We use categories and concepts to guide the hundreds of decisions and judgments we make during the course of an average day. Some decisions are easy (what to wear, what to eat for breakfast, and whether to walk or drive to class) and some are more difficult (which car to buy, which apartment to rent, and even which job to take after graduation). Some decisions are made based on sound judgments. Others are not.

Economists contend that if we are rational and are free to make our own decisions, we will behave as predicted by rational choice theory: the classical view that we make decisions by determining how likely something is to happen, judging the value of the outcome, and then multiplying the two (Edwards, 1955). This means that our judgments will vary depending on the value we assign to the possible outcomes. Suppose, for example, you were asked to choose between a 10% chance of gaining $500 and a 20% chance of gaining $2,000. The rational person would choose the second alternative because the expected payoff is $400 ($2,000 × 20%), whereas the first offers an expected gain of only $50 ($500 × 10%). Selecting the option with the highest expected value seems very straightforward. But how well does this theory describe decision making in our everyday lives? In many cases, the answer is not very well.

Is the ability to classify new events and objects into categories always a useful skill? Alas, no. The same principles that allow cognition to occur easily and accurately can pop up to bedevil our decision making.

Judging Frequencies and Probabilities

Consider the following list of words:

block table block pen telephone block disk glass table block telephone block watch table candy

You probably noticed that the words block and table occurred more frequently than the other words did. In fact, studies have shown that people are quite good at estimating frequency, or the number of times something will happen. In contrast, we perform poorly on tasks that require us to think in terms of probabilities, or the likelihood that something will happen.

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In one experiment, 100 physicians were asked to predict the incidence of breast cancer among women whose mammograms showed possible evidence of breast cancer. The physicians were told to take into consideration the rarity of breast cancer (1% of the population at the time the study was done) and radiologists’ record in diagnosing the condition (correctly recognized only 79% of the time and falsely diagnosed almost 10% of the time). Of the 100 physicians, 95 estimated the probability of cancer at about 75%! The correct answer was 8% (Eddy, 1982). But dramatically different results were obtained when the study was repeated using frequency information instead of probability information. Stating the problem as “10 out of every 1,000 women actually have breast cancer” instead of “1% of women actually have breast cancer” led 46% of the physicians to derive the right answer (Hoffrage & Gigerenzer, 1998).

The frequency format hypothesis is the proposal that our minds evolved to notice how frequently things occur, not how likely they are to occur (Gigerenzer, 1996; Gigerenzer & Hoffrage, 1995). Thus, we interpret, process, and manipulate information about frequency with comparative ease because that’s the way quantitative information usually occurs in natural circumstances. For example, the 20 men, 15 women, 5 dogs, 13 cars, and 2 bicycle accidents you encountered on the way to class came in the form of frequencies, not probabilities or percentages. Therefore, the frequency format provides an explanation for why physicians do so much better at predicting incidence of breast cancer when the relevant information is presented as frequencies rather than as probabilities.

Availability Bias

Take a look at the list of names in FIGURE 9.8. Now look away from the book and estimate the number of male names and female names in the figure. Did you notice that some of the women on the list are famous and none of the men are? Was your estimate off because you thought the list contained more women’s than men’s names (Tversky & Kahneman, 1973, 1974)? People typically fall prey to availability bias: Items that are more readily available in memory are judged as having occurred more frequently.

The availability bias affects our estimates because memory strength and frequency of occurrence are directly related. Frequently occurring items are remembered more easily than infrequently occurring items, so you naturally conclude that items for which you have better memory must also have been more frequent. Unfortunately, better memory in this case was not due to greater frequency, but to greater familiarity.

The Conjunction Fallacy

Consider the following description:

Linda is 31 years old, single, outspoken, and very bright. In college, she majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice and also participated in antinuclear demonstrations.

Which state of affairs is more probable?

a. Linda is a bank teller.

b. Linda is a bank teller and is active in the feminist movement.

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In one study, 89% of participants rated option b as more probable than option a (Tversky & Kahneman, 1983).

This is called the conjunction fallacy, which is when people think that two events are more likely to occur together than either individual event. Actually, the reverse is true: The probability of two or more events occurring simultaneously (in conjunction) is always less than the probability of each event occurring alone, as you can see in FIGURE 9.9.

Representativeness Heuristic

Think about the following situation:

A panel of psychologists wrote descriptions of 100 people, including 70 engineers and 30 lawyers. You will be shown a random selection of these descriptions. Read each and decide if it is more likely that the person is an engineer or a lawyer.

The majority of research participants thought that Jack was more likely to be a lawyer and that Tom was more likely to be an engineer. Harry’s description doesn’t sound like a lawyer’s or an engineer’s, so most people said he was equally likely to hold either occupation (Kahneman & Tversky, 1973). But remember that the pool of descriptions contains more than twice as many engineers as lawyers, so based on this proportion, it is far more likely that Harry is an engineer. People seem to ignore information about base rate, or the probability of an event, and instead base their judgments on similarities to categories. Researchers call this the representativeness heuristic, a mental shortcut that involves making a probability judgment by comparing an object or event to a prototype of the object or event (Kahneman & Tversky, 1973).

Framing Effects

If people are told that a particular drug has a 70% effectiveness rate, they’re usually pretty impressed. Tell them instead that a drug has a 30% failure rate and they typically perceive it as risky and potentially harmful. Notice that the information is the same: A 70% effectiveness rate means that 30% of the time, it’s ineffective. The way the information is presented, however, leads to substantially different conclusions (Tversky & Kahneman, 1981). This is called the framing effect, which occurs when people give different answers to the same problem depending on how the problem is phrased (or framed).

One of the most striking framing effects is the sunk-cost fallacy, a framing effect in which people make decisions about a current situation based on what they have previously invested in the situation. Imagine waiting in line for 3 hours, paying $100 for a ticket to the Warped Tour to see your favorite bands, and waking on the day of the outdoor concert to find that it’s bitterly cold and rainy. If you go, you’ll feel miserable. But you go anyway, reasoning that the $100 you paid for the ticket and the time you spent in line will have been wasted if you stay home.

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Notice that you have two choices: (1) Stay comfortably at home or (2) endure many uncomfortable hours in the rain. The $100 is gone in either case: It’s a sunk cost. But because you invested time and money, you feel obligated to follow through, even though it’s something you no longer want.

Even the National Basketball Association (NBA) is guilty of a sunk-cost fallacy. The most expensive players are given more time on court and are kept on the team longer than cheaper players, even if the costly players are not performing up to par (Staw & Hoang, 1995). Coaches act to justify their team’s investment in an expensive player rather than recognize the loss. Framing effects can be costly!

Another kind of framing effect occurs when information is presented in terms of losses instead of in terms of savings. For example, imagine you’re renting a new apartment, and, as part of a promotion, you’re given a choice between a $300 rebate on your first month’s rent or spinning a wheel that offers an 80% chance of getting a $400 rebate. Which would you choose? If you’re like most people, you’ll choose the sure $300 over the risky $400.

But suppose the lease offers you a choice of penalty for damaging the apartment: either $300, or spin a wheel that offers an 80% chance of a $400 fine. Now which would you choose? Most people will choose to gamble by spinning the wheel, taking the chance of avoiding the fine altogether, even though the odds are they’ll wind up paying more than the sure $300.

Prospect theory states that people choose to take on risk when evaluating potential losses and avoid risks when evaluating potential gains (Tversky & Kahneman, 1992). This asymmetry in risk preferences shows that we are willing to take on risk if we think it will ward off a loss, but we’re risk-averse if we expect to lose some benefits.

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A man identified as Elliot (whom you met briefly in the Psychology: Evolution of a Science chapter) was a successful businessman, husband, and father prior to developing a brain tumor. After surgery, his intellectual abilities seemed intact, but he was unable to differentiate between important and unimportant activities. He lost his job and got involved in several risky financial ventures that bankrupted him. He had no difficulty discussing what had happened, but his descriptions were so detached and dispassionate that it seemed as though his abstract intellectual functions had become dissociated from his social and emotional abilities.

Research confirms that this interpretation of Elliot’s downfall is right on track. In one study, researchers looked at how healthy volunteers differed from people with prefrontal lobe damage on a gambling task that involves risky decision making (Bechara et al., 1994, 1997). Participants were allowed to choose cards one-at-a-time from any of four decks; each card specified an amount of play money won or lost. Unbeknownst to the subjects, two of the decks had mostly cards with large payoffs and large losses (the “risky” decks). The other two decks had mostly cards with small payoffs and small losses (the “safe” decks). Early on, most healthy participants chose from each deck equally, but they gradually shifted to choosing primarily from the safe decks, where potential payoffs were smaller—but so were potential losses. In contrast, patients with prefrontal damage continued to select equally from the risky and safe decks, leading most to eventually go bankrupt in the game. This performance mirrors Elliot’s real-life problems. The healthy participants also showed galvanic skin responses (GSR) that jumped dramatically when they were thinking about choosing a card from the risky deck—an anticipatory emotional response (Bechara et al., 1997). The participants with prefrontal damage didn’t show these anticipatory feelings when they were thinking about selecting a card from the risky deck. Apparently, their emotional reactions did not guide their thinking, so they continued to make risky decisions, as shown in FIGURE 9.10.

Further studies of the participants with prefrontal damage suggest that risky decision making grows out of insensitivity to the future consequences of behavior (Naqvi, Shiv, & Bechara, 2006). Unable to think beyond immediate consequences, they cannot shift their choices in response to a rising rate of losses or a declining rate of rewards (Bechara, Tranel, & Damasio, 2000). Recent neuroimaging studies suggest that the area of prefrontal cortex that is typically damaged in these patients is also activated in healthy people who perform well on the gambling task (Fukui et al., 2005; Lawrence et al., 2009). Together, the neuroimaging and lesion studies show that aspects of risky decision making depend critically on the prefrontal cortex.

Interestingly, substance-dependent individuals, such as alcoholics, binge drinkers, and cocaine addicts often perform as poorly on the gambling task as do individuals with prefrontal damage (Bechara et al., 2001; Johnson et al., 2008). In one study, people who had been convicted of driving while impaired with alcohol (DWI or driving while intoxicated) were tested on the gambling task; those who performed poorly were much more likely to commit repeated DWI offenses than those who performed well (Bouchard, Brown, & Nadeau, 2012). Related recent work has documented gambling task impairments in binge eaters, another group in which there is an insensitivity to the future consequences of behavior (Danner et al., 2012).

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