What You See Affects What You Want

Visual attention guides the choices we make.

When it comes to making choices, say Caltech's Antonio Rangel and his colleagues, much depends on which items catch—and keep—your eye.

"We're interested in how the brain makes simple choices, like which item to pick from a buffet table," says Rangel, professor of neuroscience and economics. "Why is it that when we look at the buffet table, our gaze shifts back and forth between the items in order to make a choice? What is the role of visual attention in all this?"

To find out, Rangel—along with Caltech postdoctoral scholar Ian Krajbich and Stanford University's Carrie Armel—designed a mathematical model to describe the impact of what they call "visual fixation" on the making of these sorts of choices. (Simply put, visual fixation is the amount of time you spend gazing in one direction or at one item versus another.) They recently published their research online in the journal Nature Neuroscience.

"The model makes very specific predictions about how the pattern of fixation is related to our choices," Rangel explains. "For example, after controlling for other variables, items that are looked at more should be chosen more often."

But are they? To make sure, the team carried out an eye-tracking experiment in which subjects were shown pairs of food items on a computer screen, allowed to look at the items for as long as they wanted, and then were asked to choose one or the other. While the subjects gazed, the researchers tracked the movements of their eyes.

Just as predicted, the item a subject looked at longest was the one he or she most often picked—nearly three-quarters of the time, in fact.

Interesting stuff on its own merit, right? It also has implications for the business world—putting increased emphasis on things like packaging and in-store displays. "[T]he model explains how cultural norms (for example, reading left to right) can interact with comparator processes to produce cultural choice biases," the scientists write. "These biases help to explain, for example, why shelf and computer screen space on the top-left is more valuable than other positions."

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Lori Oliwenstein
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Consumers Will Pay More for Goods They Can Touch, Caltech Researchers Say

PASADENA, Calif.—We've all heard the predictions: e-commerce is going to be the death of traditional commerce; online shopping spells the end of the neighborhood brick-and-mortar store.

While it's true that online commerce has had an impact on all types of retail stores, it's not time to bring out the wrecking ball quite yet, says a team of researchers from the California Institute of Technology (Caltech).

Their investigations into how subjects assign value to consumer goods—and how those values depend on the way in which those goods are presented—are being published in the September issue of the American Economic Review.

The question they address is at the heart of economics and marketing: Does the form in which an item is presented to consumers affect their willingness to pay for it?

Put more simply, says Antonio Rangel, professor of neuroscience and economics at Caltech, "At a restaurant, does it matter whether they simply list the name of the dessert, show a picture of the dessert, or bring the dessert cart around?"

Most behavioral theories assume that the form of the presentation should not matter, notes Caltech graduate student Benjamin Bushong. "Some models suggest that choices amongst objects shouldn't vary with their descriptions or by the procedure by which the choice is made," he says. "However, our experiments show that the form in which the items are presented matters a lot. In fact, our research measures in monetary terms just how much those different displays matter."

Initially, the Caltech team made these measurements by presenting foods to hungry subjects in three different forms: in a text-only format; in a high-resolution photograph; and in a tray placed in front of the subjects. "Then we measured their willingness to pay for the food," explains Rangel.

As it turned out, there was no difference between the values subjects put on the food depicted in the text and in the picture. But the bids on the food on the tray right in front of the subjects were an average of 50 percent higher than the bids on either of the other two presentations.

"We were quite surprised to find that the text display and the image display led to similar bids," admits Bushong. "Initially, we thought people would bid more in the face of more information or seemingly emotional content. This finding could explain why we don't see more pictorial menus in restaurants—they simply aren't worth the cost!"

While the food experiments' results were intriguing, says Rangel, "We couldn't stop there." After all, the smell of the food might have made it more appealing to the experiment's subjects. And so, to take that variable out of play, the team chose different "goods" to present—a variety of trinkets from the Caltech bookstore—and again measured the effect of display on willingness to pay.

The results were the same as during the food experiments. The subjects were willing to pay, on average, 50 percent more for items they could reach out and touch than for those presented in text or picture form. "We knew then that whatever is driving this effect is a more general response," says Rangel.

But what was driving the effect? The team's initial hypothesis was that the behavior is driven by a classic Pavlovian response. "Behavioral neuroscience suggests that when I put something appetizing in front of you, your brain activates motor programs that lead to your making contact with that item and consuming it," Rangel explains. "We hypothesized that if there's no way for you to touch the item, then the Pavlovian motor response would be absent, and your drive to consume the item thus significantly lessened."

To test this hypothesis, the team put up a plexiglass barrier between the subject and the items up for bid. And, as predicted, once the possibility of physical contact with the item had been extinguished, the value the subjects gave to that item dropped to the same level as the text- and picture-based items.

"Even if you don't touch the item," says Rangel, "the fact that it is physically present seems to be enough. This Pavlovian response is more likely to be deployed when making contact with the stimulus is a possibility."

What does all this mean in the real world? At the very least, it suggests that your local bookstore—where you can reach out and ruffle a paperback's pages—may have more staying power than e-commerce experts might think.

In addition to Rangel and Bushong, the coauthors on American Economic Review paper, "Pavlovian Processes in Consumer Choice: The Physical Presence of a Good Increases Willingness-to-pay," are former Caltech undergraduate student Lindsay King and Colin Camerer, the Robert Kirby Professor of Behavioral Economics. Their work was supported by the Gordon and Betty Moore Foundation.

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Lori Oliwenstein
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Learning Strategies are Associated with Distinct Neural Signatures

PASADENA, Calif.—The process of learning requires the sophisticated ability to constantly update our expectations of future rewards so we may make accurate predictions about those rewards in the face of a changing environment. Although exactly how the brain orchestrates this process remains unclear, a new study by researchers at the California Institute of Technology (Caltech) suggests that a combination of two distinct learning strategies guides our behavior. 

A paper about the work will appear in the May 27 issue of the journal Neuron.

One accepted learning strategy, called model-free learning, relies on trial-and-error comparisons between the reward we expect in a given situation and the reward we actually get. The result of this comparison is the generation of a "reward prediction error," which corresponds to that difference. For example, a reward prediction error might correspond to the difference between the projected monetary return on a financial investment and our real earnings.

In the second mechanism, called model-based learning, the brain generates a cognitive map of the environment that describes the relationship between different situations. "Model-based learning is associated with the generation of a 'state prediction error,' which represents the brain's level of surprise in a new situation given its current estimate of the environment," says Jan Gläscher, a postdoctoral scholar at Caltech and the lead author of the study.

"Think about a situation in which you always take the same route when driving home after work, but on a particular day the usual way is blocked due to construction work," Gläscher says. "A model-free learning system would be helplessly lost; it is only concerned with taking actions that in the past were rewarding, so if those actions are no longer available it wouldn't be able to decide where to go next. But a model-based system would be able to query its cognitive map and figure out an efficient detour using an alternative route."

"Although the simpler model-free learning mechanism has been well studied and its basic learning mechanism—which is driven by reward prediction errors—is relatively well understood, the mechanisms underlying the more sophisticated model-based learning system, with its rich adaptability and flexibility, are less well understood" says John P. O'Doherty, professor of psychology at Caltech and the Thomas N. Mitchell Professor of Cognitive Neuroscience at Trinity College in Dublin, Ireland.

To further characterize the neurological underpinnings of these two learning systems, Gläscher, O'Doherty, and their colleagues designed a computer-based decision-making task that allowed them to measure when and where the brain computes both reward and state prediction error signals, and to determine if the two types of errors actually produce different neural signatures. 

In the task, subjects had to make choices between a left and right movement that allowed them to shift between different "states"—denoted by graphical icons—in a virtual environment; the process is similar to that of navigating around in a simple video game. Each left-or-right choice made in this virtual environment led the subject to a new state. Their objective was to reach a particular goal state to obtain a monetary reward, "and their chances of ending up in that goal state strongly depended on the particular pattern of sequential choices they made," O'Doherty explains.

A model-based system can learn about the structure of the virtual environment and then use this information to compute the actions needed to get to the reward state, in a manner analogous to how a chess player might try to think through the sequential chess moves needed to win a match. A model-free system, on the other hand, would only learn to blindly choose those actions that gave reward in the past, without evaluating the consequences in the current situation.

Eighteen participants were scanned using functional magnetic resonance imaging as they learned the task. The brain scans showed the distinctive, previously characterized neural signature of reward prediction error—generated during model-free learning—in an area in the middle of the brain called the ventral striatum. During model-based learning, however, the neural signature of a state prediction error appeared in two different areas on the surface of the brain in the cerebral cortex: the intraparietal sulcus and the lateral prefrontal cortex. 

These observations suggest that two unique types of error signals are computed in the human brain, occur in different brain regions, and may represent separate computational strategies for guiding behavior. "A model-free system operates very effectively in situations that are highly automated and repetitive—for example, if I regularly take the same route home from work," Gläscher says, "whereas a model-based system, although requiring much greater brain-processing power, is able to adapt flexibly to novel situations, such as needing to find a new route following a roadblock." 

These two distinct learning mechanisms serve complementary roles in controlling human behavior, Gläscher says. "Because the processing power of our brains is limited, it doesn't make sense to deploy the more computationally intensive model-based system for controlling everything we do. Instead, it is better to rely on the model-free system for a lot of our everyday behavior and use the model-based system only for new or complex situations. An important area for further research will be to try to understand the factors governing how these systems interact together in order to control behavior, and to determine how this is implemented in the brain."

The other coauthors on the paper, "States versus rewards: Dissociable neural prediction error signals underlying model-based and model-free reinforcement learning," are Nathaniel Daw of New York University and Peter Dayan of University College London. The work was supported by the Gordon and Betty Moore Foundation, the National Institute of Mental Health, the German Academy of Natural Sciences Leopoldina, and the Gatsby Charitable Foundation.

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Burton H. Klein, 92

Burton H. Klein, professor of economics, emeritus, at the California Institute of Technology (Caltech) passed away on February 12 at a retirement home in Santa Barbara, California. He was 92.

An expert in dynamic economics, Klein was particularly interested in the economics of innovation and how it related to organizational structure.

Born October 16, 1917, in Minneapolis, Minnesota, Klein received both his AB (1940) and his PhD (1948) from Harvard. After stints with the U.S. Strategic Bombing Survey-where he worked with noted economist John Kenneth Galbraith on an overall economic assessment of the German mobilization prior to World War II-he became a staff member of the President's Council for Economic Advisors serving from 1948 to 1952.  Klein's work in Germany led to his 1959 book, Germany's Economic Preparations for War, a widely cited publication detailing that country's economic policies leading into the war.  

Klein joined the RAND Corporation in 1952 and, in 1961, became head of RAND's economics department. He served as special assistant to the U.S. Secretary of Defense from 1963 to 1965, and in 1967 he joined Caltech as professor of economics. He became emeritus in 1983.

Klein also served as a consultant to the Bureau of Budget, the Arms Control and Disarmament Agency, the Brookings Institution, and the Swedish and Israeli governments.

At Caltech, Klein took lessons learned from other disciplines when looking at economic considerations. In his 1977 book, Dynamic Economics, he contrasted a static concept of stability with a dynamic concept. He argued that unpredictable behavior at the micro level leads to smooth progress at the macro level. And in his 1984 book, Prices, Wages, and Business Cycles: A Dynamic Theory, he provided a statistical demonstration of how the quest for micro stability, especially if aided by government, can lead to increasingly larger economic downturns.

"My basic thesis is that firms in industries that lack significant challenges become overcentralized, inflexible, and highly incapable of dealing with all kinds of negative feedback," said Klein when he spoke of his 1984 book. "This means that, instead of dealing with risks, they can transmit these risks to the public at large, especially if they are aided by the federal government."

His later interests were in explaining the particular behaviors necessary to assure the long-run survival of firms.

Klein continued his passion for writing long after his retirement. He was working on a new book at the time of his passing.

His family noted he also maintained his interest in current affairs and politics, even campaigning for Barack Obama at his retirement home.

An avid woodworker, Klein kept up his interest by crafting furniture and clocks. 

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Jon Weiner
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African-American Babies and Boys Least Likely to Be Adopted, Study Shows

Chance of adoption also drops after baby's birth, say economists from Caltech, NYU, and the London School of Economics

PASADENA, Calif.— Parents pursuing adoption within the United States have strong preferences regarding the types of babies they will apply for, tending to choose non-African-American girls, and favoring babies who are close to being born as opposed to those who have already been born or who are early in gestation. These preferences are significant, according to the findings of a team of economists from the California Institute of Technology (Caltech), the London School of Economics, and New York University (NYU), and can be quantified in terms of the amount of money the potential adoptive parents are willing to pay in finalizing their adoption.

While the data are intriguing, the real value of the study, the researchers say, is that it can give policymakers a more rational, evidence-driven base from which to consider the implications of policies and laws, such as those that restrict adoption by single-sex and foreign couples.

The key to these findings, the research team says, was the data set they were able to put together. "These data are unique," says Leeat Yariv, associate professor of economics at Caltech.

What makes them so unusual? Detailed data on adoption generally are difficult to come by. The researchers, however, were able to gather information—from a website run by an adoption intermediary—over a five-year period (between 2004 and 2009). The intermediary works to bring together—to match—potential adoptive parents with birth mothers seeking to relinquish their children for adoption.

Achieving such a match is not an easy task, says Leonardo Felli, professor of economics at the London School of Economics. He notes that adoption in the United States has "been characterized, for years, by two conflicting imbalances: On the one hand, a considerable number of potential adoptive parents are left unmatched. On the other hand, the number of children who are not adopted and end up in the foster-care system is disproportionately high."

Hence the need for adoption facilitators, says Yariv. "The website operates somewhat like an online real estate site," she explains. "We could see the attributes of the children—race, gender, age—and even the finalization costs, or the amount of money the adoptive parent would need to pay to finalize the adoption. In addition, we could see which children the potential adoptive parents applied for."

In other words, the team could see which babies attracted interest from potential adoptive parents, and determine which traits were most likely to lead to a successful adoption. This revealed three main patterns.

First, the researchers found that a non-African-American baby is seven times more likely to "attract the interest and attention of potential adoptive parents than an African-American baby," says Felli. This difference, he adds, is not seen when comparing parents' preferences for Caucasian versus Hispanic babies—a finding that is somewhat surprising, given that the adoptive parents in the sample are all Caucasian.

The second pattern shown was the gender preference. "A girl has a higher—by slightly more than one-third—chance of attracting the attention of potential adoptive parents than a boy," says Felli.

The preference for girls is arguably unexpected. "With biological children, the literature shows that there's a slight but significant preference for boys over girls," says Yariv. "But, in adoption, there's a very strong preference for girls over boys."

These preferences come with what is essentially a price tag, the researchers note. The data showed that parents are willing to pay an average of $16,000 more in finalization costs for a girl as opposed to a boy, says Yariv—and $38,000 more for a non-African-American baby than for an African-American baby.

Mariagiovanna Baccara, assistant professor at NYU's Stern School of Business, says these results are especially interesting because "the same race and gender biases persist across all categories of adoptive parents that we identified." In fact, she says, "the gender bias in favor of girls is somewhat stronger for both gay men and lesbian couples."

The researchers also found that the interest of potential adoptive parents in a particular baby depends on the stage of gestation. "While unborn children become increasingly attractive over the birth mother's pregnancy, probably because the match involves less uncertainty from the adoptive parents' perspective," says Baccara, "we find that the desirability of a child decreases dramatically right after birth." This means, Baccara adds, that “bureaucratic obstacles disrupting an adoption plan at the time of birth are extremely detrimental to the future prospects of the child.”

The economists feel their data should be used to address some of the existing political debates concerning the U.S. domestic adoption process.

The first involves the restrictions some states impose on same-sex and single-parent adoptions; the second involves the 2008 Hague treaty, which placed significant roadblocks in the path of potential parents from other countries who want to adopt children from the United States.

To assess the impact these restrictions have on the successful placement of children for adoption, the researchers turned to the data they had collected.

In one analysis, the researchers dropped all same-sex parents from their data set. When they did this, the number of successful matches dropped too—by 6 percent. "This is a substantial amount," says Felli, "considering that only 18 percent of the birth mothers in our pool allow adoption by same-sex couples in the first place."

Excluding foreign parents from the pool had an even greater effect—producing a 33 percent decline in the number of babies successfully placed with an adoptive family.

Yariv thinks this remarkably steep decline may have to do with the fact that foreign parents have "more flexible" preferences.

"These data suggest that caution should be used in some of these political debates," says Felli. "When asking whether adopted children should find a home with a single-sex or foreign family as opposed to a U.S. heterosexual family, one should account for the considerable chance that the child in question may not be matched with a family at all and will end up in foster care instead."

"And statistically," Yariv adds, "long-term foster care leads to bad outcomes."

To see how such bad outcomes can be avoided the team will look at alternatives to the U.S. adoption system.

"In most European countries," Yariv explains, "there’s a more centralized system, where effectively a judge makes the matches. We'd like to see how much efficiency we'd gain or lose by looking at intermediate levels of this kind of centralization."

The paper, "Gender and Racial Biases: Evidence from Child Adoption," was also coauthored by Allan Collard-Wexler of NYU's Stern Business School.

An abstract of the paper, released by London's Centre for Economic Policy Research, can be found at http://www.cepr.org/pubs/new-dps/dplist.asp?dpno=7647. The full, unpublished, text of the paper can be found at http://www.hss.caltech.edu/~lyariv/Papers/Adoption.pdf.

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Lori Oliwenstein
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Caltech Receives More than $33 Million from American Recovery and Reinvestment Act

Neuroeconomics and the fundamentals of jet noise just some of the many projects supported

PASADENA, Calif.-Research in genomic sciences, astronomy, seismology, and neuroeconomics are some of the many projects being funded at the California Institute of Technology (Caltech) by the American Recovery and Reinvestment Act (ARRA).

As part of the federal government program of stimulating the economy, ARRA is providing approximately $21 billion for research and development. The goal is for the funding to lead to new scientific discoveries and to support jobs.

ARRA provides the funds to federal research agencies such as the National Institutes of Health, the National Science Foundation, and the Department of Energy, which then support proposals submitted by universities and other research institutions from across the country.

Caltech has received 82 awards to date, totaling more than $33 million. Spending from the grants began in the spring of 2009 and thus far has led to the support of 93 jobs at the Institute.

"This funding will help lead to substantive and important work here at Caltech," says Caltech president Jean-Lou Chameau. "We're grateful to have this opportunity to advance research designed to benefit the entire country."

For biologist Paul Sternberg, the Thomas Hunt Morgan Professor of Biology at Caltech and a Howard Hughes Medical Institute investigator, the ARRA funds mean an opportunity to improve upon WormBase, an ongoing multi-institutional effort to make genetic information on the experimental animal C. elegans freely available to the world.

"All biological and biomedical researchers rely on publicly available databases of genetic information," says Sternberg. "But it has been expensive and difficult to extract information from scientific research articles. We have developed some tools to make it less expensive and less tedious to get the job done, for WormBase and many other groups."

Sternberg's ARRA funds-$989,492-will go towards developing a more efficient approach to extracting key facts from published biological-science papers.

Among the other diverse Caltech projects receiving ARRA funds are:

  • a catalog of jellyfish DNA;
  • improving the speed of data collection at Caltech's Center of Excellence in Genomic Science;
  • studies into the fundamentals of particle physics;
  • the California High School Cosmic Ray Observatory (CHICOS) program, which provides high school students access to cosmic ray research;
  • the search for new astronomical objects such as flare stars and gamma-ray bursts, and the means to make those discoveries accessible to the public; and
  • a $1 million upgrade of the Southern California Seismic Network.

Caltech Professor of Mechanical Engineering Tim Colonius received ARRA funds for research into better understanding how noise is created by turbulence in the exhaust of turbofan aircraft engines and what might be done to mitigate it. Jet noise is an environmental problem subject to increasingly severe regulation throughout the world.

"To meet the ambitious noise-reduction goals under discussion, a greatly enhanced understanding of the basic physics is needed," says Colonius. "Very large-scale computer simulations and follow-up analyses will bring us much closer to the goal of discovering the subtle physical mechanisms responsible for the radiation of jet noise and allow us to develop methods for suppressing it."

Colonius received $987,032 in ARRA funds from the National Science Foundation.

Colin Camerer, the Robert Kirby Professor of Behavioral Economics, received his ARRA funds to explore the application of neurotechnologies to solving real-life economic problems.

"Our project, with my Caltech colleague Antonio Rangel, will explore the psychological and neural correlates of value and decision-making and their use in improving the efficiency of social allocations," says Camerer.

Camerer and his colleagues previously found that they could use information obtained through functional magnetic resonance imaging measurements to develop solutions to economic challenges.

Rangel, an associate professor of economics, has a second ARRA-funded project to analyze the neuroeconomics of self-control in dieting populations.

"Funding of this nature is critical to much of the work we do here at Caltech," adds Chameau. "And with ARRA support, dramatic discoveries may be just around the corner."

For a complete list of ARRA projects, visit: http://www.recovery.gov

# # #

About Caltech:

Caltech is recognized for its highly select student body of 900 undergraduates and 1,200 graduate students, and for its outstanding faculty. Since 1923, Caltech faculty and alumni have garnered 32 Nobel Prizes and five Crafoord Prizes.

In addition to its prestigious on-campus research programs, Caltech operates the Jet Propulsion Laboratory (JPL), the W. M. Keck Observatory in Mauna Kea, the Palomar Observatory, and the Laser Interferometer Gravitational-Wave Observatory (LIGO). Caltech is a private university in Pasadena, California. For more information, visit http://www.caltech.edu.

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Caltech Scientists Find First Physiological Evidence of Brain's Response to Inequality

Brain images during money-transfer experiments show "rich" participants prefer to see others get financial windfall

PASADENA, Calif.—The human brain is a big believer in equality—and a team of scientists from the California Institute of Technology (Caltech) and Trinity College in Dublin, Ireland, has become the first to gather the images to prove it.

Specifically, the team found that the reward centers in the human brain respond more strongly when a poor person receives a financial reward than when a rich person does. The surprising thing? This activity pattern holds true even if the brain being looked at is in the rich person's head, rather than the poor person's.

These conclusions, and the functional magnetic resonance imaging (fMRI) studies that led to them, are described in the February 25 issue of the journal Nature.

"This is the latest picture in our gallery of human nature," says Colin Camerer, the Robert Kirby Professor of Behavioral Economics at Caltech and one of the paper's coauthors. "It's an exciting area of research; we now have so many tools with which to study how the brain is reacting."

It's long been known that we humans don't like inequality, especially when it comes to money. Tell two people working the same job that their salaries are different, and there's going to be trouble, notes John O'Doherty, professor of psychology at Caltech, Thomas N. Mitchell Professor of Cognitive Neuroscience at the Trinity College Institute of Neuroscience, and the principal investigator on the Nature paper. 

But what was unknown was just how hardwired that dislike really is. "In this study, we're starting to get an idea of where this inequality aversion comes from," he says. "It's not just the application of a social rule or convention; there's really something about the basic processing of rewards in the brain that reflects these considerations."

The brain processes "rewards"—things like food, money, and even pleasant music, which create positive responses in the body—in areas such as the ventromedial prefrontal cortex (VMPFC) and ventral striatum.

In a series of experiments, former Caltech postdoctoral scholar Elizabeth Tricomi (now an assistant professor of psychology at Rutgers University)—along with O'Doherty, Camerer, and Antonio Rangel, associate professor of economics at Caltech—watched how the VMPFC and ventral striatum reacted in 40 volunteers who were presented with a series of potential money-transfer scenarios while lying in an fMRI machine.

For instance, a participant might be told that he could be given $50 while another person could be given $20; in a second scenario, the student might have a potential gain of only $5 and the other person, $50. The fMRI images allowed the researchers to see how each volunteer's brain responded to each proposed money allocation.

But there was a twist. Before the imaging began, each participant in a pair was randomly assigned to one of two conditions: One participant was given what the researchers called "a large monetary endowment" ($50) at the beginning of the experiment; the other participant started from scratch, with no money in his or her pocket.

As it turned out, the way the volunteers—or, to be more precise, the reward centers in the volunteers' brains—reacted to the various scenarios depended strongly upon whether they started the experiment with a financial advantage over their peers.

"People who started out poor had a stronger brain reaction to things that gave them money, and essentially no reaction to money going to another person," Camerer says. "By itself, that wasn't too surprising."

What was surprising was the other side of the coin. "In the experiment, people who started out rich had a stronger reaction to other people getting money than to themselves getting money," Camerer explains. "In other words, their brains liked it when others got money more than they liked it when they themselves got money."

"We now know that these areas are not just self-interested," adds O'Doherty. "They don't exclusively respond to the rewards that one gets as an individual, but also respond to the prospect of other individuals obtaining a reward."

What was especially interesting about the finding, he says, is that the brain responds "very differently to rewards obtained by others under conditions of disadvantageous inequality versus advantageous inequality. It shows that the basic reward structures in the human brain are sensitive to even subtle differences in social context."

This, O'Doherty notes, is somewhat contrary to the prevailing views about human nature. "As a psychologist and cognitive neuroscientist who works on reward and motivation, I very much view the brain as a device designed to maximize one's own self interest," says O'Doherty. "The fact that these basic brain structures appear to be so readily modulated in response to rewards obtained by others highlights the idea that even the basic reward structures in the human brain are not purely self-oriented."

Camerer, too, found the results thought provoking. "We economists have a widespread view that most people are basically self-interested, and won't try to help other people," he says. "But if that were true, you wouldn't see these sort of reactions to other people getting money."

Still, he says, it's likely that the reactions of the "rich" participants were at least partly motivated by self-interest—or a reduction of their own discomfort. "We think that, for the people who start out rich, seeing another person get money reduces their guilt over having more than the others."

Having watched the brain react to inequality, O'Doherty says, the next step is to "try to understand how these changes in valuation actually translate into changes in behavior. For example, the person who finds out they're being paid less than someone else for doing the same job might end up working less hard and being less motivated as a consequence. It will be interesting to try to understand the brain mechanisms that underlie such changes."

The research described in the Nature paper, "Neural evidence for inequality-averse social preferences," was supported by grants from the National Science Foundation, the Human Frontier Science Program, the Gordon and Betty Moore Foundation, and the Caltech Brain Imaging Center.

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Caltech Neuroscientists Find Brain System Behind General Intelligence

Finding opens the door for more studies on biology of intelligence

PASADENA, Calif.—A collaborative team of neuroscientists at the California Institute of Technology (Caltech), the University of Iowa, the University of Southern California (USC), and the Autonomous University of Madrid have mapped the brain structures that affect general intelligence. 

The study, published the week of February 22 in the early edition of the Proceedings of the National Academy of Sciences, adds new insight to a highly controversial question: What is intelligence, and how can we measure it? 

The research team included Jan Gläscher, first author on the paper and a postdoctoral fellow at Caltech, and Ralph Adolphs, the Bren Professor of Psychology and Neuroscience and professor of biology. The Caltech scientists teamed up with researchers at the University of Iowa and USC to examine a uniquely large data set of 241 brain-lesion patients who all had taken IQ tests. The researchers mapped the location of each patient's lesion in their brains, and correlated that with each patient's IQ score to produce a map of the brain regions that influence intelligence. 

"General intelligence, often referred to as Spearman's g-factor, has been a highly contentious concept," says Adolphs. "But the basic idea underlying it is undisputed: on average, people's scores across many different kinds of tests are correlated. Some people just get generally high scores, whereas others get generally low scores. So it is an obvious next question to ask whether such a general ability might depend on specific brain regions."

The researchers found that, rather than residing in a single structure, general intelligence is determined by a network of regions across both sides of the brain. 

"One of the main findings that really struck us was that there was a distributed system here. Several brain regions, and the connections between them, were what was most important to general intelligence," explains Gläscher. 

"It might have turned out that general intelligence doesn't depend on specific brain areas at all, and just has to do with how the whole brain functions," adds Adolphs. "But that's not what we found. In fact, the particular regions and connections we found are quite in line with an existing theory about intelligence called the 'parieto-frontal integration theory.' It says that general intelligence depends on the brain's ability to integrate—to pull together—several different kinds of processing, such as working memory." 

The researchers say the findings will open the door to further investigations about how the brain, intelligence, and environment all interact.

Other coauthors on the paper, "The distributed neural system for general intelligence revealed by lesion mapping," are David Rudrauf and Daniel Tranel of the University of Iowa; Roberto Colom of the Autonomous University of Madrid; Lynn Paul of Caltech; and Hanna Damasio of USC. The work at Caltech was funded by the National Institutes of Health, the Simons Foundation, the Deutsche Akademie der Naturforscher Leopoldina, and a Global Center of Excellence grant from the Japanese government.

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Caltech Neuroscientists Discover Brain Area Responsible for Fear of Losing Money

Finding offers neuroscientists insight into economic behavior

PASADENA, Calif.—Neuroscientists at the California Institute of Technology (Caltech) and their colleagues have tied the human aversion to losing money to a specific structure in the brain—the amygdala.

The finding, described in the latest online issue of the journal Proceedings of the National Academy of Sciences (PNAS), offers insight into economic behavior, and also into the role of the brain's amygdalae, two almond-shaped clusters of tissue located in the medial temporal lobes. The amygdala registers rapid emotional reactions and is implicated in depression, anxiety, and autism.

The research team responsible for these findings consists of Benedetto de Martino, a Caltech visiting researcher from University College London and first author on the study, along with Caltech scientists Colin Camerer, the Robert Kirby Professor of Behavioral Economics, and Ralph Adolphs, the Bren Professor of Psychology and Neuroscience and professor of biology. 

The study involved an examination of two patients whose amygdalae had been destroyed due to a very rare genetic disease; those patients, along with individuals without amygdala damage, volunteered to participate in a simple experimental economics task.

In the task, the subjects were asked whether or not they were willing to accept a variety of monetary gambles, each with a different possible gain or loss. For example, participants were asked whether they would take a gamble in which there was an equal probability they'd win $20 or lose $5 (a risk most people will choose to accept) and if they would take a 50/50 gamble to win $20 or lose $20 (a risk most people will not choose to accept). They were also asked if they'd take a 50/50 gamble on winning $20 or losing $15—a risk most people will reject, "even though the net expected outcome is positive," Adolphs says.

Both of the amygdala-damaged patients took risky gambles much more often than subjects of the same age and education who had no amygdala damage. In fact, the first group showed no aversion to monetary loss whatsoever, in sharp contrast to the control subjects.

"Monetary-loss aversion has been studied in behavioral economics for some time, but this is the first time that patients have been reported who lack it entirely," says de Martino.

"We think this shows that the amygdala is critical for triggering a sense of caution toward making gambles in which you might lose," explains Camerer. This function of the amygdala, he says, may be similar to its role in fear and anxiety.

"Loss aversion has been observed in many economics studies, from monkeys trading tokens for food to people on high-stakes game shows," he adds, "but this is the first clear evidence of a special brain structure that is responsible for fear of such losses."

The work in the paper, titled "Amygdala damage eliminates monetary loss aversion," was supported by the Gordon and Betty Moore Foundation, the Human Frontier Science Program, the Wellcome Trust, the National Institutes of Health, the Simons Foundation, and a Global Center of Excellence grant from the Japanese government.

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Kathy Svitil
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Caltech Scientists Develop Novel Use of Neurotechnology to Solve Classic Social Problem

Research shows how brain imaging can be used to create new and improved solutions to the public-goods provision problem

PASADENA, Calif.—Economists and neuroscientists from the California Institute of Technology (Caltech) have shown that they can use information obtained through functional magnetic resonance imaging (fMRI) measurements of whole-brain activity to create feasible, efficient, and fair solutions to one of the stickiest dilemmas in economics, the public goods free-rider problem—long thought to be unsolvable.

This is one of the first-ever applications of neurotechnology to real-life economic problems, the researchers note. "We have shown that by applying tools from neuroscience to the public-goods problem, we can get solutions that are significantly better than those that can be obtained without brain data," says Antonio Rangel, associate professor of economics at Caltech and the paper's principal investigator.

The paper describing their work was published today in the online edition of the journal Science, called Science Express.

Examples of public goods range from healthcare, education, and national defense to the weight room or heated pool that your condominium board decides to purchase. But how does the government or your condo board decide which public goods to spend its limited resources on? And how do these powers decide the best way to share the costs?

"In order to make the decision optimally and fairly," says Rangel, "a group needs to know how much everybody is willing to pay for the public good. This information is needed to know if the public good should be purchased and, in an ideal arrangement, how to split the costs in a fair way."

In such an ideal arrangement, someone who swims every day should be willing to pay more for a pool than someone who hardly ever swims. Likewise, someone who has kids in public school should have more of her taxes put toward education.

But providing public goods optimally and fairly is difficult, Rangel notes, because the group leadership doesn't have the necessary information. And when people are asked how much they value a particular public good—with that value measured in terms of how many of their own tax dollars, for instance, they’d be willing to put into it—their tendency is to lowball.

Why? “People can enjoy the good even if they don’t pay for it,” explains Rangel. "Underreporting its value to you will have a small effect on the final decision by the group on whether to buy the good, but it can have a large effect on how much you pay for it."

In other words, he says, “There’s an incentive for you to lie about how much the good is worth to you.”

That incentive to lie is at the heart of the free-rider problem, a fundamental quandary in economics, political science, law, and sociology. It's a problem that professionals in these fields have long assumed has no solution that is both efficient and fair.

In fact, for decades it's been assumed that there is no way to give people an incentive to be honest about the value they place on public goods while maintaining the fairness of the arrangement.

“But this result assumed that the group's leadership does not have direct information about people's valuations,” says Rangel. “That's something that neurotechnology has now made feasible.”

And so Rangel, along with Caltech graduate student Ian Krajbich and their colleagues, set out to apply neurotechnology to the public-goods problem.

In their series of experiments, the scientists tried to determine whether functional magnetic resonance imaging (fMRI) could allow them to construct informative measures of the value a person assigns to one or another public good. Once they’d determined that fMRI images—analyzed using pattern-classification techniques—can confer at least some information (albeit "noisy" and imprecise) about what a person values, they went on to test whether that information could help them solve the free-rider problem.

They did this by setting up a classic economic experiment, in which subjects would be rewarded (paid) based on the values they were assigned for an abstract public good.

As part of this experiment, volunteers were divided up into groups. “The entire group had to decide whether or not to spend their money purchasing a good from us,” Rangel explains. “The good would cost a fixed amount of money to the group, but everybody would have a different benefit from it.”

The subjects were asked to reveal how much they valued the good. The twist? Their brains were being imaged via fMRI as they made their decision. If there was a match between their decision and the value detected by the fMRI, they paid a lower tax than if there was a mismatch. It was, therefore, in all subjects' best interest to reveal how they truly valued a good; by doing so, they would on average pay a lower tax than if they lied.

“The rules of the experiment are such that if you tell the truth,” notes Krajbich, who is the first author on the Science paper, “your expected tax will never exceed your benefit from the good.”

In fact, the more cooperative subjects are when undergoing this entirely voluntary scanning procedure, “the more accurate the signal is,” Krajbich says. “And that means the less likely they are to pay an inappropriate tax.”

This changes the whole free-rider scenario, notes Rangel. “Now, given what we can do with the fMRI,” he says, “everybody’s best strategy in assigning value to a public good is to tell the truth, regardless of what you think everyone else in the group is doing.”

And tell the truth they did—98 percent of the time, once the rules of the game had been established and participants realized what would happen if they lied. In this experiment, there is no free ride, and thus no free-rider problem.

“If I know something about your values, I can give you an incentive to be truthful by penalizing you when I think you are lying,” says Rangel.

While the readings do give the researchers insight into the value subjects might assign to a particular public good, thus allowing them to know when those subjects are being dishonest about the amount they'd be willing to pay toward that good, Krajbich emphasizes that this is not actually a lie-detector test.

“It’s not about detecting lies,” he says. “It’s about detecting values—and then comparing them to what the subjects say their values are.”

“It’s a socially desirable arrangement,” adds Rangel. “No one is hurt by it, and we give people an incentive to cooperate with it and reveal the truth.”

“There is mind reading going on here that can be put to good use,” he says. “In the end, you get a good produced that has a high value for you.”

From a scientific point of view, says Rangel, these experiments break new ground. “This is a powerful proof of concept of this technology; it shows that this is feasible and that it could have significant social gains.”

And this is only the beginning. “The application of neural technologies to these sorts of problems can generate a quantum leap improvement in the solutions we can bring to them,” he says.

Indeed, Rangel says, it is possible to imagine a future in which, instead of a vote on a proposition to fund a new highway, this technology is used to scan a random sample of the people who would benefit from the highway to see whether it's really worth the investment. "It would be an interesting alternative way to decide where to spend the government's money," he notes.

In addition to Rangel and Krajbich, other authors on the Science paper, “Using neural measures of economic value to solve the public goods free-rider problem,” include Caltech's Colin Camerer, the Robert Kirby Professor of Behavioral Economics, and John Ledyard, the Allen and Lenabelle Davis Professor of Economics and Social Sciences. Their work was funded by grants from the National Science Foundation, the Gordon and Betty Moore Foundation, and the Human Frontier Science Program.

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Lori Oliwenstein
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