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ECONOMY OF THE MIND
http://biology.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pbio.0000077
http://biology.plosjournals.org/archive/1545-7885/1/3/figure/10.1371_journal.pbio.0000077.g001-M.jpg

BY Kendall Powell

Frans de Waal’s laboratory monkeys won’t work for unequal pay. If a
partner monkey gets a grape (big bucks) for little or no work (trading
a token), a monkey will reject her measly cucumber pay from her human
“boss.” And she makes her disdain known, hurling her cucumber or
token out of her cubicle-even though she would happily gobble down
cucumbers in other circumstances.

De Waal’s work at the Yerkes Primate Center at Emory University in
Atlanta has shown an aversion to inequality in non-human primates
(Figure 1), drawing an evolutionary link between how humans and monkeys
make decisions. Humans reject inequality, too, even if it means walking
away empty-handed. This behavior cannot be explained by classical
economic theory that says both monkeys and humans should take whatever
reward they are offered to maximize gain. But in species like de Waal’s
monkeys and humans that rely heavily on cooperation for survival,
evolution has favored a complex calculus for even simple decisions.

In a simplified way, de Waal’s experiments and others blend
neurobiologists’ ability to track behavior and brain processes with
economists’ models of the cost-benefit analyses behind every decision
made by an animal. The two fields have each been working toward
explaining decision-making behavior, using widely different approaches
for decades. Recently, researchers in both fields have recognized that
using tools from the other trade might speed their own work along,
resulting in the emerging field of neuroeconomics.
Teaming Up

The principle of Expected Utility says that a person facing uncertainty
will rank the possible payoffs or outcomes as a function of their
expected values and probabilities of happening. Using this principle,
experimental economists tested the idea that humans should interact
with a self-interest that gives the highest possible gain. In the
Ultimatum game, one person is given a sum of money and must decide how
much of that sum to share with a second person. The second person can
then decide to accept or reject the offer, but the catch is that if he
rejects the offer, neither player gets any money.

Although rational-decision theory predicts that the first player should
make a low offer and the second player should accept because it would
maximize how much each player leaves with, the results were
resoundingly irrational. Most first players offered close to half of
the money and most second players rejected sums lower than half.
Economists were stumped when their models fell far short of explaining
human decision-making.

“Standard economic theory uses models where players are calculating
complicated numbers, thinking far ahead to figure out what the other
person will do, and there are no temptations,” explains Colin
Camerer, a behavioral economist at the California Institute of
Technology in Pasadena. Those models tended to be mathematically
simple, but realistically hard on the players, he says. “People
aren’t that smart. An 18-year-old doesn’t plan out his entire lifetime
savings.”

Camerer has teamed up with neurobiologists looking at brain scans of
people while they play games like Ultimatum. The results of such
experiments should reveal new mechanisms at play in the brain during
decisions, like aversion to inequality, that economists can add to
their models to reflect the sophistication of human choices more
accurately.

On the other side of the decision-making fence, neurobiologists in the
last decade had begun to look beyond mapping how the brain processed
sensory input or motor output and began asking questions about what was
happening in between those two systems. Once they turned away from
simple experiments in which a single stimulus elicits a uniform
response, giving meaning to neural activity was no longer easy. For
example, Paul Glimcher and his colleagues at New York University in New
York City gave monkeys a visual cue indicating that a gaze shift either
to the left or right would result in some level of juice reward. All
things being equal, monkeys had no reason to favor one side or the
other. However, when the experimenters increased the amount of juice
reward for one side on random trials, the same visual cue now elicited
a very different pattern of movement, favoring that side. And the
neural activity they recorded appeared to reflect the monkeys’ sense of
how they could get the most reward, rather than any clear association
with sensation or action.

To account for these results, Glimcher and others turned to economic
models of decision-making that took into account the probability of a
reward, the size or value of the reward, and the cost of work to get
the reward. These variables, the neuroscientists hypothesized, might
lie between environmental stimulus and action and be the link between
sensory neurons and motor neurons in the brain.

“We know that to make efficient decisions, you have to know the
utility of the decision,” says Glimcher. “Economists had beautiful
computational models to describe all of these processes” used to
calculate utility. Now, he says, the next step is to look for these
variables in the brain at a cellular level.

Cell Decisions

One laboratory has shown that neurons can indeed “code” for some of
the variables weighed during simple decisions or choices. Wolfram
Schultz and his colleagues at Cambridge University in the United
Kingdom studied dopamine-releasing neurons in the ventral midbrain of
monkeys. Dopamine neurons have long been implicated in reward-seeking
behavior and are targets of highly addictive drugs like nicotine and
cocaine.

In their experiment, monkeys responded to five distinct visual stimuli
that matched the probability of the juice reward. Each cue represented
either a probability of 0 (no reward), 0.25, 0.50, 0.75, or 1 (certain
reward). Because uncertainty and probability are inherently
linked-that is, uncertainty is highest at a probability of 0.50 and
lowest at 0 and 1-the researchers could then look for neuronal
responses to both variables.

What they found were two distinct ways in which the same dopamine
neurons code for probability and uncertainty. With decreasing
probability of reward, the monkey’s dopamine neurons fired stronger
bursts at the time of the reward delivery. At the same time, with
greater uncertainty of reward, a sustained increase in activity
occurred between the flashing of the visual cue and the reward
delivery. In other words, this intervening activity was not seen when
the probability equaled 0 or 1 and was greatest when the probability of
getting a reward was 50/50, the highest level of uncertainty.

“To make decisions about rewards or money, a person has to make
predictions about the future, and in any prediction there is some
uncertainty that is critical,” says Christopher Fiorillo, a
neurophysiologist in Schultz’ lab who led the study. “This is the
first demonstration of a single neuron coding uncertainty.” Fiorillo
says there are probably many other types of neurons in the brain that
can code uncertainty, but the fact that dopamine neurons do it adds
another intriguing layer to decision-making behavior.

Dopamine neurons, he explains, have been shown to have a reinforcing
effect, so that an animal will seek out stimuli or actions that are
followed by a release of dopamine. So, Fiorillo says, he was surprised
to see the activity of dopamine neurons increased by uncertainty about
a reward, as if uncertainty itself were rewarding in some way. The
finding might help explain why people are drawn to gambling even though
they tend to lose money on average. Fiorillo and his colleagues
speculate that outside the artificial conditions of a laboratory or a
casino, an uncertain situation presents a learning opportunity that may
help the decision-makers “beat the odds” the next time they face
it. And so evolution would favor paying attention to highly uncertain
scenarios.

In another experiment, Glimcher and colleagues had monkeys play a
“game” in which there were two ways of getting juice, with each
choice having a different probability of reward. After 100 trials, the
probabilities were changed.

“Basically, he’s playing a two-armed slot machine whose payoff rates
are constantly switched on him,” says Glimcher. “His behavior looks
pretty erratic, but he’s getting it about right. He spends two-thirds
more time on the one that is two-thirds more likely to give him
juice.” And, he says, an economic model of this choice predicts the
monkey’s behavior with 90% accuracy. This might indicate that,
subconsciously, humans tally probabilities, expected gain, and the cost
of the work to get the reward in all manner of simple choice decisions.
At the level of neurons, we might all be math-whizzes.

But game-theory work has shown that humans rarely think ahead in
complex interactions far enough to arrive at the most rewarding
decision. Glimcher, by applying the principles of the utility decision
theory of maximal gain, has found neurons that may code the variables
that go into such a decision. But, others say, since humans do not
always act in a way that maximizes their gain, other computational
models may give a better answer to how we make decisions in more
complex contexts.

“The difference is a question of perspective, since we’re really all
interested in the same issues,” says Joshua Gold, a neuroscientist at
University of Pennsylvania in Philadelphia. He and his collaborators
have used a mathematical framework called Banburismus to model
decision-making in monkeys performing a difficult visual task.

British codebreakers used Banburismus in World War II to break the
German navy’s Enigma code (Figure 2). It consists of three components:
a method to quantify the weight of evidence, a method to update this
quantity with additional evidence, and a decision rule that determines
when there is enough evidence to make a decision. Gold and his
colleagues apply the framework to monkeys watching a cluster of dots
moving across a screen of randomly moving dots. The monkeys earn juice
by shifting their gaze in the same direction as the dot cluster. By
increasing the number of randomly moving background dots, they can push
the monkeys’ visual system to its limits.

Alan Turing and colleagues at Bletchley Park broke the German Navy’s
unbreakable Enigma code with the help of a mathematical framework they
called “Banburismus.” Some neuroscientists think this framework
could also help to break the neural code. (Image modified from the
National Cryptologic Museum of the National Security Agency;
http://www.nsa.gov/museum/enigma.html.)

It is at this point, Gold says, that other factors besides visual cues
come into play as the monkey decides how to answer. “By getting the
monkey to work in a regime where he’s coming close to guessing, then we
see much more influence by extraneous factors such as bias [i.e., the
monkey’s previous experiences] and size of reward,” says Gold. The
computational model gives a way to represent these factors
mathematically and can also predict the error rates and reaction times
of the monkeys’ decisions.

“If it really does explain behavior mathematically,” adds Gold,
“it will be a nice way of studying how those variables predict
behavior.” These models, whether based in economic decision theory or
statistics like Banburismus, give physiologists good candidates in
their search for decision-making functions in neuronal circuits.
Neurobiologists chasing the perfect model that can incorporate all the
factors that go into a decision say it will show how humans calculate
the mental “currency” that allows us to literally compare apples to
oranges and decide which to buy.

Some biologists, however, are cautious about translating what happens
in an economics-based neurobiology experiment in the lab to more
complicated human behavior in business or courtroom decisions. “We
don’t want to say things that are wrong, incomplete, or could be
miscontrued,” says Jeff Schall, a vision researcher at Vanderbilt
University in Nashville, Tennessee. “And we don’t need to, to make
scientific progress.”

His lab has found two sets of neurons in the anterior cingulate cortex
that respond when a monkey shifts his gaze to a target-those that
signal success and those that signal mistakes. He has also recorded
similar signals from electrodes placed on humans performing the same
task. These signals can be thought of as the “oops” or
“high-five” feeling that tells an animal how to proceed in the next
trial. Both humans and monkeys slow down on the next trial after an
“oops” signal. Schall says his work shows another aspect of
decision-making, adaptation, not accounted for in classical economic
views of reward influences.

“Neuroeconomics is just part of the bigger picture of goal-directed
action,” he says. Others say economic theories lack another critical
component-how to calculate how much value the reward has to the
decision-maker.

“There’s a lot of emphasis on game theory and it’s very exciting, but
there’s one flaw that everyone recognizes,” says Barry Richmond, a
systems neuroscientist at the National Institute of Mental Health in
Bethesda, Maryland. “How do you measure value at any given moment
when it is changing both because of personal situation and because of
external things?” Richmond sees that monkeys, like humans, exhibit
different levels of aversiveness to work.

Richmond’s monkeys have been trained to learn a visual cue, a
brightness bar, that indicates how much work is left before getting the
reward. As the reward gets closer, its “value” appears to go up,
because the monkeys work harder (by making fewer errors) in the last
trials before the reward. Obviously, value pivots on the timing of the
reward, among many other considerations.

It is easy to see a little bit of ourselves in this monkey business.
Students study furiously the night before an exam to be rewarded with a
grade. As a project deadline looms, employees put in longer hours in
order to keep their job. But some experimentalists have gone even
further in making connections between neurons firing in a monkey’s
brain and what’s going on in ours.
Let Me Pick Your Brain

Kevin McCabe, a neuroeconomist at George Mason University in Fairfax,
Virginia, was among the first social scientists to set up a
neurobiology experiment to answer his questions about how humans make
decisions. His early work showed that if you changed the Ultimatum Game
into the Dictator Game, where the first person simply dictated how much
the second person got, then humans still gave a fairly large sum away,
about one-third of the total. Only when the experimenter and the second
person could not see the decision of the dictator did the dictator
begin acting in the rational, self-serving manner of giving away tiny
amounts. Only in the socially isolated context did the dictator follow
economic principles.

“We wanted to design an imaging experiment to demonstrate that when
people reciprocate, brain processing is different than when they are
not cooperating,” says McCabe. The subsequent experiment, where
people played the Ultimatum Game inside a scanner that takes a
functional magnetic resonance image (fMRI), showed that blood flow and,
by proxy, neuronal activation increased in the frontal brain areas of
cooperators. These areas included human homologs of the lateral
intraparietal area that Glimcher had seen activated by reward size and
probability and the anterior cingulate cortex that Schall found to send
success or failure messages (Figure 3).

The ventral midbrain is active when humans receive an unpredictable
juice reward. Monetary rewards, although defined by cultural agreement,
also engage the same subcortical reward processing structures. (fMRI
image courtesy of P. Read Montague.)

McCabe’s experiment hints that humans are wired to cooperate. “We’re
biologically endowed to engage in personal exchange,” he says. “And
what makes economies run so well is not personal exchange per se, but
our ability to trade with people we don’t even know-to buy food at
the grocery store from a farmer we’ve never met.”

Another group, led by Read Montague, director of the Human Neuroimaging
Laboratory at Baylor College of Medicine in Houston, Texas, has also
looked at brains of cooperators in the Trust Game. Here, an investor
decides to trust a trustee with some of her money. The investment is
increased by the experimenter and then the trustee decides how much to
give back to the investor. This game is played out ten times by two
people who meet each other at the beginning and whose brains are
scanned simultaneously as they play.

The researchers wanted to see what happens in each player’s brain when
the trustee’s decision is revealed to both on a computer screen. “The
trustee’s brain shows the visual cortical activity only of seeing the
screen,” Montague explains. “But the investor’s brain goes haywire,
with both emotional and cognitive reactions to what they see.”
Presumably, the activity represents the investor trying to assimilate
the information into her decision of how much to invest in the next
round.

Montague, a physicist by training, says he’s found a home in the
computational nature of neuroeconomics, which adds a “fresh look at a
bunch of problems that were previously only at the margins of
behavioral psychology.” But he also sees the advantages that the
field brings to economists by shoring up their models with physical
evidence: “Let’s face it, they don’t have good models now or they
could tell you what’s going to happen [in the stock market] tomorrow.
This is starting to give economists a way to loop back into
experiments-they realized they’ve got to crack the head open.”

Montague’s collaborator Camerer agrees that knowing how individual
humans make decisions could certainly improve our understanding of
larger markets. After all, global trade institutions are still run by
individuals who draw on their own ability to trade and make decisions.
Unraveling the decision-making code would open windows on economic
questions ranging from the global (Why do certain countries enjoy
economic growth?) to the very personal (What causes compulsive behavior
when reward systems go bad?).

Camerer sees neuroeconomics as trying to “make a one-to-one mapping
from economic theory to the brain. We have a head start, but it’s very
difficult to produce clear neuroscience that also has economic
significance.” In just a few decades, he envisions that economic
theory may look very different, perhaps throwing out utility altogether
and instead having a system of mechanisms found in the brain that
interact to help a shopper decide, “What’s for dinner?”

And the knowledge coming out of the fledgling field-how the brain
codes motivation and reward value-could be used to increase work
output, promote more effective addictive drug rehab programs, and
stabilize economies. Camerer adds, “This work can really go from
synapses seen in brain imaging to explaining the most important thing
in the world-why is Africa poor and Singapore rich?”

Kendall Powell is a freelance science writer, living in Broomfield,
Colorado, United States of America. E-mail: kendall2 [at] nasw [dot] org

Published: December 22, 2003

DOI: 10.1371/journal.pbio.0000077

Copyright: © 2003 Kendall Powell. This is an open-access article
distributed under the terms of the Public Library of Science
Open-Access License, which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly
cited.

Citation: Powell K (2003) Economy of the Mind. PLoS Biol 1(3): e77

Further Reading

1. Brosnan S, de Waal F (2003) Monkeys reject unequal pay. Nature
425: 297-299. Find this article online
2. Fiorillo CD, Tobler PN, Schultz W (2003) Discrete coding of
reward probability and uncertainty by dopamine neurons. Science 299:
1898-1902. Find this article online
3. Gold JI, Shadlen MN (2002) Banburismus and the brain: Decoding
the relationship between sensory stimuli, decisions, and reward. Neuron
36: 299-308. Find this article online
4. Liu Z, Murray EA, Richmond BJ (2000) Learning motivational
significance of visual cues for reward schedules requires rhinal
cortex. Nat Neurosci 3: 1307-1315. Find this article online
5. McCabe K, Houser D, Ryan L, Smith V, Trouard T (2001) A
functional imaging study of cooperation in two-person reciprocal
exchange. Proc Natl Acad Sci U S A 98: 11832-11835. Find this article
online
6. Platt ML, Glimcher PW (1999) Neural correlates of decision
variables in parietal cortex. Nature 400: 233-238. Find this article
online
7. Sanfey AG, Rilling JK, Aronson JA, Nystrom LE, Cohen JD (2003)
The neural basis of economic decision-making in the Ultimatum Game.
Science 300: 1755-1758. Find this article online