Much of the recent discussion on “Your Brain is Reading This” has focused on what sorts of substantive conclusions (if any) can be drawn about cognitive neuroscience based on reflections on ordinary language. One contributor to that thread is neuroscientist Eric Thompson and I’m reminded of remarks he’s made over at the Brains blog about the relative poverty of natural language (as opposed to mathematical theories) in capturing neural and mental phenomena.
I’ve been thinking about this a lot more recently and want to link to these past comments of Eric’s. For starters, there’s his dream curriculum for a cognitive science program here. But for real food for thought, there are his remarks here on the differences between two groups of researchers who don’t collect their own data. Here’s an excerpt:
Unfortunately, philosophy training is not very helpful for thinking about data or coming up with precise theories of how brains work. Many philosophers I talk to think that theoretical neuroscience is just philosophical neuroscience, but there are really two groups of people that don’t collect their own data. The theoretical neuroscientists (Sejnowski, Abbott, Hopfield, etc) who are trained in lots of mathematics (typically they come from physics) on one hand, and the philosophers who typically use natural language to think about brains. I think the philosophical branch of the armchair neuroscientists have done, and will do, very little to push neuroscience in fruitful directions. The mathematical branch of the armchair dwellers, though, will continue to bear fruit.
While it is possible, I just don’t see neuroscience becoming data rich and theory poor: theoretical neuroscience is exploding, especially as theoretical physicists are realizing that it is much easier to find jobs in biophysics than string theory. Theories to explain given data are a dime a dozen. While the amount of data is quite daunting, if you ask an experimentalist for their speculations about their data, you typically won’t find any shortage. However, they will tend to be quite cautious, journal editors tend to cut such speculations out of papers, and experimentalists don’t want to come off as mushy theorists in their presentation of data. There is a strong selection effect to make it look like there lots of theories. Also, in practice, it is typically experimentalists who come up with predictions that can actually be tested: this is very hard to do even if you are an experimentalist with an understanding of the nuances of the techniques.
Also, while I don’t think philosophical naturalists should necessarily be doing experiments, as I mentioned above, they would be better served by learning more mathematics and actually analyzing some data.