Authors: David Eagleman
This led Damasio to propose that the feelings produced by physical states of the body come to guide behavior and decision making.
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Body states become linked to outcomes of events in the
world. When something bad happens, the brain leverages the entire body (heart rate, contraction of the gut, weakness of the muscles, and so on) to register that feeling, and that feeling becomes associated with the event. When the event is next pondered, the brain essentially runs a simulation, reliving the physical feelings of the event. Those feelings then serve to navigate, or at least bias, subsequent decision making. If the feelings from a given event are bad, they dissuade the action; if they are good, they encourage it.
In this view, physical states of the body provide the
hunches that can steer behavior. These hunches turn out to be correct more often than chance would predict, mostly because your unconscious brain is picking up on things first, and your consciousness lags behind.
In fact, conscious systems can break entirely, with no effect on the unconscious systems. People with a condition called
prosopagnosia cannot distinguish between familiar and unfamiliar faces. They rely entirely on cues such as hairlines, gait, and voices to recognize people they know. Pondering this condition led researchers
Daniel Tranel and Antonio Damasio to try something clever: even though prosopagnosics cannot consciously recognize faces, would they have a measurable skin conductance response to faces that were familiar? Indeed, they did. Even though the prosopagnosic truly insists on being unable to recognize faces,
some
part of his brain can (and does) distinguish familiar faces from unfamiliar ones.
If you cannot always elicit a straight answer from the unconscious brain, how can you access its knowledge? Sometimes the trick is merely to probe what your gut is telling you. So the next time a friend laments that she cannot decide between two options, tell her the easiest way to solve her problem: flip a coin. She should specify which option belongs to heads and which to tails, and then let the coin fly. The important part is to assess her gut feeling after the coin lands. If she feels a subtle sense of relief at being “told” what to do by the coin, that’s the right choice for her. If, instead, she concludes that it’s ludicrous for her to make
a decision based on a coin toss, that will cue her to choose the other option.
So far we’ve been looking at the vast and sophisticated knowledge that lives under the surface of awareness. We’ve seen that you don’t have access to the details of how your brain does things, from reading letters to changing lanes. So what role does the conscious mind play, if any, in all your know-how? A big one, it turns out—because much of the knowledge stored in the depths of the unconscious brain began life in the form of conscious plans. We turn to this now.
Imagine that you have risen through the ranks to the top tennis tournament in the world and you are now poised on a green court facing the planet’s greatest tennis robot. This robot has incredibly miniaturized components and self-repairing parts, and it runs on such optimized energy principles that it can consume three hundred grams of hydrocarbons and then leap all over the court like a mountain goat. Sounds like a formidable opponent, right? Welcome to Wimbledon—you’re playing against a human being.
The competitors at Wimbledon are rapid, efficient machines that play tennis shockingly well. They can track a ball traveling ninety miles per hour, move toward it rapidly, and orient a small surface to intersect its trajectory. And these professional tennis players do almost none of this consciously. In exactly the same way that you read letters on a page or change lanes, they rely entirely on their unconscious machinery. They are, for all practical purposes, robots. Indeed, when Ilie Nastase lost the Wimbledon final in 1976, he sullenly said of his winning opponent, Björn Borg, “He’s a robot from outer space.”
But these robots are
trained by
conscious minds. An aspiring tennis player does not have to know anything about building robotics (that was taken care of by evolution). Rather, the challenge is to
program
the robotics. In this case, the challenge is to program the machinery to devote its flexible computational resources to rapidly and accurately volleying a fuzzy yellow ball over a short net.
And this is where consciousness plays a role. Conscious parts of the brain train other parts of the neural machinery, establishing the goals and allocating the resources. “Grip the racket lower when you swing,” the coach says, and the young player mumbles that to herself. She practices her swing over and over, thousands of times, each time setting as her end point the goal of smashing the ball directly into the other quadrant. As she serves again and again, the robotic system makes tiny adjustments across a network of innumerable synaptic connections. Her coach gives feedback which she needs to hear and understand consciously. And she continually incorporates the instructions (“Straighten your wrist. Step into the swing.”) into the training of the robot until the movements become so ingrained as to no longer be accessible.
Consciousness is the long-term planner, the CEO of the company, while most of the day-to-day operations are run by all those parts of her brain to which she has no access. Imagine a CEO who has inherited a giant blue-chip company: he has some influence, but he is also coming into a situation that has already been evolving for a long time before he got there. His job is to define a vision and make long-term plans for the company, insofar as the technology of the company is able to support his policies. This is what consciousness does: it sets the goals, and the rest of the system learns how to meet them.
You may not be a professional tennis player, but you’ve been through this process if you ever learned to ride a bicycle. The first time you got on, you wobbled and crashed and tried desperately to figure it out. Your conscious mind was heavily involved. Eventually, after an adult guided the bicycle along, you became
able to ride on your own. After some time, the skill became like a reflex. It became automatized. It became just like reading and speaking your language, or tying your shoes, or recognizing your father’s walk. The details became no longer conscious and no longer accessible.
One of the most impressive features of brains—and especially human brains—is the flexibility to learn almost any kind of task that comes its way. Give an apprentice the desire to impress his master in a chicken-sexing task, and his brain devotes its massive resources to distinguishing males from females. Give an unemployed aviation enthusiast a chance to be a national hero, and his brain learns to distinguish enemy aircraft from local flyboys. This
flexibility of learning accounts for a large part of what we consider human intelligence. While many animals are properly called intelligent, humans distinguish themselves in that they are so
flexibly
intelligent, fashioning their neural circuits to match the tasks at hand. It is for this reason that we can colonize every region on the planet, learn the local language we’re born into, and master skills as diverse as playing the violin, high-jumping and operating space shuttle cockpits.
When the brain finds a task it needs to solve, it rewires its own circuitry until it can accomplish the task with maximum efficiency.
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The task becomes burned into the machinery. This clever tactic accomplishes two things of chief importance for survival.
The first is
speed
.
Automatization permits fast decision making. Only when the slow system of consciousness is pushed to the back of the queue can rapid programs do their work. Should I swing forehand or backhand at the approaching tennis ball? With a ninety-mile-per-hour projectile on its way, one does not want to cognitively slog through the different options. A common
misconception is that professional athletes can see the court in “slow motion,” as suggested by their rapid and smooth decision making. But automatization simply allows the athletes to anticipate relevant events and proficiently decide what to do. Think about the first time you tried a new sport. More-experienced players defeated you with the most elementary moves because you were struggling with a barrage of new information—legs and arms and jumping bodies. With experience, you learned which twitches and feints were the important ones. With time and automatization, you achieved speed both in deciding and in acting.
The second reason to burn tasks into the circuitry is
energy efficiency
. By optimizing its machinery, the brain minimizes the energy required to solve problems. Because we are mobile creatures that run on batteries, energy saving is of the highest importance.
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In his book
Your Brain Is (Almost) Perfect
, neuroscientist
Read Montague highlights the impressive energy efficiency of the brain, comparing chess champion
Garry Kasparov’s energy usage of about 20 watts to the consumption of his computerized competitor Deep Blue, in the range of thousands of watts. Montague points out that Kasparov played the game at normal body temperature, while Deep Blue was burning hot to the touch and required a large collection of fans to dissipate the heat. Human brains run with superlative efficiency.
Kasparov’s brain is so low-powered because Kasparov has spent a lifetime burning chess strategies into economical rote algorithms. When he started playing chess as a boy, he had to walk himself through cognitive strategies about what to do next—but these were highly inefficient, like the moves of an overthinking, second-guessing tennis player. As Kasparov improved, he no longer had to consciously walk through the unfolding steps of a game: he could perceive the chess board rapidly, efficiently, and with less conscious interference.
In one study on efficiency, researchers used brain imaging while people learned how to play the video game Tetris. The subjects’ brains were highly active, burning energy at a massive scale while
the neural networks searched for the underlying structures and strategies of the game. By the time the subjects became experts at the game, after a week or so, their brains consumed very little energy while playing. It’s not that the player became better despite the brain being quieter; the player became better
because
the brain was quieter. In these players, the skills of Tetris has been burned down into the circuitry of the system, such that there were now specialized and efficient programs to deal with it.
As an analogy, imagine a warring society that suddenly finds itself with no more battles to wage. Its soldiers decide to turn to agriculture. At first they use their battle swords to dig little holes for seeds—a workable but massively inefficient approach. After a time, they beat their swords into plowshares. They optimize their machinery to meet the task demands. Just like the brain, they’ve modified what they have to address the task at hand.
This trick of burning tasks into the circuitry is fundamental to how brains operate: they change the circuit board of their machinery to mold themselves to their mission. This allows a difficult task that could be accomplished only clumsily to be achieved with rapidity and efficiency. In the logic of the brain, if you don’t have the right tool for the job,
create it
.
So far we’ve learned that consciousness tends to interfere with most tasks (remember the unhappy centipede in the ditch)—but it
can
be helpful when setting goals and training the robot. Evolutionary selection has presumably tuned the exact amount of access the conscious mind has: too little, and the company has no direction; too much, and the system gets bogged down solving problems in a slow, clunky, energy-inefficient manner.
When athletes make mistakes, coaches typically yell, “
Think out there!
” The irony is that a professional athlete’s goal is to
not
think. The goal is to invest thousands of hours of training so that in the heat of the battle the right maneuvers will come automatically,
with no interference from consciousness. The skills need to be pushed down into the players’ circuitry. When athletes “get into the zone,” their well-trained unconscious machinery runs the show, rapidly and efficiently. Imagine a basketball player standing at the free-throw line. The crowd yells and stomps to distract him. If he’s running on conscious machinery, he’s certain to miss. Only by relying on the overtrained, robotic machinery can he hope to drain the ball through the basket.
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Now you can leverage the knowledge gained in this chapter to always win at tennis. When you are losing, simply ask your opponent how she serves the ball so well. Once she contemplates the mechanics of her serve and tries to explain it, she’s sunk.
We have learned that the more things get automatized, the less conscious access we have. But we’re just getting started. In the next chapter we’ll see how information can get buried even deeper.
*
It is currently an open question whether courts of law will allow these tests to be admitted as evidence—for example, to probe whether an employer (or attacker or murderer) shows signs of racism. At the moment it is probably best if these tests remain outside the courtroom, for while complicated human decisions are biased by inaccessible associations, it is difficult to know how much these biases influence our final behavior. For example, someone may override their racist biases by more socialized decision-making mechanisms. It is also the case that someone may be a virulent racist, but that was not their reason for a particular crime.