Welcome to Your Brain (26 page)

subtle and can only be detected by comparing averages for groups (see
Chapter 25)
. In contrast,

sexual behavior areas of the brain show large enough differences that you can tell whether any

particular brain is male or female just by looking at these regions.

These sex differences begin to develop before birth. First a gene on the male-specific Y

chromosome directs the production of a factor that induces formation of testicles in male fetuses. The

testicles then release testosterone to promote masculinization of the brain and the sex organs, and

other hormones to suppress the development of female sex organs. Curiously, female sexual

development doesn’t require any hormones at this stage, which has led scientists to speculate that

female may be the “default” sex.

Aside from a couple of exceptions, hormones act on the brain in two stages. Around the time of

birth, hormones organize the brain by controlling the development of regions that will be important

for sexual behavior. These behaviors are not expressed, though, until they are activated by male or

female hormones after puberty. Both stages must be successful for normal sexual behavior to occur.

Sexual behavior is controlled by an area of the brain called the hypothalamus, which is also

important for other basic functions like eating, drinking, and body temperature regulation. In rats,

damage to a part of the hypothalamus called the preoptic area prevents male sexual behavior entirely.

Several areas of the hypothalamus of rodents show sex differences in their size, with some regions

larger in males and others larger in females. For most regions, these size differences are created by

hormones during a sensitive period in early life; if hormones are not available when they are needed,

these areas will not develop their sex-specific anatomy. However, sex hormones affect the sex-

specific anatomy of some regions in adulthood as well, notably a nucleus in the amygdala that is

important for male sexual arousal and some regions with AVP receptors that are important for pair-

bonding.

As with pair-bonding, we have more detailed information about these pathways in rodents, but

there’s some reason to believe that the basic system is similar in humans. One reliable sex difference

has been found in the human hypothalamus, in an area called the third interstitial nucleus, which is

more than twice as large in men as in women. Activation of sexual behavior in adulthood seems to

depend on testosterone, the hormone associated with libido in both men and women. Human sexual

behavior also depends on a variety of social interactions that are more complex than those of other

animals, of course. However, you might be surprised to learn that anthropologists find behavior

patterns during flirtation to be very similar across a variety of cultures, suggesting that they too might

be strongly influenced by biology rather than cultural experience.

As we’ve shown, science can explain a lot about love and sex, but certainly not everything. That’s

fine with us. We’re happy to live with a bit of mystery.

Part Five

your rational brain

One Lump or Two: How You Make Decisions

Intelligence (and the Lack of It)

Vacation Snapshots: Memory

Rationality Without Reason: Autism

A Brief Detour to Mars and Venus: Cognitive Gender Differences

Chapter 21

One Lump or Two: How You Make Decisions

The physicist Richard Feynman was miles ahead of his peers in many ways: he had unmatched

intuitions about physical law, he was a lightning-fast calculator—and in his spare time he was a

brilliant practical joker. But he had trouble making big decisions, especially when they needed to be

settled quickly. He once wrote, “I never can decide anything very important in any length of time at

all.”

When Feynman joined the Manhattan Project, he encountered a new, critical challenge. Many of

the usual rules of academic life—waiting to publish until everything is perfect, proving theorems

rigorously—had to fall by the wayside. The crash program to beat the Nazis in the race to build an

atomic bomb forced academic physicists to abandon their usual stately pace of progress.

During this period, Feynman was very impressed with a colonel who had to decide whether to

allow Feynman to provide a classified briefing to a team at Oak Ridge. The colonel was able to

identify the need for a rapid decision—and then make the decision—in five minutes. Once Feynman

was cleared to go, he then showed his own particular strength: he told the assembled staff how a

nuclear chain reaction works.

Although the conditions of wartime were extreme, decisions are almost always constrained in

some way. You rarely have the luxury of all the time or information you want before you make a

decision. To take a mundane example, you usually don’t know in advance what route will get you to

work through morning rush hour most quickly, but you have to pick one or you’ll never get there.

Until the last few years, neuroscientists had not studied decision making. Their focus had been on

processes more directly related to input (how sensory information is encoded) or output (how actions

are encoded). Recently, though, researchers have begun to understand a rudimentary act that comes

between input and output: deciding when and where to turn your eyes. This extremely stripped-down

version of decision making captures the feature of trading off accuracy against speed.

In such an experiment, a monkey sits in a chair looking at patterns of dots moving around on a

screen in front of him. He knows that if he can guess which way most of the dots are moving, the

experimenter will give him some juice—orange, his favorite. He peers at the dots, some of which are

moving left, some right. It’s a confusing mess at first, but he looks a moment longer, then presses a

button. Mmmm, juice.

Meanwhile, a researcher sits in the next room, out of sight of the monkey, near a large bank of

computers. One video monitor displays the movements of the monkey’s eyes, while a loudspeaker

clicks in conjunction with electrical signals from neurons in the animal’s brain, recorded from an

electrode placed in the parietal cortex. The eye movements and neural activity (and juice dispensing,

of course) are recorded for analysis later. What is already apparent, though, is that the neuron on the

loudspeaker is anticipating the eye movements. The clicks, which represent spikes (see
Chapter 3
),

quicken, reach a crescendo just before the animal’s eyes move to the right, and then become quieter.

Eyes to the left—no change, just a steady low level of activity. A decision to the right—lots of spikes.

Over and over again, this neuron’s activity presages a decision to look to the right.

The decision-related signals are found in a brain region called LIP (short for lateral intraparietal

area). In other brain regions that send their output to LIP, the information about the dots is of a more

immediate, sensory nature. LIP seems to integrate the incoming signals to determine which eye

movements are more likely to result in juice, though researchers are still arguing over exactly what

information it calculates. Delivering small electrical stimuli to LIP can influence decisions, biasing

the monkey to look in the wrong direction.

Neural responses in LIP are also affected by manipulations that make the animal more or less

motivated. Responses build up more quickly when the animal is paying attention, expecting more

juice, or intending to make movements. In each case, neurons in LIP and behavior are affected in the

same way. Scientists think that these neurons accumulate evidence of many kinds, and that LIP helps

other parts of the brain to decide whether and where to move the eyes.

The neural activity in LIP even reflects the quality of the incoming information. If the dot patterns

are less organized, activity speeds up more slowly than with dot patterns that are more clear. A

certain level of activity, a “decision threshold,” is then reached sooner, allowing a decision to be

made more quickly. Thus clearer information leads to more certainty, what engineers call a higher

signal-to-noise ratio.

Feynman observed a version of low-noise integration of information when he went to a meeting of

a Manhattan Project committee composed of distinguished scientists, four of whom, including

Feynman himself, would eventually receive the Nobel Prize. He was amazed to find that debates in

this illustrious group could be settled after each member had stated his case exactly once. Anyone

who has been through an average corporate meeting can understand why this efficient decision making

impressed him.

The simple picture from the monkey experiment, that neurons gather information and figure out

when there’s enough evidence to stop and choose, might lend insight into the more sophisticated

decisions that we humans make. Like Feynman’s committee, groups of neurons make decisions by

working together to integrate information. Once a threshold amount of evidence is accumulated, the

decision is made to move the eyes. However, currently there is no way to observe the interplay

among neurons. The nearest anyone has come is to do computer simulations that reproduce what might

be happening. In real life, a principal challenge is to find a way to watch the whole group of

decision-making neurons at the same time.

Outside the laboratory, decision making is a much more complex business. Human decisions can

be about outcomes as large as whether to take a job, or as small as what to have for dinner. In such

situations, our brains are called upon to integrate extremely disparate types of information.

Unfortunately, our brains are not naturally equipped to do a good job at integrating complex

quantitative facts, probably because they evolved primarily to negotiate social situations and survive

natural threats, not to do quantitative puzzles. Classical economic reasoning assumes that individuals

are able to evaluate costs and benefits rationally, but the brain’s methods of estimation are not good at

making such valuations. The payoffs of extremely low-probability events, such as winning the lottery,

do not appear to be represented accurately in the brain. If we don’t have any intuitive idea of what it

means when a probability is below, say, one in one hundred, then the incredible unlikelihood of a

lottery payout is not scored rationally. Even though long-term losses are a virtual certainty, just one

anecdotal story of a big winner remains a motivating factor that is weighted out of all proportion to

any reasonable expectations. (This is not even to mention that a massive financial reward such as

winning the lottery still has only transient effects on happiness, as we explained in
Chapter 18.
)

Practical tip: Maximizers and satisficers

Both of us have difficulty with decisions. We demand the best outcome, whether we’re

deciding where to go on vacation or what to have for lunch. That’s very hard to achieve. As

a result, we’re always in danger of spending forever on a decision. For example, when

shopping for a plane ticket, we look at dozens of choices, trying to get the lowest fare, the

closest airports, the fewest connections … Whoops, that one’s now sold out. Time to try

again. After the decision is made, we waste more time wondering if we were right, which

drives our spouses crazy.

Our decision-making style follows a pattern that can be classified as the maximizer

model. Maximizers spend a lot of time worrying about differences, no matter how small. In

a consumer society with choices everywhere, maximizers suffer from an inability to

recognize when an alternative is good enough. Indeed, from an economic perspective,

spending the additional time on maximization doesn’t make sense since your time itself has

some monetary value.

A second category of decision-making style brings more contentment: satisficing, a term

that refers to the act of choosing an alternative that is just sufficient to satisfy a goal.

Satisficers look until they find something good enough, then stop. Satisficers are decisive,

don’t look back, and have little regret, even about mistakes. As the saying goes, “The

perfect is the enemy of the good.” The quintessential example of a satisficer is the Wall

Street trader who has to make hundreds of decisions every day and doesn’t have the time to