Decades of neuro research wasted because "we wanted to be REAL SCIENTISTS." @BrentWRoberts at 15:00 on @fourbeerspod
NIMH funded only candidate-gene and fMRI studies, because SCIENCE, and both of those turned out to be science-free mirages.https://fourbeers.fireside.fm/22
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The 2014 dead-salmon paper was a major wakeup call for “fMRI mostly isn’t a thing,” rather like the 2010 Bem ESP paper:https://twitter.com/Meaningness/status/479081792192802817 …
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The point is not that it was wrong to pursue candidate-gene or fMRI work. There were valid reasons for excitement amid uncertainty in both cases (although also good a priori reasons to think neither could work, which turned out to be correct).
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This is what we need most I think! More scientists pursuing lines of inquiry that probably won’t work. So long as they MIGHT work, and haven’t been reasonably thoroughly shown to fail. “We KNOW this doesn’t work, but I can get funding for it”: time to reevaluate your career.
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(If I sound ranty about this, it’s because I left my scientific career after concluding that my very-well-funded field was a dead end.)
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Shortly before she died, my sister said that as a graduate student, she expected that neuroscience would actually figure out how brains work over the course of her career. In fact, it’s made almost no progress in decades. Tons of detailed factoids but no new broad understanding.
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My sister said that over her career, the number of funded neuroscientists and papers published increased ten-fold, but the number of reliable papers published was constant. She would never say this, but think she believed >>90% of scientists should not be doing science.
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It’s hard (and probably pointless) to divvy up blame between individual scientists and the perverse institutional incentives. In the
@BrentWRoberts@fourbeerspod episode, there’s plenty of interesting examples of both.Show this thread -
Common bad preferences of science funders, who usually lack vision and understanding, include: - Superficially looks more like physics than other approaches in the field - Requires large, expensive new equipment - Has “momentum” (created by their own funding)
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I worked briefly at Thinking Machines, which built the Connection Machine, which was by far the largest, most expensive supercomputer of the late 80s. Nearly all were sold on the basis of “it’s a very large, very sleek black box that costs a gigantic amount of money.”
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Billions of dollars are being spent on the current wave of “deep learning” AI. - Superficially looks more like physics than other approaches in the field - Requires large, expensive new equipment - Has “momentum” (created by previous funding)
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