The Invisible Gorilla: And Other Ways Our Intuitions Deceive Us (23 page)

The cousin of neurobabble is “brain porn,” the colorful images of blobs of activity on brain scans that can seduce us into thinking we have learned more about the brain (and the mind) than we really have. Neuroscientists have recognized that these pictures can sometimes be more of a sales tool for their research than a true aid to understanding. In one clever experiment, David McCabe and Alan Castel had subjects read one of two descriptions of a fictitious research study. The text was identical, but one description was accompanied by a typical three-dimensional brain image with activated areas drawn in color, while the other included only an ordinary bar graph of the same data. Subjects who read the version with the brain porn thought that the article was significantly better written and made more sense. The kicker is that none of the fictitious studies actually made any sense—they all described dubious claims that were not at all improved by the decorative brain scans.
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Neurobabble has crept into advertising, alongside technobabble and
other irrelevant information that makes consumers feel that they understand something better than they really do. In a ubiquitous magazine ad, Allstate Insurance asks, “Why do most 16-year-olds drive like they’re missing a part of their brain?” and answers, “Because they are.” The company attributes their risky driving to an immature dorsal lateral prefrontal cortex, a region critical for “decision making, problem solving and understanding future consequences of today’s actions.” Beneath the headline, a cartoon depicts a brain with a car-shaped hole right in this location.
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The ad copy might be right about the science, but the information about the brain is entirely irrelevant to its point. Teenagers are indeed risky drivers, but that’s all you need to know to be persuaded that parents should talk more to their children about road safety, which is the point of Allstate’s ad. If you’re more likely to talk to your kids (or to buy Allstate’s insurance) because you know what part of the brain is responsible for risk-taking, you are a victim of the illusion of knowledge—courtesy of neurobabble and brain porn.

In the 2005 comedy-drama
The Weather Man
, the title character (played by Nicolas Cage) is paid well but receives little respect for his job, which consists entirely of acting authoritative while reading forecasts prepared by others. It’s easy to mock a class of professionals whose work comes to mind mainly when a game is rained out or a flight is delayed. There are some places, though, where the weather really is important news, and accurate weather forecasts can make millions or even billions of dollars of difference in people’s lives. Dan lives in Champaign, a college town in east-central Illinois. The University of Illinois, where he teaches, is the largest employer in the area, but the dominant economic force in the region is large-scale farming of corn and soybeans. Illinois produces a larger soybean crop than any other state and is the second-largest corn producer.
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The weather influences all of the important decisions a farmer makes, including when to plant and harvest, what to plant, and
how to plan ahead for future supply and demand. Farmers in Illinois monitor conditions far outside their own region. A bumper corn crop during Argentina’s summer can affect which crops Illinois farmers plant in the spring. Even the world markets for oil and other forms of energy affect planting decisions, since Illinois corn is responsible for 40 percent of the ethanol produced in the United States.

Few National Public Radio stations have more than one weather forecaster on staff, and even fewer have one with a meteorology degree. The Champaign NPR station, WILL, has one full-time meteorologist, two part-time meteorologists, and another weather forecaster on staff. WILL gives detailed weather forecasts throughout the day, devoting as much time to the weather as any station in the United States. It has to, because farmers depend on weather forecasts for their livelihood.
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If weather forecasters really know how much they know—in technical terms, if they are “well calibrated”—then farmers can rely on their predictions when making major decisions.

Although people have attempted to predict the weather for millennia, the first published forecast appeared in print less than 150 years ago, in Cincinnati on September 1, 1869: “Cloudy and warm this evening. Tomorrow clear.”
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The addition of probabilities expressed as percentages didn’t begin until 1920, when Cleve Hallenbeck, the head of the U.S. Weather Bureau office in Roswell, New Mexico, published an article advocating their use. Hallenbeck had tested his method with an informal experiment that lasted 220 days. On each day he estimated the probability of rain and then recorded whether it rained. His forecasts turned out to be remarkably well calibrated: It rained on most of his high-probability days and on few of his low-probability days. However, only in 1965 did the U.S. National Weather Service begin to regularly include percentage probabilities of rain in its forecasts. In 1980, meteorologists Jerome Charba and William Klein undertook a massive examination of more than 150,000 precipitation forecasts during the two years from 1977 to 1979. The forecasted likelihood of rain matched the actual probability of rain almost perfectly. Tellingly, the only systematic errors happened when the forecasters assigned a 100 percent chance of
rain—it turned out to rain on only about 90 percent of those days. Beware of certainty!

What makes weather forecasts, at least good ones, different from other forms of reasoning and prediction? When meteorologists say that there is a 60 percent chance of rain, they are estimating the probability that, given the existing atmospheric conditions, it actually will rain. And these estimates are highly accurate over a long series of forecasts. Meteorologists continually adjust their predictions—and the mathematical and statistical models and computer programs that generate those predictions—based on feedback from previous predictions. If a 60 percent probability of rain is attached to certain climate patterns, but it only rains 40 percent of the time, then the models are refined so that the next time those atmospheric conditions occur, the estimated probability of rain will be lower. Weather forecasting is unusual in that forecasters receive immediate and definitive feedback about their predictions, and their knowledge of probabilities accumulates over time. For example, during the period from 1966 through 1978, skill at forecasting precipitation thirty-six hours in advance nearly doubled.
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Like weather forecasters, when we receive appropriate feedback, we can sometimes calibrate our judgments and eliminate the illusion of knowledge. In a demonstration Dan has used in an introductory psychology class, students are each given a playing card, which they proceed to hold to their forehead so that they can’t see it, but everyone else can.
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Then each person in the class tries to get the person with the highest possible card to pair up with him or her. Remember, the students can’t see their own card, but they can see everyone else’s—so they can see who rejects them.

Initially, most people in the class will try to pair up with an Ace or King (the highest cards), but most will be rejected. Only those who have a really high card are likely to be accepted by someone who has an Ace or a King. People with an Ace or King don’t know what they have, but they know that they really can’t do better than an Ace or King and they aren’t likely to accept an invitation from someone with a 6 or 7—they hope to match with someone higher. Surprisingly, people pair off
quite quickly with others who have cards comparable to their own. They are able to rapidly use the feedback they get from rejection to calibrate their expectations. The same principle can be used to explain why people of widely different attractiveness rarely end up as couples
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—people reach for the best they can get, and dating allows for some calibration of your self-impressions.

The card-matching game and the real world of dating and mating provide immediate and direct (and sometimes painful) feedback in the form of rejection. Unfortunately, for most of the judgments that we make in our lives, we never receive the precise feedback that weather forecasters do of seeing the next morning whether we were right or wrong, day after day, year after year. This is an important difference between meteorology and fields like medicine. Information about the correctness of a diagnosis, or the outcome of a surgical procedure, is available
in principle
. In practice, though, it is rarely collected systematically, stored, and analyzed the way data about the weather is; a doctor who diagnoses pneumonia and prescribes a treatment will have to wait awhile to learn—or may never learn—whether the treatment worked. Even then it may be difficult to distinguish the effects of the treatment from improvements that happened spontaneously. If you’ve recently switched from a film camera to a digital camera, you have experienced the benefits of instant feedback. You no longer have to wait for your film to be developed before you know what you did wrong (or right) in composing your shots. And when you do make a mistake, you can fix it right away. As any student knows, whether in photography, psychology, or business, it’s harder to improve if you don’t get immediate feedback about your mistakes.

Scientists, architects, and hedge fund managers are respected, but weather forecasters are parodied. Yet weather forecasters have fewer illusions about their own knowledge than do members of these other professions. In
Chapter 3
we saw that doctors who consulted books and computers were
underappreciated by patients, whereas a rape victim who expressed no doubt in her testimony was praised as a model witness. There we argued that our love of confidence can reward people for acting as though they are more skilled and accurate than they really are. The illusion of knowledge has similar consequences: We seem to prefer the advice of experts who act like they know more than they really do—or who honestly believe their knowledge is greater than it is.

Do people actually prefer expressions of knowledge that exude more certainty to more tentative statements, even when the tentative ones are better calibrated? Try answering the following simple question devised by the Dutch psychologist Gideon Keren:

This question pits our preferences for accuracy and certainty against each other. Betty said it should rain 75 percent of the time, and it did, so her predictions reflected no illusion of knowledge. Anna thought she knew more about the likelihood of rain than she really did: It would have to have rained on all four days for her forecasts to be more accurate than Betty’s. When we conducted an experiment using a variant of this question, nearly half of our subjects, however, preferred Anna’s forecast.
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The conditions of this experiment differ from most real-world situations, in which we rarely get to choose among experts with such clear track records of success or failure in prediction. A study of experts on international politics—a field in which it can take years or decades to see whether predictions are borne out—found that their forecasts were
significantly less accurate than those of simple statistical models. The way the forecasts were worse was revealing: In general, the experts predicted that political and economic conditions would change (for the better or the worse) more often than they actually did. So a strategy of simply assuming that the future will be the same as the present would have yielded more accurate predictions (but probably less airtime for the pundit). Unlike the weather forecasting experiment, though, people listening to these political experts have no way to tell in advance how accurate their forecasts will be.
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Compared to the laboratory, in the real world it’s much harder to make a correct choice, precisely because we either lack the necessary information, or we have it but lack the time, attention, and insight we need to evaluate it properly.

The Anna/Betty experiment shows that even when we have all the necessary information to recognize which expert knows the limits of her own knowledge, we often prefer the one who does not. Self-help authors who say precisely what to do (“eat this, not that”) have larger audiences than those who give a menu of reasonable options for readers to try out in order to find out what works best for them. TV stock-picking guru Jim Cramer tells you to “buy buy buy” or “sell sell sell” (with a hearty “Boo-yah!”) rather than to analyze investment ideas in the context of your overall financial goals, weighting of different types of assets, and other nuanced considerations that might undermine the dazzling sense of conviction that he exudes.
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