The Happiness of Pursuit: What Neuroscience Can Teach Us About the Good Life (3 page)

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Authors: Shimon Edelman

BOOK: The Happiness of Pursuit: What Neuroscience Can Teach Us About the Good Life
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The focus on the
pursuit
of happiness, endorsed by the Declaration of Independence, fits well with the idea of life as a journey—a bright thread that runs through the literary canon of the collective human culture.
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With the world at your feet, the turns that you should take along the way depend on what you are at the outset and on what you become as the journey lengthens. Accordingly, the present book is an attempt to understand, in a deeper sense than merely metaphorical, what it means to be human and how humans are shaped by the journey through this world, which the poet John Keats called “the vale of soul-making”—in particular, how it puts within the soul’s reach “a bliss peculiar to each one’s individual existence.”
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The fundamental insight that serves as the starting point for my story is that the mind is inherently and essentially a bundle of ongoing computations, the brain being one of many possible substrates that can support them. I make the case for these claims by constructing, in plain sight and out of readily available materials, a conceptual toolbox that affords the reader a glimpse of the computations underlying the mind’s faculties: perception, motivation and emotions, action, memory, thinking, social cognition, and language. This conceptual buildup culminates in an explanation that states, in plain language, the nature of the phenomenal self and of consciousness. Readers who are interested in the details that I omit can follow the leads offered by the many notes at the end of the book.
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These conceptual tools prove to be useful in making new sense of the notion of the pursuit of happiness. Quite satisfyingly, it emerges that the framers of the Declaration of Independence presaged the findings of the scientific inquiry into happiness: the dynamics of the self and of happiness is such that the pursuit itself—the journey rather than the destination—is what really matters (hence the title of the book). This insight, such as it is, informs the book’s conclusion: the seeker after happiness returns home, only to grow restless and eventually succumb to the lure of a new journey. On the basis of the understanding developed throughout the book, the following practical advice is offered as a way of summing up its lessons in seven words: when fishing for happiness, catch and release.
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Computing the Mind
 
A great metaphor that isn’t. Concerning
computation. No cognition without
representation. Three things everyone should
know about life, the universe, and everything.
Promethean probabilities and amazing Bayes.
Minds within brains. Minds without brains.
 
And if the body were not the Soul, what is the Soul?
—WALT WHITMAN,
Leaves of Grass: I Sing the Body Electric
(19:1)
 
A Great Metaphor That Isn’t
 
Let me tell you a short story about the brain. The brain is the most complex object known to science. Because scientists have been unable to explain exactly how the brain gives rise to the mind, they keep resorting to technological metaphors of complexity. The best metaphors are those that draw on concepts associated with newfangled technologies, which still exude a certain aura of mystery. In the past, such metaphors came from mechanics (“the brain is an intricate clockwork”) and electronics (“the brain is a vast telephone exchange” ). For some time now, everyone’s favorite source of metaphors has been computer engineering. Still, no matter how much we like to compare brains (“meat computers”) and computers (“electronic brains”), the computer metaphor is merely the latest installment in a long series of fads to which brain science periodically succumbs.
If you find yourself liking this story, you are in good company. The “computer metaphor” view of how the mind works sounds sophisticated and modest at the same time and is particularly intellectually appealing to progressive-minded people who know their history of science and value openness toward the prospect of continued replacement of good theories by better ones. It is also popular with science writers, including those practicing cognitive scientists who are eager to share their findings and insights with anyone who is interested in how the mind works. For them, the computer metaphor offers a neat way to introduce and explain the tremendous progress made by cognitive science in the three decades since computational theorizing first started to prove uncannily effective. Better yet, they can do so without actually calling the reader a computing machine.
But what if you really are one?
In our daily lives we routinely encounter devices that can only be understood in terms of computation. Take grocery-store cash registers as an example. These come in different sizes and colors and may rely on diverse mechanical and electronic components, but they all have one inalienable, categorical, defining feature in common: they compute. Take away a cash register’s ability to compute and you’re left with a heap of junk, the machine equivalent of a dead body. In the deepest possible sense, computing is what cash registers are fundamentally about. It would be intellectually irresponsible to insist that doing sums is only one among many equally valid ways of describing the function of a cash register or to argue that a cash register is only metaphorically a computing machine.
This observation sets the stage for the unveiling of what is undoubtedly both the most important and the least-kept secret of cognitive science. Although it has entire books devoted to it, this secret has so far managed to elude the attention of most of the general public and even of some cognitive scientists. It has been able to hide in plain sight because of its revolutionary implications. (We humans often blissfully ignore inconvenient truths, even as we stare them in the face.) Here’s the secret, then: computation is just as much a defining feature of brains as it is of cash registers. Moreover, in both cases it is the most important such characteristic: a cash register’s very existence (let alone its mechanics or electronics) can be really understood only by resorting to the concept of computation, and so can the brain’s.
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Because it takes a while to do justice to the idea that cognition is computation, let me offer you right away a few quick examples that illustrate it. My first example is as simple as black on white—the black of the print against the white of the paper in front of you. Just as all cats are black in the dark, both the paper and the print would be equally invisible if it were not for the light that illuminates the page. Intuitively, the paper reflects more of the light that falls on it than the print does, which is why the letters are seen as darker than their background. It would seem, therefore, that telling apart the print and the paper boils down to gauging the amount of light reflected from each. There is, however, a complication: the amount of light that enters your eye after hitting the page is determined by two independent factors: the quantity of light that is available to begin with (the intensity of the illumination) and the fraction that is reflected from the page (its reflectance).
To fully appreciate the challenge faced (and met!) by your visual system, even as you are reading these lines, let’s state it in concrete and precise terms. (It’s okay to skip the numerical example that follows if you already saw the light.) Suppose your eye is registering 100 photons per second arriving from the region of the page that you are looking at. This measurement can result from various combinations of illumination and page reflectance: for example, 10,000 photons (strong illumination) falling on it with only 1 percent of them being reflected back (low-reflectance or “dark” surface), or 125 photons (weak illumination) falling, of which 80 percent are reflected (high-reflectance or “light” surface). A quick reflection (do try it at home!) reveals that there is an infinity of possible pairings of illumination and surface reflectance values that can give rise to the very same number of photons reaching the eye. Which pairing is the right one?
To find that out, the brain must solve a problem that is fundamentally computational: given a product of two numbers (illumination and reflectance), determine what they are individually. Your ability to perceive the ink as black and the paper as white in direct sunlight as well as in deep shade is clear evidence that your brain indeed manages to solve this inherently arithmetical problem. How it does that is beside the point for the moment.
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Let’s just admit, as we must, that at least one everyday perceptual task can only be solved through computation, because this task cannot even be stated without resorting to numbers (which is why I had no choice but to mention numbers while introducing it just now).
My second example involves the task of thinking. Intuitively, thinking is what follows perception (sizing up the situation you’re in) and precedes action (doing something about it). Humans are pretty good at abstract thinking (witness the ability of some of us to stand up to chess-playing computers, sometimes for as long as a couple dozen moves), but it would be more useful for us to focus here on more mundane challenges, such as figuring out which register line in a supermarket checkout area to join. The simplest approach to this problem, which I personally face much more often than I play chess, is to estimate the length of each line in person-units and then to join the shortest one. Those of us with some supermarket experience are likely to see the simple head-counting approach as unsatisfactory. We know that a better estimate of the amount of time one is likely to spend standing in a line depends not only on the number of people ahead of you, but also on the number of items each of them is buying and on the efficiency of the cashier, as measured by the number of items he or she can scan and bag per minute.
This example illustrates nicely the value of thinking before doing: a few seconds spent on observing a line and thinking about what you see can save you quite a few minutes’ worth of waiting time, which seems like a reasonable return on investment. During these seconds, your brain forms estimates of the number of people in line and the number of items each one has, multiplies these two numbers together, and divides the product by an estimate of the cashier’s efficiency. Do it for each line you’re considering, and you have the proper grounds for making an informed choice about the most promising line. Thus, at least some problems that require thinking, just like perception, reduce to the manipulation of numbers according to certain rules, in this case multiplication, division, and comparison.
My third and for now last example has to do with a drinking problem, albeit not of the kind that necessarily involves alcohol. This particular problem arises in the planning of bodily movements. Imagine yourself sitting at a dining table, with your hand around a glass of water that rests in front of you. You are thirsty, but before you can quench your thirst your brain must crunch some numbers. Because the incident that we are imagining is set in the three-dimensional space of the dining room, it takes three numbers to specify the location of the hand that is holding the glass. If you single out one corner of the room and measure how far your hand is from each of the three surfaces (two walls and the floor) that meet at that corner, you can pinpoint its location precisely. As far as your brain is concerned, however, many more than three numbers are needed. This is because the brain does not measure or control directly the distances between your hand and the walls.
What the brain controls is the angles of the various joints of the body, of which there are many. Looking just at your upper extremities, you can count three independently controllable angles at the shoulder (direction in the horizontal plane, direction in the vertical plane, and rotation), two at the elbow, and two at the wrist. So the very formulation of the problem of planning how to get your hand with the glass from its resting position on the table to your mouth involves a whole series of pretty scary-sounding computations. First, the brain must establish a correspondence between locations specified in the “room” format (as triplets of numbers) and the same locations specified in the “body” format (as lists of seven numbers). Second, it must use this correspondence to compute the seven-number setting that would bring your hand with the glass to your mouth.
Formidable as it is, this is merely a simplified version of the actual, full-blown problem of hand movement control, which requires more than bringing the glass to your mouth (rather than, say, to your ear). For one thing, you would probably like to be able to do it without spilling the water along the way or smashing in your teeth at the end. Let me set these complications aside and reiterate instead the key point: in the three examples offered here, the problems that arise, which I chose from the three main areas of cognition—perception, action, and what’s in between—are all inherently computational. These problems, which the brain encounters and solves as a matter of daily routine and usually outside of conscious deliberation, cannot even be stated without recourse to numbers, and their solution must therefore involve some kind of number crunching.
This realization spells a certain kind of doom for the venerable computer metaphor for the brain, with which I opened this chapter. In science, a metaphor may wither away after being made irrelevant by new insights into the phenomenon that motivated it in the first place. Alternatively, it may crystallize into an accepted explanation, if theoretical advances and empirical findings vindicate it. Such is the fate of the computer metaphor in brain science—a truly great metaphor that isn’t.

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