How the mind works, by Steven Pinker (1rst. chapter)
Campo, Jorge
bellow.
From How the mind works, by Steven Pinker
CHAPTER ONE STANDARD EQUIPMENT
Why are there so many robots in fiction, but none in real life? I would pay
a lot for a robot that could put away the dishes or run simple errands. But
I will not have the opportunity in this century, and probably not in the
next one either. There are, of course, robots that weld or spray-paint on
assembly lines and that roll through laboratory hallways; my question is
about the machines that walk, talk, see, and think, often better than their
human masters. Since 1920, when Karel Capek coined the word robot in his
play R.U.R., dramatists have freely conjured them up: Speedy, Cutie, and
Dave in Isaac Asimov's I, Robot, Robbie in Forbidden Planet, the flailing
canister in Lost in Space, the daleks in Dr. Who, Rosie the Maid in The
Jetsons, Nomad in Star Trek, Hymie in Get Smart, the vacant butlers and
bickering haberdashers in Sleeper, R2D2 and C3PO in Star Wars, the
Terminator in The Terminator, Lieutenant Commander Data in Star Trek: The
Next Generation, and the wisecracking film critics in Mystery Science
Theater 3000.
This book is not about robots; it is about the human mind. I will try to
explain what the mind is, where it came from, and how it lets us see, think,
feel, interact, and pursue higher callings like art, religion, and
philosophy. On the way I will try to throw light on distinctively human
quirks. Why do memories fade? How does makeup change the look of a face?
Where do ethnic stereotypes come from, and when are they irrational? Why do
people lose their tempers? What makes children bratty? Why do fools fall in
love? What makes us laugh? And why do people believe in ghosts and spirits?
But the gap between robots in imagination and in reality is my starting
point, for it shows the first step we must take in knowing ourselves:
appreciating the fantastically complex design behind feats of mental life we
take for granted. The reason there are no humanlike robots is not that the
very idea of a mechanical mind is misguided. It is that the engineering
problems that we humans solve as we see and walk and plan and make it
through the day are far more challenging than landing on the moon or
sequencing the human genome. Nature, once again, has found ingenious
solutions that human engineers cannot yet duplicate. When Hamlet says, "What
a piece of work is a man! how noble in reason! how infinite in faculty! in
form and moving how express and admirable!" we should direct our awe not at
Shakespeare or Mozart or Einstein or Kareem Abdul-Jabbar but at a four-year
old carrying out a request to put a toy on a shelf. In a well-designed
system, the components are black boxes that perform their functions as if by
magic. That is no less true of the mind. The faculty with which we ponder
the world has no ability to peer inside itself or our other faculties to see
what makes them tick. That makes us the victims of an illusion: that our own
psychology comes from some divine force or mysterious essence or almighty
principle. In the Jewish legend of the Golem, a clay figure was animated
when it was fed an inscription of the name of God. The archetype is echoed
in many robot stories. The statue of Galatea was brought to life by Venus'
answer to Pygmalion's prayers; Pinocchio was vivified by the Blue Fairy.
Modern versions of the Golem archetype appear in some of the less fanciful
stories of science. All of human psychology is said to be explained by a
single, omnipotent cause: a large brain, culture, language, socialization,
learning, complexity, self-organization, neural-network dynamics. I want to
convince you that our minds are not animated by some godly vapor or single
wonder principle.
The mind, like the Apollo spacecraft, is designed to solve many engineering
problems, and thus is packed with high-tech systems each contrived to
overcome its own obstacles. I begin by laying out these problems, which are
both design specs for a robot and the subject matter of psychology. For I
believe that the discovery by cognitive science and artificial intelligence
of the technical challenges overcome by our mundane mental activity is one
of the great revelations of science, an awakening of the imagination
comparable to learning that the universe is made up of billions of galaxies
or that a drop of pond water teems with microscopic life.
THE ROBOT CHALLENGE What does it take to build a robot? Let's put aside
superhuman abilities like calculating planetary orbits and begin with the
simple human ones: seeing, walking, grasping, thinking about objects and
people, and planning how to act. In movies we are often shown a scene from
a robot's-eye view, with the help of cinematic conventions like fish-eye
distortion or crosshairs. That is fine for us, the audience, who already
have functioning eyes and brains. But it is no help to the robot's innards.
The robot does not house an audience of little people--homunculi--gazing at
the picture and telling the robot what they are seeing. If you could see the
world through a robot's eyes, it would look not like a movie picture
decorated with crosshairs but something like this:
225 221 216 219 219 214 207 218 219 220 207 155 136 135 213 206 213 223 208
217 223 221 223 216 195 156 141 130 206 217 210 216 224 223 228 230 234 216
207 157 136 132 211 213 221 223 220 222 237 216 219 220 176 149 137 132 221
229 218 230 228 214 213 209 198 224 161 140 133 127 220 219 224 220 219 215
215 206 206 221 159 143 133 131 221 215 211 214 220 218 221 212 218 204 148
141 131 130 214 211 211 218 214 220 226 216 223 209 143 141 141 124 211 208
223 213 216 226 231 230 241 199 153 141 136 125 200 224 219 215 217 224 232
241 240 211 150 139 128 132 204 206 208 205 233 241 241 252 242 192 151 141
133 130 200 205 201 216 232 248 255 246 231 210 149 141 132 126 191 194 209
238 245 255 249 235 238 197 146 139 130 132 189 199 200 227 239 237 235 236
247 192 145 142 124 133 198 196 209 211 210 215 236 240 232 177 142 137 135
124 198 203 205 208 211 224 226 240 210 160 139 132 129 130 216 209 214 220
210 231 245 219 169 143 148 129 128 136 211 210 217 218 214 227 244 221 162
140 139 129 133 131 215 210 216 216 209 220 248 200 156 139 131 129 139 128
219 220 211 208 205 209 240 217 154 141 127 130 124 142 229 224 212 214 220
229 234 208 151 145 128 128 142 122 252 224 222 224 233 244 228 213 143 141
135 128 131 129 255 235 230 249 253 240 228 193 147 139 132 128 136 125 250
245 238 245 246 235 235 190 139 136 134 135 126 130 240 238 233 232 235 255
246 168 156 144 129 127 136 134
Each number represents the brightness of one of the millions of tiny patches
making up the visual field. The smaller numbers come from darker patches,
the larger numbers from brighter patches. The numbers shown in the array are
the actual signals coming from an electronic camera trained on a person's
hand, though they could just as well be the firing rates of some of the
nerve fibers coming from the eye to the brain as a person looks at a hand.
For a robot brain--or a human brain--to recognize objects and not bump into
them, it must crunch these numbers and guess what kinds of objects in the
world reflected the light that gave rise to them. The problem is humblingly
difficult. First, a visual system must locate where an object ends and the
backdrop begins. But the world is not a coloring book, with black outlines
around solid regions.
The world as it is projected into our eyes is a mosaic of tiny shaded
patches. Perhaps, one could guess, the visual brain looks for regions where
a quilt of large numbers (a brighter region) abuts a quilt of small numbers
(a darker region). You can discern such a boundary in the square of numbers;
it runs diagonally from the top right to the bottom center. Most of the
time, unfortunately, you would not have found the edge of an object, where
it gives way to empty space. The juxtaposition of large and small numbers
could have come from many distinct arrangements of matter. This drawing,
devised by the psychologists Pawan Sinha and Edward Adelson, appears to show
a ring of light gray and dark gray tiles. In fact, it is a rectangular
cutout in a black cover through which you are looking at part of a scene. In
the next drawing the cover has been removed, and you can see that each pair
of side-by-side gray squares comes from a different arrangement of objects.
Big numbers next to small numbers can come from an object standing in front
of another object, dark paper lying on light paper, a surface painted two
shades of gray, two objects touching side by side, gray cellophane on a
white page, an inside or outside corner where two walls meet, or a shadow.
Somehow the brain must solve the chicken-and-egg problem of identifying
three-dimensional objects from the patches on the retina and determining
what each patch is (shadow or paint, crease or overlay, clear or opaque)
from knowledge of what object the patch is part of.
The difficulties have just begun. Once we have carved the visual world into
objects, we need to know what they are made of, say, snow versus coal. At
first glance the problem looks simple. If large numbers come from bright
regions and small numbers come from dark regions, then large number equals
white equals snow and small number equals black equals coal, right? Wrong.
The amount of light hitting a spot on the retina depends not only on how
pale or dark the object is but also on how bright or dim the light
illuminating the object is. A photographer's light meter would show you that
more light bounces off a lump of coal outdoors than off a snowball indoors.
That is why people are so often disappointed by their snapshots and why
photography is such a complicated craft.
The camera does not lie; left to its own devices, it renders outdoor scenes
as milk and indoor scenes as mud. Photographers, and sometimes microchips
inside the camera, coax a realistic image out of the film with tricks like
adjustable shutter timing, lens apertures, film speeds, flashes, and
darkroom manipulations. Our visual system does much better. Somehow it lets
us see the bright outdoor coal as black and the dark indoor snowball as
white. That is a happy outcome, because our conscious sensation of color and
lightness matches the world as it is rather than the world as it presents
itself to the eye.
The snowball is soft and wet and prone to melt whether it is indoors or out,
and we see it as white whether it is indoors or out The coal is always hard
and dirty and prone to burn, and we always see it as black. The harmony
between how the world looks and how the world is must be an achievement of
our neural wizardry, because black and white don't simply announce
themselves on the retina. In case you are still skeptical, here is an
everyday demonstration. When a television set is off, the screen is a pale
greenish gray. When it is on, some of the phosphor dots give off light,
painting in the bright areas of the picture. But the other dots do not suck
light and paint in the dark areas; they just stay gray. The areas that you
see as black are in fact just the pale shade of the picture tube when the
set was off. The blackness is a figment. a product of the brain circuitry
that ordinarily allows you to see coal as coal. Television engineers
exploited that circuitry when they designed the screen. The next problem is
seeing in depth. Our eyes squash the three-dimensional world into a pair of
two-dimensional retinal images, and the third dimension must be
reconstituted by the brain. But there are no telltale signs in the patches
on the retina that reveal how far away a surface is. A stamp in your palm
can project the same square on your retina as a chair across the room or a
building miles away (top drawing, page 9). A cutting board viewed head-on
can project the same trapezoid as various irregular shards held at a slant
(bottom drawing, page 9). You can feel the force of this fact of geometry,
and of the neural mechanism that copes with it, by staring at a lightbulb
for a few seconds or looking at a camera as the flash goes off, which
temporarily bleaches a patch onto your retina. If you now look at the page
in front of you, the afterimage adheres to it and appears to be an inch or
two across. If you look up at the wall, the afterimage appears several feet
long. If you look at the sky, it is the size of a cloud.
Finally, how might a vision module recognize the objects out there in the
world, so that the robot can name them or recall what they do? The obvious
solution is to build a template or cutout for each object that duplicates
its shape. When an object appears, its projection on the retina would fit
its own template like a round peg in a round hole. The template would be
labeled with the name of the shape--in this case, "the letter P"--and
whenever a shape matches it, the template announces the name: Alas, this
simple device malfunctions in both possible ways. It sees P's that aren't
there; for example, it gives a false alarm to the R shown in the first
square below. And it fails to see P's that are there; for example, it misses
the letter when it is shifted, tilted, slanted, too far, too near, or too
fancy: And these problems arise with a nice, crisp letter of the alphabet.
Imagine trying to design a recognizer for a shirt, or a face! To be sure,
after four decades of research in artificial intelligence, the technology of
shape recognition has improved. You may own software that scans in a page,
recognizes the printing, and converts it with reasonable accuracy to a file
of bytes. But artificial shape recognizers are still no match for the ones
in our heads. The artificial ones are designed for pristine,
easy-to-recognize worlds and not the squishy, jumbled real world. The funny
numbers at the bottom of checks were carefully drafted to have shapes that
don't overlap and are printed with special equipment that positions them
exactly so that they can be recognized by templates. When the first face
recognizers are installed in buildings to replace doormen, they will not
even try to interpret the chiaroscuro of your face but will scan in the
hard-edged, rigid contours of your iris or your retinal blood vessels. Our
brains, in contrast, keep a record of the shape of every face we know (and
every letter, animal, tool. and so on), and the record is somehow matched
with a retinal image even when the image is distorted in all the ways we
have been examining. In Chapter 4 we will explore how the brain accomplishes
this magnificent feat. * Let's take a look at another everyday miracle:
getting a body from place to place. When we want a machine to move, we put
it on wheels. The invention of the wheel is often held up as the proudest
accomplishment of civilization. Many textbooks point out that no animal has
evolved wheels and cite the fact as an example of how evolution is often
incapable of finding the optimal solution to an engineering problem. But it
is not a good example at all. Even if nature could have evolved a moose on
wheels, it surely would have opted not to. Wheels are good only in a world
with roads and rails. They bog down in any terrain that is soft, slippery,
steep, or uneven. Legs are better. Wheels have to roll along an unbroken
supporting ridge, but legs can be placed on a series of separate footholds,
an extreme example being a ladder. Legs can also be placed to minimize
lurching and to step over obstacles. Even today, when it seems as if the
world has become a parking lot, only about half of the earth's land is
accessible to vehicles with wheels or tracks, but most of the earth's land
is accessible to vehicles with feet: animals, the vehicles designed by
natural selection. But legs come with a high price: the software to control
them. A wheel, merely by turning, changes its point of support gradually and
can bear weight the whole time. A leg has to change its point of support all
at once, and the weight has to be unloaded to do so. The motors controlling
a leg have to alternate between keeping the foot on the ground while it
bears and propels the load and taking the load off to make the leg free to
move. All the while they have to keep the center of gravity of the body
within the polygon defined by the feet so the body doesn't topple over. The
controllers also must minimize the wasteful up-and-down motion that is the
bane of horseback riders. In walking windup toys, these problems are crudely
solved by a mechanical linkage that converts a rotating shaft into a
stepping motion. But the toys cannot adjust to the terrain by finding the
best footholds.
Even if we solved these problems, we would have figured out only how to
control a walking insect. With six legs, an insect can always keep one
tripod on the ground while it lifts the other tripod. At any instant, it is
stable. Even four-legged beasts, when they aren't moving too quickly, can
keep a tripod on the ground at all times. But as one engineer has put it,
"the upright two-footed locomotion of the human being seems almost a recipe
for disaster in itself, and demands a remarkable control to make it
practicable." When we walk, we repeatedly tip over and break our fall in the
nick of time. When we run, we take off in bursts of flight. These aerobatics
allow us to plant our feet on widely or erratically spaced footholds that
would not prop us up at rest, and to squeeze along narrow paths and jump
over obstacles. But no one has yet figured out how we do it.
Controlling an arm presents a new challenge. Grab the shade of an
architect's lamp and move it along a straight diagonal path from near you,
low on the left, to far from you, high on the right. Look at the rods and
hinges as the lamp moves. Though the shade proceeds along a straight line,
each rod swings through a complicated arc, swooping rapidly at times,
remaining almost stationary at other times, sometimes reversing from a
bending to a straightening motion. Now imagine having to do it in reverse:
without looking at the shade, you must choreograph the sequence of twists
around each joint that would send the shade along a straight path. The
trigonometry is frightfully complicated. But your arm is an architect's
lamp, and your brain effortlessly solves the equations every time you point.
And if you have ever held an architect's lamp by its clamp, you will
appreciate that the problem is even harder than what I have described. The
lamp flails under its weight as if it had a mind of its own; so would your
arm if your brain did not compensate for its weight, solving a
near-intractable physics problem. A still more remarkable feat is
controlling the hand. Nearly two thousand years ago, the Greek physician
Galen pointed out the exquisite natural engineering behind the human hand.
It is a single tool that manipulates objects of an astonishing range of
sizes, shapes, and weights, from a log to a millet seed. "Man handles them
all," Galen noted, "as well as if his hands had been made for the sake of
each one of them alone." The hand can be configured into a hook grip (to
lift a pail), a scissors grip (to hold a cigarette), a five-jaw chuck (to
lift a coaster), a three-jaw chuck (to hold a pencil), a two-jaw pad-to-pad
chuck (to thread a needle), a two-jaw pad-to-side chuck (to turn a key), a
squeeze grip (to hold a hammer), a disc grip (to open a jar), and a
spherical grip (to hold a ball). Each grip needs a precise combination of
muscle tensions that mold the hand into the right shape and keep it there as
the load tries to bend it back. Think of lifting a milk carton. Too loose a
grasp, and you drop it; too tight, and you crush it; and with some gentle
rocking, you can even use the tugging on your fingertips as a gauge of how
much milk is inside! And I won't even begin to talk about the tongue, a
boneless water balloon controlled only by squeezing, which can loosen food
from a back tooth or perform the ballet that articulates words like
thrilling and sixths. * "A common man marvels at uncommon things; a wise
man marvels at the commonplace." Keeping Confucius' dictum in mind, let's
continue to look at commonplace human acts with the fresh eye of a robot
designer seeking to duplicate them. Pretend that we have somehow built a
robot that can see and move. What will it do with what it sees? How should
it decide how to act? An intelligent being cannot treat every object it
sees as a unique entity unlike anything else in the universe. It has to put
objects in categories so that it may apply its hard-won knowledge about
similar objects, encountered in the past, to the object at hand. But
whenever one tries to program a set of criteria to capture the members of a
category, the category disintegrates. Leaving aside slippery concepts like
"beauty" or "dialectical materialism," let's look at a textbook example of a
well-defined one: "bachelor." A bachelor, of course, is simply an adult
human male who has never been married. But now imagine that a friend asks
you to invite some bachelors to her party. What would happen if you used the
definition to decide which of the following people to invite? Arthur has
been living happily with Alice for the last five years. They have a
two-year-old daughter and have never officially married. Bruce was going to
be drafted, so he arranged with his friend Barbara to have a justice of the
peace marry them so he would be exempt. They have never lived together. He
dates a number of women, and plans to have the marriage annulled as soon as
he finds someone he wants to marry. Charlie is 17 years old. He lives at
home with his parents and is in high school. David is 17 years old. He left
home at 13, started a small business, and is now a successful young
entrepreneur leading a playboy's lifestyle in his penthouse apartment. Eli
and Edgar are homosexual lovers who have been living together for many
years. Faisal is allowed by the law of his native Abu Dhabi to have three
wives. He currently has two and is interested in meeting another potential
fiancee. Father Gregory is the bishop of the Catholic cathedral at Groton
upon Thames. The list, which comes from the computer scientist Terry
Winograd, shows that the straightforward definition of "bachelor" does not
capture our intuitions about who fits the category. Knowing who is a
bachelor is just common sense, but there's nothing common about common
sense. Somehow it must find its way into a human or robot brain. And common
sense is not simply an almanac about life that can be dictated by a teacher
or downloaded like an enormous database. No database could list all the
facts we tacitly know, and no one ever taught them to us. You know that when
Irving puts the dog in the car, it is no longer in the yard. When Edna goes
to church, her head goes with her. If Doug is in the house, he must have
gone in through some opening unless he was born there and never left. If
Sheila is alive at 9 A.M. and is alive at 5 P.M., she was also alive at
noon. Zebras in the wild never wear underwear. Opening a jar of a new brand
of peanut butter will not vaporize the house. People never shove meat
thermometers in their ears. A gerbil is smaller than Mt. Kilimanjaro. An
intelligent system, then, cannot be stuffed with trillions of facts. It must
be equipped with a smaller list of core truths and a set of rules to deduce
their implications. But the rules of common sense, like the categories of
common sense, are frustratingly hard to set down. Even the most
straightforward ones fail to capture our everyday reasoning. Mavis lives in
Chicago and has a son named Fred, and Millie lives in Chicago and has a son
named Fred. But whereas the Chicago that Mavis lives in is the same Chicago
that Millie lives in, the Fred who is Mavis' son is not the same Fred who is
Millie's son. If there's a bag in your car, and a gallon of milk in the bag,
there is a gallon of milk in your car. But if there's a person in your car,
and a gallon of blood in a person, it would be strange to conclude that
there is a gallon of blood in your car. Even if you were to craft a set of
rules that derived only sensible conclusions, it is no easy matter to use
them all to guide behavior intelligently. Clearly a thinker cannot apply
just one rule at a time. A match gives light; a saw cuts wood; a locked door
is opened with a key. But we laugh at the man who lights a match to peer
into a fuel tank, who saws off the limb he is sitting on, or who locks his
keys in the car and spends the next hour wondering how to get his family
out. A thinker has to compute not just the direct effects of an action but
the side effects as well. But a thinker cannot crank out predictions about
all the side effects, either. The philosopher Daniel Dennett asks us to
imagine a robot designed to fetch a spare battery from a room that also
contained a time bomb. Version 1 saw that the battery was on a wagon and
that if it pulled the wagon out of the room, the battery would come with it.
Unfortunately, the bomb was also on the wagon, and the robot failed to
deduce that pulling the wagon out brought the bomb out, too. Version 2 was
programmed to consider all the side effects of its actions. It had just
finished computing that pulling the wagon would not change the color of the
room's walls and was proving that the wheels would turn more revolutions
than there are wheels on the wagon, when the bomb went off. Version 3 was
programmed to distinguish between relevant implications and irrelevant ones.
It sat there cranking out millions of implications and putting all the
relevant ones on a list of facts to consider and all the irrelevant ones on
a list of facts to ignore, as the bomb ticked away. An intelligent being
has to deduce the implications of what it knows, but only the relevant
implications. Dennett points out that this requirement poses a deep problem
not only for robot design but for epistemology, the analysis of how we know.
The problem escaped the notice of generations of philosophers, who were left
complacent by the illusory effortlessness of their own common sense. Only
when artificial intelligence researchers tried to duplicate common sense in
computers, the ultimate blank slate, did the conundrum, now called "the
frame problem," come to light. Yet somehow we all solve the frame problem
whenever we use our common sense. * Imagine that we have somehow overcome
these challenges and have a machine with sight, motor coordination, and
common sense. Now we must figure out how the robot will put them to use. We
have to give it motives. What should a robot want? The classic answer is
Isaac Asimov's Fundamental Rules of Robotics, "the three rules that are
built most deeply into a robot's positronic brain." 1. A robot may not
injure a human being or, through inaction, allow a human being to come to
harm. 2. A robot must obey orders given it by human beings except where
such orders would conflict with the First Law. 3. A robot must protect its
own existence as long as such protection does not conflict with the First or
Second Law. Asimov insightfully noticed that self-preservation, that
universal biological imperative, does not automatically emerge in a complex
system. It has to be programmed in (in this case, as the Third Law). After
all, it is just as easy to build a robot that lets itself go to pot or
eliminates a malfunction by committing suicide as it is to build a robot
that always looks out for Number One. Perhaps easier; robot-makers sometimes
watch in horror as their creations cheerfully shear off limbs or flatten
themselves against walls, and a good proportion of the world's most
intelligent machines are kamikaze cruise missiles and smart bombs. But the
need for the other two laws is far from obvious. Why give a robot an order
to obey orders--why aren't the original orders enough? Why command a robot
not to do harm--wouldn't it be easier never to command it to do harm in the
first place? Does the universe contain a mysterious force pulling entities
toward malevolence, so that a positronic brain must be programmed to
withstand it? Do intelligent beings inevitably develop an attitude problem?
In this case Asimov, like generations of thinkers, like all of us, was
unable to step outside his own thought processes and see them as artifacts
of how our minds were put together rather than as inescapable laws of the
universe. Man's capacity for evil is never far from our minds, and it is
easy to think that evil just comes along with intelligence as part of its
very essence. It is a recurring theme in our cultural tradition: Adam and
Eve eating the fruit of the tree of knowledge, Promethean fire and Pandora's
box, the rampaging Golem, Faust's bargain, the Sorcerer's Apprentice, the
adventures of Pinocchio, Frankenstein's monster, the murderous apes and
mutinous HAL of 2001: A Space Odyssey. From the 1950s through the 1980s,
countless films in the computer-runs-amok genre captured a popular fear that
the exotic mainframes of the era would get smarter and more powerful and
someday turn on us. Now that computers really have become smarter and more
powerful, the anxiety has waned. Today's ubiquitous, networked computers
have an unprecedented ability to do mischief should they ever go to the bad.
But the only mayhem comes from unpredictable chaos or from human malice in
the form of viruses. We no longer worry about electronic serial killers or
subversive silicon cabals because we are beginning to appreciate that
malevolence--like vision, motor coordination, and common sense--does not
come free with computation but has to be programmed in. The computer running
WordPerfect on your desk will continue to fill paragraphs for as long as it
does anything at all. Its software will not insidiously mutate into
depravity like the picture of Dorian Gray. Even if it could, why would it
want to? To get--what? More floppy disks? Control over the nation's railroad
system? Gratification of a desire to commit senseless violence against
laser-printer repairmen? And wouldn't it have to worry about reprisals from
technicians who with the turn of a screwdriver could leave it pathetically
singing "A Bicycle Built for Two"? A network of computers, perhaps, could
discover the safety in numbers and plot an organized takeover--but what
would make one computer volunteer to fire the data packet heard round the
world and risk early martyrdom? And what would prevent the coalition from
being undermined by silicon draft-dodgers and conscientious objectors?
Aggression, like every other part of human behavior we take for granted, is
a challenging engineering problem! But then, so are the kinder, gentler
motives. How would you design a robot to obey Asimov's injunction never to
allow a human being to come to harm through inaction? Michael Frayn's 1965
novel The Tin Men is set in a robotics laboratory, and the engineers in the
Ethics Wing, Macintosh, Goldwasser, and Sinson, are testing the altruism of
their robots. They have taken a bit too literally the hypothetical dilemma
in every moral philosophy textbook in which two people are in a lifeboat
built for one and both will die unless one bails out. So they place each
robot in a raft with another occupant, lower the raft into a tank, and
observe what happens. [The] first attempt, Samaritan I, had pushed itself
overboard with great alacrity, but it had gone overboard to save anything
which happened to be next to it on the raft, from seven stone of lima beans
to twelve stone of wet seaweed. After many weeks of stubborn argument
Macintosh had conceded that the lack of discrimination was unsatisfactory,
and he had abandoned Samaritan I and developed Samaritan II, which would
sacrifice itself only for an organism at least as complicated as itself.
The raft stopped, revolving slowly, a few inches above the water. "Drop it,"
cried Macintosh. The raft hit the water with a sharp report. Sinson and
Samaritan sat perfectly still. Gradually the raft settled in the water,
until a thin tide began to wash over the top of it. At once Samaritan leaned
forward and seized Sinson's head. In four neat movements it measured the
size of his skull, then paused, computing. Then, with a decisive click, it
rolled sideways off the raft and sank without hesitation to the bottom of
the tank. But as the Samaritan II robots came to behave like the moral
agents in the philosophy books, it became less and less clear that they were
really moral at all. Macintosh explained why he did not simply tie a rope
around the self-sacrificing robot to make it easier to retrieve: "I don't
want it to know that it's going to be saved. It would invalidate its
decision to sacrifice itself.... So, every now and then I leave one of them
in instead of fishing it out. To show the others I mean business. I've
written off two this week." Working out what it would take to program
goodness into a robot shows not only how much machinery it takes to be good
but how slippery the concept of goodness is to start with. And what about
the most caring motive of all? The weak-willed computers of 1960s pop
culture were not tempted only by selfishness and power, as we see in the
comedian Allan Sherman's song "Automation," sung to the tune of
"Fascination": It was automation, I know. That was what was making the
factory go. It was IBM, it was Univac, It was all those gears going clickety
clack, dear. I thought automation was keen Till you were replaced by a
ten-ton machine. It was a computer that tore us apart, dear, Automation
broke my heart.... It was automation, I'm told, That's why I got fired and
I'm out in the cold. How could I have known, when the 503 Started in to
blink, it was winking at me, dear? I thought it was just some mishap When
it sidled over and sat on my lap. But when it said "I love you" and gave me
a hug, dear, That's when I pulled out ... its ... plug. But for all its
moonstruck madness, love is no bug or crash or malfunction. The mind is
never so wonderfully concentrated as when it turns to love, and there must
be intricate calculations that carry out the peculiar logic of attraction,
infatuation, courtship, coyness, surrender, commitment, malaise,
philandering, jealousy, desertion, and heartbreak. And in the end, as my
grandmother used to say, every pot finds a cover; most people--including,
significantly, all of our ancestors--manage to pair up long enough to
produce viable children. Imagine how many lines of programming it would take
to duplicate that! * Robot design is a kind of consciousness-raising. We
tend to be blase about our mental lives. We open our eyes, and familiar
articles present themselves; we will our limbs to move, and objects and
bodies float into place; we awaken from a dream, and return to a
comfortingly predictable world; Cupid draws back his bow, and lets his arrow
go. But think of what it takes for a hunk of matter to accomplish these
improbable outcomes, and you begin to see through the illusion. Sight and
action and common sense and violence and morality and love are no accident,
no inextricable ingredients of an intelligent essence, no inevitability of
information processing. Each is a tour de force, wrought by a high level of
targeted design. Hidden behind the panels of consciousness must lie
fantastically complex machinery--optical analyzers, motion guidance systems,
simulations of the world, databases on people and things, goal-schedulers,
conflict-resolvers, and many others. Any explanation of how the mind works
that alludes hopefully to some single master force or mind-bestowing elixir
like "culture," "learning," or "self-organization" begins to sound hollow,
just not up to the demands of the pitiless universe we negotiate so
successfully. The robot challenge hints at a mind loaded with original
equipment, but it still may strike you as an argument from the armchair. Do
we actually find signs of this intricacy when we look directly at the
machinery of the mind and at the blueprints for assembling it? I believe we
do, and what we see is as mind-expanding as the robot challenge itself.
When the visual areas of the brain are damaged, for example, the visual
world is not simply blurred or riddled with holes. Selected aspects of
visual experience are removed while others are left intact. Some patients
see a complete world but pay attention only to half of it. They eat food
from the right side of the plate, shave only the right cheek, and draw a
clock with twelve digits squished into the right half. Other patients lose
their sensation of color, but they do not see the world as an arty
black-and-white movie. Surfaces look grimy and rat-colored to them, killing
their appetite and their libido. Still others can see objects change their
positions but cannot see them move--a syndrome that a philosopher once tried
to convince me was logically impossible! The stream from a teapot does not
flow but looks like an icicle; the cup does not gradually fill with tea but
is empty and then suddenly full. Other patients cannot recognize the
objects they see: their world is like handwriting they cannot decipher. They
copy a bird faithfully but identify it as a tree stump. A cigarette lighter
is a mystery until it is lit. When they try to weed the garden, they pull
out the roses. Some patients can recognize inanimate objects but cannot
recognize faces. The patient deduces that the visage in the mirror must be
his, but does not viscerally recognize himself. He identifies John F.
Kennedy as Martin Luther King, and asks his wife to wear a ribbon at a party
so he can find her when it is time to leave. Stranger still is the patient
who recognizes the face but not the person: he sees his wife as an amazingly
convincing impostor. These syndromes are caused by an injury, usually a
stroke, to one or more of the thirty brain areas that compose the primate
visual system. Some areas specialize in color and form, others in where an
object is, others in what an object is, still others in how it moves. A
seeing robot cannot be built with just the fish-eye viewfinder of the
movies, and it is no surprise to discover that humans were not built that
way either. When we gaze at the world, we do not fathom the many layers of
apparatus that underlie our unified visual experience, until neurological
disease dissects them for us. Another expansion of our vista comes from the
startling similarities between identical twins, who share the genetic
recipes that build the mind. Their minds are astonishingly alike, and not
just in gross measures like IQ and personality traits like neuroticism and
introversion. They are alike in talents such as spelling and mathematics, in
opinions on questions such as apartheid, the death penalty, and working
mothers, and in their career choices, hobbies, vices, religious commitments,
and tastes in dating. Identical twins are far more alike than fraternal
twins, who share only half their genetic recipes, and most strikingly, they
are almost as alike when they are reared apart as when they are reared
together. Identical twins separated at birth share traits like entering the
water backwards and only up to their knees, sitting out elections because
they feel insufficiently informed, obsessively counting everything in sight,
becoming captain of the volunteer fire department, and leaving little love
notes around the house for their wives. People find these discoveries
arresting, even incredible. The discoveries cast doubt on the autonomous "I"
that we all feel hovering above our bodies, making choices as we proceed
through life and affected only by our past and present environments. Surely
the mind does not come equipped with so many small parts that it could
predestine us to flush the toilet before and after using it or to sneeze
playfully in crowded elevators, to take two other traits shared by identical
twins reared apart. But apparently it does. The far-reaching effects of the
genes have been documented in scores of studies and show up no matter how
one tests for them: by comparing twins reared apart and reared together, by
comparing identical and fraternal twins, or by comparing adopted and
biological children. And despite what critics sometimes claim, the effects
are not products of coincidence, fraud, or subtle similarities in the family
environments (such as adoption agencies striving to place identical twins in
homes that both encourage walking into the ocean backwards). The findings,
of course, can be misinterpreted in many ways, such as by imagining a gene
for leaving little love notes around the house or by concluding that people
are unaffected by their experiences. And because this research can measure
only the ways in which people differ, it says little about the design of the
mind that all normal people share. But by showing how many ways the mind can
vary in its innate structure, the discoveries open our eyes to how much
structure the mind must have. REVERSE-ENGINEERING THE PSYCHE The complex
structure of the mind is the subject of this book. Its key idea can be
captured in a sentence: The mind is a system of organs of computation,
designed by natural selection to solve the kinds of problems our ancestors
faced in their foraging way of life, in particular, understanding and
outmaneuvering objects, animals, plants, and other people. The summary can
be unpacked into several claims. The mind is what the brain does;
specifically, the brain processes information, and thinking is a kind of
computation. The mind is organized into modules or mental organs, each with
a specialized design that makes it an expert in one arena of interaction
with the world. The modules' basic logic is specified by our genetic
program. Their operation was shaped by natural selection to solve the
problems of the hunting and gathering life led by our ancestors in most of
our evolutionary history. The various problems for our ancestors were
subtasks of one big problem for their genes, maximizing the number of copies
that made it into the next generation. On this view, psychology is
engineering in reverse. In forward-engineering, one designs a machine to do
something; in reverse-engineering, one figures out what a machine was
designed to do. Reverse-engineering is what the boffins at Sony do when a
new product is announced by Panasonic, or vice versa. They buy one, bring it
back to the lab, take a screwdriver to it, and try to figure out what all
the parts are for and how they combine to make the device work. We all
engage in reverse-engineering when we face an interesting new gadget. In
rummaging through an antique store, we may find a contraption that is
inscrutable until we figure out what it was designed to do. When we realize
that it is an olive-pitter, we suddenly understand that the metal ring is
designed to hold the olive, and the lever lowers an X-shaped blade through
one end, pushing the pit out through the other end. The shapes and
arrangements of the springs, hinges, blades, levers, and rings all make
sense in a satisfying rush of insight. We even understand why canned olives
have an X-shaped incision at one end. In the seventeenth century William
Harvey discovered that veins had valves and deduced that the valves must be
there to make the blood circulate. Since then we have understood the body as
a wonderfully complex machine, an assembly of struts, ties, springs,
pulleys, levers, joints, hinges, sockets, tanks, pipes, valves, sheaths,
pumps, exchangers, and filters. Even today we can be delighted to learn what
mysterious parts are for. Why do we have our wrinkled, asymmetrical ears?
Because they filter sound waves coming from different directions in
different ways. The nuances of the sound shadow tell the brain whether the
source of the sound is above or below, in front of or behind us. The
strategy of reverse-engineering the body has continued in the last half of
this century as we have explored the nanotechnology of the cell and of the
molecules of life. The stuff of life turned out to be not a quivering,
glowing, wondrous gel but a contraption of tiny jigs, springs, hinges, rods,
sheets, magnets, zippers, and trapdoors, assembled by a data tape whose
information is copied, downloaded, and scanned. The rationale for
reverse-engineering living things comes, of course, from Charles Darwin. He
showed how "organs of extreme perfection and complication, which justly
excite our admiration" arise not from God's foresight but from the evolution
of replicators over immense spans of time. As replicators replicate, random
copying errors sometimes crop up, and those that happen to enhance the
survival and reproduction rate of the replicator tend to accumulate over the
generations. Plants and animals are replicators, and their complicated
machinery thus appears to have been engineered to allow them to survive and
reproduce. Darwin insisted that his theory explained not just the
complexity of an animal's body but the complexity of its mind. "Psychology
will be based on a new foundation," he famously predicted at the end of The
Origin of Species. But Darwin's prophecy has not yet been fulfilled. More
than a century after he wrote those words, the study of the mind is still
mostly Darwin-free, often defiantly so. Evolution is said to be irrelevant,
sinful, or fit only for speculation over a beer at the end of the day. The
allergy to evolution in the social and cognitive sciences has been, I think,
a barrier to understanding. The mind is an exquisitely organized system that
accomplishes remarkable feats no engineer can duplicate. How could the
forces that shaped that system, and the purposes for which it was designed,
be irrelevant to understanding it? Evolutionary thinking is indispensable,
not in the form that many people think of--dreaming up missing links or
narrating stories about the stages of Man--but in the form of careful
reverse-engineering. Without reverse-engineering we are like the singer in
Tom Paxton's "The Marvelous Toy," reminiscing about a childhood present: "It
went ZIP! when it moved, and POP! when it stopped, and WHIRRR! when it stood
still; I never knew just what it was, and I guess I never will." Only in
the past few years has Darwin's challenge been taken up, by a new approach
christened "evolutionary psychology" by the anthropologist John Tooby and
the psychologist Leda Cosmides. Evolutionary psychology brings together two
scientific revolutions. One is the cognitive revolution of the 1950s and
1960s, which explains the mechanics of thought and emotion in terms of
information and computation. The other is the revolution in evolutionary
biology of the 1960s and 1970s, which explains the complex adaptive design
of living things in terms of selection among replicators. The two ideas make
a powerful combination. Cognitive science helps us to understand how a mind
is possible and what kind of mind we have. Evolutionary biology helps us to
understand why we have the kind of mind we have. The evolutionary
psychology of this book is, in one sense, a straightforward extension of
biology, focusing on one organ, the mind, of one species, Homo sapiens. But
in another sense it is a radical thesis that discards the way issues about
the mind have been framed for almost a century. The premises of this book
are probably not what you think they are. Thinking is computation, I claim,
but that does not mean that the computer is a good metaphor for the mind.
The mind is a set of modules, but the modules are not encapsulated boxes or
circumscribed swatches on the surface of the brain. The organization of our
mental modules comes from our genetic program, but that does not mean that
there is a gene for every trait or that learning is less important than we
used to think. The mind is an adaptation designed by natural selection, but
that does not mean that everything we think, feel, and do is biologically
adaptive. We evolved from apes, but that does not mean we have the same
minds as apes. And the ultimate goal of natural selection is to propagate
genes, but that does not mean that the ultimate goal of people is to
propagate genes. Let me show you why not. * This book is about the brain,
but I will not say much about neurons, hormones, and neurotransmitters. That
is because the mind is not the brain but what the brain does, and not even
everything it does, such as metabolizing fat and giving off heat. The 1990s
have been named the Decade of the Brain, but there will never be a Decade of
the Pancreas. The brain's special status comes from a special thing the
brain does, which makes us see, think, feel, choose, and act. That special
thing is information processing, or computation. Information and
computation reside in patterns of data and in relations of logic that are
independent of the physical medium that carries them. When you telephone
your mother in another city, the message stays the same as it goes from your
lips to her ears even as it physically changes its form, from vibrating air,
to electricity in a wire, to charges in silicon, to flickering light in a
fiber optic cable, to electromagnetic waves, and then back again in reverse
order. In a similar sense, the message stays the same when she repeats it to
your father at the other end of the couch after it has changed its form
inside her head into a cascade of neurons firing and chemicals diffusing
across synapses. Likewise, a given program can run on computers made of
vacuum tubes, electromagnetic switches, transistors, integrated circuits, or
well-trained pigeons, and it accomplishes the same things for the same
reasons. This insight, first expressed by the mathematician Alan Turing,
the computer scientists Alan Newell, Herbert Simon, and Marvin Minsky, and
the philosophers Hilary Putnam and Jerry Fodor, is now called the
computational theory of mind. It is one of the great ideas in intellectual
history, for it solves one of the puzzles that make up the "mind-body
problem": how to connect the ethereal world of meaning and intention. the
stuff of our mental lives, with a physical hunk of matter like the brain.
Why did Bill get on the bus? Because he wanted to visit his grandmother and
knew the bus would take him there. No other answer will do. If he hated the
sight of his grandmother, or if he knew the route had changed, his body
would not be on that bus. For millennia this has been a paradox. Entities
like "wanting to visit one's grandmother" and "knowing the bus goes to
Grandma's house" are colorless, odorless, and tasteless. But at the same
time they are causes of physical events, as potent as any billiard ball
clacking into another. The computational theory of mind resolves the
paradox. It says that beliefs and desires are information, incarnated as
configurations of symbols. The symbols are the physical states of bits of
matter, like chips in a computer or neurons in the brain. They symbolize
things in the world because they are triggered by those things via our sense
organs, and because of what they do once they are triggered. If the bits of
matter that constitute a symbol are arranged to bump into the bits of matter
constituting another symbol in just the right way, the symbols corresponding
to one belief can give rise to new symbols corresponding to another belief
logically related to it, which can give rise to symbols corresponding to
other beliefs, and so on. Eventually the bits of matter constituting a
symbol bump into bits of matter connected to the muscles, and behavior
happens. The computational theory of mind thus allows us to keep beliefs and
desires in our explanations of behavior while planting them squarely in the
physical universe. It allows meaning to cause and be caused. The
computational theory of mind is indispensable in addressing the questions we
long to answer. Neuroscientists like to point out that all parts of the
cerebral cortex look pretty much alike--not only the different parts of the
human brain, but the brains of different animals. One could draw the
conclusion that all mental activity in all animals is the same. But a better
conclusion is that we cannot simply look at a patch of brain and read out
the logic in the intricate pattern of connectivity that makes each part do
its separate thing. In the same way that all books are physically just
different combinations of the same seventy-five or so characters, and all
movies are physically just different patterns of charges along the tracks of
a videotape, the mammoth tangle of spaghetti of the brain may all look alike
when examined strand by strand. The content of a book or a movie lies in the
pattern of ink marks or magnetic charges, and is apparent only when the
piece is read or seen. Similarly, the content of brain activity lies in the
patterns of connections and patterns of activity among the neurons. Minute
differences in the details of the connections may cause similar-looking
brain patches to implement very different programs. Only when the program is
run does the coherence become evident. As Tooby and Cosmides have written,
There are birds that migrate by the stars, bats that echolocate, bees that
compute the variance of flower patches, spiders that spin webs, humans that
speak, ants that farm, lions that hunt in teams, cheetahs that hunt alone,
monogamous gibbons, polyandrous seahorses, polygynous gorillas.... There are
millions of animal species on earth, each with a different set of cognitive
programs. The same basic neural tissue embodies all of these programs, and
it could support many others as well. Facts about the properties of neurons,
neurotransmitters, and cellular development cannot tell you which of these
millions of programs the human mind contains. Even if all neural activity is
the expression of a uniform process at the cellular level, it is the
arrangement of neurons--into bird song templates or web-spinning
programs--that matters. That does not imply, of course, that the brain is
irrelevant to understanding the mind! Programs are assemblies of simple
information-processing units--tiny circuits that can add, match a pattern,
turn on some other circuit, or do other elementary logical and mathematical
operations. What those microcircuits can do depends only on what they are
made of. Circuits made from neurons cannot do exactly the same things as
circuits made from silicon, and vice versa. For example, a silicon circuit
is faster than a neural circuit, but a neural circuit can match a larger
pattern than a silicon one. These differences ripple up through the programs
built from the circuits and affect how quickly and easily the programs do
various things, even if they do not determine exactly which things they do.
My point is not that prodding brain tissue is irrelevant to understanding
the mind, only that it is not enough. Psychology, the analysis of mental
software, will have to burrow a considerable way into the mountain before
meeting the neurobiologists tunneling through from the other side. The
computational theory of mind is not the same thing as the despised "computer
metaphor." As many critics have pointed out, computers are serial, doing one
thing at a time; brains are parallel, doing millions of things at once.
Computers are fast; brains are slow. Computer parts are reliable; brain
parts are noisy. Computers have a limited number of connections; brains have
trillions. Computers are assembled according to a blueprint; brains must
assemble themselves. Yes, and computers come in putty-colored boxes and have
AUTOEXEC.BAT files and run screen-savers with flying toasters, and brains do
not. The claim is not that the brain is like commercially available
computers. Rather, the claim is that brains and computers embody
intelligence for some of the same reasons. To explain how birds fly, we
invoke principles of lift and drag and fluid mechanics that also explain how
airplanes fly. That does not commit us to an Airplane Metaphor for birds,
complete with jet engines and complimentary beverage service. Without the
computational theory, it is impossible to make sense of the evolution of the
mind. Most intellectuals think that the human mind must somehow have escaped
the evolutionary process. Evolution, they think, can fabricate only stupid
instincts and fixed action patterns: a sex drive, an aggression urge, a
territorial imperative, hens sitting on eggs and ducklings following hulks.
Human behavior is too subtle and flexible to be a product of evolution, they
think; it must come from somewhere else--from, say, "culture." But if
evolution equipped us not with irresistible urges and rigid reflexes but
with a neural computer, everything changes. A program is an intricate recipe
of logical and statistical operations directed by comparisons, tests,
branches, loops, and subroutines embedded in subroutines. Artificial
computer programs, from the Macintosh user interface to simulations of the
weather to programs that recognize speech and answer questions in English,
give us a hint of the finesse and power of which computation is capable.
Human thought and behavior, no matter how subtle and flexible, could be the
product of a very complicated program, and that program may have been our
endowment from natural selection. The typical imperative from biology is not
"Thou shalt ...," but "If ... then ... else."
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