I. The Brains of Animals

Even more animal cognition. I admit to quoting perhaps half of the article here. But the other half is also quite pretty, and worth a read.

[…] I confess I am astonished at how much mammalian brains resemble one another in their organization, architecture, and complexity. Just as human beings possess only a marginal genetic difference from the next “lower” order of primate–all our languages, sciences, tools, and arts the result of this smidgen of code–it appears that the raw matter of thought and perception is, neuroanatomically, subject to only minor variations in organization.

But considerable differences, I notice, in developmental emphasis. That is, animal brain MRIs, compared to human brain MRIs, show strikingly “superhuman” development in selected areas. We have known for centuries that elephants have long memories; the hippocampus is the seat of memory in human beings; the elephant brain looks like a human brain, only with the most gargantuan (the most elephantine) hippocampus imaginable, which is in turn intensely crinkled and convoluted. (Which means there’s a greater surface area, and hence more “processing” going on there.) On that scan, the hippocampus was the place my radiologist’s eye jumped to as I realized—slowly—that I was not looking at a human brain. My first radiological impression, though, was one of familiarity.

Similarly, the olfactory bulb, responsible for the sense of smell, is several times larger in dogs compared to humans, as you would expect. The orca happens to have an extremely crinkly (compared to us) limbic and paralimbic area, two areas which in mammals process emotion. This superhuman development correlates well with the behaviors of orca pods and human families. Human families fragment when the young reach adulthood, with the young splitting off and starting new families of their own, sometimes in farflung cities. The pair bonding between the father and mother may break down well before that (we call this “divorce”). Among orcas, adults never leave their mothers; everybody travels together, and apparently each pod has its own language—a well-known communicative prowess that may relate to its unusually well-developed operculum.

There may come a time when we cease to regard animals as inferior, preliminary iterations of the human—with the human thought of as the pinnacle of evolution so far—and instead regard all forms of life as fugue-like elaborations of a single musical theme.

II. Time Scales/Coherence

The Internet exists so people can make me seem more coherent and insightful than I actually am.“-Adam Gurri on my post on Probabilistic Minarchism, where I condensed his articles separated by weeks (and other articles) into a single topic post.

Eliezer Yudkowsky, earlier this week:

Myself at the decision theory workshop: “X, I think you’re overestimating my intelligence. I know I’ve written some good blog posts but that’s when I’ve got hours to edit them. You need to drop your estimate of my intelligence on 10-second timescales by 20 IQ points.”

Something about this. Not sure what. This idea certainly makes sense (without insulting the intelligence of the people involved, of course).

I think I’d like to work on impromptu speaking next year?

III. An Old Kind of Science

Not relevant to any thread I’ve been running with recently, but Greer wrote about Wolfram lately. In “An Old Kind of Science” (lol) he argues that the narrative of “Man: Conqueror of Nature” is a recent one, born long after the establishment of secular science and raised by the exploitation of “fossil sunlight” into the dominant historical narrative. Trapped by this story, we have developed a tendency to confuse our territories with our maps, to mistake our concepts for the definite truth instead of as useful descriptors, and to view the ultimate and inevitable aim of science as the total deliberative manipulation of nature. Wolfram’s “new method” is in fact based an older way of viewing knowledge: instead of dealing exclusively with programs dictated and designed to perform a purpose for man, he wants to allow for arbitrary programs to naturally develop and observe how they behave.

That’s the heritage of science as a quest for power over nature: like all other machines, computers are there to do what human beings tell them to do, and so computer science tends to focus on finding ways to make computers do more things that human beings want them to do.

That same logic pervades many fields of contemporary science. The central role of experiment in scientific practice tends to foster that, by directing attention away from what whole systems do when they’re left alone, and toward what they do when experimenters tinker with them. Too often, the result is that scientists end up studying the effects of their own manipulations to the exclusion of anything else. Consider Skinnerian behaviorism, an immensely detailed theory that can successfully predict the behavior of rats in the wholly arbitrary setting of a Skinner box and essentially nothing else!
The alternative is to observe whole systems on their own terms—to study what they do, not in response to a controlled experimental stimulus, but in response to the normal interplay between their internal dynamics and the environment around them.  That’s what Wolfram did. He ran cellular automata, not to try to make them do this thing or that, but to understand the internal logic that determines what they do when left to themselves. What he discovered, to summarize well over a thousand pages of text in a brief phrase, is that cellular automata with extremely simple operating rules are capable of generating patterns as complex, richly textured, and blended of apparent order and apparent randomness, as the world of nature itself.
IV. Misc