Brain Buys (26 page)

The associative architecture of the brain predicts that any cue that is consistently associated with a given product (including logos, and the design and color of packages) has the potential to influence how the actual product is perceived at the sensory level. One cue that is pervasively associated with quality is price. Which raises the question, can the price of a product influence how it tastes? One of a number of studies aimed at answering this question asked subjects to judge the taste of different types of wine, each one identified by a price. They were presented with five samples, priced at $5, $10, $35, $45, or $90, but unbeknownst to the subjects, there were only three different wines. While the $5, $35, and $90 labels did reflect the actual purchase price of the wines, the $10 and $45 wines were simply the $90 and $5 wines, respectively, presented with fictitious prices.
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Subjects rated the same wine significantly higher when they were told it was $45 than when they were told it was $5, and again when they believed it was $90 compared to $10. Additionally, in a blind taste test that does not bode well for our gustatory sophistication, or for the wine industry, there was no significant preference for the more expensive wines. In fact, there was actually a slight preference for the cheaper wine.

The influence of the associations between price and quality on our judgments has also been demonstrated by a study by the behavioral economist Dan Ariely and his colleagues. They examined the effects of the price on the efficacy of a purported analgesic. Volunteers were given a pill that they were told was a new type of fast-acting analgesic, but it was actually an inactive placebo. The effectiveness of this pill against pain was measured by applying shocks to the subjects before and after taking the pill. It is well established that placebos can be highly effective (a fascinating brain feature/bug in and of itself), but the point of this study was to determine if price of the medication altered the placebo effect. Half the subjects were told the drug cost $2.50 per pill, and the other half were told it cost $0.10. Indeed, in the high-priced group, subjects endured higher voltages than in the low-priced group after taking the same pill.
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The belief that better things cost more (that is, the association between quality and cost) seems to be a self-fulfilling prophecy. It compels us to believe that more expensive items are actually better (even if they are not), and believing they are better actually makes them better. Of course, in many cases superior products do cost more; but we tend to overgeneralize and implicitly assume that price in and of itself is an indicator of quality. This brain bug can be exploited by companies that increase the prices of products to convince us we are buying a higher-quality product.

DECOYS

In addition to the brain’s proclivity to learn by imitation and build associations between the objects and concepts to which it is exposed, there are likely many other factors contributing to our susceptibility to marketing. For example, simple exposure and familiarity with a brand goes a long way, as we are more comfortable buying brands we recognize. But marketing strategies also take advantage of a number of other far more subtle mental loopholes. One of my favorite examples of such a loophole is termed the
decoy
or
attraction effect
.

Imagine that you are buying a new car and are down to two options. Everything is essentially identical between them, except for two differences. Car A has better mileage, 50 versus 40 miles per gallon. But car A also has a lower quality rating, 75 versus 85 (we are pretending automobiles have some objective and accepted quality rating bestowed upon them by some impartial party). Which one do you choose? There is really no right or wrong choice, as the answer depends on some personal view of how you balance the trade-off between quality and fuel efficiency. Next imagine that instead of two options you had three options: the same A and B cars, plus car C, which has a quality rating of 80 and gets 40 miles per gallon. In other words, car C is unambiguously worse than car B because it has the same mileage and a lower quality rating (Figure 7.2). Do you think the presence of the inferior choice C will alter whether you choose A or B? The answer is yes. In one study 39 percent of the subjects chose car B when picking between A and B, but 62 percent chose B when picking among A, B, and C. Logically, adding another choice should never increase the likelihood of choosing one of the original options; it’s the equivalent of picking chocolate ice cream when presented with a choice between chocolate and vanilla, but then switching to vanilla when the waiter informs you that they also have almond ice cream.
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Figure 7.2 The decoy effect: Two car choices are represented as points on a two-dimensional graph where one dimension is quality and the other mileage (
upper left
). We can imagine that these choices are presented as two points on a two-dimensional grid of neurons. Because different numerical quantities should activate a population of neurons centered around each value, we can visualize the neural representation of both choices as a grid with two “hills of activity.” Since there is a balanced trade-o? between both choices the hills are of equal height (
upper right
), and neither choice is favored. When a third and clearly inferior choice (car C) is presented (
lower left
) the activity hill “B” grows because the activity produced by option B overlaps and sums with that of option C, potentially biasing choices toward car B.

Suppose you are a restauranteur who has an expensive and lucrative shrimp entrée on your menu, but it does not sell very well. How can you go about increasing the sales of this dish? You could, of course, lower its price. But a more lucrative approach might be to add another even more expensive shrimp dish—one that you don’t intend to make very often. The target dish now looks rather reasonable in comparison. This decoy strategy was knowingly or unknowingly used by Williams-Sonoma to increase the sale of their first bread maker. Their first machine was introduced at a price of $279, and did not sell very well. The company next introduced a larger bread maker for $429, which ended up doubling the sales of the original, and cheaper, one.
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Why are we more likely to pick an item or product that is next to another similar option than when it stands alone? Why are our neural circuits swayed by the presence of a decoy option? There are a number of cognitive hypotheses as to why this may be the case. For example, perhaps it is simply easier to justify our decisions in relation to a similar choice. For the sake of “mental convenience,” we might eliminate the harder option whenever possible (in the above example the decision between car B and C is straightforward, so we ignore car A). But such an explanation is psychological in nature, it does not address the neural mechanisms responsible for the decoy effect, and in the end all choices are the result of computations performed by circuits of neurons.

What exactly does it mean for the brain to make a decision? One hypothesis is that the decision between two options is determined by the levels of activity within the two populations of neurons that represent those options.
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Let’s suppose you are hanging out at your desk and hear two simultaneous and unexpected sounds, one to the left and one to the right. Which one do you look toward? It is more or less instinctive to look toward the loudest stimulus. Why? Decisions, even unconscious and automatic ones, often involve two groups of neurons competing with each other in a tug-of-war match—in this case one group would make your head turn left and the other right. The population of neurons being driven by the louder sound is likely to reach a higher level of activity quicker, and emerge the victor. How we make more complex decisions is much more mysterious, but at some level your decision between having a pizza, sandwich, or salad for lunch might come down to competition between “pizza,” “sandwich,” and “salad” neurons. Whatever group of neurons is the most active wins, and the level of activity in different populations is determined by a potpourri of intangible factors: what your companion chose, what you ate yesterday, whether you are on a diet, price, and who knows what else.

Consider the car example that involves evaluating the relative trade-off between two numeric dimensions: quality and mileage. How are these different options represented in the brain? As we have seen, quantities seem to be represented by neurons that respond preferentially to different numerical values. These neurons are, however, broadly tuned, meaning that a neuron that fires maximally to a value of 85 will also fire in response to 80, albeit a bit less. Now, imagine a two-dimensional grid of neurons: one dimension encoding the mileage and the other quality. Each option can be thought of as being encoded as a hill centered at the coordinates representing mileage and quality. The height of the hill (the activity of the population of neurons) would be proportional to the absolute value of the option, which would take into account both mileage and quality.
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When only options A and B are present on this grid, the total neural activity elicited by each is more or less the same, because of the mileage-quality trade-off. Thus, there will be no clear bias, resulting in an equal chance of choosing A or B. However, when considering all three options, the neurons encoding the mileage of B and C will overlap because they have the same mileage. Precisely because options B and C are similar to each other, they “share” some neurons. This translates into more activity in the B option neurons than would have otherwise occurred if A and B were presented alone, because some of the B neurons are now also driven by option C. In a manner of speaking, the “B” and “C” hills are added together, increasing the height of “B.” So if we assume that decisions are based on the most active group of neurons, the group encoding option B should be the winner.

Let’s look at it another way to help visualize the process. Suppose you are looking straight ahead and two bright lights go on, one to the left of center and the other to the right. Do you reflexively look left or right? There should be a 50/50 chance of making an eye movement to the left or right because both stimuli were of equal magnitude; thus, the neurons responsible for a leftward and rightward glance will be similarly active. But if an additional dimmer light (a “decoy”) went on close to the location of the right light, it might shift the odds toward the right. Because the total input from that site is now more intense, your gaze shifts to the brighter area on the right. Loosely speaking, the presence of the inferior option C could boost activity in the general B/C area, and since B is the clear victor between B and C, choices are biased toward the local winner. To place this in the language of the nodes and links we have used to describe semantic memory, we can say that options B and C are more closely linked to each other. Thus activity “spreads” between them, increasing their profile in relation to A, which stands alone.

The way the brain represents and encodes different options may inherently bias our choices. In other words, odd little bugs like the decoy effect may not be a consequence of a flaw in the sophisticated cortical circuits responsible for reasoning and logic, but of the fact that similar things (like colors, intensities, numbers, or cars) are encoded in such a way that they share neurons. And since decisions may rely on the relative magnitude of activity of different populations of neurons, the number of options boosts the perceived value of the local best option.

Whether marketing is executed through TV ads, Web sites, product placement in movies, or through sales representatives, it unquestionably influences what we buy and desire. And I suspect that more often than not, the net effect of these influences is not in our own best interests. There is no single cause to why our neural operating system is so susceptible to the sway of marketing. But propensity to learn by imitation and the associative architecture of the brain are surely two of the main reasons.

Today, few details are small enough to be ignored in the game of marketing. Cereal companies package their product in oversized boxes to provide the illusion of quantity. Many restaurants now list their prices without the dollar sign (12 instead of $12) because studies suggest people spend more if they are not reminded of “$.”
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Companies engage in stealth marketing campaigns in which people are paid to frequent bars or Web sites to covertly chat about and promote certain products or movies. They perform studies in which they track the eye movements of people viewing displays, and they carefully craft the names, packages, taglines, jingles, and odors associated with their products. Web sites also track our surfing and shopping habits. And in industries whose target audiences are essentially unreachable by conventional marketing techniques, companies resort to direct person-to-person marketing. This mode of marketing is perhaps epitomized by the pharmaceutical industry. To promote new drug sales, representatives visit doctors and may provide them with free samples, gifts, dinners, and tickets to events (although this practice is now coming under tighter regulation). The sales representatives themselves are generally not hired based on their knowledge of pharmacology, but on their outgoing personalities—drug companies have been known to target college cheerleaders for these positions. By creating databases from pharmacy sales and information from the American Medical Association, sales representatives categorize doctors into low and high prescribers, and develop targeted strategies aimed at influencing different types of doctors depending on their professional and personal profiles.
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To deny the effect of these marketing strategies on prescribing practices, and thus on the supposed impartiality of medical treatments (as well as the cost of medical care), requires ignoring the fact that companies invest hundreds of millions of dollars in this form of marketing, as well as to reject everything we know about the brain.