Autopilot Read online

  Andrew Smart wants you to sit and do nothing much more often—and he has the science to explain why

  “A hugely entertaining read about what we do most of the time, i.e. nothing. If you are to read one pop science book this year, this should be it.”

  —PROF. HAKWAN LAU, DEPARTMENT OF PSYCHOLOGY, COLUMBIA UNIVERSITY

  At every turn, we’re pushed to do more faster, more efficiently: that drumbeat resounds throughout our wage-slave society. Multitasking is not only a virtue, it’s a necessity. But Autopilot argues that slackers may have the last laugh. It makes a compelling case— backed by science —that filling life with activity at work and at home actually hurts your brain.

  Autopilot is a witty, informative and wide-ranging book that draws on the most recent research into brain power. Use it to explain to bosses, family, and friends why you need to relax—right now.

  A human factors research scientist, ANDREW SMART received B.S. and M.S. degrees from Lund University in Sweden, where he worked on using noise to improve memory and attention in children with ADHD. While at New York University, he analyzed brain imaging data from experiments on the neural basis of language. Autopilot is his first book.

  © 2013 Andrew Smart

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  First printing 2013.

  All rights reserved. No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage retrieval system, without permission in writing from the publisher, except brief passages for review purposes.

  Library of Congress Cataloging in Publication Data: A catalog record for this book is available from the Library of Congress.

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  The U.S. printed edition of this book comes on Forest Stewardship Council-certified, 30% recycled paper. The printer, BookMobile, is 100% wind-powered.

  ISBN 978-1-939293-10-7 paperback

  ISBN 978-1-939293-11-4 e-book

  CONTENTS

  Introduction

  1: That Loathsome Monster Idleness

  2: Someone Else’s Noise

  3: Aha! moments and Self-Knowledge

  4: Rilke and the Idle Examined Life

  5: You Are a Self-Organizing System

  6: Revolution or Suicide

  7: The Signal Is the Noise

  8: Six Sigma Is a Seizure

  9: Work Is Destroying the Planet

  Acknowledgements

  References

  INTRODUCTION

  I have often wondered whether especially those days when we are forced to remain idle are not precisely the days spent in the most profound activity. Whether our actions themselves, even if they do not take place until later, are nothing more than the last reverberations of a vast movement that occurs within us during idle days.

  In any case, it is very important to be idle with confidence, with devotion, possibly even with joy. The days when even our hands do not stir are so exceptionally quiet that it is hardly possible to raise them without hearing a whole lot.

  —Rainer Maria Rilke

  This book is about being idle. Being idle is one of the most important activities in life, and I have roused myself to share my thoughts on the subject, and hope to convince others as well. This, despite the fact that all over the world our working hours are increasing and every time management book on the market claims that you can and should get more done. The message of this book is the opposite. You should get less done; in fact you should be idle. Neuroscientific evidence argues that your brain needs to rest, right now. While our minds are exquisitely evolved for intense action, in order to function normally our brains also need to be idle—a lot of the time, it turns out.

  We are too purposeful, too directed; we should let ourselves go on autopilot more often. In aviation, an autopilot is a system for controlling airplanes without input from pilots, developed because flying an airplane manually requires absolute, constant attention from the pilot. As flying got higher, faster, and longer, manual flying caused serious (and dangerous) levels of pilot fatigue. The introduction of autopilots allowed pilots to take a break from physically controlling the airplane so they could save mental energy for higher risk phases of the flight, like takeoff and landing. Today, autopilots use software to fly the plane.

  The downside of autopilots is that sometimes pilots become confused about whether the autopilot or they themselves are flying the plane. This is called “mode confusion” and has resulted in fatal accidents.

  Interestingly, your brain has an autopilot. When you enter a resting state, relinquishing “manual control” over your life, your brain’s autopilot engages. The autopilot knows where you really want to go, and what you really want to do. But the only way to find out what your autopilot knows is to stop flying the plane, and let your autopilot guide you. Just as pilots become dangerously fatigued while flying airplanes manually, all of us need to take a break and let our autopilots fly our planes more of the time. The trick is to avoid “mode confusion” by taking it easy, putting away our schedule, and not getting things done.

  Psychological research has shown that humans, especially American humans, tend to dread idleness. However, this research also shows that if people do not have a justification for being busy, on average they would rather be idle. Our contradictory fear of being idle, together with our preference for sloth, may be a vestige from our evolutionary history. For most of our evolution, conserving energy was our number one priority because simply getting enough to eat was a monumental physical challenge. Today, survival does not require much (if any) physical exertion, so we have invented all kinds of futile busyness. Given the slightest or even a specious reason to do something, people will become busy. People with too much time on their hands tend to become unhappy or bored. Yet as we will see in this book, being idle may be the only real path toward self-knowledge. What comes into your consciousness when you are idle can often be reports from the depths of your unconscious self—and this information may not always be pleasant. Nonetheless, your brain is likely bringing it to your attention for a good reason. Through idleness, great ideas buried in your unconsciousness have the chance to enter your awareness.

  Our long-standing “idlephobia” has lead inexorably to our current near-obsession with busyness. In a prescient 2006 editorial in the journal Medical Hypotheses, Bruce Charlton argued that modern society is dominated by jobs characterized by busyness. Busyness refers to multitasking—performing many sequential jobs, and switching frequently between them on an externally imposed schedule. In most careers, the only path to advancement is through the seeming mastery of busyness. Francis Crick, who co-discovered DNA and won a Nobel Prize, famously resisted rising through the administrative ranks of the academic world because he detested managerial busyness.

  The definition of idleness I explore in this book is the antithesis of busyness: perhaps doing one or two things a day, crucially on an internally imposed schedule. Chronic busyness is bad for your brain, and over the long-term busyness can have serious health consequences. In the short term, busyness destroys creativity, self-knowledge, emotional well-being, your ability to be social—and it can damage your cardiovascular health.

  From a neuroscientific perspective, studying idleness in the lab turns out to be easy. And in fact, the incredible brain activity that only happens when you are doing nothing was discovered by accident, when subjects in brain imaging experiments just lay in the brain machines daydreaming. I extend this laboratory definition to include any time during your day that you are not on an externally impose
d schedule and have the chance to really do nothing, or when you have the freedom to let your mind wander toward whatever it is that comes into your awareness in the absence of busyness. True insight, whether artistic or scientific, emotional or social, can really only occur in these all-too-rare idle states.

  * * *

  Even scientists admit that you may never really understand some of the recurring concepts of neuroscience: you just get used to them. But it is useful to be familiar with these ideas early on in this discussion, if only because they are part of your excuse for taking it easy. If you can fire off a sentence explaining your laziness such as, “I’m letting the hub of my default mode network oscillate so I can figure what I want to do with my life,” people will leave you alone. And acquaintance with these concepts allows you to put many facts about the brain into some kind of a context.

  Consider this a crash course in complexity theory and neural science. The human brain is a creative machine; a complex, nonlinear, natural object that has the following features:

  Nonlinearity or chaos: exponentially sensitive dependence on initial conditions. What? Most systems that engineers deal with are linear or deterministic. Most systems, even if they are nonlinear, are modeled as linear systems because it’s easier (or even possible) to do the math. A linear system is one for which, given sufficient knowledge about the values of the variables that describe the system at a given time, and given sufficient knowledge about how those variables change, the future of the system can be predicted very accurately. If you “input signal” to a linear system, you know exactly what kind of “output signal” you will get. This is obviously very handy when you’re trying to design a communication network, a dam, or an airplane. With a nonlinear system, on the other hand, even if you have complete information about the state of the system at a particular time, and a very good model of how the variables interact, it is impossible to predict the future of the system. This is because small changes in the initial conditions of the system can get amplified down the road and cause enormous changes to the system later on. So the further into the future you try to predict, the less accurate your predictions will be. What’s more, a small input signal to a nonlinear system can cause a huge output. Or, no output at all. The best example of a nonlinear system is the weather. We can estimate how likely it is that the weather will be in a certain state in the future, and the current state of the system is a function of past states (i.e., it has a memory), but we still cannot predict its future trajectory with certainty. Fortunately for us, and unfortunately for scientists, brains are nonlinear. In nature, there are no linear systems outside the mineral world.

  Threshold: a value which, when reached, causes an excitable system to leave its normal dynamic trajectory and enter into an excited or active state. We are all familiar with thresholds in our everyday lives. A thermostat is a good example of a device that makes use of a threshold. You set the thermostat to a certain value and when the thermometer drops below that value, the heat kicks in. The value you set the thermostat at is a threshold. Neurons, by contrast, are nonlinear threshold devices. Each neuron has a threshold for firing off an action potential. A neuron has a resting state, and a threshold set by the electrical and chemical properties of each neuron. The value of a neuron’s threshold changes over time. Put very crudely, signals arriving from other neurons converge on a given neuron, and when enough of those signals arrive at the right time and are of the right type, the threshold is reached and the neuron fires. The neuron then requires what is called a refractory period to recover after it has fired. In other words, there is an upper limit to how fast a neuron can spike.

  Self-organization: the spooky tendency of a nonlinear system to rearrange itself in such a way as to develop long-range temporal and spatial correlations. In other words, when you look at an ant colony what you see is the appearance of an overall structure and organization. However, each ant in the colony interacts only locally with other ants in its immediate vicinity. Each ant is oblivious to the existence of the whole colony, yet through the simple interactions of individual ants the colony emerges. It is the same with neurons. Each neuron in our brains has no idea that it is part of a brain, much less a part of “you.” The key is that self-organization emerges from the system’s internal dynamics without an external “teaching signal.” Self-organization can only emerge from nonlinear systems. Examples include brains, societies, economies, and ant colonies. Very complicated behavior can emerge from the interaction of simple elements that make up a self-organized system. Some ant colonies have millions of members, and the colony itself displays complicated and very organized behavior. It learns over time. However each ant is a relatively simple organism following chemical trails laid down by other ants. Self-organization is why your brain and your sense of self remain nearly constant from day to day. Self-organization is also why climates are relatively stable and change very gradually. A nonlinear threshold is why even a relatively small increase in carbon dioxide might cause a huge change to the climate.

  Oscillations: any periodic or rhythmic signal. An oscillation describes the upward and downward motion of a signal like the electroencephalogram, a fan that moves back and forth, or the stock market. Single neurons oscillate, and we can measure the oscillatory activity of many neurons as the sum of electrical current in a patch of brain. One of the most striking things about neurons is that they tend to oscillate spontaneously. Oscillations at different frequencies are a key mechanism by which different regions of the brain communicate with each other, and by which neurons communicate with each other.

  Network structure: the brain has about a hundred billion neurons with an estimated two hundred trillion (yes, that says trillion) connections between the neurons. Try wiring a computer network with two hundred trillion connections. Despite these incomprehensibly large numbers, each neuron is only a few connections away from all the other neurons. This is because of the brain’s architecture. It has been estimated that each neuron only needs to send a signal through an average of seven path lengths to reach any other neuron. This is called a “small-world” network, and it is exactly like the Kevin Bacon number, or six degrees of separation. These networks have local clusters called hubs, through which many connections pass. A few large hubs dictate much of the action. Think of the FedEx hub in Memphis—all FedEx flights fly through the Memphis hub no matter where they originate and this greatly reduces the number of connections necessary to get a package from any city in the world to any other city.

  Randomness or noise: Noise is good. This might be one of the most counter-intuitive things to understand about the brain. Noise is almost always thought of as bad or harmful, especially in man-made linear systems like telephone lines. However, in complex nonlinear systems like our brains it turns out that a certain amount of noise actually helps. Through a phenomenon called “stochastic resonance,” noise in the brain controls the onset of order. Too little noise and neurons cannot pick up the signals sent from other neurons, too much noise and the neurons cannot detect the correct signals. With the right amount of noise, the brain functions normally. This noise benefit can also only occur in nonlinear systems. Put noise in a linear system and you just get noise out; put noise in a nonlinear system like a brain and you might get a symphony or a novel. Noise researcher Bart Kosko, who discovered many of the principles of stochastic resonance, calls it “the Zen of noise.” We will return to the important role that noise plays in our creativity.

  Variability: every time your brain encounters something like the flashing of a simple shape on a computer screen, the neural response is slightly different. Variability in neural responses is what gives our brains the flexibility and adaptability to survive in our complex societies and environments. Because the brain is a nonlinear system, a reduction in its variability is actually the sign of pathology. During an epileptic seizure, the neurons in a patch of the brain become “hypersynchronized.” That is, they lose their variability. The complete absence of variability in
a brain region is what a seizure is. In Chapter 8, I make the argument that many time management approaches such as Six Sigma similarly induce organizational seizures by suppressing variability where it is most needed. In this way, Six Sigma can be thought of as an organizational pathogen.

  Synchronization: also called entrainment. While “healthy variability” is important so that the brain maintains itself in a perpetual critical state (homeostatic, but always ready and anticipating the environment), information in the brain needs to be sent. There is a competition between variability and synchronization in the brain. Roughly, and in very simple terms, it turns out that when a neuron sends a signal down an axon, over a synapse to the dendrites of the next neuron, that target neuron can only receive the signal if the two neurons are synchronized. Synchronization is when two or more coupled (a physicist’s fancy word for “connected”) nonlinear oscillators start to follow each other in time. This was first noticed by Dutch scientist Christian Huygens in the 18th century. The story goes that as Huygens lay in bed with a fever he watched the pendula of two clocks swinging. He noticed that after a while the pendula began swinging in the same phase together. Even when he stopped one pendulum and let it go out of phase with the other, eventually the two pendulum clocks would synchronize again. This only happened when the pendulum clocks were on the same wall because of small vibrations in the wall that were large enough to allow each rhythm to affect the other. The vibrations or noise provided the coupling mechanism between the oscillators. So it turns out that our old friend “noise” helps achieve synchronization. However, as I noted above, too much synchronization and you may get a seizure, but too little and you may not get any communication at all. And this is also yet another example of a profound scientific insight taking place while a scientist did nothing (in this case recuperating in bed from an illness).