Brace yourself for a spate of stories about how “what you think may affect what you earn,” as the press release from the American Psychological Association puts it. Sounds innocuous. But the "what you think" refers to whether you believe that a woman's place is in the home. “A new study has found that men who believe in traditional roles for women earn more money than men who don’t,” APA continues. There you have it: if you want to rake it in, guys, adopt attitudes that value keeping the little lady barefoot and pregnant.

Before we look at how this conclusion is not exactly supported by the actual study, which I encourage anyone with half an hour or so to read for themselves, it’s only fair to see what the researchers did. Timothy Judge, a management expert at the University of Florida’s Warrington College of Business, and graduate assistant Beth Livingston analyzed data from 12,686 men and women who were interviewed four times between 1979 and 2005; the participants were 14 to 22 when the study began, and 40% of them dropped out or disappeared before the fourth interview. Nonetheless, it's a lot of data, as the researchers report in the September issue of the Journal of Applied Psychology.

At each interview, participants answered questions about their views on gender roles: do you believe a woman’s place is in the home? If a mother works outside the home, are her children more likely to be juvenile delinquents? Should a man be the achiever while the woman takes care of home and family? (Like other studies, this one finds many factors that influence people’s attitudes on all this, including marital status, gender, race, ethnicity, whether they live in a city, religiosity, and whether their parents held traditional views of gender roles. People living in Northeastern cities had less traditional views of gender roles, for instance, as did people whose parents both worked outside the home, while married and religious people had more traditional views.)

Then the researchers correlated these views with how much people earned. Bottom line: men with traditional views of gender roles made an average of about $8,500 more per year than their enlightened peers, while women who held traditional views made an average of $1,500 less per year than women with more egalitarian views.

The last finding has a straightforward explanation. If you’re a woman with a job, and if you think you shouldn’t be at work but back home with the kids or hubby, it’s not surprising that, 1) you don’t push yourself to work that hard and get promotions or bonuses or overtime, and 2) you probably didn’t try for a well-paying, high-powered job in the first place if working made you feel guilty.

But how about the men? As they write in the paper, “One straightforward possibility is that traditional gender role orientation advantages men and disadvantages women because traditional men show greater dedication to their work and traditional women show greater dedication to their homes.”

This is as good a place as any to pause and point out the obvious. Although this study is being cast as showing a cause-and-effect relationship between gender-role attitudes and earnings, it proves nothing of the sort: a man who believes in gender equality has the option of working just as hard at his career, and earning a lot, as does a traditionalist. It does not show that egalitarians are doomed to penury.

Similarly, although the researchers say that “traditional men, especially when compared with traditional women or egalitarian individuals, may negotiate their salaries more aggressively and effectively,” that, too, is hardly inevitable or even logical. Guys, if you believe that the little woman has just as much a right to work outside the home as you are, you don't have to slack off on salary demands.

The key to the whole thing, it seems to me, is a little sentence dropped into the paper’s summary (the abstract): “Occupational segregation partly explained these gender differences.” That is, a woman who doesn’t think she should be working—but, perhaps, has to for economic reasons—may “feel uncomfortable in a high-paying, complex job or in those jobs in which she is surrounded by men,” so she chooses to be, say, a secretary rather than an entrepreneur. Similarly, a “keep ‘em barefoot and pregnant” kind of man is probably not going into academia (where the male-female wage gap is small) or teaching (ditto), but is more likely to be drawn to, say, construction, engineering or finance, where the wage gap can be large. As the researchers admit, "traditional men (vs. egalitarian men) and traditional women (vs. egalitarian women) make different occupational choices. . . . For instance, traditional gender role orientations led to higher earnings in male-dominated jobs than in female-dominated jobs, and men were more likely to hold male-dominated jobs.”

So for all you young men out there just entering the work force, let’s be clear: holding egalitarian views does not mean your earnings will inevitably suffer. You have a choice about which fields to enter, and you don't have to check your enlightened views at the office door.

 A Times Roman font walks into a bar. The bartender says, “Sorry, we don’t serve your type here.”

No, what I meant was, a guy walks into a bar with a duck on his head. The bartender says, “Can I help you?” The duck says, “yeah, you can get this guy off my butt!” Or maybe, two guys walk into a bar; the third one, not being an idiot, ducks (thank you, funny2).

But seriously, one guy walks into one bar, has a few, and then into another one down the street, and has a few more, faster. What’s the difference between the two bars? If scientists who study drinking behavior are right, it may be the loud music in the second one. That, researchers will report in the October issue of Alcoholism: Clinical & Experimental Research (it’s also available at the journal’s online “Early View” page), can make you drink more, in less time.

“Previous research had shown that fast music can cause fast drinking, and that music versus no music can cause a person to spend more time in a bar,” said Nicolas Guéguen, a professor of behavioral sciences at the Université de Bretagne-Sud in France, who led the study. But “this is the first time that an experimental approach in a real context found the effects of loud music on alcohol consumption.”

By “real context,” he means the two bars in the west of France that he and his colleagues visited—purely for research purposes!—on three Saturday evenings. They surreptitiously observed 40 men between the ages of 18 to 25 who had ordered a glass of draft beer. They also toggled the sound levels of the top 40 songs on the bars’ playlist between 72 decibels, which is normal, and 88 dB, considered loud. Result: the louder the music, the more the guys drank, and in less time than when the volume was turned down.

One reason may be that loud music causes higher physiological arousal—a faster heart rate, higher blood pressure and the like, which led the men “to drink faster and to order more drinks,” said Guéguen. Alternatively, loud music may make it so hard to hold a conversation that patrons drink more because they talk less.

The lesson he takes from this is that “we need to encourage bar owners to play music at more of a moderate level ... and make consumers aware that loud music can influence their alcohol consumption.” We won’t hold our breath while bars weigh the increased revenue from getting patrons to drink more, faster, against the social virtue of doing something as simple as turning down the volume in order to reduce how much booze they sell.

We’ve all been there (though some of us longer ago than others): the cruising bar, fraternity party or other gathering place where men vastly outnumber women. As the men trip all over themselves trying to make their competitors look like losers and themselves like desirable partners, women get the upper hand: they have their pick of partners, and can crush the already-sensitive egos of the men with the back of their manicured hand.

If you assumed that this kind of female-over-male dominance was a freak result of humans’ peculiar mating habits, biologists in Germany have some monkeys they’d like you to meet. The higher the percentage of males in troops of lemurs, macaques and other primates, they report in the journal PLoS ONE, the more dominant over males the females are.

That there are any circumstances in which female primates lord it over males in a social hierarchy may come as a surprise, but it's actually not that uncommon. Although in most species females rank below the males (which means most males win aggressive encounters), in the lemurs of Madagascar the females are dominant, in bonobos the males and females are roughly equal in dominance, and among macaques females are weakly dominant, with “the most dominant females rank[ing] above approximately a third of  the males,” says biologist Charlotte Hemelrijk of the University of Groningen, who led the new study.

There are two competing hypotheses for how this female dominance develops. One holds that dominance is inborn; you are more likely to be dominant if you are born big and strong, or if you inherit it from your mother, and that’s that. The alternative holds that there is a “winner-loser effect.” Primates have chance encounters, and if they win they are more likely to win again, while if they lose they are more likely to lose the next time; it's a snowball effect.

The reason is that the outcome alters an individual’s fighting ability. Winning raises, and losing lowers, self-confidence, which can be self-fulfilling (animals filled with swagger are more likely to win the next time, too). As Hemelrijk puts it, if an individual monkey wins an aggressive interaction, “the monkey’s self-confidence grows and it also wins other aggressive interactions. It’s a self-reinforcing effect.” Also, losing is so traumatic that it raises an animal’s levels of corticosteroids (stress hormones) and lowers its levels of testosterone; that makes for a wimpy monkey more likely to lose its next encounter.

So imagine what happens in troops with many more males than females. The males are always mixing it up, playing one-upmanship in the drive to be the alpha male. That provides many chances for males to lose and hence to feel bad about themselves and have a losing mix of testosterone and stress hormones. The females take advantage of this. “In groups with more males, males are more often defeated by other males,” says Hemelrijk. “Consequently, high-ranking females may be victorious over these losers. Furthermore, the presence of more males in the group leads to more interactions between males and females, causing more chance winnings by females. Through a self-reinforcing effect, these females will go on to win more frequently and grow more dominant.”

In other words, the large number of losing males in a group with a preponderance of males makes them more likely to lose a fight with another male, and therefore with a female; the female gains confidence (and higher testosterone levels), enabling her to go on to lord it over more males. As a result, say the scientists, “high ranking females may beat low ranking males and rank above them.” In contrast, in less-aggressive primate groups, such as the egalitarian societies of macaques, the presence of many more males than females does not lead to female dominance over males: the males don’t fight enough to produce enough losers for the females to lord it over.

The scientists were particularly struck by their finding that whether females dominate males has little to do with the difference in their sizes, or what’s called sexual dimorphism. That is “unexpected,” they say, because size seems to explain male dominance in species where males are way bigger than females, such as gorillas. But when it comes to whether females can be the top bananas, the relative sizes of males and females matters less than the percentage of each sex in the group.

Says Hemelrijk, “It would not surprise me if [similar mechanisms] play a role in the development of dominance between the sexes among human beings, too.” Keep it in mind next time you find yourself in a group where the sex ratio veers far from 50-50.

Here’s one of those phrases that The New Yorker would label as “sentences we never read past”:

"I was skimming the program for the annual meeting of the American Statistical Association . . ."

But really, where else can you find not only research on “Modeling Sparse Generalized Longitudinal Observations with Latent Gaussian Processes” but also on managerial strategies in baseball, parity in the NFL and the accuracy of sports predictions? It’s striking how many statisticians who study weighty matters—how to tell if a cancer drug works or a compound is dangerous—got their start studying sports statistics.

“A lot of us really enjoyed baseball statistics when we were growing up, and that’s how we got into the field,” biostatistician Michael Schell of the Moffitt Cancer Center in Tampa told me.

So I got in touch with Jack O’Gara, who wrote the book on using statistical techniques to spot chicanery in business (that would be the 2004 “Corporate Fraud: Case Studies in Detection and Analysis”). Now retired, O’Gara has put his statistical skills to use analyzing baseball, especially cheating.

In the business world, he focused on what he calls inflection points, a sudden discontinuity in data. That is what he saw, galore, when he analyzed the career stats of pitcher Roger Clemens.

Clemens, of course, was named in the Mitchell Report, which last December reported that an alarming number of baseball players had taken performance-enhancing drugs such as steroids. (Clemens' section starts on p. 215.) Clemens and his camp deny it. O’Gara decided to see if stats could tell us anything.

One of the most telling is ERA Margin, which compares a pitcher’s earned run average in a given year to the league average. It’s more informative than ERA alone because it controls for weird things like hitters league-wide being in a slump (which would reduce every pitcher’s ERA but not ERA Margin), or the use of a juiced ball that year, which would raise pitchers’ ERAs but, again, not the margin. The ERA Margin tells you how one hurler is doing compared to his peers.

O’Gara compared Clemens’ ERA Margins to those of the 20 post-World War II pitchers with the most wins, turned in by legends such as Warren Spahn, Tom Seaver and Bob Gibson. Through age 34, Clemens’ margin was 1.09, notably better than the others’ 0.6. Fine, the guy was an ace.

But from age 35 to 40, when most pitchers fade, Clemens’ margin was 1.18, compared to 0.43 for the other greats. Here's where it gets weird: from age 41 to 45, it was 1.30, while the others’ was a negative 0.01. That is, the other great pitchers’ margin shrank as they got older, falling more in line with the league average and normal aging patterns, but Clemens’ soared. As O’Gara put it, “Clemens is the only pitcher who gets progressively better as he ages into the post-40 category.”

When the ERA Margins for baseball’s top 10 or top 20 pitchers each year is graphed, Clemens is better than the rest when he was 29 and 30, then twice more—three performance peaks while none of the top 20 had more than two. “More significantly, the second two peaks were higher than the initial peak, which occurred in the presumed prime of his life, contrary to normal aging patterns,” O’Gara says. “At age 43, Clemens had the seventh-best season [measured by ERA Margin] since World War II.”

Of the 20 best ERA Margins since 1945, all came when the pitcher was 34 or younger (average age: 28), with the exception of Clemens, who did it when he was 35 and again when he was 43. The best two-year average ERA Margins cluster when pitchers were in their late 20s (Sandy Koufax: 29-30; Greg Maddux: 28-29), and again Clemens’ best coming when he was 43-44 stands out. Clemens’ ERA margin at age 43 was the best in the majors that year and the best-ever for a 43-year-old.

Testimony taken for the Mitchell Report and given to Congress this spring included accusations from a trainer that he injected Clemens, which the pitcher denies. As it happens, the three periods when the trainer said he administered shots “correspond to performance bursts by Clemens,” says O’Gara. “The ERA for these three periods totaled 1.92 over 183 innings, significantly better than his career average ERA of 3.12.”

As has been widely reported, in 1996 Clemens, then 34, was coming off a sub-par 1995 season and struggling through the first months of the '96 season, his last of 14 with Boston. “Then he suddenly went from being mired in the worst multiple year performance of his career (the preceding one and 2/3 years) to his best two-year-plus performance of his career,” says O’Gara. “He averaged a 2.91 ERA margin for the remainder of 1996, better than for any single calendar year.”

One baseball statistician I asked about this analysis warned me against “guilt by graph”—that is, concluding that someone was juiced based on stats alone. “Stats can tell you if someone’s performance is unusual, but by definition a great player has an unusual performance,” he said. See, for instance, this post by another stats guru.

So in Clemens’ case, do the stats lie—or expose a lie?

Not that anything about global warming is fair, but one of the most unjust things about it is that the nations that have spewed most of the greenhouse gases into the atmosphere tend to be in the north (the U.S., Europe and now China), while the nations that stand to suffer the most--as in having their entire island covered by the rising seas--tend to be in the south. If a German researcher is right, it looks like nations will reap what they sow.

According to a new paper by Detlaf Stammer of Hamburg University, once Greenland melts most of the water will hang around in the Atlantic Ocean rather than spreading through the world's seas. As New Scientist reported, most of the meltwater will add to the Atlantic for some 50 years, causing sea levels to rise--and rise more than if the water were evenly distributed around the globe, which it will not be. As Stammer told the magazine, a melting Greenland "is much less of a threat to tropical islands in the Pacific than it is for the coasts of North America and Europe."

Call it poetic justice, climatologically.

Yeah, I know that headline echoes yesterday's, but I can't help it: we have now moved beyond studies showing that mental training alters the structure and function of the brain to studies showing that it alters the structure and function of our genes.

Regular readers may have noticed that I’m not a big fan of the “my genes made me do it” school of life, whether “it” is acting in a certain way (as genes “for” shyness or neuroticism supposedly make you do) or developing a particular disease. As I’ve written, the genes in our cells don’t matter one iota if they’re not turned on, and there are many things in life that can turn off bad genes such as those that raise the risk of disease such as breast cancer. That’s why it seems to me that personal genome scans are just a couple of steps removed from palm reading. As it happens, last year New York started telling 31 private companies that they need licenses to take DNA samples from state residents, and in June California sent cease-and-desist letters to 13 of the companies with the same message.

Reading what genes a person has is so 20th century. Determining which genes are turned on is where the action is. A rat study I’ve mentioned before, for instance, showed in 2004 that the way a mother rat treats her pups determines whether genes related to neuroticism and fearfulness are on or off.Now comes a study that looks at something similar in people.

The variable wasn’t how mom treats you—though I’d bet a nickel that study is just around the corner—but the relaxation response. Back in the 1960s Herbert Benson of Harvard Medical School coined this term to refer to the opposite of the stress response, which floods the body with stress hormones, raises blood pressure and elevates heart rate. In contrast, the relaxation response is a state of deep rest that decreases metabolism, relaxes muscles, slows heart rate and lowers blood pressure. Over the years, Benson and colleagues developed a sure-fire way to elicit it.

Now they’ve figured out how it works to, among other things, treat hypertension (high blood pressure), alleviate pain, even help with infertility and rheumatoid arthritis. As they report in PLoS One this evening, the relaxation response alters which genes associated with the body’s response to stress are on and which are off. As Benson said in a statement, “we’ve found how changing the activity of the mind can alter the way basic genetic instructions are implemented.”

It’s being billed as “the first comprehensive study of how the mind can affect gene expression.” By “mind,” they mean mental practices such as meditation and prayer, which are among the techniques used by the 19 long-term practitioners of the relaxation response who were studied, along with 19 volunteers who had never engaged in such practices. After the latter went through eight weeks of training, the scientists compared before-and-after patterns of gene expression, finding that mental training alters the expression of genes involved in inflammation, in the form of cell suicide called apoptosis (which can keep damaged cells from forming cancers), and in how the body handles damaging free radicals.

It really is time to stop thinking of our DNA as immutable. Even thinking can change it.

You think you have big travel plans for the Memorial Day weekend? I guarantee they're nothing like what the first humans managed as they walked all over the globe after leaving their African homeland.

The human genome project has been a veritable treasure trove for scientists trying to tell the story of humankind’s migrations out of Africa. A couple of terrific books have chronicled this, and the use of genetics to reconstruct human history was a focus of a cover story we did last year. The fun part is when genetics throws a wrench into supposedly settled accounts, and that’s what a paper posted today in the open-access journal PLoS Genetics does.

In it, scientists from the University of Oxford and University College Cork describe a new technique they developed. It analyzes not just the Y chromosome, as many studies using genetics to trace human history do, but parts of chromosomes across the entire human genome. The details are complicated, but the bottom line is an ability to probe further back in time and identify smaller genetic contributions.

The technique confirms the out-of-Africa model, in which all human populations outside that continent today are the descendants of a single pulse of wanderers who left Africa. Hominids who originally lived in the regions of Asia and Europe colonized by the migrants contributed nothing to the modern gene pool, which is a polite way of saying that our ancestors wiped them all out (or at least prevented them from mating). Or, as I wrote in the cover story:

“The first modern humans—and therefore, unlike the earlier wave of Homo erectus into Asia a million years ago, the ancestors of everyone today outside Africa—departed Africa about 66,000 years ago. These pilgrims were strikingly few.... The best estimate: 2,000 men. Assuming an equal number of women, only 4,000 brave souls ventured forth from Africa.”

Now the technique is throwing up surprises about what happened next. Among them:

*The most northerly East Asian population that the scientists analyzed, Siberians called the Yakut, carry genes of the most northerly European population, the Orcadians (whose descendants live in the Orkney Islands), suggesting that northern Europeans walked into north Asia and hooked up with native peoples there.

*Populations in Central Eurasia have genes from the Near East (Bedouins and Palestinians) and even Kenyan Bantus.

*In Europe, the most ancient populations are the French, followed by the Tuscans and then other Italians, all of whom trace their ancestry to north Africans called Mozabites, today called Berbers, and to several Near Eastern and Central Asian populations. Europeans have more genetic ancestors than any non-European population, making Europe the world’s true melting pot.

*The youngest Europeans are the Sardinians, Russians, Orcadians and Basques—which makes sense, since they are all at the geographic extremes of the continent. People arrived there last. All four have big genetic contributions from the Near East and Central Asia, suggesting multiple waves of migrants into Europe.

*In the Americas, the Colombians are the oldest population. They can trace 47 percent of their ancestry to the Hazara of East Asia but, oddly, they also have genetic contributions from the French. That probably reflects intermarriage after Europeans arrived in the New World.

*The Pima are the oldest people of North America. They trace their ancestry to the Colombians but also, surprisingly, to Mongolians, who are not ancestors of the Colombians. That suggests multiple distinct colonizations into North America from Asia.

*The Mayans have Bantu and Tuscan donors, presumably due to intermarriage after the Europeans arrived.

For two cool little movies of all this, scroll to the bottom of the paper and click on Movie 1 and Movie 2.

Are you better off today than you were 10 years ago? Some version of that is a favorite question of politicians looking to oust the party in power. As of today, if the “you” refers to American adults with a high-school education or less, and if the “better off” refers to the most basic measure you can think of—whether you are alive or dead—the answer is a shameful “no.”

Last month I blogged about a study that underlined how we truly are Two Americas (though the idea never gained traction for John Edwards this primary season). That study found that, since the early 1980s, death rates in wealthy counties of the United States have fallen—but those in poorer ones have stagnated or risen, despite the huge strides in disease prevention and treatment. Those are just not reaching the poor. Now another study uses a different proxy for “haves” or “have-nots”—education—and reaches another shameful conclusion: the gap in death rates between Americans with less than a high school education and college graduates has soared since 1993, they will report tomorrow in the May 14 issue of PLoS One.

The scientists analyzed death certificates (which indicate the last year of schooling that the person completed, as well as cause of death) for blacks and whites between the ages of 25 and 64. The age cut-off was chosen because, for older generations, education is not as strong a proxy for socioeconomic status—class—as it is for younger ones.

The numbers are shocking. Among white men who did not graduate from high school, there were 837 deaths per 100,000 of them in 1993; that same year, only 285 white men with college degrees died per 100,000 in this age group. But it gets worse. In 2001, those respective rates were 931 and 213—the death rate for less-educated white men had risen, while that for college grads had fallen. Do the math: white men who did not graduate from high school were dying at a rate 2.9 times that of college grads in 1993—and at a rate 4.4 times higher in 2001. For black men, the comparable mortality rates were 2.1 times higher in 1993 and 3.4 times higher in 2001.

For white women who never graduated from high school, the death rate was 422 per 100,000 in 1993, and for white women with a college degree it was 165. In 2001? It rose to 553 per 100,000 in the first group, and dropped to 146 in the highly-educated group. Breaking that down, the death rate from cancer among white women with only 12 years of education rose 1.1 percent per year during the period studied; for heart disease and stroke, it rose 1.8 percent per year among these women. All three of these diseases have become more preventable and more treatable—but, apparently, only for some.

Conclusion: the widening death gap was due to sharp decreases in mortality from all causes—but especially in heart disease, cancer and stroke, all of which have benefited from new forms of prevention and treatment—among the most educated. The less educated have benefited hardly at all from medical progress.

Why are the death rates from the major causes of death falling among the educated but rising among the less educated? Think of lower educational attainment as a marker of social and economic class—which has become a big issue in the presidential campaign, as Clinton grabs the votes of those lower on the socioeconomic ladder and Obama gets the votes of the higher-ups. The have-nots are not only poorer; they also are less likely to have health insurance or stable employment, which means little to no preventive care, and lower health literacy. The last factor means less likelihood of knowing when some small symptom means big trouble, and greater difficulty navigating the medical system. Those with less education are also more likely to smoke, be obese, get little exercise, and suffer from high blood pressure due to the stress of unemployment.

“Risk factors are higher in less well-educated groups, and they have less access to preventive medicine and treatment,” says Ahmedin Jemal of the American Cancer Society, who led the study.

The death gap isn’t going away. In 2005, the most recent year the researchers analyzed, the all-cause mortality rate for those with less than a high-school education was 3.2 times higher than that for people with even some college.

The poor will always be with us, as the saying goes, and so will inequality in education. But other countries have socioeconomic inequality also—with no comparable death gap, says Jemal, because they do not make access to health care (especially non-emergency and preventive care) contingent on having health insurance. Two Americas, indeed.

Here’s a moral dilemma that seems tragically timely, given the chaos surrounding attempts to deliver aid to Burma’s cyclone victims. There are 60 orphans at the Canaan Children’s Home in Buziika, Uganda, and their meal allotment has to be cut. What do you want to do: take six meals away from each of two kids, or 10 meals away from one? You have eight seconds to decide.

In this and similar moral dilemmas, efficiency (the total number of meals lost) is pitted again against equity (how evenly the burden of lost meals is shared among the children). You have to take away a total of 12 meals if two children share the loss, but only 10 (which would seem better) if a single orphan bears the entire burden. You have to decide whether to sacrifice efficiency (losing fewer meals) to equity (spreading the loss over more children).

Here’s another way to think about it. You are driving a truck to the Burmese cyclone victims. It holds 1,000 pounds of rice. The time it will take to deliver the rice to everyone in the Irrawaddy Delta village you are headed for means that 200 pounds will spoil. If you deliver the rice to people you meet en route, you will be distributing it to only half the population of the village, but only 50 pounds will spoil. Do you deliver the rice to only half the number of victims, maximizing the total amount of food provided (efficiency), or do you sacrifice 150 pounds to distribute it to more people (equity), giving rice to more people but also causing more rice to go to waste?

In a study reported online today in the journal Science, researchers posed the orphan dilemma to people while scanning their brains with functional magnetic resonance imaging (fMRI). Unlike most studies of the brain basis of ethical decision making ("neuroethics"), this one was grounded in reality: the volunteers’ choices would determine how many meals the research team actually donated to the Ugandan orphans. The volunteers knew this, which made the dilemma painful in the extreme. “Quite a few came out saying: ‘This is the worst experiment I’ve ever been in. I never want to do anything like this again!’,” said study co-author Ming Hsu of the University of Illinois’s Beckman Institute for Advanced Science and Technology.

So, which is more critical to our sense of justice, equity or efficiency? And how does the brain decide?

In the experiment, the volunteers (26 men and women, ages 28 to 55) first read short bios of the orphans. Then they watched a video on a computer screen, showing a ball rolling toward a lever. By moving the lever, they could steer the ball toward either of two depictions of the moral choices: photographs of the actual orphans who would be affected by that choice, with numbers for the number of meals that would be lost to those children if that option were chosen.

By an overwhelming margin, people chose to preserve equity at the expense of efficiency—lose a few more meals, but spread the burden among as many children as possible, rather than making one hungry child—whose imploring little face stared back at them from the screen--shoulder the entire loss.

According to the fMRI, different brain regions became active at different points in the decision-making. The insula, which is involved in processing emotions and the awareness of bodily states as well as (in some studies) evaluating fairness, was active when the volunteers wrestled with questions of equity. The putamen, which is activated during learning that brings rewards, lit up when people thought about efficiency.

Since equity won, it suggests that decisions about fairness are rooted in emotion more than in cold-eyed cost-benefit analysis. “That the brain has such a robust response to unfairness suggests that sensing unfairness is a basic evolved capacity,” Steven Quartz of Caltech and co-author of the study said in a statement. “The emotional response to unfairness pushes people from extreme inequity and drives them to be fair,” suggesting that “our basic impulse to be fair isn’t a complicated thing that we learn,” but an instinctive one.

And whoever said scientists have no heart? After the experiment, and based on the volunteers’ decisions, the team donated $2,279 to the orphanage.

Bad enough that antidepressants fail to help an estimated one-third of people suffering from depression. Even worse is that it can take 6 to 8 weeks before that becomes clear: the patient dutifully swallows Zoloft after Zoloft or Paxil after Paxil, only to find after two months that she is no better off—at which point her doctor typically puts her on a different med, and the whole process of trial-and-error starts all over again. There’s got to be a better way—and now there may be.

Last September I wrote about a new use of EEGs—the decades-old technology that measures brain waves—in which psychiatrists compare the EEG of a patient to thousands of EEGs in a huge database that matches it to an effective treatment. (This is different from using EEGs to diagnose a mental illness, something that doesn’t seem to work, perhaps because there are many, many ways for a brain to have an underlying pattern of electrical activity that adds up to “depression” or “bipolar disorder” or other psychiatric disease.) CNS Response, the California company that runs the database, looks for matches between EEG and effective drug. In about 75% of cases, that produces surprising pairings—such as an anticonvulsive drug for a patient with depression—that the physician would never have thought of.

A new study being reported this afternoon at the annual meeting of the American Psychiatric Association finds another use for EEGs: predicting which patients will respond to the antidepressant they have just started. Rather than waiting for months, patients suffering from major depression—as nearly 15 million Americans do—take the drug for a week and then undergo an EEG (which is painless, noninvasive and relatively cheap, on the order of $150).

The study, led by Andrew Leuchter of UCLA and called BRITE (Biomarkers for Rapid Identification of Treatment Effectiveness), had 73 patients take the antidepressant escitalopram, which is sold as Lexapro and belongs to same category—selective serotonin reuptake inhibitors, or SSRIs—as Prozac and many others. That’s the quandary: all of the drugs supposedly work by targeting the brain’s serotonin system (which is actually a questionable claim, but that’s a story for another day), so which one will help a particular patient? Before starting the drug and again after taking it for 48 hours, for one week, and for two and seven weeks, the patients underwent EEGs. At the one-week visit, doctors assessed how well they were responding to the drug; the researchers also identified genetic markers that have been reported to predict how well patients will respond to SSRIs, and measured how much of the drug was in the patients’ blood, which is thought to be an indication of whether it is likely to work.

The bad news: the docs were terrible at predicting, based on how well the patients were doing after a week on Lexapro, whether the drug would alleviate their depression. The genetic markers fared no better. Neither did the blood levels.

But of the 38 patients who got a little better and the 28 who recovered completely by the end of the seven weeks, the EEG readings—measuring brain-wave changes after one week on the drug—did pretty well, predicting who would get a little better or even recover with 74% accuracy (compared to 51% accuracy for the docs’ evaluation).

“Early changes in frontal EEG signals carry important information about future clinical response,” Leuchter said in a statement, suggesting that EEGs have “the potential to help clinicians improve the care of patients suffering from depression.”

Caveats: the company that sells the EEG system, Aspect Medical Systems, funded the study, and Leuchter has been a paid consultant to Aspect, served on its board and received grant money from Aspect. (I have blogged before on how company-funded studies can be skewed.) And seven weeks is not exactly long-term. Still, anything that moves us beyond the current hit-and-miss approach to treating depression is to be welcomed.

If a stock market that swings 400 or more points in a single session has been making you queasy—not to mention too terrified to check your 401(k) balance—at least we now know who to blame: men and their raging hormones.

It’s a Wall Street cliché that the most successful traders are those with nerves of steel and you-know-whats the size of softballs. Scientists from the University of Cambridge therefore decided to measure what, exactly, is going on with traders’ testosterone (which is linked to the latter) and cortisol, the stress hormone. What they found, as they report in this week’s online edition of the Proceedings of the National Academy of Sciences], is that when male traders have high levels of testosterone in the morning, they make more profits than their daily average that day, and when market volatility is high their cortisol levels soar. Since both hormones are well-known to impair thinking, the scientists warn, high levels can make traders “display the irrational behavior often observed in real markets,” make traders take more risks, and exaggerate downturns in the market.

Sound familiar?

For their study, Cambridge’s John Coates and Joe Herbert recruited 17 traders, all men working for a financial firm in London. Most of the traders in the study, who were 18 (!; that’s not a typo) to 38 years old, focused on German interest-rate futures, making trades valued at £100,000 to £500 million, or about $196,000 to $980 million. For eight business days in a row, the traders gave the scientists saliva samples, from which the researchers measured levels of testosterone and cortisol.

Tons of studies on testosterone have shown that this “male” hormone (that’s in quotes because women have some, too) rises in athletes preparing for a competition, spikes even more in winning athletes and falls in losers. Testosterone seems to increase both confidence and risk taking. That can increase the chance of winning again.

That seemed to be what happens with the traders. Daily testosterone levels were significantly higher on days when they made especially high profits on their transactions, Coates and Herbert find. How? Because, according to other studies, high testosterone levels have been found to make men “increase search persistence” (that is, you keep looking for information of the wisdom of a trade), take greater risks (which can be a winning strategy if you usually make profitable trades) and display “fearlessness in the face of novelty,” such as when unexpected news hits the markets.

As for cortisol, it seemed to reflect how volatile the German market was: dizzying swings stressed out the traders. No surprise there.

But perhaps some red flags. Levels of both hormones were high enough to have cognitive and behavioral consequences, “specifically by shifting risk preferences or disturbing the neural basis for rational choice,” write the scientists. Exactly how this happens is the subject of intense research. But what neuroscientists know is that a handful of brain regions—the amygdala, anterior insula and nucleus accumbens—are the culprits behind irrational choices. If they’re overactivated, as can occur when they’re bathed in hormones, “then investors will display the irrational behavior often observed in real markets,” warn the scientists. “If testosterone continued to rise or became chronically elevated, it could begin to have the opposite effect on [profits and losses] . . . because testosterone has also been found to lead to impulsivity and sensation seeking [and] to harmful risk taking.”

The traders’ cortisol levels might also lie behind one of the U.S. stock market’s recent tendencies: once it starts to fall, especially at the end of the trading day, it often falls off the cliff. There you are, checking the Dow at 3:45, and see it’s down a couple dozen points—only to find when you check back after the close that it plummeted 200 points. Blame cortisol. It makes people more risk-averse, so in a slightly-down market men with soaring cortisol will “exaggerate the market’s downward movement,” the scientists warn, by selling like crazy.

There are also risks on the upside. Testosterone rises in a bubble. Since it also increases risk taking and irrationality, high levels in traders can exaggerate market rises. Hence the roller-coaster: every bit of news, good or bad, has an exaggerated effect on financial markets.

As Herbert puts it in a statement, “Our work suggests that [financial] decisions may be biased by emotional and hormonal factors.” And this, from Coates (an ex-trader himself): “If testosterone reaches physiological limits, as it might during a market bubble, it can turn risk-taking into a form of addiction, while extreme cortisol during a crash can make traders shun risk altogether. In the present credit crisis traders may feel the noxious effects of chronic cortisol exposure and end up in a psychological state known as ‘learned helplessness.’ If this happens central banks may lower interest rates only to find that traders still refuse to buy risky assets.” ”

Sound like any market conditions you know?

Scientists have not done a comparable study on female traders, who are rarer than hens’ teeth. But the implication is clear. Men are just too hormonal for the public to entrust them with something as crucial as the global financial system. Their raging hormones will be the ruin of us all.

Just for the record, reporters take no pleasure in questioning the power of drugs to treat depression. To the contrary: journalism is notorious for attracting curmudgeons, grumps and depressives—some of my best friends are one or more of the above—so we wish with all our hearts that antidepressants would work.

And that scientists wouldn't keep finding evidence that they do not.

In January I reported on the file-drawer effect in studies of antidepressants. The file-drawer effect refers to the fact that scientifically-sound studies on the efficacy of antidepressants are not published, as The New England Journal of Medicine article described. Most of those studies were negative—that is, the drugs did not help patients much more than a sugar pill (placebo) did, if they helped at all. That skews the perception of doctors, scientists and you and me about these drugs; basing our assessment of antidepressants on published studies alone is like evaluating the prowess of a baseball team when only its wins and not its losses are reported.

Now a team of scientists has examined many of those unpublished studies, obtained through a Freedom of Information Act request for the U.S. Food and Drug Administration. As many people feared, once you include the deep-sixed studies, antidepressants look hardly more effective than a placebo at lifting patients’ black cloud of despair.

For their analysis, scientists led by Irving Kirsch of Britain’s University of Hull started with the data dump they got from the FDA on fluoxetine (Prozac), venlafaxine (Effexor), nefazodone (Serzone), and paroxetine (Seroxat /Paxil). They zeroed in on differences between the improvement reported by patients receiving the drug and those receiving a placebo. As is standard in such clinical trials, neither the patients nor the scientists running the study knew which patients were receiving real drugs and which were receiving placebos.

In short, there was virtually no difference in the response to drug vs. placebo of patients who suffered moderate levels of depression, and a small difference for patients with very severe depression, they report in the study published this evening in the journal PLoS Medicine. That small difference was, however, clinically insignificant—that is, the difference was so small that government health authorities do not recognize it as a meaningful improvement: on a standard scale of depression, patients should improve by 3 points, but the spread between placebo and drug was only 1.8. The difference between drug and placebo was clinically meaningful only for patients at the upper end of the very severely depressed category.

The reason for the tiny, or nonexistent, differences? Patients respond so well to placebo—to the mere thought that something might be helping them—that there was little room for an actual drug to do more. Across all groups, response to placebo accounted for more than 80 percent of any improvement. (In contrast, the placebo response to pain drugs is estimated at about 50 percent.) That suggests that even when patients are taking and benefiting from, say, Zoloft, the vast majority of the improvement is due to what their minds are telling them—that is, the belief that they would be helped. Only the most depressed patients showed little placebo response.

The scientists conclude that there is little reason to prescribe the new antidepressants to any but the most severely depressed patients except as a last resort. Kirsch summarized the findings this way: “Although patients get better when they take antidepressants, they also get better when they take a placebo, and the difference in improvement is not very great. This means that depressed people can improve without chemical treatments.”

But it seems that there is a larger message here. The placebo response—the belief that treatment will make you better—is enormously powerful. Surely it’s time to investigate further how it works and how it can be harnessed.

Bringing Sexy Back: Leah and Partner. Photo:Thomas Breuer - WCS/MPI-EVA.

Scientists from the Wildlife Conservation Society and the Max Planck Institute for Evolutionary Anthropology were minding their own business recently while studying western lowland gorillas in Nouabalé-Ndoki National Park in the Republic of Congo when they saw what no one has ever recorded: Leah and her sweetheart trying to make baby gorillas through face-to-face copulation. Curiously, Leah was also the first wild gorilla to be observed using tools, scientists reported in 2005. (She used a stick to determine the depth of a pool of water before wading into it.) A true pioneer, indeed.

Face-to-face (or, technically, ventro-ventral) copulation is extremely rare in the animal kingdom. Before lowland gorillas joined the club, only people and bonobos (a chimp cousin known for really, really enjoying recreational sex) had been known to look at their partner while mating. From time to time, a scientist in the field reported seeing mountain gorillas mate face-to-face, but the sightings were like those of Bigfoot: no photo, no count. Captive western gorillas have also been known to mate face-to-face, but scientists always wondered if that was an artifact of living in a zoo, not natural.

Thomas Breuer of the Max Planck says, “we can’t say how common this manner of mating is, but it has never been observed with western gorillas in the forest. It is fascinating to see similarities between gorilla and human sexual behavior.” Let that serve as inspiration for the 14th.

Department of unintended consequences: with wild fish populations plummeting worldwide, aquaculture seemed like the best way to satisfy the world’s ever-growing appetite for fish. Oops: according to a new study in the journal PLoS Biology, Atlantic salmon, sea trout and pink, chum, and coho salmon that come into contact with salmon farming in Scotland, Ireland, Atlantic Canada and Pacific Canada are being decimated by the fish farms.

Salmon aquaculture now produces more than one million tons of fish per year. But that doesn’t mean wild salmon stocks are left alone to rebuild. Farm-raised salmon that escape from the open-net pens along coasts—which is how most salmon farms are set up—breed with wild populations, earlier studies showed, meaning the progeny are no longer truly wild. Also, the crowding in salmon farms breeds sea lice (which can be lethal to juvenile fish), as well as other parasites and disease-causing pathogens, which also escape—right into the coastal waters where wild salmon swim en route to and from the open sea. The effect of sea lice alone could be disastrous: according to a study published last December in Science, the parasites are so ravaging opulations of one species of salmon in British Columbia that the populations are projected to plummet 99 percent within eight years.

But while the detrimental effect of salmon farms on wild fish has been known generally, the quantitative impact of swimming past salmon farms has been murkier.

Enter this study, led by Jennifer Ford of the Ecology Action Center and the late Ransom Myers of Dalhousie University. In five regions around the world, they find that the number of wild salmon surviving and returning to spawn after swimming past salmon farms is less than half the survival rate of salmon that do not get anywhere near the farms. Combined with the earlier studies, the findings point to a grim future: salmon farming is so seriously compromising natural populations that scientists expect a 99 percent collapse of wild salmon stocks in four years, or two salmon generations.