The Invisible Gorilla: And Other Ways Our Intuitions Deceive Us
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In a powerful demonstration of the extent to which confidence sways juries, psychologist Gary Wells and his colleagues conducted an elaborate experiment that resembled the entire criminal law process, from the initial witnessing of a crime to the jury’s decision on guilt or innocence. First, the researchers staged a crime for each of 108 different subjects: An actor pretended to steal a calculator from the room where each subject was completing some forms.45 Wells varied the amount of time that the perpetrator was in the room, how much he said to the subject, and whether he wore a hat (which made his face harder to see). Shortly after the “criminal” left the room, the experimenter entered and asked the subject to select the criminal from a photographic lineup and to state a level of confidence in that selection. Not surprisingly, subjects who had viewed the criminal only briefly were more than twice as likely to make an incorrect selection from the lineup as those who viewed the perpetrator for a long time. Yet they were nearly as confident in their selection as those who saw the perpetrator for a long time.
The most interesting part of this experiment wasn’t the finding of overconfidence, which had been demonstrated before. After selecting a person from the lineup and judging their confidence in their selection, the subjects were then “cross-examined” by another experimenter who had no information about which choice they had made or how confident they were. Videotapes of these cross-examinations were shown to a new group of subjects—the “jurors”—who were asked to judge whether the witness had made an accurate identification. The jurors trusted the selections of highly confident witnesses 77 percent of the time and less confident witnesses 59 percent of the time. More important, the jurors were disproportionately swayed by a highly confident witness when the witness experienced poor viewing conditions (only a brief exposure to a hat-wearing perpetrator). That is, confidence had the most detrimental effect on juror judgments when the witnesses had the least information to go on.
At Ronald Cotton’s trials, the juries relied on confidence as a way to distinguish an accurate witness from an inaccurate one. A group of scientists led by Siegfried Sporer, a psychologist at the University of Giessen in Germany, reviewed all of the studies done on the identification of suspects from lineups—a crucial step in the investigation of Cotton for the Thompson rape. Several of these studies showed no relationship between the accuracy of witnesses and the level of confidence they expressed, but others found that higher confidence is associated with greater accuracy. Considering all of the relevant studies, they found that on average, high-confidence witnesses are accurate 70 percent of the time, whereas low-confidence witnesses are accurate just 30 percent of the time.46 So, all other things being equal, a confident witness is more likely—much more likely—to be accurate than an unconfident one.
But there are two problems here. First, the level of confidence witnesses express depends as much on whether they are confident in general as on whether they are accurate in a given instance. If jurors could observe the confidence of a particular witness under a wide variety of situations, they could better judge whether that witness’s testimony was unusually confident. In the absence of any information about whether or not a witness generally acts with confidence, we tend to trust people who appear confident. The effect of a confident witness holds so much sway that 37 percent of respondents in our national survey agreed that “the testimony of one confident eyewitness should be enough evidence to convict a defendant of a crime.”
Second, and even more important, is that while higher confidence is associated with higher accuracy, the association is not perfect. Highly confident witnesses are right in their identifications 70 percent of the time, which means they are wrong the other 30 percent of the time; a criminal conviction based entirely on a confident eyewitness identification has a 30 percent chance of being erroneous. As eyewitness testimony expert Gary Wells and his colleagues at Iowa State University put it, “We would expect to encounter a highly confident mistaken eyewitness (or a nonconfident accurate eyewitness) about as often as we would encounter a tall female (or a short male).”47 This should make us question verdicts that rely exclusively on eyewitness memories, no matter how confidently they are recalled in court.
The Ronald Cotton case is often described as one of mistaken eyewitness identification due to the fallibility of memory. It is. But if the illusion of confidence did not exist, the authorities and the jurors would not have given Thompson’s identifications and recollections the inordinate weight they did. They would have recognized that her lack of doubt still left much room for error, and that physical and even circumstantial evidence are necessary backstops for eyewitness testimony—no matter how articulate, persuasive, and confident its delivery.48 The illusion of confidence obscures all of this, often with disastrous consequences.
For Ronald Cotton, the consequence was eleven years in prison for crimes he didn’t commit, but it could easily have been his entire life. At his second trial, on the basis of new testimony by the second victim, he was convicted of both rapes that were committed on that July night. His lawyers later wanted to test his DNA against samples from each crime scene, but the material from the second rape had deteriorated too much. If the samples taken from Jennifer Thompson were not testable—or were gone entirely—there would have been no way to prove Cotton’s innocence. Instead, he was set free on June 30, 1995. He was offered $5,000 in compensation by the state of North Carolina, an amount later raised to over $100,000 by changes to the law. These days, he travels and speaks on the issue of false convictions, often in tandem with Jennifer Thompson, who is now a married mother of triplets and an advocate for criminal justice reform.
In our view, what is most in need of reform is the legal system’s understanding of how the mind works. The police, the witnesses, the lawyers, the judges, and the jurors are all too susceptible to the illusions we have discussed. Because they are human, they believe that we pay attention to much more than we do, that our memories are more complete and faithful than they are, and that confidence is a reliable gauge of accuracy. The common law of criminal procedure was established over centuries in England and the United States, and its assumptions are based precisely on mistaken intuitions like these.
The mind is not the only thing we think we understand much better than we actually do. From physical mechanisms as simple as a toilet or a zipper, to complex technologies like the Internet, to vast engineering projects like Boston’s “Big Dig,” to abstract entities like financial markets and terrorist networks, we easily deceive ourselves into thinking that we understand and can explain things that we really know very little about. In fact, our dangerous tendency to overestimate the extent and the depth of our knowledge is the next everyday illusion we will discuss. The illusion of knowledge is like the illusion of confidence, but it is not a direct expression of one’s level of certainty or ability. It doesn’t involve telling someone else that you are “confident,” “certain,” “better than the average person,” and so on. It involves implicitly believing that you understand things at a deeper level than you really do, and it lurks behind some of the most dangerous and misguided decisions we make.
should you be more like a weather forecaster or a hedge fund manager?
IN JUNE 2000, U.S. president Bill Clinton and British prime minister Tony Blair jointly announced the completion of the initial phase of the Human Genome Project, the celebrated international effort to decode the DNA sequence of all twenty-three human chromosomes. The project ultimately spent about $2.5 billion over ten years to produce a “first draft” of the sequence, and over $1 billion more to fill in the gaps and polish the results.1 One of the most intriguing questions that biologists hoped the project would answer seemed to be a simple one: How many genes are there in the human genome?2
Before the sequence was completed, prevailing opinion held that the complexity of human biology and behavior must be the product of a large number of genes, probably between 80,000 and 100,000. In September 1999, a high-flying biotech comp
any called Incyte Genomics proclaimed that there were 140,000 genes in the human genome. In May 2000, top genelicists from around the world converged at the “Genome Sequencing and Biology” conference at the Cold Spring Harbor Laboratory in New York, and a lively debate about the true count ensued. Yet no consensus estimate emerged; some agreed with counts as high as those claimed by Incyte, and others argued that the number might be lower than 50,000.
With so many different opinions on offer, Ewan Birney, a geneticist at the European Bioinformatics Institute, started a betting pool for his fellow researchers to predict the final count. Each participant put in a dollar, and the winner would receive the total amount collected, plus a signed, leather-bound copy of Nobel Prize–winner James Watson’s memoir, The Double Helix. Incyte’s Sam LaBrie came in with the highest initial estimate: 153,478 genes. The average of the first 338 predictions entered was 66,050. Birney raised the entry fee to five dollars in 2001, and then to twenty dollars in 2002—it wouldn’t really be fair to let later bettors in for the same amount as earlier ones, since the late bettors could use the earlier estimates as well as their own research findings to guide their guesses. The 115 later entries averaged 44,375, and the pot grew to $1,200. Over the full two-year betting period, the lowest entry was 25,747, submitted by Lee Rowen from the Institute for Systems Biology in Seattle.
The terms of the competition, set in 2000, required Birney to declare a winner in 2003. However, to Birney’s surprise, there was still no consensus “final count” at that point. Based on evidence available at the time, Birney estimated the total count to be about 24,500. He decided to award portions of the pool to the three entrants who bet on the lowest numbers, with Rowen getting the largest prize. The final number is still in dispute, but the most accepted value has dropped to 20,500, squarely in the range between the roundworm called C. elegans (19,500) and the mustard plant called Arabidopsis (27,000).
The bettors all were leaders in the field of genetics, and they were sure that the number was higher than it actually was; the range of their 453 predictions, from the highest to the lowest estimate, did not even include the correct count. Francis Collins of the National Institutes of Health and Eric Lander of the Massachusetts Institute of Technology, leaders of the Genome Project, were each off by more than 100 percent, no better than the average guess. The collective also had a pretty poor idea of how quickly the gene-count question would be resolved (predicted: 2003, actual: 2007 or later). Collins reacted stoically: “Oh well, live and learn.”
This is far from the only example of scientists overestimating their knowledge in their own fields of expertise. In 1957, two of the pioneers of computer science and artificial intelligence, Herbert Simon and Allen Newell, publicly predicted that within ten years a computer would be able to defeat the world chess champion in a match.3 By 1968 no one had come close to creating a machine capable of that feat. David Levy, a Scottish computer programmer and chess player who would later achieve the title of international master (one level below grandmaster), met with four other computer scientists and bet them £500 of his own money—an amount equal to about one-half of his annual income at the time—that no computer would be able to beat him in a match within the next ten years. In 1978, with the pot sweetened to £1250 by further wagers, Levy in fact defeated the best computer program by a score of 3½—1½. Together with Omni magazine, he then offered a new prize of $5,000 to anyone whose computer could beat him, with no time limit on the bet. Finally, in 1989, Levy lost to Deep Thought, a predecessor of IBM’s Deep Blue computer. Only in 1997 did Deep Blue, with its multiple processors and custom-designed chess chips, defeat world champion Garry Kasparov 3½—2½ and fulfill the Simon-Newell prophecy—thirty years behind schedule.4
In 1980, ecologist Paul Ehrlich, a professor at Stanford University, and his colleagues John Harte and John Holdren of the University of California at Berkeley, were convinced that global overpopulation would lead to drastic increases in the prices of food and other commodities that were in finite supply. Indeed, Ehrlich had been convinced that this threat was dire for some time, having written in 1968, “In the 1970s the world will undergo famines—hundreds of millions of people are going to starve to death.”5 He and Holdren predicted the imminent “exhaustion of mineral resources.”6
Julian Simon, an economist at the University of Maryland, had the opposite view. He published an article in the journal Science titled “Resources, Population, Environment: An Oversupply of False Bad News.”7 Simon, whose previous claim to fame was inventing the system under which airlines reward passengers for giving up their seats on overbooked flights, proceeded to challenge the doomsayers to put their money where their mouths were: Pick five commodities and bet that their prices would increase over the next ten years, as one would expect if demand were always increasing and supply were constant or decreasing. Ehrlich was outraged by the apostasy displayed by Simon (whom he referred to as the leader of a “space-age cargo cult”), so he got Harte and Holdren to join him in accepting the wager proposed by the economist. They selected five metals—chrome, copper, nickel, tin, and tungsten—and calculated the amount of each that could be purchased for $200 in 1980. If these metals’ prices were higher ten years later, Simon would pay Ehrlich, Harte, and Holdren the difference; if the prices were lower, they would pay him. By 1990, all five commodities had gone down in price. In fact, they had collectively dropped more than 50 percent. Simon received an envelope containing a check in the amount of his winnings. There was no cover note.8
You might object that we’ve cherry-picked examples in which experts made their most horribly errant predictions. We agree that these examples are atypical, and we’re not arguing that experts know nothing and are always wrong. Especially in scientific domains, they know a lot more and are right much more often than the average person. But these stories show that even scientific experts can dramatically overestimate what they know. Every single geneticist guessed high on the gene count, and some were off by a factor of five; the computer scientists were off by a factor of four; and the ecological doomsayers were wrong about every one of the metals they selected. If expert judgments can be so misguided, the rest of us must also be capable of overestimating what we know. Whenever people think they know more than they do, they are under the influence of our next everyday illusion: the illusion of knowledge.
The Virtue of Being Like an Annoying Child
Spend a moment now and try to form an image in your mind of a bicycle. Even better, if you have a piece of paper, draw a sketch of a bicycle. Don’t worry about making a great piece of art—just focus on getting all the major parts in the right place. Sketch out the frame, the handlebars, the wheels, the pedals, and so on. For simplicity, just make it a singlespeed bicycle. Got it? If you had to rate your understanding of how a bicycle works on a 1 to 7 scale, where 1 means “no understanding” and 7 means “complete understanding,” what score would you give yourself?
If you are like most of the people who participated in a clever study by British psychologist Rebecca Lawson, you thought you had a pretty good understanding of bicycles; her subjects rated the level of their knowledge at 4.5 out of 7, on average.9 Now either look at your drawing or refresh your mental image and then answer the following questions: Does your bicycle have a chain? If so, does the chain run between the two wheels? Does the frame of your bicycle connect the front and back wheels? Are the pedals connected to the inside of the chain? If you drew a chain connecting the two wheels of your bicycle, think about how the bicycle would turn—the chain would have to stretch whenever the front wheel rotated, but chains aren’t stretchy. Similarly, if a rigid frame connected both wheels, the bicycle could only go straight. Some people draw pedals outside the loop of the chain, making it impossible to turn the chain by pedaling. Errors like these were common in Lawson’s study, and they are not trivial details of the functioning of a bicycle—the pedals turn the chain, which causes the back wheel to rotate, and the front wheel must be free to turn or
the bicycle cannot change direction. People are much better at making sense of a bicycle’s workings when the thing is sitting right in front of them than they are at explaining (or drawing) a bicycle purely from memory.
This example illustrates a critical aspect of the illusion of knowledge. Because of our extensive experience and familiarity with ordinary machines and tools, we often think we have a deep understanding of how they work. Think about each of the following objects and then judge your knowledge of it on the same 1 to 7 scale: a car speedometer, a zipper, a piano key, a toilet, a cylinder lock, a helicopter, and a sewing machine. Now try one more task: Pick the object that you gave the highest rating, the one you feel you best understand, and try to explain how it works. Give the kind of explanation you would give to a persistently inquisitive child—try to generate a detailed step-by-step description of how it works, and explain why it works. That is, try to come up with the causal connections between each step (in the case of the bicycle, you would have to say something about why pedaling makes the wheels turn, not just that pedaling makes the wheels turn). If you aren’t sure how two steps are causally connected, you’ve uncovered a gap in your knowledge.
This test is similar to a series of ingenious experiments that Leon Rozenblit conducted as part of his doctoral research at Yale University with Professor Frank Keil (who, incidentally, was also Dan’s graduate school adviser).10 For his first study, Rozenblit approached students in the hallways of the psychology building and asked them if they knew why the sky is blue or how a cylinder lock works. If they answered yes, he then played what he calls the “why boy” game, which he describes as follows: “I ask you a question and you give me an answer, and I say ‘why is that?’ Channeling the spirit of a curious five-year-old, I then just keep following each explanation with another ‘why is that?’ until the other person gets really annoyed.”11 The unexpected result of this informal experiment was that people gave up really quickly—they answered no more than one or two “why” questions before they reached a gap in their understanding. Even more striking were their reactions when they discovered that they really had no understanding. “It was clearly counterintuitive to them. People were surprised and chagrined and a little embarrassed.” After all, they had just claimed to know the answer.