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Posts Tagged ‘theory’

Daniel Simons (of The Invisible Gorilla) just reviewed my book, The Vision Revolution. Here’s how the review ends…

For anyone who studies vision, this book is a must read — it will change the way you think about the vision sciences.  It is an accessible read for anyone interested in human cognition.  Read it to see how a masterful theorist revisualizes one of the oldest subdisciplines of psychology.

The entire review can be linked here.

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Mark Changizi is Professor of Human Cognition at 2AI, and the author of The Vision Revolution (Benbella Books) and the upcoming book Harnessed: How Language and Music Mimicked Nature and Transformed Ape to Man (Benbella Books).

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Benchfly’s Alan Marnett hit me with an in-depth interview Dec 16, 2009. In addition to getting into the science, the nice thing about the interview was the opportunity to talk about different ways of being a scientist. As you’ll see, I suggest being an aloof son-of-a-bitch, something I also talk about in this piece titled “How Not to Get Absorbed in Someone Else’s Abdomen“.

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As research scientists, many of us spend a very large amount of time working on a very small subject.  In fact, it’s not unusual for a biochemist to go through their entire career without ever physically observing the protein or pathway they work on.  As we hyper-focus on our own niche of science, we run the risk of forgetting to take the blinders off to see where our slice of work fits in to the rest of the pie.

Changizi

For Dr. Mark Changizi, assistant professor and author of The Vision Revolution, science starts with the pie.  We spoke with Dr. Changizi about why losing focus on the big picture can hurt our research, how autistic savants show us the real capacity of the brain and what humans will look like a million years from now.

BenchFly: Your book presents theories on questions ranging from why our eyes face forward to why we see in color.  Big questions.  As a kid, was it your attraction to the big questions that drew you into science?

Mark Changizi: I sometimes distinguish between two motivations for going into science. First there’s the “radio kid,” the one who takes apart the radio, is always fascinated with how things work, and is especially interested in “getting in there” and manipulating the world. And then there’s the “Carl Sagan kid,” the one motivated by the romantic what-does-it-all-mean questions. The beauty of Sagan’s Cosmos series is that he packaged science in such a way that it fills the more “religious” parts of one’s brain. You tap into that in a kid’s mind, and you can motivate them in a much more robust way than you can from a here’s-how-things-work motivation. I’m a Carl Sagan kid, and was specifically further spurred on by Sagan’s Cosmos. As long as I can remember, my stated goal in life has been to “answer the questions to the universe.”

While that aim has stayed constant, my views on what counts as “the questions to the universe” have changed. As a kid, cosmology and particle physics were where I thought the biggest questions lied. But later I reasoned that there were even more fundamental questions; even if physics were different than what we have in our universe, math would be the same. In particular, I became fascinated with mathematical logic and the undecidability results, the area of my dissertation. With those results, one can often make interesting claims about the ultimate limits on thinking machines. But it is not just math that is more fundamental than physics – that math is more fundamental than physics is obvious. In a universe without our physics, the emergent principles governing complex organisms and evolving systems may still be the same as those found in our universe. Even economic and political principles, in this light, may be deeper than physics: five-dimensional aliens floating in goo in a universe with quite different physics may still have limited resources, and may end up with the same economic and political principles we fuss over.

So perhaps that goes some way to explaining my research interests.

Tell us a little about both the scientific and thought processes when tackling questions that are very difficult to actually prove beyond a shadow of a doubt.

This is science we’re talking about, of course, not math, so nothing in science is proven in the strong mathematical sense. It is all about data supporting one’s hypothesis, and all about the parsimonious nature of the hypothesis.  Parsimony aims for explaining the greatest range of data with the simplest amount of theory. That’s what I aim for.

But it can, indeed, be difficult to find data for the kinds of questions I am interested in, because they often make predictions about a large swathe of data nobody has. That’s why I typically have to generate 50 to 100 ideas in my research notes before I find one that’s not only a good idea, but one for which I can find data to test it. You can’t go around writing papers without new data to test it. If you want to be a theorist, then not only can you not afford to spend the time to become an experimentalist to test your question, but most of your questions may not be testable by any set of experiments you could hope to do in a reasonable period of time. Often it requires pooling together data from across an entire literature.

In basic research we are often hyper-focused on the details.  To understand a complex problem, we start very simple and then assume we will eventually be able to assemble the disparate parts into a single, clear picture.  In essence, you think about problems in the opposite direction- asking the big questions up front.  Describe the philosophical difference between the two approaches, as well as their relationship in the process of discovery.

A lot of people believe that by going straight to the parts – to the mechanism – they can eventually come to understand the organism. The problem is that the mechanisms in biology were selected to do stuff, to carry out certain functions. The mechanisms can only be understood as mechanisms that implement certain functions. That’s what it means to understand a mechanism: one must say how the physical material manages to carry out a certain set of functional capabilities.

And that means one must get into the business of building and testing hypotheses about what the mechanism is for. Why did that mechanism evolve in the first place? There is a certain “reductive” strain within the biological and brain sciences that believes that science has no role for getting into questions of “why”. That’s “just so story” stuff.  Although there’s plenty of just-so-stories – i.e., bad science – in the study of the design and function of biological structure, it by no means needs to be. It can be good science, just like any other area of science. One just needs to make testable hypotheses, and then go test it. And it is not appreciated how often reductive types themselves are in the business of just-so-stories; e.g., computational simulators are concerned just with the mechanisms and often eschew worrying about the functional level, but then allow themselves a dozen or more free parameters in their simulation to fit the data.

So, you have got to attack the functional level in order to understand organisms, and you really need to do that before, or at least in parallel with, the study of the mechanisms.

But in order to understand the functional level, one must go beyond the organism itself, to the environment in which the animal evolved. One needs to devise and test hypotheses about what the biological structure was selected for, and must often refer to the world. One can’t just stay inside the meat to understand the meat.

Looking just at the mechanisms is not only not sufficient, but will tend to lead to futility. An organism’s mechanisms were selected to function only when the “inputs” were the natural ones the organism would have encountered. But when you present a mechanism with an utterly unnatural input, the meat doesn’t output, “Sorry, that’s not an ecologically appropriate input.” (In fact, there are results in theoretical computer science saying that it wouldn’t be generally possible to have a mechanism capable of having such a response.) Instead, the mechanism does something. If you’re studying the mechanism without an appreciation for what it’s for, you’ll have teems and teems of mechanistic reactions that are irrelevant to what it is designed for, but you won’t know it.

The example I often use is the stapler. Drop a stapler into a primitive tribe, and imagine what they do to it. Having no idea what it’s for, they manage to push and pull its mechanisms in all sorts of irrelevant ways. They might spend years, say, carefully studying the mechanisms underlying why it falls as it does when dropped from a tree, or how it functions as crude numchucks. There are literally infinitely many aspects of the stapler mechanism that could be experimented upon, but only a small fraction are relevant to the stapler’s function, which is to fasten paper together.

In explaining why we see in color, you suggest that it allows us to detect the subtleties of complex emotions expressed by humans – such as blushing.  Does this mean colorblind men actually have a legitimate excuse for not understanding women?!

…..to see my answer, and the rest of the interview, go to Benchfly.

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