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By Michael White | March 18th 2009 05:20 PM | 17 comments | Print | E-mail | Track Comments
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About Michael White

Welcome to Adaptive Complexity, where I write about genomics, systems biology, evolution, and the connection between science and literature, government, and society.

I'm a biochemist


... Full Bio


Bell curves are everywhere. Pick 100 random people and measure them: measure their height, their weight, their blood pressure, their time to run a mile, or to sprint 50 yards, and their IQ, and you find that most of us fall in the middle of the spectrum, while there are always some people on either extreme. Why?

The puzzle grows deeper when you think about genetics. If a trait like height is controlled largely by genes, how is it that height falls into a bell-curve pattern? Bell-curves seem completely at odds with what we learn about the discrete genetics of Mendel's round and wrinkled peas in high school biology.

It turns out that the solution to this puzzle is fairly simple (although the details get messy). In fact, Darwin's cousin hit on the right answer (long before he or anyone else knew about Mendel's genetics), with what he called the "Supreme Law of Unreason": a bell curve is exactly what you expect when you toss together "a large sample of chaotic elements." In other words, genetics is like one big game of The Price Is Right.

The puzzle comes from Mendel's pioneering (but ignored) genetic experiments. He found that genes act like discrete units: Snapdragons have (in a simplified example) two different versions of a color gene: one version of the color gene produces red snapdragons, while another version of that same gene produces white ones. Since each snapdragon plant gets two copies of each gene, it can have two red copies, two white copies, or one red and one white copy. These plants are either red, white, or pink, depending on which two copies of the gene they have.

Under this arrangement, only three shades of color are possible; red, pink, and white. But in nature, most traits don't fall into distinct categories. Think of human height - there is obviously no single 'height gene' that comes in small, medium and large versions. If genes can be lumped into discrete categories, how do you produce a bell curve pattern? Are there hundreds of different versions of the 'height gene'?

The first person to come up with the right answer, although he didn't know about Mendel, was Darwin's cousin, Francis Galton. Francis Galton was a pioneer when it came to applying statistics to biology, and he argued that scientists weren't paying enough attention to the message of bell curves:

It is difficult to understand why statisticians commonly limit their inquiries to Averages, and do not revel in more comprehensive views. Their souls seem as dull to the charm of variety as that of the native of one of our flat English counties, whose retrospect of Switzerland was that, if is mountains could be thrown into its lakes, two nuisances would be got rid of at once.

Bell curves, argued Galton, are telling us something about the underlying causes of biological traits - why some people are taller, stronger, heavier, or faster than others. He illustrated this idea with what is basically a modified pin-ball machine turned on its side. This device is called a Galton Board - better known today as a Plinko board, from the Price is Right:



Today's Plinko Board:



Galton in 1889:




The idea is that you drop a ball in at the top, it knocks around among the pins for awhile and then lands in one of the compartments at the bottom. If you have enough compartments, and drop in enough balls, you're guaranteed to get (with an unbiased board) a bell curve  distribution of the balls among the compartments. The ball's final position depends on the sum of all of its encounters with the pins - it gets knocked to the right a little, to the left a little, and, in most cases, it ends up somewhere in one of the middle compartments. A few lucky balls get knocked to the right, over and over, and thus end up at the far right; the opposite happens for some balls that end up on the left. But by far the most likely scenario is that the knocks to the left and the right cancel each other out, and the ball ends up in the middle. There are only a few possible paths a ball can take to the extremes, and many, many ways for a ball to end up in the middle.

"This," Galton explained, "illustrates and explains the reason why mediocrity is so common."

Galton called this the supreme law of unreason:

The supreme law of Unreason: Whenever a large sample of chaotic elements are taken in hand and marshaled in the order of their magnitude, an unsuspected and most beautiful form of regularity proves to have been latent all along.

The more respectable and boring name used today is The Central Limit Theorem.

So what does this mean for genetics? It means that for most traits, multiple genetic factors (and, in most cases, environmental factors too) contribute to the final outcome. Imagine genes (or more accurately, genetic variants) as pins on a Plinko board: you have some genetic variants that make you a little taller, some which knock you in the smaller direction; their combined effect determines how tall you are. Only a few people are really tall, or really short, because to be really tall or short, you have to get a statistically unlikely combination of genetic variants.

This solves the puzzle posed by Mendel's experiments: individual genes may be discrete, but combinations of genes can produce traits. There is no single height gene, coming in hundreds of flavors to produce different human heights; height is instead affected by multiple genes. Bell curves are exactly what we expect to see if our height (or weight, or blood pressure, or IQ) was the combined result of multiple independent factors.

Galton puts it better:

The beautiful regularity in the statures of a population, whenever they are statistically marshaled in the order of their heights, is due to the number of variable and quasi-independent elements of which Stature is the sum.

Comments

jtwitten's picture
Dude, there is nothing mediocre about Plinko.

adaptivecomplexity's picture
Of course not! There are only mediocre players of Plinko.

Really nicely explained!

logicman's picture
The supreme law of Unreason: Whenever a large sample of chaotic elements are taken in hand and marshaled -

- they spontaneously group themselves into an inverted bell curve distribution and start trying to run the country.

At least 'Plinko' is a lot easier to pronounce than quincunx. :)

adaptivecomplexity's picture
At least 'Plinko' is a lot easier to pronounce than quincunx. :)

Yeah, I had 4-5 different games to choose from to illustrate a Galton board. The Price is Right won hands down.

The more respectable and boring name used today is The Central Limit Theorem.

Some (like me) would say "the sum of a sufficiently large number of independent random variables, each with finite mean and variance, will be approximately normally distributed" is a much more succinct and elegant statement, equivalent in information and insight to this entire blog post. :)

Hank's picture
Ouch.  Nash makes a fine distillation, though I like the example using Mendel.    We can't be too simple just for the sake of being simple, else we end up with the whole existence of humans as something people say like "Adam had'em".

 :)

jtwitten's picture
equivalent in information and insight to this entire blog post

Information? Perhaps.  Insight.  No.  Provided that one accepts that the goal of the post is to show how the Central Limit Theorem applies to genetics (i.e., how effectively binary genotypes can combine to generate normally distributed phenotypes), not to simply explain the basis of the theorem.  Mike manages to do so in a manner that engages the reader's intellect and interest.

adaptivecomplexity's picture
If this post were just about the Central Limit Theorem, the whole point could be stated more elegantly. I don't really know the history of the Central Limit Theorem - who first proved it, etc. Galton was the first, as far as I know, to use it to explain bell curves in human physical traits - an insight that was later worked out more rigorously by Fischer, who was the one to reconcile Galton and Mendel.
What's interesting about Galton's approach is that he focused directly on natural variation in genetically controlled traits. Mendel did is work with a much more simplified system, and as a result discovered that genes segregate as discrete units, something Galton never would have been able to discover by beginning with the full complexity of quantitative traits.


logicman's picture
"the sum of a sufficiently large number of independent random
variables, each with finite mean and variance, will be approximately
normally distributed" is a much more succinct and elegant statement,
equivalent in information and insight to this entire blog post.

Nope!   If you want 'succinct and elegant', try Euler's identity, but not at the airport check-in.     :)

edit: @Hank - Adam's first words were edited out of most editions of the Bible:

"Wow!  Looka teh trees, man!  Like, is this garden coool or waht?"

adaptivecomplexity's picture
If you want 'succinct and elegant', try Euler's identity,

Nothing in biology can compete with that - probably one of the most elegant and succinct ideas ever.
But the Central Limit Theorem comes close, because it explains so much.


Wow thanks for the enlightening article.

I passed my stats classes, but I have to admit I have trouble conceptualizing the theory behind the bell curve & normal distributions. This article helps, but I never understood why so many traits are thought (or found) to be normally distributed. How do we know that this isn't a function of the traits that we decide to measure, or how we measure them?

On the subject of an intelligence, Spearman found that the construct of IQ statistically breaks down at the higher end of the intelligence spectrum (he deemed this the law of diminishing returns). While the overall IQ scores from smarter people might still fall in line at the right end of the curve, the individual attributes that make up this score are less correlated & tied together. In other words, high IQ means something different than low IQ - the former is a less unified & statistically sound construct. I've always interpreted this as evidence of the asymmetrical nature of intelligence, & perhaps of the world in general (http://cntrly.blogspot.com/2008/11/good-bad-and-positive.html)

Intelligence - as conceptualized in IQ - is a combination of multiple sub-traits (eg, working memory, visual/verbal, etc). But why would these sub-traits correlate w/each other more at the lower end & less at the higher end? My hunch is that it's b/c a minimal amount or even avg. amount of each of these sub-traits is necessary for survival, but beyond, perhaps, avg or slightly above avg, the construct loses meaning. Which implies that the natural laws that govern stupidity are categorically different from those that cover genius. I think this also speaks to the piecemeal or categorical nature of intelligence rather than the continuous normally distributed picture that's often given. In the end, IQ might just be normally distributed b/c of IQ tests' psychometric properties.

I also think that it speaks to the question of "why mediocrity is common", b/c really you're looking at mediocrity *relative* to some sort of characteristic or trait, not relative to a whole organism. & along these lines, it's easier to see how most people are bound to be mediocre at most things while at the same time being better or exceptional at a few things, call them specialties. Perhaps it's nature's way of pushing us towards division of labor. Afterall, as people get smarter, they're most likely not getting smarter in every aspect of intelligence, but in a certain sub-facet of it.

logicman's picture
Kerrjak: I'm glad you posted this comment -  I'm currently preparing an article on intelligence.

the natural laws that govern stupidity are categorically different from those that cover genius.

I think this is, to a degree, an artifact of the skewed IQ data that you have commented. I am actually using an inversion model (intelligence = non-stupidity) in an effort to show the underlying mechanisms of intelligence.

I had quite forgotten about Phineas Gage - that case turned up very early in my studies of cognition and brain function. The surviving evidence from the Gage incident is too fragmentary to make a valid cognitive theory out of it, but it does show the 'un-wisdom' of using a simple iron bar for tamping explosives!

From your blog:
there are a few tangible and quantifiable ways to be dumb, but there are many more ways to be smart.

Oh, yes!

adaptivecomplexity's picture
This article helps, but I never understood why so many traits are thought (or found) to be normally distributed. How do we know that this isn't a function of the traits that we decide to measure, or how we measure them? 

The distribution can be a function of what we decide to measure - as long as whatever you decide to measure is the cumulative effect of multiple, independent, random factors, you can get something that approximates the normal distribution. You can make up a completely artificial trait (comprised of other traits), and this will turn out normal. 
So, even if IQ is a somewhat arbitrary construct made up of various 'sub-traits' or specialized forms of intelligence (however you want to define them), it's not surprising that IQ would be normally distributed.

There are all sorts of qualifiers to add here - for example, I'm not familiar enough with the stats to know what happens if the underlying factors are somehow correlated with each other (as you mention for IQ), and obviously other distributions are observed in nature. Things get messier here.




logicman's picture
Things get messier here.

Here as in: in this area of study, or here as in: on this website?  Or both? :)
Ambiguity is a whole lot more fun than ambivalence.  On the other hand ...

Re:  IQ testing. 
When I was at junior school I paid attention when the teachers were showing us how to pass an IQ test.  I was in the group selected for grammar school.  I am confident that training must invalidate such tests to a very high degree.  Surely, given the training, an IQ test measures either ability to learn or ability to make exceedingly good guesses based on observational data.  Perhaps it just measures the ability of kids to pay attention to teachers in a classroom?  In the latter case, suppose a teacher to be a climate change and/or evolution denier.  Is paying attention to what such a person says anything at all to do with what the IQ test is supposed to be measuring?  If so, then surely  I.Q. = Impressionability Quotient.

Glad you found the comment/post interesting, Patrick.

I'd be curious to see that paper on intelligence if you'd be willing to post a link whenever it's up. I like the model of intelligence as non-stupid. It flies in the face of positive psychology, but I think that those sort of deficit models have the most potential, similar to defining healthiness by a lack of disease, rather than by, I don't know, a high healthiness quotient. Part of the key, I suspect, lies in the fractionated nature of intelligence. It really is a combination of smaller sub-facets, which are only partially related/hierarchical.

You might also be interested in the executive functions literature, if you haven't read up on them already. It's tied to intelligence, a "cognitive" construct, but it's solely defined by a deficit model, as there's no such thing as high executive functioning aside from, perhaps, a lack of poor executive functioning. In particular you might check out Russell Barkley's perspective, as it borrows heavily from some of Bronowski's language work (along with Fuster's work on brain damaged patients); he mainly tied executive functions into an overarching theory of ADHD, but he also had an interesting 2001 paper (http://www.springerlink.com/content/g3161x021050t454/) speculating about their role in evolutionary/social development.

The picture I'm getting is that it's more about sub-facets of intelligence than say an overall IQ. This makes sense in an evolutionary/social context, where it would be pointless for everyone's intelligence to be along the same dimension like height. Instead, wouldn't it be more likely for evolution to push for diverse & specialized types of intelligence among humans, similar to more extremely social animals like ants and bees? Along these lines I would think that there would also be a sort of baseline or threshold for minimal intelligence along many of these facets, particularly pertaining to verbal skills & communication. Not everyone needs to be a master poet or really good at SAT word analogies, but everyone has to have a basic degree of verbal intelligence to communicate. Hence again the assymtetical nature of intelligence, where a lack of it leads to much worse, or at least much different, costs, than a plethora of it leads to benefits. & maybe(?) there's a case that since we're dealing with so many quasi-distinct traits, you would get a bell-curve if you averaged across all of them.

logicman's picture
The 'intelligence' blog will be a while, but will be posted in my blog here.  (I have a 3-parter on 'time' to finish first.)  Briefly, I agree with the multi-facet model of human intelligence.  But what I'm looking at is: what is the smallest possible mechanism that might be called intelligent?  Is there such a thing as a smart molecule, or does it take two to be smart, or three?  The proof that there exists a smart S lies in determining if people can agree that there is such a thing as a stupid S.   Stupid is as NOT(intelligence) does, so to speak.

At the other end of the scale, the human communication level of intelligence, there are multiple mechanisms to consider - body language, sign language, semiotics, pragmatics and a heck of a lot of other things.  There is a whole world of difference between talking and communicating.  There is a whole bundle of know-how facets of intelligence, and a whole other bunch of know-that intelligence.

IQ tests as on-paper tests do not measure practical-heuristic intelligence.  Right, so you can calculate pi to 3,000 decimals in your head, but can you figure out all on your own how to change a spark-plug?

edit:
Final thought for now.  In programming, there is a device called the CEO function - it can step in and grant or withdraw permissions for other functions - very useful.  A step towards computer intelligence?

(For those who may not know, CEO = Chief Executive Officer.)

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