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As instructed, No. 7 (her subject number) came dressed in T-shirt and sweats—nothing metallic—since the principal component of any MRI scanner is a giant doughnut-shaped electromagnet, and within its field, even the smallest shards of metal become deadly projectiles. In fact, just to be eligible for MRI experiments, which pay a minimum of $20 per hour, subjects must undergo scanning for metal implants, a battery of medical history questions, and even a pregnancy test.
The MRI scanning room in the Brain Imaging Analysis Center is reached via a pair of secure-access double doors. A spacious waiting area-cum-control room, its walls are lined with desks laden with computers; a walk-through metal detector fronts the entrance to the scanner room proper.
Before No. 7 enters the scanner, Venkatraman gives her the standard orientation spiel: The experiment involves a series of lotteries, each with a mixture of prizes (both positive and negative) at varying probabilities. After an initial stage in which she is required only to think about the gamble, No. 7 will subsequently be offered a pair of modifications—something like an enlargement of the largest prize or a positive result instead of a no-money outcome—and will have to choose which one she prefers. The results of a subset of these gambles will be played out, and the results added to (or subtracted from) the base pay for the study, which represents her endowment.
As the study begins, Venkatraman and I watch together as the gambles and No. 7's subsequent choices appear onscreen. Most people, when presented with the option of modifying a gamble, prefer a guarantee of some gain to a larger potential prize, and an increased potential of breaking even to a smaller worst-case loss (though both depend on the amounts and probabilities involved). Since any decision involving risk implies at least some weighing of gains and losses, and since the brain appears to possess multiple (and interdependent) systems for evaluating each, Venkatraman and collaborators are hoping to detect in the fMRI signal interaction between these several reward systems. One of the systems, for instance, may be responsible for calculating what is known as expected value, the average reward earned per gamble; another, responsible for loss-aversion, may prefer a guaranteed return.
Right now it's not even completely understood how many such systems there are in the brain, or whether such a partition is even sensible. For the moment, the focus is on narrowing down the list of the key players, trying to understand which regions are most important for our willingness to take risks.
As Huettel puts it, "The basic reward systems of the brain are pretty much co-opted learning systems. They can become pathological in cases of addiction, in cases of gambling. There may be components that are helping us evaluate probability—how likely I am to be successful at a given action; other components that may be pushing us away from options that have negative consequences. I think what we have to recognize is that we evolved for very different environments than we are in now. In our evolution, we never had to deal with something like winning a million dollars. There was never a situation where one had to make decisions about quantities that large."
At close range, the eye of the rhesus monkey, macaca mulatta, can appear almost unnervingly human—wide with amazement, pupil darting back and forth—an illusion broken only by the long strands of fur drooping downward from its brows. That image, courtesy of a laser eye-tracking system mounted above the monkey's head, fills the screen of a computer monitor in a neural recording room in the Duke vivarium, part of the laser-and-mirror assembly used to track the millisecond shifting of the animal's eyes.
The monkey does not appear to notice. Staring through the transparent glass of his eyepiece, attending to the glow of a giant computer monitor, he appears unaware that at this same moment, in the room next door, his own brain is a subject of intense study, its electrical activity traced out across a computer monitor.
These monkeys, and these rooms, are part of the lab of neurobiologist Michael Platt, Huettel's co-director at CNS and president of the Society for Neuroeconomics. His 1999 Nature paper, which showed that neurons in an area of monkey brains known as LIP encoded the expected value of risky choices, became one of the founding documents of the field.
"What we think of as neuroeconomics is still being defined," Platt says. "Initially, neuroeconomics was defined by the people who were associated with the branding of the discipline, the branding of the society, twelve or fifteen people who met on Martha's Vineyard. This year, the abstracts are much broader in what they cover, in the methods they employ. I see neuroeconomics as just a way of getting a handle on the information that's used during decision-making."
For a biologist like Platt, the allure of neuroeconomics stems from a broader interest in the underlying mechanisms that allow our brains to choose. And the juncture at which those neurobiological questions take over from techniques like fMRI is at the level of the neurons themselves, the hundred billion or so cells bundled together to form the human brain. And while techniques for studying the activity of individual nerves have been around for nearly a century, such experiments generally require direct access to brain tissue, narrowing the pool of potential human subjects.
Most of the time, researchers must make do with so-called "homologues" of the human brain, close cousins like those of rhesus macaques. "The kinds of techniques that we're comfortable using in animals we would not be comfortable using in people," Platt explains. "We're not comfortable sticking electrodes in people's heads. But the neuroimaging methods we have now are not up to giving us the temporal and spatial resolution at the level of a single neuron.
"So that's the number-one reason we use animals. And we can actually [access] the fundamental units of information-processing in the brain while these animals are performing tasks that are similar, if not identical, to the ones we use in humans. We can tap into some of the basic principles underlying decision-making."
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