The Brain is not a computer but an organism!

February 12, 2017

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’. 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 behavior have been asserting that the human brain works like a computer. 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 a doubt, built to make social connections. 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. 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. 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. 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. 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’) organized into small chunks (‘bytes’). On my computer, each byte contains 64 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. 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 on 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’. 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. 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?  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. 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. 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. 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. 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 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. 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. 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, consumers 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. 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 behavior that proceeds without employing this metaphor, just as no form of discourse about intelligent human behavior 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. 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. 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 behavior 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 about them. 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. 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. 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?  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. Because you might never have seen a demonstration like this, or because you might have trouble imagining the outcome. 

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.

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).

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, the orderly change could involve a thousand neurons, a million neurons or even the entire brain, with the pattern of change different in every brain.

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.

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.) 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 revolutionize 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. 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.



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