Tuesday, May 25, 2010

TEMPLETON DISPROVES GENE ANALYSIS THAT APPEARED TO SUPPORT OUT-OF-AFRICA REPLACEMENT MODEL

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In the sometimes opaque world of statistics, Alan R. Templeton, Ph.D., professor of biology in Arts & Sciences at Washington University in St. Louis, has found that it’s good to know your ABCs.

Templeton, with a doctorate in human genetics and a master’s in statistics, has determined that a recently published genetic analysis of deep human DNA evolution is mathematically erroneous and formally illogical.

The flaws of the analysis are due to the incorrect application of a statistical method known as approximate Bayesian computation (ABC), which led Nelson J. R. Fagundes of the Universidade Federal do Rio Grande do Sul, in Porto Alegre, Brazil, and colleagues to support the validity of the controversial “Out of Africa” replacement hypothesis in a 2007 paper.

This hotly debated hypothesis claims that modern humans emerged out of Africa thousands of years ago and replaced or wiped out existing human populations. Templeton has done an acclaimed 2005 analysis that disputes this model, showing instead a trellis relationship between different human populations, supporting gene “admixture,” or intermingling.

Templeton got the notion to re-address the Fegundes paper when he noticed claims in a student’s paper using Bayes factors that were just too good to be true. Researching Bayes factors in the primary statistics literature, he found a 1999 paper that the probabilities generated by Bayes factors can be incoherent (result in conclusions that violate logic). He then re-read the Fegundes paper.

“When I first read the paper, I thought something was wrong with it, but I’ve got to admit I didn’t see the incoherence,” Templeton says. “I just saw these probabilities, and they didn’t make any sense to me, but I couldn’t quite pin it down. As soon as I read the 1999 paper, I went back to the 2007 one, and I saw that it was massively incoherent. It arrives at probabilities for different models of human evolution that violate the constraints of formal logic.”

Templeton published his analysis, “Coherent and incoherent inference in phylogeography and human evolution,” in a March 22 early addition online issue of PNAS.

In this paper Templeton points out that two models Fegundes examined, the out-of-Africa replacement model and the assimilation model are not independent of one other. The replacement model is just a special case of assimilation model with interbreeding falling to zero.

The problem is says, is that the authors found the probability of the replacement model being true, which is the special case, to be three orders of magnitude greater than the probability that the assimilation model, which is the general case, is true.

“These probabilities are in the wrong direction because they used a technique that was not designed to test nested models and used it on nested models,” Templeton explains. “I show in my paper that the fundamental equation that they used was logically incorrect and mathematically incorrect whenever you have logically overlapping models. In every case where you apply ABC to overlapping models it will give you a mathematical error.”

Templeton is not saying that Bayes factors or ABC cannot be used coherently. In fact, when it’s run on the simulated model B, a general model that is not nested, or not “the special case,” you get a coherent conclusion.

“The ABC method can be used legitimately to test nested hypotheses, but you can’t use the mathematically incorrect equations they use by simulating these models as if they were separate logical entities,” he says. “You can do the general model and then look at the parameter that defines the special case, and when you do that you have a complete reversal.”

Surprise of surprises, when the coherence is corrected in the Fegundes model, “it’s completely compatible with my earlier trellis model,” Templeton says.

(Photo: Wikimedia Commons/Antropological Institute, University of Zurick)

Washington University in St. Louis

BRAIN MECHANISM EVOLVED TO IDENTIFY THOSE WITH A PROPENSITY TO CHEAT

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New research by scholars at UC Santa Barbara indicates that the uncanny human ability to detect cheaters reflects the operation of a reasoning system that evolved for that narrow purpose, and cannot be explained by more general abilities to reason about conditional rules, moral violations, or social interactions. Their findings appear in the current issue of the Proceedings of the National Academy of Sciences (PNAS).

According to the authors, this system becomes activated only when detecting a violation that has the potential to reveal a specific aspect of someone's character –– his or her propensity to cheat.

The new findings, which build on research presented in a 2002 PNAS paper highlighting neuroscientific evidence of a distinct cheater detection system, specifically debunk the blank-slate theory of human intelligence. This competing view attempts to explain special abilities like cheater detection as the product of experience plus a general capacity to learn or reason.

"The thing that's startling about the results is how specialized this reasoning mechanism turns out to be," said Leda Cosmides, a co-author of the paper. She is a professor of psychology and co-director of UCSB's Center for Evolutionary Psychology. Cosmides wrote the current PNAS paper with John Tooby, a professor of anthropology and also co-director of the Center for Evolutionary Psychology; and H. Clark Barrett, formerly of the Center for Evolutionary Psychology and now associate professor of anthropology at UCLA.

Social exchange is the form of cooperation that occurs when people trade or reciprocate favors. "Evolutionary analyses have shown that social exchange cannot evolve unless individuals are able to detect those who cheat," said Barrett. "Therefore, from an evolutionary standpoint, the function of detecting acts of cheating is to connect them to an identity –– to deduce character."

However, only some violations of social contracts are relevant to assessing character. "For example, someone can be deprived of what he or she is entitled to by an innocent mistake or when something accidentally interferes. In those cases, mentally flagging a violation would not reveal the presence of a cheater," said Cosmides.

"If this ability was produced by general learning abilities operating on experience," Tooby pointed out, "then you would expect it to detect the broad range of violations that people actually experience and suffer from –– incidents of cheating, accidents, innocent mistakes, and so on. All of these equally deprive people of what they are entitled to, and what they are motivated to recover. Indeed, the fastest, simplest, and most informative cognitive step would be to learn to uniformly detect all violations of social contracts."

Yet that is not what the mind does. The researchers found that the violation detection system is more complex and selective, with computational steps that respond to the intentions of the partner, whether the partner was in a position to cheat, and whether the partner could have benefited by the violation. The system remains inactive –– that is, it tends not to notice violations –– when confronting situations where people are deprived of what they are entitled to, but for reasons that are unlikely to expose cheaters.

"This reasoning system does not respond to economic consequences per se. It focuses only on those violations that are likely to reveal cheaters –– individuals who take the benefit offered in an exchange while intentionally failing to do what the other person required in return," Cosmides said. "It ignores the others. This matches the evolutionary prediction that the system's function is sifting for people who cheat."

"The system is most strongly activated when there are cues that the violator is acting intentionally, will get the benefit regulated by the rule, and has the ability to do all of this," Barrett explained. "Take away one of these three elements and reasoning performance drops sharply; take away two and it drops to the same baseline incompetence the mind exhibits when reasoning about most conditional rules, such as moral rules." That is, only a narrow range of conditions activate the cheater detection system: "It does not search for violations of social exchange rules when these are accidental, when they do not benefit the violator, or when the situation would make cheating difficult," he said.

"These experiments were designed to rule out every alternative hypothesis that we know of about why people are skilled at detecting cheaters. No other theory predicts this pattern of results," said Cosmides.

"It takes a moment to appreciate how inconsistent these results are with traditional ways of thinking," noted Tooby. "Learning theories, economic theories, and motivational theories all predict that skill acquisition or performance should be at least partly a function of payoff. Here, innocent mistakes, cheating, and accidents all lead to the same payoff for the people who did not get what they were entitled to –– zero –– and detection of the violation is a necessary first step toward recovering the lost benefit. Yet, the mind tends to disregard those losses that don't expose cheaters."

"If you take away the cues that indicate a person is predisposed to cheat, the mechanism isn't activated," Cosmides added. "That's what falls out of the evolutionary theorizing. Evolutionary theory says you should be looking for people who are cheating by design, not by accident," she said.

(Photo: UCSB)

UC Santa Barbara

COBALT CATALYSTS FOR SIMPLE WATER SPLITTING

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Researchers from UC Davis and the Massachusetts Institute of Technology are studying how a simple cobalt catalyst can split water molecules. Such inexpensive catalysts could one day be used to convert sunlight into fuel that can run domestic fuel cells.

In 2008, MIT chemists, led by Professor Dan Nocera, reported that a simple cobalt catalyst could split water at neutral pH to produce oxygen, protons and electrons. The catalyst actually seems to assemble itself over several hours as an electric current is applied, and then begins to bubble oxygen.

"This got a lot of attention from the chemistry community, but no one knew how it worked," said R. David Britt, professor of chemistry at UC Davis.

Britt's lab is working with Nocera's group to use a technique called electron paramagnetic resonance to study the chemical state of cobalt atoms in the catalyst. They found that as more water is split, the proportion of cobalt (IV) increases and the proportion of cobalt (II) decreases. The work opens the door to further studies on these catalysts, the authors write.

Ultimately, catalysts based on relatively abundant elements like cobalt, as opposed to platinum or gold, could make it economical to convert electricity from solar panels or other renewable sources into hydrogen fuel for storage or use. The protons and electrons produced from splitting water would be used in the next step of the process to make hydrogen.

Electron paramagnetic resonance is a technique similar to the nuclear magnetic resonance used in medical imaging. Britt's lab uses it to study catalysts that split water, including both artificial catalysts and those used by plants in photosynthesis.

"Plants figured this out a couple of billion years ago," Britt said.

UC Davis

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