Brain Buys (22 page)

Cognitive biases have been intensely studied, and their implications vigorously debated, yet little is known about their actual causes at the level of our neural hardware. Brain-imaging studies have looked for the areas in the brain that are preferentially activated during framing or loss aversion effects.
³¹
At best, these studies reveal the parts of the brain that may be involved in cognitive biases, not their underlying causes. Understanding how and why the brain makes good or bad decisions remains a long way off, yet the little we have learned about the basic architecture of the brain offers some clues. For instance, the similarity between some cognitive biases and priming suggests that they are a direct consequence of the associative architecture of the brain in general, and of the automatic system in particular.
³²

We have discussed two principles about how the brain files information about the world. First, knowledge is stored as connections between nodes (groups of neurons) that represent related concepts. Second, once a node is activated its activity “spreads” to those it connects to, increasing the likelihood they will be activated. So asking someone if she likes sushi before asking her to name a country increases the likelihood she will think of Japan. Once the “sushi” node has been activated it boosts activity in the “Japan” node. We also saw that merely exposing people to certain words can influence their behavior. People who completed word puzzles with a high proportion of “polite” words waited longer before interrupting an ongoing phone conversation than those completing puzzles with “rude” words. Somehow the words representing the concepts of “patience” or “rudeness” weaseled their way past our semantic networks and into the areas of the brain which actually control how polite or rude we are (behavioral priming). In another study people were asked to think of words related to being angry (that is, words that might be associated with being “hotheaded”), which resulted in higher guesstimates of the temperatures of foreign cities.
³³

To understand the relationship between behavioral priming and framing let’s consider a hypothetical framing experiment in which we give people $50, and then offer them two possible options:

You of course will pick option B, but for argument’s sake, let’s suppose the automatic system is tempted to blurt out “let’s keep the 49 percent” until the reflective system steps in and vetoes option A. Within our semantic networks the word
keep
has developed associations with related concepts (
save
,
hold
,
have
), which by and large can be said to be emotionally positive—a “good thing.” In contrast, the word
lose
is generally linked with concepts (
gone
,
defeat
,
fail
) that would be related to negative emotions—a “bad thing.” The connections from the neurons that represent the “keep” and “loss” nodes in our semantic networks must directly or indirectly extend past our semantic network circuits to the brain centers responsible for controlling our emotions and behavior. Because option A has the word
keep
in it, it will tickle the circuits responsible for “good things”; the net result is that our automatic system will be nudged toward option A.

Studies have demonstrated the connection between the semantic networks and the circuits responsible for emotions and actions by flashing a word that carries positive or negative connotation on a computer screen for a mere 17 milliseconds—too quick to be consciously registered. A second later, they showed the volunteers a painting and asked them to rate how much they liked it. Paintings preceded by positive words (
great
,
vital
,
lively
) were rated higher than the paintings preceded by negative words (
brutal
,
cruel
,
angry
).
³⁴
Again, the internal representations of words are contaminating the computations taking place in other parts of the brain.

Let’s take a closer look at the anchoring bias. You may have noted that the informal experiment in which thinking of Brad Pitt’s age resulted in lowballing Joe Biden’s age is similar to a priming study, except that it is a number that primes the numbers closer to it. Some instances of anchoring may be a form of numerical priming.
³⁵
The notion is that just as thinking of “sushi” might bias the likelihood of thinking of “Japan,” thinking of “45” makes it more likely to think of “60” than “70” when estimating Joe Biden’s age.

As we have seen, studies have shown that some neurons respond selectively to pictures of Jennifer Aniston or Bill Clinton, and we can think of these neurons as members of the “Jennifer Aniston” and “Bill Clinton” nodes. But how are numbers represented in the brain? Scientists have also recorded from neurons that respond selectively to numbers or, more accurately, to quantities (the number of items in a display). Surprisingly, these experiments were performed in monkeys. The neuroscientists Andreas Nieder and Earl Miller trained monkeys to look at a display with a certain number of dots in it, ranging from 1 to 30. One second later the monkeys viewed another picture with either the same or a different number of dots,
³⁶
and the monkeys were getting paid (in the form of juice) to decide if the number of dots was the same or different between the first and second images. They held a lever in their hands, and they had to release it if the numbers in both displays were a match, and continue to hold the lever if the quantities were different. With a lot of training the monkeys managed to perform the task fairly accurately. As represented in Figure 6.3 when a display with eight dots was followed by one with four dots they judged this to be a match only 10 percent of the time; whereas when an eight-item display was followed by another with eight items, the monkeys judged that as a match 90 percent of the time. No one is suggesting that the monkeys are counting the number of dots (the images were only shown for a half second); rather they are performing a numerical approximation (automatically estimating the number of items without actually counting them). When the experimenters recorded from individual neurons in the prefrontal cortex they found that some neurons were “tuned” to the number of items in the display. For example, one neuron might respond strongly when the monkey was viewing a display with four items, but significantly less when there were one or five items in the display (Figure 6.3). In general the tuning curves were fairly “broad,” meaning that a neuron that responds maximally to 8 items would also spike in response to 12 items, and conversely a neuron that responded maximally to 12 items would also respond to 8 items, albeit less vigorously. Therefore, the numbers 8 and 12 would be represented by a different but overlapping population of neurons, much in the same way that the written numbers 32,768 and 32,704 share some of the same digits.

In numerical priming the activity produced by one number “spreads” to others. We have seen in Chapter 1 that we are not sure what this spread of activity corresponds to in terms of neurons. One hypothesis is that it is a fading echo, the decaying activity levels of a neuron after the stimulus has disappeared. A not-mutually-exclusive hypothesis is that priming may occur as a result of the overlap in the representation of related concepts. Here, it is not that activity from the neurons representing “sushi” spreads to those representing “Japan,” but that some of the neurons participate in both representations, in the same manner that in the monkey experiments the same neurons participate in the representation of 8 and of 12. Let’s say you are illegally forging the numbers in a document; substituting the number 9990 for 9900 is much easier than for 10207 because there is more digit overlap between 9990 and 9900. Similarly, in the anchoring bias, numbers may prime similar numbers because of the overlap in the neural code used to represent them. Many of the neurons representing the number 45 will also participate in the representation of 60 and 66, but the overlap between 45 and 60 will be more than 45 and 66. Assuming that recently activated neurons are more likely to be reactivated again we can see that if the “unbiased” estimate of Biden’s age was 66, this value would be “pulled down” by increased activity in the neurons that were activated by 45 when subjects were first asked Pitt’s age.

Figure 6.3 How neurons represent numbers: (
Upper panel
) Monkeys can be trained to discriminate the number of items shown on a computer monitor (displays with one, four, and five items are shown). Recordings in the prefrontal cortex during the task determined that some neurons are tuned to the number of items. The lines show the number of spikes (spike frequency) in response to each of the three displays. Shaded areas mark the time window in which the stimuli were presented. Note that this neuron was “tuned” to the value 4 because it fired more in response to four items than to one or five. (
Lower panel
) The brain may encode numerical quantities in a population code: different neurons vary in levels of activity in response to specific numbers. Here the grayscale level represents the number of spikes in response to the number 3 or 7. (Adapted with permission from Nieder, 2005.)

Priming, framing, and anchoring may all be interrelated psychological phenomena attributable to the same neural mechanisms: the spread of activity between groups of neurons representing associated concepts, emotions, and actions. As we have seen, priming implements a form of context sensitivity. It is not only our decisions and behavior that are dependent on context; not surprisingly, context-dependency is also observed at the level of individual neurons. In sensory areas of the brain, including the auditory and visual cortices, neurons will often fire action potentials in response to a specific “preferred” stimulus, such as a particular syllable or oriented line. The response of many neurons is modulated by the context in which that preferred stimulus is presented; the context encompasses both the stimuli that preceded it as well as other stimuli presented simultaneously. For example, in the auditory system of songbirds some neurons will only fire to a specific syllable of their song, which we’ll call syllable B, if it is preceded by syllable A. Neurons in the visual cortex of mammals typically respond to lines of a particular orientation in a specific part of the visual field. The orientation tuning of these cells can also be sensitive to context. For example, when a single line is presented in the exact center of your field of vision on an otherwise empty screen, by definition a “vertical” neuron will fire more to a vertical line than a “forward slash” line; however, this same neuron might fire more to the forward slash in the context of an entire screen filled with “backward slashes.”
³⁷

Context-sensitivity at the neural level is ultimately responsible for our ability to use context to quickly make sense out of the barrage of information impinging on our sensory organs. But our exquisite context-sensitivity will inevitably encourage us to favor the option in which one-third of the people live over one in which two-thirds of the people die, because life provides a more welcoming context than death.

The decisions that shape our lives are in part the product of two highly complementary neural systems. The automatic one is rapid and unconscious, and relies to a large extent on the associative architecture of the brain. This system is the more emotional one; it attends to whether things sound good or bad, fair or unfair, reasonable or risky.
³⁸
The second one, the reflective system, is conscious, effortful and is at its best when it has benefited from years of education and practice.

The automatic system can learn to reevaluate established assumptions, but it often requires the tutelage of the reflective system. When we were children we automatically assumed that there was more milk in the tall skinny glass than in the short wide glass. Part of normal cognitive development involves correcting numerous misconceptions of the automatic system, but some bugs remain.