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Superstitions evolved to help us survive

Superstitions evolved to help us survive according to this New Scientist article.

Darwin never warned against crossing black cats, walking under ladders or stepping on cracks in the pavement, but his theory of natural selection explains why people believe in such nonsense.

The tendency to falsely link cause to effect – a superstition – is occasionally beneficial, says Kevin Foster, an evolutionary biologist at Harvard University.

For instance, a prehistoric human might associate rustling grass with the approach of a predator and hide. Most of the time, the wind will have caused the sound, but “if a group of lions is coming there’s a huge benefit to not being around,” Foster says.

Foster and colleague Hanna Kokko, of the University of Helsinki, Finland, sought to determine exactly when such potentially false connections pay off.
Simplified behaviour

Rather than author just-so stories for every possible superstition – from lucky rabbit’s feet to Mayan numerology – Foster and Kokko worked with mathematical language and a simple definition for superstition that includes animals and even bacteria.

The pair modelled the situations in which superstition is adaptive. As long as the cost of believing a superstition is less than the cost of missing a real association, superstitious beliefs will be favoured.

Read the full article.

Men are from mars, women are crybabies

Howstuffworks has an interesting article on the biological and psychological differences between men and women. Some interesting excerpts:

Women’s tear ducts are also shaped a little differently from men’s, which could be either a cause or an effect of increased crying [Source: New York Times]. In addition, people who are depressed may cry four times as much as people who are not, and two-thirds of people diagnosed with depression are women [Psychology Today].

but things are more complicated, and in general there’s a good reason why women and hormones have such a bad relationship:

Studies show that, in addition to worrying more often, women may be physiologically prone to experiencing more stress. For example, the amygdala of the brain processes emotions like fear and anxiety. In men, the amygdala communicates with organs that take in and process visual information, like the visual cortex. In women, though, it communicates with parts of the brain that regulate hormones and digestion. This may mean that stress responses are more likely to cause physical symptoms in women than in men [Source: Live Science].

and to top it all off:

In addition, women’s bodies produce more stress hormones than men’s bodies do. Once a stressful event is over, women’s bodies also take longer to stop producing the hormones. This may be a cause or an effect of women’s tendency to replay stressful events in their minds and think about upsetting situations [Source: Psychology Today].

But the most interesting part is this:

In one German study, researchers showed participants images of several scenarios. The participants used a computer to describe which of the scenarios would be more upsetting. The results suggest that, across cultures, women find emotional infidelity more upsetting than sexual infidelity. Men’s responses varied across cultures, but in general they were jealous of sexual infidelity [Source: Human Nature].

Sounds like it should be OK to have sex with other women as long as you continue bringing flowers to your wife.

Want to Remember Everything You’ll Ever Learn? Surrender to This Algorithm

Wired an interesting article on a guy who, after a detailed study of how human memory works, has developed SuperMemo, a software program that will allow you to remember many more things than you currently can:

SuperMemo is based on the insight that there is an ideal moment to practice what you’ve learned. Practice too soon and you waste your time. Practice too late and you’ve forgotten the material and have to relearn it. The right time to practice is just at the moment you’re about to forget. Unfortunately, this moment is different for every person and each bit of information. Imagine a pile of thousands of flash cards. Somewhere in this pile are the ones you should be practicing right now. Which are they?

Fortunately, human forgetting follows a pattern. We forget exponentially. A graph of our likelihood of getting the correct answer on a quiz sweeps quickly downward over time and then levels off. This pattern has long been known to cognitive psychology, but it has been difficult to put to practical use. It’s too complex for us to employ with our naked brains.

Twenty years ago, Wozniak realized that computers could easily calculate the moment of forgetting if he could discover the right algorithm. SuperMemo is the result of his research. It predicts the future state of a person’s memory and schedules information reviews at the optimal time. The effect is striking. Users can seal huge quantities of vocabulary into their brains.

While initially I found myself tempted by the idea of trying this software, or at least the techniques used in it, I soon decided that in my current situation in life, I don’t really want to memorize anything so desperately. More generally, if I read something, and then I forget it later because I did not encounter it again in my readings soon enough, I believe that it was not important enough to remember in the first place. I only want to memorize the things that keep showing up in my readings.

Your mileage may vary. And even if it doesn’t, you should still read the full article. It’s rather long, but parts of it are quite interesting.

Why I cannot resist surfing the web

Apparently, scientists are doing research into why I spend so much time surfing the web (and indeed why you are reading this). New information, or information that needs to be analyzed gives us a high. Wall Street Journal has an article about this research:

Dr. Biederman first showed a collection of photographs to volunteer test subjects, and found they said they preferred certain kinds of pictures (monkeys in a tree or a group of houses along a river) over others (an empty parking lot or a pile of old paint cans).

The preferred pictures had certain common features, including a good vantage on a landscape and an element of mystery. In one way or another, said Dr. Biederman, they all presented new information that somehow needed to be interpreted.

When he hooked up volunteers to a brain-scanning machine, the preferred pictures were shown to generate much more brain activity than the unpreferred shots. While researchers don’t yet know what exactly these brain scans signify, a likely possibility involves increased production of the brain’s pleasure-enhancing neurotransmitters called opioids.

In other words, coming across what Dr. Biederman calls new and richly interpretable information triggers a chemical reaction that makes us feel good, which in turn causes us to seek out even more of it. The reverse is true as well: We want to avoid not getting those hits because, for one, we are so averse to boredom.

It is something we seem hard-wired to do, says Dr. Biederman. When you find new information, you get an opioid hit, and we are junkies for those. You might call us ‘infovores.’ ”

For most of human history, there was little chance of overdosing on information, because any one day in the Olduvai Gorge was a lot like any other. Today, though, we can find in the course of a few hours online more information than our ancient ancestors could in their whole lives.

Apparently, this is hardwired into our brain due to evolutionary forces. Just like cats and laser pointers:

Many cat owners know that the lasers are the easiest way to keep the pet amused. The cats will ceaselessly, maniacally chase it as it’s beamed about the room, literally climbing the walls to capture what they surely regard as some form of ultimate prey.

Obviously, cats are hard-wired to hunt down small, bright objects, like birds. But since nothing in nature is as bright as a laser, they are powerless to resist its charms.

[...]

Watching a cat play with a laser, you realize the cat never learns there is no real “prey” there. You can show the cat the pointer, clicking it off and on, and it will remain transfixed.

But we can hope that:

People presumably are smarter than cats, and as we become more familiar with the Web and its torrent of information, maybe we’ll do a better job learning what is useful and what isn’t.

Then again, maybe not.

See full article.

Bloggers are happier - feel bloggers

ABCnews is reporting on a study which says that after a couple of months of regular blogging bloggers feel they have a better social life than non-bloggers. Excerpt:

The research, from Swinburne University of Technology in Melbourne, found after two months of regular blogging, people felt they had better social support and friendship networks than those who did not blog.

[...]

Bloggers reported a greater sense of belonging to a group of like-minded people and feeling more confident they could rely on others for help.

All respondents, whether or not they blogged, reported feeling less anxious, depressed and stressed after two months of online social networking.

Blogging is good for your (perceived?) social life. And online social networks are good for your mental health. Cool. (Looks like the bloggers in question hadn’t started receiving comments from users yet!)

See full article (via techcrunch)

The Nocebo Effect: Placebo’s Evil Twin

The Washington Post has an interesting article about the nocebo effect:

Ten years ago, researchers stumbled onto a striking finding: Women who believed that they were prone to heart disease were nearly four times as likely to die as women with similar risk factors who didn’t hold such fatalistic views.

The higher risk of death, in other words, had nothing to with the usual heart disease culprits — age, blood pressure, cholesterol, weight. Instead, it tracked closely with belief. Think sick, be sick.
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That study is a classic in the annals of research on the “nocebo” phenomenon, the evil twin of the placebo effect. While the placebo effect refers to health benefits produced by a treatment that should have no effect, patients experiencing the nocebo effect experience the opposite. They presume the worst, health-wise, and that’s just what they get.

Another example:

Fifteen years ago, researchers at three medical centers undertook a study of aspirin and another blood thinner in heart patients and came up with an unexpected result that said little about the heart and much about the brain. At two locations, patients were warned of possible gastrointestinal problems, one of the most common side effects of repeated use of aspirin. At the other location, patients received no such caution.

When researchers reviewed the data, they found a striking result: Those warned about the gastrointestinal problems were almost three times as likely to have the side effect. Though the evidence of actual stomach damage such as ulcers was the same for all three groups, those with the most information about the prospect of minor problems were the most likely to experience the pain.

So why haven’t you heard of this nocebo effect before? Here’s why:

Despite the smattering of doctors’ anecdotal reports and a few modest clinical studies, research on the phenomenon has not been robust, mostly for ethical reasons: Doctors ought not to induce illness in patients who are not sick.

See full article (found via A.Word.A.Day).

Why is beauty?

I know the title does not make grammatical sense. But think about it. Normally we focus a lot on who is beautiful. Not so much on the why. I’ve alluded in the past to the evolutionary reasons behind beauty. This idea is expounded in detail by Nancy Etcoff in her book Survival of the Prettiest: The Science of Beauty, which makes the case that “looking good has survival value, and that sensitivity to beauty is a biological adaptation governed by brain circuits shaped by natural selection”.

This is what boing-boing has to say about the idea:

Why do we think that certain things are beautiful? Because our ancestors did; it connotes an advantage to survival and reproduction.

When people are asked to describe a beautiful landscape they say the same thing: lake, river, mountain trees. We evolved to think it is beautiful becuase it is safe with escape routes.

When asked to describe beautiful people: clear skin, bright eyes, shiny hair — all of these things connote health fertility, protection.

There is interesting research backing up these claims:

Psychologists find that babies stare significantly longer at the faces adults find appealing, while the mothers of “attractive” babies display more intense bonding behaviors. The symmetrical face of average proportions may have become the optimal design because of evolutionary pressures operating against population extremes. Gentlemen may prefer blondes not so much for their hair color as for the fairness of their skin–which makes it easier to detect the flush of sexual excitement.

(source: Amazon’s review of the book.)

While looking into the background of these claims, I stumbled onto a bunch of fascinating facts about beauty and related aspects (like the handicap principle) that I plan to cover in future posts. Stay tuned.

Why Don’t The French Get As Fat As Americans?

This article reports on new research that has the answer:

Because they use internal cues — such as no longer feeling hungry — to stop eating, reports a new Cornell study. Americans, on the other hand, tend to use external cues — such as whether their plate is clean, they have run out of their beverage or the TV show they’re watching is over.

See full article. (Found via boing-boing.)

Scientists Create See-Through Fish, Watch Cancer Grow

Through cross-breeding, scientists have created a zebra-fish with a transparent skin so that they can actually see the insides of the fish. Zebra fish are genetically similar to humans in a number of ways, and hence serve as good subjects for experiments like this. These are now being used to understand cancer. Excerpt:

White created the transparent fish, which he nicknamed Casper, by mating two existing zebrafish breeds, one that lacked a reflective skin pigment and the other without black pigment. The offspring had only yellow skin pigment, essentially appearing clear.

In one experiment, White and his colleagues inserted a fluorescent melanoma tumor into the abdominal cavity of the transparent fish. By observing the fish under a microscope, they found that the cancer cells started spreading within five days. White could actually see individual cells spreading.

See full article. They also have a picture of the fish for you to ogle.

Reducing Teacher absenteeism in rural India

Earlier, I had blogged about the use of cameras to tackle teacher absenteeism in rural India’s schools. One commenter asked for more details, and I went digging for the original research paper and found that it is quite interesting and worth writing about.

This data comes from a paper by Esther Duflo, a professor at MIT. The actual teacher program incentive was run by Seva Mandir an NGO. Duflo and her colleagues analyzed the results and reached some interesting conclusions.

What exactly is the question that we are trying answer?

We ask three main questions: If teachers are given incentives to attend school, will they actually attend school more? If they attend school more, will they teach more? Finally, if teacher absenteeism is reduced, will children learn more?

Consider the first question. It is not obvious that increased incentives to teachers will result increased attendance:

[...] the incentives could fail to improve attendance for a variety of reasons. First, teachers may be unable to take advantage of the incentives if they must participate in village meetings, training sessions, election or census duty. These pressures may be particularly high on para-teachers, who are often among the few literate individuals in the village. Second, the incentive schemes may crowd out the teacher’s intrinsic motivation to attend school (Benabou and Tirole, 2006). Finally, some teachers, who previously believed that they were required to work every day, may decide to stop working once they have reached their target income for the month (Fehr and Gotte, 2002).

The last two sentences of that paragraph point towards some interesting research showing how well-meaning schemes can actually be counter productive. (I am lazy so I haven’t included the full references here. If you are interested, go to the original paper and find the references at the end of the paper.)

Anyway, even if we manage to get teachers into the classrooms, that might not mean anything:

Even if incentives increase teacher attendance, it is unclear whether child learning levels will actually increase. Teachers may multitask (Holmstrom and Milgrom, 1991), reducing their efforts along other dimensions. Such schemes may also demoralize teachers, resulting in less effort (Fehr and Schmidt, 2004), or may harm teachers’ intrinsic motivation to teach (Kreps, 1997). On the other hand, incentives can improve learning levels if the main cost of working is the opportunity cost of attending school and, once in school, the marginal cost of teaching is low. In this case, an incentive system that directly rewards presence would stand a good chance of increasing child learning. Thus, whether or not the incentives can improve school quality is ultimately an empirical question.

Again, things are not as simple as one would initially have though. Will all that background out of the way, we get on to the real research:

We study a teacher incentive program run by the NGO Seva Mandir. Seva Mandir runs single teacher NFEs in the rural villages of Rajasthan, India. As in many rural areas, teacher absenteeism is high, despite the threat of dismissal for repeated absence. In our baseline study (August 2003), the absence rate was 44 percent. Faced with such high absenteeism, Seva Mandir implemented an innovative monitoring and incentive program in September 2003. In 57 randomly selected program schools, Seva Mandir gave teachers a camera, along with instructions to have one of the students take a picture of the teacher and the other students at the start and close of each school day. The cameras had tamper-proof date and time functions, allowing for the collection of precise data on teacher attendance that could be used to calculate teachers’ salaries.

Yup, the attendance records were used to determine the salaries of the teachers. So there was a financial (dis)incentive scheme at work:

Each teacher was then paid according to a non-linear function of the number of valid school days for which they were actually present, where a “valid” day was defined as one for which the opening and closing photographs were separated by at least five hours and both photographs showed at least eight children. Specifically, they received Rs 500 if they attended fewer than 10 days in a given month, and Rs 50 for any additional day (up to a maximum of 25 or 26 days depending on the month). In the 56 comparison schools, teachers were paid a fixed rate for the month, and were told (as usual) that they could be dismissed for repeated, unexcused absences.

Well, that is the experiment. Now on to the results:

The program resulted in an immediate and long lasting improvement in teacher attendance rates in treatment schools, as measured through monthly unannounced visits in both treatment and comparison schools. Over the 30 months in which attendance was tracked, teachers at program schools had an absence rate of 21 percent, compared to 44 percent baseline and the 42 percent in the comparison schools. Absence rates stayed low after the end of the proper evaluation phase (the first fourteen months of the program), suggesting that teachers did not change their behavior simply for the evaluation.

Absenteeism halved! And stayed that way.

Now on to another interesting question. How much of the improvement was because of cameras (the monitoring) and how much of the effect was because of the financial incentive scheme? What if the financial incentives were different, or not present at all? That is where the MIT economists broke out their complex math formulae to figure this out:

To answer these questions, we exploit the non-linear nature of the incentive scheme to estimate a dynamic labor supply model using the daily attendance data in the treatment schools. The identification exploits the fact that the incentive for a teacher to attend school on a single day changes as a function of the number of days they attend school in the month, and the number of days left in the month. This is because they have to attend at least 10 days in a month to begin to receive the incentive (by working in the beginning of the month, the teacher builds up the option to work for Rs 50 per day at the end of the month). Indeed, regression discontinuity design estimates show that teachers work significantly more at the beginning of the month than at the end of the previous month, when they had not accumulated at least 10 days of work in that month.

We use this fact to estimate the teachers’ marginal utility of money. We allow serial correlation in the opportunity cost of attending school and heterogeneity in teachers’ outside option, and we use the method of simulated moments to estimate the parameters. Allowing for serial correlation and heterogeneity considerably complicates the estimation procedure, but we show that these features are very important in this application.

And according to their calculations:

We find that teachers are very responsive to the financial incentives: our preferred estimates suggest that the elasticity of labor supply with respect to the level of the financial bonus is 0.306. Furthermore, decreasing the number of days that workers must work until they are eligible for the incentive by a single day increases the expected number of days worked by about 1.29 percent. An unusual feature of this application is the ability to carry out convincing out-of-sample tests based on the randomized evaluation (as in Todd and Wolpin (2007)). When allowing for serial correlation and heterogeneity, we find that our model accurately predicts the difference in attendance in the treatment and the control group, as well as the number of days worked under a new incentive system initiated by Seva Mandir after the experiment.

I don’t think I really understand the meaning of the numbers in that paragraph, but basically, the financial incentives were very important. i.e. You can’t implement this scheme with just cameras alone. Simple threats “that they could be dismissed for repeated, unexcused absences” are not good enough. You need to be able to control their salaries.

But at the end of all this, the fruits of labor are sweet:

Student attendance when the school was open was similar in both groups, so student in treatment group received more days of instruction. A year into the program, test scores in the treatment schools were 0.17 standard deviations higher than in the comparison schools. Two and a half years into the program, children from the treatment schools were also 10 percentage points (or 62 percent) more likely to transfer to formal primary schools, which requires passing a competency test.

See full paper. More interesting than watching Twenty20 these days…