More details on the science, and amazing double levitation:
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We can only really have 150 friends.
Why LDS wards are the size they are. I suspect it might have something to do with the average size of apartment buildings in NYC. Belies the idea of 1000 facebook friends.
So there’s this:
The elephant, a huge package of food that is easy to hunt, disappeared from the Middle East 400,000 years ago — an event that must have imposed considerable nutritional stress on Homo erectus….
Unlike other primates, humans’ ability to extract energy from plant fiber and convert protein to energy is limited. So in the absence of fire for cooking, theHomo erectus diet could only consist of a finite amount of plant and protein and would have needed to be supplemented by animal fat. For this reason, elephants were the ultimate prize in hunting — slower than other sources of prey and large enough to feed groups, the giant animals had an ideal fat-to-protein ratio that remained constant regardless of the season. In short, says Ben-Dor, they were the ideal food package for Homo erectus.
When elephants began to die out, Homo erectus “needed to hunt many smaller, more evasive animals. Energy requirements increased, but with plant and protein intake limited, the source had to come from fat. He had to become calculated about hunting,” Ben-Dor says, noting that this change is evident in the physical appearance of modern humans, lighter than Homo erectus and with larger brains.
And then there’s this:
The mathematics of cities was launched in 1949 when George Zipf, a linguist working at Harvard, reported a striking regularity in the size distribution of cities. He noticed that if you tabulate the biggest cities in a given country and rank them according to their populations, the largest city is always about twice as big as the second largest, and three times as big as the third largest, and so on. In other words, the population of a city is, to a good approximation, inversely proportional to its rank. Why this should be true, no one knows.
…Around 2006, scientists started discovering new mathematical laws about cities that are nearly as stunning as Zipf’s. But instead of focusing on the sizes of cities themselves, the new questions have to do with how city size affects other things we care about, like the amount of infrastructure needed to keep a city going….For instance, if one city is 10 times as populous as another one, does it need 10 times as many gas stations? No. Bigger cities have more gas stations than smaller ones (of course), but not nearly in direct proportion to their size. The number of gas stations grows only in proportion to the 0.77 power of population.
…Now comes the spooky part. The same law is true for living things. That is, if you mentally replace cities by organisms and city size by body weight, the mathematical pattern remains the same….on a pound for pound basis, the cells of an elephant consume far less energy than those of a mouse. The relevant law of metabolism, called Kleiber’s law, states that the metabolic needs of a mammal grow in proportion to its body weight raised to the 0.74 power.
This 0.74 power is uncannily close to the 0.77 observed for the law governing gas stations in cities. Coincidence? Maybe, but probably not. There are theoretical grounds to expect a power close to 3/4. Geoffrey West of the Santa Fe Institute and his colleagues Jim Brown and Brian Enquist have argued that a 3/4-power law is exactly what you’d expect if natural selection has evolved a transport system for conveying energy and nutrients as efficiently and rapidly as possible to all points of a three-dimensional body, using a fractal network built from a series of branching tubes — precisely the architecture seen in the circulatory system and the airways of the lung, and not too different from the roads and cables and pipes that keep a city alive.
Oh, and here’s an even better summary of the city-elephant connection.
I really wanted this to be true, but alas, it wasn’t.
New research on the seminal Bargh priming effect shakes up our understanding of what is going on. Looks like some of the effect was from experimenter bias. Even more interestingly, evidence emerged that when there is a measurable effect, it is somehow “cued or amplified via experimenter signaling“—it didn’t appear when the experimenters didn’t know what they were testing for.
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