before, her first drawing would probably have not resembled the second drawing at all. Having seen dollar bills before, she was changed in some way. Specifically, her brain was changed in a way that allowed her to visualise a dollar bill – that is, to re-experience seeing a dollar bill, at least to some extent.
那麼，當 Jinny 在美元鈔票不在的情況下提取美元鈔票時會發生什麼呢？如果 Jinny 以前從未見過美元鈔票，她的第一幅畫可能根本不像第二幅畫。以前見過美元鈔票，她在某種程度上發生了變化。具體來說，她的大腦發生了變化，使她能夠想像一美元鈔票——也就是說，至少在某種程度上，重新體驗看到一美元鈔票的感覺。

The difference between the two diagrams reminds us that visualising something (that is, seeing something in its absence) is far less accurate than seeing something in its presence. This is why we’re much better at recognising than recalling. When we re-member something (from the Latin re, ‘again’, and memorari, ‘be mindful of’), we have to try to relive an experience; but when we recognise something, we must merely be conscious of the fact that we have had this perceptual experience before.
這兩個圖之間的差異提醒我們，想像某物（即在不存在的情況下看到某物）遠不如在它面前看到某物準確。這就是為什麼我們更擅長識別而不是回憶。當我們重新回憶起某件事時（來自拉丁語 re，「再次」和 memorari，「留意」），我們必須嘗試重溫一次經歷;但是當我們認識到某件事時，我們只需要意識到我們以前有過這種感知體驗的事實。

Perhaps you will object to this demonstration. Jinny had seen dollar bills before, but she hadn’t made a deliberate effort to ‘memorise’ the details. Had she done so, you might argue, she could presumably have drawn the second image without the bill being present. Even in this case, though, no image of the dollar bill has in any sense been ‘stored’ in Jinny’s brain. She has simply become better prepared to draw it accurately, just as, through practice, a pianist becomes more skilled in playing a concerto without somehow inhaling a copy of the sheet music.
也許你會反對這個演示。金妮以前見過美元鈔票，但她並沒有刻意去「記住」這些細節。你可能會爭辯說，如果她這樣做了，她大概可以在沒有法案的情況下繪製第二張圖片。然而，即使在這種情況下，金妮的大腦中也沒有任何意義上“存儲”美元鈔票的圖像。她只是變得更有準備來準確地畫出來，就像通過練習，鋼琴家在不知何故吸入樂譜副本的情況下演奏協奏曲變得更加熟練一樣。

From this simple exercise, we can begin to build the framework of a metaphor-free theory of intelligent human behaviour – one in which the brain isn’t completely empty, but is at least empty of the baggage of the IP metaphor.
從這個簡單的練習中，我們可以開始構建一個關於智慧人類行為的無隱喻理論的框架——在這個理論中，大腦並非完全空虛，但至少沒有IP隱喻的包袱。

As we navigate through the world, we are changed by a variety of experiences. Of special note are experiences of three types: (1) we observe what is happening around us (other people behaving, sounds of music, instructions directed at us, words on pages, images on screens); (2) we are exposed to the pairing of unimportant stimuli (such as sirens) with important stimuli (such as the appearance of police cars); (3) we are punished or rewarded for behaving in certain ways.
當我們在世界上導航時，我們會被各種經歷所改變。特別值得注意的是三種類型的體驗：（1） 我們觀察周圍發生的事情（其他人的行為、音樂的聲音、針對我們的指示、頁面上的文字、螢幕上的圖像）;（2） 我們暴露在不重要的刺激（如警笛聲）與重要刺激（如警車的出現）的配對中;（3） 我們因某些行為而受到懲罰或獎勵。

We become more effective in our lives if we change in ways that are consistent with these experiences – if we can now recite a poem or sing a song, if we are able to follow the instructions we are given, if we respond to the unimportant stimuli more like we do to the important stimuli, if we refrain from behaving in ways that were punished, if we behave more frequently in ways that were rewarded.
如果我們以符合這些體驗的方式進行改變，我們的生活就會變得更有效 – 如果我們現在能背誦一首詩或唱一首歌，如果我們能夠遵循別人的指示，如果我們對不重要的刺激的反應更像我們對重要刺激的反應，如果我們不做那些受到懲罰的行為， 如果我們以更頻繁的方式行事，從而獲得回報。

Misleading headlines notwithstanding, no one really has the slightest idea how the brain changes after we have learned to sing a song or recite a poem. But neither the song nor the poem has been ‘stored’ in it. The brain has simply changed in an orderly way that now allows us to sing the song or recite the poem under certain conditions. When called on to perform, neither the song nor the poem is in any sense ‘retrieved’ from anywhere in the brain, any more than my finger movements are ‘retrieved’ when I tap my finger on my desk. We simply sing or recite – no retrieval necessary.
儘管有誤導性的標題，但沒有人真正了解我們學會唱歌或背誦詩歌後大腦是如何變化的。但這首歌和詩歌都沒有被「儲存」 在裡面。大腦只是以一種有序的方式發生了變化，現在允許我們在特定條件下唱歌或背誦詩歌。當被要求表演時，這首歌和這首詩在任何意義上都不是從大腦的任何地方 「檢索 」出來的，就像當我用手指在桌子上敲擊時，我的手指動作 「檢索」一樣。我們只需唱歌或背誦 – 無需檢索。

A few years ago, I asked the neuroscientist Eric Kandel of Columbia University – winner of a Nobel Prize for identifying some of the chemical changes that take place in the neuronal synapses of the Aplysia (a marine snail) after it learns something – how long he thought it would take us to understand how human memory works. He quickly replied: ‘A hundred years.’ I didn’t think to ask him whether he thought the IP metaphor was slowing down neuroscience, but some neuroscientists are indeed beginning to think the unthinkable – that the metaphor is not indispensable.
幾年前，我問哥倫比亞大學的神經科學家埃裡克·坎德爾（Eric Kandel）——諾貝爾獎得主，因為他確定了海兔（一種海洋蝸牛）在學習后神經元突觸中發生的一些化學變化——他認為我們需要多長時間才能理解人類記憶是如何運作的。'他很快回答說：'一百年。我沒有想問他是否認為IP隱喻正在減慢神經科學的速度，但一些神經科學家確實開始思考不可思議的事情——這個隱喻並不是必不可少的。

A few cognitive scientists – notably Anthony Chemero of the University of Cincinnati, the author of Radical Embodied Cognitive Science (2009) – now completely reject the view that the human brain works like a computer. The mainstream view is that we, like computers, make sense of the world by performing computations on mental representations of it, but Chemero and others describe another way of understanding intelligent behaviour – as a direct interaction between organisms and their world.
一些認知科學家——特別是辛辛那提大學的安東尼·切梅羅（Anthony Chemero），他是《激進體現認知科學》（Radical Embodied Cognitive Science，2009）的作者——現在完全拒絕了人腦像計算機一樣工作的觀點。主流觀點認為，我們和計算機一樣，通過對世界的心理表徵進行計算來理解世界，但 Chemero 和其他人描述了另一種理解智能行為的方式——有機體與其世界之間的直接互動。

My favourite example of the dramatic difference between the IP perspective and what some now call the ‘anti-representational’ view of human functioning involves two different ways of explaining how a baseball player manages to catch a fly ball – beautifully explicated by Michael McBeath, now at Arizona State University, and his colleagues in a 1995 paper in Science. The IP perspective requires the player to formulate an estimate of various initial conditions of the ball’s flight – the force of the impact, the angle of the trajectory, that kind of thing – then to create and analyse an internal model of the path along which the ball will likely move, then to use that model to guide and adjust motor movements continuously in time in order to intercept the ball.
我最喜歡的例子是智慧財產權觀點與現在一些人所說的人類功能的“反表徵”觀點之間的巨大差異，涉及兩種不同的方式來解釋棒球運動員如何設法接住飛球——現在在亞利桑那州立大學的邁克爾·麥克比斯和他的同事在 1995 年的一篇論文中完美地闡述了《科學》雜誌.IP 視角要求球員對球飛行的各種初始條件進行估計——撞擊的力、軌跡的角度等等——然後創建和分析球可能移動的路徑的內部模型，然後使用該模型及時引導和調整運動，以攔截球。

That is all well and good if we functioned as computers do, but McBeath and his colleagues gave a simpler account: to catch the ball, the player simply needs to keep moving in a way that keeps the ball in a constant visual relationship with respect to home plate and the surrounding scenery (technically, in a ‘linear optical trajectory’). This might sound complicated, but it is actually incredibly simple, and completely free of computations, representations and algorithms.
如果我們像計算機一樣工作，那一切都很好，但 McBeath 和他的同事給出了一個更簡單的解釋：要接球，球員只需要保持移動，使球相對於本壘板和周圍風景保持恆定的視覺關係（技術上，在“線性光學軌跡”中）。這聽起來可能很複雜，但實際上非常簡單，而且完全沒有計算、表示和演算法。

we will never have to worry about a human mind going amok in cyberspace, and we will never achieve immortality through downloading
我們永遠不必擔心人類的思想在網路空間中失控，我們永遠無法通過下載實現不朽

Two determined psychology professors at Leeds Beckett University in the UK – Andrew Wilson and Sabrina Golonka – include the baseball example among many others that can be looked at simply and sensibly outside the IP framework. They have been blogging for years about what they call a ‘more coherent, naturalised approach to the scientific study of human behaviour… at odds with the dominant cognitive neuroscience approach’. This is far from a movement, however; the mainstream cognitive sciences continue to wallow uncritically in the IP metaphor, and some of the world’s most influential thinkers have made grand predictions about humanity’s future that depend on the validity of the metaphor.
英國利茲貝克特大學（Leeds Beckett University）的兩位堅定的心理學教授——安德魯·威爾遜（Andrew Wilson）和薩布麗娜·戈隆卡（Sabrina Golonka）——將棒球的例子列為眾多可以在智慧財產權框架之外簡單而明智地看待的例子。多年來，他們一直在博客上討論他們所謂的「更連貫、更自然的人類行為科學研究方法......與佔主導地位的認知神經科學方法不一致」。然而，這遠非一場運動;主流認知科學繼續不加批判地沉溺於智慧財產權的隱喻中，世界上一些最有影響力的思想家已經對人類的未來做出了巨集偉的預測，這些預測取決於這個隱喻的有效性。

One prediction – made by the futurist Kurzweil, the physicist Stephen Hawking and the neuroscientist Randal Koene, among others – is that, because human consciousness is supposedly like computer software, it will soon be possible to download human minds to a computer, in the circuits of which we will become immensely powerful intellectually and, quite possibly, immortal. This concept drove the plot of the dystopian movie Transcendence (2014) starring Johnny Depp as the Kurzweil-like scientist whose mind was downloaded to the internet – with disastrous results for humanity.
未來學家庫茲韋爾（Kurzweil）、物理學家斯蒂芬·霍金（Stephen Hawking）和神經科學家蘭德爾·科恩（Randal Koene）等人都做出了一個預測，因為人類的意識被認為就像計算機軟體一樣，所以很快就有可能將人類的思想下載到計算機上，在這個電路中，我們將變得非常強大的智力，而且很可能是不朽的。這個概念推動了反烏托邦電影《超越》（2014 年）的情節，該電影由約翰尼·德普 （Johnny Depp） 主演，飾演類似庫茲韋爾的科學家，他的思想被下載到互聯網上——給人類帶來了災難性的後果。

Fortunately, because the IP metaphor is not even slightly valid, we will never have to worry about a human mind going amok in cyberspace; alas, we will also never achieve immortality through downloading. This is not only because of the absence of consciousness software in the brain; there is a deeper problem here – let’s call it the uniqueness problem – which is both inspirational and depressing.
幸運的是，因為IP的隱喻甚至沒有一點點有效，我們永遠不必擔心人類的思想在網路空間中失控;唉，我們也永遠無法通過下載實現不朽。這不僅是因為大腦中沒有意識軟體;這裡有一個更深層次的問題——我們稱之為獨特性問題——它既鼓舞人心，又令人沮喪。

Because neither ‘memory banks’ nor ‘representations’ of stimuli exist in the brain, and because all that is required for us to function in the world is for the brain to change in an orderly way as a result of our experiences, there is no reason to believe that any two of us are changed the same way by the same experience. If you and I attend the same concert, the changes that occur in my brain when I listen to Beethoven’s 5th will almost certainly be completely different from the changes that occur in your brain. Those changes, whatever they are, are built on the unique neural structure that already exists, each structure having developed over a lifetime of unique experiences.
B因為大腦中既不存在“記憶庫”，也不存在刺激的“表徵”，而且因為我們在這個世界上運作所需要的只是大腦根據我們的經歷以有序的方式發生變化，所以沒有理由相信我們中的任何兩個人都會因相同的經歷而以相同的方式發生變化。如果你和我參加同一場音樂會，那麼當我聽貝多芬第五交響曲時，我大腦中發生的變化幾乎肯定會與你大腦中發生的變化完全不同。這些變化，無論它們是什麼，都是建立在已經存在的獨特神經結構之上的，每個結構都是在一生的獨特經歷中發展起來的。

This is why, as Sir Frederic Bartlett demonstrated in his book Remembering (1932), no two people will repeat a story they have heard the same way and why, over time, their recitations of the story will diverge more and more. No ‘copy’ of the story is ever made; rather, each individual, upon hearing the story, changes to some extent – enough so that when asked about the story later (in some cases, days, months or even years after Bartlett first read them the story) – they can re-experience hearing the story to some extent, although not very well (see the first drawing of the dollar bill, above).
這就是為什麼，正如弗雷德里克·巴特利特爵士在他的著作《記住》（1932 年）中所證明的那樣，沒有兩個人會以相同的方式重複他們聽到的故事，以及為什麼隨著時間的推移，他們對故事的背誦會越來越不同。這個故事從來沒有被製作過;相反，每個人在聽到這個故事時，都會在一定程度上發生變化——足以讓以後被問及這個故事時（在某些情況下，在 Bartlett 第一次給他們讀這個故事后的幾天、幾個月甚至幾年後）——他們可以在某種程度上重新體驗聽到這個故事，儘管不是很好（見美元鈔票的第一張圖， 上面）。

This is inspirational, I suppose, because it means that each of us is truly unique, not just in our genetic makeup, but even in the way our brains change over time. It is also depressing, because it makes the task of the neuroscientist daunting almost beyond imagination. For any given experience, orderly change could involve a thousand neurons, a million neurons or even the entire brain, with the pattern of change different in every brain.
我想這很鼓舞人心，因為這意味著我們每個人都是獨一無二的，不僅在我們的基因構成上，甚至在我們的大腦隨時間變化的方式上也是如此。這也是令人沮喪的，因為它使神經科學家的任務變得幾乎超乎想像。對於任何給定的體驗，有序變化可能涉及一千個神經元、一百萬個神經元甚至整個大腦，每個大腦的變化模式都不同。

Worse still, even if we had the ability to take a snapshot of all of the brain’s 86 billion neurons and then to simulate the state of those neurons in a computer, that vast pattern would mean nothing outside the body of the brain that produced it. This is perhaps the most egregious way in which the IP metaphor has distorted our thinking about human functioning. Whereas computers do store exact copies of data – copies that can persist unchanged for long periods of time, even if the power has been turned off – the brain maintains our intellect only as long as it remains alive. There is no on-off switch. Either the brain keeps functioning, or we disappear. What’s more, as the neurobiologist Steven Rose pointed out in The Future of the Brain (2005), a snapshot of the brain’s current state might also be meaningless unless we knew the entire life history of that brain’s owner – perhaps even about the social context in which he or she was raised.
更糟糕的是，即使我們有能力拍攝大腦中所有860億個神經元的快照，然後在計算機中類比這些神經元的狀態，這種巨大的模式在產生它的大腦體外也沒有任何意義。這也許是IP隱喻扭曲我們對人類功能的看法的最令人震驚的方式。雖然計算機確實存儲了數據的精確副本——即使電源已經關閉，這些副本也可以長時間保持不變——但大腦只有在還活著的時候才能維持我們的智力。沒有開關。要麼大腦繼續運作，要麼我們消失。更重要的是，正如神經生物學家史蒂文·羅斯 （Steven Rose） 在《大腦的未來》（2005 年）中指出的那樣，除非我們知道大腦所有者的整個生活史——甚至可能瞭解他或她成長的社會背景，否則大腦當前狀態的快照也可能毫無意義。

Think how difficult this problem is. To understand even the basics of how the brain maintains the human intellect, we might need to know not just the current state of all 86 billion neurons and their 100 trillion interconnections, not just the varying strengths with which they are connected, and not just the states of more than 1,000 proteins that exist at each connection point, but how the moment-to-moment activity of the brain contributes to the integrity of the system. Add to this the uniqueness of each brain, brought about in part because of the uniqueness of each person’s life history, and Kandel’s prediction starts to sound overly optimistic. (In a recent op-ed in The New York Times, the neuroscientist Kenneth Miller suggested it will take ‘centuries’ just to figure out basic neuronal connectivity.)
想想這個問題有多難。要瞭解大腦如何維持人類智力的基本知識，我們可能不僅需要瞭解所有 860 億個神經元及其 100 萬億個互連的當前狀態，不僅需要了解它們所連接的不同強度，還需要瞭解存在於每個連接點的 1,000 多種蛋白質的狀態。 而是大腦每時每刻的活動如何促進系統的完整性。再加上每個大腦的獨特性，部分原因是每個人生活史的獨特性，坎德爾的預測開始聽起來過於樂觀。（在《紐約時報》最近的一篇專欄文章中，神經科學家肯尼斯·米勒 （Kenneth Miller） 表示，光是弄清楚基本的神經元連接就需要“幾個世紀”的時間。

Meanwhile, vast sums of money are being raised for brain research, based in some cases on faulty ideas and promises that cannot be kept. The most blatant instance of neuroscience gone awry, documented recently in a report in Scientific American, concerns the $1.3 billion Human Brain Project launched by the European Union in 2013. Convinced by the charismatic Henry Markram that he could create a simulation of the entire human brain on a supercomputer by the year 2023, and that such a model would revolutionise the treatment of Alzheimer’s disease and other disorders, EU officials funded his project with virtually no restrictions. Less than two years into it, the project turned into a ‘brain wreck’, and Markram was asked to step down.
與此同時，人們正在為大腦研究籌集大量資金，在某些情況下，這些資金是基於無法兌現的錯誤想法和承諾。最近在《科學美國人》的一篇報導中記錄了神經科學出錯的最明顯的例子，涉及歐盟於 2013 年發起的 13 億美元的人腦計劃。魅力非凡的亨利·馬克拉姆 （Henry Markram） 相信他可以在 2023 年之前在超級計算機上創建整個人腦的類比，並且這樣的模型將徹底改變阿爾茨海默病和其他疾病的治療，歐盟官員幾乎不受限制地資助了他的專案。不到兩年，該專案就變成了“腦殘”，Markram 被要求辭職。

We are organisms, not computers. Get over it. Let’s get on with the business of trying to understand ourselves, but without being encumbered by unnecessary intellectual baggage. The IP metaphor has had a half-century run, producing few, if any, insights along the way. The time has come to hit the DELETE key.
我們是有機體，而不是計算機。克服它。 讓我們繼續努力瞭解自己，但不要被不必要的知識包袱所束縛。IP 隱喻已經運行了半個世紀，在此過程中產生的見解很少（如果有的話）。現在是按下 DELETE 鍵的時候了。