Computational behavior theory and cultural evolution

Tag Archives: science

Confidence Intervals

I have not found an accepted way to write asymmetrical confidence intervals. Two possibilities are rendered in the image below. I personally prefer the second one, which uses the standard ± sign already in use for symmetrical confidence intervals, simply splitting the confidence interval boundaries into two stacked numbers.

Another possibility is to write directly the upper and lower limits:

Catching Extra Time

Here I am on the Riva di Ca' di Dio in Venice visiting the 53rd International Art Exhibition. The black shirt and the tie entitle me to comment on the artworks with authority.

The shiny cube I'm leaning on is a showcase for Nikola Uzunovski's My Sunshine project. Nikola, an applied artist, wants to improve the quality of life at northern latitudes (or deep mountain valleys). The problem with these places is that, although they may well be wonderful in all other respects, they get light for a rather short time in winter. In the best of cases, lack of sunlight makes you a bit sleepy, in the worst case it makes you clinically depressed.

Nikola's solution is a device that lengthens the day: a mirror that floats up in the sky, electronically controlled to shine the sun's image on an otherwise dark spot on earth.

How does it work? Simply, the device exploits the fact that up in the sky the day lasts longer than at the earth's surface – as shown in the diagram at left (please do not blame Nikola for that). The mirror, floating inside a helium filled balloon, reflects 90% of the afternoon rays onto the earth below creating a virtual sun.

My sunshine is a step toward bringing Italian climate to Swedish civilization – the Eden of humanity. Bringing Swedish civilization to Italian climate, unfortunately, remains a daunting challenge for artists and scientists alike.

Mind the Data!

Gapminder is a wonderful, public tool that lets you visualize current data and historical trends in many demographic, economic and social indicators. Do you want to know how life expectancy in the USA has developed compared to, say, Cuba and Qatar? Just go here and click Play (then come back here, please). Three rather different development paths toward the same life expectancy. Fantastic.


The image to the right is the first thing you see on gapminder. It shows
the relationship between income (horizontal axis) and life expectancy
(vertical axis) in countries throughout the world. Each circle is a
country and its size is proportional to the country’s inhabitants (the
big red circle is China, the big blue one India).
There is clearly a positive relationship.

In a TED lecture, Gapminder founder Hans Rosling uses this image to argue that focusing on economic growth is the best thing to do to improve life expectancy in developing countries (the low-income, short-life blue dots in the image represent Africa). I think this is true, but the statement should be qualified. The catch is that the horizontal dimension does not show raw income, but orders of magnitude of income – notice how the distance between, say, $200 and $400 is the same as between $2000 and $4000.  This is called a logarithmic scale and is used for better display of certain patterns.

If you display income dollar-for-dollar, as in the image on the right, the pattern looks a bit different. Now it shows clearly that a very small income, by Western standards, can be associated with a rather long life: people in big red China achieve a life expectancy of 73 years with under $5000 per year. And so do many other countries, like Vietnam (where under $2500 per year buy you 74 years of life) or Syria (74 years for $2200). Beyond this point – about 75 years and $5000 per year – additional income buys relatively little, as the fat yellow dot (the USA) says: 78 years for $42000 per year.

Thus there seems to be a lot to gain from economic development in very poor countries (Africa is blue), and relatively little to gain from further increase in income in moderately rich to very rich countries.

Animal and Hum(e)an homosexuality

A report that lesbian pairs are common in a Hawaiian Albatross colony (Young et al, Zuk & Bailey) is the latest finding on animal homosexuality to raise some media attention (Daily Telegraph, Wired, Times Online). Many people are afraid to find out that homosexuality exists in animals, and therefore is "natural," because what is natural is often deemed morally acceptable.

David Hume, making perhaps the most important point in the history of ethics, stated over 200 years ago that we should not argue about how the world ought to be based on how the world is. Yet research on animal homosexuality still brings people on the verge of this error, by triggering the question: is it "natural"?

Hume himself did not speak enthusiastically of the "shameful and unnatural lusts […] which, by our law, […] justly expose the offender to be punished by death" (Commentaries on the Law of Scotland). He excused, however, the "Greek loves" as arising "from a very innocent cause, the frequency of the gymnastic excercises" (An Enquiry Concerning the Principles of Morals, Schmidt's summary of Hume's moral ideas).

Hume thus did not follow his own advice against confusing what is natural and what is moral, nor do many people today. The "unnaturalness" of homosexuality should not figure in discussions of homosexuality and human society. Lesbian Albatrossess are an interesting biological phenomenon, but should not burden us with moral dilemmas.

And from my neck so free
The Albatross fell off, and sank
Like lead into the sea

Tool use by insightful rooks?

[Update: A slightly more technical piece on this topic has been published in PNAS]

BBC News report that, in recent experiments, rooks (a species of crow) have demonstrated surprisingly sophisticated tool use. For instance, the rooks learned to insert a stone into a plastic tube to gain access to a second stone, which they then inserted into another tube to finally retrieve a juicy maggot.
I am a big fan of corvids. But what do these new findings say about their intelligence? What do rooks understand about causes and effects in the physical world? The controversy that this experiment touches upon boils down to the question: How much did the rooks figure out on their own, and how much did the researchers help them? Time-honored animal training techniques, in fact, allow to "shape" (as animal psychologists say) behavior of amazing complexity. Just think of what animals do in movies. The key technique is to break down a complex behavior into small, simple components that the animal can learn without much difficulty (and without much understanding).

The rooks' tool use behavior was shaped at least to some extent. For instance, stones where initially placed near the rim of the plastic tube, so that they could easily (and accidentally) be nudged down the tube. After the rooks mastered this step, stones were placed besides the apparatus, and finally they were moved further so that rooks learned to pick them up and transport them for some distance to the tube.

Such a use of shaping does not exclude that, by the end of the experiment, the rooks had developed an understanding of the task. For instance, they reliably chose stones small enough to fit into the tube. Thus I am not criticizing this brilliant experiment. I am
an even bigger fan of corvids now. But we do not know whether the rooks could have understood everything on their own.

Indeed, we do not know what "understanding" means. Animal psychologists have traditionally contrasted "insight" and "trial and error" learning. Insight is what happens when you realize the solution to a problem in your head, using your knowledge of causes and effects in the world. It is considered an advanced cognitive skill, available to humans and perhaps apes and, now, corvids. Trial and error is a more mundane process, whereby an organism learns to repeat behavior which, performed randomly or accidentally at first, has brought about desirable consequences. Shaping exploits animals' abilities of trial and error learning, by rewarding them only for the behavior we want them to produce (a classical example here). Can we understand animal intelligence by contrasting insight and trial-and-error learning? We do not know. Perhaps they are not fundamentally distinct phenomena, and a deeper understanding will come from looking at the problem from a different angle.

Main reference: Insightful problem solving and creative tool modification by captive nontool-using rooks, by C. D. Bird & N. J. Emery.