Here is a brief excerpt from an article by Alison Gopnik for the Wall Street Journal. To read the complete article, check out other resources, and obtain subscription information, please click here.
Image credit: Magoz
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Over the past decade, popular science has been suffering from neuromania. The enthusiasm came from studies showing that particular areas of the brain “light up” when you have certain thoughts and experiences. It’s mystifying why so many people thought this explained the mind. What have you learned when you say that someone’s visual areas light up when they see things?
People still seem to be astonished at the very idea that the brain is responsible for the mind—a bunch of gray goo makes us see! It is astonishing. But scientists knew that a century ago; the really interesting question now is how the gray goo lets us see, think and act intelligently. New techniques are letting scientists understand the brain as a complex, dynamic, computational system, not just a collection of individual bits of meat associated with individual experiences. These new studies come much closer to answering the “how” question.
Take a study in the journal Nature this year by Stefano Fusi of Columbia University College of Physicians and Surgeons, Earl K. Miller of the Massachusetts Institute of Technology and their colleagues. Fifty years ago David Hubel and Torsten Weisel made a great Nobel Prize-winning discovery. They recorded the signals from particular neurons in cats’ brains as the animals looked at different patterns. The neurons responded selectively to some images rather than others. One neuron might only respond to lines that slanted right, another only to those slanting left.
But many neurons don’t respond in this neatly selective way. This is especially true for the neurons in the parts of the brain that are associated with complex cognition and problem-solving, like the prefrontal cortex. Instead, these cells were a mysterious mess—they respond idiosyncratically to different complex collections of features. What were these neurons doing?
In the new study the researchers taught monkeys to remember and respond to one shape rather than another while they recorded their brain activity. But instead of just looking at one neuron at a time, they recorded the activity of many prefrontal neurons at once. A number of them showed weird, messy “mixed selectivity” patterns. One neuron might respond when the monkey remembered just one shape or only when it recognized the shape but not when it recalled it, while a neighboring cell showed a different pattern.
In order to analyze how the whole group of cells worked the researchers turned to the techniques of computer scientists who are trying to design machines that can learn. Computers aren’t made of carbon, of course, let alone neurons. But they have to solve some of the same problems, like identifying and remembering patterns. The techniques that work best for computers turn out to be remarkably similar to the techniques that brains use.
Essentially, the researchers found the brain was using the same general sort of technique that Google uses for its search algorithm. You might think that the best way to rank search results would be to pick out a few features of each Web page like “relevance” or “trustworthiness'”—in the same way as the neurons picked out whether an edge slanted right or left. Instead, Google does much better by combining all the many, messy, idiosyncratic linking decisions of individual users.
With neurons that detect just a few features, you can capture those features and combinations of features, but not much more. To capture more complex patterns, the brain does better by amalgamating and integrating information from many different neurons with very different response patterns. The brain crowd-sources.
Scientists have long argued that the mind is more like a general software program than like a particular hardware set-up. The new combination of neuroscience and computer science doesn’t just tell us that the gray goo lets us think, or even exactly where that grey goo is. Instead, it tells us what programs it runs.
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To read the complete article, please click here.
Alison Gopnik is a professor of psychology and affiliate professor of philosophy at the University of California at Berkeley. She received her BA from McGill University and her PhD. from Oxford University. She is an internationally recognized leader in the study of children’s learning and development and was the first to argue that children’s minds could help us understand deep philosophical questions. She is the author of over 100 journal articles and several books including Words, thoughts and theories (co-authored with Andrew Meltzoff), MIT Press, 1997, and the bestselling and critically acclaimed popular books, The Scientist in the Crib (co-authored with Meltzoff and Patricia Kuhl) William Morrow, 1999, and The Philosophical Baby: What children’s minds tell us about love, truth and the meaning of life, Farrar, Strauss and Giroux, 2009. She has also written widely about cognitive science and psychology for Science, The Times Literary Supplement, The New York Review of Books, The New York Times, New Scientist and Slate, among others. And she has frequently appeared on TV and radio including The Charlie Rose Show and The Colbert Report. She has three sons and lives in Berkeley, California, with her husband Alvy Ray Smith.